In this episode, Jacob and Saloni explore the cholesterol revolution: from statins discovered in fungi to new drugs that cut LDL cholesterol by 60% and last for months, driven by breakthroughs in genetics, monoclonal antibodies, RNA therapies, and modern medicinal chemistry. They talk about how cholesterol travels through the bloodstream, how it causes atherosclerosis and heart disease, and why it took nearly a century for scientists to form the consensus that lowering cholesterol saves lives.

Hard Drugs is a podcast from Works in Progress and Coefficient Giving about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Chapters:
0:00:00 Introduction
13:35 The decline in heart disease mortality
31:02 Surprising facts about cholesterol
55:40 The lipid hypothesis: 7 lines of evidence for the harms of LDL cholesterol
1:22:15 How cholesterol works
1:30:40 The discovery of statins
1:48:44 Should everyone be on statins?
1:57:10 PCSK9 drugs and beyond
2:22:56 Summary

Saloni’s substack newsletter:

https://www.scientificdiscovery.dev/

Jacob’s blog:

https://blog.jacobtrefethen.com/

Transcript

Jacob Trefethen: Statins are one of the coolest things invented by medical science. It’s just so rare you can have something that has a population-wide preventive effect.

Saloni Dattani: The first statin originated from penicillium citrinum, the same genus as the fungi that gave us penicillin.

Jacob Trefethen: Mortality rates for cardiovascular disease are down by around 75% since the 1950s.

Cholesterol treatment has actually gone through a revolution in the last few decades.

Saloni Dattani: siRNA drugs reduce the levels of lipoprotein A by… can you guess?

Jacob Trefethen: I’ll say 60%.

Saloni Dattani: 95%!

It’s a short sequence of RNA gets into your liver cells. It binds to the RNA and it says, what if I destroy you?

[jingle]

Jacob Trefethen: Heart disease is the most common cause of death globally, and it’s one of the most common health conditions. But what might surprise you is that we’ve made a huge amount of progress against it. Age-standardized mortality rates for cardiovascular disease are down by around 75% in the US since the 1950s, which means that for people of the same age, the annual risk of dying from cardiovascular disease is now just one quarter what it was in 1950.

Saloni Dattani: That’s the result of many, many efforts in biomedical research and public health. Smoking is much less common. People are much more aware of the signs of heart attacks and strokes. Emergency care actually exists. Remember a time before anyone knew CPR? It was only invented in 1960! And there are surgeries like bypass surgery and devices like pacemakers and medicines as well: statins, PCSK9 drugs, blood pressure medications, and clot-busting drugs - just to name a few.

And surprisingly, in some areas, we’ve also improved our dietary patterns.

Jacob Trefethen: In this episode, going to focus on cholesterol, how it causes disease, what lifestyle changes affect it, and the drugs that reduce it. Cholesterol treatment has actually gone through a revolution in the last few decades and has some nice recent results in the last few years.

There are now drugs that can reduce LDL cholesterol levels by more than 60%, and some newer drugs about to come out are effective for months with a single dose.

Saloni Dattani: But in the 20th century, very few people believed that blood cholesterol could be harmful. After all, cholesterol is a vital component of our cell membranes and it’s a precursor for sex hormones.

So what changed our understanding? How did we get here? What’s the science behind cholesterol and what are the treatments for it?

Jacob Trefethen: Welcome to Hard Drugs, hosted by me - Jacob Trefethen - and Saloni Dattani!

Saloni Dattani: Welcome!

Jacob Trefethen: You ready for this one?

Saloni Dattani: Yeah. I’m very excited for this one.

Jacob Trefethen: I like that we are just going for the jugular, picking the biggest cause of death and doing an episode on heart disease, that makes me feel good. And cholesterol for me, I mean, as a child, cholesterol was a very sort of chic adult-realm kind of concept.

You know I’d hear my grandmother or father talk about cholesterol, cholesterol. And it was sort of, this “Is this coming for me one day?” And all I remember as a child was the, of course you gotta stop eating so many eggs because that will give you too much cholesterol, which is as the one fact I got transmitted from culture, actually incorrect, I believe.

So maybe we’ll get to that later. But I realized coming into this episode, gosh, my background knowledge from society is not so strong, but it’s such an important concept.

Saloni Dattani: You’re right. I had the same thing. I guess I thought of cholesterol as just this boring, abstract- it’s just there, it’s like high blood pressure.

You hear it all the time from older people and it’s not very interesting. And it seems to affect almost everyone. I guess I just hadn’t thought about how it actually all works and how do the drugs work and anyway, so very excited for this.

Jacob Trefethen: Okay. Shall we get into it?

Saloni Dattani: Yes.

Jacob Trefethen: So maybe the right place to start before getting to cholesterol is with the diseases themselves. So what is cardiovascular disease?

Saloni Dattani: Cardiovascular disease? Well, we can split it into two, cardio and vascular. So it’s a range of diseases that include diseases of the hearts, “cardio”, and diseases of the blood vessels, which are the “vascular” part. And together they’re very related to each other and they’re the most common cause of deaths worldwide.

It’s estimated that in 2019, 18 million deaths occurred that were caused by cardiovascular disease. So it’s really a lot of deaths.

Jacob Trefethen: I think there’s 60 million deaths total around the world every year. So 18 million, we’re just almost a third of all deaths, oh my god.

Saloni Dattani: And so making progress on this has also meant, it’s sort of translated to an improvement in life expectancy overall. It’s so common and we have actually advanced a lot in our understanding and treatment and prevention of it.

Cardiovascular diseases, there are many of them. There’s ischemic heart disease; that’s when you have a blockage of blood flow to the heart. There’s stroke and there are different types of stroke.

There’s ischemic stroke, which is also a blockage. There’s hemorrhagic stroke, which is when there is a bleed in the blood supply and that causes pressure on, that puts pressure on the cells that it bleeds into, and it also means a loss of blood. And then there are hypertensive heart diseases, when you have high blood pressure, and there are various other circulatory diseases.

And one of the common risk factors for a lot of these, especially ischemic stroke, is atherosclerosis. And atherosclerosis is a disease of the blood vessels where a cholesterol plaque develops and it occludes, or blocks, part of the blood vessel because it’s so big, there’s less space for blood to get through, and that reduces blood flow to organs.

And also parts of that plaque can break off or get eroded and block a smaller part of your blood vessel later on, which can, if it gets blocked in the blood vessels in your brain, can cause a stroke and cause a heart attack. It can cause various other artery diseases where you lose blood flow in certain parts of your body.

And so that - atherosclerosis - is one of the things that is most driven by cholesterol, and that’s why we’re gonna talk about that in this episode.

Jacob Trefethen: Okie doke. And before we get into too much more about all the ways the heart is failing and our blood vessels are failing, and all of that, I just wanna say a note of appreciation to the heart.

You know, that organ has to beat every second for your entire life and every part of your body is getting oxygen delivered to it at all. I mean, that’s a very impressive job. I’m very grateful. Thank you to the heart. And it makes sense intuitively of - well, if that starts failing and, for many different reasons it could fail, your whole body’s gonna be in trouble.

Similar to when you just mentioned with a stroke actually, if your brain starts failing for some reason, you’re gonna be in trouble. The brain and the heart is, you gotta be on at all times, basically. So, thank you heart. All that said, I’m gonna be mad about some of the misfunctions for the rest of the episode.

Saloni Dattani: Well, I have a question for you on, because you brought up heartbeats. Do you know how many heartbeats a human has in their lifetime on average?

Jacob Trefethen: Oh gosh. Okay, so this feels like - so once a second. Oh, what’s the rent song? (he sings) 2,560 mana minutes is how many minutes there are in a year? Unfortunately, I can’t remember that number.

So we’re gonna do 60 per minute. We’re gonna do 60 minutes in an hour. So up to 3,600, we’re gonna do times up by 24. That’s a big number already. And then we’re gonna by - we’re gonna times by 80 I think the answer is like loads. I’ll go with a billion.

Saloni Dattani: It’s actually no, 10 times 10 to the power eight. Wait, that’s a trillion. That’s a trillion!

Jacob Trefethen: Trillion beats. Now imagine if you took all of those beats and put a baseline underneath them.

Saloni Dattani: Boom. Literally. Yeah. So you’re right. So the heart is really important for pumping blood around your body, making sure energy and nutrients get to lots of tissues. And because of that, the blood vessels that deliver that blood are by extension really important. And so if you get a buildup of fat or cholesterol and stuff in the walls of your blood vessels, that’s really bad. And this is something that accelerates with age. And for a long time people just assumed that cholesterol and cardiovascular disease was just this inevitable outcome of aging.

It wasn’t something that we could change. And now we know that that’s not true. It’s not inevitable; it does happen and it does accelerate, but we can actually reduce the rates in total across the lifespan with different behaviors, different environments, and then also drugs and treatments.

Jacob Trefethen: So I’d love to hear more about how that’s gone so far. So how has cardiovascular disease changed over time and what are the things that have led to reductions?

Saloni Dattani: Yeah, this was really surprising to me a few years ago when I was writing about cardiovascular mortality and how that’s changed. One of the problems is that we don’t really have long-term data on diagnoses of cardiovascular disease. We don’t really know how the rates of cardiovascular disease have changed over time. In part just because the data’s quite patchy. There are different definitions and we just don’t have this long-term view. But we do have that for cardiovascular deaths because deaths are recorded on death certificates.

And people will mention what, the doctor who’s signing off the death certificate will write down what they think the cause of death is. And we can collect that information every year and see what the overall mortality rate is. And if you look at that since 1950 in the US, there has been a decline of more than 75%.

So that’s an enormous decline. And it’s something that I hadn’t known about. And part of the reason that I didn’t know about it was that it’s affected by aging. So if we have a population that is older than in the past, the mortality rate will actually increase. So this 75% decline is when you standardize that age.

So you’re saying if we kept the population’s age constant, what is the reduction in death rates that you would see? And what that means is looking at people who are 50 or 60 now compared to in 1950, what are their chances of dying? And that is what has declined by 75%. So looking at people who are 50 years old now, or 70 years old now, they have a quarter of the chance of dying from cardiovascular disease as people who were 50 or 70 in the 1950s. So that is a huge drop.

Jacob Trefethen: The number of heart attacks hasn’t gone down 75%. But that’s actually in a strange way, a reflection of a happy fact, which is that people are living longer and as you age, your risk goes up. And so standardizing in the way you’re saying sort of gives a fairer look on our progress.

Saloni Dattani: Right.

So people are still dying. We haven’t solved death yet, but when they die, they die later and these diseases progress slower and that I think is worth celebrating. You have more years to live on this planet.

Jacob Trefethen: I’ll celebrate that.

Saloni Dattani: Yeah. So how did it happen? I think just so many different things.

One thing that I like to say, which is probably very annoying from a quantitative point of view, is that it’s really hard to separate out the different causes because they kind of interact with each other. If one of them reduces your risks by 20% and another one separately would reduce your risks by 20%, the total decline might be less than 40%, and it usually is, because there are interactions. They’re very difficult to cleanly distinguish from each other because they kind of interact with each other, but anyway, there are just a lot of different changes that we’ve made.

So there are new medicines. There are statins, which were only introduced in the 1980s, and they help keep our arteries cleaner by clearing LDL cholesterol and also by stabilizing the plaques that develop that can block our blood vessels.

There are also newer drugs called PCSK9 inhibitors and they help reduce cholesterol even more. Then there’s blood pressure medications, beta blockers, ACE inhibitors, all of these things that help keep your blood pressure under control and reduce your risk of stroke, heart attacks and heart failure.

And then there’s clot-busting medicines that break up these blockages and help restore your blood flow. And then there are dietary changes as well. This really surprised me. I feel like we hear a lot that people’s diets have worsened, and in many ways that is true. We eat a lot more than we did in the past and we eat a lot of unhealthy processed foods.

But there are certain ways that our diets have improved, such as reduced consumption of trans fats. So I think maybe you know more about this, but trans fats are banned in some countries.

Jacob Trefethen: Many, most, maybe.

Saloni Dattani: Right. And there are also recommendations that people get if they are diagnosed with different types of cardiovascular disease to reduce how much saturated fats they eat to change their dietary patterns. And I think that has actually, at least in some, in these higher risk populations and in certain food groups have actually made a difference.

And then there are surgeries and devices that have been introduced, there are pacemakers, for example, there are things like bypass surgery, there’s angioplasty where you try to unblock an artery by threading a balloon into the clogged artery and then inflating the balloon, opening up the artery and restoring blood flow.

And there are stents, which are tiny mesh tubes that keep arteries open. And then there are drug coated versions of those stents, which prevent the stents from breaking down and prevent re-narrowing. And then there are various other surgeries that we’ve introduced, like heart transplants, which didn’t exist before the 1960s.

And we have ways to completely replace heart valves with mechanical heart valves. Or you can have robot-assisted surgeries to make really precise changes that are very hard for someone to do as an individual.

And the most surprising thing to me was thinking about how emergency care has changed. So, before 1960 CPR didn’t exist, people didn’t - if you collapsed on the street, people generally wouldn’t really know what to do. They might have some thought that, I don’t know, let’s try to open up your chest or put you in a different position-

Jacob Trefethen: You would see someone collapse on the street and open up their chest? Come on.

Saloni Dattani: No one would know how, well not- I mean move their arm out of their, I don’t know.

Jacob Trefethen: Oh, open.

Saloni Dattani: Not, not literally open.

Jacob Trefethen: I was like, do you carry around a scalpel with you? It’s too late for this CPR. It’s time to go deeper.

Saloni Dattani: I meant put them in a different position like-

Jacob Trefethen: Now I understand why this 75% drop, you’ve gone from people performing unsterilized surgery on the street, which used to kill people and then when you stopped doing that…

Saloni Dattani: Well… no, but so I mean, just introducing basic things like that, like telling people what the signs of a heart attack are and what the signs of a stroke are. People didn’t know those before. I think until at the end of the 1930s, there was no 999 hotline here in the UK. There was no 911 hotline, I think until the 1970s in the US.

Then of course there were big anti-smoking efforts as well. Smoking increases the risks of heart disease and heart attacks, and all of the efforts, like the campaigns to reduce smoking, have also made a difference in reducing heart disease deaths.

Jacob Trefethen: It’s really interesting to hear that list of so many different contributors and you have this beautiful graph that’s probably a top five favorite Saloni graph for me.

Actually it might be a top three.

Saloni Dattani: Oh, really?

Jacob Trefethen: Putting a lot of those contributors dated on a declining line of, “Oh wow. We’re just making all of these improvements that stack on top of each other.” I mean, it’s interesting to think of that in comparison to our lenacapavir episode on HIV where with HIV there was, you know, there’s not just one thing that changes HIV rates, of course, that said one class of drugs, of antiretrovirals and combination therapy are so much of the driver of decline that it doesn’t feel like there’s 27 different contributors.

So it’s almost an easier story to tell than this story, which is, a lot of stacked improvements across medical research, societal infrastructure, knowledge, all of that. So that’s, yeah, the graph really stands with me for that reason, sticks with me. One question I have for you is, so you were just describing the adoption of these technologies, I think based on the US and UK. Do you have a sense of whether there’s a global drop that’s similar or have these technologies, practices been adopted in most other countries?

Saloni Dattani: So I think you do see decline in cardiovascular mortality in most countries, if you’re looking at the age standardized rate and doing this fair comparison by age. But you see an increase in the number of deaths more in poorer countries especially, that as populations grow in size and get older, we’ve had more deaths from cardiovascular disease.

Actually, what’s interesting is that in some richer countries, the number of cardiovascular disease deaths has also declined. The improvements that we’ve seen are so massive that even though we have an aging society, fewer people are dying from heart disease than in the past.

Jacob Trefethen: Okay. That’s great.

And then you mentioned anti-smoking efforts, which brings me to another question. Are there other risk factors for cardiovascular disease and how are those going?

Saloni Dattani: So I think there are a few really big ones. Smoking is a big one. Cigarettes carry a lot of carcinogens, chemicals that injure your blood vessels and cause inflammation. And that can eventually, that can accelerate atherosclerosis. It can increase blood pressure and that can worsen heart disease.

There’s also obesity, which is a big risk factor for heart disease that raises your blood pressure and cholesterol, but it also drives insulin resistance, inflammation and just puts more strain on your heart and other parts of your body. And so that worsens cardiovascular disease as well.

And then there’s high blood pressure, which has many different causes, but that’s when your blood vessels become stiffer or narrower, and that drives further damage because it puts extra pressure on your heart, it puts extra pressure on your artery walls and it means that they’re more likely to weaken or get clogged up or actually break and rupture. And that can lead to strokes, heart attacks and heart failure.

And then there’s the two that we’ll talk about mostly in this episode, which are cholesterol and triglycerides. And they will mostly put them together and they sort of fall under the category of lipids, but we’ll mostly focus on cholesterol and specifically LDL cholesterol and why that raises the risk of cardiovascular mortality.

So every increase of 40 milligrams per deciliter translates to an increase in cardiovascular disease by around 20%. And that’s an average. And the way that we know that is by looking at randomized control trials where we’ve reduced LDL cholesterol and how much does that reduce the risk of cardiovascular disease. So we know that if we reduce it by that much, we reduce the risk of heart attacks, strokes, and so on by around 20%.

So because cardiovascular disease is such a big proportion of deaths overall, that translates to a big change.

Jacob Trefethen: So the people who want to cure death, they can cure half of death if they just cure-

Saloni Dattani: Well, I guess maybe more like a third or somewhere between a third and half. One thing that was surprising to me about this, thinking about the risk factors is that some of these risk factors have actually improved over time. This also was surprising to me. So if we look, there are big surveys in the US for example, with people’s levels of cholesterol in their blood or their blood pressure, or you know, how many people smoke and things like that. And if we look at them over time, you’ll look in the last 20 years, let’s say, they’ve actually declined.

So having high levels of cholesterol, that used to be, that used to affect maybe around 20% of people at the turn of the century, so 1999. Now only around 10% have high cholesterol levels.

Jacob Trefethen: Hmm, wow.

Saloni Dattani: And then if you look at blood pressure, if you look at uncontrolled blood pressure that has declined slightly.

So from around 42- 43% to around 35%,

Jacob Trefethen: Blood pressure has gone down. I feel like in the last 10 years, everyone’s blood pressure’s been through the roof.

Saloni Dattani: So, this was really confusing to me, and I’ll tell you why. And it’s because the way that we measure it is very confusing- the definition is if you have high blood pressure or if you’re on blood pressure drugs, even if they’ve reduced your blood pressure, if you’re taking those drugs, you count as having high blood pressure.

And that is very confusing, right? Because more and more people have been taking blood pressure drugs and they’ve reduced those people’s blood pressure. But that’s not reflected in the statistic that usually goes around. And so when I was looking at this, I was incredibly confused. And you can instead look at a different metric, which is called uncontrolled blood pressure. And that’s when you have blood pressure that’s higher than 130 over 80, you’re considered having uncontrolled blood pressure.

Jacob Trefethen: Not that high. God.

Saloni Dattani: Yeah. So that has reduced from 43% ish to 35%.

Jacob Trefethen: You’re saying that, so I thought the word uncontrolled meant that you’re not on drugs, so you’re not controlling it, but you’re saying uncontrolled means? Uncontrolled just means high.

Saloni Dattani: Yep. Yeah.

Jacob Trefethen: Okay, so then is the drop attributable to blood pressure reducing medications, do you think?

Saloni Dattani: I don’t know, I would guess that part of it is, and then I would guess that another part of it might be reductions in smoking and so on, and lifestyle changes. It’s probably a combination of both of those things. But yeah, that was very confusing to me and also surprising.

And then of course, this thing that a lot of people will find kind of obvious and noticeable is that smoking rates have dropped. So, you know, in 1999, about 25% or so of people smoke cigarettes. Now it’s just above 10%.

Jacob Trefethen: Let’s say it’s a very special night and it’s late, and you have had a few glasses of wine and you have one single cigarette. Do I appear in the 10% or the 90%? Obviously I’d never do that.

Saloni Dattani: Oh, you actually would count under a non-smoker. So the definition of cigarette smoking, according to this survey, which is the National Health and Nutrition Examination survey, or NHANES, the definition is: people who have smoked at least a hundred cigarettes in their lifetime and currently smoke every day or some days.

Jacob Trefethen: I think I’m under a hundred. I’m definitely under, I’m under a hunderd. Are you under a hundred?

Saloni Dattani: If you’re just, I’ve never smoked a cigarette.

Jacob Trefethen: Zero?!

Saloni Dattani: Yeah.

Jacob Trefethen: Zero. Zero? Wooooow!

Saloni Dattani: I have, I have no temptation. It smells bad. It’s weird. Feels so outdated.

Jacob Trefethen: Have you ever hit a vape?

Saloni Dattani: No.

Jacob Trefethen: Oh my God. Saloni.

Saloni Dattani: I’m really boring.

Jacob Trefethen: You’re quite the opposite. You’re living a dream that all of us can aspire to.

Saloni Dattani: This will surprise a lot of people. I don’t drink coffee either.

Jacob Trefethen: Oh my, that’s, okay. So people on the video-

Saloni Dattani: I also don’t really drink tea, except occasionally.

Jacob Trefethen: I have a jug, a mocha pot right next to me to keep topping myself up. So what is your favorite drug then?

Saloni Dattani: None.

Jacob Trefethen: Don’t tell, don’t tell me. Tell me offline.

Saloni Dattani: You know what I do instead of drinking coffee? I sleep more.

Jacob Trefethen: Noooo! Owned. Nooooooooo! Oh damn. We’re hosting a podcast called Hard Drugs with the reveal that she sleeps well and has smoked zero cigarettes.

Saloni Dattani: Well, I don’t really sleep that well. I guess I just sleep more, like I have naps and, you know.

Jacob Trefethen: Oh, that’s fun.

Saloni Dattani: My watch tells me that I’m a very poor sleeper.

Jacob Trefethen: Oh, that’s not fun. Do you sleep with the watch on? That might be the cause.

Saloni Dattani: I have always slept with a watch since I was like five.

Jacob Trefethen: Oh, cool.

Saloni Dattani: Learning a lot of facts about me.

Jacob Trefethen: Yeah. You’re learning lot of facts—

Saloni Dattani: I mean, you’re learning.

Jacob Trefethen: Okay. Well, I refuse to share anything, but back to risk factors. I mean it’s interesting that cigarettes have gone down a lot in the US over the last 25 years.

Obesity, sadly has gone up. Is that true? Am I lying?

Saloni Dattani: Yeah. Yeah. Obesity. So yes, some risk factors have worsened for sure, and specifically obesity has increased from around 30% to 40% in the last 20 years.

Jacob Trefethen: And it’s interesting, those two risk factors we’re mostly talking about cholesterol today, the risk factor of obesity and risk factor of cigarettes though, or smoking, both also increase your risk of cancer.

So it’s not just a heart disease story. Those are having chronic negative effects that, you know, hit the other main cause of death too.

Saloni Dattani: Mm-hmm. Yep. Lots of different problems. That’s why I don’t smoke.

Jacob Trefethen: Got it. Okay. Well now that you’ve told me that smoking can have bad effects, maybe I won’t smoke either! Why didn’t you tell me until this episode?

Saloni Dattani: Sorry!

Jacob Trefethen: In case my mom is listening, I don’t smoke. I actually don’t. I actually don’t.

Saloni Dattani: I think that just to make me sound more like a normal person, I think that I’m afraid that I could get addicted to stuff and so I’m just unwilling to start taking them ever.

Jacob Trefethen: Okay. That is very sensible. That is very sensible. Don’t hit a vape. That’s my advice.

Saloni Dattani: It’s not just because I’m a boring person.

Jacob Trefethen: Okay, good.

Saloni Dattani: So the other thing that is one of the reasons that cholesterol levels have declined is because the usage of statins has increased. So I think there’s probably multiple reasons for the reduction in cholesterol.

One of them is statins. And statins have increased from about, 30% of people who are eligible, taking them to around 40% now. So in the last 20 years, we’ve increased that by around 10%.

Jacob Trefethen: Statins, statins, statins, those again, when I was growing up, were a very sort of chic concept of ‘a statin?’. You know I grew up in England but with an American father.

And so anything to do with taking medication preventively was very American. It was very American. And the idea that Americans used to take aspirin, for example. Taking aspirin? Maybe if I grow up one day I can take aspirin.

Saloni Dattani: Life goals!

Jacob Trefethen: Life goals. And I asked my British mother one day about aspirin or statins, I can’t remember what, and she was like, “Oh yeah, no, that’s just an American thing. They do checkups every year as well.”

Saloni Dattani: That has actually been so shocking to me. So I grew up in Hong Kong. And my parents are quite health conscious and we’ve tried to, when we were at school we’d have a health checkup every year. And now my parents also try to get us to have a health checkup every year. And the fact that people don’t do that here is crazy to me.

Jacob Trefethen: You wanna take up a doctor’s time? They could be dealing with a sick person.

Saloni Dattani: But it’s also funny that you considered statins to be chic. I just thought they were boring adult things that I wasn’t interested in. I was like, “Oh God, they keep talking about that.”

Jacob Trefethen: Yeah. I dunno something about any daily pill when you’re a child, it’s like, “Ooh, ooh, ooh..”

Saloni Dattani: “Can I have that?”

This must be also the difference between our drug consumption. And I was just like, “My god, that sounds boring as hell.”

Jacob Trefethen: Fair enough, yeah.

Saloni Dattani: There’s so many things that I think were new to me when I was doing research for this episode that I think you will enjoy. I have a bunch of fun facts or questions, trivia, that I wanted to ask you.

Jacob Trefethen: Okay, I’m ready.

Saloni Dattani: So first question. Out of the top 10 most prescribed drugs in the US, how many are statins?

Jacob Trefethen: Ooh. Okay, let me check I understand. Top 10 most prescribed. Does that mean by volume? So if I get a daily pill that gets renewed every month, is that counting here or you mean it’s just per person?

Saloni Dattani: No, this is per person.

Jacob Trefethen: Okay, got it. Okay, cool. Okay. Well, statins are absolutely gonna be up there because they’re one of the few drugs that has very broad population effect.

So I’m gonna say it’s gonna be more than one at the top 10. And then of course, no one person is gonna be on, oh, maybe they are gonna be on more than one. You can correct me on that if you want… So I’m going to go with… three.

Saloni Dattani: Three. That’s very close. It’s two actually, and they are Rosuvastatin, whose brand name is Crestor. And then there’s Atorvastatin, which is commonly called Lipitor.

Jacob Trefethen: Yes, yes. Lipitor, a classic.

Saloni Dattani: And it made me think that, it sounds like Lickitung, the Pokemon.

Jacob Trefethen: It does, it’s true. And there’s actually a relation, that’s the organism it was discovered in.

Saloni Dattani: Oh?? Well, we were talking about this before with our amazing video editor, Graham, and he said, Lipitor sounds like a water or steel Pokemon. It’s moving slowly, smoothly through the bloodstream. It’s a defense against cholesterol, and maybe it involves into ‘Statin-ite’, and its final form is ‘Lipi-titan’.

What do you think?

Jacob Trefethen: I think that that is a evolution that I wanna watch, and I guess this is a very small Pokemon because it’s in my bloodstream.

Saloni Dattani: So I had a question for you.

Jacob Trefethen: Hit me.

Saloni Dattani: On Lipitor, which is, what is your favorite drug name?

Jacob Trefethen: My, I mean, actually Lipitor does roll off the tongue - Lickitung reference.

Saloni Dattani: Mm.

Jacob Trefethen: And so I do like Lipitor. What’s my favorite ever? Probably.. Oh, here, here, ready?

Saloni Dattani: Okay. Okay.

Jacob Trefethen: Acid. You have to admit that is the coolest!

Saloni Dattani: Yeah, I wasn’t expecting that.

Jacob Trefethen: You’re just naming something after acid? Wow, I can’t wait to take alkaline.

Ok but actual drug name, I’ve said Atorvastatin a lot in my life as well. The generic name.

Saloni Dattani: You have?

Jacob Trefethen: Because we funded a trial involving Atorvastatin.

Saloni Dattani: Mine is ‘axicabtagene ciloleucel’.

Jacob Trefethen: No, no, no, no, no, no, no, no. Nope.

Saloni Dattani: I was like, I remember reading this for the first time and I was reading about, so this is a type of CAR T-cell therapy.

Axicabtagene ciloleucel. And I was reading this and I was like, what? How do you read that? How do you pronounce that? And then there’s another one called ‘tisagenlecleucel’.

Jacob Trefethen: No.

Saloni Dattani: What is going on with these?

Jacob Trefethen: Why do they do that?

Saloni Dattani: So that is a very good question and the answer is the suffix, this thing at the end, ‘-leucel’ is what you call - that is the suffix for CAR T-cell therapies. All of them have that ending. And then the starting thing is just something unique to distinguish it from other CAR T-cell therapies.

Jacob Trefethen: I see. I think ‘leucel’ is too long of an ending. We need to contract it. Like an antibodies ending ‘ab’ that’s shorter.

Saloni Dattani: Yeah. ‘mAb’. Like Ipilimumab.

Jacob Trefethen: Ipilimumab. Aducanumab.

Saloni Dattani: We have a lot of statins that end in -statin, Atorvastatin.

Jacob Trefethen: Very true. That’s a long ending. I like, well you know, one brand name I really like - it’s just so iconic is Tylenol. I think it’s also very American. It’s very, Americans love it.

Saloni Dattani: I didn’t know what that meant ’cause it’s paracetamol here.

Jacob Trefethen: Exactly. Yeah. If you hear the word Tylenol in England, you’re like, oh, is that a far away land? Where they- is that Kansas? Where they get a headache?

Saloni Dattani: When I’ve gone to the US and I’ve asked for paracetamol, the people are just like, what? Who?

Jacob Trefethen: Who’s that? Oh, another classic - ethanol. Pretty good, ethanol’s pretty good.

Saloni Dattani: So there’s a difference between the chemical names like Atorvastatin, ipilumumab, axicabtagene ciloleucel, et cetera. And then those are kind of governed by this prefix suffix thing, right?

But there are brand names that are just something unique that should distinguish it from other drugs. And I think that’s quite hard ’cause there are quite a lot of drugs and you have to find a name that’s sufficiently unique that people won’t accidentally prescribe the wrong thing.

Jacob Trefethen: The thing I love about some of these cases is that the brand name is so forgettable that people just end up calling it the original thing, like malaria vaccines.

It’s like that’s RTS,S!, It’s like, no, no, no, that’s technically called Mosquirix. Yeah that’s RTS,S!

Saloni Dattani: I actually like Spikevax as a name. It’s pretty cool.

Jacob Trefethen: Nice and violent.

Saloni Dattani: True.

Jacob Trefethen: That’s steel type for sure.

Saloni Dattani: Yeah. Yeah. But it’s very memorable. There’s another one that I found called Xeljanz, and I wonder if you can guess how it’s spelled.

Jacob Trefethen: Xeljanz… C-E-L-L-G-A-N-S?

Saloni Dattani: No, it’s X-E-L-J-A-N-Z.

Jacob Trefethen: Okay. That’s it. That sounds like an insurance company.

Saloni Dattani: That is an enzyme. Well, that is a drug that inhibits an enzyme and it’s very important in autoimmune diseases and inhibits JAK enzymes.

Jacob Trefethen: I’ll take your word for it.

Saloni Dattani: Alright, next question. I’ve actually given you a bit of a hint on this one, that’ll help you figure it out, I think. So the question is: How many people in the US take statins?

Jacob Trefethen: 40% of eligible people. I’m just gonna squint and pretend eligible means like you’re over 40 and male and if you’re over 50 and female you can, I dunno. And then that’s gonna leave me with, let’s just call it half the American population, which is probably 170 million people. So what’s 40% of that? And so I get to, you know, something along the lines of 70 million, but that sounds too high, so I’m gonna take it down to 50 million.

Saloni Dattani: Wow. That was incredibly close, mostly because of your adjustment. It is roughly 50 million people in 2023.

Jacob Trefethen: Yes!!!

Saloni Dattani: And this is according to the medical expenditure panel survey, which is a large survey that asked people what drugs they are prescribed and what they’re taking. So, roughly 40% of American adults are recommended or eligible to take statins, but only 40% of them actually take them.

And so that recommendation, if you have high levels of LDL cholesterol and family history and various other things, you’re usually recommended to just start taking statins. And then there’s a broader population of people who could take it.

Jacob Trefethen: Cool. Okay. Yeah. I must have got something wrong in the age pyramid slightly, if only 40% or- I was probably yeah, I was claiming more, but whatever. Whatever. Let’s just take the win.

Saloni Dattani: Alright, another question. How much blood cholesterol comes from your diet?

Jacob Trefethen: Now, I think I know the answer to this.

Saloni Dattani: Okay.

Jacob Trefethen: Because I think that the egg thing is a lie, meaning that most of the cholesterol in my body and my blood is- oh wait, what do you mean by comes from your diet? I mean, everything in some sense comes from my diet.

Saloni Dattani: Oh, yeah, well, I guess more immediately.

Jacob Trefethen: Yeah, yeah, yeah. Yes. I think it’s probably, I think most of it’s produced in my body, so I think that’s coming from my diets less than 50% and I’m gonna go with 10%.

Saloni Dattani: Very good guess; it’s somewhere between 15 to 20%, is the general estimate. And this actually varies a lot because you can get cholesterol from mostly animal products, so meat, dairy, and eggs.

And obviously people’s diets vary lot. Like I’m vegetarian, I basically get very little cholesterol from my diet. And then vegans don’t get any cholesterol from their diet. And then depending on how much dairy or meat you eat, you might get more. So the estimates are roughly 15 to 20% of your blood cholesterol comes directly from your diet.

Jacob Trefethen: So just to be explicit about, when I keep saying the egg thing, what I mean is if you’re trying to reduce cholesterol, but your dietary intake is only, as you just said, 15 to 20% of your cholesterol, you probably shouldn’t be going by the direct contribution of diet.

You should be thinking about other things that are leading your body to produce cholesterol and not, you know, get rid of the LDL.

Saloni Dattani: And in fact, this was actually something that I updated on while sort of reading for this episode. One thing that I didn’t know was, okay, dietary cholesterol, there isn’t very good evidence that changing that will affect your blood cholesterol.

It’s a small effect, probably real, but modest. But saturated fats increase your LDL cholesterol, and if you eat more saturated fats, that actually increases the LDL cholesterol that’s circulating in your body. And so changing how you eat in terms of the fats you eat, that can actually make a difference. It’s still small compared to the effects from drugs, but it is pretty meaningful, I think it’s like you can get up to a 10 to 15% decline in cholesterol from strict dietary changes.

Jacob Trefethen: Okay. Well, if the bigger effect is drugs, then ask me a question about drugs, please.

Saloni Dattani: All right. So statins. Statins were one of the first effective cholesterol drugs. They were actually not the first, and we’ll maybe talk about that later, but statins were discovered from an organism and a lot of these organisms were screened before discovering them.

So can you guess what the organism was? Where, what organism did the first statin come from? And this is funny because if you think about a different drug, you might just land up on the same organism and it’ll be the same answer.

Jacob Trefethen: Oh?

Saloni Dattani: There’s another drug that has the same source.

Jacob Trefethen: Oh my gosh. Wow. Now I’m gonna be doubly embarrassed when I don’t come up. Gila monster?

Saloni Dattani: Gila monster? No.

Jacob Trefethen: Okay. How about…

Saloni Dattani: This is very hard. There’s so many organisms, obviously.

Jacob Trefethen: Yeah. Give me a size of the organism.

Saloni Dattani: Okay. I will say that it is a different kingdom from us and also that it was a big breakthrough in the 1920s and ’30s, getting a very important drug from the same genus.

Jacob Trefethen: 1920s and ’30s. Oh my God. It’s not because ’30s or ’40s, I would’ve said maybe it’s the microbes in the soil and we’re talking about some TB drugs, but now you’re telling me twenties or thirties, so maybe that means it’s, oh God, maybe it’s a fungus?

Saloni Dattani: It’s a fungus. Yes.

Jacob Trefethen: Okay, great.

Saloni Dattani: Very good.

Jacob Trefethen: Before, I have to guess, what is it?

Saloni Dattani: What is it? It is actually the same genus as the first natural antibiotic as - it’s the fungus Penicillium citrinum.

Jacob Trefethen: Wow. Penicillium gave us statins?

Saloni Dattani: Yeah.

Jacob Trefethen: How did I not know that? That’s wild. That is a real, I love that family.

Saloni Dattani: I did not know that. I was like, wow, that’s so cool.

Okay, so this first statin was discovered from the fungus Penicillium citrinum by a Japanese researcher called Akira Endo, and he screened many samples of this fungus before discovering the first statin.

So how many steps does it take? For your liver to synthesize cholesterol?

Jacob Trefethen: Oh. Oh, well, these diagrams always have so many steps that you can’t remember them. So that means it must be over six. And so, but probably way over six. I’m gonna go with 12 though. I’ll go 12.

Saloni Dattani: Oh, it’s actually around 30. So it depends how you count. Like do you count the individual, I don’t know, something combining and then breaking apart as two steps or one? But it’s roughly 30 depending on how you count.

Jacob Trefethen: The liver is a magical place. It, there’s just so much going on down there; I personally think of it as none of my business.

Saloni Dattani: The liver’s doing so many amazing things. It’s cleaning up the toxins in your body. It’s producing important stuff like cholesterol. Did you know that the liver is the organ that can regenerate itself the most? It can regenerate, I think up to 80% of it.

Jacob Trefethen: That is, yeah, I did know that. And it is wild. Did you know that the liver is the organ along with the brain that uses the most energy?

Saloni Dattani: Oh, I didn’t know that.

Jacob Trefethen: It’s kind of amazing because if you ask an 8-year-old what organ of the body’s doing the most?

Saloni Dattani: Mm-hmm. I feel like as an 8-year-old, I wouldn’t even know what the organ, I would be like. I know five organs, maybe.

Jacob Trefethen: Fives a lot though. And I just don’t think the liverthe liver, what the heck is going on down there? Like it’s not a very charismatic organ, but it’s actually doing, it’s doing an awful lot.

And I once was talking to a friend who’s a software engineer who was like, yeah, it’s sort of like the body’s sort of like, you really put a lot of time into coding up the brain, and you know the heart maybe, and some of the other organs, and then you have a bunch of leftover resources and haven’t really got time to be very careful about how you do the liver and you’re like, just, just let it do all the other stuff. You can have - it’ll figure it out. It’s kind of a junkyard doing all this crap. And we’ve never sort of optimized it to use energy very well.

Saloni Dattani: I also think of the liver as just like a big purple blob. It’s obviously doing lots of stuff.

Jacob Trefethen: True. Yeah. Confirm. That was actually the question I was gonna ask you. What is the liver? And the answer was ‘a big purple blob’.

Saloni Dattani: You know what’s funny is that I have been listening through our episodes and in probably every episode we’ve done, we’ve talked about blobs in some ways, and this episode is all about blobs. It’s all about fatty blobs and how they circulate around your body and how they cause disease.

Jacob Trefethen: We’re obsessed with fatty bobs.

Saloni Dattani: Yeah.

Jacob Trefethen: Also, we’re slightly obsessed with the liver. Now I think about it, you know, hepatitis B, last episode.

Saloni Dattani: Oh, that’s true. That’s true. Okay, last trivia question. What common vitamin also reduces LDL cholesterol?

Jacob Trefethen: Oh, I think I know the answer to this. But I know it for reasons that are slightly circuitous. Do you have, do you have a couple minutes?

Saloni Dattani: Yes.

Jacob Trefethen: So I think it is B3, niacin.

Saloni Dattani: Mm-hmm. Correct.

Jacob Trefethen: Okay. And the reason I think that’s true is because there’s also a relation between vitamin B3 in the form of nicotinamide, which I’m sure we all know and love, to tuberculosis.

But I don’t think that the relation is mechanistically actually relevant to LDL cholesterol, but I will just explain it in a little bit more detail.

So tuberculosis, the bug, can create a latent infection that sticks around for a long time where it’s inside one of your cell types, macrophages, and when it’s inside there, it’s actually eating cholesterol.

Saloni Dattani: Oh!

Jacob Trefethen: You know that?

Saloni Dattani: Oh yeah. Well we will talk about that. Oh, macrophages sometimes try to eat up cholesterol and sometimes it goes wrong.

Jacob Trefethen: Right? Well, now imagine you’re a bug and you are taking up residence inside a macrophage and you’re like, wow, there’s a lot of food here. I don’t need glucose. I’m just gonna eat the cholesterol. And so that is one of the tricks they do.

Now, I think that is all intracellular, I think. So I don’t actually think that that is that related to what we’re discussing today, which is cholesterol in the blood mostly.

That said, there’s this strange connection with B3 as well where tuberculosis is particularly, can be a problem for people who are malnourished. So if you have more macronutrients, more micronutrients, you have a better chance of fighting off tuberculosis. And you might wonder well, what are the most important micronutrients?

It looks that nicotinamide is one of the most important. So there’s some evidence in mice and there’s some evidence in humans. So if you supplement people with nicotinamide plus maybe one other thing, take one other favorite micronutrient, you might have quite a protective effect against tuberculosis. Now..

Saloni Dattani: Wait, wait. So the nicotinamide or the vitamin B3 reduces cholesterol, LDL cholesterol, and that means there’s less of it for the tuberculosis bacteria to eat. Is that what you mean?

Jacob Trefethen: That is, what I would ask you to do is, forgive me for the digression, because I’ve told a story where that’s the natural conclusion. I think in fact it’s not true.

Saloni Dattani: What?! What??

Jacob Trefethen: Surprising, what I think, in fact what is happening mechanistically is more like you just needed B3 to get healthier, generally speaking.

Saloni Dattani: Okay.

Jacob Trefethen: And the healthiness than helps your immune system fight back more. I don’t think the B3s get inside the macrophage and- I don’t think-

Saloni Dattani: Right.

Jacob Trefethen: Now, that’s why-

Saloni Dattani: But if it’s reducing cholesterol in general, then there’s less cholesterol for the macrophages to eat.

Jacob Trefethen: That could be the right story. And I just don’t wanna go on record in case someone’s listening who’s a TB researcher like, oh my gosh. But there is a connection to land the plane here in a way that is more positive.

People do- there is some suggestive evidence that if you give people who are getting treated for TB, also give them a statin, you might be able to reduce the amount of time that they are on TB treatment. And we’re funding a trial actually, in my day job on that right now.

And that is, I don’t wanna over claim the mechanistic story, but it is possibly related. Yeah.

Saloni Dattani: Wow. I had no idea. I didn’t know that. I didn’t know that tuberculosis bacteria eat cholesterol and sit in your macrophages. That’s crazy.

Jacob Trefethen: Well, you knew they sit in your macrophages.

Saloni Dattani: I didn’t know that.

Jacob Trefethen: Oh, really? Oh, okay. They do. Yeah.

Saloni Dattani: I didn’t, no.

Jacob Trefethen: No, they do. It’s really sad. I mean, it’s very clever because if you wanna hide somewhere, just like HIV was doing with a different immune cell, they hide in one of your immune cells. So it’s like, oh my God, there’s sneaky bugs. It’s like, oh yeah, well I’ll be fine here.

Saloni Dattani: Right. It’s like you’re trying to eat me. Well guess what? I’m part of you now.

Jacob Trefethen: A hundred percent. It’s creepy.

Saloni Dattani: Can you eat yourself? Exactly.

[jingle]

Saloni Dattani: Have you heard of the lipid hypothesis?

Jacob Trefethen: I have heard of the lipid hypothesis.

Saloni Dattani: So just for our listeners, the lipid hypothesis is the idea that high blood cholesterol levels, especially LDL cholesterol causes atherosclerosis and heart disease, and reducing them reduces those risks. So that is the lipid hypothesis, and it took a really long time for people, for scientists, to actually come to the consensus that that hypothesis was true.

It took, I would say, almost a century. So the first evidence for it was in the early 20th century, and it was really only in the 1980s and 1990s that it became the scientific consensus. But there are still some people out there, often podcasters not like us, who don’t believe it.

Jacob Trefethen: Yeah, I’m not convinced yet. Wots your evidence?

Saloni Dattani: Okay, so I have seven lines of evidence, and maybe you can rate them on how convincing they are to you.

Jacob Trefethen: I will, but before you tell me, I just wanna say what I agree with going in. I agree that heart disease exists. I agree that lipids exist and I agree that atherosclerosis exists.

What I’m not yet convinced of is the causal relation. Is it what you are telling me it is or not? So that’s what you gotta convince me of.

Saloni Dattani: Fair enough, fair enough. Okay. So first point, number one. So there are rare cases of, there are severe cases where people have something that’s called familial hypercholesterolemia, which is when they have an inherited condition where they have really high levels of cholesterol.

And those people, if they have two copies of that gene, often also get heart attacks and strokes at very young ages. So often when they’re children or teenagers, they can still get heart attacks even then. And if you look at autopsies after they’ve died, you’ll often find cholesterol deposits in their arteries. And this is something that was noticed in the early 20th century. So what do you think, are you convinced?

Jacob Trefethen: It’s suggestive, but I am not convinced! And here’s why. Let’s just think of the parallel with Alzheimer’s. There’s also familial Alzheimer’s where you can have a gene mutation, PSEN2 I believe, whole families can get early onset Alzheimer’s - so you are very likely to get Alzheimer’s in your forties, for example. The reason is that, there’s similarities of course, between the Alzheimer’s that people in those families develop and the Alzheimer’s that people outside of those families develop, but I don’t believe that it’s the same causal pathway fully.

I mean, there’s probably that hundreds of genes are implicated in how most people get Alzheimer’s, and I don’t think that a drug that targeted that one gene would work for most people. Sure enough, they make some mice for Alzheimer’s research that the way that they give them Alzheimer’s or sort of the phenotype of Alzheimer’s will give them dementia is by editing one gene.

And I just don’t believe a lot of that’s gonna translate because it’s more complicated when it shows up outside of the rare genetic case.

Saloni Dattani: It’s like when I see those medical studies where they found something and it works in mice and it’s far away from working in human trials.

But it makes me think, what if we could benefit from all of those early treatments by turning ourselves into mice? If someone could correct that, we would have so many treatments available to us.

So basically you’re saying maybe in the general population the condition has other causes or that is just one contributor and it’s not the cause? That seems fair, but I feel like there are some drugs and there are some diseases where studying a family that was inheriting that condition was quite helpful in developing an effective drug, and that will come in later in this episode.

Jacob Trefethen: If I’m being less the skeptic. Do I think that studying those families in my comparison to Alzheimer’s is irrelevant to Alzheimer’s? No, I actually think it’s pretty useful because you can learn some very important things from that. For example, in that case, there’s a couple people in an extended Colombian family are actually resilient to Alzheimer’s.

So they’re in their forties, they’re in their fifties, they’re in their sixties, and they haven’t developed Alzheimer’s or haven’t developed dementia, but do actually have plaques in their brain. So, oh my gosh, what’s happening there? And you can really hone in on the gene variants that those people have and learn a lot from that.

So that’s true. But I stick by my original point that I don’t think the causation is exactly the same. So we can learn stuff, but it’s not the same.

Saloni Dattani: Okay. Okay. Fair. What if… Second line of evidence: animals. So you mentioned mice, but, in the early 20th century, there were some Russian scientists who decided to feed rabbits with pure cholesterol.

And they were like, let’s see what happens. And what happened was, sadly they got hypercholesterolemia, which is high levels of cholesterol in their blood. And they developed things that looked like atherosclerotic plaques. And so you could see that giving them cholesterol gave them atherosclerosis. How about that? What do you think?

Jacob Trefethen: I think that … here’s what I’m interpreting. I’m interpreting in a different animal it’s sufficient for atherosclerosis to jam ’em with a bunch of cholesterol. Now that’s useful. What is it missing? Well, it might not be the only cause of atherosclerosis.

Maybe atherosclerosis just happens when you jam full of anything. And guess what? Sticks to some of their blood vessels. Okay, number one. And number two, of course, animals are not humans. Okay, but whatever. And then number three…

Saloni Dattani: What if rabbits are just weird?

Jacob Trefethen: What if rabbits are weird? Number three, I would say it doesn’t give me the link to heart disease.

I mean, it gives me a link to atherosclerosis, so, okay. It ain’t nothing, I’ll take it, but I’m not convinced yet.

Saloni Dattani: There’s another thing, which is that when these Russian scientists did these experiments in the 1910s, other scientists were not convinced because they tried to do the same experiments in other species and they didn’t find the same effect.

So they did this in rats and dogs, and they didn’t find the same effect, and so they just didn’t believe it. And we know now that actually the reason for that is that rats and dogs actually process cholesterol differently. So they mostly don’t even have LDL cholesterol. They have very low levels.

They mostly carry them around in HDL. But yeah, I think you’re right that they’re not humans, and also maybe it’s just jamming them with stuff that’s bad. Okay. Third piece of evidence, I think this might update you a little bit.

Jacob Trefethen: We’ll see.

Saloni Dattani: If you correlate cholesterol levels with heart disease, you will find that certain particles, especially LDL, are predictive of coronary heart disease. And cholesterol levels in general are also predictive of coronary heart disease. What do you think?

Jacob Trefethen: Well, I remember learning in school that correlation equals causation, so I’m convinced!

Saloni Dattani: Well, at least this is in a human general population.

Jacob Trefethen: Fair enough. I do think it is useful evidence. I really want to see that present. I bet you I could find a ton of correlates though.

Saloni Dattani: Yeah.

Jacob Trefethen: So I’m not, yeah, I definitely don’t feel convinced by that, that there’s an important singular driver that is LDL, but I’m glad it’s there.

Saloni Dattani: Okay, that failed.

Jacob Trefethen: Useful. Useful info. Useful info.

Saloni Dattani: You know, when I was reading about this, it was very funny because I was reading this historical review that said Michel Macheboeuf is known for being the father of plasma lipoproteins. And I was like, what? The father of what?

Jacob Trefethen: It’s like mitochondrial Eve and lipoprotein Macheboeuf.

Saloni Dattani: No, you know when people are like, oh, you know the father modern epidemiology, John Snow, and the father of bacteriology, Robert Koch, and have you heard of the father of plasma lipoproteins? This is getting outta hand. This is so specific.

Jacob Trefethen: He’s still a father to me.

Saloni Dattani: So anyway, Macheboeuf discovered lipoproteins, which include LDL cholesterol, but he didn’t know what it was made of.

And then another scientist, John Gofman, he was studying the serum and he was ultra centrifuging it. He was spinning it around really fast in an ultra centrifuge, and he found that there was some component of it that floated to the top that was partially made of lipids and partially made of proteins.

Jacob Trefethen: Was he also the singer behind ‘you spin me right round baby, right round, like a record baby’? Because I think he was the father of that pop music as well.

Saloni Dattani: That should be the motto of ultra centrifuges.

Jacob Trefethen: Every time we press go, they play that song. Like, yeah, I gotta leave this one overnight, it’s gonna be a lot of repeats of the song.

Saloni Dattani: Well, it’s kind of a shame because often the process is quite fast and you don’t really do the spinning process for very long. You probably only hear the first few words of the song.

Jacob Trefethen: Noooo… ‘You spin-’ ‘Oh, we got a result!’

Saloni Dattani: Unfortunate. Okay, next piece of evidence. So not just a correlation, but this time: longitudinal study, the Framingham Heart study, of that town in Massachusetts, was it?

Jacob Trefethen: Framingham.

Saloni Dattani: Framingham. And that was launched in 1950 and it followed thousands of people who lived in that town, and they initially got measured for their cholesterol levels, blood pressure, smoking, obesity, diabetes, family history, blah, blah, blah.

After two decades of following those participants up, you could see that people who started off with higher levels of cholesterol had much higher rates of heart attacks, strokes, and other cardiovascular risks, even if you adjusted for their smoking or hypertension.

Jacob Trefethen: Oh, that last bit is quite interesting.

Okay. Yeah, basically I would put this as a notch better than the last correlation you told me. Longitudinal data is really cool. You get to follow an actual person’s whole trends, so that’s great. Now, that said, once again, there’s just a bunch of other stuff that might be co-correlated. Or, you know, maybe there was confounding there and you know, you can, that’s great that they tried some way of adjusting for smoking or adjusting for hypertension, but yeah, there might be a third thing you forgot to ask?

Saloni Dattani: Right. A secret third thing that caused both high cholesterol levels and cardiovascular risk. Okay. Fair enough. I think my fifth one might be more convincing.

So number five, if you change people’s diet…

So this is what I found really interesting because I feel like a lot of diet research today is observational, right?

It’s like, let’s see what people say they’re eating, and then let’s see how that correlates with their risks and stuff like that. Usually we’re looking at food diaries. So people are just writing down what they ate and then we’re correlating that. And I mean, who sits down and writes in a food diary.

Jacob Trefethen: I dunno if you’ve ever tried a food diary, but the classic thing I’ve experienced is you completely forget what you ate and it’s all made up.

Saloni Dattani: Right. And there are lots of- it’s very noisy as a measure that you can record. Instead, and this really shocked me. But it turns out that people actually did randomized controlled trials where they made people have the same diet for every day for years in the 1950s and ’60s.

Jacob Trefethen: You’re kidding.

Saloni Dattani: Isn’t that crazy?

Jacob Trefethen: How did they - wait? There were doctors in your home cooking for you?

Saloni Dattani: No, no, no. So it would be like a cafeteria. Where you get the standard meal every day.

Jacob Trefethen: Really?

Saloni Dattani: Yeah, yeah. There were several of these. There were some that were carried out in hospitals or mental asylums.

But then there were also others that were the general population, or they were like, the NIH ran a study called the National Diet Heart Study. There were a bunch of these, right, where they actually just randomized dietary interventions where some of the food that they were eating was, they were substituting the saturated fat parts of their food for polyunsaturated fats. So it was just that same thing that they were eating for every day.

Jacob Trefethen: ‘Please, sir, could I have some more?’ ‘No,’ said the NIH. ‘You’ll have your allocated fat.’ People wonder about scientific institutions declining and really it’s said they don’t get to control what we eat anymore.

Saloni Dattani: But I found this so interesting and I was like, wow, how come we don’t do that anymore?

And I guess maybe one of the reasons that it was possible to do the study at the time was that maybe food was expensive then. And if you were in the study, you would just get your food every day for free.

Jacob Trefethen: What the hell? This is crazy. This is one of those things you hit, this feels genuinely like Victorian England, but you’re telling me 1950s America, it’s like, wow, I’m running low on sloppy joes, so I’m gonna need state provided.

Saloni Dattani: So there was- This diet heart study was like around a thousand men who were in this trial and there were different cafeterias serving the different foods. So some of them were allocated to the normal saturated fat diet and some of them were allocated to the unsaturated fat diet and they found that blood cholesterol levels increased in the saturated fat group.

I think it was maybe the same trial, or maybe it was a different trial, they actually did a crossover trial. So the people who were randomized to one of those options were then later on randomized to the opposite one.

Jacob Trefethen: You know what? I like it. I like this, this makes me feel good.

I like it. I’m being too nitpicky. What I’m trying to say is show me someone who - I wanna see the contraindications. Someone got elevated heart disease risk but didn’t have cholesterol, or someone had lower heart disease risk but did have cholesterol. And I get, every time you give me more evidence that isn’t of that shape, I do get more confident.

Saloni Dattani: Well, this is the average. Yeah.

Jacob Trefethen: Yeah. But the anomalies would help me dis-confirm. So the fact I haven’t seen the anomalies fair enough. I’m more confident. I’m more confident. So I don’t know. What do you think about that one?

Saloni Dattani: I think, I feel like that is pretty good evidence, but it’s a bit indirect because it’s about saturated fat.

The thing that they change in their diet is how much saturated fat they eat, not how much cholesterol they eat. And we know that saturated fat reduces your cholesterol clearance, but people didn’t know that back then and they sort of just lumped saturated fats and cholesterol together. So it’s a bit indirect.

Jacob Trefethen: If they fed them cholesterol then, because we now know that most cholesterol is made in your body not consumed, that also might have not been- quite got it. So this stuff is tricky. Wow.

Saloni Dattani: Yeah, it’s tricky. So we know now, and I think since the ’80s, we’ve known that saturated fats work through this pathway in the liver and they reduce your clearance of LDL cholesterol. But people didn’t know that back then.

Jacob Trefethen: Okay. So that was, that was five. Were we already in a LDL world? Did they have, did they say LDL-?

Saloni Dattani: In LDL world? I love that. This made me think of the Barbie World song.

Jacob Trefethen: I’m an LDL. Yeah.

Saloni Dattani: I’m a fatty blob in a fatty world, life in fatcholesterol…

Okay, let’s try again. I’m a fatty blob in a fatty world. Life in lipids, it’s insipid!

Jacob Trefethen: Woo. Yay!

Saloni Dattani: Thank you.

Jacob Trefethen: That’s beautiful. That’s the career pivot we should be talking about.

Saloni Dattani: Okay. Number six is. Before statins were introduced, there were a few drugs that reduced cholesterol levels. One of them was called Cholestyramine, a bile acid sequestrant.

And there was this big trial that randomized about 4,000 men to either that cholesterol drug or placebo for about seven to 10 years. And they found a reduction in LDL cholesterol, so that was reduced by about 20%. And heart disease deaths or heart attacks were reduced by 20% as well.

And then the same thing was true also in another trial after that with a different drug that reduces cholesterol called niacin, which is vitamin B3, and also similar. So they had a reduction in cholesterol levels and also a reduction in heart attacks and strokes.

Jacob Trefethen: It’s nice, I can feel myself yearning for more on the mechanism or ruling out alternative, basically, and I guess you told me in the case of- the closest I remember of the six so far was the rabbits.

So there we’re like really getting at, okay, we squeezed this cholesterol in and it led to, you know, so we don’t yet have necessarily the molecular mechanism of why, I guess that maybe happened earlier, but at least there’s some sort of link I can visualise.

What I’m struggling with in the trials you just mentioned is I want to know what else other than LDL went down and why are we singling out LDL? Why are we singling it out?

Saloni Dattani: So you’re saying maybe these drugs do reduce heart attacks and strokes, but what if they’re doing it from a different mechanism? Like what if they just happen to reduce cholesterol and happen to reduce heart attacks and strokes for some other reason that we don’t know?

Jacob Trefethen: Yes.

And what is special about cholesterol now for people who- you know, ’cause it might reduce all sorts of things; maybe it reduces your blood pressure by 20%? You know, I don’t know. Anyway, I don’t want to come off too skeptical. Basically, in all of these cases, that type of question is looming for me.

But the more you layer on these different lines of evidence, my skepticism is reducing. So the thing that would be lovely is if I knew there was a, either you gimme some mechanism, stuff that I can hang my hat on, or you’re like, okay, actually this is not just a vitamin, this is a drug literally just going after cholesterol and that we got rid of that only, and then heart attack went down. That would be, I’d be excited about that.

Saloni Dattani: Well, you are in for a treat because that is the seventh line of evidence.

Jacob Trefethen: Oh my goodness.

Saloni Dattani: So statins, I dunno if you’ve heard of them.

Jacob Trefethen: Statins. I’m gonna cry.

Saloni Dattani: They directly block an enzyme that is essentially vital to produce cholesterol in your liver.

So the pathway that the liver synthesizes cholesterol is very long, and there’s one really important step called the rate limiting step. It’s the bottleneck, if you don’t have that, if that’s step is blocked, then you get no cholesterol the out the other end. And statins block that enzyme to an extent.

So they were initially tested in the lab to see if they could block the activity of this enzyme, and some of them did, and then they were tried in clinical trials given to human participants.

And there were various clinical trials, but one of the biggest ones was in the 1990s and it’s called the 4S study, and it took place in Scandinavia. And they found that statins reduced LDL cholesterol and they also reduced heart attacks and strokes, and they’re actually quite big reductions.

So they reduced total cholesterol by about 25%, LDL cholesterol about 35%, and heart disease deaths by 42%. And that was highly significant as well. And this was done with Simvastatin, which is the second statin produced by Merck.

Jacob Trefethen: WOO!

Okay. I love it. And before I reveal that I’m convinced, I will give the final skeptical take, which is the, I would wanna read the paper in depth and be like, what else is that enzyme involved in?

Are we sure it’s only-? You’ve always gotta go a few steps deeper just to make sure there’s nothing more. That said, given the description you just gave me, that seems like pretty solid evidence. If you have a trial, you’re randomizing people, you’re going after the one fricking thing that we’re interested in. That’s pretty cool.

Saloni Dattani: And you know the mechanism because you know that that drug inhibits cholesterol production.

Jacob Trefethen: Love it. Okay. So that was pretty good.

Saloni Dattani: Oh wow! You’re convinced, boom.

Jacob Trefethen: That took a while though. I mean, could we know about statins earlier, that would’ve helped me.

Saloni Dattani: So I found this really interesting because I was reading this book called The Cholesterol Wars, and it’s about how the evidence was put together and all of these different lines of evidence and when people were convinced, and most scientists were convinced at the sixth step.

So when you had the earlier cholesterol reducing drugs. So they said, let’s look at all of these lines of evidence. We’ve seen the correlations. We’ve seen the correlations are longitudinal. We’ve seen the animal evidence, we’ve seen the families with high cholesterol levels, and we’ve seen a few drugs that reduce cholesterol and also reduce heart attacks.

And they said, this is enough evidence. And the FDA decided that they were happy to accept or approve any drug that reduced LDL cholesterol before seeing that it reduced heart attacks and strokes, but they also asked for that evidence afterwards. So they treated it as causal and they said, this is okay as a surrogate endpoint, and if you can show that your drug reduces LDL cholesterol, that’s enough for us to approve it, as long as you also collect this other evidence afterwards to see how much it reduces those things.

But yeah, there were still a bunch of holdouts and people who were not convinced until the statin trials.

Jacob Trefethen: So are there still holdouts after the statin trials?

Saloni Dattani: Yeah, but I wouldn’t take them very seriously. I think it’s possible to just not know about the evidence, but I think there’s some people who have read about all this and are still skeptical.

Jacob Trefethen: Right, right. Yeah. I would love to read their takes. I mean, basically I think it’s always useful to have holdouts who are up to date on all the evidence and then they can give you- there always will be some story that is technically possible. And then. You know, well, not always. In some cases you get so decisive, but you know, the human body’s complicated.

And so there’s effects that are different for different people and all that. So I’d love to read that take. I would be surprised with statins in particular, it’s just they’ve been used by tens, hundreds of millions of people. We’ve got so much data that I’m like, okay, well that’s actually, that would be extremely hard to convince me. I wanna stay open-minded, obviously. But that one is so studied. Oh my goodness.

Saloni Dattani: So instead of going through the enormous and complicated description of how LDL and other cholesterol transport works, I thought I would do a three minute version of how everything works in cholesterol. Why it’s bad.

Jacob Trefethen: Should have brought my timer.

Saloni Dattani: You should have brought a little sand glass.

Jacob Trefethen: Yes. Red sand. Like in Wizard of Oz, at the end there’s booming.

Saloni Dattani: Ooh… that would be very stressful. But I’m gonna try to explain how lipids are transported in your body, what cholesterol is, why LDL cholesterol is bad… in three minutes.

Jacob Trefethen: 3, 2, 1. Start the clock.

Saloni Dattani: Alright. Alright. So fats and cholesterol have to be transported around your body, but they can’t dissolve in water. They’re hydrophobic, they’re literally like oil and water, right? So they have to be transported in a special route. And the way that they’re transported is that they’re carried around by lipoproteins.

And the lipoprotein is a blob that contains the fats and cholesterol, so it carries them around, and it has an outer shell that’s made of proteins and phospholipids. So this blob, the lipoprotein, travels around the bloodstream and it delivers fats and cholesterol to the organs that need them.

So fats are really useful because they can be stored for energy use later on, or they can be used by our muscles for energy, so they can be broken down and go through the Krebs cycle and generate ATP, and they can be used for energy.

Cholesterol is really important for lots of reasons. So one, it helps keep our cell membranes stable. It’s also a precursor to lots of hormones like aldosterone, testosterone, progesterone, and estrogen.

And so basically these fats in the cholesterol are being carried around the body in lipoproteins to many different organs for these types of functions: energy use, storage, making hormones, and keeping cells stable.

You can get cholesterol in two ways. One, you can consume it in your diet. And when you do that, it gets absorbed in your small intestine. Or your body can synthesize cholesterol itself through other food that you’ve eaten before. And most of the cholesterol in your bloodstream is actually synthesized by your body, specifically by your liver. And your liver can synthesize cholesterol from other stuff. And that’s a very long process that involves many enzymes.

So either way, you have fats on cholesterol and they’re transported around your body in lipoproteins. And there are many different types of lipoproteins. You’ve probably heard of LDL, which is bad. (boo) And HDL, which is good. (they cheer)

Saloni Dattani: So those are two types of lipoproteins and there are others as well. And the different types of lipoproteins are classified by how large they are, how dense they are, how bouncy they are, what they contain.

The basic system is the fats and cholesterol transported around your body, starting in bigger lipoproteins, and then they get dropped off at different organs and then the particles get smaller and denser.

The lipoproteins start off big. It’s a bit like they’re carried around in a bus; imagine a blobby bus going around your body, and then as more people get off the bus, the bus shrinks. And technically that bus or the lipoprotein is being remodeled at each step, but we don’t need to get into that.

At a certain point, the buses or the lipoproteins are small enough that they’re called LDL, the bad one. (they boo) And that’s bad. Higher levels of LDL cholesterol cause higher risks of atherosclerosis, which is when you have a cholesterol plaque in your blood vessel. So what’s going on there?

What happens is, these particles, which are circulating in the bloodstream, they can actually pass through your blood vessel wall, especially if the lining is damaged, and then they can just get stuck there.

Jacob Trefethen: ’cause they’re so small?

Saloni Dattani: Yeah, they’re very small. And that means more particles containing cholesterol and carrying certain proteins on their outer layer. And those proteins get them stuck. And when they get stuck, they send out signals that make immune cells try to come and clear them up. And those immune cells are macrophages. So macrophages-

Jacob Trefethen: I wanna say, yay. Not too many.

Saloni Dattani: No - it goes wrong. So the macrophages usually try to eat things up like pathogens and debris, and we talked about them in our hepatitis episode, I think. They’re trying to clear up the mess of these LDL cholesterols that have gotten stuck. But instead of actually clearing them, the immune cells just get filled with fatty blobs.

So they eat up, they’re trying to eat up the LDL, and then they just get fat. They get foamy, they become foam cells, and that makes things even worse. So that attracts smooth muscle cells, which cover up that whole mess. They turn it into a plaque and they add a cap to it and that is an atherosclerotic plaque, so it’s like an outgrowth in your blood vessel wall.

Jacob Trefethen: So you got a blood vessel wall with these foamy cells trapped underneath.

Saloni Dattani: And cholesterol, lots of fat.

Jacob Trefethen: Right?

Saloni Dattani: And so this plaque can slow down your blood flow and if it’s loose, the whole thing can just break off and form a blood clot and then it can get stuck.

And if it gets to smaller blood vessels, as it travels through your bloodstream, it can just trap- it can just block the entire blood flow. And that’s very bad. But it depends on where it happens: if it happens near the heart, it can cause a heart attack. If it happens near the brain, it can cause a stroke or it can block the blood going through your legs or your arms, and that causes peripheral artery disease.

So there is a way to reverse this, and the thing that does that is called HDL.

Jacob Trefethen: H! D! L! Woo!

Saloni Dattani: And that can help reverse this to some degree. So HDL is made in a totally separate process, also by the liver and the intestines. And what it tries to do is it tries to scavenge for cholesterol, tries to pick it back up from the tissues.

It gets into the blood vessels where the LDL gets stuck, and it tries to take the cholesterol away from it. Or it can even go to the foamy immune cells and it can try to take up cholesterol from there, so it’s a good guy. HDL is generally good, and LDL is generally bad. And that’s my summary.

Thank you.

Jacob Trefethen: Very efficient. We have a good witch. We have a bad witch. Although the good witch is kind of like a little scavenger. And the bad witch is older, in this case.

Saloni Dattani: Right? Well one of them, the bad witch is making a mess everywhere.

Jacob Trefethen: Yes. Everywhere.

Saloni Dattani: And the good witch is cleaning it up.

Jacob Trefethen: Ah. Marie Kondo, the good witch.

My takeaway is: fat’s gotta get transported around so they start in a big package and drop stuff off along the way, different organs. Then they get so small that they turn into LDL, the bad word and can get stuck to your blood vessels in a way that you don’t actually want. Then your immune cells, macrophages, come to try and deal with the problem, but they can end up making the problem worse.

Eating a bunch of cholesterol, getting really foamy, and then getting trapped under a layer of more cells. And look, we do not want our blood vessels to be clogged up. We do not.

Saloni Dattani: Alright. Welcome back. I think we should talk about how statins were actually developed. What do you think?

Jacob Trefethen: I would love to.

Saloni Dattani: Alright. So drug development, how does it all work?

Jacob Trefethen: Well, you sort of stir something up in a test tube and drink it and then…

Saloni Dattani: Pour it in a big witch cauldron.

Jacob Trefethen: Right?

Saloni Dattani: That’s how they did it back in the day.

Jacob Trefethen: Those cauldrons are so regulated these days. They don’t even let you stir your cauldron anymore.

Saloni Dattani: The first statin called mevastatin originated from a fungus called Penicillium citrinum, and it is the same genus as the fungi that gave us penicillin, which is called Penicillium notatum. And I was like, this is whoa.

How? What? Well, I remember reading this and I was just like, wow, that’s shocking. And I think there are actually more coincidences than that. So the guy who discovered this, a scientist called Akira Endo, he’s a Japanese scientist. He was working in a company called Sankyo, which began as a fermentation company.

So he was working on fermentation, broth, and he was inspired by Alexander Fleming, he said, and he loved fungus. He loved studying fungi. So yeah, it was very interesting to hear. I had no idea about this. Did you know that?

Jacob Trefethen: The relation between them or..?

Saloni Dattani: Like that they were both from fungi and that they were both from penicillin, like penicillium fungi?

Jacob Trefethen: I did not know that. And of course it makes me wonder, number one, is there something special about that fungus? But it also makes me wonder, oh God, what else is out there that we just haven’t studied? There’s probably a ton of stuff that if we had taken into the lab, we would’ve learned by now. But this particular fungus just happened to be the one that we leaned in on.

Saloni Dattani: Right? The chosen one.

This is really also interesting because the way that he discovered it at least was quite different from Fleming. So it wasn’t just some sort of weird accident, but he did trial and error. He tested over 6,000 samples of fungi and different compounds before finding one that seemed effective in lab studies at inhibiting the specific enzyme, HMG-CoA reductase, which is crucial for synthesizing cholesterol.

And the source of this sample was that it came from a fungal mold that was growing on rice samples at a grain shop in Kyoto.

Jacob Trefethen: So if we’d just eaten more rice.

Saloni Dattani: Well, moldy rice.

Jacob Trefethen: I got it. Yeah.

Saloni Dattani: So this is really interesting because, okay, you found this compound that works in the lab. What are you gonna do next?

Jacob Trefethen: Eat it, sorry.

Saloni Dattani: Hopefully not.

Jacob Trefethen: Okay. I’ll try and test it out in a lab experiment or in animals. See if it does what I think it does.

Saloni Dattani: Fair enough. Fair enough. So probably purify the fungi, grow the fungi in the lab, try to produce more of this compound, and then let’s test it out in… dogs. And so they tested it out in a bunch of different dogs, and this was in the 1970s, late 1970s. So they were doing studies in animals, but they were also, immediately they started to treat patients with familial hypercholesterolemia because they had severe disease. And they were like, okay, well let’s try to see if it improves their condition, they have this very serious condition. And unfortunately the experiments in dogs seem to show what looks like intestinal tumors.

Jacob Trefethen: Oh God.

Saloni Dattani: And so they just shut down the clinical trials. They don’t report specifically what they found in the animals.

Jacob Trefethen: No!! Report! Report!! Oh my. You’re triggering me. You’re triggering me.

Saloni Dattani: They didn’t report it.

Jacob Trefethen: Anytime you generate negative data. Let. It. Out.

Saloni Dattani: Report!

Jacob Trefethen: Oh my.

Saloni Dattani: We need the negative data.

Jacob Trefethen: The negative data’s so useful.

Saloni Dattani: So they didn’t report it. And actually, I only found this out from a paper that was published by Akira Endo in 2017 or something.

Jacob Trefethen: When did his career start? I’m confused.

He was publishing in 2017?

Saloni Dattani: So he was working in the 1970s. And then after that in recent decades he was kind of writing retrospectives. So this was a bit scary, right? Like they just shut down their trials. They saw these intestinal tumors or what they thought were intestinal tumors.

And meanwhile, there was another big company that you might have heard of called Merck, and they were testing a very similar compound called Lovastatin. And Lovastatin, it’s almost the same as Mevastatin, it just has an extra methyl group. And they had, I think, heard of Akira Endo’s work.

They also started looking at fungal extracts and trying to test them in the lab, and they found this compound. And for them it was their 18th sample, not their 6000th or whatever. And they heard that these clinical trials had suddenly been shut down. And they’re like, what? Why did they shut it down?

Because they thought, we found a treatment for cholesterol. Cholesterol is such a big problem, right? So many people have high cholesterol levels and they expected it would be at least a multimillion dollar drug, if not a billion dollar drug. And so they were really surprised by this and they tried to find out what happened and they asked- they paused their own trials and they asked the people at Sankyo for further details, but they didn’t share them.

Jacob Trefethen: No. Well, the classic way that you do this now is that you hire someone who used to work there and in fact, what you’re doing is smuggling intellectual property from their brain.

Saloni Dattani: Isn’t that illegal?

Jacob Trefethen: Oh, for sure. Yeah. I mean, depends how it goes down. But yeah, this is why there’s so much litigation in biotech.

Saloni Dattani: So according to the book that I was reading, The Cholesterol Wars, and that book is by a guy called Daniel Steinberg, who was a scientific advisor to Merck at the time. So that is pretty good at a source of this information. So he says that executives at Merck offered Sankyo a business deal, if they shared this data.

And the head of Sankyo was interested and then he declined the offer and he said, I wanna cooperate, but other people are objecting. And that’s it.

I mean, imagine if you were in this situation, you’re a Merck, what would you do? You’ve heard these clinical trials shut down. They’re not sharing any data on exactly why, you’ve heard that it’s because of tumors and dogs, but you don’t really-

Jacob Trefethen: Oh, you have heard that on the grapevine?

Saloni Dattani: Yeah. You’ve heard rumors. Yeah.

Jacob Trefethen: Got it. Interesting. I mean, the honest answer is that these days there’s enough risk aversion in the system. It probably would just get shut down. I wouldn’t be surprised. Well, what I would want to do is, if I think that I have good enough signal that it’s gonna work out the other end, I just wanna figure out what the heck is going wrong with these tumors.

Saloni Dattani: I mean, you’re already in the middle of clinical trials at this point.

Jacob Trefethen: Oh, you’re in the middle of clinical trials?

Saloni Dattani: Yeah, you’ve paused your clinical trials, you’ve told the FDA something might be wrong.

Jacob Trefethen: I see.

Saloni Dattani: And yeah.

Jacob Trefethen: That’s really tough. Oof. I guess I would, in whatever way you can try and get more data from the people who’ve already gone through the clinical trials, see if there’s any side effects you didn’t expect, hone in on stomach cancer or whatever. Then other than that, I don’t know.

Saloni Dattani: So with your own, you would look for the people who’ve already been treated in your own clinical trial?

Jacob Trefethen: Yeah.

Saloni Dattani: Yeah. Okay. Got it.

Jacob Trefethen: Collect more data if you can that relates to the side effect in question.

Saloni Dattani: Okay. Next step. I feel like we’re playing a interactive video game, but what happens is they do that, they look at the trial data that they’ve already collected. They haven’t seen any increased risks, but there’s still this rumor, right? What are they gonna do next?

Jacob Trefethen: Also, they’re not collecting all the data that you could.

Saloni Dattani: So they’ve asked all the doctors-

Jacob Trefethen: Yeah. Okay. Got it, got it, got it.

Saloni Dattani: Have you seen anything going wrong?

Jacob Trefethen: Got it, got it. So then I’m thinking… you fly to Japan and you try and find these dogs.

Saloni Dattani: You try to find the exact dogs.

Jacob Trefethen: Have a reasonable conversation.

Saloni Dattani: Well, unfortunately the dogs were probably dead, ’cause that’s the only way, you know that they had tumors.

Jacob Trefethen: The tissues still preserved? I dunno, anyway. I don’t know, what do you do? I don’t know.

Saloni Dattani: Infiltrate the pharmaceutical company and try to find those dogs.

Jacob Trefethen: I guess, I mean it’s an interesting case of the benefits of and drawbacks of industry versus academia, where it’s industry is really good for iterating, changing a molecule, testing it out, trying it, and this, trying it and that, really pushing forward on the boring engineering stuff and, no offense industry for saying boring, and then taking things into trials and all of that.

Now, academia can take things into trials. It’s possible. It’s often a little bit less of a tweaking, boring, tweaking. Now the issue is if this had just been academics, there would definitely be egos involved, but in theory they would be publishing in public! So you would know!

Saloni Dattani: Well, they might have just not published their null or negative results.

Jacob Trefethen: That said, if you are an academic and you’re not publishing that kind of result, look yourself in the mirror. Look yourself in the mirror, don’t you believe in knowledge? Come on, what we doing? We’re advancing human understanding. Come on. Okay.

Saloni Dattani: What’s interesting is in this case it is the people in industry who have discovered the possibility that there are drugs that inhibit this enzyme. So they’ve actually done the basic research.

Jacob Trefethen: Right? Yeah. Which sometimes happens. Anyway, I’m really stalling for time because I don’t know, what should I do if I’m Merck?

Saloni Dattani: What are your options?

Jacob Trefethen: Shut down the trial and never return.

Saloni Dattani: And do what? Like move to a different field or?

Jacob Trefethen: Yeah, maybe move to the Caribbean, maybe move toMexico City’s gorgeous this time of year. Or I could do an early readout of the efficacy data if I’m allowed.

Saloni Dattani: Okay. Okay.

Jacob Trefethen: I guess I’m just still so hazy about what happened with the dogs. What happened with the dogs? What happened with the dogs? And I don’t know how to select a better candidate until I-

Saloni Dattani: Who let the dogs out?

Jacob Trefethen: Who let the dogs out? Would be my question, posed in flawless Japanese. Okay.

Saloni Dattani: So I will tell you what happened. I’ll finally reveal the mystery. So what they do is they continue the toxicology experiments. They try to see, do our dogs have the same side effects, do our animals have the same side effects?

And they do see those same tumor like appearances, but those changes are benign and they can be reversed by inhibiting the next step of cholesterol synthesis. So it’s not a huge deal.

Jacob Trefethen: So basically they’re like, okay, number one, do we generate the problem with the dogs? If not, let’s just pretend it never happened. And then they’re like - we are giving dogs tumors.

Saloni Dattani: That, yeah, yeah. Well, it’s something that looks like intestinal tumors but isn’t, and it’s reversible, and it’s benign.

Jacob Trefethen: Okay. So… it’s reversible. It’s reversible. You know, honestly, if I was a patient in those trials and they were like, look, it looks like intestinal tumors, but the great news is it’s reversible. I’m like, sorry, what?

Saloni Dattani: Well, it’s not that they’re actually tumors, they just look like tumors. And whatever has changed is reversible.

Jacob Trefethen: Do they look like… flesh? Like in what sense do they look like tumors?

Saloni Dattani: They’re just like blobby.

Jacob Trefethen: Okay. So if a patient comes up to me and says-

Saloni Dattani: So many blobs in this episode.

Jacob Trefethen: “Doctor, Doctor, I think there’s only a tumor.”

But then I’ll just say, “No, it’s actually just blobby. It’s just blobby. I know it looks-” Like, “Right, so why do I have a blob in my intestine?” “Don’t worry, it’s reversible.” It’s like, “Right, but did you give it to me?” “That’s correct. I did give it to you, and you should be grateful.”

Saloni Dattani: So what happens next is there are a lot of other doctors that are like, these patients really need these drugs.

They clearly are reducing cholesterol levels and they’re not causing tumors in humans, only the dogs.

Jacob Trefethen: Don’t-

Saloni Dattani: Which are not actually tumors.

Jacob Trefethen: I don’t think that Merck should hire you as their press person.

Headline: They’re not causing tumors in humans.

Saloni Dattani: Well, so what happened was they presented this evidence to the FDA and they said, let’s at least continue the clinical trials and see what happens. And the FDA said, okay. And it took three years until they resumed their clinical trials.

Jacob Trefethen: Really? Oh my gosh.

Saloni Dattani: Yeah.

Jacob Trefethen: So they had to re-enroll new people?

Saloni Dattani: I think so.

Jacob Trefethen: Oh no.

Saloni Dattani: And they did a bunch of the toxicology stuff, they also tried to get the data, didn’t get it. They asked a bunch of the doctors in the trial to see what their patients- what happened with their patients. Seemed like no side effects.

And so they resumed their clinical trials. And when the clinical trials were completed, they did show a large reduction in blood cholesterol reduction and very few side effects. And then the drug was approved in 1987. So they didn’t see the tumors in humans there either. And that was the first statin, lovastatin. Was approved in 1987 and we are now, almost 40 years since then. Millions, hundreds of millions of people have been treated with statins and monitored. And there is no increased risk of cancers from statins, which is good news. And they’ve probably saved millions of people’s lives by reducing the risks of heart attacks and strokes by roughly 20% and annual death rates by around 10%. And this is according to big meta-analyses of dozens of clinical trials.

Jacob Trefethen: That’s amazing. And I feel like there’s some, many drug development lessons encoded in that story. And one is just how messy the animal to human translation is, where people really wanna make sure they’re not harming patients, as they should. And that means that you have to do a ton of work to prove something’s safe enough before you even give it to a person.

And then you give it to people in a phase one trial to just collect more data to make sure it’s safe. If there’s any hint of something that could be a problem, you pause so that you don’t harm people and that’s great, but the animal- other animals are not humans.

And so you end up doing these trials that- you’re generating hints and if there’s a hint of efficacy, it might be worthless. If there’s a hint of safety issues, it might be worthless. And so it’s just not a good system, but it’s the best we got currently.

Saloni Dattani: Yeah. I don’t know. I feel like it’s strange that if something happens in dogs, we stop the whole process without- what if it didn’t happen in any other animals? What if it was just dogs? What if the dogs were just being weird?

Jacob Trefethen: So you do it, you do a trial on chocolate and you’re Cadburys, and you’re trying to sell new, a variant of dairy milk.

It’s like, okay, well we gotta do animal trials and then we give it to dogs first. And it’s like the chocolate’s harming the dogs!

Saloni Dattani: No, no!

Jacob Trefethen: Don’t let any humans near this!

Saloni Dattani: No! Don’t give them chocolate!

Jacob Trefethen: It’s like, yeah. Wow. That was a lot of people listening just threw their phone down and dogs should not have chocolate.

However, humans should, in fact, humans must, in fact, it’s part of a healthy diet.

Saloni Dattani: I love, yeah. You need chocolate or you’ll die.

Jacob Trefethen: You heard it here first. Yeah.

Saloni Dattani: So. Should everyone get statins? What do you think?

Jacob Trefethen: Well, it’s - putting my cards on the table, I think that statins are basically one of the coolest things invented by medical science.

Saloni Dattani: Thank you! Oh, I didn’t do it-

Jacob Trefethen: Actually, you didn’t invent those, Saloni. I can’t believe you-

Saloni Dattani: I just love saying thank you.

Jacob Trefethen: Saloni accepted the award on behalf of medical science. Now the reason-

Saloni Dattani: Well, thank Akira Endo.

Jacob Trefethen: Yeah, thank you Akira Endo, and thank you, everyone else involved.

Saloni Dattani: But they should shared the data though, goddammit.

Jacob Trefethen: Yeah, no, that is annoying me. Now the reason I think that’s so cool is that it’s just so rare you can have something that has a population wide preventive effect.

The other obvious cases of childhood vaccines where you have a population-wide preventive effect, but in this case it’s like, wow, this is the biggest killer, heart disease, and we found something that reduces your chance of getting it meaningfully. And that’s just unbelievable. There’s just a huge number of people alive who simply would not be. So I’m…

Saloni Dattani: That’s your top drug?

Jacob Trefethen: It’s definitely up there, I think with, is it my top drug?

Saloni Dattani: Should we do a top 10 drugs episode?

Jacob Trefethen: Yeah. Write in the comments if you want us do a top 10 drugs episode. Okay. Now getting back to your original question, should everyone take statins?

Okay, well there’s this population wide effect. We have a really good biomarker LDL that we think should go down. Does that mean everyone should take statins and drive it down? You know, my guess is that it actually doesn’t matter that much, for people who are low risk, whether they take a statin or not is less important than for people who are high risk, and probably it’s good on net, if you made me guess, but then some people might experience some side effects they don’t like and it’s not the biggest decision in their life if they’re low risk. So that’s kind of my take. But what’s the correct answer?

Saloni Dattani: I think that I could come up with arguments for both sides.

So I think the argument for is that if we look at the relationship between cholesterol levels and risks of heart attacks and strokes and heart disease and all of that, it is basically linear as far as we know. In general, scientists agree that the lower the better. I think there’s another thing which is that people who have lower risks- we’re often thinking about an annual risk in these trials, we’re not talking about a lifetime risk. And if you think about their lifetime risk, most people are going to develop heart disease if they live long enough. And so if you could slow that down, that would be helpful.

Jacob Trefethen: So should I think about that as a linear accumulation type of- if I could go on statins now, or I could go on statins in 15 years, what would you recommend? Just hypothetically. I’m actually not on statins right now.

Saloni Dattani: Well, me neither. But the recommendation from scientists is the lower the better, the earlier the better. The lower cholesterol, the better. The earlier you start taking statins, the better. But in practice, they actually look at like, what is your overall risk of getting heart attacks or whatever in the next 10, 15 years? And it’s sort of based on that. But if you read the academic literature, they basically say earlier is better.

Jacob Trefethen: Cool. Okay.

Saloni Dattani: I assume that it’s also this thing of you’re taking it every day, that’s kind of annoying. Is that worth it? It’s other considerations that you would have.

Okay, but I can make a case for the other side as well. And that is that it does, in rare cases, cause side effects. And this isn’t like vaccines where you create a herd immunity effect and that it’s good for more people to be taking it even if the total risk is low, because you’re preventing it from spreading. That’s not happening here. It’s not contagious.

And the side effects are quite rare. They’re sort of one in a thousand or less of serious side effects. And those mostly are things like muscle weakness or muscle loss. And then I think there’s also a slight increase in diabetes, but it’s quite small and it’s only something that you can tell with large trials.

And yeah, this was quite interesting because there was recently a really big study of some 19 or so clinical trials where they had individual data from each of the patients in those trials, and they looked at this pooled analysis and they were like, which side effects on the labels of statins are actually increased in people who are taking statins versus people on the placebo?

And it turned out that most of the things on the labels of statins are not actually increased in risk. Basically here you’re seeing no difference in the risks of these things between the drug and placebo, or they’re so small that they’re not significant even in a trial with hundreds of thousands of people.

Jacob Trefethen: Fair enough.

Saloni Dattani: What I found weird about this though was like, why do the drugs have so many side effects on their labels, if that’s the case…

Jacob Trefethen: This is America. You heard of the legal system?

Saloni Dattani: What I heard was that it’s a legal liability issue and that they sort of want to avoid people saying, your drug gave me the side effects. And if you put that on the label, you can just say well, we told you.

Jacob Trefethen: We warned ya.

Saloni Dattani: But that seems bad, that probably reduces- that deters people from taking a medicine that could save them.

Jacob Trefethen: Oh, well, a hundred percent. And not only that, but it also, it degrades the trust that people have in the labels because of course people aren’t stupid.

If you hear a drug ad that says side effects may include nausea, vomiting, death, killing your mother-in-law, you know. People know that they have friends who are on statins and their friends are fine, so therefore I can ignore this label. So yeah, the whole thing just degrades.

Saloni Dattani: I heard from someone taking statins that- taking statins to reduce cholesterol levels because he has a family history of heart disease, but he also thinks it carries a risk of blindness and things like that. And I’m just like, this is just sad. This is not true and it probably means that a bunch of people don’t take it.

Jacob Trefethen: Yeah. You know, as I introspect on, why am I not on statins already, just for fun? One of the reasons is-

Saloni Dattani: Yeah, why are we both not on statins?

Jacob Trefethen: I think so one is laziness. I’m like, there’s other things I wanna spend my mental attention on and I’ll get around to that in a decade. Then the other one though is, I think there might be even better options than statins coming, which is part of my- I kind of want some of these newbies that are even better, but I don’t know if we’re allowed to talk about that.

Saloni Dattani: Yeah. That is next.

Jacob Trefethen: Okay.

Saloni Dattani: I think I have the same-ish take. Well, so I think reading for this episode has made me think I need to get my cholesterol level checked and if it’s high then I should probably be taking one of these drugs and that is something that I’ve been persuaded of, but I also think it’s kind of annoying to take a pill every day. I do take multivitamins every day though, so maybe it’s not a big deal.

Jacob Trefethen: Buckle up. We’re getting older. We’ll be taking a lot of pills everyday.

Saloni Dattani: Oh no.

Jacob Trefethen: Gonna get that pill bottle lined up.

Saloni Dattani: Okay. Okay. Back to the drugs. What is the next drug that you have heard of related to cholesterol?

Jacob Trefethen: Well, I probably am skipping ahead.

I don’t know, but I hear a lot about PCSK9 inhibitors, but probably there’s something in between. Is there?

Saloni Dattani: There’s stuff in between, but it’s not really important. So let’s just talk about PCSK9.

Jacob Trefethen: PCSK9. Sexy. Sexy, sexy.

Saloni Dattani: What, the name?

Jacob Trefethen: The target.

Saloni Dattani: PCSK9.

Jacob Trefethen: Yeah, firstly the name, because every time I say it I’m like, PSC, PCS? You know?

And then there’s sort of these, when you hang out with- Let’s just say nerds. There are these-

Saloni Dattani: Okay. I’ve done that.

Jacob Trefethen: Like biotech nerds. There are these particular targets where, yeah, it’s like, oh my god, oh my gosh. And I feel like PCSK9. I’m like, oh my God. You know, a couple years ago someone made it really deliverable, I can’t even remember, and it won molecule of the year.

You know, that that’s what I was like, oh my God. That’s kind of how I feel about PCSK9.

Saloni Dattani: That’s amazing. I haven’t heard that kind of effusive praise for it yet.

Jacob Trefethen: Oh, I actually genuinely, it’s one of those ones where I’m excited about progress because I’m like, oh my God, this one’s actually gonna be something I use, and it’s very helpful to me, is my guess.

Saloni Dattani: This it has a really interesting story. It also has some really effective drugs against it and there are some newer drugs that are probably even better. And so I think it is a very exciting story to tell.

Jacob Trefethen: I’d love to learn about it.

Saloni Dattani: Do you know how it started?

Jacob Trefethen: I have no idea actually, no.

Saloni Dattani: Okay. So it did start with familial cases of people who had inherited high cholesterol levels and there were- so this is kind of before, if you’ve heard of GWAS and if you’ve heard of genome sequencing and stuff, it came before that. So this was the 1990s and early 2000s.

Back then, people used something called genetic linkage studies where they were trying to trace markers that were shared between- in families, that were seen in people with the condition and not in people without.

Jacob Trefethen: I dunno if we’ve said out loud, PCSK9 is a gene, so that is the name of the gene.

Saloni Dattani: It is also the name of a protein that the gene encodes for.

So they were, people were doing these genetic studies. They were like, let’s try to find out what is causing this high cholesterol levels that these people are inheriting, you know, in families. It’s running through families, must be a gene, seems like it’s a single gene.

And in a bunch of these families, they did find a signal on chromosome one and it was a gene that was then called NARC-1, but they had no idea what the function was.

And so there’s something, some gene on chromosome one that is somehow linked to high cholesterol levels. And then in the early 2000s, people actually identified a specific mutation in that gene that was connected to having high LDL levels and they renamed the protein PCSK9.

Jacob Trefethen: Just to make it more catchy.

Who could remember NARC? Make it so catchy like PCSK.

Saloni Dattani: I think that it’s named after the specific mechanism, but it’s shortened. Well, so what they found was people with this specific mutation had an increased protein activity of PCSK9, and somehow that was linked to increased cholesterol levels. And so they were like, let’s investigate this further: What is this protein doing? What do you think the protein is doing?

Jacob Trefethen: I actually, I actually… I’m embarrassed to say I actually don’t know.

Saloni Dattani: I didn’t know either.

Jacob Trefethen: I haven’t really read about which people are gonna listen. Like wow. He works in medical research and doesn’t even know, but yeah.

Saloni Dattani: I didn’t know this until a few weeks ago.

Jacob Trefethen: Okay.

Saloni Dattani: I guess I just, I only remember the stuff that I personally have read and written about, and this fell out of that. So let me explain how your liver tries to clear out cholesterol in the first place because that’s how this drug works.

So in your liver cells there is a receptor called the LDL receptor. You can guess what that attaches to.

Jacob Trefethen: HDL?

Saloni Dattani: No. So imagine there’s an LDL cholesterol, swimming, swimming, it’s in the liver. It gets caught by the LDL receptor and now, the LDL receptor gets taken in by the liver cell; the whole particle with the LDL gets internalized. And then LDL gets released into the rest of the cell, and meanwhile the LDL receptor comes back to the cell surface and tries to grab more LDL cholesterol.

So it’s basically trying to clear out LDL cholesterol with this receptor. It’s grabbing it, taking it into the cell, releasing it for further processing, and then it’s coming back up. It basically comes back up and does this, it gets recycled and it’s grabbing these LDLs probably about a hundred times per receptor, which is pretty cool.

Jacob Trefethen: Yes, that’s a Sisyphean task.

Saloni Dattani: But what PCSK9 does is, it attaches to the LDL receptor, and instead of allowing it to come back up, it just gets degraded.

Jacob Trefethen: I see. I see.

Saloni Dattani: So it can’t catch cholesterols because it’s getting degraded and so your liver isn’t able to clear enough cholesterol if this protein is overactivated.

Jacob Trefethen: Which now makes sense to me because I know we’re gonna be dealing with PCSK9 inhibitors, so now I get why we’re gonna try and inhibit them.

Saloni Dattani: Yeah, we don’t want that. We want the LDL receptors to keep grabbing the LDL and internalizing it and clearing it from our bloodstream. And so if you had seen that mechanism alone, would you think this is a good target and what would you do next?

Jacob Trefethen: Well, I mean, that is pretty good evidence that it might be a good target, but then, you know, PCSK9 might be doing a bunch of other stuff too, so I’d wanna know that, but what would I do? I’d try and generate something that basically inhibits it.

Saloni Dattani: Great point. So I think what is difficult about this, I think there’s so what the next step is just like you said, what if PCSK9 has some important function? So what people did was, they had these big genetic studies at this point, so this is like the early 2000s.

There have been some big cohorts where people have been genotyped and they’re like, let’s see if anyone does not have PCSK9, or they have a mutation where PCSK9 is not produced at all. And turns out, there are a bunch of people who actually have that in both copies of their gene. They don’t produce any PCSK9 functionally at all, and they’re still alive.

So, okay. It’s not gonna kill you basically. Right? If you don’t have it, it’s not gonna kill you. And so, okay, let’s now try to generate a drug against it. What’s difficult about this is that PCSK9, the way that we want to develop something that specifically blocks its interaction with the LDL receptor.

Jacob Trefethen: Well, so let me just get there. So there’s a particular part of the protein that’s probably gonna do the binding. So that’s maybe what you wanna be inhibiting, blocking.

Saloni Dattani: Indeed. Yeah, yeah. I think you could develop a drug against it. I think it’s hard because I think the shape of it means that there isn’t a clear binding spot. There isn’t a clear pocket to bind to. So what are you gonna do?

Jacob Trefethen: Ooh, good question. Okay, so I… but this is extracellular, is that right?

Saloni Dattani: Correct.

Jacob Trefethen: So that gives me more options because I can use different modalities. So, but what would I really do? I mean really what I want to do is if I’m allowed to use all the tools in my toolkit, I wouldn’t mind doing a little bit of a, maybe a little gene edit or something. Or maybe do a little-

Saloni Dattani: That’s expensive though.

Jacob Trefethen: Yeah, that’s true. I mean, that’s where we’re headed presumably. I mean, that’s why I was asking is it extracellular? Because then I can use proteins.

Saloni Dattani: Yeah.

Jacob Trefethen: And I can use an antibody, I can use a binder. So antibody makes sense.

Saloni Dattani: Indeed. Yeah.

Jacob Trefethen: So the antibody tags it and we destroy it. Or the antibody blocks it, or what does the antibody do?

Saloni Dattani: I think the antibody blocks it. There are different mAbs that do that; Monoclonal antibodies. And what do they do? They bind and they block and they prevent it from binding to the LDL receptors.

Jacob Trefethen: Great. Love it.

Saloni Dattani: So there are a bunch of drugs with funny names like ‘Alirocumab’ and ‘Evolocumab’.

Jacob Trefethen: Evolocumab.

Saloni Dattani: Evo- Very good.

Jacob Trefethen: That sounds like a Pokemon.

Saloni Dattani: That does sound like a Pokemon. And both of these were developed in the mid 2010s, so in 2015 or so, that’s when they were approved. And they both have this same kind of mechanism that they’re blocking the PCSK9 protein from binding to the LDL receptor, which means the LDL receptor doesn’t get destroyed, so it can clear more cholesterol from your blood.

Jacob Trefethen: It’s so clean. It’s so clean. I love it.

How, wait, how do I get administered the antibody? Because I don’t wanna have to do blood, like-

Saloni Dattani: Injection.

Jacob Trefethen: Like IV or like?

Saloni Dattani: They are given by subcutaneous injection.

Jacob Trefethen: Okay. That’s way better than blood- that’s just a little pin-prick vibes.

Saloni Dattani: Yeah. So that’s not too bad. What’s so interesting about PCSK9 inhibitors and I think probably why you were, and many biotech people are interested in them is that they’re really effective. They’re effective over and beyond, beyond statins. So if you’re taking statins and you also take PCSK9 inhibitors, they reduce cholesterol levels by an additional 60%.

Jacob Trefethen: Epic.

So cool. Oh, also, do they have fewer side effects than statins or?

Saloni Dattani: Uh, yeah.

Jacob Trefethen: Okay. That is so cool. Oh, I want it so bad. Okay.

Saloni Dattani: And that’s with a injection that’s once every month.

Jacob Trefethen: That is so cool. Do you know if you just go straight to them and don’t do statins? Is that something people have tried?

Saloni Dattani: I am not sure. I think that’s possible. I think in the UK, the recommendation is that you should take statins first, but I think that might not be the case in the US.

Jacob Trefethen: I assume that for cost reasons alone, you would make that recommendation. I don’t know if there’s additional medical reasons.

Saloni Dattani: There’s another way to attack PCSK9 too, with siRNA drugs.

I think maybe to help understand, a lot of people have heard of gene editing and they’re like, okay, well if you don’t like this protein, let’s just edit the gene. Well what if we don’t wanna edit the gene? What if that’s too difficult? What if that’s too expensive?

You’d have to get the cells first, then you’d have to edit them and then you’d have to replace them, and that’s just time consuming. And there aren’t really great ways yet of gene editing in vivo. Like you can’t just put something in your body that just edits the cell.. yet, not very well, anyway.

And so instead there’s another idea, which is what if we don’t target the gene but we target the intermediate RNA that’s gonna produce the protein? And that is a lot easier because the RNA is kind of hanging out in your cells and you can just get something like siRNA to go into those cells and block the RNA. So we stop that intermediate step instead of editing the gene. And that’s what’s happening here.

There is an siRNA drug against PCSK9 that was approved in Europe in 2020 and in the US in 2021. And its name is Inclisiran. And what it does is it blocks the production of that protein entirely. So it’s not blocking it from attaching or anything, it’s just saying. What if you just didn’t get produced at all?

And it’s basically a tiny bit of, it’s a short sequence of RNA and it’s either encapsulated in a lipid or a liposome. And it goes specifically to your liver, it gets taken up by your liver, gets into your liver cells, and it binds to the RNA that is going to produce the PCSK9 protein and it says, shut up. What if I destroy you? And then you can’t produce your protein anymore. And that’s what happens.

Jacob Trefethen: Pretty cool.

Saloni Dattani: That is a scientific explanation of what is happening here.

Jacob Trefethen: Well, I mean, just spelling out why that can be easier in more detail. So presumably we’re interfering in the cytoplasm of the cell, are we?

Saloni Dattani: Yep.

Jacob Trefethen: And whereas if you’re trying to edit a gene, you gotta get into the nucleus, you gotta somehow find that gene, you don’t wanna hit the other. It’s like, oh my goodness, that’s kind of crazy. Whereas, you know, cytoplasm, that’s a mess in there. You know? Everything can get in there.

Saloni Dattani: That’s easier. Yeah. Well, it’s a little bit hard, and it was kind of tricky to figure out because when siRNA initially gets into a cell, it gets trapped in an endosome and then-

Jacob Trefethen: Ah, right, right.

Saloni Dattani: It’s like, how do I get out of here?

Jacob Trefethen: Yeah. The cell’s like, no!

Saloni Dattani: But what is really cool is that that is actually a feature in this case.

Because what happens is, it’s stored in those endosomes and then occasionally, once in a while, one of them trickles out.

Jacob Trefethen: So it’s like a long lasting drug.

Saloni Dattani: Yeah!

Jacob Trefethen: Wow. That’s-

Saloni Dattani: So, it’s like they trickle out slowly. Slowly. Like one by one.

Jacob Trefethen: The original lenacapavir.

Saloni Dattani: Slowly. We silence that RNA. It prevents the protein because it’s released quite slowly, these drugs can last really long times. So there are some siRNA drugs that last a few months, so you only have to be dosed every three months or six months. And there are new ones that are gonna be once a year, and I just think that’s so cool.

Jacob Trefethen: You know what I’m, I really don’t wanna jinx it, but you know what I’m so hopeful for with siRNA, due to the property you just mentioned? Hepatitis B.

Saloni Dattani: Ooh-

Jacob Trefethen: Hepatitis B.

Saloni Dattani: We’re back to the previous episode. Why Hepatitis B?

Jacob Trefethen: Well, I mean, imagine, imagine, imagine, imagine if you could have- you know, gene editing is how you might wanna conceptually deal with Hepatitis B because it’s integrated into it.. it creates its own little chromosome or integrates into your own in the nucleus. But if that’s too hard, siRNA. That might be…

Saloni Dattani: Oh, you could block the Hepatitis B virus from producing itself or producing its proteins.

Jacob Trefethen: Oh my goodness. Oh, wow. Imagine. And there’s actually a siRNA going into a phase one two trial right now that’s recruiting.

Saloni Dattani: Oh.

Jacob Trefethen: So I forget the name of the company, but my friend McKayla sent me a link and I went, oh my God.

She’s like, I know one’s gonna really freak you out. Look at this. And I was like, oh my God. I’m so excited!

Saloni Dattani: Well, I really wanna talk about this a little bit more, but before we move on, there is another drug against PCSK9 that is newer, and it’s also really effective. It’s actually even more effective than the other one probably. And it’s not an siRNA.

Jacob Trefethen: What is it?

Saloni Dattani: Have you heard of it? It’s called Enlicitide. It just finished its phase three trial.

Jacob Trefethen: What does it do?

Saloni Dattani: What does it do? It’s a peptide.

Jacob Trefethen: Oh, it’s a peptide?

Saloni Dattani: It’s a peptide, and it’s a pill!

Jacob Trefethen: Yay! (he cheers) Oh, I did actually hear about oral availability. I didn’t know that was a peptide though. Okay. Interesting.

Saloni Dattani: It’s a macrocyclic peptide.

Jacob Trefethen: That’s quite a peptide. I’m looking at it right now.

Saloni Dattani: It’s quite a big it’s not that it’s quite small for a peptide, I guess.

Jacob Trefethen: Yeah, it exactly looks-

Saloni Dattani: -but it has a lot of rings.

Jacob Trefethen: I’m used to seeing small molecule diagrams. They’re like, oh my goodness, what a big small molecule. But it’s actually a peptide.

Saloni Dattani: So what is this? So this is an oral drug, so it’s a pill and it blocks the interaction of PCSK9 with LDL receptors. And like I said, that’s quite hard to do, to design a drug that can fit into a little gap.

But what they did was they use this RNA assay, and they said which of the peptides made by these mRNA bind to the PCSK9. And when they found some that bound, they then try to see what do they look like? And they found the crystal structures and they’re like, let’s tweak this to make it even better at binding.

And so what they did was, added a bunch of chemical rings to it, and that’s what we got. So we have this chemical ring thing, and there’s an additional small fatty acid which helps it be better absorbed in the small intestine. And so what I think is really cool about this is that it is really effective. So it reduces LDL cholesterol levels by an additional 60%.

Jacob Trefethen: The oral version? That is so cool.

Saloni Dattani: This was in people with the familial condition, so they have hypercholesterolemia, they’re already taking statins and other drugs, and this cuts cholesterol levels by an additional 60%.

Jacob Trefethen: Any side effects?

Saloni Dattani: Any side effects? I think there were maybe a few that were just like diarrhea and stuff, but I think they were very limited.

Jacob Trefethen: Yeah. If you go oral, usually get nausea or diarrhea or something. Yeah.

Saloni Dattani: But yeah. Very cool. It’s called a macrocyclic peptide.

Jacob Trefethen: Okay. That I have to admit, that’s really exciting. It’s funny. It’s very exciting. I’m like, that’s, you know, whoever invented that probably in my life, I will take that. How cool is that?

Saloni Dattani: Yeah. Yeah.

Jacob Trefethen: It’s so cool. Wow.

Saloni Dattani: Well, unless… there’s something even better.

Jacob Trefethen: Oh my goodness.

Saloni Dattani: Wait, I need, I should say this drug hasn’t been approved yet, so it probably is gonna be submitted for approval this year and it’s probably gonna become available next year.

Jacob Trefethen: Got it. And just to depress people because I was too excited, it probably will cost some number of tens of thousand dollars, so you won’t want to pay out of pocket and then your insurance probably won’t cover it…

Saloni Dattani: You might as well just take statins.

Jacob Trefethen: Now, that said, in the grand sweep of time it’ll go off patent and become cheap.

Saloni Dattani: And well, I guess there are other- because PCSK9 is such a good target, there are lots of other things in the pipeline.

There are other small molecule drugs, there are other monoclonal antibodies, there are peptides, and there are even some vaccines to help you make antibodies against PCSK9 yourself.

Jacob Trefethen: Yeah, why not? And the benefit there is everyone will remember is you only gonna need that once or twice and then it’s there forever.

Saloni Dattani: Boom. Long lasting. Have you heard of any other cholesterol drugs in recent times?

Jacob Trefethen: I probably have, but none are coming to mind. If you mention them, I might recognize them.

Saloni Dattani: They target something called lipoprotein A. Have you heard of that?

Jacob Trefethen: Yes. Yes. I have.

Saloni Dattani: LPA. What is lipoprotein A?

Jacob Trefethen: You know, I generously will let you explain.

Saloni Dattani: I didn’t know this until recently. So what it is, is a lipoprotein with a weird long peptide that is stuck to the outside. So the lipoproteins, the fatty blobs that are carrying around the cholesterol and fatty acids through your body, this is just a big long peptide that’s attached to that- It’s part of the shell of that fatty blob.

The reason that we think that it’s bad is that there’s evidence from epidemiological studies. So people who have higher levels of lipoprotein A tend to have worse outcomes, so they have higher risks of cardiovascular diseases.

They also have higher risks of aortic valve calcification, which is when your aortic valve - your artery valve from your heart - gets calcified. And so that has been seen in a bunch of epidemiological studies. And so people were like, what if we just blocked that? And that has actually happened with siRNA drugs.

So there are a bunch of drugs in the pipeline that are extremely effective. So there’s a phase two trial of one of them recently called Lepodisiran. Actually, there are a bunch of these which are really effective. And these siRNA drugs reduce the levels of lipoprotein A by… can you guess?

Jacob Trefethen: Um, I’m gonna guess a lot. So I’ll say, 60%.

Saloni Dattani: 95%.

Jacob Trefethen: WHAT? Okay, then I’m nervous. Are we sure I don’t need that?

Saloni Dattani: Well, I guess we’ll see.

Jacob Trefethen: 95%. Oh my goodness.

Saloni Dattani: That is crazy. So it’s like a single injection at the highest dose of this siRNA drug. There are two of them, Olpasiran and Lepodisiran. The first one, Olpasiran is taken every three months. The second one, Lepodisiran, is taken every six months. And it just reduces (she gestures), and then you just stay really low for a really long time, for at least six months, you don’t need another dose. And then there’s another one that’s an oral small molecule, which also does something similar and they. There’s one called Muvalaplin, which is by Eli Lilly.

Jacob Trefethen: That’s not a real one.

Saloni Dattani: That’s what it’s called, Muvalaplin.

Jacob Trefethen: The way that I would recommend reducing your risk of heart disease is if you move a lot. Oh, did you say Muvalaplin?

Saloni Dattani: This is in phase two trials, this just finished that phase two trials as well and the oral drug reduced lipoprotein A by 85% on the highest dose. I feel like these numbers are crazy to me.

Jacob Trefethen: They’re crazy. But at the same time, we went through how it took a hundred years to establish the LDL cholesterol as epic a biomarker as we thought.

So I’m gonna hold my applause until we get a little bit more knowledge about whether dropping something by 85% actually affects disease that much. But let’s hope.

Saloni Dattani: Let’s see what happens. I mean, I think the last thing to say about siRNA is that people have been developing drugs that target various liver diseases with these, and the reason is that people have sort of figured out how to target or how to deliver the siRNA to liver cells.

And so it can treat a bunch of different liver-related conditions like hemophilia, high cholesterol, and various other genetic conditions that are quite specific and have long names that I won’t pronounce. And this is just the start, I think, because the specific way that you address those siRNA drugs to the liver; if you use a different addressing system, you could in theory target them to the brain or the lungs or the muscles, and the principle of it is essentially the same.

We have to get the siRNA into the cells so that it can target the RNA, degrade it, and then whatever protein you choose doesn’t get produced, or at least it massively gets reduced in production. And the amazing thing is that these are really effective, they have very high potency and they’re also long-acting like Lenacapavir. One dose, it gets stuck in this endosome and then it trickles out one by one. And you could just have a long lasting drug that lasts for months. Instead of taking a pill every day instead of taking an injection every few weeks or whatever, once.

Saloni Dattani: Okay. Let me give you a quick summary of the different types of drugs. So there’s statins, which were one of the first cholesterol reducing drugs invented. They block cholesterol synthesis directly and they’re given as tablets, that are taken daily. They reduce cholesterol levels by 20 to 40% and reduce annual mortality rates by about 10%.

Then there’s PCSK9 drugs, which increase LDL clearance. They actually cause a 50 to 60% reduction, on top of statins. So if you’re taking statins, they cause an additional 50 to 60% reduction. They’re taken by subcutaneous injection every month or every two weeks.

And there’s some newer versions of PCSK9 drugs like siRNA therapies, like Inclisiran; those last much longer. They last about three to six months per dose, and they’re also very effective, reducing cholesterol by about 50%. And what they do is they silence the mRNA, so they silence the production of that protein itself.

And there are some new tablets that are kind of on their way, like Enlicitide, which is a pill. It’s a pill that inhibits PCSK9 as well. And that is, taken daily as an oral medication, again around a 60% reduction. So that’s really large. It hasn’t arrived yet. Probably, it’ll be available in the US next year if the review goes well.

And finally there are lipoprotein A drugs, and what these do is they block a particular harmful type of LDL particle and they are mostly in development right now. And there are various siRNA drugs and various other RNA drugs that essentially block the production of lipoprotein A levels. These seem to last for a very long time, so some of the drugs last three to six months per injection, but newer ones are taken every six months or maybe even longer than that.

And they cause around 90% reduction in lipoprotein A levels per dose, so that’s a very long lasting drug. And finally, there are some oral drugs that also block this. Their efficacy is around 50 to 85%, in reducing lipoprotein A, so these are really large effect sizes focusing on this one protein.

So if you’re listening to this and you probably have, or are thinking about, you know, is this something that I’m gonna be using, or is this something that I should check? I would say consider getting your cholesterol levels checked. If it’s high, it’s probably a good idea to start taking cholesterol drugs.

The earlier, the better; the lower that you can reduce them, the better – that is the general recommendation. But the decision is also made based on your overall risks of cardiovascular disease, like age, your smoking habits, blood pressure, diabetes, family history, et cetera.

There might be some people listening who have a family history of not just cholesterol but hypercholesterolemia - so inheriting particular genes that increase your risk to a very large extent. The recommendation for that is that people start taking cholesterol reducing drugs if they are diagnosed. If you test positive for that, generally you’re recommended to take drugs early and aggressively to reduce LDL as much as you can. And so, yeah, that’s my little summary.

Jacob Trefethen: So that is so much progress on all these different fronts quite recently. So what is going on? Why has the last decade been such a flourishing decade for these drug development programs?

Saloni Dattani: I don’t know for sure. I have a lot of hypotheses. So I think that one, we’re sort of starting to see the fruits of genetic data coming through.

So we’re starting to see the things that we’ve discovered from genome-wide association studies, genome sequencing, and even the linkage studies of the 1990s, it takes an average of 10 years or so to get a drug through clinical trials. And that’s when you have the drug.

So I think if we start off with the genetic studies, we find these targets like PCSK9, we test a bunch of candidate drugs, we developed them, and then there’s, it’s still another 10 years after that.

And so I think that that has only really started to come out in the last 10 years that final trickle of innovation that we’re finally seeing now. I think the other is that this sort of drug design and chemistry has really improved in the last two decades, so there are new things that we can do that we couldn’t do before.

And we’ve talked about some of them. Protein design, we talked about in our fourth episode, hallucinating proteins and tweaking proteins with the use of AI tools. But also before that, the use of other statistical models and data collection with things like Protein Data Bank.

And so I think it’s- partly it’s that, so it’s having more knowledge about which places to target with these drugs, having better ways to actually design drugs to get to those targets.

And then I think it’s various other related things as well. Like we know how to make siRNA drugs now, right? And we have much more research on drugs that are long acting in many different ways, like Lenacapavir, like siRNA, like oligonucleotides.

And I think those are some of the reasons, but I also wonder what you think, like are there other reasons that I haven’t thought of, and are there other types of drugs that might be developed soon?

Jacob Trefethen: Well, what stands out to me, because this is an area of drug development- heart disease is an area of drug development I know less well than some other areas. And so what stands out to me hearing about all these drugs is just how good the targets are, compared to some other areas. And so I wonder how come they’re so good. But you know, PCSK9 is just a really good target.

Saloni Dattani: I wonder if maybe it’s because a lot of them are liver related.

Jacob Trefethen: Yeah. Yeah. You can, the liver easy to get to, easy to mess with.

Saloni Dattani: A lot of things get to the liver, and the liver usually filters things and clears out the toxins. But that is also very useful for drugs that you want to target to the liver.

Jacob Trefethen: Yeah. And then it’s just so useful to have this clear biomarker in LDL cholesterol. And you know, I wish that other diseasesthat it’s this combination of an unbelievably clear biomarker plus the biggest killer.

I’m like wow, what an amazing situation. Whereas in a lot of other drug development you have kind of dodgy markers, or you have an okay thing you don’t really understand, and it isn’t one of the biggest killers. So yeah. Anyway, it’s a nice combination.

Saloni Dattani: I do wonder if it just happens to also be one of the most studied areas. It’s probably an area where there is a lot of pharmaceutical investment into it because there’s so many people who are affected by these conditions. So there is a lot of incentive to develop drugs in the field.

Jacob Trefethen: That makes sense. So what is not yet known, do you think, and what does the future hold?

Saloni Dattani: There are a bunch of things that are not yet known. So some like the lipoprotein A, we don’t really have a great idea of how exactly that in seems to increase the risk of cardiovascular disease.

But I think there are also other questions that are unsolved. Are there ways to develop oral long-acting drugs?

Can we develop siRNA drugs that you can take by pill? I don’t know. I don’t think so yet, but maybe there will be.

And then I think there’s the other question about can we develop drugs or therapies that actually reverse the cholesterol buildup that people have? So most of the drugs that we have, they sort of have multiple effects.

The first is to reduce cholesterol circulating in your bloodstream. They also have smaller effects on plaques that are already in your blood vessels. So if you’ve already developed plaques, it’s not too late, these drugs do still have an effect. And the way that they seem to have an effect is by stabilizing those plaques so they don’t fall off, and that’s important, but I think that we don’t really have cures yet, right? We don’t have things that can reverse the damage that much.

And then there’s the question of HDL. Like, we don’t have drugs that can improve HDL and that are actually beneficial yet, so I think that’s another target that people could be working on in the future.

And then I think the next question is, can we develop drugs that are oral? So they’re easy to take, they’re cheap, they last a long time, and they’re really effective. Can we do something that is all of those things? And that hasn’t happened yet. But maybe it will.

Jacob Trefethen: I’m optimistic about this one.

Saloni Dattani: I’m pretty optimistic as well, and I think there’s a lot in the pipeline right now, and I think there’s a lot that’s just around the corner.

But I also think that in the future we will have a lot of even better drugs. So I think there’s a bunch of cool things, one is the programmable medicine, like the siRNA, the oligos, the gene editing techniques - basically we found a target, let’s go and hit that target directly with either a gene edit or an siRNA to stop the protein from being produced.

That I think is really cool. One because of how precise it is. You’re honing in on that one thing. And then I think the second thing is that if you develop a way to do that in general, then you can easily kind of swap out the gene that you wanna target for a different gene. You can develop dozens or hundreds of different drugs to target different, you know, rare conditions, let’s say, by just swapping that out, as long as you have a platform, you have a specific way to get it into a particular organ, let’s say. So I think that is really cool.

And then I’m excited for the vaccines that are in the pipeline against PCSK9 and like the other, these other targets like, wow, what if you just make your own body produce antibodies against these proteins that are doing harmful things?

One of the things that I was thinking about when reading about this was like, why doesn’t our liver just solve this itself? And also why are these proteins even there, if they’re bad for- if blocking them is so good, why do they even exist in the first place?

Jacob Trefethen: Yes. Always a question one must ask. I mean, I assume that a lot of the answer is that our diet and environment has changed so much versus the ancestral environment. Is that true?

Saloni Dattani: I think that’s, I think there are a bunch of reasons. I think that’s one reason. So we probably eat very differently to how people ate before - we eat more. We also eat more saturated fats and saturated fats reduce LDL cholesterol clearance and your diet can also increase the amount of cholesterol you produce, like these lipoproteins you produce.

And then there’s some people who just have genetic conditions where these normal proteins are dysfunctional and they’re not clearing up LDL cholesterol properly or they’re producing too much cholesterol or something like that.

And then I think that the other reason is that it’s a problem of aging and that after the point at which you have produced children, then evolution isn’t really acting that strongly.

Jacob Trefethen: It’s not picking up on that signal.

Saloni Dattani: Yeah.

Jacob Trefethen: It is a little.

Saloni Dattani: It’s not really selecting against that very much. And if you have high cholesterol and that causes heart attacks, if you’re 50 or 60 or 70, evolution is kind of like, yeah, I don’t care.

Jacob Trefethen: Evolution is very happy for us to die, which is very harsh.

Saloni Dattani: Yeah, that’s true. The way that I see it is also we’re pretty smart. We have developed lots of technologies to solve these problems. What if that’s… what if that’s the evolutionary goal? What if that’s the extended phenotype?

Jacob Trefethen: Oh my. That is quite an extension. Wow. So you are saying that evolution acted through our brains to build artificial intelligence so that-

Saloni Dattani: Yes.

Jacob Trefethen: Thank you, evolution.

Saloni Dattani: I mean a lot of these things are a result of age. When you get older, this balance between producing cholesterol and clearing it out from your bloodstream gets worse. There’s too many particles made and too few are removed, and this gets worse with age.

And so it’s sort of this thing where you’re like, okay, well maybe it’s fine if you’re young, but after a certain age, this is harmful, and we should reduce it. So that’s my answer, which I hope is correct, but I don’t know.

Jacob Trefethen: I can’t wait for the evolutionary biologist to come into the comments and say, “That’s not true because-!!”

But I think that you are right, we’re in a wonderful situation where we don’t die young as much as we used to. And so a lot of evolutionary pressure got spent on stuff that, you know, we would maybe not select ourselves.

Saloni Dattani: You know, I don’t trust evolution as well, ’cause I’m like, what about, you know, child mortality and infant mortality? That is surely bad, but it’s still common.

Jacob Trefethen: We have a few questions for you, evolution.

Okay Saloni! That was a lot of material and I think we reached the end for now. I learned a lot from you this episode, and we discussed a lot of different topics about cholesterol, about heart disease. What sticks out to you most? Let’s do a little summary.

Saloni Dattani: Yeah. I guess.. I learned quite a lot from learning about this.

One of the things that really stood out was just how common statins are. How common it is for people to be on these drugs. But even then it’s, so it’s the estimate is 50 million Americans were prescribed statins in the last year, and I also didn’t realize that even that is only about 40% of the people who are eligible to take them.

So a lot more people should be taking statins than are. And then I think, I didn’t realize how common it was to have high cholesterol, how common it was to have cardiovascular disease. And the other thing that I found really interesting, but was not new to me, was that cardiovascular disease mortality has reduced massively over history.

And if you look at the trend since 1950, the death rate from heart disease has dropped by three quarters. That’s enormous. And I didn’t know that. And that has many causes, not just statins, but you know, emergency medicine and public health efforts and surgeries and medicines, different diet and people changing their lifestyle sometimes if they’ve been diagnosed.

And it’s just a lot of different things coming together and making a massive reduction in cardiovascular disease. So I thought that was really interesting to me.

Jacob Trefethen: That was amazing to see. One thing that stuck out to me about cholesterol is just how long it takes for scientific consensus to build.

And you know, in this case with cholesterol now there is consensus that LDL cholesterol is bad and drives heart disease and these bad outcomes, including mortality. The different lines of evidence it took to get there were animal studies with cholesterol, were observational studies and correlations, were longitudinal observational studies like the Framingham heart study, and then experiments that were randomized or had controls with different drugs: first the vitamins and then drugs that specifically targeted cholesterol. And once you layer that all on top of each other, you can get to consensus. And that consensus is often what is needed to really have impact on a lot of people’s lives, because we are all sort of at the mercy of the recommendations of doctors.

Now that that theory has been proven out, we can all benefit from these wonderful drugs and of the different layers of evidence that I really want to give a shout out to the longitudinal evidence because a lot of people are just coming back and back over years and decades in these incredibly useful studies and you know, giving up biological samples, blood tests, whatever that doing it out of the goodness of their heart for the sake of learning these difficult truths to pin down, and it’s just so cool to think about the people of Framingham, Massachusetts! What an amazing town and what amazing people!

Saloni Dattani: You know, I’ll have to check that this is right, but I think what I read was that it was over 70% or so of the people in that town were part of the study.

Jacob Trefethen: So cool.

Saloni Dattani: Which is massive, I think that’s so cool. The other thing that I thought was really interesting was that in some cases, like with cholesterol, but also in other diseases, it’s often quite helpful to study rare conditions in order to understand a more common condition.

So in this case, the way that we found out that cholesterol was linked to heart disease was from people who had an inherited condition called familial hypercholesterolemia, which had really high levels of cholesterol and had really high risks of heart attacks at a young age. That I thought was really interesting.

But also the same population, that same condition, helped us to get PCSK9 drugs. That was the same condition that helped people find, specifically, what genetic mutation was linked t high cholesterol levels and find a protein that they could target with a drug. So that was really interesting and I think it’s, it just happens to be the case that that same mechanism is also the case in the general population.

So that is something that maybe isn’t as common, but you know, when you find people with rare conditions and they have really high levels of some traits, it makes it much easier to study biologically, what is the mechanism. It’s easier to study than predictors and, you know, what are the risks? And it’s easier to see the effects of reducing those risks with certain drugs, and I think that’s a great starting point for a lot of drug development.

Jacob Trefethen: I also found it interesting in this episode to hear more about the path of lipids through the body, and what LDL cholesterol really is, and how it can lead to atherosclerotic plaques and what those plaques are, how macrophages can get foamy in the middle.

Atherosclerosis is something that I sort of know of, but don’t know the details that in detail, so that was really interesting to hear. And the ways that can lead to downstream strokes or downstream blood clots in different areas makes sense to me and helps chain the mechanism all the way through.

Saloni Dattani: Yeah that was- I really didn’t know that macrophages could get foamy. I didn’t know that if you’re a macrophage and you eat fat particles, the fat kind of just remains in you and you’re foamy now? That was so strange to me and you can actually see this in microscope slides as well, so that was fun.

The other thing that was new to me, which I didn’t know, was that you actually don’t need to eat any cholesterol in your diet; your liver can synthesize all the cholesterol that you need, and if you’re vegan or vegetarian, you probably consume very little cholesterol and it doesn’t matter and it might even be good for you. That was wild. I didn’t know that.

Jacob Trefethen: Another one like that for me is you think saturated fats sound bad because it’s gonna be just fat floating around and then, but actually one of the main reasons saturated fats are bad is because they stop the removal of LDL cholesterol. Like, oh, that’s the reason.

Saloni Dattani: Yeah, it’s a bit sneaky. The whole pathway of cholesterol synthesis, transport, metabolism was really complicated and I sort of boiled it down to a much simpler version here. But I thought that it was so interesting because there’s so much stuff going on, and maybe that means that there are actually lots of different things that you can target, and the different drugs target many different parts of this pathway.

And I don’t know if you have come across this, but occasionally I used to come across these memes on Twitter that were like, they would just show you the entire biochemical pathway of loads of things. ‘Oh, if you just change one thing, you think you can solve this disease? No, you can’t!’ But sometimes you can!

And I thought that was so interesting, the whole pathway doesn’t necessarily correct itself. Right? Sometimes there is a rate limiting step or there is a bottleneck and if you stop that particular protein, you can actually make a really big difference.

Jacob Trefethen: I love how most people are seeing political memes, you’re seeing metabolic memes. Yeah. Very relatable. Me too. Another thing that stuck with me is funguses! Funguses rock. And the fact that it was the same genus of that got us the penicillin, and the first statin? That’s wild.

Saloni Dattani: That’s crazy. Maybe mold is actually good?

Jacob Trefethen: I’ve always had a suspicion, and that’s why my bedroom’s full of it.

Saloni Dattani: I don’t know if I told you this, but I had this very funny experience, I think one or two years ago. I was thinking about tick-borne diseases and diseases spread by ticks. And I was like, should we just get rid of all the ticks?

Would that be good? And I asked chatGPT, and this was 3.5 or something, and I asked chat

GPT: are there any benefits of ticks? And it said: ticks are very important for researchers to study tick-borne diseases.

Jacob Trefethen: Hahahaha! It’s not wrong.

Saloni Dattani: But then I thought the relation to this was, the mold is good because you can study the moldy diseases.

Jacob Trefethen: Hey, and that’s why you don’t want to get rid of mold or ticks.

Saloni Dattani: Yeah, well I do wanna get rid of ticks.

Jacob Trefethen: Okay, fine. If I’m allowed the mold, you can get rid of the ticks.

Saloni Dattani: The other thing I found that was interesting and was surprising to me, I wasn’t convinced before doing research for this episode that diet was important.

I was like, yeah, people just say that junk food is bad, but is it really? And what convinced me was that there were actually randomized trials where people randomized people’s diet and they actually had the same meal every day for months or years. And it was like, that surprised me.

Because I think a lot of nutrition research today is observational and it’s people recording stuff in their diaries and there are all kinds of things that correlate with eating healthier. And so I just thought this is kind of messy, and a lot of those studies, they had sort of messy conclusions, there wasn’t something clear. And I just thought we should do more of this randomizing people’s diet to see what is good for you or bad for you.

Jacob Trefethen: I’ll volunteer so long as I get the bagel arm.

Saloni Dattani: Fair enough.

Jacob Trefethen: I think finally the thing that I really enjoyed talking about this episode and stuck with me is the different ways that drug development has succeeded here.

And honestly, it sounds like it’s gonna succeed further in the next decade or two. The original drugs being.. I mean, there were drugs even before the statins that were not as targeted. And then the development of statins, the trial and error, the testing of different fungal samples, you know, really trying to figure out what was going on with those dogs and eventually benefiting humans.

Saloni Dattani: Sometimes more than 6,000 samples just to find a drug that inhibited cholesterol synthesis.

Jacob Trefethen: And of course my favorite, the PCSK9. I mean, really cool to have discovered that target from genetic evidence, from people who are at higher risk of cholesterol problems. And then to develop a drug, in this case, starting with monoclonal antibodies that targeted this one thing. And that one thing is really important because it binds to LDR receptors.

Saloni Dattani: I also thought it was cool to see, people were wondering, well do we need this protein? What if we block this, will it go bad? And actually using the genetic evidence to find people who just lacked the functional gene and they were healthy.

I think there’s a survivorship bias thing there that makes it complicated, but it does tell you that it’s not vital and I think that’s helpful. And then the siRNA, I think it’s just so cool that you can just decide to just silence the specific gene and you can just, you know, you pick a gene, you found that it’s bad for whatever reason, and you’re like, let’s just switch that off.

And there, you have a medicine, and I feel like we’ve sort of figured this out for a lot of liver diseases and a lot of things that act somehow through the liver, like cholesterol. But eventually we’re gonna find ways to do that for other organs as well. We’re gonna be able to just switch off genes that do things that we don’t like.

Jacob Trefethen: One of my main lessons from this episode is we’re going to have to do a future episode on siRNAs, because that’s just a whole new way of thinking about what you can do in medical intervention and oh my goodness. Oh my goodness. There’s gonna be a lot more that we’ll need to discuss.

Saloni Dattani: Do you have any final thoughts?

Jacob Trefethen: My final thoughts are… Despite what evolution is trying to get us to do, namely die, I don’t think it’s inevitable, and I think it’s great to see how you can really, as a medical research community, tackle the biggest problems and make progress on ’em. And it takes a lot of people’s work over many decades, its a really collaborative effort, and that’s a lot of doctors, a lot of scientists, a lot of patients, and even some podcasters too. No, I’m kidding.

And then we get to benefit, you know, you and I love to hear how all of this stuff is invented and why there’s this scientific consensus, but if you’re just going about your day, you can take a statin and you never have to know all of these arguments that happen between tens of thousands of people.

Saloni Dattani: But they’re so interesting!

Jacob Trefethen: That’s true. I know it’s horrible to consider that people might not get into the scientific weeds.

Saloni Dattani: Yeah. I think this is maybe one of those episodes that for a lot of people it’s personally relevant to them. It’s either them themselves or a family member or someone they know very closely that’s affected by one of these conditions.

And they are curious about a lot of the things that we talk about. And so hopefully it was interesting to a lot of you and fun, and you learnt a lot, I hope.

Jacob Trefethen: I hope so too. And I think one of the joys of medical progress is when it disappears into the background and you don’t think about it. So if you are someone who takes a statin every day without thinking about it much, I hope you had fun thinking about it a bit more for a few hours with us, and now you can go back to normal life.

Saloni Dattani: So if you enjoyed this episode, please share it with all your friends and subscribe and tell everyone you know about it.

Jacob Trefethen: And if you have a future episode request you’d like to make, we’re everywhere online. So just find us somewhere and make a suggestion.

Saloni Dattani: Oh yeah, I’m everywhere. I’m behind you right now.

Jacob Trefethen: No! She’s practically cholesterol, she’s everywhere. I had a lot of fun doing this one.

Saloni Dattani: I thought this was a great episode for the Fatty Blob fans.

Jacob Trefethen: True.

Saloni Dattani: This was an episode all about fatty blobs.

Jacob Trefethen: The fatty blob community has been served well today.

Saloni Dattani: Alright, bye!

Jacob Trefethen: Bye!

Show Notes

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Graham Bessellieu, video editor

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer
  

Works in Progress & Coefficient Giving

Books

• Daniel Steinberg (2007) The Cholesterol Wars.

• Jie Jack Li (2009) Triumph of the Heart: The Story of Statins.
  

Blog posts

• James Stein (2025) Lipid and lipoprotein basics series.

James H. Stein, MD

Part I: Lipid and Lipoprotein Basics for Clinicians

It’s amazing how often I have conversations with patients and health care professionals who genuinely do not understand what they are asking or talking about when it comes to cholesterol, triglycerides, lipoproteins, and their treatment. That’s not because they are dumb or ignorant - they certainly are not. It’s because most people never have had the ti…

Read more

4 months ago · 137 likes · 25 comments · James H. Stein, MD

Articles

• Akira Endo (2017) Discovery and Development of Statins https://doi.org/10.1177/1934578X1701200801

• Joseph L Goldstein, Michael S Brown (2010) History of discovery: The LDL receptor. https://pmc.ncbi.nlm.nih.gov/articles/PMC2740366/

• Patty W. Siri-Tarino and Ronald M. Krauss (2016) The early years of lipoprotein research: from discovery to clinical application https://pubmed.ncbi.nlm.nih.gov/27474223/

• Eun Ji Kim and Anthony S. Wierzbicki (2020) The history of proprotein convertase subtilisin kexin-9 inhibitors and their role in the treatment of cardiovascular disease https://pubmed.ncbi.nlm.nih.gov/32537117/

• Patrick W. Siri-Tarino et al. (2010) Saturated fat, carbohydrate, and cardiovascular disease. https://www.pnas.org/doi/10.1073/pnas.94.9.4312

• Saloni Dattani (2025) Death rates from cardiovascular disease have fallen dramatically — what were the breakthroughs behind this? https://ourworldindata.org/cardiovascular-deaths-decline

• Cholesterol Treatment Trialists’ (CTT) Collaboration (2010) Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. https://doi.org/10.1016/S0140-6736(10)61350-5

• E. J. Mills et al. (2011) Efficacy and safety of statin treatment for cardiovascular disease: a network meta-analysis of 170,255 patients from 76 randomized trials. https://pubmed.ncbi.nlm.nih.gov/20934984/

• Julia Brandts and Kausik K. Ray (2023) Novel and future lipid-modulating therapies for the prevention of cardiovascular disease. https://www.nature.com/articles/s41569-023-00860-8
  

Videos

• Ninja Nerd (2018) Lipoprotein metabolism

In this episode, and follow the trail of strange jaundice outbreaks that scientists traced to a stealthy liver virus, how scientists turned one viral surface protein into a lifesaving shot for newborns, and how it was all built upon breakthroughs in immunology.

Hard Drugs is a new podcast from Works in Progress and Coefficient Giving about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Chapters:

0:00:00 Introducing the hepatitis B vaccine
0:15:46 The mysterious trail of jaundice outbreaks
0:28:03 How a tiny virus causes cirrhosis and liver cancer
0:53:19 Maurice Hilleman’s purified hep B vaccine
1:17:36 Turning the hep B vaccine recombinant
1:29:14 The impact of hep B vaccination
1:39:27 The 19th century battle for immunology
2:01:34 How the body almost infinite antibodies
2:30:57 How subunit vaccines took over
2:45:33 Conclusion

Saloni’s substack newsletter: https://www.scientificdiscovery.dev/

Jacob’s blog: https://blog.jacobtrefethen.com/

Transcript

Saloni Dattani:

It took 90 years after Jenner developed the smallpox vaccine for scientists to make another one. For the next 50 years after that, scientists tried making many more vaccines through attenuation and inactivation. They tested them, found success multiple times, but still had very little understanding of why they worked.

Jacob Trefethen:

That is where we ended last episode, but the discovery of germs and the invention of attenuated and inactivated vaccines were just the beginning. One thing that changed over the 20th century were massive breakthroughs in immunology, the science of improving our understanding of our immune responses and how we learn from past infections. That meant that scientists could start making safer and more precise vaccines, which allowed particular scientists to develop the first hepatitis B vaccine.

Saloni Dattani:

The hepatitis B vaccine is special in many ways. It was the first protein subunit vaccine which contained just a small protein from the virus instead of the whole thing. It was then improved on to make the first recombinant vaccine, with genetic engineering technology to produce it in yeast. Third, it was the first vaccine against a cancer: liver cancer.

Jacob Trefethen:

It has also been one of the most successful vaccines, preventing hundreds of thousands of children from getting chronic hepatitis B infections each year and reducing liver cancer rates by about 85% in those who receive it.

Saloni Dattani:

In this episode of Hard Drugs, we are going to talk about all of that: how people figured out the science of immunology, how they made vaccines more precise, how they made hepatitis B vaccines, and how those vaccines have protected babies from jaundice and a deadly form of liver cancer.

[podcast jingle]

Saloni Dattani:

What’s your opinion about hepatitis? Good, bad, ugly?

Jacob Trefethen:

Hepatitis? God, I have a lot of opinions about hepatitis, if I am being honest. I mean, hepatitis is caused by many things. Do you want my opinions about hepatitis B or my opinions about hepatitis?

Saloni Dattani:

Yes, hepatitis B; so there are different letters and they correspond to different diseases.

Jacob Trefethen:

Well, my opinions about hepatitis B are that in my day job at Coefficient Giving, we have worked on trying to make new cures against hepatitis B, trying to improve animal models for drug development, trying to put hepatitis B vaccines in microneedle patches so that a baby can have a patch instead of an injection. We’ve done a fair bit on hepatitis B.

Saloni Dattani:

I want a patch.

Jacob Trefethen:

Yes, well...

Saloni Dattani:

It’s only for the babies.

Jacob Trefethen:

What would you put in your patch?

Saloni Dattani:

Everything. All the vaccines in one patch.

Jacob Trefethen:

Oh, wow. That would be... hey, that’s the future. Unfortunately, today we are just talking about history. I mean, I basically think hepatitis B is it is the hepatitis virus of many that leads to the most deaths. It’s hard to quite attribute how many, but somewhere in the region of 750,000, maybe 800,000; some estimates go as low as 500,000, but hundreds of thousands around the world.

Saloni Dattani:

Per year.

Jacob Trefethen:

Per year. I have a lot of respect begrudgingly for that virus because it is fucking tiny; it is four genes. It is astonishing. It is unbelievably compact. I mean, some of those genes literally overlap, that is how efficient it is. It also has a reverse transcription stage, like HIV does. It’s just unbelievably complicated for something that is so tiny. I am just like, bloody hell. Anyway. It forms its own chromosome inside your liver cells. Anyway, I did not know I had this many feelings about hepatitis B.

Saloni Dattani:

Hepatitis B has also been in the news recently because in the US, the Vaccine Advisory Committee just voted to stop universally recommending hepatitis B vaccination at birth. It’s possible they limit the vaccines even further, potentially keeping them only for teenagers or adults. That’s another reason that I think this is a really interesting and important topic to talk about.

Currently, in many countries around the world, hepatitis B vaccines are universally recommended at birth to all newborns. There are some exceptions, though. In various countries in Africa, they do not provide it at birth. That is generally because it’s just difficult to do that logistically, because many births happen outside of health facilities. The vaccine requires cold chain storage, and instead, the recommendation is to give vaccines around six weeks after birth to babies. In Europe, many countries only provide- or only recommend the vaccine to children of mothers who test positive for hepatitis B, but they have a lot of screening and high vaccination rates for those newborns.

This is interesting as well because of the epidemiology, how hepatitis B spreads, and how that transmission occurs nowadays in different countries. In poorer countries, there are a lot of infections that happen after birth in early childhood, and kids catch the virus from their siblings or other kids that they’re playing with.

In richer countries, the prevalence of hepatitis B is generally very low, and that’s generally because vaccination has been so successful so far. Most infections nowadays are from mother to child, not from other kids.

I think one reason that I find this concerning is that if there is more vaccine hesitancy against hepatitis B because of this change, it could mean that even mothers who test positive for hepatitis B would be unwilling to get their kids vaccinated. A second is that it would be very bad if other countries, especially poorer countries, follow suit.

Early vaccination is important because infections in babies and young children are much more likely to turn into long-term infections, which can lead to liver disease and then cancer. The reason for that is that infants and kids have less developed immune systems, and they cannot control the virus as well as adults can. It slips past their immune system and establishes itself in the liver for years or decades. If it was me, I would recommend universal vaccination, but that does not mean that it’s mandatory; it just means that it’s free and recommended.

Jacob Trefethen:

Yeah, and the case that people in favor of this change in the US would make is that if some mothers already know that they are negative for hepatitis B and will not pass on hepatitis B in childbirth to their child, they should be allowed to skip vaccination. Their kid will be at more risk from other sources, but at birth at less risk.

The case against, from a lot of the public health community is, well, we know that this vaccine is safe and works. It has already driven down, you know, 90-plus percent, 99% maybe in the US of hepatitis B cases because it works so well. Implementing more complicated guidelines where people have to judge for themselves when they are not health experts will mean that more kids are at risk of hepatitis B.

Yeah, I think what you said is absolutely right: if this is step one to restricting access further, that is especially concerning. That is also true for other countries where they have not yet seen the big drops in hepatitis B that the US has, as a result of the vaccine, that the risk for kids is higher.

We recorded most of this episode before this recent news from the US because hepatitis B vaccines happen to be the best next part of the story to tell of vaccine development. We wanted to talk about them as a vaccine using modern methods to become even more safe than some of the vaccines we talked about in the last episode.

Saloni Dattani:

So let’s get into the topic of hepatitis B, the vaccine, and how it was made.

[podcast jingle]

Jacob Trefethen:

Firstly, what is it?

Saloni Dattani:

What is it? What is hepatitis? Hepatitis is a liver infection. That is what it means: hepa- is liver, -itis is inflammation or infection. What happens when someone gets a hepatitis infection? Well, there are two types. There are acute infections, which are not cute. They are basically a short-term infection, reaction, that lasts weeks to months, and people feel very tired; they throw up a lot. They also could have yellowing of their skin and of the whites in their eyes. Most people recover from that.

Then there’s chronic hepatitis, which means that the virus stays around in your body. It’s hanging out for a long time, usually years, and it causes inflammation in your liver, causes your liver to get scarred, and over time, that can lead to cancer.

Jacob Trefethen:

Long-time listeners may recall that the fifth episode in our AI series, we talked about hepatitis antigens you could go after with AI designed proteins. It came up there before, but I think the thing you just said about it being a chronic infection is crucial to the link with cancer. And the issue with hepatitis C infections as well, if you have a chronic infection, then it’s sticking around, it’s spitting off new viral particles somewhat regularly, and they are going to start integrating with your own DNA sometimes, which can lead to... problems!

Saloni Dattani:

Right. We mentioned hepatitis B, and you mentioned hepatitis C, and there are many different kinds of hepatitis. There is A, B, C, D, E, and are there more? Maybe there’s an F as well.

Jacob Trefethen:

I only know those five, and those five actually, again, with my Coefficient Giving, we have worked on four of the five. Any guesses which one we have ignored?

Saloni Dattani:

A.

Jacob Trefethen:

Correct, yes.

Saloni Dattani:

I was right!

Jacob Trefethen:

A has got a great vaccine and spreads through food contamination and all that. Of those, it is kind of amazing how different they are.

Saloni Dattani:

What I did not know until doing research for this episode is that they are caused by completely different viruses. They’re just completely genetically unrelated. I remember reading about this and I was like, what the heck is going on? Why do they all have the same name then?

Jacob Trefethen:

They are named so badly. They just happened to be involved with the liver. Well, I mean, my one defence is that hepatitis D, or hepatitis delta, is related to hepatitis B.

Saloni Dattani:

Oh, okay.

Jacob Trefethen:

In the sense of you actually can’t get- it’s a real tiny, tiny, tiny, tiny, tiny virus, Delta. And you can’t even get infected with it unless you have hepatitis B, and so it only hops on in.

Saloni Dattani:

It’s a hanger-on.

Jacob Trefethen:

It’s a hanger-on. I mean, viruses themselves are making use of your cell machinery because they are no good on their own. Hepatitis delta is like, I am actually no good without another virus. I mean, that is pretty no good about it, but it’s dangerous if you do get it.

Saloni Dattani:

Well, hepatitis A causes a short-term acute infection. B and C both cause liver cancer. What does D do?

Jacob Trefethen:

Same again as B.

Saloni Dattani:

Also liver cancer?

Jacob Trefethen:

Yeah.

Saloni Dattani:

What about E?

Jacob Trefethen:

E is pretty, well, firstly, with the liver cancer ones, also often it is just cirrhosis where you do not necessarily get cancer, but you get so much liver damage that it causes- it can lead to death, that is over many years. E is one that I wish there was more epidemiology on, in the sense that I do not think it is well known how widespread E is around the world, but it is more often acute, so similar to A, where if you have infections at a particular water source, E is hanging around in some water source, then you might get transmission in a particular community that has a big explosive outbreak.

Saloni Dattani:

Huh. Yeah, I didn’t know about that. I wonder if people are going to identify more after this. Especially if they actually have no genetic relationship to each other and it is just different viruses that can infect the liver.

Jacob Trefethen:

Absolutely. Do you remember which one came up last episode?

Saloni Dattani:

No?

Jacob Trefethen:

Hepatitis C, because, do you remember the injections in Egypt?

Saloni Dattani:

Oh, yes, yes, yes, yes, yes.

Jacob Trefethen:

To cure schistosomiasis. Those introduced hepatitis C in the ‘60s and ‘70s to a lot of people through sterilized needles- or not sufficiently sterilized. And that was before hepatitis C was discovered.

Saloni Dattani:

Right, right.

Jacob Trefethen:

Who knows what else is down there in our liver?

Saloni Dattani:

Right. That is very scary. They are all different infections of the liver. They have different sources or ways that they transmit. We are going to focus in this episode on hepatitis B. Hepatitis B, as we said, has a short-term infection, and then it can also lead to a chronic infection. It is mostly spread through blood, right? Mother to child, or through shared needles, or blood products. That is actually how it was discovered and how people figured out that it was a virus at all.

People had seen the symptoms of hepatitis for thousands of years. Hippocrates, over 2,000 years ago, described the symptoms of hepatitis. People were wondering what caused it. At the time they said, humoral imbalance, and I can see why you would think that. It’s also really difficult to figure out what the cause is because often the illness that you get happens months after you get infected by the virus, so it’s hard to make the connection and figure out what is the source of an infection.

There are situations in which there are outbreaks of hepatitis. Historically, that would have been in wars – so you would potentially have blood contamination through injury and things like that. But also there would have been outbreaks sometimes after the use of medical products, such as blood transfusions, but also the use of, as you said, unclean sterilized syringes. When people were doing vaccination campaigns against smallpox or against yellow fever or against other diseases, sometimes, in some contaminated lots of the vaccine, it led to small outbreaks. You would have many people who suddenly develop jaundice around the same time. The only shared thing they have is that they all got a particular medical product at some point months ago.

There is this interesting quote that I was reading about this that said, “Vaccination against smallpox, the glass syringe, injectable antimicrobial drugs, passive immunization, the yellow fever vaccine, and blood transfusion have all been of enormous benefit to humanity, but each of these medical advances has left a trail of jaundice in its wake.”

Jacob Trefethen:

Oh my gosh, is not that crazy to think about?

Saloni Dattani:

Yeah, but thankfully it’s not that common because it’s only- typically in the past, it would have just been a few small percentage of people who had it circulating in their blood and that could contaminate those products and lead to outbreaks.

Jacob Trefethen:

Well, just as a PSA to listeners, if you, for any reason, do use needles — so people who inject drugs with needles are some of the most at risk of hepatitis C — so you have to be really careful because you do not want to introduce microbes into your blood system.

Saloni Dattani:

But also if, like me, you just like getting vaccinated.

Jacob Trefethen:

So long as you are not getting vaccinated in the 19th century, I think you will be okay.

Saloni Dattani:

That’s true. There is also another theory of the cause of hepatitis, and that is by Rudolf Virchow, the father of pathology. He thought that hepatitis was caused by overeating; that overeating inflamed the intestines, and it closed the bile ducts, and that led to a lot of problems. The only reason he basically thought this was that he had heard about an autopsy of one person who had jaundice and whose bile duct was closed and swollen by mucus.

Jacob Trefethen:

N of 1, okay.

Saloni Dattani:

He’s like, problem solved. Now I know what causes the disease.

Jacob Trefethen:

Wow. Okay. Was he right?

Saloni Dattani:

He was not right, but people believed... this theory led to a particular name of hepatitis before it was called that, catarrhal jaundice, that it was related to the intestine. And that name lasted for 80 years. People just thought it was related to the intestine for a very long time because of him.

Jacob Trefethen:

We cannot be too mad, because the current names of the diseases / viruses, hepatitis A, B, C, D, E, as just discussed, are still terrible.

Saloni Dattani:

But I’m also like, well, probably you would have been able to at least observe these outbreaks of jaundice. When you saw them, you could have done more than one autopsy, and you could have done some more pathology research, and figured out that it was the liver that was the most damaged. And that is what people eventually figured out over the 1920s and ‘30s.

Also in World War I, pathologists who were looking at soldiers who were killed in the war, but also suffered from jaundice, saw that they had a lot of abnormalities in their liver tissue. There were a lot of cells that were infiltrating their liver tissue and inflaming it. They concluded that the bile duct jaundice or the catarrhal jaundice was actually due to liver inflammation. They resolved this old debate and threw out his theory.

Jacob Trefethen:

Okay. Pew. Trash can! Okay. Now, the question I have is what is causing that inflammation, Saloni?

Saloni Dattani:

What is causing that inflammation? People do not know. People do notice sometimes there are outbreaks. The outbreaks occasionally happen months after a vaccination campaign, but only in certain places, only with certain lots of contaminated vaccine. Sometimes they happen when people are treated for diseases, so syphilis patients that are treated with antibiotics are sometimes developing jaundice months later. Doctors are like, well, it doesn’t seem like it is because of the drug, because the illness is only happening months later.

Jacob Trefethen:

These are old school antibiotics with a needle, you mean? Not oral.

Saloni Dattani:

Yes. This would have been things like salvarsan, which I think is the first antibiotic. This jaundice is probably not caused by syphilis because their other symptoms are improving by the time they have developed jaundice, which is months later. They are like, hmm, what’s going on?

Jacob Trefethen:

Something else here, yeah.

Saloni Dattani:

People keep studying, and they’re like, okay, let us try to deliberately infect people and see what happens. They try to do different things to inactivate whatever is in the serum, and they figure out that it is something that is smaller than a bacterium; it gets through bacterial filters. It can also survive heating at 56 degrees for 30 minutes.

Jacob Trefethen:

Really? That is impressive. Wait, on the temperature thing, I think that is 56 degrees celsius, I assume you mean.

Saloni Dattani:

Yes, yes.

Jacob Trefethen:

Not Fahrenheit, not Kelvin. Okay, got it. Yes.

Saloni Dattani:

It is caused by something that can get through bacterial filters, can survive heating for half an hour. It can also be transmitted by blood of people who have symptoms.

Jacob Trefethen:

Wait, so I have a question for you, Saloni, which is, do you think you could survive 30 minutes in 56 degree heat?

Saloni Dattani:

I don’t think I could, and I do not mean like physiologically, but I think psychologically I would just leave after two minutes. You know what? I like normal temperatures.

Jacob Trefethen:

Yeah. Yeah. That is... I do too, actually. Okay.

Saloni Dattani:

I was once in a sauna, and I just got bored. I was like, you know what? No more of this.

Jacob Trefethen:

Everyone else is like, “I paid 50 pounds for this.” You’re like, “This is boring. I want to get back to my books.”

Saloni Dattani:

So it’s really difficult to figure out what exactly is causing hepatitis. It’s not a bacterium; it’s probably too small for that. But it’s somehow resistant to a bunch of different types of chemical processes. It survives for heat for half an hour. And it’s really hard to grow in cell culture. And it’s really hard to cause jaundice in other animals by injecting infected serum with them.

Jacob Trefethen:

What year are we in? What year?

Saloni Dattani:

This is the 1930s.

Jacob Trefethen:

Okay. 1930s were when the electron microscope came out, and we discovered viruses-

Saloni Dattani:

True.

Jacob Trefethen:

So did they know yet the virus concept or is...

Saloni Dattani:

1931, I think, was the first electron microscope, but I think they just could not figure out the specific pathogen. I think that would have still been kind of hard.

Jacob Trefethen:

Got it. I’ve got a guess as to why it’s not working in other animals.

Saloni Dattani:

Why?

Jacob Trefethen:

I mean, working is a horrible word, but it is not causing disaster in other animals. Now we know hepatitis B only really forms a chronic infection in humans, right?

Saloni Dattani:

Doesn’t it also infect chimpanzees? I think I read that.

Jacob Trefethen:

Yes. And woodchucks, but that is a whole different story.

Saloni Dattani:

Yeah, and woodchucks. Yeah, I saw that as well.

Jacob Trefethen:

How much wood would a woodchuck chuck if a woodchuck had hepatitis?

Saloni Dattani:

Hepatitis is causing long-term infections. It hides in the liver. It causes liver damage, scarring, and then liver cancer. This was not known for a very long time, but people did see that there seemed to be different types of hepatitis. Some of them are infectious through the blood, through the serum, and some of them seem to come after serum-related things.

There are others that don’t, and there are others that seem to be spreading through contaminated food or water or something else. They have not discovered hepatitis C at this point, so that is out of the question.

They then separate these into two, and they call hepatitis A “infectious hepatitis” and hepatitis B “serum hepatitis.”

Jacob Trefethen:

That’s a terrible name.

Saloni Dattani:

They are like, hepatitis A usually seems to be spreading in outbreaks through contamination.

Jacob Trefethen:

Right, yes.

Saloni Dattani:

And that can pass from person to person. But hepatitis B seems to happen mostly after blood transfusions, and people develop jaundice weeks or months after those, so that is “serum hepatitis.”

Jacob Trefethen:

Okay. Okay.

Saloni Dattani:

Kind of makes sense.

Jacob Trefethen:

I mean, I think I could, it is going to make me sound bad, but I think I could give hepatitis A via serum, and I could give B via infection.

Saloni Dattani:

Yeah, yeah, but it’s the typical pattern, I guess.

Jacob Trefethen:

Yeah, I am with you.

Saloni Dattani:

Now we know that they are caused by totally different virus families. Hepatitis A is caused by an RNA virus of the Picornavirus family, which is the same family as poliovirus. Hepatitis B is a DNA virus in the Hepadnavirus family, which is very directly named after, I guess, hepatitis and DNA.

Jacob Trefethen:

Hepadna?

Saloni Dattani:

Yeah.

Jacob Trefethen:

Cool. I mean, it is double-stranded DNA, right? I mean, I think it’s a little strand that hangs off, but yeah, it’s a little circle basically.

Saloni Dattani:

And as you mentioned before, it also has this very weird replication strategy where it turns its DNA into RNA and then back into DNA and then double-stranded DNA.

Jacob Trefethen:

So it reverse transcribes. So I’m a little virus that then enters the bloodstream via a needle, and then I am going to find my way to the liver. It probably won’t take very long at all. And then I’m going to enter a liver cell, and I’m going to unfurl and unpackage myself into the cytoplasm of that liver cell. I’m in the nucleus, I form a little mini-chromosome of my own, which is crazy. Then that mini chromosome uses all your little machinery in the nucleus, your histones. It’s forming this nice little circle-of-eight kind of thing.

Saloni Dattani:

Aw.

Jacob Trefethen:

I know, it’s cute until it’s not. And then it’s so stable, it can stick around for decades, hence the chronic infection. And your body’s going to start transcribing it again, and once it transcribes it, it’s going to leave the nucleus. It’s in the cytoplasm, it’s going to sort of self-assemble. Maybe that’s when it does its reverse transcription, I’m not sure, and then it’s going to form a little packet - boop! - and then leaves as a new virus.

Saloni Dattani:

But it also, it reverse transcribes- so it turns its RNA back into DNA and then it uses its polymerase to make another DNA strand, and so that makes it a double-stranded DNA again.

Jacob Trefethen:

Oh, right. Yeah.

Saloni Dattani:

What the heck is going on, though? That is so complicated.

Jacob Trefethen:

That should not be allowed.

Saloni Dattani:

Why so many steps?

Jacob Trefethen:

Yeah. Why so many steps for something so small?

Saloni Dattani:

The other thing about the life cycle, or its strategy, is that the way that it infects the liver is by binding to your liver cells - on the surface of your liver cells. People make antibodies against this surface protein it uses to attach - the hepatitis B surface antigen - which we will come back to.

People are making antibodies that stick to this viral surface protein, prevent it from attaching to our liver cells, and that would normally prevent an infection if it cannot bind to the liver cells. But guess what? The virus has a strategy to fight back.

The way that it does that is by making so many of that same viral surface protein. Basically, the idea is that all of these excess surface proteins will soak up antibodies from the blood that you have developed against it, and allow the virus to attach itself to our liver cells. There’s a quote from a book that I was reading, which will come up again, called ‘Vaccinated’ by Paul Offit. He says, “Hepatitis B virus is so committed to this method of survival that people infected with the virus have about 500 quadrillion particles of viral surface proteins circulating in their bodies during an infection.”

Jacob Trefethen:

That’s... that’s a lot. I’m no mathematician.

Saloni Dattani:

That is five with 17 zeros after it.

Jacob Trefethen:

That’s kind of crazy to think about.

Saloni Dattani:

It is just like, “Oh, guess what? You think you can attack me? Can you attack 500 quadrillion-?” I guess that’s what you do if you only have seven proteins, you only have four genes. You are like, “Guess what? You can’t fight so many-

Jacob Trefethen:

Yeah, it is like one horse-sized duck versus a hundred-

Saloni Dattani:

500 quadrillion. Would you rather get infected by one tetanus bacterium or 500 quadrillion hepatitis B surface antigens?

Jacob Trefethen:

Yeah. Well, you have given me two great options, so thank you. I’ll take the Hep B. I’ll take the Hep B.

Saloni Dattani:

Really? I would have taken the tetanus.

Jacob Trefethen:

Let’s see who makes it out.

Saloni Dattani:

None of us, probably. So hepatitis B, how does it cause disease? One is, it manages to evade our immune response by making 500 quadrillion hepatitis B surface antigens.

Jacob Trefethen:

As I think of Hep B getting into your nucleus of your liver cells, which is why it stays so chronic, because it gets so stable down there, I think of two forms of it. The main one is what is called cccDNA, covalently closed circular DNA. There’s a second form, though, which is called integrated DNA, where basically sometimes it does slip in and similar to HIV that sort of gets integrated into your DNA, it will get into your main chromosomes. Oh my gosh, imagine trying to get that out. That’s... difficult. I think that that leads to some co-genetic activity.

Saloni Dattani:

I think that was basically as far as I got. The virus’s DNA integrates into your liver cells’ genomes, and then the insertions could be in places that are important for protecting your cells against cancer- or cancerous mutations. But I think it is the long-term inflammation. Basically, the virus is sticking around in your liver cells for a very long time, your immune system keeps trying to control the infection, but it never manages to fully clear it, because I guess it’s integrated itself. And this creates long-term inflammation and repeated cycles of cell death and regrowth.

I guess the liver is also the only organ that we know of that can regenerate itself almost fully. There are just many different cycles of this inflammation, and those cycles raise the chance of harmful mutations as there’s regrowth of liver cells. So, over a lot of time, over many years or decades, the combination of these things — the chronic inflammation, the cycles of cell replacement, and the insertion of its viral DNA, and also just various other crazy things it does with its HBX protein, right?

Jacob Trefethen:

Thank you for bringing up HBX. Yes! HBX, part four of our AI series, I was trying to make binders against. I don’t know the most recent literature to be sure of, but I think how that could be implicated in cancer is that HBX is somehow getting your body to relax its usual way of controlling oncogenes.

Saloni Dattani:

It affects the signalling of your cell.

Jacob Trefethen:

Yes, definitely.

Saloni Dattani:

Yeah.

Jacob Trefethen:

It is the transcriptional signalling. Well, it’s a little bit beyond our pay grade, but we might need to get an HBX expert on a future episode.

Saloni Dattani:

Oh. We’re going to do another episode on this?

Jacob Trefethen:

Great.

Saloni Dattani:

Hold on to your hats. This isn’t over. So all of this stuff eventually causes liver cancer, specifically in your hepatocytes, is that right? Hepatocellular carcinoma.

Jacob Trefethen:

HCC won’t let me be.

Saloni Dattani:

So why is this important to prevent? Because it causes liver cancer, but also because it infects babies, right? It causes jaundice in babies. Most cases of hepatitis B are acquired by infants from their mothers at the time of birth.

Jacob Trefethen:

There’s something I don’t understand the driver of, but I’ve heard that if you get infected when you are an infant or a baby, you have a very high chance of forming a chronic infection, maybe over 90%. If you get infected as an adult, you have a very low chance of forming a chronic infection, maybe under 10%. I’m not sure why that is, but that means the risk per infection changes a lot.

Saloni Dattani:

Right, I’m also not sure what it is. If I had to guess, it would be that adults have various protective factors in their humour, in their serum that destroy hepatitis B virus, at least for some people, at least that’s what I would guess. Or we have other immune cells that manage to kill it, and that doesn’t happen in babies. Around 90% of babies infected at birth or in the first year of their life go on to develop chronic hepatitis, and that compares to five to 10% of adults who catch the infection.

15 to 40% of infants who develop that chronic infection will then die prematurely from liver failure or cirrhosis, which is severe scarring of the liver, or from liver cancer if they’re not treated for it. That’s really bad because there weren’t vaccines for a very long time, and we didn’t really have a good idea of what was causing that liver cancer until, I think, the 1970s and 80s.

Jacob Trefethen:

You mean that those babies will very often die of liver cancer, but as adults, not as babies?

Saloni Dattani:

Right.

Jacob Trefethen:

That’s a high percentage. It’s surprising to me how liver cancer overall is very high up there and it’s mostly caused by viruses. There’s almost an opportunity there: if you can get rid of the viruses, then maybe we actually can reduce cancer a lot more than some other cancers that might be even trickier.

Saloni Dattani:

Very true. There are a bunch of cancers that we can get rid of by getting rid of infections. One is liver cancer. Then there’s cervical cancer, which is caused by human papillomavirus. There’s stomach cancer, which is predominantly caused by Helicobacter pylori, often spread through contaminated food. So we should just get rid of all of these pathogens and then we won’t have a bunch of cancers, isn’t that cool?

Jacob Trefethen:

That is really cool.

Saloni Dattani:

I was interested in the fact that people figured out this was related to liver cancer, and as I mentioned, that was not clear until the early 1980s, around 1981. There were various lines of evidence that suggested it. People doing lab experiments or animal experiments — which I presume would have been with chimpanzees, I guess — showed hepatitis B caused liver cancer, probably because of the long-term liver damage caused by the virus. There were case-control studies where if you looked at people who had liver cancer, they had a much higher prevalence of hepatitis B antigens.

Then in the 1970s, the CDC researcher called Palmer Beasley carried out a longitudinal study in Taiwan where he tested adults for whether they had the hepatitis B surface antigen, which is a marker of them having the infection, and followed them up for several years. The people who had this chronic infection marker went on to develop liver cancer at much higher rates than people who were not infected.

And as we’ll come to later on, there’s even stronger evidence that hepatitis B causes liver cancer, because when the vaccine was rolled out, you could see a massive drop in liver cancer rates years later. This was done in a randomized trial in China, where they randomly gave the vaccine to some towns and not other towns. They gave them to all newborns in certain towns but not others, and they had an 85% reduction in liver cancer 30 years later in the towns where the newborns got vaccinated.

Jacob Trefethen:

Yeah, it’s a pretty stark result. How did we actually get to those vaccines?

Saloni Dattani:

So we have lots of hints that hepatitis B - the serum one - is caused by a virus. We know it can get through the filters, blablabla. The person who discovers the marker of hepatitis, or this hepatitis B surface antigen, is a guy called Baruch Blumberg, or I think Barry Blumberg is his nickname. He figured that out in the 1960s, but he didn’t actually know that it was related to hepatitis B. He was a geneticist studying in Africa I think, looking at elephantiasis, which is a condition where people have very swollen limbs after... that’s a parasitic infection, I think. Elephantiasis... parasitic worms. Lymphatic filariasis.

Jacob Trefethen:

Oh yes, yes. Lymphatic filariasis.

Saloni Dattani:

I think elephantiasis is the old name, and the idea is that people’s limbs have swollen so much that they look like they have turned into elephants.

Jacob Trefethen:

Not to be confused with encephalitis.

Saloni Dattani:

... which is inflammation of the brain.

Jacob Trefethen:

Absolutely.

Saloni Dattani:

So he was studying elephantiasis and noticed that different people in Africa had very different susceptibilities to it, and he figured out at some point, I don’t actually remember how, that this was because of polymorphisms in their protein. They had genetic differences that made some people more susceptible than others. There are many examples of polymorphisms related to different risks of diseases; another is sickle cell disease, where some people are protected from malaria and other blood related infections because they have a different hemoglobin protein so their blood cells look more like sickles than bouncy cells.

He was trying to figure out if there were any other examples of diseases where there were polymorphisms that could affect people’s susceptibility to those diseases. So what did he do? He’s trying to find other protein polymorphisms, and the way you can try to find protein polymorphisms is to see if people develop antibodies against other people’s proteins that are not their own.

Jacob Trefethen:

Yeah, just like the blood types earlier.

Saloni Dattani:

Exactly. So he thought, if someone has received many different blood transfusions, they would probably have antibodies to other people’s proteins.

Jacob Trefethen:

Very good point, yes.

Saloni Dattani:

Very good idea. He looked at people who had had at least 25 blood transfusions in the 1960s. He found a man who had hemophilia in New York City. So he had these samples and he tried to see basically, what this person who had hemophilia would react to.

He found that man had antibodies in his blood to a protein found from someone halfway across the world, an Australian aborigine, and he was like, oh wait, this is reacting to a very, very different person’s serum. He called this protein that it was reacting to the “Australia antigen.”

Later on, he found that the same antigen was present in a lot of people with leukemia, and then also a lot of children with Down syndrome, and he didn’t really know why at the time, I think he was quite confused by it. The reason we now know is that it’s because they were much more likely to have been infected by hepatitis B virus, and that the Australia antigen is... the hepatitis B surface antigen.

Jacob Trefethen:

Oh... Whoa! So wait, so this guy, Blumberg, was looking for genetic polymorphisms, not looking for viruses?

Saloni Dattani:

He was looking for protein polymorphisms. Because it was the 1960s, genetic sequencing was really hard.

Jacob Trefethen:

He wasn’t looking for viruses?

Saloni Dattani:

No.

Jacob Trefethen:

He was just out there. He went, I want to go for someone who’s had many blood transfusions. If you have hemophilia, you’re often going to have to have many blood transfusions. He wanted to see if they have antibodies that react against particular proteins. He found one and it reacted against a very distant protein. He was like, wait, hold on a second, hold on a second, hold on a second.

Saloni Dattani:

He’s like, why’s that going on?

Jacob Trefethen:

He found that antigen elsewhere, he found that antigen in-

Saloni Dattani:

a sample from an Australian aborigine.

Jacob Trefethen:

But then in addition, people with leukemia...

Saloni Dattani:

...and children with Down syndrome.

Jacob Trefethen:

And then it turns out that’s because there’s a common source!

Saloni Dattani:

Yeah. The levels of this antigen seem to vary a lot across the world and in different demographics.

And there’s actually a different guy who figures out what it is. There’s a virologist called Alfred Prince, and he figured out that that antigen was part of hepatitis B virus. He, again, was looking at blood samples from people who had received transfusions. What he did, was he looked at their samples before and after they received the transfusions. In 1968, he found a patient who had serum hepatitis, or hepatitis B, and the early samples of their blood didn’t have this Australia antigen, but the later samples did.

Jacob Trefethen:

Wow! Okay, gosh.

Saloni Dattani:

So he’s like, this antigen has appeared after they have developed serum hepatitis. So he concluded that the antigen was part of the virus, which is now called hepatitis B.

Jacob Trefethen:

Well done! That’s, hey, that’s clever.

Saloni Dattani:

I’ll mention some of these names later on, but the next step is to think, well, maybe we can develop vaccines. We now have figured out this one particular viral antigen that people develop antibodies to. Why don’t we develop vaccines with it? You know, in the previous episode, we talked about attenuated vaccines and inactivated vaccines. Why not try one of those routes to develop a hepatitis B vaccine?

Jacob Trefethen:

That seems key to me, because I don’t want to give just any antigen, I want to give an antigen that actually prompts an immune response that then stops the infection.

Saloni Dattani:

Right, that’s what the next person does, they try it out. There’s a scientist called Saul Krugman, who you might have heard of, or maybe not, and he is a first cousin of Albert Sabin — this is going to come up later. He did some experiments that are now considered controversial, actually also slightly after the time.

He was working with a school of intellectually disabled children. They often, the children with Down syndrome, had higher rates of hepatitis B infections. He was trying to figure out if people with hepatitis B infections had the virus in their blood, so he took blood from someone with a hepatitis B infection, he let it clot, and then he injected it into the veins of other healthy children in that same school. Almost all of them got sick with hepatitis, that confirmed that the virus was, in fact, in the blood.

It’s considered very controversial now, because at the time people didn’t know it could cause chronic infections, and also the fact that the children were disabled, but he did take their parents’ consent.

Jacob Trefethen:

But no children can themself fully consent.

Saloni Dattani:

Right. So he did figure out that the virus was in their blood, so now he’s like, let’s see if we can develop a potential vaccine.

He took the infectious serum, diluted it in water, and heated it. He hoped this would kill the virus but keep the hepatitis B surface protein intact. Then he injected that heated serum into the kids, I think at the same school. He gave some of them two doses, some of them one, and then tried again infecting them and seeing if they were protected. And it worked! It protected all of the kids who had received two doses of this heated serum vaccine, and he then showed that was because of the surface protein, so you could use that surface protein to make a vaccine.

Jacob Trefethen:

It is amazing, in that era of vaccinology- the difference back then between the bug itself and the vaccine was not as much. These things were just way more dangerous, my god. Now if you volunteer for a vaccine trial, you’re not getting injected with a virus.

Saloni Dattani:

Right, right. You’re not going to get tested to see if you’re protected by getting injected with the virus itself... unless you’re in a challenge trial.

Jacob Trefethen:

Indeed, indeed.

Saloni Dattani:

The other question I had was, why not make whole vaccines? Why not use the entire virus? But it turns out hepatitis B is really difficult to culture in cells, right?

Jacob Trefethen:

Oh, okay, yeah. That does make it much harder because you want to inject a big volume so that you get enough response.

Saloni Dattani:

So it doesn’t grow very well and it depends on very specific liver cells and liver cell conditions, and that’s very difficult to do in the lab. And you couldn’t also produce it outside of the body in a different living animal; there’s no animal model except chimpanzees and woodchucks, which we found out much later. So we can’t make a traditional vaccine, we’re instead going to try making a hepatitis B surface antigen vaccine, a protein subunit vaccine. And do you know who’s going to make it?

Jacob Trefethen:

I think it’s going to be... Sabin!

Saloni Dattani:

No.

Jacob Trefethen:

Okay... I think it’s going to be... Blumberg?

Saloni Dattani:

No, it’s Maurice Hilleman.

Jacob Trefethen:

Hilleman! Hilleman, Hilleman, Hilleman. Of course, I could have guessed that. He made approximately all of the vaccines.

Saloni Dattani:

Yeah, he made 40 vaccines, so if I asked you for a random one, you might as well have guessed Maurice Hilleman and you’d have been right most of the time.

So in summary, Baruch Blumberg found the Australia antigen, Alfred Prince showed that this Australia antigen was actually a surface protein on the hepatitis B virus - which is the hepatitis B surface antigen - which is produced in 500 hundred quadrillions in an infection. Then there’s another scientist called Saul Krugman who showed that if you inactivate this serum hepatitis, you can create a vaccine, and that it can spread through the blood. And now it’s time finally to develop a vaccine, and it’s time for Maurice Hilleman to develop it.

Jacob Trefethen:

Enter stage left.

Saloni Dattani:

So how did Hilleman produce this vaccine? What do you think?

Jacob Trefethen:

What year are we in?

Saloni Dattani:

We are... I think he started this project in 1968.

Jacob Trefethen:

So I bet he went, well, we know where the protein is, it’s in infected people’s blood. I’m going to guess he went there and took it.

Saloni Dattani:

There was literally that kitchen sink type vaccine that Saul Krugman had made by just heating it up.

Jacob Trefethen:

But he went more systematic, somehow?

Saloni Dattani:

He did! First, he said, let me get some plasma, let’s get some serum from the groups who are most likely to have hepatitis B infections. So he goes to New York City and gets serum from groups who are most likely to have infections at that time, which are gay men and drug users who live in The Bowery in New York City.

Now he needs to find a way to purify their serum and get only hepatitis B surface antigen; he doesn’t want to have anything else because some of them are injecting themselves with drugs. He really wants to make sure it’s not contaminated with something else and also that it’s not contaminated with other microbes - other viruses, other microbes.

He has two plans. The first plan is to make a big continuous flow system, a purification system where you pass the serum through hot water, UV light, formaldehyde, and all of this is just happening continuously. It didn’t really work practically because you had to build that flow system before you tested it, and it was just unfeasible.

Jacob Trefethen:

I don’t know what you mean by flow, as in you have an initial stock of serum?

Saloni Dattani:

Yes, I think what I’m thinking is like a factory line and you start out with serum and it goes through all of these tubes and then it comes out at the end, purified.

Jacob Trefethen:

But you’re not saying that I then get injected with that vaccine. I, then, it’s not like I’m part of the factory.

Saloni Dattani:

No, no, no, no, no, no. You are definitely not part of the factory. So that doesn’t work. Plan B, he tries a bunch of things that he knows are going to inactivate various contaminants. So first, pepsin; pepsin is an enzyme that breaks down proteins. In this case, he’s like, it’s going to break down other proteins in the blood serum, and hopefully it doesn’t break down hepatitis B surface antigen — and correctly, it didn’t. This reduced the number of infectious hepatitis B virus particles by 100,000-fold. But that’s not good enough. He was like, ‘I need to remove every single particle of hepatitis B from this serum. I just want the hepatitis B surface antigen.’

Jacob Trefethen:

He wants the antigen, not the virus.

Saloni Dattani:

Exactly. He doesn’t want it to be able to infect people; he just wants the antigen to trigger an immune response. Okay, so he’s added pepsin. Now he adds urea, which is the protein in urine, and which destroys other proteins as well, so it’s a bit like pepsin.

Urea is really important here because he was scared that if you derive the vaccine from serum, you could be infecting people with prion diseases; he had heard of Creutzfeldt-Jakob disease, which is an infectious prion disease, and he was like, we really need to inactivate those prions, and they had figured out by then that urea broke down the prion proteins that are involved in that. So he’s like, okay, let’s introduce urea as well. That’s going to destroy the prions.

Then next step, he adds formaldehyde. Formaldehyde, as people will probably know, is used as a fixative. Formaldehyde is going to destroy other contaminating viruses; formaldehyde can destroy poliovirus and hepatitis B virus, but it doesn’t destroy the surface antigen.

Now we have these three very important steps: pepsin, urea, and formaldehyde. Somehow the hepatitis B surface antigen is really sturdy and it remains intact even after all of these steps. I think the reason is, one, there’s just so many particles of hepatitis B surface antigen that some of them are going to survive. I think probably other proteins might survive, or other antigens might survive, but you would get rid of other infectious particles. So at this point, after these three steps, there’s a quadrillion fold decrease in infectious hepatitis B virus particles.

Jacob Trefethen:

So you’re saying there’s a chance!

Saloni Dattani:

But he’s still like, you know what, what if this isn’t good enough? So he continues to do more testing and he tries to test for specific pathogens that might still be in the serum. He’s like, maybe let’s test for rabies, let’s test for polio, influenza, measles, mumps, smallpox, herpes, and the common cold. He tested for lots of different viruses and none of them remained. And this plan succeeded.

Jacob Trefethen:

What he’s doing basically is not just targeting hepatitis B virus. Those steps are going to make it hard for any virus to stay intact. But I’m glad he checked, I don’t want to... It’s good to confirm because you’re putting- I mean, you’ve really made me realise... People say this is a subunit vaccine, but really it’s an inactivated vaccine, by which I mean, he inactivated all of the dangerous stuff.

Saloni Dattani:

But if people are not developing an immune response to most of the virus, only the surface virus remains. But it’s also really interesting because by the end of this three-step procedure, the remaining plasma-derived hepatitis B vaccine is almost purely hepatitis B surface antigen. I think that’s because of the 500 quadrillion surface antigen proteins; there’s just so much of it.

Jacob Trefethen:

That’s so interesting because my intuition would be if whatever you’ve done to destroy the stuff in there has not destroyed Hep B surface antigen, I would have thought there’s some other stuff I hadn’t destroyed either.

Saloni Dattani:

Yeah, that’s what I would have thought. I think it’s maybe two things. One, it’s just quite sturdy, and then second, there’s so much of it.

Jacob Trefethen:

Got it.

Saloni Dattani:

The other thing that’s interesting is that all this stuff happened in the 1970s before people had identified AIDS. So some of his sources of serum probably had HIV, but it didn’t matter because all of the steps that he took would destroy the virus anyway.

Jacob Trefethen:

Wow.

Saloni Dattani:

There’s this quote from a microbiologist called Harvey Alter. He says: “He had done all the right things to kill the AIDS virus, even if he didn’t know it was in there.” which I think is so cool.

Jacob Trefethen:

Yeah, that’s astonishing to think about.

Saloni Dattani:

So he has now got a plasma-derived hepatitis B vaccine, at least in principle, and now he needs to test whether it works. What’s going to happen next? He has to convince people to actually take this vaccine to see if it works. Even though he’s done all of these steps to decontaminate it, people are really scared because the vaccine is ultimately derived from the blood of intravenous drug users. Then, in the 1980s, people are like, “Wait, this is from people who might have HIV.” So they’re really terrified. And he has to do human testing. This is going to be kind of shocking, and this was really surprising to me, but guess who comes into the picture now?

Jacob Trefethen:

Uh, Sabin!

Saloni Dattani:

Yes! Albert Sabin, the developer of the oral polio vaccine, for some reason, he seems to hate other people’s vaccines. In the last episode, we talked about how he hated Jonas Salk’s vaccine and he wanted to “kill the killed vaccine.” But it turns out that he also tried to block Stanley Plotkin’s rubella vaccine, because that was grown in fetal cell culture. And he was like, this is very bad. Now he tries to block Hilleman’s hepatitis B vaccine.

Jacob Trefethen:

The Plotkin thickens and the highest Hilleman to climb comes.

Saloni Dattani:

So I was reading this from this book by Paul Offit about Maurice Hilleman and his life. And I have it right here.

Jacob Trefethen:

Paul Offit.

Saloni Dattani:

Paul Offit, ‘Vaccinated: One Man’s Quest to Defeat the World’s Deadliest Diseases.’ Hilleman is sort of recalling this episode and he says: “Albert Sabin hears about it and he says that our vaccine will not be used in any human being. Sabin said that if there was a lawsuit, he would go to court to testify against us. And he would sue Saul Krugman [who was Sabin’s first cousin by the way] if there were any problems with the studies.”

Jacob Trefethen:

Ugh.. stupid.

Saloni Dattani:

So stupid! And Maurice Hilleman’s response to this — this is also a quote — “My feeling was, screw you, Albert.” And he just proceeds anyway.

So now he’s trying to find volunteers to take this vaccine. He is going to find it really hard. So what he does is convince some of Merck’s employees to try it. Maurice Hilleman works at Merck, and he’s like, you know who’s going to take this? My own team!

Jacob Trefethen:

Do you want a good performance review, this cycle?

Saloni Dattani:

So he comes to this big meeting and he says, “I need volunteers, damn it. Just decide who among you are going to take this vaccine. Give yourselves a little bit of time to regain your senses.”

Jacob Trefethen:

Wait, what? He literally said, damn it?

Saloni Dattani:

Yeah. He tries to get volunteers twice. The first time, no one volunteers. The second time, he’s really upset and he’s like, “I need volunteers, damn it.”

Jacob Trefethen:

I wonder if him being pressure-y is why I think nowadays, there’s legislation in the US that means that employees can’t take their own vaccine. Maybe it comes from this pressure, I’m not sure?

Saloni Dattani:

Could be. There is also a quote from someone who took the vaccine in Merck, a woman called Joan Staub. She was one of those who was asked to take the vaccine. She says: “Consent forms? What consent forms? We got that vaccine because we had to get it. If Hilleman told you to do something, you did it.”

Then she learned months later, after she had taken the vaccine, where it came from and that there was a possibility that it might be contaminated with HIV. And she says: “We were scared to death. I thought I was going to die. Maurice pulled us all into one room and had to explain to us over and over again about the inactivation process and that we were going to be okay.”

Jacob Trefethen:

Well, he sounds like a very trustworthy and even-keeled man, so I’m sure I would have been convinced too.

Saloni Dattani:

I think... I would have taken it, maybe.

Jacob Trefethen:

He does seem scientifically trustworthy, to be fair, even if he’s a bit pushy.

Saloni Dattani:

Yeah, he’s very pushy. He was known for being very strict and intimidating. He made his team work seven days a week. They were very dedicated and loyal to him anyway, because he protected them whenever there were layoffs in the company as a whole, and he also made sure that his department’s budget rose 10% every year.

Jacob Trefethen:

I love it. I love- yeah, that’s great.

Saloni Dattani:

So next step, some people in his company have taken the vaccine, they’ve tested it out, and they’ve then done larger trials, and then there’s the manufacturing process. The manufacturing process is not done by Hilleman’s team, but by Merck’s manufacturing division, which was controlled by unions like the Teamsters Union, the Oil, Chemical, and Atomic Workers Union. And there’s this famous story about this, which is why I’m bringing it up.

Because the manufacturing process, it was done by other people, not on his team, he didn’t have that amount of control over it anymore. At some point, someone in the manufacturing division had slightly changed the chemical inactivation process, in the hopes of increasing production. And he was furious to hear about this. There’s a quote that I think maybe you should read out because I don’t think I will sound angry enough, and it will just sound funny coming from me.

Jacob Trefethen:

This is from Maurice Hilleman in response to the unions tweaking his process: “There is no fucking test for absolute safety except to put the vaccine in fucking man,” said Hilleman. “A procedure was developed to make the fucking vaccine and was shown to make the vaccine safe. And there are always fucking people who can’t wait to make fucking brownie points by changing the process to get more yield. You have to adhere to the goddamn process. We know that the vaccine is safe, and you have to adhere to the goddamn process. What worries me is that [someone] will get a bonus if he can get more yield, so he changes the fucking process. Goddamn meatheads are everywhere.” That’s actually the original recording, I think.

Saloni Dattani:

This is actually from a recorded memo. And the “someone” that you mentioned, I think it was censored in the quote, but I think it might have been the name of the actual employee; I think he screamed this at the employees and named the person who did it, but I don’t know for sure what that person’s name was. Does he even have a Cockney accent?

Jacob Trefethen:

Maurice Hilleman, yes. He was actually a famed Cockney. I think Merck at that time was based in East London.

Saloni Dattani:

Wait, wasn’t Merck in the US? Wasn’t he in the US?

Jacob Trefethen:

Well, maybe just the R&D division then.

Saloni Dattani:

You’ve tricked me! He was born on a farm in Montana, near the plains of Montana.

Jacob Trefethen:

“There’s no fucking tests for absolute safety, except to put the vaccine in a fucking man,” said Hilleman.

Saloni Dattani:

You somehow managed to top the previous one.

Jacob Trefethen:

I’ve been practicing my Montana accent for a long time. So, Saloni, you got any more Hilleman quotes?

Saloni Dattani:

I do. We talked about how strict he was, and the fact that he developed 40 vaccines in his lifetime... might have been related to each other. I have this quote from the Paul Offit book, where Paul Offit interviews him near the end of his life to try to understand how he developed all of these vaccines.

He said that his work style was very different from everyone else’s. He said: “If I ever caught anybody delaying a set of tests because [the results] might come out on a weekend, it would be grounds for dismissal. You can imagine how that went over. They all had wives and that sort of thing. Now Merck tells [employees] that they don’t need to put in any extra time and that you have to balance your life and that you have to have enjoyment with your job; [that way] you can do a better job and have fun. It’s all just a pile of shit. What the company should be doing is kicking ass. But that’s from the old school. I was told that I had a very unusual management style.”

Jacob Trefethen:

“What the company should be doing is kicking ass!”

Saloni Dattani:

I mean, you can see how he developed 40 vaccines.

Jacob Trefethen:

Oh, yeah. Maybe we should be making 41 podcasts. We have to start kicking ass, Saloni.

Saloni Dattani:

I think the four hour episodes are a way of doing that. So he has now developed a successful hepatitis B vaccine from the plasma of people who might have had HIV and some who used drugs. A lot of people were still scared at this point, and they then ran larger trials. What was weird about this to me was that there is this conspiracy theory that his trials of the hepatitis B vaccine are the source of the AIDS epidemic. As we have learned, his very strict purification process would have killed the HIV virus anyway.

So someone else is going to make a comeback in the story now, and it’s Baruch Blumberg, the guy who discovered the Australia antigen, which is later called hepatitis B surface antigen. Why is he involved? Well, Hilleman is trying to patent his hepatitis B vaccine and he discovers, strangely enough, that someone has already patented it. So strange! So Baruch Blumberg had patented a hepatitis B vaccine containing hepatitis B surface antigen without making it.

Jacob Trefethen:

Oh my god, not one of those, no.

Saloni Dattani:

He basically has a patent that says that he’s going to make a hepatitis B vaccine through a process that removes impurities, including other infectious components, to attenuate any virus that remains, and that the vaccine would be free of all other blood components aside from the Australia antigen, and that’s it. He hasn’t actually made the vaccine at all; he just has this patent.

There’s a quote from Maurice Hilleman about this, where he says: “People in the hepatitis field were aghast at the guts of this son of a bitch. Somebody had actually issued a patent for that crap.”

So Hilleman then goes to Baruch Blumberg’s company. He now works at the Fox Chase Cancer Center. He tries to convince them to license that patent to him. Initially, when they heard his request, they said yes, but only if Baruch Blumberg can direct the whole vaccine manufacturing process! And Hilleman was like, “Are you fucking kidding me?” And he refuses, obviously, because that guy wasn’t involved at all. He manages to convince them to license the patent to him at a cost, and then he finally manages to license the vaccine and introduces it in the US in 1981.

Jacob Trefethen:

1981.

Saloni Dattani:

But there’s more. Baruch Blumberg is back. Later on, Baruch Blumberg writes a book called ‘Hepatitis B: The Hunt for a Killer Virus’ in which he claims that the hepatitis B vaccine was invented by him and that they had simply licensed Merck to develop it.

Jacob Trefethen:

Oh, no. No, no, no, no, no. No, no, no, no.

Saloni Dattani:

This story is crazy.

Jacob Trefethen:

People need to stop this.

Saloni Dattani:

Why is everyone arguing with each other? But it really is a moment worth celebrating anyway. Because his hepatitis B vaccine “derived from plasma was made from the most dangerous starting material ever used, but was probably the safest, purest vaccine ever made.” That’s according to Paul Offit, who wrote this book, and who co-invented the rotavirus vaccine.

Jacob Trefethen:

You know, I think it is a nice quote, but is obviously false, given that last episode we talked about the rabies vaccine, which is made from literal rabies.

Saloni Dattani:

That is true, but it wasn’t the safest; that wouldn’t have been the safest.

Jacob Trefethen:

True, the first half of the quote is false. The second half, I’m fine with.

Saloni Dattani:

Yes, I agree.

Jacob Trefethen:

But I have a question for you. Because 1981, that’s far enough along... that I think we have a new technology that we could maybe use here? Because in 1977, Genentech went splashy with recombinant DNA, so hold on a second, does that get involved?

Saloni Dattani:

That’s next. So Hilleman’s program to develop the hepatitis B vaccine from plasma took 13 years, and that includes the vaccine trials and whatever other development they had to do. I think by the time recombinant DNA technology became usable, that was like 1978 or ‘79 when they first cloned insulin? So yes, recombinant DNA technology is available, but it’s still pretty new. And it would have taken much longer to get through the whole pipeline.

Jacob Trefethen:

Got it.

Saloni Dattani:

But that is next. So even though he’s made this incredibly safe vaccine, people are really scared still about using it. People are very uncomfortable because it’s made from human blood. So he decided, let’s try to find another way to make it: recombinant DNA technology!

Jacob Trefethen:

Doo doo doo!

Saloni Dattani:

Do you want to do a quick recap of what that is?

Jacob Trefethen:

Sure. So the end goal, as a reminder, is to make protein, in this case, the hepatitis B surface antigen - that’s a type of protein. How do you make proteins? Well, the body makes it by DNA, transcribing to RNA, translating to proteins. So you want to copy that gimmick, and the way you’re going to copy it is you’re going to take a DNA slice that is going to code for the protein you want, and you’re going to inject it - pleurgh - essentially, into a system that’s not human. Maybe it’s a bacteria, maybe it’s yeast, some other cell that you can get it to be produced en masse. That’s the gimmick, I’ve cut out some of the detail, but that’s the gimmick.

Saloni Dattani:

Yes. So that happened in the 1970s. The first recombinant protein that was made was human somatostatin and then insulin in 1978, and that transformed diabetes treatment, as we talked about in our third episode. Next, people think, hey, let’s use this to make vaccines better!

Why do they want to make vaccines better? I think one, this purification process is quite difficult; you have to rely on getting plasma from people. It’s also various steps and you have to check for contaminants along the way. Also, the amount of yields could in some cases be quite low, whereas with recombinant technology, if you find out general ways to improve the yields, you could find ways to scale up production by quite a lot. And it’s much safer, so you aren’t that worried anymore about accidentally contaminating people with other viruses or with other blood products or drugs or things like that.

Jacob Trefethen:

Yeah, instead of getting rid of everything and getting left with only the hepatitis B antigen, the only thing you’re making in the first place is the hepatitis B antigen. Who are we talking about here? Is this still Merck or someone else?

Saloni Dattani:

This is still Merck.

Jacob Trefethen:

Ah, so the innovators’ dilemma. They decided to eat their own lunch.

Saloni Dattani:

Yes, but in this case, I think people were just still uncomfortable with using this plasma-derived vaccine. So they were now like, okay, well, we do know quite a lot about hepatitis B, so why don’t we just make a better version that everyone will take?

So now they decide to use Genentech’s skills and all of these new recombinant DNA technologies to produced- to mass-manufacture a hepatitis B surface antigen. The first recombinant DNA molecule was made in 1972 by Paul Berg. Then Stanley Cohen and Herbert Boyer, who worked at Genentech, then improved upon that process; they developed restriction enzymes, which basically cut up DNA, and then they found a way to insert that into bacterial plasmids. Then Boyer’s lab found ways to purify the enzymes and this whole system, so that they could replicate the plasmids. Basically, you can grow proteins from other organisms in bacteria or yeast in a little bacterial manufacturing tank or something like that. They had essentially invented this cloning system so you can clone genes and produce the proteins that they code for; they can use bacteria and yeast as factories to make proteins that previously were only available from blood or tissue.

So why not make loads of hepatitis B surface antigen, but make them in... so they did manage to produce it in bacterial plasmids with one of the researchers at the UCSF group who worked with Stanley Cohen. But this guy was called William Rutter.

Jacob Trefethen:

Oh yeah, Bill Rutter. Yeah.

Saloni Dattani:

You know him?

Jacob Trefethen:

Yeah, of course. He’s a famous biotech investor. I think he actually sadly passed away this year.

Saloni Dattani:

Oh. And so they get William Rutter, they recruit him, Bill Rutter, and he is working at UCSF. Bill Rutter removes the hepatitis B surface antigen from the virus and inserts it into one of the bacterial plasmids. He then makes the bacteria reproduce; they make large quantities of the hepatitis B surface antigen. But unfortunately, this doesn’t induce an immune response in animals, so now they’re going to try something else. They’re going to try yeast; they’re going to use common baker’s yeast, which must have a more specific name, but I don’t know what it is.

Jacob Trefethen:

Wait, I’m lost. Why does it matter that it doesn’t induce an immune response in animals?

Saloni Dattani:

Well, they wanted to have some indication that it would be effective. They did manage to reproduce hepatitis B surface antigen in the bacteria, but it seems to have some kind of different configuration, which means it’s not causing an immune reaction.

Jacob Trefethen:

Got it, sorry. It wasn’t absence of evidence; it was evidence of absence.

Saloni Dattani:

Yes, yes, yes. Yup. I guess one thing that I wasn’t super sure about was how come, sometimes, the recombinant DNA technology varies depending on which species you insert it into? Like how come it’s sometimes working in yeast and sometimes it’s working in bacteria? Some species produce it better than others?

Basically there was research showing that yeast cells can also produce the hepatitis B surface antigen and assemble those proteins into particles that look very similar to the ones that are made in human plasma and that are immunogenic in mice.

This is when electron microscopy is useful; you can actually see the shape of the proteins. So I have this image here, if you’re watching the video, of these very interesting looking hepatitis B surface antigen particles. They look kind of like individual cereals, like breakfast cereal.

Jacob Trefethen:

Yeah, I would say a little... maybe... corn pops?

Saloni Dattani:

Yes. Very tiny corn pops.

Jacob Trefethen:

Very tiny corn pops and probably a bit less tasty.

Saloni Dattani:

Oh my God. I wouldn’t want to eat that.

Jacob Trefethen:

Do you think they make milk go yellow?

Saloni Dattani:

They make humans go yellow.

Jacob Trefethen:

Oh! The original corn pop!

Saloni Dattani:

Basically the yeast are producing the virus’s proteins much more closely than our own cells would produce them. Viruses that depend on our own cell’s machinery will have some other modifications, after just producing the protein itself. In order to try to replicate that further modification, it can often take a long time to figure out how exactly to do that, which specific type of system to use for recombinant DNA or which optimization pathway you can use to make sure the shape and the structure match the real version.

Jacob Trefethen:

And I think that’s still true.

Saloni Dattani:

So Hilleman develops another recombinant hepatitis B vaccine by making the hepatitis B surface antigen in yeast. That, he finds, creates protective antibodies in chimpanzees and later in people. He uses this to make the next hepatitis vaccine.

Jacob Trefethen:

Woo!

Saloni Dattani:

Then it’s licensed in 1986 and it’s still used today.

Jacob Trefethen:

Wow. Okay. So we went full circle. To summarize, how did we get to this vaccine that is made with recombinant DNA and is going to protect you against hepatitis B? Well, we had to do the whole drive of immunology. What actually is a productive immune response against infections and against different infective agents?

We had to then try and isolate the parts of the hepatitis B virus, instead of the whole thing, that generate a productive immune response. In this case, just one protein, the hepatitis B surface antigen. And the wonderful interlocking of this idea of a subunit vaccine — just one thing, not the whole thing — with recombinant DNA that came in the 70s and 80s, is that now we have a way of producing proteins really easily.

And proteins are often the one part of the virus you want to use as a vaccine. So, lo and behold, we have a lot of yeast cells in a big vat churning out hepatitis B surface antigen, and that can be used as a vaccine. And you know what? I think I’ve got that vaccine.

Saloni Dattani:

Oh, really? I guess I have as well. I don’t remember getting it though, I guess I was a baby at the time. So I think in 1981, the first one was introduced in the US and then 1986, the recombinant vaccine was introduced.

How much of an impact did it have? Just to recap, we said that newborns who are infected with hepatitis B at birth or soon after have about a 90% chance of developing a chronic infection from it. And then 15 to 40% of them, who have that chronic infection, will eventually die from liver failure, cirrhosis, or liver cancer if they’re not treated.

So the impact this vaccine is going to have is actually massive. The way that we know that is from a large randomized control trial that was done in Qidong in China. There, entire towns were randomly selected to give hepatitis B vaccine to all of their newborns who were born there, and some towns were not.

Jacob Trefethen:

So a cluster randomized trial, yeah.

Saloni Dattani:

Right. We have entire towns getting it or not getting it. The reduction that you see in the vaccinated towns is an 84% reduction in the incidence of liver cancer and a 70% reduction in deaths from liver disease.

Jacob Trefethen:

So this is a decades-long study?

Saloni Dattani:

Yes. This study was done, well, they were followed up over more than 20, almost 30 years. The thing about China’s health system is that everyone born there has an ID and that you can track that ID decades later in their medical system and see what happened to them.

Jacob Trefethen:

That is a big and long trial. Most products, we do not get that kind of information.

Saloni Dattani:

But of course, we did know that it was protecting against liver disease and hepatitis infections much before that. So they knew that it was going to have this reduction, but I think they probably didn’t know how much of a reduction it would have.

I have this chart here showing the decline in hepatitis B after the vaccines were introduced. In 1987 in the US, it was recommended that all parents who have hepatitis B surface antigen in their bodies — meaning that they have been infected — should have their infants get vaccinated. Then in 1991, it was recommended that all infants actually should get vaccinated. You can see this gradual decline in hepatitis B cases after that.

I think it’s interesting because this kind of graph actually looks quite different from the other types of graphs you often see when a vaccine is introduced. There isn’t just a sudden drop after the vaccine is introduced.

I think the reason for that is, one, there’s a long delay between infection and the disease, sometimes months, sometimes years. I think the second is that generally it doesn’t spread like other diseases in an epidemic. It’s mostly spread from mother to child. So it’s not going to have this herd immunity effect; it’s not going to massively reduce the number of cases very soon, but it’s more of a generational thing. Mothers are not going to pass it on to their children if they’re chronically infected. So there’s a much slower, but still in the long term very large, decline from hundreds of thousands of cases of hepatitis B in the 1980s and 90s to only less than tens of thousands now.

The other thing that was interesting was, they have testing for hepatitis B surface antigen, so they can test if the mother has been infected. But it’s still important to move to universal vaccination to, one, just skip that process; a lot of people are just going to miss that testing procedure, so you might as well just give it to everyone, also because it’s extremely safe. And I think the other reason is that even if infants don’t get infected at birth, they can still get infected after that. That’s quite common in some places where young children often get infected from contact with other people’s blood or small wounds or injuries or shared objects that they’re using. So just getting it to all newborns instead means that they’re protected from all of these exposures, which is really cool.

Jacob Trefethen:

On hepatitis B, lots of people around the world have chronic infections. Actually, globally, probably 300 million people have chronic infections, and most people who do don’t know that they do. Just for listeners out there, if you find out you have a chronic infection, it’s not the end of the world. There are drugs you can go on to help control the infection. There’s not complete cures, and hopefully that will change in the next generation, but there are treatments that can help. So it’s definitely worth getting tested and your doctor will be able to advise when you should go on treatment for what different level of current infections.

Saloni Dattani:

Nice. What are the treatments? Are they antivirals?

Jacob Trefethen:

Yes, they are. They are nucleoside analogs, which actually some of them also came up in our HIV episode, because they can be prescribed as PrEP for HIV.

Saloni Dattani:

Oh, so they block the virus from replicating.

Jacob Trefethen:

And it’s a good reminder that a lot of progress in one area can help progress in another. We have seen that over the course of this episode where progress in immunology on hepatitis B, guess what, it’s ended up helping other viruses. Progress on recombinant DNA for the sake of insulin, guess what, it’s helped hepatitis B. So thank you, interlocking strands of science.

Another form of innovation from one area helping another is: you know how they make a malaria vaccine?

Saloni Dattani:

Yes, with the hepatitis B surface antigen.

Jacob Trefethen:

With the hepatitis B surface antigen. I’m afraid you’re just going to have to let that linger because we are not going to get all into it now, but it self-assembles into this beautiful viral particle, those corn pops we saw earlier. Now imagine if the corn pops had little malaria antigens laced through them.

The first malaria vaccine: stir up one out of every four is a malaria protein. And then they’re going to - whoo, putiputoo - have a tendency to form a nice little corn pop. The second malaria vaccine: stir up way more. Hardly any hepatitis B, more malaria. And it just happens that that happens to get attracted together into a beautiful little self-assembling little ball.

Saloni Dattani:

You know, we didn’t talk about that. We didn’t talk about why does it self-assemble into a sphere? The surface antigen has regions that are water-loving and fat-loving, which makes the proteins gather in curved shapes.

We made this huge impact with this one hepatitis B vaccine, and it’s just the first protein subunit vaccine and it’s just the first recombinant technology vaccine that’s made. Then there are many more that are made after that. That includes the human papillomavirus (HPV) vaccines, some influenza vaccines like FluBlok, which is a recombinant hemagglutinin vaccine. The shingles vaccine is also a recombinant, Shingrix. There’s a hepatitis E vaccine, which is also a recombinant vaccine. And then there’s a few more, I think meningococcal vaccines, MenB, are a recombinant.

Jacob Trefethen:

And if a vaccine is recombinant, that also means it’s a protein subunit vaccine. So we’ve gone from the whole virus, or the whole bacteria, weakened to only a part, a subunit. And that is, in general, going to be much safer.

Saloni Dattani:

Yeah. And there are many other types of vaccines alongside the protein subunit or the recombinant DNA technology ones, including polysaccharide vaccines or toxoid vaccines or viral vector vaccines, and finally mRNA vaccines. We might talk about them in future episodes... if we feel like we should.

Jacob Trefethen:

If you subscribe.

Saloni Dattani:

Yeah. So if you’re watching the video, you might notice that I’m wearing a shirt with the names of some vaccines on them. It is an acrostic, which says VACCINATED, and it has the names of many different vaccines.

Jacob Trefethen:

Stand up again, stand up again. COVID, Measles, Varicella, Meningococcus, Polio, Tetanus, Hepatitis, Pertussis, Influenza, Diphtheria. VACCINATED!

Saloni Dattani:

I love this shirt because I saw someone on Twitter wearing it as a hoodie, and they posted a picture with it. And I was like, I want that.

Jacob Trefethen:

Yeah, that was made for you. Also, I like that when you sit down, we can only see the first couple. So COVID will be in shot for people throughout, and everyone’s going to get anxiety.

Saloni Dattani:

Well, I’m sorry.

Jacob Trefethen:

So I think we should talk about how we even got here to those modern subunit vaccines, hepatitis B and the others you mentioned. This wasn’t just the story of recombinant DNA, and of microbiology and learning about those different microbes; it was also reliant on many breakthroughs in immunology - in what prompts your immune system to actually create a protective response in the future, and understanding how vaccines actually work in that way.

So that’s why many modern vaccines are quite different than the ones we talked about last episode, because of these immunology breakthroughs. Those older ones were mostly whole viruses or whole bacteria that were killed or inactivated.

Saloni Dattani:

I mean, that’s really interesting, right? Because the vaccines that we talked about, so many of them were developed without even knowing which virus they were for or what the microbe was. People developed vaccines with these systematic methods through attenuation or inactivation, but it wasn’t until breakthroughs in immunology until people figured out how they actually worked and how to make them much more precise, safe, and sometimes more effective. Sometimes as we’ll talk about, it’s difficult or it’s impossible to make vaccines with those older methods, without these newer tools that we have today.

So let’s do it. Let’s talk about how we got here. But in order to do so, we’re going to have to go all the way back to the 19th century.

Jacob Trefethen:

Okay, let’s get back in that time machine. *time machine sound effect*

Saloni Dattani:

Yeah, so as we discussed, Pasteur, Koch, and all these other people who were working on germ theory and trying to make new vaccines were doing it through attenuation and through inactivation. They were trying to force the microbes to evolve under different conditions or trying to inactivate them with chemicals or other procedures to weaken them so that we would recognize the pathogen or the weakened pathogen and be able to attack it in the future.

But while they knew how to make some vaccines, they didn’t really understand why they worked. They didn’t understand immunology, and all of that was going to be discovered in the next few decades.

So maybe we should start with what the different theories were at the time. I think I mentioned at one point that Pasteur thought vaccines worked because they depleted the body of nutrients that that specific disease needed. And if you had depleted them once through a vaccine, then the next time it wouldn’t be able to attack you again.

There are just other mysterious reasons that people thought you would be protected from a second attack. I guess at this time, people just thought that, well, there was the very popular theory of the imbalances of humors.

Jacob Trefethen:

Yes. Humoral imbalance. What’s a humor again?

Saloni Dattani:

What is a humor? A humor... Well, it’s not very funny, they’re all quite disgusting. One of the humors is blood, the other is phlegm, and then there’s yellow bile, and there’s black bile.

Jacob Trefethen:

Right. Tag yourself.

Saloni Dattani:

This was an ancient theory by Hippocrates, so thousands of years ago. I guess I can sort of see how you might think that, because sometimes people are bleeding, sometimes people cough up blood, sometimes their skin looks yellow. Sometimes their liquids, their fluids look black and they’re spitting up lots of mucus and phlegm. I don’t know, I guess I can see how you might think something’s going wrong there.

Jacob Trefethen:

I guess I see blood and phlegm way more than yellow bile and black bile. What about you?

Saloni Dattani:

I think... I mean, as we’ll talk about, I guess yellow bile maybe refers to jaundice or something, or maybe it’s like vomit. What do you think?

Jacob Trefethen:

Oh, maybe... I don’t know. I guess I do see vomit sometimes.

Saloni Dattani:

Or is it diarrhea?

Jacob Trefethen:

Black bile is old school. I feel like if I ever saw black bile 2,000 years ago, I’d be like, “That’s a demon. That’s coming from a demon.”

Saloni Dattani:

Well, that’s scary. What’s so funny about the theory of humoral imbalance is that people kind of continue to believe this for hundreds, or thousands, of years after. So even in the 19th century, a lot of scientists still basically think that all diseases are caused by this imbalance of humors.

What changes is one, I think, microscopy, and people start to study human organs and tissues and cells and stuff. And some people who are studying this, pathologists, are claiming that, “Okay, no, you know what? It’s not because of humors, it’s because of cells, and it’s the cells that are malfunctioning.”

There’s this famous German pathologist called Rudolf Virchow, and he thinks that- in 1858, he tries to challenge humoral theory, and he says all diseases are caused by the malfunction of cells. What is also notable about him is that his colleagues referred to him as the Pope of Medicine.

Jacob Trefethen:

Oh?

Saloni Dattani:

I don’t know why. I think it’s because he was involved in politics or something.

Jacob Trefethen:

Well, yeah. I mean, I feel if black bile were demons, you do want a pope to bless you away from them. But if he believes in cells, it doesn’t quite add up.

Saloni Dattani:

No, it doesn’t... So he thinks, okay, everything is caused by the malfunction of cells instead. At the time, in the mid-19th century, people are starting to learn about infectious diseases that are caused by specific microbes. So this ‘germ theory’ is starting to gain acceptance. There’s another guy who comes on the scene called Ilya Metchnikoff. Have you heard of him?

Jacob Trefethen:

I think I have, but I couldn’t tell you why.

Saloni Dattani:

So he was a zoologist living in the Russian Empire, and he was born in present-day Ukraine. He had this theory of “phagocytosis”, and you probably know what that is.

Jacob Trefethen:

Yes, I do know that one, yeah. Cytosis, that’s to do with cells, and phago is... Oh god, the derivation, I don’t know.

Saloni Dattani:

Eating.

Jacob Trefethen:

I’m trying to think of another word that has that in there, anyway. But...

Saloni Dattani:

I don’t know if any others do, I guess it’s Greek. But eating cells and killing them. He looks under the microscope and he suggests that there are cells that can eat microbes; they’re phagocytic cells. He says these phagocytic cells, which appear during inflammation after an infection, they’re actually not harmful. He says “they’re the first line of defense because they can ingest and digest invading organisms.” That was quite radical at the time.

Jacob Trefethen:

Do you ever play that computer game Agario, where you had a little blob that had to go around eating smaller blobs until you got bigger and bigger?

Saloni Dattani:

Oh, I haven’t played that one, but I feel like this is also like Snake.

Jacob Trefethen:

Yeah, it’s like Snake. So you’ve got to imagine the snake is actually good instead of bad, and it’s one of your cells. But if I’m a cell and I eat a microbe, do I get bigger? Might temporarily.

Saloni Dattani:

I think you... I think the cell does the equivalent of pooping it out.

Jacob Trefethen:

Right.

Saloni Dattani:

I don’t know, the extra stuff gets exuded.

Jacob Trefethen:

Right. So it’s sort of like a snake swallowing an egg and the egg goes really big, big, big, big, big, big. Got it.

Saloni Dattani:

So Metchnikoff says that actually inflammation is good. And people were like, “What?! No, it’s not!” And he’s also like, actually, the way that we develop immunity to things is by having our cells eat the invaders.

Let me just tell you how he thought of this. He has this quote in his autobiography. He says: “One day, when the whole family had gone to the circus to see some extraordinary performing apes, I remained alone with my microscope, observing the life in the mobile cells of a transparent starfish larva, when a new thought suddenly flashed across my brain. It struck me that similar cells might serve in the defense of the organism against intruders.”

So he has this thought that comes to him about this starfish, and he’s like, well, maybe our cells are like starfish that eat up different intruders. He tries to run an experiment to see if this actually happens. He pokes a starfish larva with a splinter, and then he looks under the microscope at what happens next. He sees mobile cells moving around and surrounding that splinter. And he says: “That experiment formed the basis of phagocytic theory to the developments of which I devoted the next 25 years of my life.” A long time.

He studies these phagocytes and their digesting of different bacteria: the anthrax bacteria, erysipelas (which I think is Streptococcus pyogenes), typhus, tuberculosis, and then many other bacterial infections. And he’s like, all of these infections are actually destroyed by cells that eat them up.

Jacob Trefethen:

I think that’s an amazing experiment because it’s so crisp, and to see the mobile cells. And it is worth just saying- when I think about cells in my body, I do think about them as pretty much fixed in place most of the time. I’m thinking of the neurons in my head, they’re not moving. The neurons in my gut, they’re not moving that much. The skin cells, well, maybe I’ll get some new ones if I put on the right lotion. But basically the cells, I don’t think move that much. But of course, the immune system needs to have cells that move.

Saloni Dattani:

Yeah, and sometimes when you look under the microscope, you actually will see these phagocytes moving around and engulfing the bacteria and eating them up. This is especially true with one of the bacteria that he loves to study, the anthrax bacteria. Anthrax, as we’ll come to later on, is quite resistant to other forms of immunity. So he basically only sees it getting eaten up by phagocytes. And he says, “Well, this is how immunity works.”

And lots of people are kind of angry about this. Some of them are the humoralists. They’re like, ‘No, you’re wrong. It’s actually the humors. The Greeks were right.’ There are other people who try to run more experiments and they aren’t really seeing the same thing as he is seeing. When they’re looking at other diseases, they’re like, the phagocytes aren’t succeeding; they’re not able to ingest the organisms. They’re not destroying them. Or they’re just secondary. They’re just Robin and Batman is the humor.

Jacob Trefethen:

Okay.

Saloni Dattani:

That’s a terrible analogy.

Jacob Trefethen:

I mean, Robin’s important, so let’s not do him injustice.

Saloni Dattani:

That’s true.

Jacob Trefethen:

That makes sense, and I can think of other reasons why the phagocytosis view can’t be right, but that’s all with the hindsight of what I now know about the immune system. If I put myself in their headspace, I’m like, okay, well, there’s definitely some invaders that are bigger than cells! You know, like if I get a tapeworm, I’m not going to be able to eat that with a cell, but maybe I will be able to develop some immune response? So I don’t know, I could think of some ways it can’t be the full story.

Saloni Dattani:

Right. I mean, I could imagine maybe the phagocytes are just slowly biting away, but no, I don’t think that’s actually going to work. And the other thing is that he made a lot of wild claims beyond these. He basically claimed that actually the reason for aging is also phagocytosis. The neurons in our brains are getting eaten up by phagocytes, and he says, our hair goes gray because phagocytes eat up the hair pigments.

So he’s making some crazy claims and he’s making some legit claims. But ultimately, he loses the argument, and the humoralists win. So there’s this big debate between the cellularists like Metchnikoff and the humoralists. The other kind of political aspect to this is that Metchnikoff is living in Paris and he’s working at the Pasteur Institute, and a bunch of his team and his students become cellularists. And then in Germany, humoralism is much more popular, so there’s actually a nationalistic element to it as well. They hate each other at that point.

Jacob Trefethen:

So German humour... Hmm... does exist after all?

Saloni Dattani:

Yeah, what’s so funny about this is that the humoralists win for various reasons. So the humoralists, they’re not just arguing about black bile and everything - they’re saying something in the blood is responsible for attacking pathogens, and they can actually find pretty good evidence of this.

If you take the serum of someone who has previously been infected with a disease, and then you introduce that serum into someone else, it protects the second person from an infection, often. That’s something that scientists find in the 1890s against diphtheria and some other diseases. So they’re like, oh, look, it’s just the blood itself that is protective. And you can also see that in vitro; you can see that sometimes the serum is able to destroy bacteria. This basically discredits the argument... quite significantly. The humoralists have won out, they’ve convinced the rest of the scientific community.

Jacob Trefethen:

No! I don’t think it’s true. I think both are true. I’m cheating. I’m thinking ahead.

Saloni Dattani:

No, no, you’re right. And it’s kind of sad because in the late 19th century, the humoralist theory had won-

Jacob Trefethen:

That’s sad. I didn’t know that.

Saloni Dattani:

-people just ignored cellular immunology for decades until basically the mid 20th century. They just focus on the serum. They just focus on antibodies and complements and things like that. They only figure out that immune cells produce antibodies in the 1930s and 40s.

Jacob Trefethen:

No! My heart.

Saloni Dattani:

So they don’t know what T-cells are until 1961.

Jacob Trefethen:

Right. Just to give the context, antibodies are not cells, they are proteins. And that is a big distinction. I can’t be too mad about people getting obsessed with antibodies because I probably would too. But at that time, they thought they were the be-all end-all.

Saloni Dattani:

Yes, so antibodies are... People know about them; they have never seen an antibody and no one will see an antibody until the 1960s, until electron microscopy. Until then, you’re basically seeing the reactions that form through antibodies. You’re seeing, for example, precipitin reactions, which is just when antibodies clump together and get mixed with their antigens and form a clump. You can see the effects of that and you can quantify the amount of antibody, but you don’t know what it looks like and you don’t know what it’s made of. So I think Paul Ehrlich at some point figures out that it’s a protein.

But how it was discovered? In the early 1900s, Jules Bordet and Maurice Arthus described these precipitin reactions. So if you mix up the serum with the antigen, then you would see an aggregate forming at the bottom of a tube, because the antibodies are cross-linking many antigens and they make this lattice and they sort of form a little precipitate. So yeah, you can see that, but you have no idea what they look like.

We now know a lot more that would have kind of helped resolve that debate a bit. So we now know antibodies are Y-shaped proteins. They’re produced by immune cells that circulate the blood and the lymph. Back then, they just knew about antibodies circulating in the blood and the lymph somehow; they know that they bind to pathogens like bacteria or toxins and they block them. They also think know about complement; they know that some microbes get covered in something that attracts or causes them to get destroyed.

Jacob Trefethen:

So wait, just too many compliments can destroy you?

Saloni Dattani:

Yes. So complements is a group of proteins that interact in the blood. They go through a series of enzymatic reactions, and after that, they can coat microbes. We now know that they can attract immune cells to destroy them. And they would have known back then that complements can create little holes in microbial membranes and kill them, that’s very useful for extracellular pathogens which are in the bloodstream. So now we know that there is this humoral aspect to immunity with antibodies and complement.

And we also know that there’s a cellular aspect as well, so there’s B cells which have antibodies on their surface and they recognize antigens, and they produce many antibodies that circulate in the serum; and then there’s T cells, which help them out, help them mature, and can also kill cells that themselves are infected. And there’s phagocytic cells, like the ones that Metchnikoff described, that actually eat up microbes and debris and other waste.

So now we know there’s kind of both of these different aspects, and there’s coordination between them as well. But back then they were like, okay, cellularism is basically done. Maybe it only is true for some situations like anthrax, but mostly it’s antibodies. Antibodies are the future.

Jacob Trefethen:

I wonder what we currently think is done that, in 50 years, is going to have a resurgence.

Saloni Dattani:

I’m going to bring this back up later, but in the 1960s, people basically thought that immunology was all solved. They were like, we know everything; there’s nothing else to discover. And I think they probably think that back then.

Jacob Trefethen:

That’s amazing. Now I want to pick up a modern immunology textbook and see how many pages are based on things published after 1960. It’s probably the vast majority.

Saloni Dattani:

Right. But let’s go back. We have Metchnikoff, and he’s like, it’s all cells. And then we have the humoralists, and they’re like, no, it’s the serum, and it’s the antibodies within them. In a way, they’re happy that it’s the serum. They’re like, “Guess what? The Greeks were right. They had so much foresight, and they knew all of this without having the specific, precise understanding. And we’ve figured out now why they were right.”

Essentially, they’re like, “Let’s just look at the serum. Let’s see what’s going on in there.” So various people are doing experiments in the serum. One of them is Paul Ehrlich, and he develops tests to quantify the amount of antibodies in the serum. Then there’s Karl Landsteiner. Have you heard of him?

Jacob Trefethen:

Again, I recognize the name, but couldn’t tell you why.

Saloni Dattani:

He discovered blood groups in 1901.

Jacob Trefethen:

Karl Landsteiner?

Saloni Dattani:

Yes, an Austrian doctor.

Jacob Trefethen:

That’s a big one.

Saloni Dattani:

It’s funny because his name isn’t that well known now, but he would have saved so many people through making blood transfusions safer and discovering the ABO blood groups. The way he discovered this was he tried to mix blood from different people and saw how red blood cells clump together in some samples, but not others. Just by doing this systematically and comparing them, he found different patterns: A, B, and O, and that revealed that people carry antibodies against the blood group antigens that they don’t have.

I have this silly story from when I was at school. We were preparing for an exam and we were very stressed out the day, the morning of the exam. A friend in our group was telling us, “You know, you all just got to be positive.” And another friend of ours was like, “Hey, that’s my blood group!”

Jacob Trefethen:

I thought you were going to say, “That’s my grade!” But for you I think it would have been A positive. What blood types make me think of is the British radio comedian, Tony Hancock. Have you heard of him?

Saloni Dattani:

No. Why does it make you- who is that? And why do you think of that?

Jacob Trefethen:

You weren’t around in the 1960s in the Midlands? Well, he had a TV show and a radio show; I only know the radio show. I used to a family friend gave me this one episode of it, The Blood Donor, which I listened to on repeat as I was falling asleep or if I was ever feeling sick. It was a comedy. The main character, Tony Hancock, goes in to donate blood. The doctor taking the blood sample starts with a little prick or a little smear. And Tony thinks that’s the whole donation done. And so he goes, “Okay, cool.” and he heads off. And the doctor goes, “No, no, no, that’s just the initial test. We’re going to take a pint.” And he says, “A pint!? That’s almost my whole arm!”

But the real reason I remember it is because the way that he then, the doctor then gets him to do the donation is by saying, “Well, you have a very rare blood type, you know?” He goes, “Oh, oh, me? Oh, yeah.” And every time you need to get someone to donate blood, you just have to tell them, “Well, you know, you have a pretty special blood type.”

Saloni Dattani:

Do you donate blood?

Jacob Trefethen:

I feel faint when I get blood taken, but maybe I have to donate blood these days after all. Do you donate blood?

Saloni Dattani:

I don’t, but it’s because I don’t qualify. I’m too small.

Jacob Trefethen:

They don’t let you do it if you’re small?

Saloni Dattani:

I’m too small. I don’t know, maybe they’re like, well, if we take blood from you, that’ll be it. You’ll be depleted. There won’t be any of you left.

Jacob Trefethen:

“We’re going to take a pint.” “Oh, she’s only got about a pint and a half.”

Saloni Dattani:

Which is so sad because I would like to donate blood. I feel like it’s cool. I like getting injections and I like seeing my blood taken out.

Jacob Trefethen:

Oh, that’s crazy, Saloni. Absolutely not true for me. However, one day I hope it will be.

Saloni Dattani:

Anyway, so Karl Landsteiner discovered blood groups. Blood groups are really interesting because they’re just really tiny differences in chemical structure that lead to huge differences in our reaction. If you get transfused with an incompatible blood group, your antibodies recognize that and then create this huge reaction to it; that amount of specificity of just these tiny chemical differences lead to huge differences in a medical response are what’s really key here.

Landsteiner tries to do more experimentation and tries to figure out how specific these antibodies are. So what he does is he injects animals with foreign proteins - he injects them with serum from other species - and then they develop antibodies towards it. Then he mixes those antibodies with different protein samples, and it’s only recognizing the same exact one that it was introduced to before and not with any other related species.

This suggests that there’s extreme specificity, there’s extraordinarily subtle differences in molecular structure that antibodies can recognize, and that even proteins from very closely related species can be distinguished by the body. This means antibodies are very specific and they’re also very diverse; he knows this in the 1910s, way before anyone has any idea of why. So everyone is now wondering, why are antibodies so specific? I wonder if you could go back to the 19th century and have a guess using that amount of knowledge, what would you think is the reason?

Jacob Trefethen:

Well, it’s so hard to screen off the knowledge I already have. Because the puzzle for me is how can they be so specific given humans only have 20,000 genes? But they didn’t even know humans had 20,000 genes, so they didn’t even have my puzzle.

Saloni Dattani:

They didn’t even know about DNA back then.

Jacob Trefethen:

They didn’t even know about DNA, let’s see, yeah, it was 1940s that DNA was thought to be the conveyor of genetic information, and double helix is 1953. Going all the way back then, what would I have thought? I would have thought, “Hold on a second. You mean to tell me that I have all sorts of stuff in my blood that’s ready at any moment to bind to anything? How does it fit in there? That would be crazy.”

And now we know that actually it’s not there the whole time; you actually promote certain types from the B cells that they didn’t know about. But how the bloody hell would I- oh my gosh. I think I would have thought if these things in blood can do all this magical binding, then the whole, the main purpose of blood is antibodies. That’s probably what I would have thought. I would have been like, cause that’s, that’s crazy, it can do whatever.

Saloni Dattani:

Let me tell you what they thought, which is kind of an inverse. They basically thought that antibodies didn’t exist until the antigens were introduced. So they thought that antibodies formed from the toxin, or from the antigen.

Jacob Trefethen:

So every package contains its own cure?

Saloni Dattani:

Kind of? That’s one version of it. The other version is that it’s a mold. There’s this guy called Hans Buchner in the 1890s. With diphtheria, for example, it releases toxins, right? So he was like, the antibodies to that, which are called antitoxin, are formed directly from the toxin itself. And that became very quickly accepted, but it didn’t really make very much sense even back then.

Émile Roux, who we talked about in the last episode, basically showed that if you injected a horse with tetanus toxin, and then you kept bleeding the horse, it didn’t reduce the quantity of antibodies... even after you’d removed the entire original blood volume, eventually. So I don’t know, I feel bad for the horses, but you still managed to keep that.

Jacob Trefethen:

I’m realizing that I would have done better than these idiots. Here’s another thing I would have noticed. I think I genuinely would have known back then that people or kids who are less well-nourished get more infections. I probably would have noticed, “That has to be to do with the food. That can’t be to do with the invader.” And sure enough, we now know the food gets broken down and used by your immune system. But I would have come up with that, I think.

Saloni Dattani:

Well, I feel like you might have believed in miasma theory then because they were also like, oh, it’s all about socioeconomic differences and there’s this contagion that spreads diseases through poverty and stuff like that.

Jacob Trefethen:

Yeah, but you don’t think that people specifically would have got the food thing because, in fact, nutrition is useful.

Saloni Dattani:

Hmm. I don’t know. Maybe. But they didn’t think of that. And Paul Ehrlich, who’s very famous, in 1897 basically comes upon the right theory. In 1897, he suggests that antibodies are a natural constituent of the cell surface. They’re formed inside cells, and they have, from the beginning, a configuration that is specific to a given antigen. Then the antigen selects from all of the different antibodies available, only the ones that can interact specifically with it, and then the cell that contains that antibody starts to produce many more of those molecules that it releases into the blood. I mean, he kind of comes up to essentially what we now say.

Jacob Trefethen:

He pretty much nailed that.

Saloni Dattani:

He pretty much nailed it. But then later in the 1930s, Linus Pauling proposes a different theory, which is wrong, which is called “instructional theory”. The idea is that the antigens mold antibodies into complementary shapes, like a key pressed into soft wax. Does that make sense? So that explains why they’re so specific - because the antibody just molds around it.

Jacob Trefethen:

Right, so that is the kind of thing I could have imagined. Yeah, I would have come up with something like that. It’s the thing that I sympathize with that leads to that thought. How the heck do you get something so specific if you can get loads of different specific things? If it was just in the blood the whole time, so that’s why it has to be microscopic or there’s loads of them, or it has to be the main thing about it. Or sure, you have this thing that gets molded. The thing I don’t believe is, it came in with it the whole time. I mean, that’s just crazy.

Saloni Dattani:

Right. Why would a scary microbe contain the cure to it? And so a lot of people then like reintroduce the same theory of the complementary shapes. It doesn’t make sense, and one of the reasons we already talked about Émile Roux’s experiment, where there are lots of antibodies being produced and they’re still in the blood even after the antigen has left. Second reason is, actually the difference in quantity between those two things - at the time, they were like, at least 100,000 antibodies are produced per antigen - we now know that it’s more like millions or billions - but how is that going to happen if it’s a mold?

Jacob Trefethen:

Yeah.

Saloni Dattani:

And they wouldn’t be exactly the same, probably.

Jacob Trefethen:

Yes, that’s another good, yeah. So why on earth did he was a smart chap? Why did Linus Pauling think such a silly thing?

Saloni Dattani:

I don’t know. I think he was focused on the structural similarity, and the fact that there’s this interface between the antigen and the antibody. I think that’s what he was focused on.

But the idea of a key being pressed into soft wax reminded me of those customized hand wax sculptures that were popular when I was young, I don’t know if you’ve ever had them.

Jacob Trefethen:

I don’t know what you’re talking about.

Saloni Dattani:

So when I was growing up, a lot of my friends had these- basically, they had put their hands into a molding gel and then peeled off that molding gel and then put warm wax into the mold that they’d created, and that reforms a wax sculpture that looks like your hand. And a bunch of my friends had these in their house, and I was like, I really want one.

Jacob Trefethen:

That’s a jealousy inducer. Now, the closest I came was that when I was growing up, we had inherited a sort of sculptural toy from my dad’s parents that involved a rectangle with a lot of pins and you would place your hand and it would move back only the pins that your hand pressed against. So then if you removed your hand, you would have a sculpture that was the pin against the perspex, and I probably spent at least tens of hours, maybe hundreds of hours sort of interrogating that artistic process.

Saloni Dattani:

How old were you back then?

Jacob Trefethen:

Probably in the four to nine range, I would say, which probably means hundreds of hours, because over the course of those years, it adds up.

Saloni Dattani:

Yeah, I feel like I would- it sounds a bit like bubble wrap, you could get distracted by that.

Jacob Trefethen:

Yeah, yeah, that’s true. Although with bubble wrap, you do finish, you know, you pop them all and then it’s all over.

Saloni Dattani:

Right, you pop them all. Sadly, this is not over. There’s so much more that they’re going to learn. So Linus Pauling is wrong; people increasingly have reasons to believe that he’s wrong, and they’re searching for a new hypothesis, and in the 1940s and 50s, people come up with it. And that is a theory of selection of antibodies.

There’s an immunologist called Niels Jerne, and he introduces the idea that the body contains a vast existing library of antibodies. The antigens don’t instruct the immune system, but instead they select the antibody that already fits them. Once this interaction is complete, the antigen carries the antibody to cells that can reproduce the antibody... which is also weird, for the same reason, like, why would the antigens help your body?

They sort of moved then from this chemistry-based theory of how antibodies are working to more of a biological one. They’re like, oh, this is basically like natural selection, some kind of selection is happening.

In 1957, is the big paper that describes the type of theory that we believe in. It’s called Clonal Selection Theory that was proposed by Macfarlane Burnet in 1957. His idea was each white blood cell expresses one unique antibody, and when that antibody binds to its matching antigen, the cell gets activated and then it proliferates into a clone of identical cells that secrete that same antibody in massive quantities... which is kind of true!

Jacob Trefethen:

It’s kind of true, but how did they figure that out?

Saloni Dattani:

They had found some white blood cells that released antibodies, but they hadn’t actually figured out what those cells were, and they hadn’t distinguished between B and T cells at that point. It was just a theory that would explain the observations.

So it explained how there’s so much specificity in antibodies and how fast the response is and how many thousands, or millions, or billions of antibodies are produced by the body. So we now know that immune cells can release thousands of antibodies per second, and over a lifetime, humans generate tens of millions of unique antibody-producing cells.

Jacob Trefethen:

I will say, to me, that seems unexplained how we can do that, still, but...

Saloni Dattani:

We haven’t figured that out yet. But even though we haven’t figured that out, Niels Jerne, who initially introduced the selection theory, he declares at this point that immunology is solved. We’ve basically figured it all out now, all that’s left is some details that we need to sort out. He says that it’s solved in 1957, and in 1969, he gave a talk that was called “The Complete Solution of Immunology.” This sounds so funny.

Jacob Trefethen:

May I live to one day give a talk saying “The complete solution to X.” That’s wonderful. But just to check, so he thought it was solved after his own paper?

Saloni Dattani:

No.

Jacob Trefethen:

Or he thought it was solved after the Macfarlane person?

Saloni Dattani:

Yup.

Jacob Trefethen:

Okay got it. At least he’s complimenting someone else.

Saloni Dattani:

He’s like, “That guy solved it. We’re done now.”

Jacob Trefethen:

Were these people in America? I know Linus Pauling was.

Saloni Dattani:

I think Niels Jerne was in Denmark. Let’s see. Okay, so he’s in the US at this point, but he’s Danish.

Jacob Trefethen:

Because this sounds to me...

Saloni Dattani:

What, very American?

Jacob Trefethen:

Well, I didn’t want to put too fine a point on it, but it sounds little American, but also it’s... It is interesting that so much of the last episode is happening in England and France, I suppose, and then this episode, we had a flurry in Germany and a little bit in France, but by the time we’re in the 20th century, we’re moving across; this is America! Is immunology kind of... American, do you think? I’m going to get so much hate mail for that comment.

Saloni Dattani:

Well, definitely a lot more, but the very next step happens in Switzerland by a Japanese researcher.

Jacob Trefethen:

Aha!

Saloni Dattani:

So yes, it’s so funny to me that he’s like, “Immunology is solved.” in 1957. They haven’t even figured out what T cells are or what B cells are; they haven’t figured out how all of this diversity is created. Obviously, we just know so much more about immunology now. And back then, they were like, this is the end, we’ve found out all there is to know.

Jacob Trefethen:

They don’t know about B cells?

Saloni Dattani:

Yes. Also the 1960s, B cells.

Jacob Trefethen:

Okay. B cells, 1960s, that’s wild.

Saloni Dattani:

Because only in the 1960s do people realize that the thymus is involved in immunity. And they’re like, oh, wait, if you don’t have a thymus, you’re kind of fucked.

Jacob Trefethen:

That’s so interesting because I had thought- I think of the organs as a very 19th century thing. I think of the organs as like, well, we figured out the heart, the kidney, the liver. I’m like, okay, they sort of got that. You’re telling me that the organs used in immune responses, or at least some of them, they just did not have an idea what was going on?

Saloni Dattani:

No, they didn’t. I was reading about this in this great book called A History of Immunology. It’s by a scientist called Arthur Silverstein, and it’s very good, and it basically explains this. In the early 20th century, immunology had sort of turned into this chemistry problem rather than a biology one. People just thought, okay, there are these things in the blood, antibodies, which are protecting us, and the way that we need to understand them is through chemistry. They didn’t know how these antibodies were produced. They just don’t really appreciate how important the biological understanding is there.

Jacob Trefethen:

In 1957, they hadn’t even nailed down all the planks of the central dogma. Like, it was known that DNA was a double helix, but they hadn’t nailed down loads about how DNA to RNA to proteins and, you know, all that. So, blooming heck. Anyway.

Saloni Dattani:

Maybe it’s post-World War II. They’re riding that high, they’re like, “You know what? We’ve done it. We’ve finished it. Biology is over.”

Jacob Trefethen:

We’ve finished physics, now we’ve finished immunology.

Saloni Dattani:

So this gets us to the next bit. Antibodies are super specific, they’re super diverse. How is so much diversity created? So as you said... How does it work if there are only 19,000 or so genes in the human body? How do we produce millions of unique antibodies?

Jacob Trefethen:

I do know the answer, but let me come up with some fake answers I might have thought of before I knew the real answer. But I wouldn’t have known the total scale of just how unbelievable the combinatorics in reality can go. So I would have known it’s bigger than 19,000, though, the number of antibodies.

Saloni Dattani:

Right, and they didn’t know how many genes there were; they didn’t know that until the Human Genome Project in the 2000s!

Jacob Trefethen:

I even forgot that.

Saloni Dattani:

They didn’t actually know any of this.

Jacob Trefethen:

Would they have had any sense of the finite number of proteins?

Saloni Dattani:

I think they would have just thought like a huge amount. So it’s like very diverse, very specific. They would still have been confused.

Jacob Trefethen:

I think of so much of the wonder of the human body as the... it’s just astonishing, A) that there are only four base pairs in DNA and RNA; that’s just crazy. So that’s just like, what the hell is going on there? But it’s almost less- I can get my head around it when I think about information and computers.

Something that is residually just wild from modern knowledge is that there’s a finite number. You’ve got the two really interesting macromolecules, and now lipids - people are going to get mad at me - and carbohydrates - people are going to get mad at me - but the really interesting ones are the nucleic acids, which are doing the information storage, and you’ve got the proteins that are doing everything in the book.

So it’s just crazy that there are only 20,000 proteins or 100,000 if you count, you know, post-translational stuff. So... I’m like, what?! So I’m confused about that now. But before I knew that, I’m not sure I would have been confused at all. Because I would have been like, oh, that’s just sort of a mess of stuff down here.

Saloni Dattani:

And so maybe that’s why they thought it was solved. They were like, “yeah, well, seems possible. This doesn’t seem strange at all!”

Jacob Trefethen:

But really, what do we have to do? If you take the evolutionary lens, we have to somehow have a way of defending against arbitrary infections, because the infections are going to keep evolving, so we’re going to have to keep having a way to defend against them. So you can realize, oh, hold on a second, that is going to explode and it’s going to blow well past any number in the tens of thousands.

Saloni Dattani:

Right. And they can sequence proteins in the 1960s, I think.

Jacob Trefethen:

They can?

Saloni Dattani:

Yes. So they would be able to tell that very slight differences are going to lead to different antibody responses.

Jacob Trefethen:

Got it. Because they would have been evolving some different microbes in the lab and slight variants lead to big difference. Yeah, okay, fair enough. How did they figure it out? You’re going to have to tell me.

Saloni Dattani:

It is by a Japanese scientist called Susumu Tonegawa. He is working in the Basel Institute for Immunology in Switzerland at the time.

But it takes probably months to sequence the DNA of individual cells. So instead, he uses electrophoresis, which separates segments of DNA based on their sequence, and by doing that, he compares the DNA of B cells in embryonic mice versus adult mice - like the same mice as they get older.

He discovers that their sequences have changed; so the genes have changed somehow. The sequences have moved around, they’ve recombined, they’ve been deleted in some parts. There’s just very diverse regions of their genome that are used to create antibodies. This process, basically, is called V(D)J recombination. So the DNA of B cells are randomly spliced together, and that creates an almost infinite number of different potential antibodies.

Jacob Trefethen:

So basically it’s a trick where you are not just stuck with the genes you came with, you’re inserting some tootoo-tootoo-too so that you can create some new stuff.

Saloni Dattani:

So we start out with the genes that create antibodies and those genes, like the sequence of those genes can be randomly cut up and then spliced together. And you can introduce additional diversity into that, by joining the different segments together imprecisely, or randomly adding in new bases, or just like mutating, just randomly mutating the antibody genes afterwards. All of these things happen, and it generates tens of millions of unique antibodies throughout a lifetime, which is crazy.

Jacob Trefethen:

That’s how the big, big, big human can still defend against all these tiny, tiny, tiny microbes.

Saloni Dattani:

I have a little summary of the whole process in case it was hard to follow.

So each white blood cell, each B cell expresses a single antibody. When an antigen finds a match, clones of that white blood cell multiply. And this massive diversity isn’t encoded from birth, it’s generated continuously as the DNA inside developing immune cells is actively cut, shuffled, and stitched back together in countless combinations. This process creates tens of millions of unique antibody-producing cells, generated continuously after birth, waiting for the right antigen to come along. It’s quite beautiful if you think about it.

Jacob Trefethen:

It is. It’s astonishing, the ratchet of evolution. What an amazing trick to evolve. But you’ve only got to do it when you’re facing some absolutely unpredictable invaders. Once you’ve discovered that, I would have thought, “Well, hold on a second. Why does infectious disease even keep killing us? That’s such an amazing defense mechanism.” So what do people have to say?

Saloni Dattani:

I don’t know the answer. I don’t know what they think. Maybe they again think that immunology is solved again. I don’t know. I was going to say that it sounds kind of romantic in a way that the antibodies are just waiting for the right antigen to come along.

Jacob Trefethen:

And then they become... many more... numerous!

Saloni Dattani:

They’re like, I found my match. Guess what? Now there are millions of me!

So we now have the theory that there are antibodies in our blood that recognize foreign invaders. They’re really specific; they’re also really diverse. They’re produced by our cells and this massive diversity is continuously generated over our lifetimes.

What this now means is that you don’t need to have immunity, necessarily, against entire organisms, but about specific molecular parts of an organism; and even slight chemical changes can change that immune response.

This means that you can make what are called subunit vaccines, so you can just have specific molecules in a vaccine. And the way that they figure that out is that using all of these different antibody tests, they find that not all of the antigens are causing people to develop protection. Each pathogen tends to only have a few protective antigens that we develop antibodies to, and the others are kind of irrelevant, or they’re distracting in some way.

For influenza, for example, it has two proteins on its outer surface called hemagglutinin, which “agglutinates”, or binds to blood, and then neuraminidase, which does something else which I’ve forgotten. Those are identified as the protective antigens. For other bacteria, they find other specific antigens which are the key ones.

They’re like, “You know what? We don’t need to have the whole pathogen anymore. We don’t need to have the whole bacterium or the whole virus. We can just have a vaccine with one or a few different antigens in it; we can teach the immune system to make antibodies against it. Then it will be able to recognize those same antigens in a different infection in the future.”

Jacob Trefethen:

Let me slow down and check I got it. Because to me, it’s almost a different realm. In the last episode, we talked about microbiology, learning all about these microbes. And what you’ve just told me is all about immunology, learning about our immune response to the microbes so far, we’re learning a lot about antibodies. I’m wondering though, could you have got further with microbiology before knowing about antibodies in quite as much depth?

You’re saying take flu, take TB, whatever. We’re dealing with a microbe that’s pretty big and well, take TB, that’s a pretty big one; it’s a bacteria and it’s got a whole bunch of different proteins, carbohydrates, all sorts of stuff on it. Your body is going to react to a bunch of stuff on it.

And then what you just said about antigens is that you can take, of the thousands of genes that code for proteins in TB, maybe there’s a subset - maybe the subset could be just one, maybe it could be two, which is the leading vaccine currently being tried, M72 - it has two antigens. You have to somehow say, “Okay, I’m going to get rid of the other 300 and just going to keep these two, and that’s going to be enough.” Do I need to know about antibodies for that? Or could I have studied TB more, and done a bunch of experiments with subsets of what’s in TB? Why do I need to know about antibodies?

Saloni Dattani:

I think you need to know about antibodies if you want to test them. If you want to test a lot of different subsets, you probably want to do that in vitro; you don’t want to just keep infecting people with different ones, and then you would need a very large sample size to tell the differences between them, right? So I think that’s one reason.

I think the other is trying to figure out which specific subsets is just easier if you can visualize that, instead of telling the efficacy; you’re seeing specifically which part of the microbe is getting attached to.

There are different types of subunit vaccines that people develop. One is the protein subunit vaccine, where the vaccine just contains a few or maybe even just one protein from the pathogen; another is a polysaccharide vaccine, which is a sugar from the pathogen, often a bacterium; and then there are toxoid vaccines. Some of these are created before anyone really understands the immunology either. They do have antibody tests, but Gaston Ramon, if you’ve heard of him.

Jacob Trefethen:

Gaston Ramon?

Saloni Dattani:

He developed some toxoid vaccines against tetanus and diphtheria in the 1920s. So people have developed now, much safer and purified ways of developing vaccines.

Jacob Trefethen:

What you just said actually helps me feel more at peace with the answer to my last question. It sounds like before we knew the full story about antibodies and V(D)J recombination, we, as a society, did make a few subunit vaccines - without understanding the full theory - and we didn’t make that many. Once you had the theory, once you could see the antibodies binding, then you could start identifying antigens better, you could start really ramping up your subunit vaccine discovery and innovation. That makes sense to me.

Saloni Dattani:

I’ve convinced you now.

Jacob Trefethen:

You’ve convinced me.

Saloni Dattani:

I think the other is that there are various other reasons why you might prefer to have these subunit vaccines. One is safety. You notice, for example, that some components of bacteria are really harmful to us, even if the bacteria is killed. So with the pertussis bacteria, Bordetella pertussis, it contains 3,000 antigens, or 3,000 proteins; some of them trigger really harmful reactions in us, they have rare side effects. If you had just two or three different antigens instead, without all of that waste material, or without all of those other antigens that could be harmful or trigger things, then you could avoid a lot of those side effects.

I think the third is that, in some cases, you’re just not going to be able to grow the whole pathogen in the lab. So some microbes are really difficult to culture, or they don’t infect any other organism apart from humans; and hepatitis B is one of these where there are very few animal models. It’s really hard to grow it in cell culture, and if you just had figured out the specific antigens that you needed, that would make it much easier to develop a vaccine.

But you are right that a lot are going to be produced without this knowledge. Those are some of the benefits of subunit vaccines, but there are also drawbacks of subunit vaccines. And you might know some of them.

Jacob Trefethen:

You know, it’s interesting you get these sort of couple examples before you understand a theory, then you get the theory and you get way more. And I like that, happened in the last episode with Edward Jenner; he didn’t even know what a microbe was.

Anyway, subunit vaccines. What are the drawbacks? Well, I can come up with some drawbacks. Here’s a drawback: if you don’t select a good antigen, that’s a drawback; maybe you got rid of a lot of the things that led to a good immune response. Or if the thing that controls a microbe in different people, is not always the same antigen. Maybe some people react to some bits, some people react to others. Let’s say you don’t generate as big an immune response, because if you get injected with a whole bacteria, your body’s going to start freaking out. If you get injected with a few little proteins, I don’t know, is that even going to juice my immune system enough? I’m not sure. So those are some ones off the top of the dome.

Saloni Dattani:

Yes, but that’s basically all of them, I think. There’s a really good example of this in pertussis, as I mentioned. The Bordetella pertussis bacterium has thousands of antigens. And although it was a really successful vaccine in terms of reducing the spread of pertussis in the 20th century, people were also like... “This is occasionally causing very scary, rare side effects.” There were a bunch of safety concerns about this vaccine; there were controversies and there were lawsuits in the US. In Japan, the controversies rose so much that the government actually just suspended the vaccine, even though it was quite successful.

So there was this international research effort, where scientists were thinking, “Okay, so that original whole bacterial vaccine was too risky for people, what if we can develop a better one that only contains a few antigens that we need for protection?”

There are these Japanese scientists, Yuji and Hiroko Sato, who are husband and wife, and they are two scientists working in Japan’s National Institute of Health, and they figure out the two antigens that they can use in a vaccine. They use the pertussis toxin and the filamentous hemagglutinin, and they purify them, and they detoxify them with formalin, and that resulting material becomes the vaccine, and it becomes the acellular version of the pertussis vaccine, and it was introduced in Japan in 1981. So that’s a really good example where these safety concerns of all of these different antigens are driving people to use different vaccines.

But at the same time, just the way that you said, it actually had lower efficacy, and people would develop an immune response but that immunity would wane after some months. So there is definitely a drawback to the new, safer vaccines that we have. And the way to improve them is by using adjuvants!

Jacob Trefethen:

Adjuvants! Yeah.

Saloni Dattani:

So Gaston Ramon, once again, in the 1920s, he discovers some of the first adjuvants. The way that he does this is by experimenting with lots of different random stuff in his house. Tapioca starch, breadcrumbs, soap flakes...

Jacob Trefethen:

Breadcrumbs and starch make sense because his name is Gaston. Gaston Roman makes me think, you know, like a buildings roman where it’s a novel of coming of age. In my head, a “Gaston Roman” is like a story about learning about delicious food. “Gaston Roman!” It’s like, “Oh, and then I really came into my own, and now I eat all of these delicious sweets.” Anyway, keep going.

Saloni Dattani:

I was thinking about Beauty and the Beast, and that guy is called Gaston, right?

Jacob Trefethen:

Yes, yeah, yeah. Maybe it is based on a little scientist.

Saloni Dattani:

Maybe that’s him. So he was basically trying to figure out if there were substances that could boost the immune response. I think he noticed that when animals were vaccinated with toxins, sometimes they develop stronger immunity if the injection site was inflamed; he was trying to find different substances that could inflame it. And so he found the first adjuvant, which were aluminum salts, I think.

Jacob Trefethen:

Aluminium! As we would say where I come from originally.

Saloni Dattani:

Yeah. Aluminium. I should say that as well. But for some reason, I have an American accent.

Jacob Trefethen:

This episode, the Americans are winning.

Saloni Dattani:

So he discovers that you can turn up the volume of the immune reaction and you can get a better immune response with an adjuvant. And various other people discover other adjuvants that improve subunit vaccines like saponin.

Jacob Trefethen:

Yes, yes, from the Chilean tree bark.

Saloni Dattani:

That’s right.

Jacob Trefethen:

This was something that still feels too magical. I sometimes wonder if the whole vaccine is just the adjuvant, it’s so wonderful and perfect. And I’m like, where do I get some tree bark? It’s some ancient wisdom in South America is that this particular tree bark will cure you of ills. And sure enough, it’s now used in more processed form, but essentially the same chemical in several vaccines. The first malaria vaccine...

Saloni Dattani:

The TB vaccine candidate, and the shingles vaccine! It reminds me of what we said in our AI episode where people in the past occasionally came upon really effective treatment. But I would prefer if someone figured out the purified form of the adjuvant so that I could just use that instead of, I don’t know, ingesting a bunch of insects along with it. So there are lots of different subunit vaccines!

[podcast jingle]

Saloni Dattani:

All right. So we’ve now reached the end of the episode. What did we learn? What are your favorite thoughts or what did this episode make you think about?

Jacob Trefethen:

Many different things. One is how many different steps it took and takes to make the hepatitis B vaccine. So it started with analyzing the outbreaks, identifying what was then called “serum hepatitis”, contrasting that with the other “infectious hepatitis”, hepatitis A; finding the Australia antigen, which was then discovered to be the hepatitis B surface antigen; purifying it, and getting rid of all of the dangerous stuff from blood, so that you could use it as a vaccine, and then eventually making it in recombinant form and focusing only on that one protein itself.

Saloni Dattani:

The other thing that I found really interesting was just how many different fields were involved in this whole process. There’s the microbiologists and the germ theorists of the 19th century, trying to identify the cause of each infectious disease they’re finding. There’s the epidemiologists, who are also tracing the causes of outbreaks and figuring out, “Hey, isn’t that strange that all of these people are developing jaundice... but the only thing that’s common between them is that months earlier, they all received a particular medical product.”

Then there’s all the immunology advances in both the theory and all the testing that happens to improve our understanding of how the immune system actually responds to a pathogen, and that helps people develop antibody tests, and it helps people test for the presence of pathogens, and also test the ability of new vaccines against them.

Then there’s the DNA and protein sequencing tools and all of that research that helps people figure out the specific hepatitis B surface antigen, and what its code is, and try to clone it in recombinant DNA technology, which itself is another big innovation that helps develop a better hepatitis B vaccine.

Jacob Trefethen:

Another thing that’s stuck with me from the episode was how it’s almost the hubris, that is almost the opposite to what you just said, which is that people throughout history think that science is done, or they think they’ve closed off a particular line of inquiry. The humoralists who had a fight with the cellularists in the 19th century; both of them had some good evidence for what was going on, and we now know both were right about some parts of their vision. But in the 1890s, people mostly declared victory for one side, the humoralists; we kind of dropped cellular immunology for decades, generations, and it’s only really since the 1960s, it’s popped back up. And now T-cells, one of the most studied things in immunology and in vaccinology! So yeah, you can really drop things for a long time. There was the example of Niels Jerne, who thought immunology itself was done in the ‘60s or the 50s. And I would not necessarily agree with him.

Saloni Dattani:

There was so much left to discover!

Jacob Trefethen:

Yeah, totally. Any other examples like that?

Saloni Dattani:

I have a bunch of other examples, but I just thought that itself was so incredible to me because he declared that immunology was solved before people figured out what B and T cells were! Obviously he wouldn’t have known that, they hadn’t been discovered yet. But it’s just a sign of... sometimes the field seems like it’s slowing down, and then there’s this whole new breakthrough that introduces way more complexity, and way more potential understanding and science that you could be working on. And I think people sometimes forget about that in the moment, because they’ve only been studying within this one particular paradigm of that field.

Jacob Trefethen:

I mean, we didn’t even have V(D)J recombination by then.

Saloni Dattani:

We didn’t even have good DNA sequencing at that point, or protein sequencing, or any of the tools to understand how much complexity there was. The other examples that I was thinking of was that Lord Kelvin thought physics was done... in the 19th century! His mind would have been blown by Albert Einstein.

Jacob Trefethen:

Oh my God, yeah. Imagine.

Saloni Dattani:

There’s also Francis Crick, one of the co-discoverers of DNA as the genetic code. He thought molecular biology was done in 1966! When I first read about these, I was like, isn’t that funny? What are all of these people doing research on now, then? You guys should just get a new job. But all of this research is still ongoing.

Jacob Trefethen:

Well, I will say in their defence, they really got a lot done, and in the 50s and 60s, molecular biology did nail down the central dogma. But guess what? We still don’t know what a lot of genes are doing today! There’s a lot more to do.

Saloni Dattani:

Well, in 1966, people hadn’t developed recombinant DNA technology. They hadn’t discovered that HIV or other viruses could reverse-transcribe their genes, their RNA into DNA, right?

And the other thing that that reminded me of was that, if I remember correctly, James Watson, who was also a discoverer of DNA as the genetic code, was really against IVF. He thought that this was an abomination and that if you developed babies in a test tube, they would come out all abnormal. So I just feel like there’s lots of stuff about molecular biology that they probably didn’t understand back then.

Jacob Trefethen:

Sounds right. Okay, what else stuck with you from this episode?

Saloni Dattani:

What else stuck with me? I think the way that people do innovation. So in the last episode, we talked about a very different style of trying to make vaccines than in this one. In those cases, you are doing loads of different experiments; you’re just seeing what works. You have no idea really why any of those vaccines are working, what the mechanisms are. You have some general methods of how to produce them, but you don’t really understand why.

All you do have is the germ theory, and the microscopes and trying to identify the causes of different diseases, then you have some culture techniques that you’re trying to improve, to grow them in the lab, and then to attenuate or to kill them and develop vaccines from that.

This was so different where here you’re like, ‘Okay, we’ve figured out what the virus is that’s causing this. Let’s try to get a specific protein from this virus and develop a vaccine from just that alone, and try to purify this vaccine and have only that.’ And we know, from immunology, that that alone is going to stimulate an immune response and be able to protect people from an infection.

This difference - between the serendipity and the empiricism versus the goal that you have in mind and trying to figure out how to get there - I thought was really interesting.

Jacob Trefethen:

Yeah, and for me, the fact that that innovation can lead not just to more vaccines, for diseases we don’t have vaccines for yet, but improving the safety of existing vaccines. This whole transition from going from taking a whole microbe, putting it in someone’s body as a vaccine versus just going after a really small and non-replicating protein or carbohydrate. What a transition. There’s probably things that other sectors can learn from that too. Innovation doesn’t just work on the frontier of new stuff, it can make better stuff and safer stuff.

Saloni Dattani:

Right. The other thing was all the feuds and the competition along the way. In the last episode, we talked about a bunch of them. We talked about Salk and Sabin’s rivalry. In this episode, we talked about the cellularists and the humoralists. I think there’s something positive about the competition in that, in trying to debunk the other people’s views, they developed lots of new ways to test these hypotheses, and they refined their theories.

But then it was also really sad that they just gave up on cellular immunity for so long. And then we talked about Albert Sabin, again, getting in the way of Maurice Hilleman and trying to- and getting ready to sue him for developing a hepatitis B vaccine. And then we talked about Baruch Blumberg, who had patented hepatitis B vaccine without making it, and then had later claimed that he had invented it himself. You know, their company initially refused to license it to Maurice Hilleman until they allowed him to be the director of the manufacturing program, which I thought was so crazy. You know, I do think all of these scientists have obviously developed incredible inventions, but at the same time, you’re just thinking, “What is going on? Why can’t you just let other people do their work?”

Jacob Trefethen:

I mean, finally, it’s just hepatitis B itself. And all of the science and technology development that we talked about over this episode, leading to something that has been used by hundreds of millions, probably billions of people at this point, and has prevented so much liver cancer - I don’t know even how to add it all up - so much cirrhosis, and saved millions of lives over the course of several decades. How hard-won that was to get there and how cheap and easy it is now to take. In fact, it fits into the background of our lives; it’s hard to remember whether I’ve gotten it. If I really make myself think, I know I have. And it’s kept me safe all these years, it’s something we can rely on in the modern world. So thank you, all the feudsters, as always, for making progress.

Saloni Dattani:

I thought it was so crazy how, when you described how tiny this little virus is, how few genes it has, how few proteins it has, and yet it causes so much damage over years or decades of a person’s life. And guess what? We can just prevent all of that with a simple shot and 85% reduction in liver cancer rates. That’s crazy. 70% reduction in liver deaths. That’s crazy. And all of that with a single shot.

Jacob Trefethen:

19th century, 20th century... what’s coming next, Saloni?

Saloni Dattani:

What’s coming next? You’ll have to wait and see. We’re now at the end of the story of one of the most successful vaccines that’s been taken by hundreds of millions of people, potentially billions of people, and it saved millions of lives. But this was just the start of protein subunit vaccines. Many, many more came after it. And then we got recombinant DNA technology. We got many other types of vaccines too, like vector vaccines and mRNA vaccines. And we don’t even know what the future holds... because, unlike immunology, vaccinology isn’t solved yet.

Jacob Trefethen:

And all of the future can be given to another episode. But for now, we hope you enjoyed this episode. And we hope that, if so, you rate us on Spotify and Apple or wherever you’re listening to this, and share the podcast with everyone you know, your friends, your viruses, your bacteria, and more! And your liver, for sure. Your liver’s going to love this one. And hope you enjoy listening too.

Saloni Dattani:

See you next time.

Jacob Trefethen:

Bye!

References

Books:

• Paul Offit (2007) Vaccinated: One Man’s Quest to Defeat the World’s Deadliest Diseases

• Arthur M Silverstein (2009) A history of immunology

• Ronald W Ellis (1993) Hepatitis B Vaccines in Clinical Practice

• Sally Smith Hughes (2011) Genentech: The beginnings of biotech

Articles:

• Timothy M. Block et al. (2016) A historical perspective on the discovery and elucidation of the hepatitis B virus https://doi.org/10.1016/j.antiviral.2016.04.012

• Naijuan Yao et al. (2022) Incidence of mother-to-child transmission of hepatitis B in relation to maternal peripartum antiviral prophylaxis: A systematic review and meta-analysis https://doi.org/10.1111/aogs.14448

• Jill Koshiol et al. (2019) Beasley’s 1981 paper: The power of a well-designed cohort study to drive liver cancer research and prevention https://pmc.ncbi.nlm.nih.gov/articles/PMC5866222/

• William J. McAleer et al. (1984) Human hepatitis B vaccine from recombinant yeast https://doi.org/10.1038/307178a0

• Chunfeng Qu et al. (2014) Efficacy of Neonatal HBV Vaccination on Liver Cancer and Other Liver Diseases over 30-Year Follow-up of the Qidong Hepatitis B Intervention Study: A Cluster Randomized Controlled Trial https://doi.org/10.1371/journal.pmed.1001774

• Anthony R Rees (2020) Understanding the human antibody repertoire https://doi.org/10.1080/19420862.2020.1729683

Through the stories of milkmaids and aristocrats, secret lab notebooks, microscopes and cell culture, they explore how trial and error turned gruesome folk practices into the science of immunization, and how it all began with a single pustule.

Hard Drugs is a new podcast from Works in Progress and Coefficient Giving about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter: https://scientificdiscovery.dev

Jacob’s blog: https://blog.jacobtrefethen.com/

Transcript

Saloni Dattani:

You would extract brain tissue from a rabid dog, inject it into the brains of rabbits.

Jacob Trefethen:

Frankenstein was written much earlier in the 19th century. You can sort of get why people had a view of scientists that was kind of like, “What the hell are they up to?”

Saloni Dattani:

He compiled all of it into his own manuscript, An Inquiry into the Causes and Effects of the Variolae Vaccinae.

Jacob Trefethen:

He submitted, got rejected, reviewer 2 had some comments, and then he became a Substacker?

Saloni Dattani:

Basically until the 1940s, you couldn’t see the smallpox virus at all. And all of that changes when Ernst Ruska and Max Knoll developed the electron microscope. There’s an enormous improvement in the resolution that you can get, thousands of times higher than light microscopy. And that finally allows you to see viruses.

[podcast jingle]

Saloni Dattani:

I think we have a few little announcements to make. So we have finally a print edition of Works in Progress magazine! And I’m holding it up right now, if you’re watching the video, and it’s very pretty, and if you haven’t got one yet, you should get it. It’s full of super interesting articles, including one about inflatable space stations.

Jacob Trefethen:

Inflatable space stations. At the time of the recording, Saloni’s just arrived and mine has not, and I’m going to be checking my mailbox all day long. I can’t wait for mine, and from what I’ve heard from you, Saloni, and others who were involved in the production of it, it’s a beautiful piece in its own right just to flick through, so I definitely recommend others get a copy.

Saloni Dattani:

Yes. And then, you have some news.

Jacob Trefethen:

That is right. I work at Open Philanthropy, or I used to, because we just rebranded to Coefficient Giving to emphasize that we’re going to work with more donors to give away more money to good causes. And what I work on is the kind of stuff we talk about in this podcast so science for global health and to make new vaccines and new drugs, and we are right now hiring on my team and on some other teams at Coefficient Giving. So I would recommend for anyone interested in these topics, or in philanthropy in general, to go ahead to coefficientgiving.org/about-us/careers or maybe just Google “Coefficient Giving careers” and then we list all of the open jobs that we have. I’d love to work with you one day maybe, so make sure to check it out.

[podcast jingle]

Jacob Trefethen:

According to the latest estimate in The Lancet, vaccines have saved 154 million lives over the last 50 years. Not bad, but where did they come from? How do you make a vaccine from scratch? How did scientists make the first vaccine and why did it take another 90 years to make a second one? What were all the challenges they faced along the way?

Saloni Dattani:

In this episode of Hard Drugs, we’ll get into the mindset of a scientist living in the 18th century, then the 19th and the early 20th, trying to make vaccines for the first time with only limited tools and knowledge, and trying to find ways to make them safer, more effective, and scale them up.

Jacob Trefethen:

So, get into the time machine and let’s begin. Okay, Saloni, we’re going to try to get into the mindset of a scientist living in the 18th century to try and protect people against smallpox. Take me there.

Saloni Dattani:

So smallpox was really scary. I think it’s easy for us to forget about that now because we’ve eradicated it. Just to first introduce what it was like.

Smallpox is caused by the Variola virus and you typically contract it if you inhale it, either through the air or through fluids and surfaces. The first thing that would happen is you would have fevers. You’d have fatigue and muscle pain, and then spots would start showing up on your tongue and your mouth. Then you’d have pimples and rashes all over your face, your chest, and all over your body.

All those pimples and the spots would then erupt. That would release new particles of the Variola virus into the air or into fluids, and then they could infect more people. While that’s going on, the virus is replicating in your body, in your immune system, in your lungs, in your eyes, in your bones, in your heart. And that can cause a lot of problems.

Jacob Trefethen:

Your eyes and your bones?

Saloni Dattani:

It’s horrible. Smallpox could cause blindness. It could cause arthritis. It could cause heart failure, sepsis, pneumonia. It’s estimated that 30% of people who have any symptoms die from the disease. This is before any treatment, before vaccines are available.

Jacob Trefethen:

We have chickenpox and monkeypox still today, but they are poxes that create pustules. Not exactly as bad as 30% death rate, though.

Saloni Dattani:

That’s right. Chickenpox is actually a totally different virus, but monkeypox is related to smallpox.

Trying to think of someone living in the 18th century, seeing this 30% death rate around them if there was a smallpox outbreak and someone caught it, and seeing that happen every year, every other year, that’s terrifying, I think. You wouldn’t really have the tools that we have today. You wouldn’t know how the disease spread. You didn’t know that it was caused by a virus. You didn’t know what microbes were.

You wouldn’t really have any idea of how to protect yourself, apart from staying away from other people, I guess. You wouldn’t be able to see microbes- you wouldn’t be able to see viruses under the microscope, certainly. You wouldn’t be able to study the virus in the lab because you don’t have the culture methods to do that.

All you would know is if there’s a second epidemic in the same town, people who caught smallpox the first time would usually be protected from the second one. This was just this folk knowledge that people had of some kind of immunity. They didn’t know why.

There were different kind of theories about this. One was that the first outbreak or the first time someone was infected with a disease, because you didn’t really know what was caused by pathogens at that point, was that it somehow depleted the body of the nutrients that it needed. Then the second time it couldn’t do the same thing.

Jacob Trefethen:

First cut is the deepest.

Saloni Dattani:

That’s so good. You also wouldn’t really have an understanding of contagion. Not the movie, the-

Jacob Trefethen:

Well, you wouldn’t have an understanding of the movie either.

Saloni Dattani:

Right. Well, you would probably believe in miasma. You would think that diseases were spread through bad air, sometimes bad soil, just bad smells. Something around you in the environment is causing diseases to spread. And for a lot of diseases, that makes it quite hard to get rid of them. You’re not really able to trace the disease very effectively, and you aren’t able to develop vaccines with that. So, given all of that, how did Edward Jenner even develop the first vaccine? Do you know the answer?

Jacob Trefethen:

I think that he had spotted, or had read about, or heard anecdotally, that people who had been exposed to a related cowpox were more likely to be protected from smallpox. The milkmaids with clearer skin who had been working with cows, that kind of thing.

And I think there was also a part of the story I’m less sure about, which is that earlier in the 18th century, a British aristocrat or aristocratic woman, Lady Montagu, had been to the Ottoman Empire and seen how they were doing some vario- wherever she’d visited, they were doing variolation to protect against smallpox there and brought that back to the UK and did a big press tour about infecting her own child to protect her. It was safe, you could- I don’t know if Edward Jenner was directly influenced by that background, but presumably so. I think he infected- he used the cowpox without, I guess, knowing it was a virus, but somehow exposing a young boy to it and then challenging him with a smallpox agent. And then the boy was okay! That’s the story I have.

Saloni Dattani:

Mmhmm, so I think basically everything you said was correct. The lady that you’re describing is Lady Mary Wortley Montagu, and she was an aristocrat. She traveled to the Ottoman Empire. She learned about Turkish customs and she witnessed people doing the smallpox inoculation, so she witnessed variolation at the time. Basically, they gave children a smaller, controlled dose of the virus. And she wrote about it in letters to her friends. Because she was so afraid that her children would have to go through the horrible disease, and one in three perhaps would not survive, she wanted to spare them. So she got her son inoculated, and he was the first English person in history to undergo this operation.

Jacob Trefethen:

So she didn’t try it on herself?

Saloni Dattani:

Well, I guess she probably had had smallpox by that time, because it’s mostly children who are having it for the first time. After that, she got her daughter inoculated by the doctor, and that time she publicized the event. She then persuaded the Princess of Wales at the time to also try it. She then got the prisoners at a prison to have the chance to undergo variolation instead of getting executed.

Jacob Trefethen:

Instead of getting executed? Those seem quite unrelated.

Saloni Dattani:

You know, variolation was still risky. I think that’s something that we probably forget now. The estimate is that there was a 2% or so mortality rate from variolation.

Jacob Trefethen:

Two percent. Two percent? So you intentionally infect your child and there’s 1 in 50 chance that your child then immediately dies?

Saloni Dattani:

Right. As a result of the procedure. I mean, I think that’s freakishly scary to us now, but if you think about the alternative, which is smallpox, which is going to kill one in three, then the risk is much lower and it’s very reasonable as a parent, but it’s also just a terrible rite of passage that people would go through.

Jacob Trefethen:

This is the lesson that we always learn when we go back in a time machine, which is don’t go back. Don’t go back. Both options are very bad. Now I’m wondering, imagine if Lady Montagu had done a big show about variolating her daughter and then had hit the 2% chance and the daughter had just died? Would we even have vaccines now?

Saloni Dattani:

Oh... That’s a really good point. I’m thankful that that didn’t happen statistically, but it probably did for some of the early attempts. There were different procedures of how you could variolate someone at the time. One was you would get the pus from someone who had been infected, and then you would put that through another healthy child’s nostril with a cotton, with a piece of cotton. Or with a thin silver tube, you would powder the pus and then blow it into their nostril. There were also different methods. You could use the fresh pus. You could cook the pox first and then put it into your nose.

Jacob Trefethen:

Cook the pox? Cook the pus?

Saloni Dattani:

Cook the pus, I think. But it was called cooked pox.

Jacob Trefethen:

Cooked pox. Well, that’s a tasty little treat.

Saloni Dattani:

Yeah, recipe. What would you prefer?

Jacob Trefethen:

Oh my gosh. I want it as a cooked pox in a little sugar cube I can put in my cup of tea.

Saloni Dattani:

That’s cute.

Jacob Trefethen:

Although I suppose I have to inhale it, don’t I? So that wouldn’t work. Okay. How about, I think I would do the blow it up your nose method. I think that just seems so kind of [whoosh] one and done. Moving on. What would you do?

Saloni Dattani:

I think I would... I would probably try to get the thing blown up my nose, but I think my nose is quite sensitive and I’d probably just sneeze it out.

Jacob Trefethen:

You’d sneeze it out and then two years later get smallpox.

Saloni Dattani:

Damn it.

Jacob Trefethen:

It’s kind of interesting though that they had nasal vaccines back then and we can still barely invent nasal vaccines for respiratory viruses now.

Saloni Dattani:

That is true. The other thing that’s interesting about the whole procedure was that variolation essentially involved putting actual smallpox virus into a child. You would try to guess based on the child who previously got it and that you’re taking the pus from, whether they got this mild or severe disease. You just hope that that replicates and that there’s a kind of uniform risk among different children and that some of those children are not just healthier and they’re not just getting milder disease because of that. But you wouldn’t be able to distinguish them because you didn’t have the microscopy, you didn’t have the culture techniques.

Jacob Trefethen:

Would doctors be harvesting from one child and passing it on to a hundred? Or would it be more like one to three?

Saloni Dattani:

I think you would be harvesting because you could collect- people would have so many pustules around their body that you could harvest multiple.

Jacob Trefethen:

I guess that gives you some chance of if you accidentally harvest the dangerous version, then you can just stop giving it after something bad happens.

Saloni Dattani:

Yep. You would have to make sure that you’re actually recording all the details. Tallying up the numbers. I guess some doctors would have a reputation for accidentally killing lots of people and some would not.

Jacob Trefethen:

Not a good Yelp review.

Saloni Dattani:

This is also interesting because as we’ll come back to with the smallpox vaccine, that was also transferred between people from arm to arm. But let’s first start with how Jenner actually found that in the first place.

You described how he had heard rumors of dairy- of milkmaids being protected from smallpox after they’d had cowpox. That is right. What I learned while reading about this was that there were actually other doctors who tried it before Jenner did. They had actually also tried inoculating their children with cowpox. I think there are two doctors that we know of that have done this decades before Jenner.

But the key thing that Jenner did was actually write this up in a lot of detail and publicize it. I think sometimes that’s really important. Just having the idea is not enough. You need to actually make it known and you need to be able to get other people to replicate this method and scale it up.

He had heard about these rumors and he went out into the dairy farms and he tried to find out more about it. He asked families, if there was a smallpox outbreak in the past, were any of your family members protected? Did they have cowpox before? He wrote up these case reports, dozens of case reports on people who had been protected. And then also experimented on one young boy, he inoculated a gardener’s son called James Phipps, I think he was seven at the time, with pus from a cowpox infection by a woman named Sarah Nelmes, who was a dairymaid. Then in order to test whether that vaccine worked, he then variolated him with smallpox. It turned out he didn’t have any symptoms after that.

This is the part of the story that I misunderstood at first. I thought that Jenner just gave him smallpox and tested that out. I thought that’s really unethical. Maybe it was- maybe that’s just how science was done at the time, but that’s not actually what happened. He gave him variolation, which at that time would have been considered the safe option to do.

Jacob Trefethen:

Which reminds me of some vaccine development happening today, where in tuberculosis, it would not be safe to give someone a vaccine and then expose them to the tuberculosis bacteria because if the vaccine doesn’t work, they’re in trouble; the bacteria can be deadly.

But people are trying now to develop safer challenge models where you do give someone a vaccine and then expose them to something, so they’re attenuating the bacteria. The issue, though, is that often the vaccines you’re testing are attenuated bacteria, and then you’re exposing someone to a challenge agent, which is attenuated bacteria. You’re kind of giving people the same thing twice. But in this case, it sounds like there’s one pox virus, cowpox, and then another virus.

Saloni Dattani:

Hopefully milder smallpox.

Jacob Trefethen:

Hopefully milder, yeah.

Saloni Dattani:

It’s so funny that you’re giving someone an attenuated bacterium to protect them from another attenuated bacterium. What if the second attenuated bacterium that’s the challenge just happened to be so mild that the person wouldn’t have had symptoms anyway?

Jacob Trefethen:

This is the difficulty from a scientific point of view of are you learning what you really want to learn? You’ve got to make it safer to learn anything, but you do get more distant from the reality of the bug.

Saloni Dattani:

Right. The other thing that I thought was quite interesting about the Jenner story was, he wrote up this experiment that he did on James Phipps and he submitted it to the Royal Society’s academic journal, and they rejected him. They said, ‘not sufficient evidence’ in 1797. I guess he took some of their reviews- he took that comment to heart and he put together the other cases that he had described and compiled all of it into his own manuscript that he self-published as a monograph, which he titled An Inquiry into the Causes and Effects of the Variolae Vaccinae.

Jacob Trefethen:

He submitted, got rejected, reviewer 2 had some comments, which I’d love if those were public by the way, do you know if we can read- and then he became a Substacker?

Saloni Dattani:

This is so funny because even after he published his own version of the manuscript, people were still quite critical of it at the time. He still only had that one experiment, and he sort of mixed up the drawings a little bit, and people were like, “This isn’t enough evidence.” I just found that quite funny.

The way that he persuaded people was by continuing to do the experimenting and continuing to offer people the cowpox inoculation, and by people seeing that it works. More and more doctors would then ask him for the material and he would share it with them, and he kind of improved his manuscript and added more detail to it. I don’t know, it’s just interesting to me that all of this doesn’t start with just one idea; it’s this process that’s building up over decades. And sometimes the key moment is not having the idea.

Jacob Trefethen:

That when we look back at- the stories of science end up condensing to this one eureka moment. But in fact, it’s similar to science today, it just takes a long time, and a lot of people trying to disprove what you proposed.

Saloni Dattani:

Right. I guess I find it interesting that we sort of think often of experiments in history as, “oh, we just did this one experiment and then poof, everyone was convinced.” Or maybe the opposite: it was like, “oh, they’re so stupid they didn’t believe him at first.” But actually, people were just generally rigorous. It mattered whether this method worked. You wouldn’t want to think that you’d protected your child and then actually they would die from another smallpox outbreak; that would be terrible. This stuff kind of mattered back then as well.

The other thing that I found interesting was just how different the vaccination method back then was to now. Once Jenner had developed his method, initially you would take a cowpox pustule and you would inoculate another child with it. How would you pass it on? You would either hope that there’s a local outbreak of cowpox, which wasn’t very common actually at the time, or there was this new method that people discovered, which was that you could transfer the pus from arm to arm. Once you had inoculated someone with a cowpox infection, you then remove the pus again from them and scratch it into another person. This is called arm-to-arm transfer.

And that worked, but it also kept dying out. So if you didn’t have- the virus itself would die out from the person’s arm or whatever you scratched it into them after a while. It was hard to actually spread it around the world and keep it around and keep it alive for long enough to vaccinate all the children.

I was reading about how this method got taken up in China at the time, and because the vaccine lineages just kept dying out, people in China, poorer families, were sometimes paid to get their children vaccinated just so that the vaccine didn’t go extinct. It would be paid for by merchant guilds or local officials or shops or by taxation, just to make sure that we didn’t accidentally get this extinct, because sometimes you wouldn’t have another local cowpox outbreak to derive it again.

Jacob Trefethen:

I like the present day, where we have machines and bioreactors that keep things alive.

Saloni Dattani:

Right. We have big tanks with bacteria just churning out stuff that we want instead of scratching them from arm to arm. The other thing that I think you’ll find interesting is that this process of transferring the pus from arm to arm, as you might be able to imagine, could also risk spreading other microbes between people. You could accidentally- you could have contamination. You could accidentally give someone syphilis or hepatitis B, and that happened often. I think that was quite scary for me to hear about.

Jacob Trefethen:

Oof, oof. Yeah, because you’re breaking the skin and that means you’re going to be- stuff might be getting straight into the bloodstream, right? Yeah, I’m not sure about that one.

Saloni Dattani:

Would you like a little side of syphilis with that?

Jacob Trefethen:

I think I will- No, I think I won’t actually.

Saloni Dattani:

So the early smallpox vaccine was still quite risky. Obviously, it was a lot better than actual smallpox, and it was better than variolation. But there were still lots of improvements that had to be made for this to be a solution to vaccinate entire populations. I wonder if you can guess what the next step is.

Jacob Trefethen:

Well, first I have a question. So, getting smallpox, 30% mortality. Okay, don’t want to do that. Variolation, say 2%. I mean, that is not good, I’m just going to be honest. Smallpox vaccination, this method with cowpox, how safe was it? Was it ever lethal? Would people, would kids actually go off the rails or?

Saloni Dattani:

I don’t know that, I don’t think there are numbers, but I do think we can be pretty confident that the risks are lower just because it’s cowpox and cowpox is not very- it has a very low fatality rate. I don’t think it actually caused fatalities at all. The only risks you would have are from contamination of syphilis or something else.

Jacob Trefethen:

Cool. Okay. Back to your question, was the question, how did they scale it up from here?

Saloni Dattani:

Or what was the next step after arm-to-arm transfer?

Jacob Trefethen:

After arm-to-arm, let’s see. Well, I don’t know if this fits into the story, but as I rack my brain for historical anecdotes, I remembered something about- oh no, this must have predated the vaccine. I was thinking of intentional exposure where there were sick kids who would cough in front of you, but that must have been before.

Saloni Dattani:

Oh, that might have been variolation. Or maybe you’re thinking of chickenpox.

Jacob Trefethen:

That was just variolation. I was just thinking about it. Did you go to a chickenpox party, though?

Saloni Dattani:

I did not. It still makes me mad even today because the chickenpox vaccine was available when I was a kid, but my parents didn’t get it for me, and I got actual chickenpox instead, and I was out for a week just throwing up. I remember this because I was maybe seven?

Jacob Trefethen:

Okay. You were James Phipps’ age.

Saloni Dattani:

I was James Phipps’ age, and I was in India on a holiday, and I was sick for a week. There was another little girl, my cousin’s friend, was visiting. I was like, “you shouldn’t come in here, I have chickenpox.” Her mom said, “it’s okay, she’s had the chickenpox vaccine.” I was like, “there’s a chickenpox vaccine?”

Jacob Trefethen:

The reveal.

Saloni Dattani:

I was so mad. So where were we? What’s the next step?

Jacob Trefethen:

Yes, what’s the next step? You’ve got to somehow find a way to do this at scale instead of going arm to arm. Well, do they use cows? Why not? Cows are where the cowpox comes from.

Saloni Dattani:

You have, finally- you’ve got the right idea. That’s exactly right. They moved to growing cowpox on cows. Quite strange. You’re doing the arm-to-arm transfer, and then you’re like, “What if we could just grow this on the skin of calves instead?” Yes. They’re not actually getting cowpox, but well, they are on the skin.

This was a procedure that was developed by some doctors in Italy in the 1840s. At that time, it was obviously a lot safer because you’re avoiding the other human pathogens that you could contaminate the vaccine with, so no risks of syphilis and hepatitis B anymore. But I think it took a few decades for that process to actually be passed on to other people because they didn’t understand exactly how to do the method.

Jacob Trefethen:

That’s multiple generations after Jenner.

Saloni Dattani:

Right. Until then, they’re just doing this arm-to-arm transfer. There were kind of businesses that got set up that were called vaccine farms, and they would harvest the vaccine from these inoculated calves. I thought that was kind of interesting. They sort of handled this vaccine, the lymph or the pus, that was a biological material. I think it was also- this was the first time that there was a medical regulation on biological material in the US to regulate the quality of this smallpox vaccine and different vaccine farms.

Jacob Trefethen:

Hmm, what did that actually entail?

Saloni Dattani:

It was basically the general hygiene of the vaccine farms, but you don’t want it to be contaminated with other stuff. Also, this is before antiseptic techniques are introduced or anything. It’s literally just like, “oh, does this look clean? Looks good to me.”

Jacob Trefethen:

I guess we don’t have needles either, right? We have kind of scraping your skin with it.

Saloni Dattani:

Oh, no, we do have needles. We do have needles.

Jacob Trefethen:

We do have needles. I mean, one thing that came up in a previous episode was hepatitis C and how Egypt had a surprisingly high rate of hepatitis C. That was because in the 1970s, when we’re 100 years of progress in medicine since what we’re currently discussing, still needles were not properly sterilized, and there was a campaign to get rid of schistosomiasis in people who lived along the Nile. And you get-

Saloni Dattani:

Wait, what is schistosomiasis again?

Jacob Trefethen:

An infectious disease spread by snails, randomly.

Saloni Dattani:

Oh, that’s horrible.

Jacob Trefethen:

Well, ‘spread by’ is not quite right. It’s not that the snails sort of slide on top of you. The life cycle of the pathogen, which is a worm, involves a snail portion. That happens in water, so if you live by the Nile, you are kind of at risk. There was this big public health campaign to try and injecting people to treat schistosomiasis.

The issue being you needed 10+ injections and the needles weren’t properly sterilized. Everyone got hepatitis C; not everyone, but fast forward- that generation of Egyptians, a lot of people were accidentally infected with hepatitis C, which was not discovered as a virus until 1989, so people didn’t even know it was there. So now, the story ends happily because there are cures for hepatitis C. In Egypt, people got screened and treated, and the prevalence rate went from around 10% right down to, I think now, under 1%. But yeah-

Saloni Dattani:

Wow, that’s horrible. I’m thinking, why didn’t they just use disposable needles by default? Why not just use antiseptic techniques and things like that? Weird. Mistakes of the past.

Jacob Trefethen:

Mistakes of the past.

Saloni Dattani:

So, you’ve now got the smallpox vaccine that you can grow on cows. But there are still various things that you can do to scale that up. One is you could prevent it from spoiling quickly. You could add glycerin to it and do that. Then you can learn how to freeze dry the vaccine so you can transport it longer distances without losing its strength.

In the 1960s, there was also this other tool that was invented similar to the— well, not similar, but because you mentioned the needles, I remembered it- there were bifurcated needles, which are just two prongs on the needle, and they would hold a tiny drop of the vaccine in between them. And that meant that you could use only a quarter of the usual dose to get a standard dosage. That meant that you could scale up vaccination around the world.

Lots of progress that happened in the smallpox vaccine that I think is quite interesting, but it also just shows how very preliminary Edward Jenner’s vaccine was and how little people understood. Throughout this whole process, basically until the 1940s, you couldn’t see the smallpox virus at all. You just knew that something in this material is protecting people. You don’t know what. You don’t really know why. You just have this method that empirically works.

Jacob Trefethen:

Not much to work with there, then.

Saloni Dattani:

No. That really raises the question of how you can actually replicate, how do you do this for other diseases? I don’t know if you have any guesses as to how the next step happened, but I think it’s just interesting. Also, I think smallpox is kind of a rare case in that you just happen to have this milder version of the infection in cowpox.

Jacob Trefethen:

I was just thinking how lucky that was, because you don’t get the next vaccine for so long. In order to replicate that model, you kind of would need there to be a related virus that happens to be mild.

Saloni Dattani:

Right, and that gives you protection.

Jacob Trefethen:

Yeah, exactly, that gives you cross-protection. What are cases where you can get cross-protection from something rather than having to try a different method of vaccine production?

Saloni Dattani:

There aren’t really any, even now, there aren’t really any, I think. So it just takes another 90 years to figure out how to do it.

Jacob Trefethen:

Well, let me try and come up with some on the fly. If I think about flus, there are some flus that would have been deadly back then and some flus that would have been mild. But I suppose those are more seasonal rather than ongoing, so it’s hard. If you get a mild flu, you probably would get some cross-protection against some dangerous, lethal flus. But how do you keep that going? How do you know? Any others come to mind for you that?

Saloni Dattani:

I guess monkeypox and smallpox, but again, those are the same one. Is there cross-protection for hepatitis or syphilis or? No, that doesn’t, that’s really one strain.

Jacob Trefethen:

Well, there’s meningitis B and gonorrhea. Both have an outer membrane vesicle. That might be some cross-protection. Gonorrhea is not deadly. Maybe you purposely infect yourself with that and get a little bit of protection against meningitis?

Saloni Dattani:

It would be hard to make the connection, I think.

Jacob Trefethen:

Yeah, no, that’s true. I guess it helped not only that you had this milder case of cowpox, but also that smallpox was so common that you could determine the effect maybe, or you were motivated to determine the effect as well.

Saloni Dattani:

Also, it’s quite easy to distinguish, or to tell, that someone has smallpox. It’s so easy to tell that someone has cowpox and it’s easy to get the pustules. If it was gonorrhea, maybe people just wouldn’t tell you they would be so embarrassed or something and you wouldn’t be able to replicate it.

Jacob Trefethen:

Well, okay, I’ve thought of one more.

Saloni Dattani:

Oh! Okay.

Jacob Trefethen:

Which is TB. Because, you know, the first vaccine is BCG, which is...

Saloni Dattani:

The cow version. It’s bovine.

Jacob Trefethen:

The cow version. So how come they didn’t come up with cow TB?

Saloni Dattani:

That’s a really good question. I was wondering about this as well. The answer is they didn’t know that they were different bacteria at the time. They didn’t know that they were different strains.

Jacob Trefethen:

So they just thought.

Saloni Dattani:

So it wouldn’t have worked. They would just be like, well, you’re just infecting someone with TB.

Jacob Trefethen:

I see. There wasn’t the equivalent because it’s probably less visible. If you have TB, it’s not the equivalent of cowpox. Interesting. I’m now wondering from the cow’s point of view, if they look over at human smallpox and think it’s puny and pathetic and they could have protected themselves against cowpox.

Saloni Dattani:

Right. Well, this is the other thing that’s interesting, because smallpox doesn’t infect other animals, right? It doesn’t have any animal reservoirs. It just happens to be the case that cows get a similar but different infection.

Jacob Trefethen:

That’s why to fast forward to the present, we managed to get rid of it. It doesn’t hang around. If you’re someone who wants to eradicate diseases, the first thing you should think about is “Which ones don’t have an animal host? So I could actually...” I think syphilis-

Saloni Dattani:

Or you could vaccinate the animals.

Jacob Trefethen:

That’s true. That’s true. Bit harder, but doable.

Saloni Dattani:

Yes. Well, maybe a bit easier.

Jacob Trefethen:

It depends, yeah, I guess.

Saloni Dattani:

Can you catch them all?

Jacob Trefethen:

Gotta catch ‘em all! We’ve eradicated two diseases, and one was a human disease and another- Do you know the other one?

Saloni Dattani:

I forgot. No, but it was a dairy farm one, wasn’t it?

Jacob Trefethen:

Correct. Rinderpest.

Saloni Dattani:

Rinderpest!

Jacob Trefethen:

Rinderpest, which had a ridiculously high mortality rate for cows, where cows would start dying. You’d go, “what’s going on?” You’d have an inspector come in and you’d go, “Oh, these cows have rinderpest.” Then they’d walk out of the farm with some rinderpest on the bottom of their shoes and go to the next farm and spread it around. So these poor cows. But yeah, rinderpest, we managed to eradicate. Smallpox, we managed to eradicate for humans. We’re one for one.

Saloni Dattani:

We’re almost there for Guinea worm disease as well. Guinea worm is spread by a worm. It’s a worm that you get from, I guess, swimming in stagnant lakes and things like that. Their eggs get into your body and they grow into very long worms, interfere with your joints and stuff like that, eventually they erupt from your skin and then you have to take them off and try not to let the worms lay more eggs in the water.

But we’re almost there. I think in the last few years, there were only a dozen cases worldwide that were recorded versus hundreds of thousands, probably over a million, in the 1970s. It turns out it was extremely easy to eliminate that because you could just clean up the water or you could just introduce larvicides or something, to kill the worm’s little eggs. I mean, this is totally off the point now, but I was reading about how Guinea worm unintentionally got eliminated in parts of Pakistan temporarily before the partition of India, just because there happened to be a drought. So all the eggs just died.

Jacob Trefethen:

Ooh. Wow.

Saloni Dattani:

Imagine that.

Jacob Trefethen:

Imagine that. It’s like all the things during COVID, all the other respiratory diseases that dropped because no one was seeing each other anymore. I don’t think we got rid of any, I mean, we almost got rid of flu B for a while.

Saloni Dattani:

We got rid of one strain of flu B, the Yamagata strain.

Jacob Trefethen:

Right. Is that just gone?

Saloni Dattani:

We haven’t seen it in the last five years.

Jacob Trefethen:

Well, I’ve got a surprise for you...

Saloni Dattani:

No!!!

Jacob Trefethen:

Reveal! Let’s get back on point. So what question did you ask me?

Saloni Dattani:

Okay. We’ve got the smallpox vaccine, improved it in a bunch of ways, scaled it up. Now we can store it for longer. We can use only a quarter of the usual dose. You can vaccinate basically everyone with it. The way that it was eradicated was by, if there were any cases that were remaining, people just vaccinated all of their contacts and everyone around them. That’s called ring vaccination, so you just control the outbreak before it could spread anymore. Managed to eradicate that in 1980. Pretty cool.

Jacob Trefethen:

The people who did that are still alive today. Bill Foege. It was a team effort, so I don’t want to single just him out, but I think he was the guy at the CDC at the time who was like, “Okay, we’re just going to get rid of this thing.” He’s still alive!

Saloni Dattani:

He’s still alive! There’s another scientist called Victor Zhdanov who was involved in the smallpox eradication campaign, and he persuaded the World Health Organization to actually start working on it, when they were really skeptical before.

Jacob Trefethen:

Good man. Victor-y.

Saloni Dattani:

Boom. He is the mascot of Works in Progress magazine. He’s the little guy.

Jacob Trefethen:

That guy with the mustache?

Saloni Dattani:

Yes.

Jacob Trefethen:

Okay. Good on him.

Saloni Dattani:

Pretty cool. Anyway, you have a smallpox vaccine, and for 90 years, you don’t have any other vaccines. This is because of all the challenges that we mentioned. The fact that cross-protection isn’t that common, that even if it is, it’s hard to find a mild version that’s easily visible. Then the fact that you don’t know about germ theory, you don’t necessarily even know about microbes at all. You don’t know how immunity works. You don’t have a systematic way of producing new vaccines. How do you move forward from here?

Jacob Trefethen:

Well, let me cheat by trying to work backwards from, I happen to know what the next vaccine was, which was the rabies vaccine. Now, will that give me a hint?

Saloni Dattani:

Well, that was the next human vaccine, but there were two animal vaccines in between.

Jacob Trefethen:

Oh, interesting. Okay. I think I’m stuck. You’re going to have to help me.

Saloni Dattani:

I think at this point, you probably need to figure out germ theory, and...

Jacob Trefethen:

Right, that would help a lot. Yeah.

Saloni Dattani:

You probably want to be able to have a sense that microbes are spreading these diseases, and we can study these microbes in the lab or in controlled conditions, and we can do something to them that will turn them into a vaccine from being a dangerous pathogen.

How would you work out germ theory? This is so crazy to me to think about, because it’s just something that we take for granted today. How do you figure it out from the beginning?

Jacob Trefethen:

Well, let’s take it step by step. You can do a bunch- there’s the John Snow story, which we may have covered in a previous episode, where you’re basically doing epidemiology to try and rule in or rule out competing theories that make sense of the evidence in front of you. I’m wondering though- bacteria are big enough that you should be able to see them if you can somehow make a bit of progress in microscopy. Viruses are like, “good luck.” That’s not going to- rabies is caused by a virus. I’m thinking they must not be able to see the rabies virus yet.

Saloni Dattani:

No.

Jacob Trefethen:

I guess we don’t even need microscopy yet. I take it back. I take it back. What do we need instead?

Saloni Dattani:

I think what we need instead is methods to culture bacteria or microbes in the lab. Pasteur is obviously famous for pasteurization and for fermentation and his experiments in that. But I think that the way that he got into this, he started by trying to debunk spontaneous generation, which was the alternative to germ theory at the time.

Jacob Trefethen:

Maggots.

Saloni Dattani:

Maggots, correct. The popular alternative was essentially that life was formed spontaneously, just came out of nowhere. That rotting meat had maggots that appeared that kind of came out of thin air, just came from, again, bad air or bad smells, bad environment, soil, something like that. There wasn’t really a concept of where the maggots are coming from apart from that.

There had been a few attempts to try to disprove this. One of the first ones I read was actually in the 17th century. So 1668, there’s an Italian doctor, biologist and poet called Francesco Redi.

Jacob Trefethen:

Was everyone a poet back then?

Saloni Dattani:

I mean, presumably you would have to be somewhat rich to publish your poetry.

Jacob Trefethen:

Was it a bit like how everyone who is a scientist called themselves a philosopher? Was everyone who wrote anything, did they call themselves a poet?

Saloni Dattani:

No, he actually wrote and published poetry; books that were just pure poetry.

Jacob Trefethen:

Okay, okay.

Saloni Dattani:

But I did find that interesting. He did some experiments where he had four wide-mouthed flasks, and he put into them- into each of the four flasks: one of them had a snake. One of them had a river fish. One of them had four little eels. And the fourth one had a cut of meat from a young calf that had been feeding on its mother’s milk.

Jacob Trefethen:

Two of them have water in, presumably? Or are you telling me that everything’s dead?

Saloni Dattani:

Oh, no, they are dead. They are dead. They are dead.

Jacob Trefethen:

It sounds like a stinky room. I’ll just give that spoiler. I’m a fan of the miasma theory.

Saloni Dattani:

Also, I love how this sounds like witchcraft and you’re just like, “Oh yeah, you need a tooth from a snake.” He has this set of four flasks with the snake, the river fish, the four little eels, and the cut of meat from a young calf. Then he has a duplicate of each of those flasks. But the second one is actually sealed. The first one’s open and the second one’s sealed, so he has a controlled experiment, right? He observes that only in the open jars do maggots start to appear. The second thing he notices is that in the sealed flasks, there are droppings of flies on them. The flies are probably trying to get into the flasks.

Jacob Trefethen:

Can flies smell through glass or did he sort of somehow? How do flies get attracted to carcasses? I don’t even know. Anyway.

Saloni Dattani:

I don’t know. I am trying to get into the mindset of a fly and I’m struggling.

Jacob Trefethen:

Okay. But I mean, this is a spoiler for our audience, but you’ll never guess what.

Saloni Dattani:

What?

Jacob Trefethen:

Maggots do come from flies.

Saloni Dattani:

Yes! So, the last thing that he observes is that he waits and he watches the maggots and eventually they turn into flies. He has this- he likes to interpret all of his experiments through biblical passages. So he has a famous adage that is, “omne vivum ex vivo” which is “All life comes from life.” That’s how he explains it.

Jacob Trefethen:

Which, of course, can’t be true because- but anyway, it was a nice thought. I mean, that’s so interesting as well to think that if someone had just paid attention to maggots for a bit longer, they didn’t actually need to do a controlled experiment. They just had to see them turn into flies. It’s like, they’re coming from nowhere. Then it’s like, oh, no, no, they’re actually turning into flies.

Saloni Dattani:

Well, I guess that’s true. But where would the initial maggots come from?

Jacob Trefethen:

No, you’re right.

Saloni Dattani:

It’s the chicken and the egg-

Jacob Trefethen:

It’s the chicken. You’re right, you’re right, you’re right. You need to see the droppings here.

Saloni Dattani:

Yeah. But sadly, some people still weren’t really convinced by his experiments. I guess they just were like, “Yeah, okay, but those are maggots. What about everything else?” It took another... took another two hundred years for Pasteur to finally disprove spontaneous generation.

He sort of did a similar experiment where he had glass flasks with long, curvy necks that kind of make them look like a swan’s neck. He has living material, broth, I think, inside the flasks. All of the ones with that kind of neck stay sterile indefinitely, unless the neck is broken. Just because of the shape of this swanny neck, it’s hard for microbes to actually get in and then go up. It’s like trapping the dust of microbes.

Jacob Trefethen:

But wait, what does that show?

Saloni Dattani:

I found that bizarre, though. What does that show? I think I preferred the other example.

Jacob Trefethen:

That one is, let’s just say, on the more boring end, relative to eels and snakes. I love it in the last one. It’s like, you didn’t need all of those snakes. You’re like, “But I have a spare eel. I have a spare snake. Look at this calf. Let me duplicate each of them.” Everyone’s like, “Wow, that seems really necessary for the experiment. This poet’s really onto something.” This whole time, you could have just had one swan neck.

Saloni Dattani:

He’s really over-egging it. All you needed was a broth in a flask. But I guess the point was this one flask, it is still open on the side, so air can get through. That, I think, would have disproved the idea that it was miasma. But dust and microbes wouldn’t be able to get through that whole concoction.

Jacob Trefethen:

Okay.

Saloni Dattani:

Sorry, that does make it sound better than the other version, I’m sorry.

Jacob Trefethen:

No, no, no. I mean, I think there’s also another, like maggots and flies and droppings you can visually see. You have to get a bit cleverer to get at the microbe question. So fair enough to our man.

Saloni Dattani:

True. I think this was essentially a very, I think, elegant experiment to show that spontaneous generation was not happening there. He continued working on fermentation; Pasteur was working in the wine industry, and in the silkworm industry, and trying to develop ways to prevent contamination from developing, like preventing spoilage. The famous thing that he invented is a process that’s now called Pasteurization.

He briefly heats the wine to 50 to 60 degrees and that would kill microbes and not ruin the product and eliminate the spoilage. Supposedly, these studies were so important that he essentially rescued the French wine industry at the time through developing Pasteurization. The French wine wasn’t getting spoiled.

Jacob Trefethen:

Well, I thank him, I’ve actually drank French wine myself, so thank you, Pasteur.

Saloni Dattani:

So have I!

Jacob Trefethen:

It’s amazing to think, though, that with spontaneous generation, I assume that people were thinking about life, and life as the important unit. “Oh, I can see my bread molding and my wine going off. Is there some life that’s causing that?” Then, in fact, viruses aren’t alive. They probably didn’t know much about the distinction between these bacteria that were causing their wine to spoil and the viruses that actually weren’t alive, but were using the machinery of some things that were alive. The next vaccines were not against bacteria. But those nuances are so... they’re not nuances, they’re deeply important. And yet, you don’t have to know it all at once. You can sort of hack your way there.

Saloni Dattani:

Right. You actually, it really takes until after the first vaccines are made, and it takes until the 1930s for people to confirm what viruses actually are.

Jacob Trefethen:

1930s, wow.

Saloni Dattani:

Until then, there’s just like, “What is this? It’s something that is infectious. We can’t see it. We can’t culture it in the lab.” Because viruses, as you said, are not alive. They need cells to grow; they’re obligate parasites. It was really hard to culture them. Also, people had developed bacterial filters, and that would filter out all the bacteria, but the viruses would still get through. They were just like, “What is this? Maybe it’s a fluid.” There was a popular theory that viruses were actually a fluid. It was only in the 1930s that that was resolved through microscopy and crystallography.

Jacob Trefethen:

Wow.

Saloni Dattani:

But we’re getting ahead of ourselves.

Jacob Trefethen:

We’re getting ahead of ourselves. Back to the 19th century.

Saloni Dattani:

Back to the 19th century. The first vaccines after Jenner were for chicken cholera and animal anthrax. Pasteur went into the animal industry and he’s, okay, he’s protected the French wine industry. He’s protected the silkworms from getting disease. Now, can he protect the chickens? Do you think he’s going to succeed?

Jacob Trefethen:

If you’ve already achieved the most important feat of humankind, which is protecting the French wine industry, I think something as easy as chickens, I think he’s going to be all right. He’s going to get there.

Saloni Dattani:

He’s going to get there. But it turns out he didn’t do it. This was so weird to me because I was like, oh, Louis Pasteur, he develops the chicken cholera vaccine, he developed the animal anthrax vaccine, and then he developed the rabies vaccine. But, and this is going to be controversial, and I hope there aren’t French listeners who hate me for saying this, it turns out that a lot of the big, the key, steps here were actually by his assistant, Émile Roux, well, who I guess is also French, so.

So he’s trying to protect chickens. He’s trying to develop a chicken cholera vaccine and doing lots of experiments in his lab. Then he actually goes away traveling to a different part of the country. Meanwhile, without his knowledge, his assistant, Émile Roux, is doing loads of experiments. He tries out different processes and he finds out that some of his cultures are going sour if he just leaves them out for a while. And once he then uses those cultures and injects chickens with them, some of those chickens are actually surviving chicken cholera, and all of the other ones are dying. Chicken cholera was really fatal to chickens, but the vaccine seemed to protect some of them. He was kind of surprised by this and he wrote down these observations.

When Pasteur returned from his travels, they continued doing these experiments and trying to figure out how to replicate that. What are the environmental conditions that are actually causing this broth to go sour? How are we- Is there a reproducible way to make chickens immune? They eventually figured out that it was the prolonged oxygen exposure and the acidity that was weakening the bacteria and protecting the chickens.

Jacob Trefethen:

So, okay, let me take it more slowly. Firstly, you’re saying you don’t want French people to be mad at you, but you’re saying French culture is going sour. Okay, interesting. I’m going to put that out there. Secondly, you’re saying that, Émile Roux, was he younger, which I think of him as a PhD student kind of vibe?

Saloni Dattani:

Yeah. You should think of him as a younger- so Pasteur was a chemist. Émile Roux was a doctor. Actually, a lot of the key experiments are done by Émile Roux because he knows how to work with animals and humans. So he is doing a lot of the actual experimentation. He’s inoculating people.

Just to summarize this chicken cholera vaccine, we’re kind of exposing the broth that contains the chicken cholera to oxygen for a long time and acidity for a long time. That is a method that’s called attenuation, so you’re weakening the pathogen and it sort of evolves into a different strain that is unable to cause severe disease in chickens through these different environmental conditions. Pasteur is really now talking about his theory of how this is working, that it’s weakening the microbe, it’s sort of changing it, and this is the way that you should be developing new vaccines.

He then turns to anthrax. Animal anthrax was, again, really deadly for different types of farm animals. He’s like, let me try to develop another vaccine with the same method that’s going to protect them. What is so funny about this to me is that he does a live demonstration to test whether this vaccine works. I think this is odd to us now because people don’t do that anymore. But it feels a bit like...

Jacob Trefethen:

“Gather round!”

Saloni Dattani:

Yes.

Jacob Trefethen:

Sorry, wrong accent. [over the top French accent] “Come, gather round!”

Saloni Dattani:

Very good. So 1881. Pasteur is at Pouilly-le-Fort in France, and he’s at a farm.

Jacob Trefethen:

That was the name Fort of Chickens, I think.

Saloni Dattani:

Pouilly-le-Fort.

Jacob Trefethen:

“Hey, Pasteur, where are you headed this weekend?” “Ah, Pouilly-le-Fort.”

Saloni Dattani:

So he’s going to publicly demonstrate that his animal anthrax vaccine, which he’s also developed through attenuation, is going to protect the animals. He gets 58 sheep and 10 bovines: eight cows, one ox, one bull. He brings them together for this experiment. Half of these animals receive the attenuated vaccine that he’s made of anthrax. Two weeks later, they get revaccinated with a more virulent, less weakened version of the microbe. And then, another two weeks later, all of them are injected with a highly virulent, fresh culture of anthrax. Over the next few days, we’re going to see what happens to all the animals. He invites people from around the country to come and see the results of his live experiment. What do you think is going to happen?

Jacob Trefethen:

Oh, I think that some of them are going to make it and some of them are not. That’s my guess.

Saloni Dattani:

You are, well, it was actually quite successful. It turns out that in the vaccinated group, all of the vaccinated sheep, the goats and the vaccinated cows were healthy. In the unvaccinated group, most of them died- had already died by the date of the observation when everyone came to visit them. Then two more sheep died while people were viewing the results of the experiments, and the last unvaccinated sheep died later that day. Basically all of them died.

Jacob Trefethen:

That is lethal anthrax.

Saloni Dattani:

Yeah, it’s very lethal. The unvaccinated cows didn’t die, but they did have a lot of swelling. So he’s like, “successful experiment! I have proved the efficacy of my new vaccine.” And that’s true. I remember when I was reading this, I was like, “Wow, why don’t people do live demonstrations anymore? That would be kind of cool.” You could show live how the method works. You could demonstrate that something replicates.

Jacob Trefethen:

You could do it more easily now because you can livestream.

Saloni Dattani:

Very true. I was kind of disappointed and was a little bit surprised to actually read a bit more about this experiment and what happened behind the scenes and before the experiment proceeded.

What is really odd about this is that Pasteur’s lab notebooks and also just generally some of his methods, he kept secret for most of his life. Only after he died were they published by his former assistants. By looking through those notebooks, historians have found out that he didn’t attenuate the vaccine. He actually used a different preparation. He used a different preparation that was made by his assistants, Émile Roux and Charles Chamberland, and they actually chemically inactivated the bacterium with potassium bichromate instead.

I was like, “But why would you hide that?” It still works, right? Turns out the reason was that he had a rivalry with this other scientist called Henri Toussaint. His rival had already published similar results using carbolic acid. Basically, Pasteur was trying to show that his vaccine was better and that attenuation was the key principle to developing vaccines. So he told them not to tell anyone about how the actual preparation was made until after he had died.

Jacob Trefethen:

That is so sketchy. Also, what did he think was going to happen to his reputation after he died, if all these secrets are coming out? And then fast forward to 2025, two people are roasting him on a podcast.

Saloni Dattani:

Well I mean, I didn’t know the story until I looked into it in the last few months, so I think most people didn’t actually know that.

Jacob Trefethen:

You run the parallel in the modern system and it wouldn’t work. The pros and cons of patents are numerous. But one of the pros is that if you file for a patent, it does get made public. Your competitors do know what is in your concoction. Even if you don’t file for a patent, you have to let the FDA know if you’re in the US, or MHRA if you’re in the UK. They probably won’t leak it though, they’ll treat it as a trade secret. But now that we have so many more scientists, if you actually sell a product or you put a product on the market, your competitor is going to be able to just buy a vial of it and then see, “hold on, what’s in it?”

Saloni Dattani:

Yup. I was also thinking at the time, wait, how were other people making the animal anthrax vaccine that- if they followed the method that he claimed he was using, they wouldn’t succeed? But I think his institute, which was a very big research institute at the time, was producing this for most of France. So they just had the method-

Jacob Trefethen:

They were doing it secretly, and everyone was injecting their animals.

Saloni Dattani:

Animals, yeah. It was working. It was a different vaccine.

Jacob Trefethen:

That’s ridiculous.

Saloni Dattani:

Which I think is crazy. I guess I thought maybe live demonstrations are not good after all, because maybe the stakes are just so high that you want to avoid humiliation and you’re going to do whatever it takes to do that.

Jacob Trefethen:

Yeah. Whereas a good experiment, you want to be learning, which means there has to be a chance of success and failure. Otherwise, you’re not learning anything.

Saloni Dattani:

Yes, yes! There has to be a chance of failure. But anyway, I mean, it was just such a bizarre. I was like, “Wait but... why don’t you just tell people the truth?” Anyway.

Jacob Trefethen:

Never meet your heroes. That’s why I really regret meeting Louis Pasteur.

Saloni Dattani:

Anyway, he did do amazing things with fermentation and pasteurization. Now he turns his eyes to rabies, which is terrifying because almost anyone who develops any kind of neurological symptoms from rabies then dies from it, usually within a few weeks. Can we develop a vaccine for that? That was probably- and this was a human disease, so unlike the animal experiments, now you’re doing something that’s potentially risky and exposing humans to it.

So again, as you mentioned, the rabies is caused by a virus too small to be seen under the microscope. But Pasteur and Roux and other people had reliably found that if you took brain tissue from an animal that had rabies, you could use it to cause rabies in another animal. Something in that brain tissue is infectious and is causing rabies. Don’t really know which microorganism it is, but maybe we can develop a vaccine from that.

Although Pasteur was very secretive, I think one of the good things about his approach to science was that it was very empirical. It was hundreds of experiments that he would actually write down in a lot of detail in his lab notebooks. Many of them were failures and he was trying to learn from what worked. In this case, again, it took hundreds of experiments to develop a rabies vaccine.

The process was this. You would extract brain tissue from a rabid dog and then inject it into the brains of rabbits by drilling a small hole into their skulls and injecting it. Then you do this from rabbit to rabbit. You take the brain tissue out and put it into another rabbit, after drilling a hole in their skull, again and again.

I think eventually they did this through a series of 90 rabbits in a row. They then take the brain tissue and put that into a flask and then close the flask and sort of let it dry to weaken the pathogen. Again, this took a lot of experiments to figure out. What was really difficult was that if you did this in other animals, not rabbits, then sometimes it would actually make rabies more severe. So you really have to be careful in doing this method and you have to be writing up what the experiment showed, what your failures were, how does this actually work?

Jacob Trefethen:

Instead of live attenuated, you might get live boosted.

Saloni Dattani:

Exactly, which is terrifying. Then you inject that material into dogs and sort of gradually expose them to higher and higher doses from the rabbits. As you continue doing this, exposing the dogs to higher, more virulent strains, you are helping them build up protection. And that actually seems to work. Louis Pasteur felt comfortable enough to move to treat people with this procedure.

Again, you’re hoping that it’s not worse in humans because humans are not dogs and you haven’t tested it before in humans. That’s quite scary. You are also doing this procedure where it’s not just once and done. You have to give them the lowest dose and then higher and higher. You gradually help them build up protection after they have already been infected.

Jacob Trefethen:

They’ve been infected and you’re very quickly doing this process.

Saloni Dattani:

Yeah. So he actually treated people bitten by rabid animals, who I guess wouldn’t have had really any other alternative. They were quite desperate for having some treatment and Pasteur says that he might have developed some method. He treats different people. In 1885, he and Émile Roux treated two young boys that were bitten by rabid dogs. They seemed to be fine after it. Almost everyone else would have died and they survived. This was the first sign that the procedure actually worked. That was a big breakthrough and Pasteur was very, very happy about it.

Jacob Trefethen:

I suppose that Frankenstein was written much earlier in the 19th century than this, but you can sort of get why people had a view of scientists that was kind of like, what the hell are they up to? Yeah, “I just want to drill a hole in this rabbit’s brain, so I can inject its brain into a dog, and then inject the dog’s brain into a different dog.” Like, what the hell are you doing?

Saloni Dattani:

Sometimes you’re playing with eels and you’re growing smallpox on cows.

Jacob Trefethen:

It’s like, why can’t you get a normal job like chimney sweep or something?

Saloni Dattani:

This reminds me of how surgeons, surgery used to be seen as this very low status and very just physical procedure that it was literally just someone who’s ready to chop off someone’s leg, basically, because there weren’t really any; there are very few surgeries that were worth the risks at that point, before Joseph Lister developed antisepsis. So you would only do it in the highest risk situations. You would maybe have a kidney stone removed. You would maybe have your leg chopped off if it was at risk of gangrene or something like that. There were surgeons who had trained so much in doing this that they could do a whole procedure of cutting off someone’s leg within half a minute.

Jacob Trefethen:

What? Oh, God. You want to do it quickly, I suppose, otherwise it’ll go.

Saloni Dattani:

Exactly, because it’s so painful as well. Because until 1846, no one had developed anesthetics either.

Jacob Trefethen:

You know, you could tell me that these doctors were drilling holes in people’s brains and administering vaccines there and at this point, I might believe you.

Saloni Dattani:

You know I read this very short, probably apocryphal bit about how there was this famous surgeon in the mid-19th century who had perfected this technique so much that he was famous for doing his surgeries within half a minute. One time, he accidentally chopped off someone’s testicles as well.

Jacob Trefethen:

No, come on, Saloni. Come on. God, going back within time is...

Saloni Dattani:

You don’t have modern vaccines. The vaccines you do have are developed by drilling holes into rabbit skulls.

Jacob Trefethen:

I didn’t know that story of the rabbits and the dogs. But I do think of the rabies vaccine as helping to confirm the germ theory of disease. So, I guess, what are they learning there that’s generalizable to the theory?

Saloni Dattani:

I actually, I’m not sure that it is- well, it is helpful, it’s the first human vaccine after Jenner’s vaccine, so that is definitely a big moment, but because you couldn’t- they couldn’t figure out what the microorganism was, it wasn’t that helpful for germ theory. Pasteur was kind of repeatedly asked by other scientists, “What’s causing this disease?” He was like, “We haven’t found it yet. We’re focused on the experiments. We’ve made the vaccine. It works.” He didn’t know, and he couldn’t have known.

Jacob Trefethen:

It’s amazing you’d have the persistence to keep doing so many weird steps in order involving so many animals’ brains when you just don’t have an understanding of what you’re doing. It’s amazing.

Saloni Dattani:

I guess because he had worked on fermentation. He was also just much more of an empiricist than a theorist. He was like, I’ve developed this method. It’s going to work. I’m just going to do this procedure. Somehow, for some reason, it’s going to work and I don’t know why, but it will, eventually. And he just does hundreds of experiments to try to figure out how.

But no one figures out what a virus is, or actually looks like, until the 1930s. There’s no way that he could have actually found that out. But I think what really helps was germ theory; was actually Robert Koch and him developing Koch’s postulates, which you might have heard of, and also just identifying different microbes that cause different diseases. He had four postulates, and if you fulfilled these four postulates, you would establish causation between a microbe and a disease. And not so similarly, but I was surprised to learn that Koch didn’t actually compile the postulates into four and he did not say that you needed to fulfill all four to establish causation. It was actually his student, Friedrich Löffler, who puts them together into a textbook.

Jacob Trefethen:

This is happening a lot. Students getting their work stolen constantly.

Saloni Dattani:

No but in this case, Robert Koch actually did mention in different places each of these four postulates. He just didn’t say that you needed to fulfill each one. So here are the four postulates. Number one, the microbe or a particular microbe has to be found in every case of the disease. Second, it has to be isolated and grown in pure culture. Third, it must cause the same disease when introduced into another healthy host. Fourth, it must then be recovered from that host.

What do you think? Is this going to help? Is it going to cause problems?

Jacob Trefethen:

I think it’s going to help. You’re going to get some false negatives, probably, would be my guess.

Saloni Dattani:

Yes. I think it definitely did help and it sort of increased the level of scientific rigor and what the threshold was for establishing whether a microbe was a cause for disease. I think partly as a result of him putting together these postulates and people having a systematic way of testing, is this microbe the one that’s causing the disease?

I think it just means it’s probably one of the reasons why most of the microorganisms that people traced back at that time still hold up today. I think that is something that’s quite impressive. There were dozens that people discovered during this time period of the late 1800s. Tuberculosis, cholera, typhoid, diphtheria, tetanus, meningitis, pneumonia. People discovered some of the microorganisms that caused all those.

But it has a lot of problems as well, because if you think about each of these four postulates, the first one: the microbe has to be found in every case of the disease. That’s really strict.

One thing that’s difficult about this is viruses are kind of ruled out at this point, because you can’t see them. So it’s very difficult to identify when they’re the cause of disease. Second, there are some diseases that have multiple causes like meningitis or pneumonia; they have different bacterial and sometimes different viral causes as well. So maybe you’re hoping that people can classify them more granularly and say, oh, well, ‘the bacteria does cause pneumonia, but only this version of pneumonia’ or something. But to me, it feels very strict as a definition.

Then there’s the other one, the second one, which is the microbe has to be isolated in pure culture. Sometimes that’s really hard, right? It’s really hard to grow some microbes in culture or in other animals.

Jacob Trefethen:

Syphilis is caused by a bacteria, Treponema pallidum, not by a virus. But only in 2016, ‘17 or ‘18, around then, did people first have a protocol for in vitro culture. You used to have to culture it in rabbits.

Saloni Dattani:

Wow, that’s so much later.

Jacob Trefethen:

So much later, yeah.

Saloni Dattani:

These kinds of difficulties actually tripped up Robert Koch as well. He often struggled to satisfy the postulates that he had developed.

Jacob Trefethen:

You can be your own worst enemy sometimes.

Saloni Dattani:

Yeah, sometimes you regret the things you write. But maybe rules are just meant to be broken. So he tried to figure out what the cause of cholera was, and he did very quickly actually isolate the bacterium, which is Vibrio cholerae, from patients who were dying in Egypt and India from the disease.

But he couldn’t figure out how to reproduce that in other animals. He couldn’t reproduce it in culture. He couldn’t grow it in culture. He tried repeatedly to inject mice and other animals with it. He took fifty mice from Berlin to Egypt to test whether they could get infected, and none of them developed cholera. Then he just asked people; he was like, “Are there any animals that get infected by cholera?” And the answer was no. So he was really struggling.

He writes in this report in 1884, “No one ever observes animals with cholera. Therefore, I believe that all the animals available for experimentation and those that often come into contact with people are totally immune. True cholera processes cannot be artificially created in them. Therefore, we must dispense with this part of the proof.” So he acknowledges that. Sometimes it’s just really difficult, and you don’t necessarily need to have all of these four postulates fulfilled, but they are a really high bar that I think makes you, it makes me very confident in some of the pathogens that actually fulfill all of them.

Jacob Trefethen:

Right. That makes sense. Yeah.

Saloni Dattani:

The thing is, though, when he said this, the fact that, oh, well, we must just dispense with this part of the proof, other people were not convinced by that. He had another rival. He was also rivals with Louis Pasteur, but he had another rival, Max Joseph von Pettenkofer, who was a chemist and a hygienist. He was like, “No, cholera is spread by dirty soil and dirty air. It’s miasma, it’s hygienic conditions, it’s not germs.” He wasn’t convinced by Koch’s rules that he broke himself. He was like, well, we can see that cholera is much more common in the places with worse hygiene and sanitation. That’s even true today. But he basically thinks that it was soil and air that just spread disease and it just came out of nowhere. It wasn’t spread by microorganisms that you could see.

Jacob Trefethen:

It’s funny to think today we have all of these separate subfields of, on the one hand, epidemiology, on the other hand, microbiology, on yet another hand, vaccine development. Back in the day, everyone was kind of doing everything all at once. They were trying to make progress all at once, and there were about three people doing it.

Saloni Dattani:

You’ll love this next step of the story then. Because the way that he then proves that cholera is caused by this microbe is by doing epidemiological analysis. He can’t replicate this infection in animals. Instead, he tries to find more evidence. He finds, surprisingly, that there are two adjacent cities, Altona and Hamburg- for some reason, on the Hamburg side, there’s a lot of cholera and people are always facing outbreaks. On the Altona side, people are just free of the disease. He was like, what is going on? And this is even true just at the boundaries.

He looks and he compares different aspects of those two streets and those areas. He’s like, well, the soil is the same. The sewers are the same on both sides of this boundary, but the difference is their water supply. He then studies the water supply and finds the same bacterium there; he finds cholera that’s present in the Hamburg supply, but not the Altona water supply. And this resolved the debate. Mic drop.

Jacob Trefethen:

Mic drop!

Saloni Dattani:

The other thing that I didn’t know until reading about this was that there was a political divide in this debate as well. The germ theorists versus the sanitationists. In Germany at the time, the idea was the sanitationists want to improve living standards, they want to improve, they want to reduce poverty. The germ theorists are like, you don’t need to tackle poverty; you can just eliminate diseases by vaccinating against them. I don’t know. I just found that very different and interesting. But of course, they’re not actually distinct. You can reduce the chances of microbial growth and infection by improving sanitation and hygiene. We know that now.

Jacob Trefethen:

With cholera, you can do both. People do do both right now.

Saloni Dattani:

People do do both. The sanitationists actually had a lot of victories in the 19th century because they introduced various sanitary reforms and improvements to water supply and air and getting rid of mosquitoes and keeping people away from sewers and things like that. That actually reduced a lot of different infectious diseases. I think in a way, maybe they were kind of right, but in another way they were wrong, because they didn’t understand exactly how the diseases were spreading and the fact that vaccines would work.

Jacob Trefethen:

If the sanitationists had been in power all the way through, we would not have got the smallpox vaccine and we would not have got smallpox down because that, you gotta use the vaccine.

Saloni Dattani:

It would be really hard to eradicate some diseases if you were just using sanitation and hygiene as your tools. Okay, so we’ve talked about how the first vaccine was made, Jenner’s vaccine, through cowpox. We’ve talked about then how the next vaccines were made through attenuation, getting the microbe to grow in a different environment and weakening it, so that it causes a milder disease when it’s then injected into humans or into animals. Or there’s inactivation, where you kind of kill or inactivate the pathogen in different ways, with chemicals or oxygen or something like that. This is quite a helpful technique, but I think the real breakthrough comes with better methods of culturing bacteria in the lab. So now we need to talk about culture.

What is kind of interesting about this is that really until the late 19th century, there aren’t great ways to grow bacteria in the lab. I don’t know if you know this, but in the past, people used to grow bacteria on slices of boiled potato.

Jacob Trefethen:

Eugh.

Saloni Dattani:

You could also grow them in broths of different- fluids, that’s kind of gross.

Jacob Trefethen:

The boiled potato though, I mean, that’s perfectly fine. Not gross at all.

Saloni Dattani:

Well, not if you’re growing... anthrax bacteria on it. So Robert Koch was working on anthrax as well and trying to identify the bacterium. He was working with anthrax bacilli on slices of boiled potato. It made me think, what if someone accidentally ate them?

Jacob Trefethen:

Yeah, that’s dangerous, especially if they’re lying around looking tasty. You might think it’s sort of like a blue cheese spread or something.

Saloni Dattani:

Mm... true. You have these liquid media, you have the broth and you have the potato slices. They’re not that great. They don’t work with too many bacteria and they’re not very easy to standardize. There’s this need to get some better culture method. Maybe you can find some way to grow bacteria on a solid substrate.

An ingenious idea was to use the existing liquid broths and then just solidify them. That was what Robert Koch did. He just solidified the broths with gelatin, but it didn’t work because it melted in the summer. Gelatin just doesn’t stay stable at higher temperatures, so in the summers, it would just melt and that would ruin a lot of experiments. The solution to this was to, instead of using gelatin, to use agar. Agar is a gelatinous material that comes from seaweed. This idea came from the wife of the assistant of Robert Koch. Her name was Fanny Hesse.

Koch had just developed this tool where it’s like a plate, and then there’s a solid broth, solidified broth, and it’s covered with a bell jar. That was an interesting concept. Then he had an assistant come along, and the assistant’s name was Julius Petri.

Jacob Trefethen:

I heard he was quite dishy.

Saloni Dattani:

He thought of a better method, which is also very simple, but it was just remove the bell jar and just add another dish that’s slightly larger. You’d have a flat, a lidded dish. Why not just use two dishes?

Jacob Trefethen:

Right. Sounds quite simple.

Saloni Dattani:

It is quite simple, but it’s so useful. You could keep the cultures sterile from other microbes growing in them because that second lidded dish would make it really hard for microbes to get inside. But you could still observe everything happening on the plates.

So this combination of agar, Petri dishes, other bacterial culture methods made it possible to grow colonies of bacteria, to experiment with them, to visually identify the microbes, and then eventually to turn them into vaccines.

Jacob Trefethen:

To visually identify the microbes.

Saloni Dattani:

... under the microscope. The bacterial cultures, this method is really useful to grow bacteria because they can just eat the nutrients in the broth and grow. That means that you can develop a lot of bacterial vaccines out of that. You can grow the bacteria in the lab, you can study them, you can evolve, you can attenuate them, you can inactivate them. It doesn’t work with viruses, because viruses need cells to grow in, and we don’t yet know how to grow cells in a lab.

Jacob Trefethen:

I have a question, which is, nowadays, when you’re culturing bacteria for the sake of making vaccines, I mean, if you’re making a vaccine, some of those bacteria are probably kind of dangerous. You have special lab conditions to make sure that you don’t yourself get infected and you don’t pass on an infection. In the 1880s, 1890s, 1900s, what were conditions like? Were people good at avoiding infections?

Saloni Dattani:

Well, quite scary because they hadn’t developed antibiotics back then.

Jacob Trefethen:

Oh my gosh. Yeah, of course.

Saloni Dattani:

They did have some antiseptic techniques, but they hadn’t been introduced into culture methods yet. They also were still mouth pipetting at this point, I’m pretty sure.

Jacob Trefethen:

They were mouth pipetting while doing bacterial culture vaccines.

Saloni Dattani:

Very scary stuff.

Jacob Trefethen:

Yeah.

Saloni Dattani:

Maybe you would just need a scoop, you could just get a spoon or something and just streak a bacterial colony onto the Petri dish. You could probably get infected by stuff. Some people would deliberately infect themselves as well because they wanted to prove that something caused a disease. So when Robert Koch was trying to demonstrate the bacterium the caused cholera, he couldn’t find any animals that it could infect. Not him, but other scientists who really believed him were like, ‘well, let me just try to ingest the cholera and see if it causes disease in me.’ I’m not really sure whether that worked, but it wasn’t very convincing.

Jacob Trefethen:

Yes, it used to be a bit more direct back then.

Saloni Dattani:

The other thing that people were learning at this point was that organisms were composed of cells. People didn’t know that before. I found that also so strange. They were just like, well, we’re just made of material, I guess, stuff. We just appear out of nowhere.

I think at some point microscopes got good enough that you could actually see individual cells and people started to notice that, oh, there are a lot of things that have similar looking blobby cells, are composed of them. One by one people had to demonstrate this was true for many different organisms. Eventually they’re like, maybe cells are the units that all living organisms have. But that took a long time. They didn’t know that that was kind of- you would just think, ‘well, it’s just a person. It’s just a person that gets bigger and bigger.’ We’re not made of cells. We’re just...

Jacob Trefethen:

That’s how I-

Saloni Dattani:

We’re just like an inflatable.

Jacob Trefethen:

That’s my, yeah, the miasma theory of human. You’re just bad air that keeps getting bigger.

Saloni Dattani:

I feel like I’m imagining a little baby or a fetus and then it’s just getting inflated and inflated and turned into an adult.

Jacob Trefethen:

Well, how would you disprove it? Could be true.

Saloni Dattani:

Could be true. So microscopes then become really important and they were not very good before the 1830s because there are a bunch of lens distortions. Eventually Joseph Lister’s father, who was a wine merchant, was also a lens maker in his free time.

Jacob Trefethen:

Everyone was everything.

Saloni Dattani:

He was just like, ‘oh, I like microscopy, I like looking at stuff under the microscope.’ He developed better lenses that corrected some of these distortions. Because of all of this development, you could actually see bacteria quite clearly. It wasn’t just blurry blobs under a microscope. The most famous example of this is Robert Koch identifying the bacterium that causes tuberculosis. I don’t know if you know the story behind that, since you’re a big TB fan... or a hater.

Jacob Trefethen:

Not really, tell me.

Saloni Dattani:

I think maybe we could just describe the process of how microscopy works. You’re not just putting a culture under a microscope because bacteria are not colored necessarily. It’s hard to see them and they can be quite translucent, so you need to stain them with some dyes. You have staining techniques, and then you have to get the culture or whatever material really thin so that you only have one layer and it’s not just blobs of different bacteria or microbes. There are a bunch of innovations in making better fixing methods and better staining methods and better slicers and things like that.

The difficulty with tuberculosis is that bacteria usually repel most water-based dyes. All the standard dyes that people were using at the time didn’t really work, and it was really hard to stain, and people couldn’t really see what was causing the disease. The key innovation was that Robert Koch took lung tissue from an autopsy of someone who had tuberculosis and he had the standard dyes and he then added ammonia. The ammonia somehow meant that the dyes were now working and the dyes were staining the bacteria’s cell walls. The reason for that was that bacteria has acidic cell walls and the ammonia makes it more alkaline. That makes it easier for the dyes to attach to the bacteria. This meant that he could finally see the tuberculosis bacteria, Mycobacterium tuberculosis, in 1882.

The other thing that I found quite funny was that he was not very- at the time people were doing microscopy and then they were drawing the results that they were seeing, but he was very bad at drawing. Well, he wasn’t very bad, but it’s not looking good. He was trying to find a better way to visualize this and he really wanted to reproduce his work. He thought maybe we can attach a camera to a microscope. He basically worked with microscopy developers to make a photomicroscope with a camera attached to it. He then got lots of pictures of his discoveries and they became really famous. It basically showed visually diseases are being caused by specific microbes that are moving around, changing shape, and you could actually identify their causes. It was a huge revelation.

Jacob Trefethen:

That’s epic.

Saloni Dattani:

The other thing that microscopy would be really helpful for is that you could then see if your preparation was contaminated. You could see if there were other microbes infiltrating the culture that you had. You could develop safer vaccines. You could do experiments more reproducibly and things like that, because you’re actually working with the correct microbes in the lab each time.

Jacob Trefethen:

We’ve gone from not knowing what caused these diseases we were trying to make vaccines for, to knowing the cause in some sense, but not being able to see it or control it, to we’re now growing cultures of that cause, those bacteria, in an agar plate, in a Petri dish. We are dyeing those, so we can see them and we’re looking at them under a microscope.

Saloni Dattani:

Yes. Now the challenge is, okay, let’s actually use these tools and make many more vaccines. Some people do actually do that. There’s a new cholera vaccine and a new bubonic plague vaccine, and then there’s a typhoid vaccine in 1896. There are a bunch of bacterial vaccines.

But again, virus vaccines are still really hard because you’re growing them in other animals. You need to find a way to keep cells alive for long enough that you can study the viruses that grew in them. I don’t know if you know what happens at this point, but the key moment that I’ve read about is the hanging drop technique.

Jacob Trefethen:

The hanging drop technique, yes. Your opponent is on their back after a blow, and you actually get on the corner of the wrestling ring, and you stand on both of the railings, and then you...

Saloni Dattani:

Jump on them.

Jacob Trefethen:

Yeah.

Saloni Dattani:

But it’s much less exciting than that.

Jacob Trefethen:

You’re telling me there’s a second hanging drop method?

Saloni Dattani:

Yes.

Jacob Trefethen:

Oh, how does that work?

Saloni Dattani:

The second hanging drop method is actually really simple. You have a microscope slide, you take cells from a frog embryo and then you put that into frog plasma, and you put that on the microscope slide, and then you turn the microscope slide upside down.

Jacob Trefethen:

Ooh. How did someone come up with that? Who came up with the hanging drop and what does it let you do?

Saloni Dattani:

Ross Harrison, who was at Yale. I think he was trying to find a way to visualize cells under a microscope. They kept dying if he kept them upright. He just put them upside down so that the drop was bigger and the cells would have much more fluid and they could grow for longer. He could still see it with the microscope by seeing the other side of the microscope slide.

Jacob Trefethen:

Cool.

Saloni Dattani:

He did that with neurons and he saw them growing for weeks and sometimes months, sitting in that little drop, which I think is so cool. Over the 1910s and ‘20s and ‘30s, people made many new cell culture techniques. They found different cells that viruses could infect in the lab. That helps develop a lot of different vaccines.

If we think back to the smallpox vaccine, that was growing in calves. The rabies vaccine was growing in the brain tissue of rabbits. The influenza vaccine is growing in embryonated eggs. Polio vaccines were made in monkey kidney cells.

Of course, at this point, people actually develop better techniques for sterilization. One of them is antibiotics. Another is autoclaves. I actually didn’t really know how autoclaves worked until recently. I was like, they just somehow sterilize equipment. It turns out it’s just a lot of steam.

Instead of the Petri dishes and the microscope slides, people develop other better techniques to have larger surface areas for cells to get nutrients from. One is the roller bottle system, which is just bottles filled with the cell culture and fluid and stuff.

Jacob Trefethen:

Still used today.

Saloni Dattani:

Still used today, and it’s just on this little machine, and the machine kind of moves from it, and the bottles keep rolling, and that keeps them all...

Jacob Trefethen:

There’s something that is quite gestational, maternal about it. It’s very sort of like... whoosh whoosh whoosh... rocking a baby.

Saloni Dattani:

Like a little cot. Yeah. Then there are microcarriers, where it’s a big tank filled with little spheres, and on the surface of the spheres, the cells are growing and there’s growth medium surrounding them.

We still don’t have better microscopes to see viruses until the 1930s. This is so crazy that people are sometimes developing virus vaccines basically through a lot of experimentation, but they still don’t actually know what is causing those diseases. Again, they know that... They can’t see it. They can’t grow it in bacterial cultures. Seems like it’s growing in cell cultures. They don’t know exactly what’s causing it. They can decontaminate things with antibiotics and with bacterial filters, but that’s not affecting the viruses. They just don’t know what the viruses are.

All of that changes in the 1930s when two scientists, Ernst Ruska and Max Knoll, developed the electron microscope. I also, I just don’t know that much about physics. I was like, ‘What is it? Why is this more helpful?’

The reason is that with light microscopy, the resolution is kind of limited by the wavelength of light. Light has a relatively high wavelength. That means that if any two objects are closer together than half of that wavelength, then it’s really hard to distinguish them. That meant that basically viruses and smaller materials just can’t be visualized at all.

The difference with electron microscopes is that electrons have a much shorter wavelength than light. That means that they can distinguish much smaller details. In an electron microscope, you have magnetic coils that are the lenses, and they bend the electron beam and focus it onto the sample. All of that happens in a vacuum so that the electrons aren’t just scattering around. The beam passes through or bounces off the specimen, and then that pattern is projected onto a screen or a photograph. It’s very similar in the overall principle to light microscopy, but instead of light, you’re using electrons, and the machine is also much bigger, I think.

Jacob Trefethen:

Electron microscopes are big enough, they’re pretty fun to look at because you’re like, oh, wow, this is some heavy duty. ‘If something breaks down here, I don’t know how I’m going to fix this one.’ Whereas if it’s just a nice little light microscope, I’m like, I’ll just replace the lens or something like that.

Saloni Dattani:

This also is kind of interesting because so far we’re talking about a lot of these things just being developed through experimentation, but electron microscopes are made through better theory. People figure out that the way that you get better resolution is through either changing the wavelength of light or if there was something with a smaller wavelength of light. They’re like, well, electrons have a smaller wavelength than light. They also have to figure out what electrons even are and how they get bent by magnets and things like that. Only after those things were figured out, people were like, let’s try to make better microscopes. So they make electron microscopes. I think the first ones were not very good and they had to kind of improve the lenses, improve the vacuum sealing and things like that. Eventually there’s an enormous improvement in the resolution that you can get, like thousands of times higher than you could get with light microscopy. That finally allows you to see viruses.

Jacob Trefethen:

When abouts did that happen?

Saloni Dattani:

1931.

Jacob Trefethen:

1931. We had the smallpox vaccine, which is a virus that was making use of cross-reactivity of a different virus, cowpox. Okay. We had the rabies vaccine, which was also a virus, but making use of these rabbit brain attenuation techniques without knowing necessarily what the virus was. The flurry of vaccines that you talked about, so that was 1880s, 1890s, and there was a bunch of vaccines in the decades afterwards. But those vaccines, the ones I think of, are bacteria.

Saloni Dattani:

Mostly bacteria.

Jacob Trefethen:

Right, okay.

Saloni Dattani:

They’re mostly cholera, typhoid, things like that.

Jacob Trefethen:

Got it. Because of all the culturing techniques that you were just talking about. It’s not until the 1930s we get an electron microscope. Does that then open up the avenue for viral vaccines? Are we back in business?

Saloni Dattani:

That’s what I thought. But actually, no. The key thing for developing better viral vaccines is the cell culture techniques. The microscopes are really helpful in identifying some viruses that cause diseases in the first place. Also, they’re helpful for classifying and making sure that the vaccines are not contaminated and things like that. But they’re not that helpful for vaccine development for a long time. Part of the reason for that is that electron microscopes would destroy biological tissue. The electrons themselves are very harmful and they just destroy proteins and biological material. The next big one is the polio vaccine.

Jacob Trefethen:

The polio vaccine, yeah. That’s 1950s?

Saloni Dattani:

That is 1950s, but the key thing that happens in order to enable that is in the 1940s, which is by John Enders, who later developed the measles vaccine. So John Enders and some of his colleagues are trying to figure out how to grow polio virus in the lab so that they can study them better and so that they can attenuate them or inactivate them. They know that polio affects the brain and the nervous tissue, but they don’t know how to grow it outside of those tissues in order to attenuate them or study them better.

It turned out that John Enders’ colleagues just happened to be working with chickenpox and they were working with diarrhea and they had different samples of these other viruses. John Enders suggested, why don’t you try to inoculate some of those extra tubes that you have with poliovirus and see if it manages to survive? It turned out that it did. Some of those tissues that they were using, which were human foreskin or embryonic tissue, was actually keeping poliovirus alive. You could then figure out that there are different serotypes of poliovirus, and then you can try to attenuate them or inactivate them and turn them into vaccines.

Jacob Trefethen:

Serotype meaning it’s the same virus, but you can characterize different strains by how the immune response hits them.

Saloni Dattani:

One of them typically doesn’t protect you against the other. So, for each of the polio vaccines, you need to develop them for each of the three serotypes. The vaccines are a combination of all of them.

What was so weird about the John Enders bit was how he actually became a scientist at all. So he was previously a real estate salesman.

Jacob Trefethen:

Okay.

Saloni Dattani:

He studied Celtic and Teutonic languages at Harvard. The reason that he changed and started working in infectious diseases was because he was in a boarding house sharing a room with Harvard medical students and one of those medical students was working in this famous lab and he was just captivated by his work and he thought, well, let me just abandon my PhD in English that he was almost finished with and instead do medicine and bacteriology and immunology. So he did that and he joined his roommate’s lab, did a doctorate in that instead, and then finally got an appointment in the faculty, he was 32. But again, after being a real estate salesman. It did make me think about these chance events. I’m like, what if he didn’t have that roommate?

Jacob Trefethen:

Yeah, that stuff’s scary.

Saloni Dattani:

What if no one figured it out for another 10 or 20 years? That would have been really scary.

But building on his techniques, there were two other scientists whose names are probably quite well-known, Jonas Salk and Albert Sabin. They developed the two polio vaccines that are still used today. Salk’s vaccine was a killed version, an inactivated version of the poliovirus, which he grew in monkeys’ kidney cells and then inactivated with formalin, which is a chemical. That is a very safe vaccine. It can still give you an immune response and you can still recognize the virus later on.

In contrast, Albert Sabin made a live attenuated virus, and it’s an oral vaccine. The previous one was injected. This one is oral as a drop, and that has a weakened strain that replicates harmlessly in the gut and then can also protect other people.

And both of them had a rivalry as well. Apparently, they really disliked each other. I was reading this LA Times article about them, and Jonas Salk says in it, there’s this quote from him: “Albert Sabin was out for me from the very beginning. In 1960, he said to me, just like that, that he was out to kill the killed vaccine.” I was like, calm down.

Jacob Trefethen:

We think of him as a lifesaver, but really he was a killer.

Saloni Dattani:

Albert Sabin, was also like... they just hated each other. He was talking about Salk’s vaccine and he was like, “It was pure kitchen chemistry, Salk didn’t discover anything.” Come on! They’re sort of dunking on each other’s vaccines at a time when people need to be, I think, more receptive to the fact that you can protect yourself from this scary disease that could paralyze you, put you into an iron lung.

Jacob Trefethen:

I think that Pfizer and Moderna should have come for each other’s throats during COVID. “That’s a kitchen table mRNA vaccine. I could have made that in my backyard.”

Saloni Dattani:

It reminds me of those viral memes where you see someone does a really complicated maths proof, and they’re like, how I did this step: trivial. A toddler. I read one today that was like, the proof for this step in the mathematical formula is: ask a toddler on the street.

Jacob Trefethen:

What are toddlers doing on the street? Come on.

Saloni Dattani:

How are they solving complex math proofs?

Obviously, the polio vaccine is kind of amazing because, in the mid-20th century, there were literally hundreds of thousands of cases of people getting paralyzed by polio per year, right? This just basically eliminated it in so many different countries. Two of the serotypes of polio have actually been eradicated worldwide. There’s just one left. We don’t anymore have to see lots of children having their breathing muscles paralyzed and being put into boxes, iron lungs, and trying to keep them alive. That’s a crazy change.

Jacob Trefethen:

A crazy change, and hopefully one day soon we’ll get rid of the last remaining polio cases.

Saloni Dattani:

The next one, which I think we can end on, is the measles vaccine.

Jacob Trefethen:

Another virus.

Saloni Dattani:

Another virus. I think I didn’t really know very much about how bad measles was until I was reading about it for an article I wrote a few months ago. The thing that I didn’t know was: measles basically infects your immune cells, so it infects memory T and B cells, which usually help your immune system recognize past infections. But now, instead of being able to fight the virus, the virus uses those cells to transport itself into your bloodstream and get into different cells in your body.

It uses them to spread into your spleen and bone marrow and digestive tract and kidneys and liver. Then you can develop lots of complications from it replicating in those tissues, in those organs, and causes lots of different complications, like ear infections, pneumonia, diarrhea, dehydration, also potentially blindness and brain swelling.

The fact that it infects those immune cells means that it depletes them by infecting them. That means that you can lose your memory responses to other infections that you’ve had in the past. If you’ve developed immunity to other diseases or you’ve been vaccinated against other diseases, that immunity can actually fade through a measles infection. That was something that I didn’t really know very much about before. It’s why if you look at children who have had contracted measles, they then have a higher risk of just seeing the doctor for various infectious diseases over the next two to three years, usually, which is quite bad.

So we need to get a vaccine for this. How does it happen? It’s by John Enders again. He develops culture techniques, he finds the right cell culture to grow the virus in and then to attenuate it. He and his research group find a school that is having a measles outbreak. He takes samples from lots of those kids. From one of those kids, a 13-year-old boy called David Edmonston, he finds a virus strain that he can grow in the lab, and then he names the strain after the boy. It’s called the Edmonston strain.

Jacob Trefethen:

Good! I love that.

Saloni Dattani:

I think it’s so cool to have a vaccine that’s named after you.

Jacob Trefethen:

Yeah, what would the Saloni vaccine do?

Saloni Dattani:

I don’t know... something silly.

So he takes this virus strain from this boy and then grows it in human kidney cells and fertilized chicken eggs. That forces it to adapt and lose its ability to cause disease. He then does vaccine trials in 1960, and that was very effective. But many of the kids developed fevers and rashes and things like that. There were new vaccines and different preparations that doctors were using to make them safer. Eventually, Maurice Hilleman, who developed 40 vaccines in his lifetime, developed the MMR with it. It was measles, mumps, and rubella together. He also developed a better measles vaccine. That meant you could just give them all at once. I think that was introduced in 1971, and kaboom.

Jacob Trefethen:

Kaboom!

Saloni Dattani:

So we have gone from a chance discovery of, it just happens to be the case that cowpox is protecting people from smallpox as well, and it happens to be mild, and it happens to be something you can easily identify and work with. But you have no idea what is causing the disease. You have no idea how to grow it reproducibly in the lab. You don’t know how to scale it and you’re transferring it from arm to arm.

Over time, all of these different innovations in scaling up the method, but also figuring out the theory of, how do germs cause disease and which germ causes each disease? How do we grow them and study them in the lab? How do we make them safer and how do we make them more effective? Doing that for bacteria, and viruses, and now some parasites, like malaria, has taken a very long time and a lot of different efforts from different people, including all the people that we talked about so far.

Jacob Trefethen:

We are ending this episode with MMR in 1971. I just wanted to read out all the different vaccines made starting with smallpox and Edward Jenner in 1796 through to MMR. Smallpox, rabies, cholera, typhoid, plague, pertussis, tuberculosis, diphtheria, tetanus, yellow fever, tick-borne encephalitis, anthrax, influenza, Japanese encephalitis, polio, measles, mumps, rubella.

Saloni Dattani:

Boom. So many.

Jacob Trefethen:

So many.

Saloni Dattani:

We’re going to talk about the next ones in our next episode and a different type of vaccine technology and how we do modern vaccines today. Because the process has kind of changed for a lot of different vaccines, and I think that deserves its own episode. But this whole progression from moving from not knowing how to develop a vaccine to figuring out these techniques that systematically allow you to make vaccines against different diseases is itself just an amazing set of achievements.

Coming back to your point right at the beginning, in just the last 50 years, it’s estimated that vaccines have saved more than 150 million lives. Despite the false starts and the hundreds of experiments and sometimes grisly methods, people managed to find ways to develop vaccines against so many diseases. They found ways to make them safer and more effective and found ways to scale them up, and you know, I think that’s amazing.

Jacob Trefethen:

It sure is. Before we get to those achievements, I just feel grateful for all the scientists all those generations ago, and all of their beefs and all of their feuds. I even feel grateful for Louis Pasteur, even Louis Pasteur. My favorite of everyone we mentioned today is probably that kid, Edmonston, he seemed like a good one.

Saloni Dattani:

He is good. Yeah. Who’s my favorite? My favorite is the wife of Robert Koch’s assistant, Fanny Hesse. She’s like, why don’t you use agar? That’s not going to melt in the summer. Maybe John Enders as well, the fact that he just happened to move into microbiology from studying languages before... could be an inspiration to some of you in the audience right now. Just kidding.

Jacob Trefethen:

Not kidding. Buckle up. Back to school!

Saloni Dattani:

All right, so we have now reached the end of the episode. I hope you share this with everyone you know. Subscribe, rate this on Apple, Spotify, wherever you get your podcasts, and tell everyone about it.

Jacob Trefethen:

Yes, please do. The ratings actually really help us find new audience, and we have a lot of fun making this. Thanks for lasting all the way to the end with us, and we look forward to talking to you next time.

Saloni Dattani:

Bye-bye.

Jacob Trefethen:

Bye.

Books:

• Gerald Geison (1995) The private science of Louis Pasteur

• Thomas D. Brock (1998) Robert Koch: a life in medicine and bacteriology

• Mervyn Susser and Zena Stein (2009) Eras in epidemiology : the evolution of ideas

• Angela Leung (2011) Chapter: “Variolation” and vaccination in late Imperial China, ca. 1570–1911. History of vaccine development by Stanley Plotkin

• Florian Horaud (2011) Chapter: Viral vaccines and cell substrate. History of vaccine development by Stanley Plotkin

• Samuel Katz (2011) Chapter: The role of tissue culture in vaccine development. History of vaccine development by Stanley Plotkin

• Hervé Bazin (2011) Chapter: Pasteur and the birth of vaccines made in the laboratory. History of vaccine development by Stanley Plotkin

Articles:

• Andrew Shattock et al. (2024) Contribution of vaccination to improved survival and health: modelling 50 years of the Expanded Programme on Immunization https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00850-X/fulltext

• Saloni Dattani (2020) The story of Viktor Zhdanov https://worksinprogress.co/issue/the-story-of-viktor-zhdanov/

• José Esparza et al. (2020) Early smallpox vaccine manufacturing in the United States https://doi.org/10.1016/j.vaccine.2020.05.037

• Paula Gottdenker (1979) Francesco Redi and the fly experiments https://www.jstor.org/stable/44450950

• Donald Angus Gillies (2016) Establishing causality in medicine and Koch’s postulates

• Burt A Folkart (1993) Dr. Albert Sabin, Developer of Oral Polio Vaccine, Dies https://www.latimes.com/archives/la-xpm-1993-03-04-mn-283-story.html

• Saloni Dattani (2025) Measles leaves children vulnerable to other diseases for years https://ourworldindata.org/measles-increases-disease-risk

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Graham Bessellieu, video editor

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Works in Progress & Coefficient Giving

In this episode of Hard Drugs, and trace the path of drug development from discovery to testing, manufacturing, and delivery. They explore where AI could speed things up, and where it still hits the limits of biology, data, and economics. They ask what it would take, beyond algorithms, to actually cure and eradicate diseases.

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter: https://www.scientificdiscovery.dev/

Jacob’s blog: https://blog.jacobtrefethen.com/

Transcript

Saloni Dattani:

Is AI about to cure all diseases? This year, Demis Hassabis, the CEO of DeepMind, said, “I think one day maybe we can cure all disease with the help of AI. I think that’s within reach, maybe within the next decade or so. I don’t see why not.” Is he right? In this episode, we figured we’d tackle that head on. I’m Saloni Dattani, this is Jacob Trefethen, and we’re presenting the new podcast, Hard Drugs.

Jacob Trefethen:

So far, we’ve talked about what proteins are, how they can be medicines like insulin, how AI can help scientists improve proteins to make even better drugs, and how AI can help design entirely new proteins never seen in nature.

Saloni Dattani:

Now we’ll zoom out to look at the drug development process as a whole, talk about what AI might speed up and where new drugs might still get stuck. This one is necessarily more speculative than usual; we’re going to draw on examples from the past and talk about the possibilities of the future.

Jacob Trefethen:

You can leave the end of the episode with better guesses of whether AI is about to change everything or whether it will be one tool among many that scientists can draw on. Will we cure all disease in 10 years? Let’s get into it.

Saloni Dattani:

I recently read this blog post by , and he says, “We still can’t predict much of anything in biology.” I thought that was kind of interesting because the last two episodes we’ve talked about how AI is being used to improve protein structure prediction and design new proteins. But he basically explains that even though there has been a lot of progress, there are still a lot of really unsolved problems. Biology is much more complex than people would imagine, and even the types of problems that have been solved are not necessarily representative of all the problems that are out there. We haven’t really modeled the whole complexity of cells, organs, and organisms as a whole.

He says, “I remember in the early 2000s, David Baker was revolutionizing computational protein design with his Rosetta software suite, winning CASP competitions left and right, and writing papers that gave the impression computational protein design was solved. For example, computational design of novel folds was solved by 2003. Protein docking was solved by 2003. Enzyme design was solved by 2008. Atom level co-folding of multi-peptide chains was solved by 2009. Yet here we are 20 years later. All of these topics are still active areas of research, and if you have any particular system of interest, you may find that none of the available methods perform that well.”

He gives three different reasons for this — not that the research is sloppy or anything like that, but essentially that the types of work that people have approached with AI far have been ones where the likelihood of success is much higher. They’re kind of areas where you could make those predictions well already, and we have much more data available on those topics. We have only just started solving these soluble proteins — proteins dissolved in water — and not all of these other types. In nature, proteins might be wiggling around; they might be attached to a membrane, or they might be attached to some drug. They’re doing different things, and those things haven’t yet been solved.

Jacob Trefethen:

Yeah, I found that quote so interesting that you just read out because it can always feel like you’re on the precipice of sudden change. Hearing people talk about — people who have had that feeling before especially — what was the same that time around and what’s different this time around is really useful. There are different lenses and worldviews that people apply to AI progress, and I think people come at this question from extremely different places. I just want to outline those worldviews in case you’re a listener who feels described by one of them and are worried we’re not going to hear you properly because you don’t have a microphone in front of you.

On the one hand, many people who think AI will change everything. Hold aside the debate about whether people trying to make artificial super intelligence are actually going to achieve that. And just project forward AI progress will continue and assume that we’ll get to more powerful systems of some sort. I think a lot of people look at that and think, “Well, in principle, a lot about the physical world is knowable, and we just don’t know it yet. Science is about discovering that knowledge, and scientific discovery does progress. It is often limited on having some of the smartest people in the world, Einstein and all of those lot, devote their energies to thinking about the external world around us. If we’re about to invent systems that can reason well, debate with us, and debate with each other, instead of having hundreds of thousands of working scientists alive at any time working on discovering the nature of the universe, we can have hundreds of millions maybe, but they are AI agents.”

There might be a period when you apply that to human biology, where in a few years, we ask those helpful assistants, “If we want to learn about the human body and develop drugs that will prevent death and prevent illness, what in the fewest number of experiments, just help us do this and communicate it to us in a way we’ll understand.” In a few years, those experiments will get carried out, they’ll get published, and humanity will be much better off. You’ll walk into a booth, you’ll get genotyped, you’ll maybe give a few blood samples, give your medical history, and an AI doctor will tell you, “Okay, here’s your regimen of seven daily pills you’ll take for the next year.” That will actually prevent ill health for you, based on all the knowledge we discovered of the human condition. I would say that this worldview is quite common in where I live, San Francisco, so I hear this from friends quite often.

But let me say the other view, which is very common too, and probably more common from practicing scientists and people who’ve worked in drug development, which is: human biology is really complicated. We understand very little of it. It’s hard to even take good measurements of the human body, in certain parts of the human body — the brain, the heart, and so on — without doing harm to a given person. In the case of software, sure enough, transistors have faced Moore’s law where they’re getting cheaper and cheaper to make over time, and that’s led to a big boom in technology from a software point of view.

We have the reverse in drug development. We have what people sometimes call Eroom’s law- The reverse. E-R-O-O-M.

Saloni Dattani:

It’s Moore’s law backwards.

Jacob Trefethen:

Everything’s getting harder and more expensive. Progress is really hard won. The more positive spin on that is we’ve actually already technologically solved some of the worst health problems humanity used to face, in rich countries at least. We now have antibiotics. We now have vaccines for childhood diseases. We have statins to reduce your risk of heart disease. There’s more progress coming, but it’s fiddly, it’s difficult. The bottlenecks aren’t mostly in discovery where AI might help; they’re in off-target effects and toxicity from these drugs. The expense of clinical trials is the block. Manufacturing new modalities is the block. Health systems themselves are the block in making sure that people who need new drugs can actually access them. I think that’s a whole different worldview, and sometimes one person can be both of those people, but a lot of the time those people don’t communicate that well.

Saloni Dattani:

Do you think we should get all of these different people in a room and just watch them fight, or are we going to try to mediate them and solve all of their questions?

Jacob Trefethen:

In a world where we could get them all to fight, I think that might get us even more downloads on this podcast. But for now, you and I are going to have to hash it out in this episode.

Jacob Trefethen:

To figure out how AI could affect medicine, we’re going to talk about the steps to making medical progress today and see if AI can speed that step up or let us skip that step entirely.

Drug discovery

Saloni Dattani:

So, one of the areas that AI seems most promising is drug discovery. That means finding potential drugs that could be used as treatments. You probably think understanding the disease is crucial to developing new drugs, right? Actually, wrong. Drugs can be developed without understanding the disease at all, and there are many different ways that that can happen. This was very common in the past, but it’s still common today. To explain why, I want to give you three different examples from different parts of history: one is Jenner’s smallpox vaccine, two, the discovery of a new drug for malaria in the 1960s, and three, a new schizophrenia drug that was approved last year.

Let’s start with Edward Jenner in 1796, a very long time ago. As many people have probably heard, Edward Jenner developed a vaccine against smallpox by using the pus from cowpox infections. Dairy maids who were infected by a related virus that causes small pustules on their hands were also protected from smallpox. He extracted that pus and transferred it to children, adults, and so on to protect them from a potential outbreak of smallpox. This is really interesting because the way that he had discovered that was really through other people’s case reports, observation, and collecting data. He went across many dairy farms and asked them, “Has anyone in your family been protected from smallpox in a previous outbreak? Did any of them not catch it?” He collected data on all of these different individuals who had contracted cowpox at some point and after that had been protected from a smallpox outbreak that all the rest of their family had been infected with. Through that and through experimentation, he developed a new vaccine. This is quite interesting; this is basically a very early form of epidemiological analysis. He’s collecting all of this data from these different examples in front of him, and he’s doing experimentation.

Similarly to that, the way that Louis Pasteur and Emile Roux developed the rabies vaccines in the 1880s was that they did lots of experiments in the lab. They didn’t really have any idea of how vaccines actually worked. At that point, it was quite common to think that the way a vaccine worked was it somehow depleted specific nutrients from your body that the virus or the infection needed in order to cause disease. If you used the vaccine, it would deplete those nutrients, and then you’d be protected from the real infection later on. They did lots of experimentation, seemed to find these methods that were effective, but really they had no idea why; they were very empirical in the way that they did this research. This is again, much before it became clear how immunologically any of these vaccines worked at all.

There’s another example from the 1970s of the discovery of artemisinin, which is a malaria drug. That was discovered by a Chinese scientist called Tu Youyou. She was part of this secret research project in communist China, where she was part of the small medical research institute trying to discover new malaria drugs from ancient medical texts. At this point in the 1960s, the Vietnam War was going on, and the previous drugs that people had against malaria were gradually not working anymore. The parasite and the mosquitoes were becoming resistant to them, and it was becoming more of a problem for people fighting in the war, so they needed to find new potential medicines.

What she did was, she looked through more than 2,000 different ancient medical texts — recipes of traditional medicines — for potential herbs and preparations that might be effective. She then narrowed down all of those thousands to a few hundred, and then tested some dozens of them in the lab and tested them in animals and people. Eventually, isolated this particular compound called artemisinin from the Qinghao or sweet wormwood plant. This was just one of hundreds or thousands of different potential recipes that could have worked according to those texts.

What she did is essentially a very early high-throughput screening. Even today, when pharmaceutical companies are trying to find potential drugs, they might just have this library of chemicals that they’ve used for different purposes, that they’ve experimented with before. They want to see, “Do any of those work against this disease that we’re trying to treat?” They will do this mass screening, testing all of these different drugs in the lab to see how they affect whatever — the receptor in cell culture or in animals and things like that. Sometimes they might come upon some that do work. Even in the 1970s, people were applying similar methods where they were really scrolling through all of these hundreds or thousands of different potential recipes and trying to find something.

Related to that, it’s quite funny that the two examples we’ve used seem a bit traditional or ancient herbal medicines. You might think, “Okay well, why do we need modern science then? What’s the point of trying to do drug development the way that we do it now?” I think it makes me think about the types of refinements that you can do once you’ve found these compounds. The modern versions of them tend to be a lot better; they contain fewer contaminants — once you’ve identified the key ingredient that is responsible for the effects, you can then tweak that, you can then remove the impurities. You can try to test the dosing and try to get into a range that is both safe and effective. You can also tweak and improve the efficacy, reduce the side effects, or make it more heat-stable or more soluble or more easy to manufacture. That’s what happens with artemisinin. After Tu Youyou discovered this from this plant, people then adapted and improved on the compound to make it more bioavailable, so you would require a smaller dose to have the same effect.

Jacob Trefethen:

It’s so cool to do both — to use the wisdom of the ancients and the tools of modern science. It makes me want to go read some old texts and see what I can discover.

Saloni Dattani:

I’ve given two old examples, one from the 18th century and one from the 1970s. There’s also a third example that I want to give because there really are these three or four different pathways that you can go about designing or finding a new drug without understanding the disease. That is a schizophrenia drug that was approved last year called xanomeline trospium. I’ve written about this a little bit.

What’s really interesting about it was that it was discovered, or well, it was initially tested as a potential drug for Alzheimer’s disease in the 1990s. While conducting that research, scientists discovered that, surprisingly, it seemed to reduce people’s symptoms of hallucinations, delusions, and agitation in those patients. It wasn’t really slowing down the cognitive decline, but it seemed like, “Well, if this is reducing hallucinations and so on, maybe it could be useful as a schizophrenia drug instead.” After that, they started to test whether it could be repurposed as a schizophrenia drug. But in those trials, it caused a lot of side effects like vomiting and stomach pain. Because it was so hard for people to take, they just shelved the drug and didn’t continue that work.

In the meantime, other researchers were trying to figure out what was actually going on. Why is this drug seemingly causing a reduction in hallucinations and so on, but also causing all of these horrible digestive side effects? The reason is this drug targets muscarinic receptors. This is a type of receptor on the outside of your nerve cells in the brain, and that was the way that it seemed to be reducing these hallucinations and delusions. But also, very similar receptors are in other parts of your body, and they’re doing different things. In your digestive tract, there are muscarinic receptors as well. When both of them are targeted by this one drug, then you could have both of those effects.

What they did was, they combined this original drug, which was xanomeline, with another drug called trospium, which meant that it was unable to target the muscarinic receptors outside of the brain in the gut. It could only really affect the receptors in the brain and reduce those side effects. You get the benefits of reduced hallucinations, but you also don’t have the side effects that were seen earlier. The reason I brought up these three different examples is that there are three different ways that you can develop drugs without really understanding the disease at all.

The first one, Jenner’s smallpox vaccine or Pasteur’s vaccine, is really about this empirical analysis. You’re learning from epidemiology, you’re seeing, “Oh, it seems these people are protected, don’t know why.” You’re trying to experiment with that, see if you can tweak and improve the method; that’s one way. Another way is the malaria drug development, where you’re just testing hundreds or thousands of different compounds, seeing if anything works in the lab or in animals, and then based on that, you’re tweaking and improving it. The third way is you’re repurposing an existing drug. When you’re testing for one condition, you notice something else that might be helpful for another condition. So, can AI replace these methods? What do you think?

Jacob Trefethen:

I’m in two minds about it. I think that contrasting the examples you just laid out, where we didn’t need a full mechanistic understanding, with the more rational version that people might picture, I have some hope for AI spotting patterns that we haven’t yet. If large language models had ingested those ancient Chinese texts, would that have helped prompt good ideas for scientists to investigate further? I don’t see why not. Similarly, every time some new idea comes up with repurposed drugs, I always wonder, “Well, why didn’t that pop up earlier?” So those kinds of questions, I do have quite a bit of hope for AI.

A real input there, though, is what kinds of empirical observations are accessible to different large language models or to different reasoning agents? Are they located in published form? Are they in actual medical papers, are they in case reports, in doctor’s notes? Given different privacy and legal institutional concerns there, will there be access to that data of some form that could prompt these ideas? One of the things that makes me so hopeful though, is that especially for repurposing, we already know as a society how to make very cheap versions of small molecule drugs. The more you can improve people’s health by giving people the right combination of small molecule drugs, the more hope I have that many people can access health-improving technology. There are many other more complicated routes that you might be able to develop drugs than small molecules, and those get me...

Saloni Dattani:

Like antibodies or vaccines or things like that.

Jacob Trefethen:

Antibodies, vaccines...

Saloni Dattani:

Gene editing therapies.

Jacob Trefethen:

Gene editing, surgeries, organ transplants, all of those, incredibly important, but much harder. I worry more about the ability to really reach everyone who might need it.

Saloni Dattani:

I think you’re right; I think this area of drug discovery is maybe one of the more optimistic areas that we’ll talk about. But at the same time, even when you have spotted the patterns or the similarities in diseases, or you’ve analyzed the data from healthcare records and things like that, you still need to do lots of experiments in cells and animals and humans to confirm that they work. We’ll talk about that later on, but basically, even once you do have this collection, you still have to filter that down, and it’s really going to be just a fraction of those that will work in reality; that’s one thing to remember, I think.

The other is that when you’re testing so many different combinations of different drugs and different diseases, you actually need a huge amount of data to have the statistical power for making those inferences. If some of these drugs are not being taken by that many people and they don’t have other diseases, it’s hard to actually test, are those people actually getting better? I think maybe there are some diseases that this approach works better for and some that it works worse for.

My thinking is that there’s probably much more potential for drug discovery with neglected diseases through this route, or rare diseases and things like that, because there is more of a tendency for those types of conditions to be caused by a single exposure, like a single pathogen or a single environmental pollutant. They haven’t really been studied as much yet, so if we do make some effort, you could potentially make lots of progress. But at the same time- and some of these are genetic congenital conditions that are very rare, and so trying to figure out what single gene might be responsible for some of them could help design or develop new drugs.

But at the same time, there are quite a lot of different rare diseases, right? And lots of different neglected diseases where the data collection hasn’t really been that comprehensive, and there isn’t that much for AI models to go on. They haven’t been studied as much yet. There are a lot of different rare diseases; collectively, they’re not that rare, about 5% of the population has some kind of rare disease. But studying each one is very hard because there just aren’t that many people. There are usually 20 per 100,000 people in the population who have that, so there hasn’t been that much research done on that. Once you do collect the data, I would assume that it would be easier for AI to make a lot of progress on those types of conditions. But at the same time, you still have to collect that data in the first place. If we’re thinking about where AI would have the most impact, given the existing amount of data and effort, probably that’s not the rare diseases or the infectious diseases, but if you were to collect that data, there’s a lot more potential that you could have.

Jacob Trefethen:

Got it. What sort of data should we be going after then?

Saloni Dattani:

I think one is sequencing data. People who have rare genetic conditions often they’re not in the types of genetic data that are commonly collected right now, which are... If you’ve done 23andMe, for example, it basically just tests a very small fraction of your entire genome. Those are the areas that are very common for people to vary on, and basically helps you to predict your ancestry and common differences between people. But most of the genome is not included there. People with rare genetic diseases tend to have mutations or changes in individual or specific parts that are very uncommon, and those tend to have much larger effects on their risk of diseases or things like that. That would be one that I think, if we have better sequence data from people, it’s going to be much easier to spot those patterns of people with these conditions tend to have these very rare mutations that haven’t been studied very much so far.

The other one is collecting data on environmental exposures. One that I think is quite interesting that I recently read about was ALS, which is motor neurone disease or amyotrophic lateral sclerosis. There was recently this article about how there’s this cluster of cases of some dozen people living in this small town in the Swiss Alps who all developed ALS. That’s very unusual because it’s quite a rare disease; you wouldn’t expect that in a small location. These researchers went to that town, talked to each of the individuals, their families. They talked to other people in the town, collected very detailed histories of their occupation, their genetics, their daily habits, the things that they ate and they did, things like that.

What they found was that all of the people who were cases in that town had previously eaten this type of mushroom, which is called Gyromitra venenita and there’s another one called Gyromitra esculenta. They’re wild mushrooms, and all 13 of these ALS cases had eaten these wild mushrooms in that town. None of the controls in that town had ever eaten them. I think there’s a more common name for this type of mushroom, which is false morels. There are different types of false morel mushrooms, but the specific species that they ate also contain neurotoxins; this is well-known, and people are generally recommended not to eat these types of mushrooms. I generally think this is some fairly strong evidence that this is a cause, but still obviously people need to do more research and confirm this in more studies. This idea of going out and collecting all these data from these people on these kind of rare exposures — you’re looking at mushrooms or maybe some toxic plant — that’s stuff that doesn’t exist in the existing literature unless someone actually goes out and collects it. So I do think things environmental factors, very uncommon exposures, if you do go out and collect them, you’ll find some surprising things.

Jacob Trefethen:

Okay, we need more case reports. It’s interesting just stepping back on this principle you’re discussing: you don’t always need to understand a disease in order to make a drug. I think the relation between science understanding and technology of intervening for some purpose is not always what you’d expect in fields outside of medicine as well. There’s often this perception of you have to understand something so that you can develop a tool or technology to intervene on it. How much of aerodynamics did you really have to understand before you could invent flight and airplanes?

Saloni Dattani:

Right. Steam engines were invented way before people understood thermodynamics.

Jacob Trefethen:

Yes, steam engines invented before thermodynamics. The big one, really, is fire. We were using fire well before we understood combustion or knew what oxygen was.

Saloni Dattani:

Right. This really reminds me of this article that Jason Crawford wrote for Works in Progress years ago called “Innovation is not linear.” He basically explains that people think of these as two very different approaches, and that you start off with doing basic research, just exploring, seeing what happens, developing theories, things like that. Separately, there’s engineering and tinkering and trying to make products or trying to make different tools and technologies. People generally think of that as being a linear process: you start with the basic research, you understand the disease or whatever, the theory, and then you develop the engineering outputs of that. But really, that’s not the only thing that happens. There’s a lot of feedback between these two different places, and often you start off with the product and then you figure out how it works, and then you develop these theories and those theories allow you to go forth and make way more technological improvements. You don’t have to start by understanding the disease in this case, but also once you do understand the disease, it can be really helpful.

Jacob Trefethen:

Invention feeds back into science and understanding, and they have this kind of loop together, which could have implications for AI if you think that AI is going to, for example, get better at reasoning before it gets better at taking new samples of the real world. Okay so, you talked me through some cases where we don’t need so much understanding in order to make medical progress. What about the reverse? Are there cases where medical progress is currently bottlenecked on understanding a disease, or recent cases where understanding a disease was helpful?

Saloni Dattani:

So I guess there are a lot of examples where once you understand a disease, or once you understand how a drug works, you can then tweak it and make a lot more tools related to that. Obviously, vaccines are a great example of this. In the 19th century, people didn’t really understand how they worked at all. But developing these processes of weakening a microbe in the lab — what’s called attenuation — meant that people could develop many more vaccines with that approach.

There are other approaches as well. Once you understand specifically, it’s not about having the entire microbe being weakened or something; you don’t need that entire thing as a vaccine. You could just have a specific antigen, or a specific part of this vaccine that your immune system recognizes and then matches to the pathogen in the wild. That knowledge or that theory was only really put together in the 20th century, in the 1920s to 40s. It meant that people could then develop better vaccines where you don’t include the entire pathogen, but you just have the specific proteins or the specific outer parts that are needed, and they’re much safer and easier to scale up and things like that.

We also talked about some of these other examples in the previous episodes. In the first episode, we talked about HIV treatment. One of the big breakthroughs in HIV treatments was developing protease inhibitors. Protease is an enzyme that the HIV virus has in order to mature into its infectious form. Only by understanding what the shape of that protein looked did people develop drugs that fit into one of the little gaps in that enzyme to block it. That’s another example. Gene editing, for example, is much easier if you know the specific genetic cause of a condition. You could specifically target that gene with CRISPR or RNA therapies or things to silence the specific gene that is overactive or something like that.

There are also things like devices or surgeries, where the only way to develop a pacemaker, for example, is to understand that the heart uses electricity to pump blood, right? Knowing what kind of heart rhythm is needed for that helps you develop a pacemaker. Or if you’re conducting a surgery and you’re opening up the chest — initially when people tried to do that, you would try to open up the chest, and their lungs would immediately deflate and they would just suffocate and die. Also, if you’re trying to operate on the heart, people would lose blood so quickly that, again, they would just rapidly die.

In 1950, I think, people developed this machine called the heart-lung machine, which basically replaces these two functions of the heart and lung, in order to keep people alive during a cardiac surgery. The heart-lung machine essentially pumps blood — you’re connected to this machine, it pumps blood, keeps blood pumping — and then also bubbles oxygen into it. That means people can continue to have blood flow, but you can only really make that once you know that you need to do both of those things.

Jacob Trefethen:

Making the connection to AI then, I wonder how much do you think that, firstly, in big diseases, we still don’t have that knowledge connection, and if so, will AI be able to help? Secondly, if we do have that connection between a molecular target, say, and disease, do you think that means we’ll get a drug really soon, because AI will help?

Saloni Dattani:

I think there are a lot of diseases where we don’t have the right data that’s collected yet for people to understand the causal pathway. One thing that you and I are both interested in is tuberculosis, right? A lot of people are trying to get rid of tuberculosis in the general population by targeting what’s called latent infections. The bacterium is basically just hidden around; it’s not doing very much, but eventually it might reactivate and cause disease. In order to know how many people can be targeted with that or exactly how to treat those people and get rid of tuberculosis, you need good ways to test for people who have those latent infections. My understanding is that those testing methods are currently not very good, and because of that, we have a huge uncertainty about how many people even have latent infections in the world. The previous estimates were that a quarter of the world’s population has a latent TB infection. Unfortunately, that estimate is affected by people who are also vaccinated with BCG, right?

Jacob Trefethen:

And turn up positive on the skin test.

Saloni Dattani:

Right. Or have been infected in the past at some point, but have cleared it naturally. Many of these people don’t actually still have latent infections, and that means it’s really hard to actually test whether the drug that you’ve developed is going to treat and remove the bacterium from them. The new estimates are that actually only 3 to 6% of the global population has latent infections. I feel like we just don’t really know very well right now, and we won’t know that until people go out and collect this data with better testing and better immunological methods.

Jacob Trefethen:

As good as your drug design is, you still need to figure out what’s going on in lots of different people.

Saloni Dattani:

Right. More data collection is really important. This actually reminds me of something that happened in the 1850s, which makes me sound like an extremely old person. The story I’m going to tell you is about the discovery of what caused cholera. In the 1850s, London was having this big cholera outbreak. As you might have heard, the story of John Snow tracing that to a water pump in Broad Street in London in one of the local epidemics, that pump was contaminated with the cholera bacteria. He didn’t know that it was caused by that bacterium, but he did have this idea that something in that pump was contaminated and was causing disease.

What I find really fun and interesting about these historical scientific discoveries is, you know, that the way people often hear about them is, “Oh, it’s so obvious. If only someone had gone out and collected that data, it would just be obvious that that was the cause. Why did anyone not believe him at the time?” or something like that. But actually, there were these competing hypotheses and theories even at that point. There was a very common theory that cholera was not caused by germs or anything like that; it was actually caused by either bad air or by elevation, like from how far away you are from the sea, or maybe it’s just because of poverty.

The people who proposed these theories were not really stupid. They had collected lots of data. They had mapped out cholera cases across London, and they had noticed this correlation between, “Okay, the people who live closer to the River Thames have a higher rate of developing cholera.” Maybe it’s just caused by poverty or being close to the Thames. They assumed that socioeconomic factor was the cause and it wasn’t some germ. In this case, you need to think through what’s happening here. It’s not really that obvious that just collecting data on one thing or collecting data on the other thing is going to give you the answer.

But in this case, the environmental factors are actually confounders that lead to a higher risk of many different diarrheal diseases and many different diarrheal pathogens, including cholera, that someone might be infected with. At the time, people wouldn’t have been able to distinguish these different diarrhoea diseases. Both of these theories could look correct, but only once you really understand the causal path — you’re doing experiments, actually identifying the microbe involved, and seeing which tissues in the body it infects — will you be able to identify that that’s the cause of the disease. It’s an example of where you do need more data, but you also need to model, have this theory of how these different hypotheses work together and how to distinguish between them. You still have to do lots of experiments and stuff to figure out what’s actually going on.

Jacob Trefethen:

It’s so interesting to think about the interventions that different forms of knowledge unlock in that context of cholera, because in that case, you can then have better sanitation and make sure you have cleaner water, which reduces the number of cases of cholera. At the same time, you don’t yet have a full understanding of molecular biology, certainly. Where I work now at Open Philanthropy, we just funded the development of a cholera conjugate vaccine. We’re still dealing with the problem of cholera in some parts of the world. We have only got enough knowledge about proteins and carbohydrates and the immune response of kids to different vaccine technologies that, in 2025, this is now going ahead, and we’ve co-funded a phase two being run by a vaccine developer. That would have been useful in the 1850s as well, but a lot more knowledge had to come first.

Saloni Dattani:

Right. There’s another example from COVID as well. Lots of people will know that the risk of a severe COVID infection, or dying from the coronavirus, is affected by your age, and that exponentially increases your risk. But there’s another biological factor as well. One of those is interferon antibodies. Some people, actually quite a large fraction of people who have severe disease from COVID, have antibodies in their body that are reactive to another type of protein, called type one interferon, which usually helps fight viruses. In this case, your antibodies are instead attacking this protein that you need to fight the infection off.

Some 20% of people in some research who died from COVID have these specific autoantibodies, and it raises the risk of death by 6 to 17 times if you have autoantibodies to this protein. The chances of having those kind of reactive antibodies are much higher among older people. But it’s this example of where, once you collect data on specific biomarkers or specific types of antibodies or immunological data, you can understand the causes of severe disease much better than you would from just the general data about age and things like that, and that might then help you develop better drugs.

Jacob Trefethen:

I think that example, and I guess some of the other ones you mentioned, does sort of reveal... we were at the beginning thinking, “Well, if you don’t have an understanding of a disease, can you still develop a drug?” And the answer is sometimes yes. The opposing version of that is, “If you have a perfect understanding of a disease, can you rationally develop a drug to hit certain targets?” And the answer is sometimes yes there. In fact, with most human diseases, we’re sort of in the middle. We’ve been developing our understanding, and we understand some things, and we’ve taken some forms of measurements. If we were taking other forms of measurements, we might start to understand those diseases more.

That brings me to a question of, “Okay, in the messy middle there — of where we have some knowledge, but we aren’t quite sure if the real bottleneck is knowledge or engineering and drug development, how much is AI going to help there? And will it unlock some new progress?” Take neurodegenerative diseases. I think with Alzheimer’s, it depends which Alzheimer’s researcher you ask how much they think we know versus don’t know. There’s definitely some knowledge about some protein targets or relation to some processes in the body — amyloid beta, tau, inflammation. There’s some knowledge about how it’s related, but we already have drugs now approved that reduce amyloid beta plaques. Those drugs do not cure you from Alzheimer’s all the time. We have a developed theory, and the theory can’t be simple and quite right; there must be something more complicated going on. I wonder in that case, do you think that the bottleneck is more understanding? Do you think the bottleneck is something else in drug development? Will AI help?

Saloni Dattani:

I think it’s probably lots of things. I think that’s a great summary. One thing that we don’t yet have with Alzheimer’s is really better animal models, trying to test out these drugs before they get to the clinic. The brain is also just really hard to study. Most of the research comes from post-mortem tissues instead of live brains, for obvious reasons. If we did have better methods, maybe we could learn, in a more real-time way, what these drugs are actually doing or how the disease is progressing.

The other is how to actually safely deliver drugs to the brain. The two drugs that, I think both maybe, of the drugs you mentioned that were approved to treat Alzheimer’s cause brain bleeds. I think there are various side effects and problems that a lot of medications that are targeted at the brain have. Part of the reason is that generally speaking, the brain is fairly protected from the rest of our body in terms of the toxins and the chemicals that go around our bloodstream. There’s a blood-brain barrier that means that it’s harder for certain compounds to get across and actually have any effect. That’s probably a good thing in general; you don’t want toxins to repeatedly go past and target your brain. But it also means that designing drugs in such a way that they’re effective and also safe is still quite hard.

I think the other is there are probably lots of things that we don’t know yet about the specific progression of the disease. There is also sadly lots of research fraud in this area, and that probably has slowed things down a bit where people are falsifying different experiments. That means it’s hard to know what’s actually going on. Knowing about what is not working is sometimes just as important as knowing what works. You’re not just repeating other people’s failed efforts in the past.

I think probably AI would be helpful in the fraud detection. Hopefully, it would be helpful in screening drugs for repurposing or trying to find potential drugs that target the amyloid plaques that develop in Alzheimer’s. I don’t want to say we need to solve this stuff in order to find an effective drug, because as we just talked about, you don’t need to understand these things to develop drugs sometimes. But I think that will make a difference.

Jacob Trefethen:

By the way, you know what I think we should do for delivery? It’s hard to get past the blood brain barrier, usually a good thing. Be careful what you wish for, if you get past it. My colleagues, Chris and Heather, have looked into funding a sort of gel where you go up the nose, the olfactory nerve. You know the mummies where they used to pull their brains out of their nose before they got buried?

Saloni Dattani:

Well, I don’t know any of them, but I’ll take your word for it.

Jacob Trefethen:

It’s funny, that’s not what they told me. But if you could just sniff something or rub a little gel so that you could deliver the drug through the nose, you could get hundreds of times the dosage. If you try and go through the blood, probably. You’ve got to be a little careful if you give a thousand times the dosage or something, though.

Saloni Dattani:

Have you heard of microbubbles?

Jacob Trefethen:

Microbubbles?

Saloni Dattani:

So they’re tiny bubbles. But these microbubbles basically can also be used as this drug delivery system. The bubble can be coated with something, but inside essentially it can contain some gene therapy or some chemical molecules or things like that. It’s this new type of technology that’s currently mostly being used in, I think, radiology and diagnostics. Because what you can do with these bubbles is that you can control where they go. With ultrasound, you can control when they pop. The bubbles kind of respond to sound waves. If you have a little sound, you could pop the bubble and release the drug in the right place. They can also be used to open up bits of the blood-brain barrier potentially with this little bubble getting popped over there. I’ve just been hearing about this from someone who’s writing an article about this for us, but I just thought that’s so fun. It’s still very far away from being used as a treatment for things. Right now it’s mostly being used in diagnostics.

Jacob Trefethen:

You know, that’s so fun that I really want you to have all the fun you want, so I think you should volunteer for that one first. Let me take the side of a AI advocate here. We just said that Alzheimer’s is hard to develop drugs for, for various reasons. I think some people listening might be thinking, “Well, hold on a second. If we get really advanced AI, just from the kind of reasoning agent point of view, take an LLM and make it even smarter and be able to reason in English and debate with other copies of itself in a virtual university.”

Saloni Dattani:

Wait, does that mean if it speaks in a different language, it’s just not useful to us?

Jacob Trefethen:

Well, it might be useful to communicate with itself in a different language; it’s more information dense.

Saloni Dattani:

I would love to talk to myself in different languages.

Jacob Trefethen:

You don’t do that already?

Saloni Dattani:

No.

Jacob Trefethen:

My internal monologue is sort of like, boop, boop, boop, boop, boop. But actually, you have to be a little careful about internal monologues for AI agents because one thing is so lovely from an AI safety point of view with the LLMs so far is that they reason in English, and you can read some of their reasoning output as they go. Now, of course, they may be encoding messages for each other that are not actually being reflected in English, but it’s quite nice they’re in English. In any case, they’re mostly going to be talking to each other for a while and then talking to us a bit, I would guess.

If I’m an AI booster here, I’d say, “Well, Saloni and Jacob, you’re just not being imaginative enough because you’re not taking seriously that we’re going to have the output of a lot of cognitive labor here.” Some of that output might say, “Okay, we agree that we don’t have good measurements of the brain yet, so what we’re going to do is design this non-invasive device that maybe you hold to your head and uses ultrasound safely, but actually takes better measurements.” Here’s a really, if you’re not quite comfortable with that happening with a human brain, then we’ll make it as safe as we can. All you need are 12 non-human primates, and you need to take these 17 different measurements, and then you’ll actually understand what’s going on in the brain.” What do you say to that?

Saloni Dattani:

Well, okay, but even if that is the case, we do actually need to get those non-human primates and we do actually have to apply those ultrasound techniques ourselves. But I think there’s another issue, which is that for a lot of these conditions, that data just doesn’t exist. It’s really hard to come up with these hypotheses if there isn’t much to go on. We’ve seen that with the protein design and protein structure prediction problems that we talked about in the last episodes, where it’s not as good as predicting things where the data collection is very limited. I suspect that that’s still going to be a problem here. That might just be a matter of timing; someone needs to collect that data first, but I think you still need to do lots of experiments in order to find out what is working and what different types of tools you might need for different problems.

Jacob Trefethen:

Experiments are important; you got me there. In particular, when you’re saying experiment, I assume you mean we’re not just looking at observational data we might have picked up, we’re not just looking at case reports, we’re actually perturbing the real world with controls to try and get at the causality of a given system.

Saloni Dattani:

Right. I basically think that in some disciplines or when you’re working with some types of tools and techniques, when you are able to do experiments, you can understand the pathways and how things are connected to each other much better. Essentially, there’s three reasons basically that I think experiments are really helpful. One, you can directly manipulate a specific point in this massive network of causal processes. Imagine your entire body as being a collection of all these different causal pathways. You have thousands or hundreds of thousands of different things interacting with each other. There are different nodes and connections. Maybe each one is a different hormone, signaling protein, enzyme, cell, whatever. When you have a drug or something that you can experiment with, you can intervene on these specific points in that giant network of different pathways. Doing that can help you understand the impact that that pathway is having, what is actually happening in the body or what is happening in this collection of cells and things like that. Because once you intervene on a specific area, it will vibrate and it will affect the other things that it’s connected to. Two, you have this controlled environment, so in the same way that you adjust for confounders in an observational study, you can keep the rest of the environment stable and just focus on that specific process. And third, you introduce lots of variation. You maybe introduce a new drug, something that’s never been seen before, and you see what effect that might have. Those large differences or interventions might not exist in the real world, and that can make it much harder to study what is happening outside there in observational data.

Jacob Trefethen:

How can we design systems that don’t necessarily require an entire human being put at risk in order to run experiments? Are there ways that we can simulate that system that might be experiments on a computer or experiments in something that doesn’t require the traditional ways of taking measurements that biologists might be used to? I think we have to, next up, talk about models.

Saloni Dattani:

Models. Well, yeah, let’s talk about models. Who’s your favorite fashion model?

Jacob Trefethen:

Probably Naomi Campbell, to be honest. Something about that walk.

Animal models

Saloni Dattani:

Let’s say we have discovered potential candidate drugs. What happens next? What’s the experimental process, or what are the next steps that we might have to test whether they work?

Jacob Trefethen:

Let me walk you through a toy model of that. The proviso, as you might expect, is that it does differ for different diseases and different drugs, but here’s a basic one. Let’s say you’ve got a drug candidate in hand that you think might work against some disease. You first will probably test in the lab in cells or on a plate against some biological material, whether it has some effect that you might care about. If the answer is ding, ding, ding, then you might test in non-human animals.

You, in many cases, will first test in mice, sometimes in mice that have been genetically altered to recapitulate some form of the disease that you’re trying to test against. After that, you will often try in another animal model that ends up being relevant for the disease you’re looking at. If you’re looking at a disease that affects the lungs, you might look at ferrets. Ferrets are used, I think, because their transmission of viruses — respiratory viruses — are a particularly useful property, but I don’t know the details so well. You sometimes use woodchucks or other birds when it comes to hepatitis B.

Saloni Dattani:

Wow, that’s so random.

Jacob Trefethen:

It is very random. Historically, people have used chimpanzees for a lot of research that nowadays we would think of as unethical because chimpanzees can’t consent in the same ways that humans can and yet are probably sentient and can suffer. There’s a lot of non-human primates that are not chimpanzees, but usually smaller or further away on the evolutionary tree from us who still are participants in medical research. Often a non-human primate, because of the genetic similarity to a human, will recapitulate a disease you’re looking at best of all.

Saloni Dattani:

There’s also this trade-off because primates are just probably much harder to work with than mice or mosquitoes or something like that in the lab. They’re much more expensive. They require much more space. It takes a much longer time for them to develop the disease. Their lifespan is much longer.

Jacob Trefethen:

Absolutely. People may remember in COVID that there were many vaccines and drugs that people wanted, scientists wanted, to test in different primates, but there were not many primates available because the laboratory system that we have is not so scalable to crises. You’re completely right that even just from a practical point of view, primates require a lot of space and a lot of food and a lot of care, and that does cost money and that will increase your grant budget required to do the experiments by a lot versus mice.

Saloni Dattani:

Right. You could use mice, and you have a cheaper animal model to work with, but then you might also lose these features of the disease.

Jacob Trefethen:

Yes. If you talk to anyone who works in biomedicine, most people are just very skeptical of most mice models. I don’t think that’s an exaggeration, but certainly, I mean, we just talked about Alzheimer’s, mouse models for Alzheimer’s. What the heck are they even showing you? It’s just so far from...

Saloni Dattani:

Do mice have cognitive decline?

Jacob Trefethen:

Well, exactly. They don’t even live for anywhere near as long as humans, and then they have very different brains. So what the heck are we even looking at? If you get past, usually the FDA will ask for two animal models, different species of animals. Then you can submit in the US case to the FDA, in different countries to your health regulator of choice, an IND, Investigational New Drug. That’s a whole big data package saying, “I would like to take this drug into humans.” At the same time, you can upgrade your manufacturing processes to make sure that the type of drug you’re making is what you think it is and is definitely safe- well, you don’t know if it’s safe for sure, but at least is what you think it is.

Then you can go into what’s called a phase one clinical trial. That is usually a clinical trial in healthy adults who may not be affected by the disease you care about at all, but are just participants to check that when you put this drug in the human system, it’s not causing big problems. If you get a tick there and you’re not causing big problems, then you will go to a phase two trial, which tests both for safety and efficacy. You will usually be in the population who is affected by the given disease you’re studying.

Phase one might have tens of people. A phase two might have hundreds of people. You’re measuring safety at a larger scale in terms of for many more people and maybe in more depth, and you’re measuring initial signs of efficacy. You might be trying multiple different doses of the drug and measuring in each case, how is this affecting the outcomes that we care about?

If after a phase two, you’ve got a drug that looks pretty safe and a drug that looks like it might be effective at some dose you’ve chosen, you’ll go into the most expensive stage usually, which is a phase three trial, usually with just one dose to confirm versus a placebo or versus standard of care of another drug that’s already used in the health system. That might be thousands of people, sometimes hundreds, sometimes thousands, sometimes tens of thousands, to determine that your drug is efficacious and to determine at the largest scale yet that your drug is not causing safety problems that are prohibitive.

And then if that all looks good, you’re going to submit a huge data package to the FDA and say, “Can I please sell this drug in America?” The FDA will take 6 to 10 months and review your data, review your thousands of pages of submission and get back to you with a thumbs up or a thumbs down. After you’re selling your drug, you’re still collecting data. The FDA might require further studies after they approve your drug if there are particular questions they have that things should be addressed. If in those studies you end up with a negative result, they might withdraw your ability to sell the drug; that happens somewhat frequently. Also, you’re going to be collecting in the real world, more side effect data. Once hundreds of thousands of people are using a drug, you will spot more side effects. They won’t be randomized, so you won’t necessarily get as high quality data, but you at least get more data as things come in, so the evidence collection does not stop once you get approval.

Saloni Dattani:

Wow, great. That’s a very long process. So I have two things about mice that I wanted to talk about. One is how we actually find animal models at all is kind of interesting. Second, why are mice models so common? It’s not just because they’re easy to work with. But let me start with the first one.

Imagine you’re trying to develop treatment for a disease. Let’s say you’re trying to develop drugs against malaria or something, and you don’t want to immediately test them in humans. You’re like, “Well, let’s see how this works in the different animals that we can work with.” But what you need to find out first is, “Are there any animals that have a similar disease that we have?” So let’s say you’re doing this for malaria. Malaria is caused by a parasite that is transmitted by a mosquito. What people would do in order to find a malaria animal model is look for other animals that are infected with the same or very similar parasites, which are infected by mosquitoes as well.

This was a really difficult problem in the 1930s, and or the early 20th century, in order to find animals that were also infected with malaria. They first, I think scientists first found birds and ducks that could be infected by similar parasites, but those didn’t seem to work. If you tested drugs with birds and ducks, they seem to be effective there, but then in humans, they cause all these toxic side effects. They were kind of looking for a better animal model. The way that you would do that is really you’re trying to actually test these different animals to see, are they infected by malaria right now? That really requires there to be a local malaria outbreak, which is kind of a rare occurrence maybe in some cases. You have to go out and collect that data from the individual different types of animals.

Another thing that you could do is you could test the mosquitoes. You could see basically when mosquitoes take a blood meal, they also ingest lots of other proteins that are in your bloodstream. You can test for those and see which other animals was it drinking blood from. This is one way that people tried to figure out which animals could be infected in the 1930s and 40s, I think. They tested whether the mosquitoes had ingested the blood and had particular proteins that were seen in different animals. I think cattle, sheep, dogs, primates, stuff like that. All of those seemed to be negative.

Some researchers thought, “Okay, well, maybe this is a good thing. Maybe this suggests that it’s rodents who are getting infected. Let’s try to look for any rats or mice that are infected by malaria.” There were some researchers in what was then the Belgian Congo and they were kind of just collecting lots of rats and mice in different, you know, near different rivers and villages and things like that, and testing them for evidence of malaria infection. I think I was reading this because I had written this piece on the malaria vaccine a while ago now.

What was so strange to me was that they’re trying to find, you know, where are these rats or mice that are potentially infected by malaria. They really couldn’t find any for several years. One of the reasons for that was because there was a forest fire in that area, and that deterred the mosquitoes from the area, so there weren’t any local outbreaks. It was really hard for them to- They eventually came upon this little thicket rat.

Jacob Trefethen:

Thicket rat?

Saloni Dattani:

Well it’s a type of rat, I don’t know that much about it, but it’s a type of rat.

Jacob Trefethen:

In the thick of it.

Saloni Dattani:

Right, and it happened to be infected by a different similar but different type of plasmodium strain that causes malaria. That was the first ever rodent malaria model that was found. It just goes to show how difficult it is to actually figure out which animals are potential models that you could use in the wild by doing all of this data collection, just testing things out. You kind of have to hope in some ways that there’s an animal outbreak or infection that you can capture. Or you get the species in the lab and you try to deliberately infect them, but that might not work — maybe they’re only infected by a different strain of the parasite or something like that. Sometimes it’s better to look for the natural outbreak and see what similarities it might have.

Jacob Trefethen:

It makes me quite grateful that we have division of labor in science because I really like the job of talking about it in front of a microphone. If I was out there catching thicket rats, I might go do another one line of work.

Saloni Dattani:

I think they tested some 200 rats before they found one that was infected. Even after they did find that one that was infected, they still had to optimize the... in order to study that in the lab, you have to recapitulate the environmental conditions that that rat is infected by malaria with. That was my first mouse story. Second mouse story, why are mice and rats so common in laboratory research?

Jacob Trefethen:

Yes, we sort of take that for granted. It’s just the default.

Saloni Dattani:

There was this great article recently in on the origin of the lab mouse. The author talks about, you know, the majority of lab animals are mice or rats, 95% of them. They’re probably around 30 million different rodents that are used for biomedical research every year in the US and Europe. This huge supply of mice is not just because they’re easier to work with and they’re small and have shorter lifespans than some of the other animals that you could work with, but it’s partly because there was previously in the early 20th century and the late 19th century there was this culture or community of people who collected and bred different mice varieties. They’re called mouse fanciers.

Jacob Trefethen:

Right.

Saloni Dattani:

I had never heard about this before reading this article and it was super interesting.

Jacob Trefethen:

I had not either, but I did read this article. Yeah.

Saloni Dattani:

In the early 20th century, basically they’re creating all these different varieties where some of them have spots and some of them are different colored mice breeds and things like that. People were just breeding these as a hobby. But then some of this was used in research. In the 1920s, there was this group of researchers in Maine who tried to standardize these mouse varieties and tried to basically create lineages of mice where they were very inbred. What that would hopefully help with was to reduce the amount of variation in the different mice in your lab. If you were testing a drug and it worked on some of the mice, but not the other ones, it would be really annoying if the reason for that difference was because they just had different genetics or something like that.

These researchers basically tried to have these very standardized, purebred line of mice where each of the mice has the same genetics, essentially. Their response to different drugs is not going to vary because of their genetics. This happened in the 1920s and people were developing these mouse model or lineages of mice to study the genetics of cancer. But then their funding dried up during the Great Depression and they moved to just working on creating more lineages of lab mice to supply other labs with. That is now, that laboratory, the Jackson Laboratory, is now one of the biggest providers of mice to laboratories around the world.

Jacob Trefethen:

It still is, all these years later.

Saloni Dattani:

Yeah. They have these really detailed procedures to keep their facility contamination-free. You have these physical barriers. If you want to enter the mouse room, you have to wear specialized equipment and stuff like that. They take really extreme measures to prevent any kind of contamination of those mice as well, which is a little bit grim, but also was very interesting to read about.

Jacob Trefethen:

I mean, it’s fascinating from a historical point of view. It sounds also like, you know, Hansel and Gretel, these scientists in this forest that make it American. It’s interesting, though, how much you gain by being able to control the genetics and the environment, but also how much you lose. Economists would call this internal validity and external validity, what I’m about to say. By making the mice so inbred, you can isolate causal factors of what is occurring when you give them drugs of different forms. When you’re making a drug, of course you care about, does this generalize, not just to one person in one room with one genetic code, but to all people who might benefit from the drug.

At some point, you’re going to have to have some experimental step, which is way broader, because you really need this generalizable effect in order for it to make sense, at least in the current paradigm of medicine. The way that we’ve set things up, we’ve decided to have this controlled, but something that most of the time does not generalize. That leads to a ridiculous amount of failure in the clinic where you have these drugs that look perfectly good in black mice or in whatever mouse model you’ve used and absolutely don’t work in humans.

Saloni Dattani:

You’re right. I do think that the extreme amount of control does let you understand some specific pathways, but it does also mean that once you are in the real world with a much wider variety of things, other things might be involved in that process as well. They might be modulating what is happening there and without that broader information, you can’t be sure really if these drugs are going to work for the average person. You don’t know if the average person is like those little mice in your lab. Probably they’re not.

Jacob Trefethen:

Reminds me of a story I heard about a plant biologist recently who said that, you know, you can tinker around with your theories in these great plant models as much as you want. But if he actually really wanted to know if something worked, he’d go out to the farmer’s market and get some spinach. Because if it worked in some spinach he found at the farmer’s market, well, that generalizes. Otherwise, probably it’s an artefact.

Saloni Dattani:

Right. One other thing. Do you have a favorite animal model?

Jacob Trefethen:

Gosh, I mean, if I’m being honest, to some degree, I hate all animal models. I just think it’s so awful that this is how we have to do medicine at this current stage of development. I just basically think animals can’t fully consent, so I hate to be a downer, but if I were to pick one, I do like the zebrafish because part of the reason a zebrafish gets used is because it’s transparent or translucent, so you can actually see visually things that might be going on. If you want to test something where that might be important, maybe go for a zebrafish. Zebrafish in the wild, I don’t think are transparent, but scientists have managed to change it so that whatever causes the pigmentation is different, so you can get transparent zebrafish.

The question of what’s our favorite animal model and us really kind of wishing that fewer animals were involved in medical research gets to the next question I wanted to ask you about, which is, can you design systems that might give you data that is useful, but where the systems are not alive or are not full organisms? One that comes up sometimes in the work that we’ve supported at Open Philanthropy, and we’ve funded some of this actually, organoid systems. Do you know much about organoids?

Saloni Dattani:

I know a little bit about organoids. They’re not fully organs; they’re parts of an organ, right? They’re derived from stem cells and they’re cultured in 3D. Imagine the dish that has the cells on it, but not that. It’s in a 3D shape or something like that. They’re kind of organized into little clusters of different types of cells, and they might reproduce some features of tissues in your body, including the different types of cells that are involved, or they might be doing some types of functions, but they’re not fully an organ in the lab. Is that right?

Jacob Trefethen:

I think that’s right. I think they’re sort of in between just doing experiments in cells and doing experiments in animals where you want to have a bit more complexity you can represent beyond just a cell. They’ll often have multiple cell types. You might have a lung organoid that has epithelial cells and then also some other cells. You might have a brain organoid that has neurons, but also microglial cells or something like that. I think this is a growing area that different fields or different organs honestly have gotten further along or less far along with. There’s various research going on that I’m cautiously hopeful we might get better organs on a chip over time.

Saloni Dattani:

When I hear the word organoid, I’m thinking about Futurama, like there are these jars filled with people’s heads only or different body parts or something like that. But it’s not really like that at all. It’s just cells in a 3D dish.

Jacob Trefethen:

That’s right. But they are 3D and there are some sort of bizarre things you observe when you try and grow some types of cells, neurons in particular. There’s a bit of a question of how big can this clump of neurons get before we feel sketchy about this experiment?

Saloni Dattani:

Sketchy in what way? Like it could start thinking?

Jacob Trefethen:

Yes.

Saloni Dattani:

What?!

Jacob Trefethen:

I think that people will often have up to maybe a million neurons, but the bigger you get than that, it does start resembling a brain a little bit, and then you might have some more philosophical questions about what you’re doing.

Saloni Dattani:

Huh. When I was in medical school for biomed, we had this anatomy class where we actually saw brain slices. I remember thinking how un-brain-like they were. They were fixed with this chemical to preserve them, and they had the consistency of a very thick tofu.

Jacob Trefethen:

That sounds pretty brain-like.

Saloni Dattani:

Yeah, right, but I guess when I’m imagining a brain, it’s quite active, it’s fluid filled, it’s a bit squishy, doing lots of electric pulses going on in there. That felt not very real somehow. This is a very different version of that. That’s in cell culture on a computer chip, or it’s on some 3D printed scaffold or something like that. But it is kind of alive, right?

Jacob Trefethen:

Well, I’m sure there are neurologists listening or other scientists listening who have strong opinions about that answer. It’s certainly alive in the sense that the cells are alive. The questions on top are, you watch them form these structures. You’re like, “Oh gosh, those... I don’t like the look of this.” You kind of want them to form structures so that they can be useful in experiments in terms of the similarity that they share with how a brain’s neurons are structured. I once went into a lab when I was visiting Boston a couple years ago, where they had the brain organoids in one room, basically. I did have a certain feeling of, “Oh, this room is strange.”

Let’s really bring this back to AI because everything we’ve described, even these organoids, still involves the physical world and still involves perturbing physical things in experiments. And I think part of the dream that AI boosters have is that you can do even more of the research and drug development without having to engage in a system outside of silicon. You want this to really happen computationally if you can, because then you go around way more experiments, you do way more, way quicker.

I don’t want to immediately laugh at it because we just talked about how mice don’t generalize that well to humans. I think it’s perfectly possible and I’m indeed hopeful for a world where in a few decades time, mice are much less involved in medical research and we’ve found other routes through. What’s more up for debate is what are those routes through? Do they involve perturbing biological systems, or are there things that you can do purely computationally that we’re not doing yet? Which brings me to the question I wanted to ask you next, which is around, I’m hearing a lot of talk about the new hypey area of biotech: virtual cells! Have you seen much of this talk in the last nine months?

Saloni Dattani:

I have actually. The thing that I read about this was this article by . I don’t know if you read his substack; it’s very good.

Jacob Trefethen:

It’s a great substack.

Saloni Dattani:

He has this article where he writes about the history of the virtual cell, where it’s at now, what the research is like, what the future might look like. He kind of starts by talking about one of the first mechanistic virtual cells. When I first heard the phrase “virtual cell”, I imagined it was like, I don’t know, it’s in a computer, you’re looking at this 3D diagram or something. But sadly, it’s nothing like that at all. It’s just this computer model, and you don’t really know what is happening.

But with the first system, the first virtual cell that was built, it was a little bit like that, in that there was this team at Stanford that was led by a scientist called Markus Covert, and he studied a bacterium called Mycoplasma genitalium, which is the smallest free-living bacterium probably that we know of. It causes various genital infections, and it has a tiny genome of just 600 genes, and that’s why they picked it. What they wanted to find out was if we can map what each of these genes are doing in this one bacterial cell, maybe this will help us understand how cells actually work as a whole.

What they did was they created this computational model where they represented each individual cellular process in that one bacterium, from their DNA replication, their metabolism, whatever. They collected lots of data from thousands of papers and tried to encode each of these processes as a separate module in their computer module. They linked all of that together and then tried to approximate what would happen in a living cell by updating that model at one second intervals. In 2012, I think they finished making this model and they could use it to predict cell growth and division. It was the first time that a full organism, one bacterium, was simulated in a computer. And one thing that-

This is a really cool project, obviously, but in our second episode when we talked about proteins and all the cool things they’re doing in the body, I do remember talking about how many collisions there are between different molecules in a cell and how many times enzymes collide with other molecules. It was 50,000 or something per second. I do think that even though they’ve made this computer model of the virtual cell, if it’s updating in one-second intervals and it has these, that’s missing a lot of things that happened within that one second, right, as we talked about. So even this very simple bacterium with this very complex computational model is still quite far, I think, from the real biological bacterium in a lab or in real life.

Jacob Trefethen:

Is there any way we can draw out the expectations here that we might have on future models? Is there some hope that next year there’s an even better virtual cell and the year after an even better one, and it’s that kind of problem? What do you think?

Saloni Dattani:

I think there are lots of efforts right now to improve these virtual cell projects. The Arc Institute has this Virtual Cell Atlas where they are basically putting together lots of different data sets, measuring different things within a cell and helping people to create better computer models of what’s happening inside each cell. What you’re then doing is trying to perturb each cell with a virtual gene edit. You’ve mapped out in this computer model what is happening between all of the different genes and proteins in the cell, and then according to the other literature or the data that you’ve collected, you’re saying, “Okay, what would happen if this gene was dysfunctional or something? How would that affect the cell as a whole?” They’re simulating billions of experiments and trying to predict their effects using that.

I think these things are definitely improving, but they are limited to an extent by the data that you can collect at all. We don’t have a great way to collect so much information at the level of milliseconds or microseconds, which is often how fast lots of things in biological systems happen. You can try to approximate longer processes and things like that that are happening, but it’s still going to be, I think, quite hard to get to the real life version of what is happening inside an organ or a tissue or a cell.

I think maybe it’s a little bit similar to weather forecasting. With weather forecasting, you’re also trying to make these predictions; weather is very complex, and there are loads of different local environments and things interacting with each other. There’s lots of local data collection efforts that are being pieced together for these bigger weather forecasting projects and computational models. It’s sort of similar to this, right, in that you might not have the data for each individual specific locality and you might not have the right level of data sometimes, but you can still make some predictions.

What’s going to be important is to compare those predictions with what you actually observe in the real world. Just like with weather forecasting, the way that you would improve a model, I think, is by making those predictions, comparing it to the real experiments or something like that and seeing what’s the difference between that and then can we use that to improve the performance of the model. You still do need to do lots of experiments and collect lots of data in order to improve these virtual cells at all. Even when you do, I think there’s still a lot of additional complexity that happens if you’re trying to understand how different cells work together, how different tissues develop or organs interact with each other and so on.

In order to understand those things, maybe you don’t need the specific pathways between every single protein in order to understand these wider structures. Maybe there’s a higher level of data that you need to collect and that could help you understand or predict those processes better. But if you’re really trying to recapitulate the entire thing, that’s going to be still incredibly hard.

Jacob Trefethen:

It’s interesting to try and think through if there are ways to represent complexity that mean that you don’t- to sort of reduce the number of real world samples you have to take. It’s been quite interesting to see over the last decade or so, these projects that are incredibly empirical to try and map out paradigm structures that are beyond just one cell. What comes to mind for me are the brain maps of most recently the fruit fly, where when I was growing up, I heard about scientists have mapped C. elegans, which is 302 neurons of complexity.

Saloni Dattani:

And that’s the worm.

Jacob Trefethen:

That’s a worm, yes, sorry. A small worm, in fact. But scientists know how each of the neurons in that worm’s brain connects to each other. More recently, well, what is a connection? A synapse is where neurons connect to each other. If you’ve got 300 neurons, you’ve actually got 7,000 synapses. More recently, scientists have tried to look at organisms closer to humans than worms, but not quite at the human level, of the fruit flies. They’re mapping in absolutely exhaustive detail the 50 million synapses that occur in a fruit fly’s brain. Humans, we’re dealing with tens of billions of neurons and I assume trillions of synapses, so we’re not yet at the human level of complexity, but having these models that are able to be represented computationally, but derived from a real fruit fly might be a nice kind of bridge here where we know that fruit flies share many genes, or sort of precursors and sometimes genes themselves, that we have related to our brain, so we know there’s going to be some relevance. We can hone in with a bit more detail on that. But do you- at the same time, this is not a functional model; this is a diagram of a brain more than anything else. Do you hold out hope there of those bridging models being useful for AI?

Saloni Dattani:

I mean, I think in some ways, yes. I think you’re right; this shows basically all the connections in the brain of the worm or the fruit fly or the human, but it doesn’t necessarily show how they interact with each other and what happens if one thing changes. I think that’s basically where you need the experimental information. But one thing that reminded me of was the very time-consuming, very manual effort of actually putting together these brain maps in the first place.

I don’t know if you know about how people figured out the process of programmed cell death, which is just this process by which as cells are developing in an embryo or whatever, there are some cells that basically die off at predictable points throughout an organism’s lifespan. In order to find out about this- there was this biologist called John Sulston, and he tried to map out the development of the embryo of the C. elegans, that worm that you described. He basically looked under extreme magnification at a single worm.

For two to four hours every day, for 18 months, he looked at the single worm and he noted every single cell division and death that was happening in the embryonic worm, as it was growing up. He noted down every single cell division and death as this series of circles. When he went home for his break or at the end of his shift, he kept the worm embryo in cold temperature to freeze it for them, and would come back the next day and unfreeze it and then see what happened next. He mapped out this entire worm embryo, its developmental pathway, and discovered that every C. elegans worm, the cells kind of divide and die at predictable points.

This work basically generalized later on to other species, in different ways obviously. But just the amount of care and detail and data collection and how manual some of that labor can be, in order to understand some of these processes, was really interesting to me. Hopefully, there are better models now that can recognize those things under a microscope so that someone doesn’t have to look under the microscope for two to four hours every day for 18 months to understand these cellular processes. But at the same time, it just goes to show how much data you need to collect in order to understand some of these pathways.

Jacob Trefethen:

I mean, thinking about that individual worm, there’s something almost spiritual about it, like the amount of knowledge that that one worm gave us.

Saloni Dattani:

Thank you to that worm.

Jacob Trefethen:

Yeah, thank you to that worm. It makes me think of a science fiction short story I wanted to write sometime. You know the short story by Ursula K. Le Guin, “The Ones Who Walk Away from Omelas?”

Saloni Dattani:

I don’t know it.

Jacob Trefethen:

I think I’m allowed to spoil it because it is a short story and the gist is famous, but fast forward 30 seconds if you don’t want the spoiler. Basically, there’s essentially a utopia that people are living in, but in order for this utopia to be maintained, there has to be a child who is living in terrible conditions and is at the heart of this otherwise glorious city, sort of living in dirt and not being treated like a human. There are some people who, as the title says, walk away from Omelas. I don’t know how to pronounce that. There are different interpretations of this, and I won’t get into that.

But the short story that I have sometimes wanted to write that’s maybe sci-fi horror is a more scientific version where, let’s say that people in the city called Earth or San Francisco or somewhere think that AI systems will be able to cure lots of diseases if only they had access to high quality data from a human. One person sacrifices themselves and says that for the next 18 months, like that worm, “You can study me with any sensors you want and perturb me in any way you want to generate the knowledge that will allow everyone else to be immortal and live for the rest of time.”

Saloni Dattani:

That’s amazing. I hope you write this short story.

Jacob Trefethen:

Once again, I’m not quite sure what the implication should be from it.

Saloni Dattani:

That did remind me actually of another very similar project in the 1990s called the Human Genome Project. This was again, a type of map, right? You’re trying to map what human genomes look like, where the genes are, what each gene is doing, which proteins it’s producing, and so on. I think the initial, the kind of reference genome that a lot of people use is this composite of just one single genome. They compare genetic mutations that you see in other people with that composite reference genome. While that is useful in a lot of ways, it’s also very limited because naturally there’s a lot of variation between people, and that reference might not be exactly the right reference that you should be using for a particular thing, or you could just be missing out on the internal variation, the larger structure, it’s not necessarily just that one mutation that might be different, but there could be many larger segments of your DNA that could be very different between people, and that’s something that would be hard to tell if you’re just using a single genome.

I think in the last five years, people had recently published this new research project called the PanGenome Consortium, where instead of just having a single human genome reference, they mapped some, I think it was like a few dozen people’s entire genome sequences and used diagrams to represent how different parts of their genome varied from each other. You have this more diagrammatic way to show what the entire genome looks like rather than the single linear sequence. I think it’s sort of similar because even with all of these, the brain maps or the developmental maps and the single worm that was used for this pathway, obviously, there is lots of stuff that we can learn from that. But at the same time, most of the interesting things in humanity are things that vary between us. If you aren’t able to study that and you don’t have good references for what the variation looks like, then it’s harder sometimes to tell what is causing differences between people, how different processes are linked to each other, how to actually understand the causes and effects within the body.

Jacob Trefethen:

Okay. Well, stepping back, we’ve discussed all different forms of models that currently get used in drug development and some models that are at the frontier getting more use. Let’s just see, is this going to still be a necessary part of drug development going forward, and will AI help or not so much?

Saloni Dattani:

My summary from all of what we talked about here, the animal models, the cell culture, the organoids, the virtual cells, is essentially all of them have various limitations. The animal models are easy to work with sometimes; they’re what we’ve traditionally used. But in many situations, they don’t really recapitulate what human disease is like. With the organoid models or even the cell culture, they’re quite limited specific features that you’re trying to replicate. At least with the organoids, I think they’re human cell derived, so there are some parts of them that might be more similar to us, but they don’t capture the whole complexity of what your organs are interacting with in your body and things like that.

The virtual cells, I think, are very interesting, but you still need to either experimentally or computationally perturb them in order to understand what is actually going on. Because really it’s this complex, almost black box model, and you need to do various computational things, or you need to do experiments in order to understand what the pathways are like within that cell, and it’s just a single cell. I think we are still pretty far from capturing what the biological complexity of a tissue or an organ or a human body is like.

Jacob Trefethen:

A human body, a human body, a human body. I think it’s time to talk about human clinical trials.

Drug efficacy

Saloni Dattani:

Okay, so animal models, organoid models, and virtual cells all have different limitations. But I think really the benchmark for whether something will show success in treating human disease or preventing human disease is actually by testing it in humans, in us.

Jacob Trefethen:

Fundamentally, what we’re asking in human trials can be split into two things in terms of the knowledge we’re trying to gain, I would say, which is: safety and efficacy. There are further things that a drug approver will be interested in that are to do with the manufacturing process of a given drug to assure that you’re making things to the right product quality and that kind of thing. But for simplicity, I’m just going to talk about knowledge generation of safety, knowledge generation of efficacy. You can pick, which do you want to do first?

Saloni Dattani:

Let’s do efficacy first. I want to know if things work.

Jacob Trefethen:

What are you doing when you’re testing for efficacy? Well, you are trying out a drug in a population that you think is relevant enough to the people who might end up using the drug offer approval to show that it works for those people as good or better than the other options they might have. In order to do that, there’s a few steps you got to go through. First of all, how do you get those people in this clinical trial? That’s actually one of the under-appreciated maybe bottlenecks of drug development is that very few people actually take part in a clinical trial.

There are something like 5% of people in America have taken part in a clinical trial in their lives. At least that’s the last statistic I saw. That means that you’re already dealing with a big loss of potential. If more people volunteered, we would get more medical research done. We might want to talk about why more people don’t volunteer.

I think there’s a couple reasons for that. One is just that information; people are not aware that they might actually be able to contribute to medical research in a given topic, or it just doesn’t come to mind. You have to be told by a doctor if you have a particular disease, then they might make you aware in a one-to-one communication, or maybe you’ll see a poster on a pinboard somewhere and volunteer that way. I’m such a lazy person that I, even when I do think, “Oh, I should volunteer for something,” it ends up, you know, the last one I was considering volunteering for, it just clashed with my actual work life.

Saloni Dattani:

That seems normal.

Jacob Trefethen:

Oh, thank you. I didn’t end up doing it, but just the practicalities of life get in the way. Another one is compensation. There’s a debate in the bioethics community about how much you should compensate people who participate in clinical trials. Should you just give reimbursement for travel to get to the clinical trials, or should you compensate people with actual payments that are more substantial? There are different opinions on that question, but it is clear that it does affect how many people do partake in medical research. Some people report that they’re perfectly fine with the risk, but the fact that they have to take a day off work means that they will get less income and all of that.

Saloni Dattani:

That totally makes sense. I feel like there are also maybe other types of compensation. This is just speaking from my own personal interests, but I once tried to sign up for a study just because they said that they would provide me with a scan of my brain, and I was like, “I want that up on my wall or something. I want to know what my brain looks like.”

Jacob Trefethen:

You know, there’s compensation, but some things are priceless.

Saloni Dattani:

I totally agree that these things really are incentives because you are taking time out of your day, sometimes several days per month or sometimes for a period of months or even years potentially, to participate in a clinical trial. You really have to think about what the other things that people could be doing are. What is the opportunity cost for someone to be part of this clinical trial versus continue their job or relax at home or something like that? There’s also the potential risks that are involved with taking a new treatment that’s still in an experimental stage and things like that, and how you can compensate that as well.

Jacob Trefethen:

I think just walking through other examples of cases I’ve seen where recruitment has been difficult before, you know, before proposing any solutions, I mean, there’s this example from hepatology.

Saloni Dattani:

The liver?

Jacob Trefethen:

The liver, that’s right. There’s some viruses down there. One of them is hepatitis C. A group of scientists were trying to test a vaccine to see if it worked to prevent hepatitis C infections led by Andrea Cox.

Saloni Dattani:

Hepatitis C, that’s the one that causes cancer and liver disease, right?

Jacob Trefethen:

Yes, there are a few hepatitis viruses that cause liver disease, liver cancer, cirrhosis. Hepatitis C is one of the happy medical research stories of the different hepatitis viruses, because it was discovered in the 1980s and the first cure was developed for it only 22 years later, I think 2011. We now have many different cures for hepatitis C that you can take. Now, in addition to cures, which take about three months to complete, it would be quite useful to have a vaccine because a lot of people who get cured, a lot of people who are affected are people who inject drugs. If you don’t sterilize a needle, then you’re at risk of getting reinfected. Even if you get cured, you might get reinfected.

There was a research group that tried to run a clinical trial on a hepatitis C vaccine to determine whether it works. To do that, you have to enroll a lot of people. In this case, people who often don’t have the best contact with the medical establishment, people who inject drugs are often not as likely to show up for all future events in this clinical trial, so you’re already dealing with some complexity there. In addition, in order to statistically show that the vaccine is better than the placebo, you have to accumulate enough infections in the placebo more than the number of infections in the treatment group to show that you have a vaccine that works. In this case, the limitations on how hard it was to enroll people plus the number of people you had to enroll to reach that conclusion meant that they ran the trial for six years, and it took six years to reach the conclusion that the vaccine probably didn’t work. It’s kind of horrifying how slow things can be when statistics is what is the real driver there.

Saloni Dattani:

That also reminds me of another example of Zika virus, right? It’s quite different, but similar problems in the end. Zika virus, as people might know, is transmitted by mosquitoes. It is this epidemic infectious disease. There are big outbreaks in some years, and then for many years or a decade or more, there might be very little of this spread once people have built up enough immunity.

But what’s very challenging about, therefore, developing vaccines against the next outbreak is that in the meantime, when you have this lack of outbreaks going on for years, it’s very hard to get enough infections in the trial in order to see whether the vaccine protects you more, or reduces that number further, if the number to begin with is very low. But there’s another challenge, even if there was a local outbreak, which is that the problem with Zika virus- so, for most people, they don’t tend to get symptoms from the infection. A fraction of pregnant women who get infected, that is really the concerning part of the disease because it can cause miscarriage and it can cause congenital Zika syndrome, which causes various birth defects and things like that. That’s really what you want to prevent.

If you are trying to run a normal trial and you’re trying to see, “Well, is this vaccine going to protect against reduce the number of cases of congenital Zika syndrome?” that’s really hard. You actually would need, in a typical type of trial, you would need hundreds of thousands of participants, because not very many of them develop any symptoms; there might be very few participants in the trial who are getting pregnant during the trial; and you aren’t necessarily testing that frequently for whether they’re infected.

There could be another option. I think there are different options that you can take to making this whole process faster. One is instead of only testing against congenital Zika syndrome, which is in pregnant women and babies, you could instead test, does it reduce infections of Zika virus in general? You could test everyone frequently with PCR testing or something like that to see if they’re infected by Zika virus and see how much the vaccine reduces that in the trial.

Or you could go even further and do what is called a challenge trial. You could deliberately infect volunteers, not pregnant women, but other women who are not planning to be pregnant, taking contraception and things like that in order to test whether it protects them from an infection. Those volunteers would be deliberately exposed to the virus. If you did something like that, because you can time the infection, you can actually say, you’re not just waiting for the infection to happen, you’re not waiting for an outbreak, but you’re deliberately giving them an infection. You get to monitor them closely and you can test them very frequently and actually understand the specifics of the disease and how it develops, you can test more carefully whether this vaccine works. In contrast to the hundreds of thousands of people you would need in a typical type of trial, in this case, you would only need a few hundred, or even less sometimes. You can kind of speed up this process by really just changing the design of the trial, and that’s something that I find really interesting, but it’s just one way.

I think there are many different ways that you can speed up the process of doing this recruitment or running a trial. I do think challenge trials are really cool and sometimes they’re really the only option if you can’t wait for another Zika virus outbreak or something like that. But on the other hand, they’re quite difficult to actually set up. Sometimes it’s hard to recruit volunteers to infect them with a dangerous pathogen or something like that.

Jacob Trefethen:

That’s crazy.

Saloni Dattani:

Imagine trying to run a challenge trial for rabies or something, which kills most people and infects. Or trying to run a challenge trial for some disease that infects children, you know, and that has a lot of ethical issues around it. But there are also scientific challenges as well, like trying to actually infect someone in a similar way that they would get infected naturally, to really see how well this vaccine or drug might work in the real world. Sometimes it’s quite hard to culture a pathogen or a microbe in the lab in order to do that and then to be able to infect them at all and know what the right dose is that you should give them and so on. Of course, this doesn’t really work at all for diseases that are not caused by infections. I don’t know if there’s a version of a challenge trial with other non-communicable diseases. For injury trials, I hope we’re not actually cutting off someone’s foot or something in order to test the treatment, but maybe there’s some analogy.

Jacob Trefethen:

I’m wondering if there’s, you know, those attempts to heal burns. Is there a challenge for you to get burned?

Saloni Dattani:

Burn heal!

Jacob Trefethen:

Burn heal. Final generalization point I was going to make on challenge trials, though, is about adults versus children. A lot of vaccines you’re making to try and benefit children, not always, but often. It would not be ethical to have children in a challenge trial because they can’t consent in the same ways that adults can. You end up doing trials in adults that you hope might generalize, but you really don’t know because the immune system of people at different ages is pretty different, and they’ve had different levels of exposure to the pathogen of interest before sometimes.

Saloni Dattani:

Right. I was going to say, maybe you just choose some really short adults like me or something and hope they’re representative of children. But you’re right that obviously I’m different from a seven-year-old.

Jacob Trefethen:

That’s my best guess.

Saloni Dattani:

So I mean, if we think about improving recruitment, for example, maybe you could have better websites or better search tools that match people according to the conditions that they have or their interests to the trials that are ongoing, something like that. Or in some cases, you could automatically enroll people into a trial if the different options that they would get for treatments are already things that they would get in real life. So let’s say it’s not for an experimental drug, but it’s for treatments that are already varying in the population. That might be an option. Maybe there are different administrative things you could do to make trials run faster.

Jacob Trefethen:

It sounds like most of those things that could be done are not AI-specific problems. Some of them might be amenable to some boosting from AI. So maybe the website one and the recruiting one. What do you think?

Saloni Dattani:

I think those two for sure. Right now in the US or even in the UK, it’s quite hard to actually figure out which trials are ongoing and to register your interest in them. There are some websites that do that, but if you have a disease or some condition, it’s quite hard to then get matched to trials. Even simplifying something like that would be probably really useful. But at the same time, maybe it’s just that 5% of people in America who are going to multiple trials. How are we going to actually expand the population beyond that? I think that’s something that needs more thinking. This could be a situation where you want that automatic enrollment for the different treatments that are already available to test what’s better. Or better compensation, or other incentives to get people volunteering — if the only reason that they have to not volunteer is because it’s too time consuming, or it doesn’t pay them enough, or it’s just not worth not doing other things they could be doing.

Jacob Trefethen:

Right. Just to give one more example that I think illustrates something that AI won’t be able to get around. Sometimes the data you’re collecting is more invasive than others. You just talked about a Zika challenge trial where you really want to stare at that before deciding to volunteer or not and make sure you’ve understood the risks. An example that we’ve come across in my work at Open Philanthropy is we’ve funded trials related to Alzheimer’s where you want to sample, basically do a spinal tap, sample cerebrospinal fluid to be able to test two years into a trial whether something is having a benefit or not. That’s quite invasive; having a spinal tap is painful and not exactly fun. Sure enough, we’ve seen that dampen recruitment in trials because you just can’t get people to sign up for that part unless they really have to. Sometimes trials end up switching from, “Okay, we were planning to collect through spinal fluid, but for some participants or maybe all participants, we’re actually just going to do an Alzheimer’s blood test and use that as an input.” But then you didn’t actually get as useful a sample that- the AI is not going to be able to deal with as much data there because you didn’t get to collect it. That’s absolutely as it should be. People should get to decide when they give up their cerebral spinal cord, so that’s another real-world difficulty here.

I do have some optimism on the efficacy front, though, from an AI point of view. Can I pitch you on a couple of things?

Saloni Dattani:

Sure.

Jacob Trefethen:

What you’re saying makes sense and it takes me back to our last discussion about animal models. One of the big things you got to always have in mind is how much is this animal model going to generalize to human populations we care about? Even human models, such as challenge models, you have to have generalizability fully in sight. Your example with Zika has me thinking about other ways that things won’t generalize. Hepatitis C, I just mentioned, has most people or many people get infected from injecting drugs, which means that, strangely enough, that is quite similar to how you might get infected in a lab because that’s what doctors are used to doing.

But something more like flu or strep or rhinovirus, if you’re trying to do a challenge model there, the way that you might get infected by a doctor, if they use a needle there, well, that’s not how I’m getting infected with rhinovirus usually. You’re like, “Okay, so you’re going to simulate a classroom where there’s some kids coughing next to me.” Not so similarly, you might get the exposure level way too high, for example, or if you get it too low, you won’t get an infection.

Saloni Dattani:

I was going to say it reminds me of the challenge trials that are used for malaria research, malaria vaccines, where people are in a room filled with mosquitoes or they have their hands in a little or in a big jar that’s filled with mosquitoes and they’re just waiting to get bitten by them.

Jacob Trefethen:

I really want to volunteer for a malaria one because they’re so well established and they’re actually extremely safe because malaria is so curable. But something about intentionally putting your arm in somewhere where you know you’re going to get a bite that will give you malaria is just so hardcore, I love it. It reminds me of-

Saloni Dattani:

But it’s hundreds of bites, probably.

Jacob Trefethen:

You know, those old TV shows of challenges you had to complete to win some prize. I used to watch one growing up where you had to go into a snake pit.

Saloni Dattani:

Fear Factor, right?

Jacob Trefethen:

Yes. Oh, for sure. That’s exactly the vibe.

Saloni Dattani:

Are there examples of trials that have been quite fast and successful?

Jacob Trefethen:

Yes, and in that lies some of my optimism for AI. I think we’ve talked about how hard it is to recruit and how long things can take, but all of that is a statistical question. Now, if you have a drug that’s okay versus a drug that’s excellent and cures everyone; the second drug, you will need fewer participants to statistically prove it cures everyone, you can get there pretty quickly. There was this case of Gleevec or imatinib is the drug name. Gleevec is the brand name of a cancer drug that was so good in the phase two trial that they actually got FDA authorization for the drug before the phase three trial had even reported out, and the phase three was almost more of a confirmatory trial. My hope would be that if you get AI improvements that lead to better drugs coming into clinical trials, you actually would see a benefit of cheaper and less long-lasting, hopefully, and less recruitment-contingent efficacy trials, too. What do you make of that?

Saloni Dattani:

That’s a really good point. I mean, there are other examples of this as well, right? In our first episode, we talked about AZT, azidothymidine, for HIV as a treatment, and that also was stopped early in phase two. I remember between the two different arms, in the placebo arm, it was 19 people who died from HIV, versus in the treatment arm, only one did. They stopped it early because this is clearly a big difference statistically, given the number of people in the trial. That meant you could, because it was so effective in this short amount of time, it was so dramatic, you could end this trial ahead of schedule using these preliminary results. That’s all you really need to know for that point.

But then, sometimes it’s not just about, is this treatment working on average? Because one of the reasons that people do phase three trials is not just to get a better efficacy data, but also to test it out in a larger number of people where there is more variation. Some of them are going to have rare side effects, or for some of them, it’s not going to work. In order to understand why those differences exist and to study them better, you sometimes still do need large trials.

It also reminds me of COVID. The COVID trials were very fast, finishing within a year. That’s despite them having some 30,000, 40,000 participants per trial, right, for the COVID vaccines. That really shows some of the things that can be improved in order to speed up clinical trials in general. I think there are several things that worked in those cases. One is, it was much easier to recruit people into COVID trials. Many more people were interested in it. Also, the disease was very prevalent to begin with, so it was easier to get the number of infections in the control group to see what the effect of the vaccines was.

The other reason was that the trials kind of happened in parallel. The phase one and two trials were happening at the same time, and the phase two and the phase three trials sometimes were happening at the same time. That means the full timeline can be shortened if you’re doing these different stages simultaneously.

The next thing was there was this rolling regulatory review. When you first described the drug development pathway, you said you go through phase one, phase two, phase three, and then you collect, you submit this huge data package to the regulator to see whether they approve the drug. But in this case, what happened was the regulators were looking at the data as it was coming in. They sort of started looking at the vaccine manufacturing sites as the trials were ongoing. There’s Operation Warp Speed and the amounts of funding meant that pharmaceutical companies could take risks in the sense that they could do these phase three trials very soon without waiting for the results of the phase one and phase two trials to come in, and they could decide that let’s just do them all at once.

Jacob Trefethen:

Sounds like some of those are not amenable to AI, though, I mean, you tell me if I’m wrong. I’m curious if there are alternatives to long efficacy trials that you think might be more amenable.

Saloni Dattani:

I guess the other option is instead of looking at the general outcomes for: has someone developed some disease or have they died from the infection or whatever, you look at biomarkers or things earlier on in the disease progression, in order to see whether the treatment or the vaccine works. Sometimes that can work. If you’re looking at the shrinkage of tumors or something, in trying to understand whether this is going to improve survival of people with cancer, or if you’re looking at the viral load of something in the body, maybe that is a good correlate of how severe the disease is.

When it comes to, I mean, if you know about human papillomavirus, for example, the way that’s the efficacy of those... Human papillomavirus is a type of virus that causes genital infections, and some of those can lead to cancers developing over a longer period. If you’re able to tell what these initial changes are in cells before they turn into full-blown tumors, and if you can see whether the vaccine reduces those initial stages earlier, then you could... I mean, if there’s a progression between those things, then you can hopefully have a pretty good idea that they’re also going to reduce the various cancers that it’s associated with. That’s exactly what happened. The HPV vaccines are very effective, were very effective against genital warts in clinical trials. Since then, they’ve also been shown to be extremely effective against reducing the types of tumors that people could get from HPV virus.

Jacob Trefethen:

As of now, there were no cases of cervical cancer last year in Scotland because the vaccine worked so well.

Saloni Dattani:

Yeah, so it was rolled out in schools and different cohorts. I think the cohort that was born roughly around my age, none of them had cervical cancer versus dozens or more in previous cohorts.

Jacob Trefethen:

That is so awesome. When thinking about new biomarkers, can we have the next case of that where you have some intermediary thing that does block off the really bad thing at the end? I think for me, this is an open question with AI, but I do frankly hold some hope out for it.

I think you can design systems that recognize particular signatures that someone in a disease state might be giving off, whether that’s from a blood sample, whether that’s from some other type of sample, that AIs might be better at clustering and creating those signatures from many different inputs that we might not think to put together. Now, for any one of those signatures, you’re going to really have to validate that it’s real. Wherever an AI spots some correlation, in the cytokines and white blood cell counts of some blood sample with a disease state, you then want to test in a new population, well, does that correlation hold? If you perturb these systems such that that count changes, are you definitely changing the disease state? So I don’t think it would be magical, but, you know, it might help a lot if you could validate more of those clustered setups. I do think AI is going to be better than us at some of those. Am I too hopeful? What do you think?

Saloni Dattani:

I think you’re probably right. I think it’s still quite hard because again, these are really just correlations. I think you often still do need to think about, you know, what is the causal pathway? Are these confounders? How are they related to the outcome that you’re interested in? Does the drug or the vaccine, if it reduces that biomarker, does it actually reduce the disease, or is it just reducing something else that’s a byproduct of the disease that isn’t going to cause its progression? With Alzheimer’s, for example, do we know that the amyloid plaques themselves are causing the disease or are they a confounder? I feel like I still don’t know the answer to that. If that’s the case for other diseases as well, you need stronger evidence sometimes, even if some of the correlational evidence is quite strong.

Jacob Trefethen:

Okay, you’re puncturing my optimism.

Saloni Dattani:

I do agree that I think probably AI models are going to be better than regular statistical models just testing each correlation one by one. Probably it’s easier to spot patterns and things like that with larger models and scan many different datasets and find these comparisons much better.

Jacob Trefethen:

What do you think about rare diseases?

Saloni Dattani:

With rare diseases, it’s often really hard to recruit enough participants for a regular trial, right, because it’s rare to begin with. Secondly, all the people who have some rare disease might all be contacted by different trial research groups to participate, but they can only really participate in one or two maybe, and so that’s often quite difficult. There are some registries online where people with rare diseases can sign up and then be notified if there’s a clinical trial for their condition. But the problem is really, how do you design a trial so that you can find out whether treatment is effective when there are only a small number of people who have that condition to begin with?

I think there’s maybe two different approaches to this. One is running a smaller scale trial. The people with these rare very severe conditions, don’t have any other options. If we can study them or monitor them closely and we can target the treatments very specifically to the condition that they have, then we don’t necessarily need a big trial. It’s the example that you gave before where let’s say someone has a rare genetic condition and you can use CRISPR or some kind of gene therapy to specifically target the individual gene that’s involved. If you’re able to do that, it might have a massive effect size. You don’t need that many people in the trial to see whether it’s effective. You still might want a larger trial to see what the side effects are like, to see how much heterogeneity there is, how much variation there is, between different people with that disease and how they respond to the drug or vaccine.

But there’s another option as well. That option is to set up collaborations. So not just to work in a single individual country, but set up these collaborations to recruit people from around the world for this particular condition. One really successful example of that is with childhood leukemia. When I was growing up, the movies that you would watch about kids with leukemia were extremely depressing. This child suddenly develops this horrible cancer and they only have a few years to live and their parents are really struggling with that future prospect being taken away from them. But the situation is very different now, and the survival rates are much higher than they were in the past. I think before the 1960s, there was a survival rate of 15%, I think, would survive more than five years. If they managed to survive more than five years, that generally means they’ll have a roughly average lifespan, and by that point, you can see them as effectively cured of the condition. But now, that survival rate has moved up from 15% to 85% or 90% for some types of childhood leukemia and 60% to 70% for others.

That’s really high. I think that amount of progress, there are different reasons for that. One big reason is that there have been these collaborative research groups across the world essentially set up to study the condition. There were these collaborations to enroll different children with leukemia across the United States, across Canada, and then separately in Europe. They kind of merged, and now there are these international research groups where you’re testing particular treatments or different types of regimens across kids in different hospitals in different countries in the world. Because childhood leukemia itself is quite rare, if you’re able to find more people with the condition across the world, you don’t have to just limit yourself to one particular country, you could then get enough data for a regular clinical trial. From my understanding, that collaboration has been a big driver of progress in the condition, just learning what types of regimens work better, how they work differently for different kids with leukemia, with different mutations in their cancers, genomes, and things like that. They’ve really helped to make the treatment more effective and safe.

Jacob Trefethen:

That’s amazing. It’s the least AI-able thing, human cooperation.

Saloni Dattani:

I could imagine it’s really hard to set up these trials if you don’t speak the language there, or if you’re not aware of the other trials going on. I would imagine that to some degree, AI can probably help with that.

Jacob Trefethen:

While we’re talking about different efficacy trial designs and multicenter trials and all that, is there anything clever, AI or unrelated, that you wish more drug developers would try out?

Saloni Dattani:

I think one different approach is really to change the design of the trial itself. In a regular clinical trial, you are usually testing one treatment versus one control group, right? That’s fairly inefficient, I think, because you have to repeat having this control group for each new trial that you’re developing. There are better ways to do this.

One example is a platform trial. What you’re doing there generally is you’re testing multiple treatments against one control group. Because there are multiple treatment groups as well, you have more data, which means that you can kind of see what the natural fluctuation is or the natural amount of variation is between people. That allows you to reduce the sample size overall. But it also means instead of having five control groups for five trials, you have one control group for the five different treatments. That is a much faster way to run clinical trials. The most famous example of this was during the pandemic. The RECOVERY trial in the UK was a platform trial where researchers tested more than a dozen types of treatments against the control group in the same trial, in one single trial, across a period of two years, they managed to test these 12 different treatments.

I think that kind of thing is much more efficient than a regular type of trial. These types of trials are pretty great, I think, in terms of efficiency, but it’s hard to actually coordinate them and set them up because now instead of just working with one drug developer or something, you’re working with five, and they have different timelines. Maybe they don’t want to test their drugs against their competitors’ drugs in the same trial, and they don’t want theirs to look worse and they don’t want to take that risk. So it’s hard to set those up, but if you can find better incentives for them or better protocols, then sometimes they’re much more effective.

Jacob Trefethen:

My colleague Ray Kennedy at Open Philanthropy funded a trial I thought was very cleverly put together by the researchers, which was a platform trial of two different drugs that hopefully work as antivenoms if you get bitten by a snake. The way that that worked, given the difficulties you mentioned, is that both of those drugs were repurposed drugs, I think they were both off patent, so that just makes it way easier if a philanthropy or a government wants to do that. I think that was also true in the case of most, possibly all of the drugs in the RECOVERY trial that you mentioned. They were already drugs in use for other purposes, which means that a government can fund them to be used without having to question a pharmaceutical company too much.

So we talked about human efficacy trials and we’ve tried to speculate on some of the ways AI might help and AI might not help. What’s your overall take? How much will AI help with human efficacy data?

Saloni Dattani:

My takeaway was, human efficacy — collecting data on whether things work in humans — is really the goal. If you want to develop treatments or vaccines or preventives for human diseases, you at some point need to use data from humans. The problem is sometimes it takes a very long time to collect that data. Sometimes it’s really hard to recruit people into those trials. Sometimes the trials are just not designed very well and they just take much longer, or are not very informative. There are lots of ways to speed that up through improving recruitment, improving the design of the clinical trials, maybe by having big collaborations and things like that, or testing different outcomes using biomarkers, things like this.

I think with these various different approaches, there are ways that AI can help, but it can’t really replace this need for actual human data. The human body is really complex. It’s often hard to predict how effective things are going to be. Even when you use biomarkers, sometimes they don’t correlate very well with the disease. Sometimes they’re confounded. Sometimes we might just not have good biomarkers at all for the condition. Sometimes, even if you find ways to better match people to enroll in clinical trials, they still need to actually come in. You still need the nurses and the doctors to run the test, do the operations, or actually perform different functions in order to collect that data and see whether the drug or vaccine works.

Jacob Trefethen:

That all makes sense to me. I would say my biggest hope personally for efficacy trials and improvements coming from AI to efficacy trials actually comes from AI improving drugs earlier in the design pipeline. It’s not about the trials per se, it’s that if you have better drugs that are more likely to work really well entering the clinic, then you don’t have to have as expensive trials.

Saloni Dattani:

Yeah, I think I agree with that. I also feel like in some ways we are talking about various obstacles that are still going to remain even with AI. But I think in another sense, we’re actually kind of giving people a roadmap for what other things need to be fixed, what other things need to be reformed or sped up.

Jacob Trefethen:

Absolutely right.

Drug safety

Saloni Dattani:

So, we’ve talked about collecting data on whether a drug works, but what about whether it’s safe? I guess I tend to think of safety and efficacy as really two sides of the same coin. We can often study them in similar ways. The safety data is often collected as part of a regular clinical trials, so you could analyze the difference between different safety outcomes in the same way that you would analyze the difference between the efficacy of a drug or a vaccine.

But in practice, that doesn’t really work. The reason is that it’s usually difficult to predict which side effects will develop in the participants. Usually what happens is you’re only comparing, you’re only doing a statistical analysis on the overall number of side effects that are seen in the treatment or the placebo arm, so each of the individual side effects might vary quite a lot between people.

The second thing that’s different is that, it’s quite hard to detect rare complications in a trial without a very large trial. For example, the COVID vaccine trials showed that the vaccines are very effective, and they had 30,000 or more participants in each trial. These are some of the largest vaccine trials in history, and 30,000 or more is really large, I think, for a clinical trial. But at the same time, it’s still not enough participants to detect some of the rarer side effects of the vaccines like myocarditis, which affected 1 in 100,000 people overall with the mRNA vaccines. If a trial has 30,000 participants, that’s obviously a lot if you want to study the efficacy, but it’s not if you want to look at these rare side effects. Maybe only a single person in that trial might have had this side effect. That I think means that you have to treat safety as slightly different from efficacy, and often you’re thinking about collecting that data in different ways. What do you think about that summary?

Jacob Trefethen:

I think that’s a fair summary. Also I often think about the timeline being different. You will get many adverse events that would count as safety flags near to the time that you first take the drug. If it’s a vaccine, you might get negative effects straight after taking; injection site pain is probably the most common adverse event from vaccines and you get that immediately. Within seven days, you’ll see a lot of negative events. If they’re going to show up, they’ll show up by then, within 30 days, especially. Same with drugs, if you ingest a drug that is to make you nauseous, it’s not going to make you nauseous in six months; it’s going to make you nauseous when you ingest it, sometimes the safety will show up early.

Whereas for efficacy, again, it depends on the particular case you’re wondering about. Sometimes a drug will be efficacious within the day as well. But if you’re talking about curing hepatitis C like we did earlier, that takes three months. If you’re talking about curing or treating hepatitis D, that’s actually sometimes one year or two years of treatment. If you’re talking about the new weight loss drugs to show how much weight you’ve lost, that might take months, you know? I think about that as another difference.

The statistics are also different. Basically, the worst case for efficacy is that it doesn’t work. But the worst case for safety is much worse than that. If you have a rare event, that can be absolutely awful. You have to be conscious of that, whereas there’s no equivalent with efficacy of a rare event.

Finally, there’s another difference I think about when it comes to prevention versus treatment. For a treatment, when you already have a disease, you might have a different efficacy bar you’re willing as a patient to accept. If something might work, well, I might as well take it, that kind of thing. On the safety front, you might tolerate a lot more side effects because you want to get rid of the given disease.

With preventive drugs such as lenacapavir from our first episode, such as the COVID vaccine you just mentioned, because it’s less likely that you’re going to get the worst case of a given disease, you need it or most people will want it to be much safer and have much fewer side effects. That’s the other lens I think about.

As I put those different lenses through the question of whether AI will help, I think in the best case, the answer is for cases where you can observe safety quickly. If you can get AI designing drugs early in the system that then enter clinical trials that have a higher chance of being efficacious, you could get drugs way quicker because you will have a high chance of it working and you won’t need as many people. You can do a larger safety population of thousands of people, but they don’t have to hang around forever in your trial.

Where I think AI, I personally struggle to see it helping as much is if the safety events may be happening later in time. If you are taking a daily drug for many years, that might accumulate in parts of your body that actually are bad. I start getting worried. Then I start getting extra worried if that’s a daily preventive drug, because people will want to have a higher bar on safety. If you’re taking a drug that prevents your progression of Alzheimer’s, but might risk brain bleeding because of neuroinflammation, but some of those brain bleeds happened two years in. Oh my gosh, that seems like really hard for me to imagine how AI is going to help with that one. But I just said a lot there all at once. I’m curious what you think in reaction to that or other aspects of safety trials.

Saloni Dattani:

I think I basically agree. I think it’s going to be harder to predict long-term side effects and kind of optimize for them because it’s harder to get the data for that. There is less data from past trials on this so far. The other thing that I’m thinking about where safety is different from efficacy is that you care more about the heterogeneity than you do when you’re looking at efficacy, because the types of side effects might vary quite a lot between people. You really want to be able to capture as much of that variation as you can in order to know how to treat someone, especially if it’s a preventive. Then you don’t want to be unnecessarily treating people who are going to have the side effects that could be quite severe for them.

But I do wonder, with most of these drugs, that many drugs don’t stay in the body for very long. Vaccines don’t stay in the body for very long. Why worry about these hypothetical long-term side effects that we can’t see in trials anyway?

Jacob Trefethen:

I think I worry about them more for the drugs you’re taking daily. I’m now trying to think about, do I worry about them for one-off drugs? Do you?

Saloni Dattani:

Sometimes. Imagine that it’s antiviral or is some kind of drug that causes damage to some vulnerable organ. But most of your body has a lot of reserve capacity. In your lungs, you have a lot of reserve capacity. You might not actually notice the symptoms until quite late, and that’s true for diseases, but it’s also true for potential side effects. There are things that you might only know if this drug you have been either using repeatedly or after a very long period. That’s one.

Then, I don’t know, there are kind of other, like, potentially dangerous effects that are only seen in clusters or rare or smaller demographics. One example that I often think about is clozapine. Clozapine is a schizophrenia drug, and it was initially developed in 1958, but it was only approved in the US in 1990. It’s quite effective against schizophrenia that is not responsive to other types of treatments. I remember reading about this and thinking, “Wow, that’s more than 30 years that it took to reach the clinic. What happened?”

The reason was that it was approved first in Europe in the late 1950s and early 1960s. In the countries that approved it, they saw these clusters of this rare condition called fatal agranulocytosis. Basically, the white blood cells are getting depleted, and white blood cells are really important in your immune system. If that happens, then getting an infection could be very dangerous. There were these clusters of people in Scandinavia, I think, that developed this condition after taking clozapine, and many countries in Europe then withdrew the drug. But it really was quite effective at the same time.

Is there a way to kind of manage this trade-off or find a way to reduce the side effects? There were these pharmaceutical companies in the US who tried to develop, do the trials again, try to see if they could develop a safer version. I think in order to get it approved in the US, the FDA required them to set up a safety monitoring system. In order to prescribe the drug, basically, the way that you would get a refill for the drug was to send in blood tests. That was very expensive, and until that system was set up, they weren’t able to approve it, and that only happened in 1990.

Then, of course, there are other situations like medical implants. Maybe there’s some kind of heart implants or some brain implant that someone gets that might be effective for that particular condition, but also it carries a small risk of infection, or it might just damage different parts of that organ. Over time it rusts, or something happens, and over the long term, it could cause side effects.

Jacob Trefethen:

That’s a really good point.

Saloni Dattani:

I think basically there are these long-term risks, but there can sometimes be these monitoring approaches or these secondary treatments to manage them or to understand which demographics are more affected by them. But at the same time, you do need to collect lots of data over a long period. Especially when there’s a lot of variation between people, it’s harder to skip that process, I think, and it’s hard to predict because the individual people who have these side effects might have some other differences that are not seen in a smaller clinical trial or in an AI model.

Jacob Trefethen:

Another thing that rules out some drugs from being useful for is a safety question of drug-drug interactions. If you’re on one drug and you’re going to go on another drug, are they going to interact? Do you think AI might help with that, or what do you think the story is there?

Saloni Dattani:

In one sense, that’s very hard to figure out because in order to know whether drugs have interactions in people, so not in a lab or not in animals, you need to have enough people who are taking those multiple drugs, who are taking the combinations. That’s much less common than someone just taking one drug, right? Because there are so many potential pairs of combinations, most of these are too rare to study systematically, and you often need some kind of network model or something to compare how these drugs might be interacting.

Right now, the way that we collect data on these potential interactions is either it’s in labs or it’s in clinical trials, or it’s basically through these voluntary reporting systems and these online databases that just have these scattered evidence that they’ve compiled. There are websites like Drug Bank and Drugs.com where there are potential side effects from having multiple drugs that interfere with each other. But it’s quite hard to know whether that’s causal because you just don’t have enough of the data in trials and you’re essentially just relying on people to report what effects they had after a drug.

First of all, those reports are sometimes not even verified. The US has two systems, one for vaccines and one for drugs where people can report potential side effects, and they are not necessarily side effects of a drug; they are just things that happened after the person took the drug or the vaccine. They could include things like the guy got struck by lightning or the guy got divorced; these are genuine examples of entries that people have put into these systems, so they’re not necessarily causal at all. It’s really just this system to have a public repository for people to answer potential complications, and then it’s something that other researchers will then follow up on and do proper analysis on. So really I think of that as an initial step.

With a lot of drugs like this, if you’re just relying on data that’s collected naturally from the population after a clinical trial has finished, you are often capturing things that are related to the disease, not the drug. The way that we understand whether something is linked to the drug itself usually is by running a randomized controlled trial in a clinical trial. It’s hard to fully replicate that in when you’re doing analysis of these internet databases or whatever you might collect afterwards. Right now, I think we don’t have anywhere near the amount of data that’s curated and that’s verified in order to have good predictions of drug interactions from that.

But on the other hand, I think there’s two potential ways that you could think this might be improved. One is maybe scraping the internet, not just using the databases that exist. Maybe they’re reporting on Reddit that they took these two drugs and then they felt really sick or something. Obviously this also needs verification, and maybe a robot wrote that or something, I don’t know, but it could be a first step. I can imagine sometimes that it will be helpful to use AI to scrape public databases or Reddit or Twitter or something like that.

The reason that I say this is because during COVID, one of the symptoms of COVID infections, the loss of smell, was initially- the reason that we know that that was linked at all was because people were reporting it online. People were tweeting about, you know, having something like suddenly losing their sense of smell and suddenly coffee tasted like poop or something. I remember reading a tweet along those lines and feeling really bad for the person. But it’s things like that where you wouldn’t really think to study that. That might not be a thing that people are collecting data on in a clinical trial or even thinking to input into these side effect reporting systems. They’re just these other random potential side effects. It’s only when people freely talk and report different things, their experiences from these drugs, that you can look into them. But really, I see that as a first step.

In this case as well, looking at these tweets online helps researchers then do epidemiological research on, is this loss of smell related to COVID specifically? Is it not just some natural, I don’t know, some other cause?

The second possibility is maybe like the virtual cells that we were talking about, maybe someone does a virtual liver. The way that a lot of these drug-drug interactions happen, or even how the side effects happen, is that the liver is not clearing them well enough, or one drug is interfering with the enzyme that usually clears another drug. If you’re able to predict the pathways in the liver itself, I would guess that you can go a long way.

Jacob Trefethen:

Most of the issue with drugs getting dropped from clinical trials and not making it to people is just from off-target effects and raw toxicity, right? What’s the current mechanisms for tracking that? What are we going to do about that?

Saloni Dattani:

Right now, there are a few. I mentioned the US has this drug side effect reporting system. This is people’s reports of potential side effects; it’s publicly available, but it’s not necessarily verified. There are other datasets as well. They’re more healthcare-focused ones, where the clinicians enter online reports about potential side effects using their own judgment of what would naturally happen with other people. Then there’s some other surveillance systems in the US where you use electronic healthcare records and insurance claims to try to monitor the safety of drugs. I think the US also has various toxicology and poisoning databases, and I’m sure this is true in other wealthy countries as well, where there are these systems where there’s poison control tracking and toxicology data where if someone has some very short-term reaction to something that is often investigated and followed up and entered into these databases for researchers to use.

Jacob Trefethen:

Because AI is often training on public data, one thing that sounds great about that system is that a lot of that is public, and then it takes my head to another implication of what is not yet public that would be useful for predicting off-target effects. When drug companies apply to the FDA at various stages, if they don’t get to the finish line with a drug, I imagine that some of the data that has some useful insight into toxicology for that failed drug ends up staying private rather than going public. That’s one thing. I don’t know if the FDA could release more from what they’ve already been submitted along those lines and not be quite as careful around making sure that drug companies are happy on that front and just take the public interest over the private interest there.

Another thing is, could you- if you were purposely trying to create public data sets that were useful for predicting off target effects, what would you do? I know there’s this nonprofit focused research organization called EveBio that’s trying to create a dataset like that, that is basically going in with the public interest in mind of, “Okay, I’m not actually trying myself to develop a drug, but I want everyone who’s making drugs to have some knowledge of what they can dodge and weave that’s better.” That’s another angle on it, I think.

Okay, so, putting it all together, safety is something that you want to test for in real humans and you don’t want to take for granted. What do you think about AI’s effect, if any, on safety trials?

Saloni Dattani:

I think it’s helpful probably as a first step for flagging these potential side effects that people are reporting online. Secondly, I think if you’re able to build good models of liver metabolism of different drugs, that might be pretty helpful. Trying to model drug interactions is going to be hard and it’s going to need a lot of data, I think. But at the same time, I can imagine that analyzing that with network models or AI or something like that can probably help improve what we know about interactions. And then, other side effects, the problem that we described is really the number of people. Some of these side effects are rare. In order to predict them, you’re going to need a large sample. Things like this are often quite hard to replace with computer models that are only trained on a more limited set of patients.

Jacob Trefethen:

I think I would take the more skeptical side on this one. As I introspect about especially preventive medicine, would I myself take a preventive drug that an AI predicted would be safe, or would I wait for the safety data? Would I advise a family member who’s older to take a preventive Alzheimer’s drug that had not yet been taken for at least five years by one person, but it’s a daily drug? That feels hard to me. Firstly, it might be illegal for a drug company to sell it before they accumulate that data. Secondly, if you’re putting something, if something is strong enough to affect your brain enough to reduce Alzheimer’s, it is strong enough to do some damage up there too. I think that I struggle to see AI getting around the need for multi-year safety data in real humans for each of these new drugs.

All that said, I wrote a blog post about AI and medical progress once and got a response from someone who I respect very highly, who said I was being way too conservative, and in fact, safety data is not going to be as much of a problem as I think. This is really getting at the edge of speculation and I’m not sure.

Saloni Dattani:

Well, I think there’s two last things that I wanted to mention. One is the long-term safety data that you mentioned, where that’s even harder to collect because you need such a long follow-up. The second thing I think is, I mean, sometimes the disease is bad enough that I can imagine that even with, I don’t know, with more limited safety data collection, I think some people would be willing to try them just because if you can demonstrate that this has a large efficacy, then maybe that’s worth that trade-off.

I guess I’m also thinking maybe the way that people might be thinking about the risk profile or whether they’re willing to take the risk of a drug that’s only been validated by AI or something like that. It’s a bit like participating in a trial. You don’t really know what’s going to happen. But it’s worse than participating in a trial because it’s not randomized.

Jacob Trefethen:

No!

Saloni Dattani:

So probably your experience doesn’t help as many people as it would if you were in a clinical trial.

Jacob Trefethen:

Unfortunate.

Manufacturing and healthcare

Saloni Dattani:

Once we found these potential candidate drugs and we’ve tested them in lab models or in animals, or we’ve tested them directly in humans, we found out that they’re effective and safe. I think the next part might be even more challenging for AI to solve. I think it would be fun to talk about what happens after that. Once you have a drug, how do we get it out to people? How do we scale it up? What do you think that pathway is going to look like? How hard is it, and how much can AI replace?

Jacob Trefethen:

Well, let’s break it down. First you have to manufacture the drug in question. If a lot of people are receiving it, you have to manufacture it at scale. Then what you’re going to have to do is find a way to plug into a health system of the given country you’re in and make sure that people who need the drug have access to the drug and can get delivered it.

Let’s start manufacturing. Manufacturing as it stands is very different for different modalities or different types of drug. We have really, as a society and as a species, nailed it when it comes to small molecules, at least in my opinion.

Saloni Dattani:

So those are chemical drugs, right?

Jacob Trefethen:

Well, all drugs are chemicals in a sense, but this is really, yeah, small chemicals. Just imagine a string of atoms and imagine it’s usually not that big and it’s small enough that it can diffuse into your cells and sort of do some useful property once it’s in there. When most people think of a drug, that’s probably what they’re thinking of. If you swallow a pill, it’s probably got a small molecule as the active pharmaceutical ingredient in it.

There are other forms of drugs too. In our previous episodes, we were talking a lot about protein drugs. There are protein drugs, protein therapeutics that, for example, antibodies are proteins, so if you’re ever getting antibody treatment — there are many different cancers that get treated with antibodies — that’s actually a protein, not a small molecule. You also have vaccines that are proteins, not small molecules. You have peptides like GLP-1s, which are small proteins.

Then there’s other modalities still. There’s RNA, so you can get mRNA vaccines. You can get RNA medicines, siRNA, or different forms of RNA: they can be in circles, they can be linear, they can be all sorts. There’s DNA. You can get DNA therapies, DNA is extremely cheap to make. Then on the more complicated end, there’s entire cells. Like there’s CAR T therapy where you get your own cells, T cells redone into a chimera.

Saloni Dattani:

Oh, that’s nice. Plastic surgery for my cells.

Jacob Trefethen:

Yes. Very, very chic. Then really, go way up to the other end and you can replace whole organs, not just cells. You can get a completely new, I don’t know, what do you want today? Kidney?

Saloni Dattani:

Heart.

Jacob Trefethen:

Heart, yeah, we can even replace hearts these days. Those are all interventions at different scales. Those are all things entering your body that are doing particular functions. But the ways to manufacture each of those vary a lot. My starting perspective here on AI is that there’s one vision of how AI can be useful that is mostly about small molecule medicines. I think that it would be astonishing if you only needed to take one small molecule and it magically did all sorts. GLP-1s are about as close as you can get. That’s a pretty small string. It’s a small peptide and it is pretty magical seeming.

But I think the vision of AI helping with small molecules is more like a vision of personalized medicine where people are getting a lot of different things they might need at different times. So the manufacturing, I don’t think will be a problem there.

But we can talk about what the problem might be. But if we’re thinking of a vision that is more to do with more complicated modalities, if it involves something like CAR T or involves cell therapy or involves something more like dialysis, where you have to go in every couple of weeks to have some procedure done and you have to be monitored by a healthcare professional while the procedure is being done to make sure that you don’t get harmed. That is not just going to be high price in the sense of a patented small molecule is really expensive, how are we going to afford it? It’s actually going to be high cost in that even if there was no profit in the system and it was simply paying the salaries of the people who are monitoring the equivalent of the dialysis machine and simply paying for that machine itself, that costs a lot and someone’s got to pay. It might be an individual, it might be your insurance company, it might be your government, but someone has to pay for that. So I think it’s a little underappreciated by some AI advocates who hope for a great speed up of drugs here, that the manufacturing of those more difficult modalities is not yet commoditized, and that even if it gets there, if you need a lot of monitoring the cost will be high, if you need an MRI machine to diagnose you, then that’s going to cost a thousand dollars to do a scan, and the machine itself costs a lot more than that, if you need a PET scan, it costs five thousand dollars. That’s where my head goes when it comes to scaling.

Saloni Dattani:

I’m going to, for once, take the AI booster side. I think in some cases, you could simplify this manufacturing process. One thing that I’m thinking of is when people used to try to culture cells in the lab. They used to use serum from different animals as the media to grow to help keep the cells alive. Then some researchers figured out that actually you don’t need the whole serum to keep the cells alive. You only need a small number of molecules: some amino acids, some vitamins, some key ingredients, and basically you could replace all the other stuff. Obviously, you’re not going to capture the whole complexity of a living organism, but you can at least do those basic things with just a small number of molecules. And that, I kind of wonder, with these different machines and these different testing methods and things like that, maybe there’s a simplified version that can get quite a long way and that is easier to scale up.

Jacob Trefethen:

I mean, I hope you’re right, of course, but my first response is just to look at the historical track record for modalities so far. Small molecules are now dirt cheap. You can get a drug where the active pharmaceutical ingredient maybe only took pennies to create, so let’s call that solved. The next biggest product class are antibodies. Antibodies have had 50 years now to improve the process of how they’re developed and become cheaper in how they’re developed. Still, the cost is not low enough to manufacture them at scale to provide for some use cases that would save lives. You should by now, in my opinion, be able to have preventive malaria monoclonal antibodies that kids in West Africa get given before rainy season when malaria is common. But we still cannot get the price low enough. We can’t get it below $10 per gram. That is, it’s not cost effective enough for public budgets to then pay for it. There has been more innovation in antibodies, and we’ve had 50 years, I’m like, oh my gosh. I’m curious what you respond to that.

Saloni Dattani:

I think there’s a few things that come to mind. One is, one version of antibody treatments is anti-venoms, right? You’re trying to treat people who have had a snake bite and sometimes they don’t know which snake has bitten them and they don’t know which anti-venoms they should use. I think if you had more data collection on which venomous snakes are in your area and maybe you have better recognition technology of what the bites look like versus what the snakes are like. Maybe you can kind of help predict which antivenoms the person should be taking. That hopefully reduces the delivery costs because you then get to narrow down which ones are given to people. That doesn’t really work for the other types of antibody therapy, obviously.

But I do wonder if maybe antibody therapy needs an effort like the, you know, after the human genome project, there was another project to get the thousand dollar genome. Before that, it was millions of dollars just to sequence one single human genome. Now it costs less than a thousand dollars. I wonder if, if we develop some big initiative like that, for making antibody therapy cheaper, maybe it’s possible.

Jacob Trefethen:

You got me to sign up, but I don’t think that AI is going to be the bottleneck there. I think the way to get it cheaper will probably be a lot of process improvements that you learn as you go. You’re going to have to have a bunch of capital investments in big new ways of scaling up antibody production that you’re only going to be able to improve and tweak as you go, and that will take many years.

And then that’s only antibodies. What if we want to do something more like, well, I mean, let’s say RNA that is fixed in a particular conformation that’s hard to print. That describes some pretty interesting new medicines, but are we going to be able to scale it? I’m not sure yet. What about if it needs to be personalized like CAR T and you need to take a sample and then make it in a one-off fashion? I’m not yet seeing the AI improvements there.

Saloni Dattani:

What if someone develops a 3D printer for antibodies?

Jacob Trefethen:

I mean, I won’t rule it out because that’s basically how we make mRNA vaccines. mRNA vaccines, approximately you print the RNA, then you shove it in a liquid nanoparticle machine and you’re done. So yeah, maybe someone could do an antibody printer.

Saloni Dattani:

The other thing with the CAR T cells, so I guess I agree that that is personalized right now, but maybe it doesn’t need to be personalized. So I was recently reading about what happens after you get an organ transplant or transplant of pancreatic cells.

When people get transplants, they usually have to be also given immunosuppressants because their body might react to the new cells that are from someone else and recognize that as foreign material and try to destroy it, and that can cause various problems for them. But you can probably find ways to, I don’t know, downregulate the molecules on the surface of those transplanted cells to basically make them silent to your body if they’re transplanted into you. Then you wouldn’t need to personalize the way that that transplant happens. You wouldn’t necessarily need to match it as well, as closely. Maybe that’s the same for other types of personalized treatments, that there are other ways to kind of improve them in such a way that they’re less personalized while still working. I’m not really saying that AI can solve this. I’m basically saying, what if the personalized therapies become less personalized?

Jacob Trefethen:

I am hopeful for that too. Now, let’s then go to the next part of the debate on delivery then. Okay, let’s just grant that the manufacturing costs have gone way down. You still need a trained doctor to stand next to you to do the injection, to make sure that they’re doing it right. Are you imagining a robot for that?

Saloni Dattani:

I wasn’t imagining a robot. I think this is the part that’s hardest to solve because you actually need to deliver these drugs to different places. I mean, maybe you can have drone deliveries of medicines and stuff, but you still can’t do surgical procedures with them. From what I understand, the hardware of doing robots is often much harder than the software improvements.

Jacob Trefethen:

You know, for what it’s worth, I have heard AI boosters say the opposite of you often get more limited on software, but I don’t have a strong opinion there. I mean, I think the thing that will be necessary is, at the very least, a huge amount of capital investment in robotics that pays off along these lines. I think more fundamentally, I just think you’re going to need economic growth for this to work because I think the underlying cost structure of delivering complicated, dangerous personalized medicine — dangerous in the sense of if you get it wrong, it’s dangerous — is going to be high. To deliver that to many people, we have to have a larger economy, and either growth that people’s incomes grow and they pay for it themselves if they want, sort of Brian Johnson style, or in the sense of you have a big tax base and really good healthcare and healthcare is a really large proportion of a large economy.

Saloni Dattani:

That I agree with. It sort of reminds me of this quite horrible situation, or well not that horrible, but it was horrible for the few days that I had a situation that was, this I think probably couldn’t be replaced by a robot unless it was a very skilled one.

So last year, after I had a cold, I think a few days later, I woke up one day and I turned my head to the side. I think I was just scrolling on my phone or something, and suddenly the room started spinning and I basically had developed vertigo. That was probably a complication of the infection, which was probably a virus. Basically the entire room was spinning, spinning around. The whole disorientation I had from that really made me feel, firstly that I was going crazy or something very scary had happened to me, and secondly, even after it had slowed down and stopped, I basically just felt like throwing up and just kept vomiting for a while. I was like, “Something has gone wrong in my brain. What is going on?”

I remember when it had finally calmed down, I kind of tried to Google to find out what I should be doing. Are there any, I don’t know, is there any self-treatment I can do for now? I also called up the emergency medical service here in the UK, and one of the things that is recommended online is this maneuver.

Jacob Trefethen:

Manoeuvre.

Saloni Dattani:

When you get vertigo — one of the causes of vertigo is vestibular neuritis, is I think the one that I had — what happens is in your inner ear, you have your ear and then slightly in deeper than that is your eardrum. Beyond that is this thing that the cochlea, which looks a bit like a snail, and it has these little loops and those loops are called semicircular canals, and those canals have little hairs in them, I think, and also have fluid in them. As the fluid moves around in the canals and touches the hairs, it sort of detects your position relative to gravity or whatever. That helps your brain figure out which position you’re in and stuff like that.

What is happening with this condition, with this type of vertigo that I had, is that there are these tiny little calcium crystals in a part of just at the border of those loops, in this place called the utricle. Those calcium carbonate crystals get dislodged from that usual spot, and they move up and they start floating in the semicircular canals. Because of that, they kind of mess up the fluid detection that’s happening to detect your balance and your position and that. You feel like the room is spinning around. What you’re supposed to do is try to get those crystals back in the right place.

Jacob Trefethen:

This is like the worst video game of all time.

Saloni Dattani:

It’s like those little toy games where there’s a little marble or something and you’re trying to move around the box to get the marble into the hole. That’s basically what you have to do with your head, except you don’t know what the little canals look like at all, and where the crystals are. It’s fairly elaborate, but it takes about half a minute or something to do, to guide those crystals back into the right place. You have to do these precise head and body angled movements to make that happen.

So I read these instructions online when I was feeling very sick. I think partly because I was feeling very sick, but also partly because I’d never done this before. I was trying to do this maneuver myself and just follow the instructions. Can you imagine having these crystals in this little part of your ear? If you’re doing them the wrong way, it’s going to make it worse.

Jacob Trefethen:

Oh no!

Saloni Dattani:

The crystals are moving around in the wrong direction. I was trying this maneuver on myself, and it really made me feel very sick, even worse than I did before.

Jacob Trefethen:

This story is horrible.

Saloni Dattani:

But I think this, it was a Saturday, and I also was so confused about this whole situation, I was so freaked out. I had the day before bumped my head a little bit on the wall, and I was like, “What is this? Is this because of that?” I had no idea what was going on, so I went into A&E. After a while, I finally saw a nurse and she asked me a few questions. I think she was kind of skeptical at first that I actually had any problems because I looked quite calm, even though I had a vomit bag with me and was throwing up.

Jacob Trefethen:

Ridiculous.

Saloni Dattani:

But then she noticed, she asked me to do this eye exercise. Then she noticed that actually my eyes were basically spinning around or whatever, my vision.

Jacob Trefethen:

Your eyes were moving?

Saloni Dattani:

Yeah, yeah. Your eyes can move left to right in response to the weird balance that you feel. Because they usually try to-

Jacob Trefethen:

This is giving me vertigo hearing about this. Your brain’s trying to right it, yeah.

Saloni Dattani:

Have you ever seen those videos of a pigeon or something or a chicken? And the chicken is kind of moved around, but then their head stays in the same place.

Jacob Trefethen:

Okay. I can kind of- like an owl?

Saloni Dattani:

Yeah, yeah, yeah. Your body basically tries to do that with your vision. So when you move around, it tries to make sure that you’re still looking in the same place, so your eyes are kind of fixed on the same thing, but while your whole body is moving around, so your eyes are kinda moving. This is basically what’s happening to me, so my eyes were kind of spinning around a little bit and she noticed that and she was like, “Oh, okay, I better do the maneuver.” She did the maneuver and I felt a little bit sick, but it basically solved the problem. It like moved the crystal back.

Jacob Trefethen:

She did the maneuver.

Saloni Dattani:

She did the maneuver on me.

Jacob Trefethen:

Okay. I thought you meant like a yoga instructor. She was like, “Okay, now you have to do this pose.”

Saloni Dattani:

No, no, no. She picked up my head and did the specific maneuvers on my head.

Jacob Trefethen:

She picked up your head? What are you doing? Your head is attached to your body. How did she pick up your head? Oh my god.

Saloni Dattani:

I love that we’re four hours through this. This is the best story ever.

Jacob Trefethen:

If you ever find yourself four hours into a podcast, you will start describing the maneuver.

Saloni Dattani:

She eventually, you know, moved around my head and my shoulders and stuff to reposition the little crystals in my inner ear and to move them into the right place, and it kind of solved the issue for a short while. There was still the issue that basically that viral infection was inflaming, I think, that part of my ear. If I turned in a certain direction, the crystals would dislodge again and then the whole, you know, room would spin and blablabla. It sort of took a few days to get back to normal. It was very hard, but it was only a few days.

Jacob Trefethen:

A few days of vomiting?

Saloni Dattani:

A few days of the room spinning if I moved my head a certain way.

Jacob Trefethen:

Oh my god, Saloni, no, I don’t want this.

Saloni Dattani:

But it basically just made me think, this is the connection to AI that I feel like it would be really hard for an AI to replace that because even when you do have the instructions, you’re too sick to really do the thing properly. Even if you weren’t, it’s really hard to get it right the first time and it makes you feel much worse if you get it wrong. You need a trained professional sometimes to perform these procedures in real life, and you need a person or maybe a very sophisticated robot.

Jacob Trefethen:

Our message from this podcast is no science, no robots, what we really need is a skilled professional to do crystal therapy.

Saloni Dattani:

I love that there are literally crystals in your ear that mess up your balance.

Jacob Trefethen:

The human body can’t be real. No, I think the real response here is that what we need to develop is not only software AI, but also software AI that can plug into a human-sized gyroscope and can then twiddle you around in the right orientation.

Saloni Dattani:

Wow. I don’t know what I think about that. I feel like I’m imagining one of those, when you go into a cinema and it’s a 4D cinema and the chair shakes around.

Jacob Trefethen:

Smell-O-Vision.

Saloni Dattani:

You need training and you need to make the connections. That’s very hard to replace even when you have the instructions or something for someone to try to do it themselves.

Jacob Trefethen:

Fair enough. Okay, so what’s our takeaway?

Saloni Dattani:

I think AI can kind of, to some degree, maybe improve which modalities are being used or to narrow down the way that they’re used, with the case of anti-venoms — if you’re able to better match patients who have been bitten by a venomous snake to the right antibody that they should be taking, that seems like something that AI could help, but I can’t really imagine it being that helpful for delivery systems. How does AI help develop better hospital clinics? You still need human people to do those things, even if I think there are various places where AI might help.

Jacob Trefethen:

I’m with you on that, and I’ll end the segment by being the AI booster in that maybe there’s some way around this that does involve some combination of vitamins, supplements, and small molecules that can be determined to be safe enough that you don’t need much monitoring, you don’t have to go in and get checked up all the time, and you do get really good personalized advice. I think that is some place I hold out some hope. But my head then goes to a different bottleneck, which is no longer manufacturing and delivery, but who’s going to pay to generate the knowledge that a particular combination will work for you.

Saloni Dattani:

What about drones and robots and nanobots?

Jacob Trefethen:

Drones and robots and nanobots will save us all.

Saloni Dattani:

Will they?

Jacob Trefethen:

No, I mean, take the last one of that. I think that drones already are used for delivery to rural areas; that will help in some cases. Robots are already used in surgeries, used in — massively used depending on how you define what robot means — in manufacturing, a lot of that is robotic, it’s not a human that’s holding all of that liquid.

I think my perspective on robots is that there will be a set of improvements to biomanufacturing that will make things way better with loads of modalities. But I think it will take time and iteration in the real world rather than something AI can magically do.

Then, nanobots. I mean, I hear about nanobots probably more than your average person just because I live in San Francisco. When people say nanobots, what they’re often using the term to mean is a future technology that can go into your body, swim around, and sense different things going on in your body and then tune up things it sees that are broken. That, to me, is a concept that fills in at such a level of generality that I can’t yet comment on it, really. Basically, it sounds sometimes like people want to reinvent what a cell is or something, and then replace your cells with better cells. Maybe that will lead to healthier bodies. I think we need to get a couple more steps before we can have that debate in a way where we know what we’re talking about though.

Saloni Dattani:

I think, imagine you did have one supercell that does, it swims around in your blood, maybe it detects, “Oh, something might be wrong in your finger or something.” But how is it actually going to solve that problem? Can it actually manufacture the right drugs or gene therapy or something to deliver it to that specific part of your body? That’s really hard. That’s like combining the transcription, the small space — getting through each of these different parts of your tissues not being killed off by your immune system or just by enzymes and things like that, or just falling apart. Then also it’s somehow delivering these drugs to specifically where they need to go. That does sound like it’s reinventing a cell, except a really complicated cell, and we don’t even really know how to reinvent one cell.

Jacob Trefethen:

I’d love to be wrong on this one, and I look forward to people writing in and saying that we’re simply unimaginative.

Saloni Dattani:

Well, I feel like, okay, even if that’s possible, that’s definitely not possible in the next 10 years, I would say. That’s a prediction I feel quite comfortable making.

Jacob Trefethen:

I think the other side would be, “Look, Jacob and Saloni, what you’re not taking seriously enough is that before we get all these improvements in manufacturing and improvements in nanobot technology, that’s coming from having got these enormous explosive improvements in software intelligence, where there’s going to be hundreds of millions of people, who are not people, they’re actually in a data center and they’re actually AI agents who think of themselves as scientists or who we have told to think of themselves that way. But those entities have all the time in the world to think through all the problems that we may be coming up with. They will have all the best podcast debates you could imagine about what constitutes a nanobot and what experiments you have to do to make sure that it works and doesn’t get ejected by your immune system and can recapitulate metabolism, so it still has energy to move around and all of that. ‘Jacob and Saloni, why are you even having this conversation about 10 years? What you should be having a conversation about is...’”

Saloni Dattani:

Maybe we should be listening to the Jacob and Saloni nanobots instead.

Jacob Trefethen:

I wonder if we can let those bots loose on Spotify and Apple Podcasts to give us ratings.

Saloni Dattani:

All right. Let’s say we can, in principle, develop new drugs to tackle these difficult diseases. We can test them in humans. We can test their safety and efficacy. We can manufacture them at scale. Even if all of those things are possible theoretically in the next 5 or 10 years for certain diseases, I think we’re still not going to make... a lot of things are still going to go unsolved. One of the reasons for that is bad economic incentives and just who is working on the problems, what kind of problems they’re working on. Would you say that’s fair?

Jacob Trefethen:

Well, it makes me depressed to think that’s true, but let me get into the headspace. I think that if you look at what, in fact, the world currently looks like. Imagine Jacob and Saloni two generations ago. What would that have been like? Talking through a similar set of questions, but without reference to AI and just thinking about how the future might go. We might have said something like, “I think that people are going to discover lots of new drugs and will save lots of lives.” That would have been true. We have way lower mortality rates globally than we did back then, and a good portion of the contribution to that fact is drugs, not the only contribution. Sure enough, at that time, Jacob and Saloni, two generations ago, would have been talking when artemisinin was being discovered and was being created for curing malaria.

But now let’s fast forward two generations. With all this economic growth and the fact that small molecules now cost pennies to make the active ingredient of, are people still getting malaria? “Well, they can’t be because there’s a cure for malaria. Artemisinin, we invented it in the 70s in China.” Well, I hate to bring it to you, but people are still dying of malaria. That’s because of more complicated societal and economic problems. If your kid gets malaria, but you live in rural Tanzania, far away from a clinic or hospital, then you might not have access to artemisinin in time. Or if the drug regulator of Tanzania has not found a way to verify the imports from different countries manufacturing that drug, sometimes you’ll get the drugs, but they’ll be substandard or it’ll be made up and it will be a fake drug. There’s a lot of stuff that means that even with arbitrarily cheap, essentially, technology of artemisinin, oral or injectable drugs, people are still dying of malaria; 600,000 people are dying of malaria every year.

Saloni Dattani:

I guess the same is true for maybe TB and hepatitis C, right? You mentioned that hepatitis C is now curable, but still lots of people have it today and are untreated. Are there maybe diseases like that in richer countries where there is a pretty good cure or treatment, but there’s still a big gap in how many people receive the drug or vaccine?

Jacob Trefethen:

There are, and in the US, hepatitis C is one example where many people who are in prison have hepatitis C and are not treated. Currently, the main treatment drugs are still on patent, so it’s still very expensive in the US. That means that the prison system itself would have to pay to get those people treatments that would be useful for them and they, you know, that would- that’s not something that they’ve budgeted for from their central budget, so there are cases right now. The way that health systems in different countries work, even between different high-income countries, varies a lot. In the US, you have mostly a private insurance system. In the UK, you have mostly a national payer and a nationalized healthcare. It really gets down to the specifics of who gets affected positively and negatively by each of those. I think one shared similarity between many countries though is that people rurally get worse healthcare on average.

Saloni Dattani:

Right. I mean, I guess I’m also thinking about, you know, with, let’s say, measles. We have a really effective vaccine against it, but some parents don’t take it. I guess there are other issues where maybe there is a cure, there is a treatment, but someone hasn’t gotten diagnosed for it or for the disease, or they’re skeptical of healthcare in general, or maybe they’ve, maybe it’s quite difficult for them to access hospitals or the right specialists to get treated with that therapy, or it’s some complicated procedure and they have decided that it’s not worth the risks for them, or they are not interested in taking it.

But I’m also interested in maybe some of the diseases or problems that are not solved and where there is no treatment or cure for it because it’s not seen as a priority, or the economic incentives to develop these drugs and take them through trials, or test them and manufacture them at scale, is missing. What do you think of that? Are there things that come to mind?

Jacob Trefethen:

There definitely are, and actually, I currently work at a foundation that gives away money on this theme of what R&D is undersupplied because it would develop products that would help mostly people in lower income countries, so pharmaceutical industry is not that well incentivized to produce those technologies. I mean, there’s a couple of themes I would point to there. One is the wealth gap, but another is just the way that pharmaceutical development is incentivized even in the US and in the UK and in higher income countries has kinks in the system.

If you are developing a new chemical entity that you can patent, or actually any form of chemical entity that you can patent, that gives you a 20-year window from when you invent it to get through clinical trials, and if it works, sell at a high price. If you are a company that has been invested in by different investors who are maybe pension funds originally, and they expect a return, then you will charge high prices and then maybe make a return or make some profits, and that sort of system has its own internal logic.

However, if there are drugs that have already been invented, that have passed through the 20-year window, or if there are supplements that aren’t even drugs, say, like vitamin D or like lithium orotate, which some people are now looking into whether that might be useful for Alzheimer’s delay or prevention, but it’s not well known yet. Then there’s no pharmaceutical company that would be incentivized to pay the tens or hundreds of millions of dollars of a phase three trial to determine that lithium orotate prevents Alzheimer’s because out the other end, they can’t charge a high price because it’s a commodity market and anyone can sell you lithium.

That is another relatively well-known market failure of the generation of knowledge. There is not knowledge being generated in clinical trials that would be useful for people in medical practice. There are a few ways you can try and solve that, and I think I personally am somewhat hopeful that some of those ways will get tried out because there’s some building energy around it at the moment. Again, I’m not sure that that is more of an economic problem and an incentive problem than an AI-amenable problem, I think. Do you agree with that?

Saloni Dattani:

I think so. It reminded me of antibiotic development and the incentives for that. I think now AI models to discover and develop new antibiotics are getting pretty good. What you can do in order to develop them is you can sort of mine the genome of bacteria and fungi to see if they’re potentially producing compounds that would interfere with another bacteria’s growth. If you want to develop new antibiotics that bacteria are not resistant to, it’s really important to have this pipeline of new drugs coming out.

One other interesting idea on this front is if a bacteria becomes resistant to one antibiotic, sometimes that will make it more vulnerable to other antibiotics. Can you find pairs of antibiotics that you can put together so that if it becomes resistant to one, it then becomes vulnerable to the other, so it’s really hard for it to develop resistance overall? I think there’s lots of promising potential ways that you could develop new antibiotic compounds. But at the same time, the actual development of antibiotics has really slowed down.

Most of the antibiotics that we have today came from a 20-year period in the mid 20th century. This is what was called the golden age of antibiotics, and that has really slowed down, I think, for several reasons. One big one is that, okay, there’s one thing is that infectious disease prevalence has kind of reduced in a lot of wealthier countries with better sanitation, food safety regulation, things like that, so there’s sometimes less need for antibiotics in general. But the other reason is that the economic incentives to produce a new antibiotic are kind of really limited because there’s this problem of resistance developing to most antibiotics that we see. When a new antibiotic comes along, people like doctors want to reserve it for the most serious cases, they don’t want to give it out to everyone because we might have resistance developing and then there’s no last resort remaining.

So any new antibiotic that comes onto the market now has a smaller and smaller population that can use it, and so the market is just very limited. Potentially there are economic, I don’t know, you can change the incentives around or change how you pay for these drugs to incentivize better innovation.

One thing that the UK is doing is using this subscription model, so I’ve heard, where instead of paying per consumption or per purchase of the antibiotic, you’re paying a specific amount per year in order to use stocks of new antibiotics that are developed. You’re able to have this market that drug developers know is going to be there once they finish developing a new product, but it also means that you can control how much of the drug is given out to reduce the chances of resistance developing. I think that’s, I mean, I can see how you can use different economic incentives to incentivize better innovation in these areas, but it’s something that actually needs political will. It requires better incentives; it needs more funding, and that’s not necessarily something that AI is going to be able to solve in the same computational way.

Jacob Trefethen:

I think that people who, again, with my AI booster hat on, are thinking, “Well, AI will actually help solve a lot of these problems.” Often, I think what they’re imagining is that AI will help with cognitive labor and be able to replace or augment human researchers at an increasing rate. My synthesis of these positions is that I think in the world where AI starts increasing or replacing cognitive labor, some of the conclusions that those systems will come to are ones we already know. I think that there will be recommendations coming out of that digital university, such as you should have a subscription model for antibiotics. I think that some of those, you can actually do better than just waiting. You can allocate capital better now. You can do more public funding of science that may be not exactly perfect, but is extremely reasonable, and you will have been glad that that’s how societal resources were spent in 2025, even if AI goes gangbusters from here.

R&D funding

Saloni Dattani:

I think maybe we should do a little fun fact section on just how much the R&D funding landscape is skewed and some of the statistics that people might not know about in this area.

Jacob Trefethen:

Ooh, fun, I love it.

Saloni Dattani:

My first question, how much is spent on R&D globally per year, in let’s say, 2023?

Jacob Trefethen:

Ooh, fun. Okay, here are the ways I’ll try and get to an answer. Number one, I think of R&D as being about 2% of the GDP of OECD countries, or high- and middle-income countries. Then I could back into my answer via global GDP. But now I have to think what global GDP is. I’m going to guess that it is, okay, what’s the US? The US is like 20 trillion and the US is 25%. Okay, I’m going to say global GDP is 80 trillion and I’m going to say 2% of that because it skews to richer countries, which equals one- no god, what is it? One hundred, wait hold on, this can’t be right, yeah no that’s right... 1.6 trillion.

Saloni Dattani:

That was a really good reasoning process. You’re kind of off by, well, like half the way there. The estimate is 2.75 trillion US dollars.

Jacob Trefethen:

Oh I’ll take that, I’m happy with that.

Saloni Dattani:

Maybe that is because of the national differences and the sizes of the countries that are spending more. But anyway, that was a great guess.

Jacob Trefethen:

Maybe we should stop there. I think that one went fine. Okay. Well, I’ll ask you one.

Saloni Dattani:

Okay.

Jacob Trefethen:

My question for you is, where is that research taking place? Where are the world’s researchers located? I’m going to give you, actually, let me make it more specific. What do you think the population share of the world is in sub-Saharan Africa? What do you think the research share is in sub-Saharan Africa?

Saloni Dattani:

You know, I don’t have some of these basic facts in mind with these. I’m like, “I don’t know, ten? Three?” But, what percentage of the world’s population is in sub-Saharan Africa? The world’s population is seven, eight billion people. I know that three billion or so of those are in India and China. So the remaining, and then there’s another 0.5 billion people in the U.S. There’s only three or four billion left. How many of them are in sub-Saharan Africa? I don’t know, maybe 10% of that remaining population. How much is that? That’s like 10% of 4 billion, which is 400 million, which is what percent of the world’s population is that? Is that like 20% or so?

Jacob Trefethen:

I think the last two steps you got wrong, but they kind of canceled out.

Saloni Dattani:

Damn it.

Jacob Trefethen:

I think it’s more like a billion, but the number I’ve got is 14 percent. It sounds like roughly eight billion divided by eight.

Saloni Dattani:

Well, thank you for my two errors that canceled out.

Jacob Trefethen:

For people who do back of the envelope calculations, a tip I really have is that if you don’t have systematic directional reasons that your estimates are bad, then they will cancel out pretty often because you’ll get some too low and some too high, and then it’ll end up being okay. Anyway. Okay, and what about the research share?

Saloni Dattani:

You know, I think it’s going to be more skewed than that. I don’t know how much more skewed. I’m going to say, 5 to 10 times more skewed than that. So what is that, like 2 or 3%?

Jacob Trefethen:

Good guess, but the skew is 20 times, so it’s 0.7%. 14 to 0.7.

Saloni Dattani:

Oh, wow. 0.7% of the world’s researchers are in sub-Saharan Africa.

Jacob Trefethen:

Yep.

Saloni Dattani:

I think is definitely going to affect the types of diseases that get studied and the types of potential treatments that people make. Maybe the considerations that they have, if you’re trying to develop treatment for a disease in a wealthier population, maybe you’re not thinking about the same types of bottlenecks or you’re not thinking about the same degree of variation and like, how heat stable the thing has to be or how easy it is to transport or whether you can, I don’t know, whether you can take it in pill form versus injection and lots of things like that, I would assume are affected by that amount of skew.

Jacob Trefethen:

Yes, absolutely, and there’s actually evidence in the economic literature for that.

Saloni Dattani:

Okay, I have another question. How much is spent on healthcare in the US versus in Nigeria per person?

Jacob Trefethen:

Okay. So US, I have in my head that healthcare is shockingly high percentage of GDP. I think the last number I remember is 18. That’s roughly half public spending, half private spending. So if that were true and GDP per capita is 60K, call it 20%. So say roughly 10,000.

Saloni Dattani:

Wow, that was a really good guess. That’s 12,000.

Jacob Trefethen:

Boom. Okay. Nigeria, I don’t have any of those statistics to hand, including GDP per capita, although maybe that’s 5,000 or something like that. So, but let’s say, or maybe a bit lower, but let’s say, oh God, I don’t even know how to make a guess in Nigeria, but I’ll go with the GDP per capita is 5,000 or 3,000 and that the share going to healthcare is lower and that means say 5%. So that would be, oh god, okay, $150.

Saloni Dattani:

Also, I feel like the reasoning here is great, but it’s more like $90.

Jacob Trefethen:

Okay, that’s further off to be fair. Wait, 90. Okay, so I was out in the wrong direction on each one, which means that the spread is even larger. Okay, so you’re saying 12,000.

Saloni Dattani:

12,000 per person per year in the US is spent on healthcare versus $90 per person per year in Nigeria. So the difference is more than 100 times.

Jacob Trefethen:

Wow. Yeah, my gosh, that is not exactly going to get you multiple medical interventions per year. That’s really not getting you much at all.

Saloni Dattani:

What that means is that there often, there is lots more research effort that goes into diseases that we still see in wealthier countries, but have not yet eliminated in poorer countries. Especially infectious diseases, but I think also maybe things related to food safety and things like that.

Any diseases that are caused by disease, pollutants and exposures like that are probably harder to treat because there’s less effort that’s going into them because we have other infrastructural reasons that those things have been largely reduced in wealthy countries. For example, things like cholera are still quite common and there are big outbreaks in different parts of South America and Africa, especially near monsoon season, I think, and near coastal areas. But in wealthier countries where sewage systems and clean water systems are much better, the bacteria get filtered out and you have a much less, lower chance of getting infected or having severe disease from those conditions.

Jacob Trefethen:

The happy news is that some technologies that get developed in richer countries first do spill over in the sense of they, you know, if you do all of that investment in mRNA vaccine technology, while at the time that COVID hit, that was useful in the US, but probably was not that useful in Nigeria. But if you fast forward the next 10 years and people keep iterating on mRNA and making it more stable and making it require less cold chain and delivery, and improving the profile in various other ways, maybe that will find its way into a vaccine for a totally different disease in Nigeria in 10 years time. We can’t know, but if it does, that will have been helped by the, or a necessary driver of that was the, original vaccine investment in rich countries. The clearest example recently of this for me is lenacapavir, the drug we did our first episode about, where Gilead, an American company, spent many years doing very complicated tweaking R&D of this molecule. The people who will most benefit from it are women in Southern Africa. That is not where Gilead is going to make most money from the product; they’ll make this money in the US.

Saloni Dattani:

I think that’s a really good example. What I’m wondering about as well is, let’s say you do have currently an expensive process for developing a treatment or it’s some complicated surgical operation or something like that. How does the transfer of that knowledge actually happen right now? Can AI help speed that up or improve the distribution and the access to that knowledge?

Jacob Trefethen:

I think sometimes yes, and sometimes it’s tricky. I think that often a lot of knowledge in manufacturing is fiddly and you have to be able to walk another outfit through, “Here’s how we got this cell line to work so that it would produce, you know, the protein vaccine that we’re trying to make in the right quantity and, oh, this thing we tried doesn’t work and this thing does.” It’s hard to just implement a checklist, though the checklist helps. What do you think?

Saloni Dattani:

Is that because there are just too many things to record? That sort of reminds me of how sometimes it’s quite hard to reproduce or replicate findings even if the research is done well, there might just not be enough information in an academic paper to know how to replicate it at all. Let’s say someone has developed a new method to either make a vaccine or some cell therapy or something. Unless they really give a lot of detail, it’s quite hard to do the same thing in a different lab. Is that the kind of problem that you mean? Would this be solved with, I don’t know, video cameras? If someone like a lab technician wears a helmet with a video camera and records everything they’re doing, will that solve the problem?

Jacob Trefethen:

I think that’s such a cool question. I think that in the limit with better AI, you could imagine that helping. I think the problems often helped by software that can transfer between companies already, even without the cameras, there are parts of the process that have been digitized. You’ll be taking measurements of what’s happening in a given machine or bioreactor or something. If the conditions get too warm, you will automatically turn on a cooler. That kind of thing where you’re tuning the conditions does transfer already probably pretty well and probably better than the lab example that’s hard to reproduce because in order to sell a product, you need to demonstrate to a health regulator that you have a consistent way of making a product that you are sure what you’re getting out the other end. So actually that probably transfers better. At the limit of that, small molecules transfer extremely well already. There’s many- you don’t need to do an 18-month tech transfer process that costs $10 million and lots of flights and visits. Small molecules, knowing the chemical itself is a pretty big first step.

Saloni Dattani:

That makes me think that maybe for, let’s say, biological drugs, a lot of this kind of technical know-how is probably proprietary knowledge. It’s probably quite hard for other firms to learn from the process unless it’s all documented publicly.

I think probably there are some pharma companies or biotech companies who will share some of this knowledge publicly, but it’s not that common. I do remember reading the Genentech book which is behind me on my shelf. In that, Sally Smith Hughes talks about how one of the things that drew people to Genentech was that they were willing to publish their methods and their results in academic journals. That was important for the reputation of academics who wanted to join Genentech and leave academia for it because it meant they still have this standing of, you know, they’re producing, they’re doing this research, they’re sharing it with the world and other people can trust what they’re doing because if they can see exactly how the methods work, then they can replicate it and see and kind of build on that knowledge. I think that the building on this knowledge could be really helpful. If there’s some way to make that more public information, it would be really valuable probably, to help speed up development by other research groups or other firms working on similar problems.

Jacob Trefethen:

One source of data I would love, if it was more public, is all of the decades of documents and spreadsheets and data packages that pharma companies have for drugs that never made it to the market. Where, if a drug does make it to the market, then you have to, well, most regulators, especially the EMA in Europe, will publish a lot of information about their review of your drug, which is wonderful and helps patients and helps doctors. If the drug doesn’t make it to the market, you get less of the data, even if the drug does work, you sometimes have redactions to protect the proprietary information of the company. I think what you’ll see at least at the beginning of the AI technologies getting used in pharma is that traditional pharma companies that have operated for a long time will have a big data advantage for that reason.

Saloni Dattani:

Is there some way to use AI to un-redact redacted information?

Jacob Trefethen:

That is a fun punk science project. Yeah, someone listening, give that a go, but don’t cite us.

Saloni Dattani:

The thing that I’m imagining is there was this meme a few years ago, I think, of the TV show CSI that someone would use AI or something to... You would have camera footage of some crime taking place. The camera quality is not very high. The person would be like, “Magnify!” and then the software would just somehow improve the resolution of the footage, but actually you would just be hallucinating the information there. Which is really bad because you’re probably creating this fake persona that is doing this crime and has not actually done it. I wonder if the same thing would happen here. You’re just kind of hallucinating the details by using AI to project it.

Trust and ambition

Saloni Dattani:

Let’s say we’ve fixed all of the research and development funding problems. We have more Open Philanthropies, for example, who are helping to plug in these gaps or try to realign and get people to recognize which areas are neglected. Are there still going to be problems that we can’t solve?

Jacob Trefethen:

Aside from just simply public health systems being inadequate in a general sense — it’s not worth having a whole episode on US health insurance — reforming health insurance in many countries is going to be a requirement for getting people healthcare they need. Let’s just leave that aside. The final thing I would return back to is something you said about measles, which is measles is, technologically speaking, a solved disease in the sense of the measles vaccines are great and they’ll stop you from getting measles. But not everyone trusts that that is true and not everyone trusts the messengers who say that. So, at the end of the day, if people are going to benefit from new health technologies, the final boss is societal trust and individual trust.

That sounds, as I say it, like a negative, like, “Oh my God, AI is just going to make it so much worse because AI is populating my Twitter feed with deep fakes and I don’t know what the hell’s going on and why could I trust anything.” I’m actually not sure that that’s the way it will go. I have some hope that AI will increase people’s trust in better information, but I don’t want to bank on it. I mean, if I just look anecdotally, I think that large language models so far, I’d be so curious what you think of this. What I’m going to wager is that so far, they have led to better medical information, not worse. Because if people are getting, a lot of people talk to ChatGPT or Claude or Gemini about what drug they should take, what’s going on with some symptom they’re having. I think that, although each of those hallucinate and make stuff up, they probably give better answers by a lot than the next best alternative, which is asking your friends. So you might have more trust in science if not more trust in doctors. But what do you think?

Saloni Dattani:

Hm, I think to some degree I agree with that because I think when you’re thinking about what is the effect of talking to ChatGPT about medical information and things like that, the counterfactual to that is not having information necessarily from the CDC or something, right? The counterfactual is usually asking Google or looking up the first search results and maybe the first search result isn’t very good. I know that Google has kind of changed their algorithms in a way to prioritize better healthcare websites and information from them. But at the same time, usually that information is pretty limited. It’s, you know, you’re Google searching or you’re asking your friends or you’re asking one local doctor or something like that. People tend to have more trust in those sources of information, but generally speaking, I would say that an LLM is going to be better at providing information about, you know, should you take a measles vaccine? What are the benefits and risks and things like that? It’s going to be able to actually tell you about what the literature says and summarize that, and you can kind of talk back and forth with it and try to get it to answer your questions, and ask it to explain that to you in plain language and things like that.

But at the same time, my guess is that there’s also so much variation in this. It’s sort of like I want to run a clinical trial or something to see how well ChatGPT affects trust in the population because I can imagine that sometimes it just goes wrong and the hallucinations are actually really harmful and that if someone has some, I don’t know, rare condition or maybe they even have a common condition, but ChatGPT convinces them that they need some horrible procedure for, that might actually make them take a worse decision. It’s hard to predict how that will go. But I guess I’m also thinking about things beyond the LLMs and the social media spaces that are now cluttered with bots and deepfakes and things like that. That didn’t feel like it was the case 10 years ago. Obviously there are other issues with the internet back then, but this feels new and this feels like it’s maybe reducing public trust.

Jacob Trefethen:

It’s hard for me to take the optimistic side on that presently. If I really try and summon my optimism, it might be that we’re passing through a particular era that is not so epistemologically healthy. But we may be, we all, at the end of the day, have some desire to see truly and some incentive to see truly when it comes to our own health. That’s where my optimism comes from.

Saloni Dattani:

What about the actual ambition to solve some of the problems that we talked about? A lot of them are going to take people actually deciding to go out there and solve some of these economic incentive issues, or we need to maybe train some doctors, or we need to run better clinical trial designs, or we need to find ways to improve the recruitment of participants. Maybe some of these are policy changes, or they’re cultural changes, or they’re training people up. How is all of that going to happen? Is AI going to solve all of this as well?

Jacob Trefethen:

I think that one is for humans, and probably for the listeners of this podcast. When I get again in an optimistic mood, I just think a lot of these problems require people to take them seriously, think them through, and then you can make progress. You look at some of the big pushes of the past that have made progress, and we’ve done things much harder. We eradicated smallpox — like, oh my God, that’s a big push. I think this just being a little bit more self-confident is some of what’s missing. Will AI help with our self-confidence? I don’t know, maybe. I think overall, we just got to take a deep breath in and give it a go.

Saloni Dattani:

Do you think we would be able to eradicate smallpox today?

Jacob Trefethen:

Oh, what a horrible question. I hate to contemplate that the idea might be no. Oh my gosh.

Saloni Dattani:

I’ve been meaning to write a piece about this because my view is that smallpox was surprisingly cheap to eradicate. I don’t know if that means it was surprisingly politically easy, and I think it wasn’t; I think that was the difficult part. But if you look at just the amount of spending that went into that, it was less than the annual funding that went into the malaria eradication program. That suggests to me that there are some diseases where, if we put in the effort and we have the right technology and things like that, we can actually make really huge amounts of progress, as long as people come together and decide to coordinate, develop these efforts, and target the disease in a specific or an effective way. It does make me think that there’s a lot out there that could still be solved. What are your top three public health problems that people should solve that you think could be done with more ambition and effort?

Jacob Trefethen:

I think lenacapavir in HIV is a special case where a new tool could really change the game on a massive disease. People already know a lot about HIV. People already want to keep making a dent. Now’s the time to just...

Saloni Dattani:

Eliminate it. Use this extremely effective preventive drug.

Jacob Trefethen:

Yep. Next up would be more countries should follow Egypt’s lead on hepatitis C. Egypt in a few years went from having very unusually high rates of hepatitis C, due to public health campaigns in the 70s with needles that weren’t sterilized properly. They had probably over a 10% positive rate for hepatitis C, and now they’ve got it down to near zero because they screened and treated people. It’s the same in Georgia, the country, not the state. This is an issue that other countries could pick up, get loans from an international health funder, and just do it.

My third would be, I guess, malaria vector control. It’s possible that something like gene drives or some other form of biological vector control could, in some countries, eradicate malaria. I don’t think it will suddenly eradicate malaria everywhere; you’d have to do such a strategic set of releases, and it might pop back up, but you could really have a discontinuous effect, a drop way down. That would be wonderful. What are yours?

Saloni Dattani:

What are mine? I’m going to go a different route. I think all of yours were really great ideas. I’m going to say there are specific data collection efforts that people should be working on that will help for better research, but also will help AI tools help us more.

One of them is actually to map out the world. I don’t know if you’ve heard about OpenStreetMap, which is kind of Google Maps, but the open access, open source version of that, and anyone can contribute. You have to take coordinates from where you are, and you can insert onto this global map where the roads are, where the hospitals are, where the schools are, and specific little features on a map. For, I think it’s over 10 years, people have contributed to this mapping project voluntarily. I think they also received some nonprofit funding now to do humanitarian mapping, which is basically volunteers using satellite information, or going out into the field and mapping specific roads, hospitals, and clinics and things like that in some of the poorest parts of the world. In order for people who are delivering drugs for running nonprofits to provide humanitarian aid to these remote places, so they know exactly where to go, how to travel there, and where the nearest clinics are, and things like that, to help people map and improve that distribution process. I think that kind of thing is definitely one. Just better information about the world out there seems like a really big thing that could be hopefully solved with collaboration and open source data collection efforts.

Another one is the Demographic and Health Surveys. This is a huge initiative to collect data on causes of death in children and mothers, and also to estimate various other things related to that, like fertility rates and mortality rates. I think there are some specific efforts to collect data on HIV and malaria, like the prevalence and drug resistance and things like that, and that helps to inform lots of other efforts that are involved in distributing and delivering some of these things, and also to see how effective they’ve been, what the trends are like, and if anything is ticking up or if there are new outbreaks.

Then I think maybe the third one is, I feel like there are just a bunch of diseases that could be eliminated or eradicated that we haven’t really tried hard enough for yet. Rabies is one of them, where you could just vaccinate all of the wild animals in different ways. In Europe, for example, there have been efforts to... well, you catch rabies by a bite from a rabid animal, like a bat or a dog or a fox. Humans don’t transmit it between each other. The place that the rabies virus lives, the reservoir, is in wild animals, usually dogs, foxes, and bats. If you are able to vaccinate all the bats or vaccinate all the foxes in the wild, then you could eliminate rabies without having to develop a treatment or without even having to vaccinate humans at all.

In Europe, I think in the 2000s and 2010s, there were large-scale efforts to drop oral bait vaccines from helicopters into forests and get all the foxes and dogs and things vaccinated, so they would just eat these vaccines up. That’s not super effective because some of the foxes might be eating more of the vaccines than others, and some of them are not getting protected, and it’s funny to think about gobbling up vaccines in the wild. But you could have efforts like that in other countries as well, where you’re trying to catch infected animals and vaccinate them, or educate the population to teach them what the signs are of a rabid animal. I just think there are various diseases like this where, if we put in the effort, we already have some of the tools to eliminate them. We could just do that and why don’t we, you know?

Jacob Trefethen:

I love it. Let’s do it. Let’s do it. Let’s do it.

Summary

Saloni Dattani:

We are on the last stretch of this episode, so we should do a summary of what we’ve talked about so far.

Jacob Trefethen:

Okay, what did we cover?

Saloni Dattani:

Well, in order to develop drugs at all, we said that biological understanding is not necessary. Sometimes you can develop drugs without understanding how the disease works, and that has happened in the past. It’s less common today, and we have more rational drug design but it does help to understand how things work. You can have new theories, you can improve the technologies you have, and you can make big breakthroughs once you can understand the actual mechanisms. Then you can really filter down this search from before where you might just experiment with hundreds or thousands of different compounds, and narrow that down once you understand how the disease works. Even if we do understand how diseases work, sometimes it’s still hard to develop drugs for different reasons. Sometimes it’s hard to deliver the drugs to the right place, like in the brain, or it’s hard to make drugs that are effective and safe. There’s still a lot left even if AI is able to help us develop better candidate drugs, we still have to go through lots of testing before they can cure disease.

Jacob Trefethen:

Next, we discussed models. Animal models in the status quo are okay, but not great. Sometimes animals don’t get the same disease or don’t recapitulate infection in the same way as humans. It’s hard to generalize from animal results to human results, which leads to a lot of failure later in the clinic. Organoids are currently fairly limited, but can be a more human-like approach, and involve human cells and aggregations of multiple types of cells; they’re a newer part of the toolkit. Virtual cells that can happen on a computer are still in the early stages of utility, but absolutely an exciting area to push further forward. More complicated virtual systems that involve more than just a virtual cell and involve many different cells connected to each other. Also those are in the early days, but there are some exciting things that you could keep pushing forward and do more.

Saloni Dattani:

Right, and we often need experimental data to even validate whether those virtual cells are working correctly.

The next thing is, imagine you have made a candidate drug. You then need to test it out in humans eventually, at some point, if the drug or the vaccine is meant for people to take. I think to some degree, AI can improve the pipeline of drugs that get to this stage, but it’s really hard to still collect human efficacy data. Sometimes it takes a really long time to run trials because the disease might be rare, or the outbreak might have passed and it’s going to take a while before the next outbreak happens, and in the meantime, you might be waiting years, you might have to have thousands or tens of thousands of people in your clinical trial in order to see whether a potential drug has any effect at all. Sometimes even after that long period, you might find that it has failed. There are probably lots of non-AI related reforms that you could do to improve this process: better trial design, improved recruitment of participants into trials, and maybe AI can help in some ways better matching people to trials that they might be interested in. There are still a lot of human, regulatory, and policy-related obstacles that mean efficacy data is quite hard to collect. Then there are various statistical reasons that it takes a long time, and it’s something that is very difficult to automate or predict in advance.

We also talked about biomarkers, how they are sometimes useful and can sometimes tell you about the earlier stages of a disease, but you still need to validate them. You still need to collect data to find out how well they correlate with the later stages of a disease. Then you need causal inference research. You need to find out whether the biomarkers are just a correlate or are they just a byproduct of the disease process, and if you treat them, you might not actually improve the overall disease that much. I think that to some degree, AI can help synthesize the existing research, but you still need to actually do experiments, and you still need to validate this stuff in real life.

Jacob Trefethen:

That’s the human efficacy data. We then discussed safety. Safety is probably harder, at least in my view, to skirt around or improve upon with AI than efficacy. When it comes to preventive drugs, my optimism would be for treatment drugs, where your other option is really bad, and you’re in a bad disease state already, where maybe there’ll be some acceleration that people will tolerate more, there are other things you could try and do with AI. You could try and model liver metabolism better, model drug interactions better, and that will help. A lot of the knowledge or data that already exists that would help you do that is proprietary in drug companies, rather than public, so there’s a limitation on how you can build public models there currently.

When it comes to safety data, another thing that’s hard to get around is that variety and heterogeneity between different people means you need larger samples. You need larger samples to detect rarer events, and you just need even more data to detect drug-drug interactions. Existing datasets, when they get really large, are usually the ones that are not randomized because they are people self-reporting, so it’s hard to get the causal links there. You don’t want to blast through safety too quickly for things that could do a lot of harm.

Saloni Dattani:

Once we’ve developed drugs, we’ve tested them, and it turns out they’re effective and safe, there are still various challenges that AI might not be able to solve. What are some of those?

Jacob Trefethen:

Manufacturing and delivery is what we talked about next. Some technologies here are going to be easier than others, especially small molecule drugs and anything else you can already buy over the counter, vitamins, supplements, all of that. AI may help if knowledge can be generated about what combinations of those drugs people could use to benefit themselves. Ultimately, some new modalities, in my opinion, we need more economic growth to support the high costs that they may require, and in particular, even if we manage to get manufacturing costs down, there’s always going to be the cost of delivery. The more you need an expert human or humans to be present to be doing procedures or to be monitoring procedures, the more regular those procedures are, the more expensive the cost structure here will be. So some of those technologies get invented by AI, that will be wonderful, but we’ll need some economic growth to go alongside and some good health systems and good insurance reforms to go alongside those inventions to deliver the drugs to people who need them.

Saloni Dattani:

The economic growth angle you mentioned is really interesting. It also leads to this other thing that we talked about, which is the skew of which problems we actually have decided to tackle so far. For now, most of the problems that the new technologies that are being developed in medical research are kind of rich country diseases. They’re diseases where the incentives for new drug developments are very high, the markets are high, and there’s going to be a higher return on investments. That’s much less the case for tropical diseases, for rare diseases, for areas where the funding model is just not going to work, like antibiotics, where new drugs that are developed have a very limited market and there isn’t much of an incentive to develop the drugs at all, or once they’re developed, to actually take them through clinical trials and to manufacture them at scale. Better incentives are needed, better funding models are needed. Otherwise, some products that are developed won’t get delivered, and some diseases won’t have drugs developed against them. Even when cures do exist, they won’t reach everyone who wants or needs them.

Jacob Trefethen:

The next thing we talked about was trust, societal trust in the medical system, and trust from different individuals in the information that they receive about what might benefit them. In my opinion, this is actually the biggest wild card, but a wild card in the sense that it won’t necessarily get worse from where we are right now. Possibly it will get better, if better information is delivered in ways that people trust via large language models or other routes that AI improves things, I think we’ll just have to wait and see on this one.

Saloni Dattani:

Finally, we talked about ambition. I think more ambition is generally important for solving public health problems. Lots of problems, lots of diseases can be eliminated, are solvable, but it doesn’t happen without the funding, without the incentives, without the willpower, and also the right ideas and incentives, types of reforms and ideas to solve those big challenges by focusing on the right barriers and actually making progress against some of the more tractable areas here. I feel like I am optimistic for the future of medical progress. I’m less certain about how much AI will help here. I do think that there are lots of ways that it can help, but there are just so many other problems outside of this that need other types of reform, other types of efforts.

Jacob Trefethen:

What parts of the drug development system do you think will be easier for AI to solve?

Saloni Dattani:

I think the areas where there is lots of abundant data and there’s clear structure to them, people have verified the data, collected it in a curated way. Protein and drug design, for example, that we talked about in the previous episodes, are areas where there has been a lot of progress. Even then, we haven’t really solved some of the bigger challenges. We’ve made a lot of great headway against predicting the structure of proteins in dissolved water, but we haven’t really done that for other types of interactions, how they move around, and their dynamics. Similarly, if you’re trying to repurpose drugs or discover new potential targets for drugs, I think those are things that AI could be quite helpful for.

Maybe to improve the clinical trial recruitment process or designing better trials or identifying better biomarkers, I think those are things where AI could help. I still think there are quite a lot of areas where you need someone to actually think through what are the right incentives to develop for this, what are the policy changes, and what are the specific people who are needed to be involved to improve or collect the data in the first place in order to improve. I guess some of these more trickier things where you want to solve a problem, but you need to integrate lots of different data: genomics data, imaging data, chemical data, if you wanted to simulate something; that’s going to be much easier for AI to do than for one person or a small team of researchers.

Jacob Trefethen:

Which parts of drug development do you think are going to be harder for AI to help with?

Saloni Dattani:

Well I guess in some ways the reverse. Areas where there’s very limited data, where the data isn’t digitized, things like that. Also the physical operations, the manufacturing that we talked about, manufacturing things on the frontier, the types of technologies where it’s hard to replace or simplify the process, and where the capital is very expensive. Then areas where you need regulatory reform, or maybe you need medical regulatory capacity and staffing and expertise to build. Also, the areas where the disease or the incentives are not very good, diseases of poorer countries, other conditions where developing treatment doesn’t have very good incentives for it, places where there isn’t very much political will or political capital for people to be interested in solving that problem. Maybe finally, areas where it’s hard to get data on efficacy and safety. Maybe we haven’t developed the right tools yet in order to test the right places of our body, such as the brain, or it takes a really long time to see what the outcome of these studies is going to be.

Jacob Trefethen:

Putting that all together, let’s go back to the question that we started with. Saloni, will AI cure all disease in a decade?

Saloni Dattani:

No, I think no. Not even if there’s a radical change in the drug development pipeline. For lots of diseases, the data isn’t there. Biology is really complex, it’s hard to predict. We don’t have the tools yet. We don’t have the starting point of the data to go to make these predictions very well. The economic incentives are still quite limited for many diseases. There’s a lack of access to drugs even now where there are cures for diseases or that are extremely effective vaccines. Things like getting the trust, the ambition, and the access, those are still barriers. I think yes, there are lots of things that could be improved, reforming drug development overall. We can make lots of medical progress by trying to solve all of these different issues. I think AI could be impactful, but I think no, it’s not going to solve this alone. What do you think?

Jacob Trefethen:

I don’t think AI will cure all diseases in a decade, but I do think that some of the progress we might see from AI may lead to more discontinuous types of progress within parts of the scientific system that we have so far seen in proteins and discussed in this series. So I would maintain probably actually more optimism than the average scientist that things might start looking pretty interesting.

The level of optimism I’m not at yet is that, if AI progress in software terms keeps continuing, and AI companies build new large language models or reasoning agents who can act as scientists in digital form, talk to each other, and reason with each other, I think that that could lead to a lot of scientific progress too and might be very hard for us to predict because we haven’t approached that type of system yet.

What I would emphasize to people who are thinking through that lens of AI progress is that even in that case, some of the things we’ve discussed today will keep applying, and the bottlenecks to medical progress in manufacturing, in delivery, in working on problems that affect people that don’t have great financial incentives to work on, all of those will still exist.

The AI will maybe advise us to start working on those problems, but the great news is that we can already start working on those problems right now. Those problems are not bottlenecked on more intelligence per se. They are bottlenecked on ambition, they’re bottlenecked on energy, they’re bottlenecked on money, and they’re bottlenecked on caring. Let’s get that right starting now, let’s take it seriously. Let’s take some of the advice the AIs of the future may give us, but take it in 2025.

Saloni Dattani:

We are now at the end of the episode. Thank you so much for listening. I hope you subscribe and share this with your friends and enemies. You should give us a rating on Spotify or Apple or wherever you listen to this. If we’ve changed your mind, let us know. If we haven’t, let us know still. But I hope you enjoyed this episode.

Jacob Trefethen:

Thanks for staying with us. Thanks for letting us speculate. Great to talk to all of you again.

Saloni Dattani:

Bye!

Show notes

Blogposts:

• Claus Wilke (2025) We still can’t predict much of anything in biology https://blog.genesmindsmachines.com/p/we-still-cant-predict-much-of-anything

• Elliot Hershberg (2025) What are virtual cells? https://centuryofbio.com/p/virtual-cell

• Jacob Trefethen (2025) Blog series.

  • 1) What does AI progress mean for medical progress? https://blog.jacobtrefethen.com/ai-progress-medical-progress/

  • 2) AI will not suddenly lead to an Alzheimer’s cure https://blog.jacobtrefethen.com/ai-san-francisco/

  • 3) AI could help lead to an Alzheimer’s cure https://blog.jacobtrefethen.com/ai-optimism/

Articles:

• Wendi Yan (2024) Discovering an antimalarial drug in Mao’s China https://www.asimov.press/p/antimalarial-drug

• Jason Crawford (2020) Innovation is not linear https://worksinprogress.co/issue/innovation-is-not-linear/

• Shayla Love (2025) An ‘impossible’ disease outbreak in the Alps https://www.theatlantic.com/health/archive/2025/03/als-outbreak-montchavin-mystery/682096/

• Alex Telford (2024) Origins of the lab mouse https://www.asimov.press/p/lab-mouse

• Jonathan Karr et al. (2012) A whole-cell computational model predicts phenotype from genotype https://pmc.ncbi.nlm.nih.gov/articles/PMC3413483/

• Wen-Wei Liao et al. (2023) A draft human pangenome reference https://www.nature.com/articles/s41586-023-05896-x

• Per-Ola Carlsson (2025) Survival of transplanted allogeneic beta cells with no immunosuppression https://www.nejm.org/doi/pdf/10.1056/NEJMoa2503822

• Saloni Dattani (2024) Antipsychotic medications: a timeline of innovations and remaining challenges https://ourworldindata.org/antipsychotic-medications-timeline

• Saloni Dattani (2024) What was the Golden Age of antibiotics, and how can we spark a new one? https://ourworldindata.org/golden-age-antibiotics

Books:

• Sally Smith Hughes (2011) Genentech: The beginnings of biotech

Theses:

• Alvaro Schwalb (2025). Estimating the burden of Mycobacterium tuberculosis infection and the impact of population-wide screening for tuberculosis.

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Graham Bessellieu, video editor

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Works in Progress & Open Philanthropy

01:12 Why build proteins nature never made?
06:33 Designing a hepatitis B-blocking protein from scratch
12:47 Hallucinating new proteins with diffusion models
18:20 AlphaFold changes everything
28:10 How AI models design and test proteins
32:33 What AI still can’t predict about proteins
40:45 From computer-made proteins to real-world drugs
44:33 Protein Lego: building shapes, tubes, and scaffolds
49:45 The future of AI protein design

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter: https://scientificdiscovery.dev

Jacob’s blog: https://blog.jacobtrefethen.com/

Courses:

• EMBL-EBI. AlphaFold: A practical guide https://www.ebi.ac.uk/training/online/courses/alphafold/

Articles:

• Tanja Kortemme (2024) De novo protein design—From new structures to programmable functions https://www.cell.com/cell/fulltext/S0092-8674(23)01402-2

• Jie Zhu et al. (2021) Protein Assembly by Design https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00308

Lectures:

• Rosetta Commons (2024) Diffusion models for protein structure generation (and design) https://www.youtube.com/watch?v=OEnY2yA3jy8

• Rosetta Commons (2024) AlphaFold – ML for protein structure prediction https://www.youtube.com/watch?v=SVrn8_8aKO8

• Rosetta Commons (2024) MPNN – ML for protein sequence design https://www.youtube.com/watch?v=6z4XmUAwdNA

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Graham Bessellieu, video editor

• Rachel Shu, on-site editor

• Anna Magpie, fact-checking

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Works in Progress & Open Philanthropy

Transcript

Jacob Trefethen:

I think the starting gun is basically 2022. What we’re gonna see, I believe, if people put the effort in is a lot of structural biologists who know how to use these computational tools, matching up with experts in particular fields who know a lot about diagnostics, or who know a lot about the heart, or who know a lot about a given infectious disease, or know a lot about a given agricultural problem, and in combination, I think those teams of people are gonna do really incredible things.

Saloni Dattani:

Alright. Well, we’ve talked about proteins, all the cool stuff proteins can do. We’ve talked about the history of insulin, one of the protein treatments used in treating diabetes. We then talked about improving proteins with AI, to be used in medicine. Now I wanna hear from you about how we can design entirely new proteins that have never been seen in nature. But first, why would we want to do that?

Jacob Trefethen:

Nature’s great and I’ve got nothing against it.

Saloni Dattani:

Mm-hmm.

Jacob Trefethen:

But some-

Saloni Dattani:

I have some things against it. Like, I don’t know, natural disasters, tornadoes...

Jacob Trefethen:

Tooth and claw.

Saloni Dattani:

... mosquitoes.

Jacob Trefethen:

Yeah. Mosquitoes, gosh. Some of them, I guess, are harmless, but some of the others... Okay. Nature’s not perfect, but nature’s so good when there’s a problem that evolution’s really taken a swing at. But there are many problems that face us, as humans, as a society, as a planet, that nature’s not - in the same sense - been evolving to try and solve. So, for example, plastic famously is not biodegradable.

Saloni Dattani:

Right.

Jacob Trefethen:

So nature’s not doing, in the sense of bio, ain’t doing much degrading.

Saloni Dattani:

But what if something could digest it?

Jacob Trefethen:

Exactly. Could you design something with that problem in mind, to try and digest it and get rid of all that plastic currently in the ocean?

Saloni Dattani:

Could we get a PacMan to eat all those little blobs?

Jacob Trefethen:

You know, there’s also these pathogens that we had talked about last episode, one of. But some of them we haven’t been able yet to - either our body in terms of our immune system or drug development - has not yet been able to make drugs actually work well enough.

If you could create a protein that operated as a therapeutic, as a drug, and you just created it out of thin air and you never saw anything like it before, then could be really useful.

Saloni Dattani:

I think there maybe also lots of uses outside of medicine. I recently learned that silk is a protein. And we talked about gluten in bread, that is a protein. There are all these proteins that are also doing these like structural things - they’re stretchy, or they’re super strong, or they’re really silky and smooth. Maybe for other purposes, like materials or industry, you could want lots of proteins. Maybe there are other things in industry as well, like maybe you wanna cook something, you want to ferment something in a way that’s never been done before.

Jacob Trefethen:

Mm-hmm.

Saloni Dattani:

You might want a protein; you might want an enzyme to do that for you.

Jacob Trefethen:

Yeah.

Saloni Dattani:

And then, what else? Maybe there are just a bunch of chemical reactions that you wanna do-

Jacob Trefethen:

Oh, totally.

Saloni Dattani:

-but at a really small scale, and you want to do them all at once, and you want a protein to do all of those steps of the enzyme reaction, and you could create a new protein.

Or maybe you want to create a protein that does like a bunch of different things: a protein that’s in a hot environment, it’s gonna do one thing, cold environment, it’s gonna do something else; that tunes the protein and what it’s working on.

Jacob Trefethen:

I also wanna just get energy. I mean, this-

Saloni Dattani:

To eat?

Jacob Trefethen:

To eat, to do cool stuff with, like photosynthesis. One of the main ways-

Saloni Dattani:

You want to do photosynthesis?

Jacob Trefethen:

I want to do photosynthesis.

Saloni Dattani:

Would that turn you green?

Jacob Trefethen:

By the time I succeed, I may have worse problems. I just feel like photosynthesis... I don’t mean to criticise plants, but I’ve always found it very inefficient. You’ve got so much sunlight beating down; hardly any of it gets turned into biomass, or like 1%.

Saloni Dattani:

I didn’t know that.

Jacob Trefethen:

Oh yeah, hardly any, hardly any. And you know, there’s probably some deep reasons for that. But could we design new proteins? Not just like RuBisCO that you mentioned in the first episode, but even better ones.

Saloni Dattani:

Right. So proteins are used to get carbon from the air, they’re also used to getting nitrogen from the air, right? To fix nitrogen. And so you could be trying to improve some of these processes to get stuff from the air.

Jacob Trefethen:

Yeah.

Saloni Dattani:

You could be trying to improve agricultural yields or products that we eat. What else? Maybe you would be trying to purify water. Trying to make new biofuels.

Jacob Trefethen:

Oh wow, purify water, huh?

Saloni Dattani:

Ooh, I feel like there’s just so many, so many things. Yeah, purify water; you could get rid of- maybe the proteins are really specific and they attach to dirt or something.

Jacob Trefethen:

Oh, okay.

Saloni Dattani:

And they eat them up.

Jacob Trefethen:

Okay. I eat your dirt. I eat it up! I think that everything we just described sounds wonderful, but sounds a bit magical. So how would you actually go about achieving a protein if you’re not modelling it off of something in nature?

Saloni Dattani:

Well, a lot of the things we described actually happen. They already happen in nature.

Jacob Trefethen:

That’s a good point.

Saloni Dattani:

Maybe we just want to adapt how they happen. We want to make the silk stronger, or we wanna make the gluten stretchier. Or we want to make the silk stretchy, and the gluten stronger.

Jacob Trefethen:

Or we wanna make the dirt dirtier. I hadn’t thought- Fair enough, fair enough. I think we still need a way to invent these proteins though.

Saloni Dattani:

Mm-hmm. And maybe you want to make stuff that exists but is not protein, but you want to make it protein; you want to make it biodegradable or something. Like, I don’t know, you want a flower vase that is made of proteins or something, and so you could probably make things like that with proteins if you wanted.

Jacob Trefethen:

I want to tell you about the next thing I did when I visited the University of Washington.

Saloni Dattani:

When did you visit the University of Washington? What were you doing there?

Jacob Trefethen:

I was there in March and in the last episode I talked about how I was working on a protein on the Strep A bacteria. I want to talk to you about my next project, which was designing a drug against a hepatitis B protein.

Saloni Dattani:

Ooh, wait, so you’re designing a drug, not a protein?

Jacob Trefethen:

Well, the protein is a drug; no, the drug is a protein.

Saloni Dattani:

What??

Jacob Trefethen:

I know! By drug, I just mean in this context, a binder; something that binds really tightly.

Saloni Dattani:

Okay.

Jacob Trefethen:

So before, in the last example, I was talking about taking an existing protein that exists in nature and tweaking it a bit. Here I’m talking about hallucinating... an entirely new protein... that has no previous instantiation, necessarily, in the world... using just... a diffusion model.

Saloni Dattani:

Ooh.

Jacob Trefethen:

So you, you seen DALL-E made by OpenAI?

Saloni Dattani:

I have made some cartoons with it.

Jacob Trefethen:

Nice. What about stable diffusion and-

Saloni Dattani:

No.

Jacob Trefethen:

What about...

Saloni Dattani:

... Midjourney?

Jacob Trefethen:

Thank you! Midjourney, oh yeah. What about Midjourney? I mean, those have gotten really good these days. So imagine, I’m speaking a bit loosely here, but not that loosely, that instead of hallucinating cat pictures, you started hallucinating a protein structure.

Saloni Dattani:

You know, when I was in school and I was learning how to play the piano and at the end of school, when I was like 16 or so, well, I was doing like the grades of piano, qualifying for them. And there was this one exam that you had to do where you had to play the scales, regardless of where the examiner told you to start.

Jacob Trefethen:

Oh, okay.

Saloni Dattani:

And I remember being really bad at that. Because you have to remember where your fingers go, like which order, you know? Do you use your third finger at this point or do you switch back to your thumb? And I got so stressed out by this whole situation that I would literally start dreaming of myself playing the scales at different points. And it was very-

Jacob Trefethen:

Correctly?

Saloni Dattani:

-it was genuinely helpful.

Jacob Trefethen:

Oh!

Saloni Dattani:

Yeah.

Jacob Trefethen:

Hallucinations can be helpful, and you can actually use a few AI systems in a “design, create and validation” loop to get to new proteins that might be useful.

Saloni Dattani:

Wait, wait, wait. What does this mean? Okay so, you said that you’re making a binder. So you are trying to make a protein that sticks to another protein.

Jacob Trefethen:

Yes.

Saloni Dattani:

To do what?

Jacob Trefethen:

Yes, in this case... Lots of functions begin by sticking, let me just say that to begin with. But in this case, the reason I want to stick is that I want to bind a part of hepatitis B to interrupt its life cycle, so it stops damn replicating in my liver cells.

Saloni Dattani:

You’re like, shut up, stop!

Jacob Trefethen:

Shut up! Stop! Yes. So actually I went after a little disgust protein called-

Saloni Dattani:

What? Wait, what is a disgust protein? What did you just say?!

Jacob Trefethen:

You know, everyone talks about the hepatitis B surface antigen. I’ve never-

Saloni Dattani:

I’m sorry, I’ve never heard someone talk about this. I know about it because it’s used in the malaria vaccine, right?

Jacob Trefethen:

Oh that’s absolutely true, yes.

Saloni Dattani:

But why is everyone talking about this around you?

Jacob Trefethen:

Okay, let’s do a quick side note on the- Okay, firstly, I love that you tried to say you don’t hear people talk about it, and literally you talk about it all the time. You immediately started talking about it-

Saloni Dattani:

Well, I read about it - I’ve never heard someone talk about it.

Jacob Trefethen:

Well, now’s the moment.

Saloni Dattani:

I have normal friends.

Jacob Trefethen:

I know for a fact that that is not true. Especially given I’m one of them. Okay, so let’s do a detour on the malaria vaccine ‘cause that is fascinating. The original idea is you take the hepatitis B surface antigen, which happens to self-assemble into this kind of spherical thing.

Saloni Dattani:

Particle, yeah.

Jacob Trefethen:

And that’s pretty useful because, you know, the immune system’s good at looking at spherical things and being like: “That’s a virus, kill it.” And if you can lace that spherical thing with antigens from the malaria parasite, then inject those - oh boy, we’re in business.

Saloni Dattani:

Yeah.

Jacob Trefethen:

Yeah. That’s a cool idea.

Saloni Dattani:

Right? But it’s like if I was trying to detect you and your face, even if you were wearing a wig, I should still be able to recognise you, because that’s really important.

Jacob Trefethen:

Right. I like how most of our metaphors end up with you assassinating me. So hepatitis B surface antigen, it’s the rage, everyone is always on about it, as we now fully agree. The issue is, you know, the drugs for hepatitis B are pretty good, in that you definitely want to know if you have a chronic infection because you can go on nucleoside analogues - they will help you control the infection.

They won’t cure you though, so the drugs aren’t good enough to cure you yet, although many people are working on that. So I was like, you know, let’s go for the jugular. What can you do to really get rid of this thing? The hypothesis is that if you create a protein drug that binds really tightly to this other antigen that’s actually in the middle of the life cycle, which is called... the X antigen...

Saloni Dattani:

Alright. This is very scary.

Jacob Trefethen:

It’s very scary. But the X antigen, it forms as two parts – a dimer – that come together and then start doing stuff. So I was like, okay, I’m gonna hallucinate a protein that interrupts that dimer formation. Can I distract it, get it to bind?

Saloni Dattani:

Right. So you’re imagining what might fit into the little gap between these two parts of the protein.

Jacob Trefethen:

Yeah and I used this tool, RFDiffusion, which is a particular diffusion model made, again, at the Baker lab at the Institute for Protein Design; there are other diffusion models being made too. The “RF” there stands for Rosetta Fold, which is the family of models that they’ve worked on up there for a while.

So what I’m doing there, I’m giving the computer just a few inputs of what I’m attempting to do; asking it to hallucinate many options.

Saloni Dattani:

I saw a video about this and it featured a lot of cats, and I think you mentioned some of these cats before.

It’s a bit like DALL-E, and I didn’t even know how DALL-E worked, how it was developed and stuff. But the basic idea is you have a bunch of pictures of cats or something, and you introduce some noise into that image.

Jacob Trefethen:

Right.

Saloni Dattani:

Or maybe you’re making-

Jacob Trefethen:

It’s more pixelated.

Saloni Dattani:

Yeah, more pixelated, or you just introduce random little pixels that are different colours or something, and you introduce some of that, and then you do that again. You make it even more noisy, and then you make it even more noisy, so you have different versions of the same image that are progressively noisier.

And what you’re trying to do is get the image model to try to figure out how to go backwards, how to get from the noisy version to the clearer version to the clearer version to the cats.

Jacob Trefethen:

[sound effects]

Saloni Dattani:

And this was really funny because I was watching this video and you know, if you input a cat, you’ve made it noisier, noisier, noisier, and then you’re telling the AI, “Okay, now try to predict what happened before. What did it look like before I added this noise?” And it’s like: slightly less noisy, slightly less noisy, and then it’s a dog. And you’re like, “Wait, that’s not right!” And you just keep doing this until it gets better and better at predicting what the image is.

Saloni Dattani:

And then what’s happening with the protein version of that, which you described as RFDiffusion, is that instead of having an image with noise that’s the little pixels, you instead have the coordinates of the atoms in the protein, and then you introduce a little bit of jiggle, like you mess up the coordinates a little bit.

I think in this case, they add some Gaussian noise, right? So they move it by- basically most of the time, it moves by some average amount, but sometimes it moves to a more extreme amount and stuff like that. Then each time you’re making it blurry and blurrier, and messing it up more and more, and then you’re asking the AI tool, RFDiffusion, can you go backwards and remake the protein? And then obviously, there are issues with doing that still, it’s not gonna be very accurate.

But in this case, it could be a good thing that it’s not accurate ‘cause you’re creating whole new structures that you haven’t seen before, and some of those structures might be useful for other purposes.

Why would you also want to hallucinate something that doesn’t exist? Maybe there are just so many more potential ways that a protein could fit together that have never been seen in nature before.

Instead of a cat with one head, what would it look like if this cat had three heads or something?

Jacob Trefethen:

You know, let me just describe the loop you can then go in. What RFDiffusion — which was made by this group at the Baker lab and the Institute for Protein Design, Helen Eisenach and others made it — well, you can generate these different hallucinated structures. So this is, again, think about a 3D model of: Where is this protein backbone?

What you don’t have is the sequence, so you remember from last time that you can go from structure to sequence using ProteinMPNN, so a different model? So I’m going step one, hallucinate; step two, okay, hold on, what sequences would actually lead to those solutions?

Saloni Dattani:

So what structure would fit in between the gap? And then how do we make that structure?

Jacob Trefethen:

Exactly, and then you’re going to want to do the validation with- or the first in-silico step of validation with AlphaFold.

Saloni Dattani:

And that is basically: if I make this amino acid, does it actually make the structure that fits into the little gap?

Jacob Trefethen:

Yes, I have these ideas for the amino acid sequences, but in reality, is it gonna fold up to look like that? What you’re going to end up with, at the end of this three step chain, is you’re going to end up with some hypotheses.

And AlphaFold’s going to say, look, I’m sorry, some of these are not what you thought. It’s pretty unlikely that that amino acid is the one that will lead to the thing you want. It’s pretty unlikely that the distance between these two randomly selected amino acids is gonna, after folding, actually be where you thought it was. So you want to ditch the ones that you actually accidentally messed up on the way.

Jacob Trefethen:

AlphaFold, AlphaFold, AlphaFold.

Saloni Dattani:

Yes.

Jacob Trefethen:

We should probably explain a little bit about why that was a breakthrough, how that came about, what was actually happening with protein folding before.

Saloni Dattani:

Mm-hmm. So I think I described, at some point, the fact that people used to be using physical models to predict what protein structures were like. So sometimes they had the structure in mind, and they were trying to figure out what amino acid sequence goes into that. How do the amino acids look if we make them in physical structure, with a model, with a real life model.

I think after that were these statistical models that were produced with different types of information — so you might have some data about each amino acid, what kind of features it has, how it interacts with other amino acids. Another thing that you would have is data on the amino acid sequence for a particular protein, but in different organisms. So you might have: What does insulin look like in chimpanzees? What does insulin look like in pigs? Et cetera, et cetera.

Jacob Trefethen:

Yep.

Saloni Dattani:

And by comparing all of these versions of the same protein — the different amino acid sequences for the same protein in the different organisms — you can see which parts are shared. So you can see which bits are basically in the same- or are shared between all of them, and that tells you something about the important structures that are kept in the same shape.

But it can also tell you something else. It can also tell you if some parts of the structure are changing, do other parts of the sequence also change along with it? So if leucine always changes here- whenever leucine changes here, then alanine always changes there- or often changes there, or something. And with these comparisons, of having a pair of a comparison, can tell you a little bit. The reason this is important is it means that they probably interact – that they’re probably close together in this 3D shape of the protein – and that is useful information.

Another thing that you might know is, you might have some information on secondary structures of proteins, and what that means is, in specific parts of the 3D shape, what is going on? Is there a little helix? Are there two parallel lines or something? And how does that map onto the amino acid sequence? So if you have a bunch of this information, you can try to predict what the structure would be like.

So there were people who were working on some of these models for a while and they were making some gradual improvements.

Jacob Trefethen:

Structural biologists?

Saloni Dattani:

Yeah! Uh... no. No? The structural biologists are figuring out- they’re determining the sequence by using crystallography or cryo-EM or something like that, right? Whereas what I’m saying is, can you predict it computationally?

Jacob Trefethen:

You think those were different people in computer science departments or-

Saloni Dattani:

Maybe it was some of the same-? Yeah, I mean, it’s different tools, but when I think of structural biology, I usually think of the imaging and stuff.

Jacob Trefethen:

Yeah.

Saloni Dattani:

Okay. But people are making a little bit of progress, but the predictions are still not very good, and that continued for a long time. Also there are people who are saying, “Oh, my model is really good. My model is better than yours.” And there’s no benchmark, or there’s no reference, to compare them.

What happened was, in 1994, is that right, at UC Davis, people developed a competition and they said, okay, some crystallographers have actually figured out what the structures of these previously unknown proteins looks like, and we’re not gonna tell you what that structure looks like.

Jacob Trefethen:

Okay. But that’s real. That’s documented. Not a prediction.

Saloni Dattani:

Yeah, that’s documented, that’s determined in the lab. But we will tell you the sequence of amino acids, and you have to guess what the shape is like. So now you have this very standard comparison, where you can give all of the different research groups this amino acid sequence and say, “Hey, can you guess what it looks like?”

So they came together for this competition – CASP – that was set up in 1994, and they were given a bunch of amino acid sequences, and told to predict the structure, and then you could compare how good their predictions were.

Jacob Trefethen:

Yeah, that’s the real thing.

Saloni Dattani:

And so I think until, what is it, decades, there are some gradual improvements, but basically the accuracy is under 50% generally, on average. That accuracy is about, you know, how far away are the coordinates that people are predicting of the atoms to the real structure.

And then in 2020, DeepMind released AlphaFold2, which was this model that not only used that data, but it also used structural data from a dataset called Protein Data Bank. So people had already determined this structure from x-ray studies and blablabla. They had collected lots of this data on like, if we have this amino acid sequence, this is what the protein structure looks like; and they had done this for hundreds of thousands of proteins, right?

Jacob Trefethen:

Yeah.

Saloni Dattani:

And AlphaFold was trained on all of this data, so it has a connection directly between the amino acid sequence and the structure, and that means that they were able to make a much better prediction and their prediction was so much higher, it was around 90% accuracy on average, for the particular proteins that they were asked to determine. There’s still stuff that it can’t predict even now, and there was still stuff that it couldn’t predict then, but that was an amazing- a huge leap.

Jacob Trefethen:

That’s like a game changer. Yeah.

Saloni Dattani:

Yeah.

Jacob Trefethen:

Because if you can go from the number of proteins that us mere human beings had crystallised over the last 50 years, so maybe that’s 200,000, and you suddenly have a tool that can predict pretty well a lot of proteins; I mean, you can predict how millions of proteins fold without having to crystallise them. You’re not totally sure, but you are, it gets you so far.

Saloni Dattani:

Right. It’s doing better on proteins that look similar to other proteins that people have determined in the lab, and it’s doing worse on ones that, where there’s hardly anything to go on. And also, in particular areas of the protein, some domains, are going to be determined quite well because there’s lots of data on them already; some are gonna be predicted much worse. But yeah, that’s the story.

Jacob Trefethen:

It reminds me a bit of these other parts of machine learning, before the deep learning revolution – well I don’t know if I’d even phrase it that way, but before the “shove loads of compute at it” revolution – where you had these computer vision grad students who were writing these algorithms for edge detection and trying to understand what’s in an image with all these clever algorithms, and then, sure enough, you just sweep through with a ton of compute.

A particular architecture, you can learn all that stuff without having to, yourself, get that specialised about it. And I imagine a lot of people who are working on these physics-based models, these other models before AlphaFold, that probably are quite intricate, are a bit like, “Oh my gosh, what? Like, I was working so hard on this subset of proteins; on this type of- when this alpha helices looked like this together, you know, all that. And lo and behold, I was, I got blown out of the water by a bigger machine.”

Saloni Dattani:

I think it’s also- maybe at the start, when people are building these physics-based models, they have this textbook understanding of some parts of the process; they have some training and expertise over many years that they’ve learned of what might fit together and stuff like that. But it’s really hard to consider how all of that works on a grander scale, on a larger protein, especially when it’s types of proteins that you might not have come across.

Obviously there’s hundreds of thousands, there’s so many types of proteins, right. So even when you have someone with the expertise, going from that to a statistical model can add value. And that was what came before AlphaFold. And AlphaFold is also, in a way, it is a statistical model where it’s learning from someone of this data and it’s predicting things better, and part of the reason is not only does it have that information about like particular amino acids, but it also has lots of structures that it’s remembered, and that’s very hard for a person to do, or a statistical model.

Jacob Trefethen:

A puny statistical model.

Saloni Dattani:

Right? Yep. So let me do a quick recap of all of the tools just to see if I remembered them right. So we first have AlphaFold, that’s maybe the most famous one, that people would know about. And that does: you have the amino acid sequence and you’re asking “What structure does this make if it was a protein?”

And then you have ProteinMPNN, which is the opposite: “I have the structure, what amino acid sequence makes that structure?”

Jacob Trefethen:

Exactly.

Saloni Dattani:

And then you have thirdly, RFDiffusion, which is, “given that I have some bits of the structure, can you make the full structure- or I want this protein to have these things. Can you make the full thing for me? Can you make a full thing that could be a protein?”

But then you need to check that that actually happens. “Does the protein actually fold that way? What is the amino acid sequence that makes that protein? If you had that amino acid sequence, does it actually fold into that protein?” And the reason that I guess that’s really important is because there are so many potential combinations, right?

Jacob Trefethen:

Oh yeah.

Saloni Dattani:

There are 20 different amino acids, so at any part of the chain there could be one of 20 things. And if you add those up, I think if you get- if you get a protein that is like 60 something amino acids long, the number of combinations is already bigger than the number of atoms in the universe – that’s the estimate – which is a huge amount.

So you want to make sure, is that the right structure, and is this structure going to fold into that particular shape? And that’s also hard because there are many potential ways that a protein could be folding, because there’s so many different amino acids at different places. It could just fold into a different shape or confirmation.

But I think there’s also this question of: what is the structure that it will fold into, given that it wants to reduce the amount of energy that’s required to keep in that position? So it wants to go down the easy route. But then, there might be many easy routes, right? And there might be multiple different ways that a protein folds. But in reality, it usually only folds a particular way, I think. So that was my recap.

Jacob Trefethen:

That’s a great recap. And I think the real practical output of what you’re saying about the combinatorics – it used to be the case if you had hypothesis molecules, whether they were protein binders or small molecules or something, that you were trying to achieve some function, so you were trying to bind this hepatitis B thing or whatever - you might have to run through high throughput screening; you might have to have a hundred thousand different miniature experiments of a hundred thousand different hypothesis molecules, because most just will not do what you want.

And what’s astonishing about this loop of three systems that we just described is that, for proteins at least, you can go through the loop, do the “down selection” on your computer, and once you’ve got it set up and running, I literally did that in a day.

Once it’s set up and running, it was based off of not only all the work of people building those models, but also being next to extremely helpful other postdocs and grad students who would share their Jupyter notebooks with me and show me how to do it.

But you know, you can create hypotheses, some of which do work, and you only need tens of them. So they work for the initial in-lab validation step. They don’t necessarily work as actual drugs in the field, once you go through all the future steps of getting through humans, but you don’t necessarily need a hundred thousand things, you could actually try 50 and maybe 4 will work. And that is completely different than it was literally five years ago.

Saloni Dattani:

Right. That’s crazy.

Jacob Trefethen:

Crazy.

Saloni Dattani:

I also, I have two thoughts.. or one question. One is, I think you mentioned something about the errors and how well it’s predicting stuff - is that with AlphaFold? So you’re predicting the structure from the amino acid sequence. And the question is: How well does this map onto what the protein actually looks like? And what is its real structure like? So what you’re comparing is, at each particular atom even, how far away is that, in coordinates to the atom in the real structure?

Jacob Trefethen:

Well, yes, my understanding is that what AlphaFold is giving you is its own, if you will, subjective predicted error versus reality. So you’re not actually ground truthing a lot of it.

Saloni Dattani:

But you could.

Jacob Trefethen:

You could in theory, well if you are able to crystallize a protein you can do that, but for some proteins you simply can’t do it.

Saloni Dattani:

Yeah, so you have confidence or error or something, but that is not just of the shape as a whole, it’s also about at each atom, how confident is AlphaFold that it’s got that position right.

Jacob Trefethen:

Yep.

Saloni Dattani:

And I think that’s really interesting because maybe there are certain domains of a protein, or something like that, where we have loads more data about this type of protein or this part of the protein structure. So AlphaFold can be much more confident that it’s going look like this, but in other parts, they might be completely new or the structure hasn’t been determined by anyone before in similar proteins, so it doesn’t have that much to go on. And it’s kind of just like, “Eugh, I don’t know.”

Jacob Trefethen:

Yeah.

Saloni Dattani:

But there’s the other question. Is it possible to make this protein in the lab, and maybe there are other challenges in doing that, aside from just whether it would fold into that shape. Or would the protein do the thing that you want it to do in the lab, or in real life?

Jacob Trefethen:

And it’s going to depend on the protein, the difficulty in making the protein – it is more difficult still to make large proteins, for example, gets more expensive - you gotta splice together multiple things, all that. Will it do what you’re looking for it to do? You know, it’s all about what is the initial test that you can do to get some signal, and that will be different for different things you’re trying to achieve.

Saloni Dattani:

And I think maybe there’s also differences because in, let’s say, Protein Data Bank or- the structures that are being predicted are just one static version of the protein, they’re not like- A protein in real life is wiggling, or it’s moving around, or it’s folding, or it’s turning and rotating, and stuff like that, and that is not being predicted.

Jacob Trefethen:

Nope.

Saloni Dattani:

It’s not predicting how it binds to metals or something like that, there are some other tools that do that, though, that are kind of based on this, but then you would have to have data on what that looks like as well.

Jacob Trefethen:

It’s good maybe just to summarise the useful things that aren’t yet done. So there’s that - what you just said is completely right - we just don’t have good predictions of protein dynamics. So we’re pretty good at predicting protein structure, but as you said a second ago, not perfect, but we’re getting there. We can’t yet predict protein function very well. And so I just want to distinguish what we were just talking about is: starting with an intended function, hallucinating a protein to serve it - that was very difficult five years ago and is increasingly some problems you can do that for - so that’s start with a function, hallucinate your way to the finish line.

But we can’t- if you just take a given human protein, it probably will serve multiple functions, and you’re not gonna suddenly be able to ask an AI, “Wait, what is this protein doing, by the way?” Like that is, you know, that’s one of the holy grails left, which is predict, you know, we care about function more than structure, ultimately.

Saloni Dattani:

And I guess the other thing is maybe also, is it attached to something? Is it a protein that stuck to a membrane; does that change its shape or something like that? And that’s not something it’s answering. But it’s still really use useful because a lot of proteins are kind of just hanging around, they’re just dissolved in something, and they do sometimes look like those structures.

Jacob Trefethen:

Yeah. And you know, let me give a shout out to a fourth AI model.

Saloni Dattani:

Oh?

Jacob Trefethen:

Don’t know if you’ve heard of ChatGPT... Claude...

Saloni Dattani:

I have heard of those.

Jacob Trefethen:

I was talking to a grad student who was at the frontier of these biological machine learning models, and she was emphasizing to me how useful ChatGPT was, because this loop with three models we just discussed, it’s really simple, but you do have to know how to code to get it to work, and simply learning to code takes a while.

Saloni Dattani:

Right. What language do they use, do you know?

Jacob Trefethen:

I’m sure there’s many answers to that. Python is a classic that you would start with, maybe. If you’re a grad student and have to take a year out in order to learn to code, just so that you can use these models - that’s what it used to be like all the way back in 2023. So these models were out, but we didn’t yet have really good LLMs. I mean, luckily in 2025, you no longer have to take a year out because you can basically talk to a large language model about: “What’s this bit of code doing? Okay, write me some code that does this. Now explain to me what that function is doing. Okay, now explain to me what I’m missing.”

Saloni Dattani:

You’re vibe protein coding.

Saloni Dattani:

You know, I have sometimes used ChatGPT to code stuff, but it’s kind of hard because when it makes a mistake and there’s some error, and I run the code and I say, “Hey, you made this mistake” or “This was the error.” and then it finds it really hard to fix the error; it doesn’t know where the bug is coming from. But then, I also use it sometimes because I share all of my data and graphs on GitHub.

Jacob Trefethen:

Very good.

Saloni Dattani:

And I have a background of using R, the programming language, but I don’t use Python. But I think that people who use Python should also be able to reproduce my graphs. So sometimes I have written my code in R and I tell ChatGPT “Turn this into Python” so that someone else can just run the code super easily. And again, I don’t use Python, so it’s hard for me to fix any issues that come up, but I will copy that code and then run it on the terminal and see if it makes my graph again, and it does take a while, but it does eventually get there, usually.

Jacob Trefethen:

Yeah.

Saloni Dattani:

Which is cool.

Jacob Trefethen:

That’s great. Another one that that brings to mind for me is, you are often using other people’s code, or inheriting some of other people’s code when you’re trying to achieve something, and different people document their code well or poorly, and someone who has not done a good job of factoring their code, or documenting it well, you can at least ask one of your friendly AI assistants, “What the hell is going on here?”

Saloni Dattani:

Yeah, that’s helpful. But it is helpful for the lines of codes, and when it’s a very long script, it’s just like, “I’ve forgotten what I’m doing. I’m sorry.” But in fairness, I would forget as well, so.

Jacob Trefethen:

By the way, shout out to Sebastian Ols, who has famously well-documented code for some of the systems that-

Saloni Dattani:

Sebastian Owl?

Jacob Trefethen:

Ols - O-L-S - to pronounce that Swedish name.

Saloni Dattani:

Interesting, well thank you to him.

Jacob Trefethen:

Thank you to him.

Saloni Dattani:

Where were we with hepatitis B? I’ve almost totally forgotten about it.

Jacob Trefethen:

Well, I actually have a video I can show you, of how far I got, that I took on the final day, while I was up there in Washington.

Saloni Dattani:

So wait... You were making a protein that fits into the gap between hepatitis B’s proteins, and then you were like, “how do I block them from joining together?”

And you used RFDiffusion to hallucinate a potential protein that would fit into this gap. And then you went what amino acid sequence creates that protein, with ProteinMPNN. And then you asked AlphaFold, “does this amino acid sequence actually produce the structure that I want?”

Jacob Trefethen:

Yep, absolutely right.

Saloni Dattani:

I remembered.

Jacob Trefethen:

And by the end of that system, I’ve got it down to 50, 100 different possible sequences, structures.

Saloni Dattani:

Oh, that’s a lot.

Jacob Trefethen:

It’s quite a lot. I mean, you could go down further if you want, but you know, the really fun thing is you can visually look at those structures in a- the thing I used was PyMOL, Python molecule, and you can see “does it look like it would line up and bind?” and-

Saloni Dattani:

This is a software where it shows you what proteins look like if they’re-

Jacob Trefethen:

You can twist them around.

Saloni Dattani:

-represented with ribbons, and arrows, and blobs.

Jacob Trefethen:

Exactly.

Saloni Dattani:

So in the graph we’re showing, each of the little dots is a prediction- predicted structure that you made. And the graph is showing: “What is the potential error in the structure compared to the subjective”, you said, “reference” or something. Or how far is this structure, in terms of the coordinates and stuff, from what it should be? And because you’ve made so many of these predicted structures, you then filtered down and you went like, “these are probably the ones that are gonna look actually like this.”

Jacob Trefethen:

Mm-hmm.

Saloni Dattani:

And then what did you do next?

Jacob Trefethen:

And then, you know, I went home, to be honest. Oh, but what if-

Saloni Dattani:

Well, that’s boring.

Jacob Trefethen:

I know, I know, but I love to leave things unfinished here. But what one would do next if I were a full-time lab scientist?

Saloni Dattani:

Oh wait, you didn’t just go home that day, that was the end of your time there.

Jacob Trefethen:

That was my final day there.

Saloni Dattani:

Oh, okay.

Jacob Trefethen:

So I hope that other people are carrying forward ideas of that sort. But those particular binders disappeared into the ether. That said, what I could have done was order ‘em up, order up the DNA sequences that would code for those proteins, grow up the proteins in some system, harvest those proteins, and check against hepatitis B virus, or something like hepatitis B virus, maybe the protein itself.

Saloni Dattani:

So you would be seeing, “does this protein actually block it from binding to each other, to the two parts?”

Jacob Trefethen:

Exactly. And probably, what I would find given the state of the AI models, is that most of the things I ordered wouldn’t, and some of them would. And I just can’t emphasise how astonishing that last part is. Because you used to come up with sequences and you used to make up proteins and they didn’t work.

Saloni Dattani:

Right.

Jacob Trefethen:

And now some of ‘em seem to work.

Saloni Dattani:

Yeah. I mean, it might have been a three or five year project just to work on one of these things.

Jacob Trefethen:

And what I hope we’ll talk about next is why even all of this magic sometimes is not enough.

Saloni Dattani:

I’m also wondering, okay, you mentioned binders- what are the other potential uses that you might have for this technology, of hallucinating different proteins? Could you, I don’t know, is it like Lego? Can you make little bits of the proteins and stick them together? Would people do that?

Jacob Trefethen:

There’s one way to find out, let’s try. I mean, I don’t know the answer, but maybe...

Saloni Dattani:

I think I’ve seen some of these structures, where they’re just some symmetrical thing - maybe they’re a tube, or a ring, or there’s some star-shaped thingy - and they are proteins. And people have figured out how to make those.

And I think that’s maybe easier than making an actual protein that’s doing reactions. If you know, for example, in a protein, this is how you make a little helix in one part, and this is how you make a little fold, and this is how you make some parallel structures, the computer can have a pretty good idea of putting that together and making some symmetrical thingy with it.

Jacob Trefethen:

Yeah, you know who’s done really cool work on this?

Saloni Dattani:

Who?

Jacob Trefethen:

Chelsea Fries.

Saloni Dattani:

Who’s that?

Jacob Trefethen:

Postdoc in the Neil King lab. And last time I saw her- Maybe Fries?

Saloni Dattani:

Wait, how do you spell that?

Jacob Trefethen:

Fries.

Saloni Dattani:

It’s not like frozen freeze.

No, although ironically she does a lot of cry electron microscopy, so maybe that’s nominative determinism. But she’s down there in the lab, well in the basement, with the microscopes. And I went to visit her once and she went, “Hey Jacob, you wanna take a look at this?” And on the screen she had this perfectly symmetrical long tube. I was like, “What the heck is that?” She’s like, “This is a self-assembling massive protein tube.”

Saloni Dattani:

That’s so cool.

Jacob Trefethen:

And I was like, “okay, what, that’s amazing! So what can you use it for? And she said, “Oh, I’ve got no idea.” And she just has an instinct, if she follows this further, having nano-tubes will probably be useful for something.

Saloni Dattani:

I can imagine tubes being useful for various things. Maybe as a straw for little bacteria or something, I don’t know.

Jacob Trefethen:

Yeah, those little- what are those critters that- do you know who I’m thinking of?

Saloni Dattani:

What? What the hell are you talking about?

Jacob Trefethen:

What are those- what are they called? Tetra- megalofaun- pterodactyls- the ones that look really ugly.

Saloni Dattani:

I have no-

Rachel Shu (offscreen):

Tardigrades.

Jacob Trefethen:

Say again? Tardigrades, tardigrades.

Saloni Dattani:

Oh, tardigrades.

Jacob Trefethen:

You must know tardigrade.

Saloni Dattani:

Well, I- tetrahedral poly- what? Tetrahedral...

Jacob Trefethen:

So I think that tardigrades need straws, because currently they don’t get diet coke in the right quantity.

Saloni Dattani:

Oh yeah. But do we want them to drink more?

Jacob Trefethen:

Oh, they might get really frenetic.

Saloni Dattani:

Uh-huh. But also, I think we talked about this earlier, when we were talking about our favourite proteins, and I said microtubules, and they’re a type of tube. They are used for this structure of a cell- a skeleton of a cell.

Jacob Trefethen:

Wow.

Saloni Dattani:

So I feel like there could be lots of cool uses for this.

Jacob Trefethen:

Yeah. Chelsea’s onto something.

Saloni Dattani:

Like scaffolding something.

Jacob Trefethen:

Yeah, yeah, totally. Well, you could create a mansion, but the mansion is microscopic.

Saloni Dattani:

Like a little dollhouse.

Jacob Trefethen:

Dollhouse and columns.

Saloni Dattani:

Yeah.

Jacob Trefethen:

Yeah.

Saloni Dattani:

For little... proteins to live in.

Jacob Trefethen:

Yeah.

Saloni Dattani:

I think I’d enjoy that. Maybe that’s something people would do with 3D printing, they could 3D print a protein, make a little protein house.

Jacob Trefethen:

All a protein wants is a little protein house, and a white picket protein fence.

Saloni Dattani:

A what?

Jacob Trefethen:

A white picket protein fence. You know, like a white picket fence.

Saloni Dattani:

This protein is going to be a NIMBY.

Jacob Trefethen:

Oh no! Okay, don’t make NIMBY proteins. That’s our one request.

Saloni Dattani:

Okay. So we can think about hallucinating proteins for binders, to block binding or to make things bind. We could make scaffolds. We could make little structures of their own, maybe the shape of the structure itself does something.

Jacob Trefethen:

Yes, I think so.

Saloni Dattani:

Oh, you know haemoglobin, right, is a protein complex with four heme things, and they fit together, and the oxygen fits inside them. If it’s just one heme, it can’t carry the oxygen and let go of it, I think. So maybe it’s a similar sort of thing, if you can make a structure that does something, but in a complex it can do something else, so you want to create a larger structure.

Jacob Trefethen:

Right.

Saloni Dattani:

And so what happened with your- okay, so you would have ordered the DNA, and made the amino acid sequence, and you would’ve made the protein, and then you would’ve seen: “Does this actually block hepatitis B in the lab, maybe in some animals or something, and then in humans.”

Jacob Trefethen:

Yep.

Saloni Dattani:

Yep. So that’s it, we’re done?

Jacob Trefethen:

If only, Saloni!

Saloni Dattani:

Oh.

Jacob Trefethen:

We talked through one example, of trying to make one hallucinated protein, and we hypothesised other possible uses. Do you know, have these proteins actually made it into the real world yet? And what have people worked on already?

Saloni Dattani:

I mean, this is a super new method.

Jacob Trefethen:

Yeah.

Saloni Dattani:

The first time it was published was three years ago, right, in 2022? And so it is fairly new.

I think there are a few proteins that are in the pipeline as drugs that have been developed based on methods like this, or this method.

One of them is called rentosertib, and that is a small molecule drug that is developed to treat pulmonary fibrosis. That is a lung disease where- I think there are different things that can trigger that, but basically the idea is, your lungs have all of these little empty spaces called alveoli where your blood is on the edge of that little space, and that gives it a little bit of a gap or structure where the oxygen and the carbon dioxide mix with the air, and that helps you breathe.

And what happens with this disease is that there’s some kind of injury, or some inflammation, or stress, or infection, or something, that damages these parts, and when the body is trying to repair it, it produces this fibrous structure or repair structure, and that goes too far. It’s like a scar that is just a slightly different material than what was originally there.

That actually prevents the transfer of oxygen, carbon dioxide as well as before. Over time, for whatever reason, people’s lungs get more and more scarred, so they become less and less able to breathe well, and obviously that’s very harmful.

So this new drug is basically trying to block a particular protein that is involved in this whole process, so that’s one. That is in phase two trials right now.

There’s another one called luxdegalutamide, and that is used to potentially treat prostate cancer by targeting the androgen receptor that’s involved. That is also in phase two trials.

There are a few that are in trials, or were in trials, but then got discontinued because they didn’t work. And that highlights this thing that you were saying, that even after you’ve hallucinated a thing, you still need to make sure: does it actually produce this structure in the lab, but also, does it do this function in the real world when we’re using it as a treatment?

Jacob Trefethen:

And does it avoid doing other functions that make those problems elsewhere in the body?

Saloni Dattani:

Yeah, is it causing side effects. So I guess that’s where we are now, but it’s super new, right? And because clinical trials take so long, you wouldn’t expect this to get to the market for a while. Do you have a favourite use of these tools, or thing that you think they could do?

Jacob Trefethen:

I am hopeful for several things. I mean, I’m just so curious to see how it all goes in the next few years. But one is actually proteases. If you look at one of the first protein products that was designed with recombinant DNA technology in probably the eighties, nineties, was tissue plasminogen activator-

Saloni Dattani:

Oh yeah.

Jacob Trefethen:

- which is a protease-

Saloni Dattani:

It cuts proteins. We’ve talked about an HIV protease, right, in our first episode?

Jacob Trefethen:

Absolutely. And that we talked about Strep A protease in the episode of that cleaving our signalling proteins. But in this case, when you have a stroke, what’s often happening is that a clot is lodged in your brain and you want to break up that clot and there aren’t great ways to do that chemically. The best we’ve got at the moment is tPA, which was these tissue plasma activators invented maybe 40 years ago.

Saloni Dattani:

Wait, wait. That is a natural thing that’s produced by our body, but it was produced in recombinant bacteria or yeast or something, 40 years ago.

Jacob Trefethen:

Yes, that occurs naturally, and the difference there was making it with recombinant DNA, so you can make it as a product scalably. So now imagine you could lay it on top of some tweaking or some hallucinations, and you get even more useful proteases that perform similar or better functions. It’s a tough problem ‘cause you also don’t want it to run away and perform its function too well on the wrong strokes. But millions of people die of stroke every year, so those kinds of targets just become way more tantalizing when you have more you can do with proteins.

Saloni Dattani:

Right. I guess there are also other medical uses. So proteases are one, maybe other kinds of enzymes- there are a lot of rare genetic disorders where someone is missing an enzyme, or some enzyme is dysfunctional, or something like that. And I guess there are other kinds of diseases that occur, where blocking a protein, or maybe designing a new protein, or introducing a protein or something, would help that person do some function that they weren’t able to do before.

And then, would you also maybe be able to use proteins for diagnostics? Would you be able to use them for testing? Like I think I mentioned sometimes proteins change shape or something like that, if the temperature changes - I think you mentioned that as well - and if the acidity changes or something like that.

So maybe you would be able to make these really specific proteins that bind or go to a specific place, and then if something is there, it changes shape and maybe that releases some information that-

Jacob Trefethen:

Yeah, you could design transistors with proteins. You know, you could-

Saloni Dattani:

What?!

Jacob Trefethen:

I bet you.

Saloni Dattani:

I was not expecting that. What do you mean?

Jacob Trefethen:

I mean, if you’re just- all you’re trying to do is send some signal under certain conditions and not others, you can make electronics with proteins, or I assume. I’m making this up, but it must be true.

Saloni Dattani:

I don’t know anything about engineering, so. Okay, so there are all of these really cool things that proteins could be doing, that people could be designing new proteins for the structures, the medicines, the diagnostics, the replacements for enzymes or hormones that people are missing, also the agricultural uses, or the like fermentation, or the industrial processes, or the materials, or the-

Jacob Trefethen:

There’s so many different things, and I think the starting gun is basically 2022. What we’re gonna see, I believe, if people put the effort in is a lot of structural biologists who know how to use these computational tools, but otherwise they’re essentially generalists, matching up with experts in particular fields who know a lot about diagnostics, or who know a lot about the heart, or who know a lot about a given infectious disease, or know a lot about a given agricultural problem, and in combination, I think those teams of people are gonna do really incredible things.

Saloni Dattani:

I have a last question for you.

Jacob Trefethen:

Hit me.

Saloni Dattani:

We talked about a lot of applications of this and making particular things. Is this gonna be useful for basic research as well?

Jacob Trefethen:

It’s gotta be, it’s gotta be. I’m now, my first, what is my first thought on...

Saloni Dattani:

Or I don’t know, like understanding some disease, or something like that. Yeah, understanding some process.

Jacob Trefethen:

I mean, the answer’s got to be yes, and then it’s almost like, start with the problem in hand, before I know how to answer it. But the last 10 years, we had CRISPR come through – CRISPR has proven so useful as a basic research tool, maybe even above how useful it has been as a medicine platform. So I wouldn’t be surprised, yeah.

Saloni Dattani:

Yeah. I guess we talked about how, if it can be used in diagnostics, that actually is a big research tool as well, like if you’re able to make sensors to something.

Jacob Trefethen:

Definitely. Yeah.

Saloni Dattani:

Okay, yeah, so there’s lots of cool uses.

Jacob Trefethen:

Lots of cool uses.

Saloni Dattani:

What’s gonna happen in the future?

Jacob Trefethen:

There’s two things I think we need to discuss, ’cause we’ve just gone so far with these AI models. Number one is, are they gonna cure us all of all diseases in the next couple years? Sounds so magical. Why not?

Saloni Dattani:

No, I don’t think so.

Jacob Trefethen:

Okay, great. Well, I think we should get into that. The other is, if you can hallucinate any protein for a function of interest, well, does that mean that terrorists can hallucinate proteins that attack other human beings? And we gotta talk about that too. And that probably means a whole other episode.

Saloni Dattani:

Alright. Well, thank you for listening to our episode on protein design and if you like this, share it with every single one of your friends, your family, your teachers, your haters, your colleagues, and subscribe.

Jacob Trefethen:

Couldn’t have said it better myself.

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter: https://www.scientificdiscovery.dev/

Jacob’s blog: https://blog.jacobtrefethen.com/

Courses:

• EMBL-EBI. AlphaFold: A practical guide https://www.ebi.ac.uk/training/online/courses/alphafold/

Articles:

• Monica Jain et al. (2022) Exosite binding modulates the specificity of the immunomodulatory enzyme ScpA, a C5a inactivating bacterial protease. https://pmc.ncbi.nlm.nih.gov/articles/PMC9464890/

• Jakki Cooney et al. (2008) Crystal structure of C5a peptidase https://www.rcsb.org/structure/3EIF

• Hui Li et al. (2017) Mutagenesis and immunological evaluation of group A streptococcal C5a peptidase as an antigen for vaccine development and as a carrier protein for glycoconjugate vaccine design https://pubs.rsc.org/en/content/articlelanding/2017/ra/c7ra07923k

Lectures:

• Rosetta Commons (2024) AlphaFold – ML for protein structure prediction https://www.youtube.com/watch?v=SVrn8_8aKO8

• Rosetta Commons (2024) MPNN – ML for protein sequence design https://www.youtube.com/watch?v=6z4XmUAwdNA

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Graham Bessellieu, video editor

• Rachel Shu, on-site editor

• Anna Magpie, fact-checking

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Works in Progress & Open Philanthropy

Transcript

Jacob Trefethen:

Proteins are abundant in our body, doing lots of different and amazing things to keep us alive. They are a big part of medicines too, like synthetic insulin, which we talked about last episode, antibodies, and protein vaccines – just to name a few. And over the last decade, it’s become possible to use AI to predict the structure of proteins much more accurately, and use that knowledge to design new proteins that have never been seen before in nature, improve existing proteins to use for medicines, other industries, agriculture and more.

Saloni Dattani:

Welcome to Hard Drugs. I’m Saloni Dattani, and this is Jacob Trefethen. I’m a co-founder at Works in Progress magazine and Jacob leads science and global health R&D funding at Open Philanthropy. And in today’s episode we’re talking about proteins – how to improve proteins and how to design new proteins.

Jacob Trefethen:

Last episode, you taught me about insulin – a protein that I know is pretty useful and the body makes naturally, and that’s also useful if you make it outside of the body, and provide it as a medicine for people who need it.

Saloni Dattani:

Right. It’s used for diabetes – insulin helps control blood sugar levels. So in the episode we just did, we talked about how it used to be extracted from animals in the early 20th century. Then, in the 1970s, people developed a way to reproduce it in bacteria that were used in this giant bacterial churning soup machine [a bioreactor], in such a way that you could scale up this protein much more than you could with animals – that are being factory farmed – so that they can be used for many more diabetes patients around the world.

Jacob Trefethen:

That was a game changer over the last fifty years. Today, we want to speed through to the last five years, really, and talk about some game-changers of new AI technologies that help you not just take a protein nature has designed and reproduce it, but actually tweak or improve a protein that nature has designed for an even more useful medical purpose. That really started getting more and more possible with tools invented since 2020.

Saloni Dattani:

Whoa, that’s very recent.

Jacob Trefethen:

It’s very recent and people probably have heard of AlphaFold. AlphaFold2 came out in, or was first used, in 2020. And some of the other tools that help you improve on existing proteins that were made since then, 2022 and since, so.

Saloni Dattani:

But first...

Jacob Trefethen:

Yes.

Saloni Dattani:

Why proteins? Why are proteins so cool? We talked about it a little bit in a previous episode.

I think there are a few things. One is, a single protein could be doing lots of different things. You can have modular parts of proteins that are doing a bunch of things together: maybe one part is doing an enzymatic reaction, another one is binding to something that tells it how to speed up that reaction or to slow it down. Maybe there are some other signalling parts that are like, “Hey, protein, stop working now.” and stuff like that. So that’s one.

I think the other is, yeah, it reacts to the environment – changes in temperature, or acidity, or things like that could change how much the protein is doing something. Also they’re quite small. If you were a chemist who was trying to make some reactions happen, and you’re doing a series of reactions in a bunch of machines, a protein can do that at a tiny scale. This tiny protein is doing all that stuff. It’s super specific as well. It could bind to a tiny molecule, or a metal, or another protein or another receptor or so many different things, and it’s super specific. So, lots of reasons.

Jacob Trefethen:

Lots of reasons. And I want to convince you now that that’s not enough in that-

Saloni Dattani:

Ooh. I wasn’t expecting that.

Jacob Trefethen:

-nature has not evolved all of the proteins we might find useful in our bodies, and that’s why these new AI tools are pretty useful.

Saloni Dattani:

Okay, so the potential uses of proteins are very, very large, I think.

Jacob Trefethen:

Yes.

Saloni Dattani:

But the ones that we see in nature are not necessarily, and maybe they’ve only evolved to do certain things in certain environments. Is that right?

Jacob Trefethen:

Yep, exactly. And I want to make it specific by talking about... like we had insulin last episode, let’s talk about a medicine that we don’t yet have that we might be able to make with the help of new tools.

Saloni Dattani:

What’s the protein?

Jacob Trefethen:

I picked one called ScpA.

Saloni Dattani:

ScpA.

Jacob Trefethen:

This is a protein which is on the outside of a bacteria – the bacteria is Strep A. As some of my friends are well aware, I am a bit-

Saloni Dattani:

You’re a fan boy.

Jacob Trefethen:

I’m a fan boy, or really, I hate-

Saloni Dattani:

Well, you’re a hater.

Jacob Trefethen:

I’m a hater of Strep A, and the reason is that Strep A, as a bacteria, kills half a million - or maybe more than half a million people a year, because it can lead to different diseases if you get repeated infections.

So in particular, it leads to rheumatic heart disease, which used to kill a lot of people in the US, the UK, and other high income countries; still kills a lot of people in lower and middle income countries. As a side note, Strep A is also the bacteria that- have you ever seen that flesh eating stuff that goes up your leg and then eats your whole body?

Saloni Dattani:

Ooh. Well, I guess I’ve seen a few different diseases that do that, but yeah, that’s scary.

Jacob Trefethen:

Yeah, Strep A is one of the biggies.

Saloni Dattani:

And wait, doesn’t it also- isn’t that also Strep throat?

Jacob Trefethen:

Yes, Strep throat - and Scarlet fever and different names for these diseases - yeah.

Saloni Dattani:

I think the one thing that I know about this is: the bacteria maybe first causes an infection of the throat - so it first causes Scarlet fever or Strep throat - and then maybe later on, people develop heart disease from it.

Jacob Trefethen:

Yes, exactly. Because what happens is, your body’s making an immune response to that bacteria that it can actually get confused and start attacking your own heart valves, which... not so good.

Saloni Dattani:

That’s terrible. Yeah, you also wouldn’t usually- I think the classical way people think about infections is not that they’re linked to heart disease, but in fact, there are many ways that they can be connected.

Jacob Trefethen:

Absolutely. So, we don’t have a vaccine against this. It would be pretty nice if we had one. The way that you make vaccines often, in a modern context, is you try and take protein antigens from a given pathogen.

Saloni Dattani:

What is an antigen?

Jacob Trefethen:

An antigen is a part of, in this case, this bacteria, that prompts a immune response in your body. So there’s many different things that are involved in Strep A the bacteria reproducing and living, but some of them are better things to target, for your antibodies, than other parts of your immune system.

Saloni Dattani:

So if I was trying to recognise you-

Jacob Trefethen:

Yes.

Saloni Dattani:

-from far away, it would be more helpful for me to know what your face looks like than what your clothes look like. Because you might change your clothes.

Jacob Trefethen:

That’s exactly right. And sometimes if I’m really stealthy, I might change my face, but that is a bit more costly.

Saloni Dattani:

Ooh. I used to watch these- well, I didn’t watch them- but on Indian television, there were soap operas that would be ongoing for years or decades. Sometimes the actors would not be interested in working on that show anymore, and they’d leave the show, and the producers would replace the actor with someone else!

Jacob Trefethen:

That’s what we call a stealth pathogen.

Saloni Dattani:

So they would have to explain what happened, and usually it would be like they had cosmetic surgery, or they got into an accident. So they write off the old actor from the show, but they still want the character in the show, so they just get someone else to play them. And they’re like, “Oh... now they look different.”

Jacob Trefethen:

I’m always impressed that the main characters in Harry Potter were still the main actors by the end because that was what a decade of- anyway, so someone doing good.

Saloni Dattani:

That’s impressive.

Jacob Trefethen:

Well done.

Jacob Trefethen:

So I’m Emma Watson.

Saloni Dattani:

You’re Emma Watson?!

Jacob Trefethen:

No, I’m not, I’m actually Jacob, but I am Strep A. That’s what I-

Saloni Dattani:

Okay- oh??

Jacob Trefethen:

-and we are trying to find antigens on the outside of Strep A that could be used as a vaccine, or as part of a vaccine. There’s a lot of attempts to do this already, so you might not end up needing AI, but AI is starting to help. And I’ll walk you through an experience I had, of sort of playing around with these tools to get to grips with them a bit more.

So I went to visit the University of Washington in March this year, and got to spend a couple weeks at the Institute for Protein Design, which focuses on a lot of protein design tools and has invented some themselves. And while I was there, I focused on this one antigen – ScpA – and tried to use a tool called ProteinMPNN to revise it, to make an even better antigen, or immunogen, for the immune system.

Saloni Dattani:

Okay. So let’s say again, I am trying to recognise you from far away.

Jacob Trefethen:

Yes.

Saloni Dattani:

And I know what your face looks like.

Jacob Trefethen:

Yes.

Saloni Dattani:

Why would I want to improve that?

Jacob Trefethen:

Okay, so let’s start with what ScpA looks like. This is a protein that’s stuck on the outside of the bacteria. Now that’s a good start, because your antibodies can access what’s on the outside of a bacteria much more than they can access what’s on the inside.

Saloni Dattani:

Right.

Jacob Trefethen:

So that’s a good start.

Saloni Dattani:

They don’t fit in. They can’t get in.

Jacob Trefethen:

They don’t get in. Now, you might say, okay, great, why don’t I just take that thing and use it as a vaccine? Well, you could try that, and people do try that. You can make changes, though, that improve on the properties of that protein from the point of view of making an actual vaccine - making a product that you could inject.

For example, any given protein, it’s not guaranteed to be that easy or cheap to make. You can make it in different systems - different bacteria, in yeast - and if you have to make a protein in mammalian cells, for example, instead of in yeast or in bacteria, that’s going to be more expensive. That’s not great from a product development point of view.

It also might not be as stable as it is in its natural occurrence, on the outside of a bacteria. If you’re plucking it out of this membrane and you’re just putting it in and, well, are you sure that’s going to not just clump up together with other versions of itself if you’ve got a lot of them? Are you sure it’s going to be soluble in water, which is a requirement property for a vaccine? And are you sure it’s going to not deform into something, once it’s not plugged in in the same way?

So you want to make alterations to that too. And finally, are you sure if, on its own, it’s going to be as immunogenic as when it was in its natural form on the outside. “Immunogenic” in the sense of: prompting the immune response we’re looking for. This particular antigen, it’s about a thousand amino acids long.

Saloni Dattani:

Okay. That’s like moderate, I guess.

Jacob Trefethen:

Yeah, it’s sort of medium-big, yeah.

Saloni Dattani:

Yeah, we talked about some tiny proteins before and they were like 20 to 30 amino acids. Then we talked about a huge protein, titin, which is like 30,000 - 33,000 something amino acids.

Jacob Trefethen:

Yep, so this one’s sort of in between those. And if you can get away with it, it might be nicer to take only the bits that really matter for the immune response, and make it smaller.

If you can still prompt an immune response, it’d be kind of nice if it was only 200 amino acids long – it might be cheaper to produce, for example. It might be- if you put it on a scaffold of a thing, of a soccer ball, and you want to put lots of different antigens on that soccer ball to prompt an immune response - if it’s smaller, you might be able to fit more on.

Saloni Dattani:

So I’m unfortunately still thinking about the analogy where I’m trying to recognise your face, and now this soccer ball has little Jacob faces all over it.

Jacob Trefethen:

Well, you would definitely recognise that.

Saloni Dattani:

It would be scary though.

But also, this makes me think of, okay- in history, we didn’t have vaccines that were just one antigen. They weren’t just one part of the thing. It was usually the entire virus, or the entire bacterium. And that would be killed in some way, maybe it’s chemically inactivated, or it’s attenuated - so it’s put into cell culture and made to evolve into something that doesn’t infect us or cause harm - and then we’re using the whole pathogen. And this is very different; this is a very precise part of the pathogen.

And it turns out, sometimes we only need that to recognise the whole pathogen. And maybe that’s also useful because there are other parts of the pathogen that are harmful to us in some ways.

Jacob Trefethen:

Exactly. In this case, with Strep A, if you know that the whole pathogen prompts a immune response that you might hurt your own heart, then you sure enough want to get rid of some of it.

Saloni Dattani:

Right. And then, are there other reasons that we would want that? What is this bacteria doing? What are the proteins doing?

Jacob Trefethen:

Well, actually in this case, yes! So I’m going to take a quick detour that it’s not central to the point. Are you okay to bear with me?

Saloni Dattani:

Yes.

Jacob Trefethen:

Okay, great. This particular antigen is kind of messed up. What it does is it hangs out on the outside of the bacteria and it’s a peptidase. Imagine it kind of looks like - for people watching the video. [crocodile jaw clapping sounds]

Saloni Dattani:

Oh, it’s like a crocodile.

Jacob Trefethen:

It’s like a crocodile. And what happens is that it’s evolved to cleave, or chop in half, say, some signalling proteins that our body sends to the pathogen to actually recruit even more immune proteins, so C5a. If you send those immune proteins, and they’re going to bring some buddies, it’s going, “Nope. Nope.”

Saloni Dattani:

Oh my god. So wait, this bacterium is trying to infect me, and then my immune response is trying to attack it in response, and it’s sent out all of these immune cells to attack it. But the signals just get cut up.

Jacob Trefethen:

They’re just getting cut up.

Saloni Dattani:

That’s really sad.

Jacob Trefethen:

It’s so sad because this is one of those things where our immune system is pretty good, and it is getting activated, and it’s going to try and mess up that bacteria, and the bacteria is like, “Hmm, no you don’t.”

Saloni Dattani:

Oh, that’s scary. So if we could somehow change this protein or this bacterium, we could find a way for us to recognise it without it cutting up those signalling proteins.

Jacob Trefethen:

Correct. Now, the reason this is a detour on the main point is that, that is not that hard. You can actually just look at this cleaver in question and, imagine it’s the crocodile jaws, you just pick the amino acid residue that’s most at the jaw.

Saloni Dattani:

So you put something in- you’re saying you could block the cutting by putting something into it.

Jacob Trefethen:

Yeah, essentially, but just changing the string of amino acids, so that at the position that’s usually histidine-193, you put leucine, or I forget, you put a different amino acid. You actually only have to make one change to this thousand amino acid long protein, and it will no longer perform the harmful function.

Saloni Dattani:

That’s amazing.

Jacob Trefethen:

You don’t need AI for that. You can validate that you’ve neutralised that aspect of it.

So just stepping back, we can get to a pretty useful initial test of a vaccine without AI, where what we did was, we said: we got this bacteria that’s invading us, and we don’t like. We took one of the things on the outside that antibodies do bind to, and we’re just going to inject that after changing a residue, so it doesn’t hurt us.

What will happen if that works? Well, the immune system broadly- but let’s just visualise antibodies- will bind that and you’ll generate a adaptive immune response that remembers how to produce those antibodies. If you get an infection of Strep A later, those antibodies are going to come and hit that bit of it.

Saloni Dattani:

So basically, I recognise your face, and if you were in a different place, like in a sand pit on a beach; you’ve dug up a hole...

Jacob Trefethen:

Who put me there?

Saloni Dattani:

I don’t know, maybe you did it yourself. Don’t people like doing that? And then maybe you’re inside, and only your head is sticking out, and I’m like, “That’s Jacob.” I know that it’s you and I don’t need to see the whole body to know that.

Jacob Trefethen:

Yes. And actually, in the context of the immune system, it’s a good metaphor because you’re trying to neutralise me, so you probably brought a bow and arrow, and if you shoot me in the head - that will actually kill all of me. I’m neutralised, so you don’t have to shoot my hands, you don’t have to shoot my legs. You got that kill shot in the head, so well done. And while I was in this sand! But I couldn’t-

Saloni Dattani:

Well, yeah, you probably couldn’t move at that point.

Jacob Trefethen:

I couldn’t move; that wasn’t my fault.

Jacob Trefethen:

So...

Saloni Dattani:

So.

Jacob Trefethen:

AI.

Saloni Dattani:

Wait, okay, wait, this protein- we just have the one protein. I think the other thing that’s really helpful about this is that - it’s not the entire bacteria that’s like harming us; it’s not invading different parts of our body, and things like that. We’ve just honed in on your face.

And that’s really useful and that’s like- okay, we can recognise this when it comes later on. Then maybe, if there are other types of... if you had an alter ego who shared your face, or if there was a different species, or there was a different strain that had some similarities like your face, but different body, then I would still be able to recognise that as well.

Jacob Trefethen:

You could neutralise jacked Jacob, you could neutralise short Jacob. Absolutely.

Saloni Dattani:

Cool. Okay, so we talked about protein vaccines and we can improve them. This is not good enough?

Jacob Trefethen:

Yes, so I mean, it may be, I think in this particular case, it probably wouldn’t be. You might want to improve that head of mine; maybe give me a little bit of Botox or something, so that I’m even more recognisable to your recognition system.

So there’s this new tool, ProteinMPNN. ProteinMPNN was made in the Baker lab. David Baker, who just won the Nobel Prize alongside the AlphaFold inventors, by him and by some students in particular: Justice Dauparas, I think, was one of the lead authors. It basically takes the structure of a protein and predicts what sequence of amino acids leads to that structure.

So the structure in the sense of- think visually, 3D, where do the residues - where do the carbon atoms in this backbone - actually appear once the protein’s all folded up? And sequence, think: what is the string of amino acids?

Saloni Dattani:

So I’m imagining a bead of strings [beads on a string], which is the protein, and the bead of strings is folded up into this larger structure. Maybe it’s a knitted ball like a kitten plays with, or something, and it’s not as symmetrical or anything. And we are trying to predict exactly what amino acid is each of the beads. We know what the string of beads looks like - what that shape of the folding is - but we’re trying to predict each of the beads.

Jacob Trefethen:

Correct. Exactly right. And we may not have a- there may be multiple strings that end up folding up to kind of similar- so you’re not always just trying to predict “What’s the exact string?” You’re often trying to give me some hypotheses here that you then want to test. In the case of this antigen we’ve been talking about, you can say: I want to make this smaller; it’s currently made of five domains – which are these subunits of the protein that self-assemble and then assemble together: Could I generate the same immune response for just two of them, the two most important ones?

So let’s just chop off- let’s just chop off the other three domains and look at these two together. Then I’m going to ask ProteinMPNN. I’m going to say, and I did this when I was at the University of Washington, I was like, okay, here’s what it looks like; here’s what I want it to look like...

Saloni Dattani:

... How do I make that?

Jacob Trefethen:

How do I make that? Once you chop off some of those domains, you can’t immediately use what’s left because there will be amino acid residues that have evolved to be in particular points inside the protein that are doing great. But if they’re exposed, some of those, if they’re exposed, will be hydrophobic, which means that what you’ve just created is not going to be soluble. So you’re going to want to mutate the amino acid sequence a little bit so you’re not exposing, for example, hydrophobic residues.

Saloni Dattani:

So I remember learning a little bit about protein structure, and one thing that I remember is, okay, so if a protein is soluble, then the outside has to be attracted to water, “hydrophilic”. But the inside is usually “hydrophobic”. And that makes it unlike oil, say, so if it was hydrophobic on the surface, then it wouldn’t dissolve.

Jacob Trefethen:

But we want it to dissolve. So we’re going to have to make some changes.

Saloni Dattani:

I guess, well, if I’m thinking about the environments of a protein, it’s usually in the blood or in cells. It’s in these places where there’s a lot of water content.

Jacob Trefethen:

I mean, most of life’s chemistry happens in aqueous solutions, so we got to be ready for that.

Saloni Dattani:

So that would be helpful not just to cut it down so you could only make two domains, but just generally. Let’s say you just had a mystery protein in front of you, and you’re thinking, how do I recreate this? I would maybe imagine- I don’t know if people actually do this, but I would just imagine, I don’t know, I’m a pharma company, or I’m a biotech company, and I found a protein and it’s doing something really cool. Or I found my competitor’s protein, and I’m like, how did they make this? I want to make this.

Then I would use this tool to try to figure out the letters that make it. And you know what, I really like the name “ProteinMPNN”, because I’m thinking it’s trying to predict each of the amino acids in the bead of strings [beads on a string], and you represent each of the amino acids with a letter, right? So it’s like “M-P-N-N.” Does that stand for an amino acid chain? Maybe it does.

Jacob Trefethen:

I wish... I’m going to look it up.

Saloni Dattani:

Does it?

Jacob Trefethen:

It does!

Saloni Dattani:

Oh my god, it does. Wow. What does it stand for?

Jacob Trefethen:

It stands for methionine (M), proline (P), asparagine (N), and asparagine (N).

Saloni Dattani:

[sound effects of an exploding brain] Whoa, that’s very smart. So yeah, I mean that’s a really good way to think about it. You’ve got the structure now you’re like: what amino acid sequence makes this? And as you said, there could be multiple amino acid sequences that make that particular structure.

I think what this tool is doing is that, at each point of the beads on the string, maybe it’s predicting one amino acid at a time, and it’s sort of going, “Maybe let’s try from the start of the string, and I think that’s aspargine [sic]. And okay, that’s that, and so given I know that, what could the next one be?” And you’re building it up, by thinking about the larger structure, but also, now that you have already predicted some of the previous amino acids on that string, that gives you a bit more information about what the next amino acid could be.

So you’re using the information that you already have from the neighbours to predict the next one. But sometimes that probably leads to you getting stuck in dead ends, sometimes it doesn’t work, and maybe that’s why it produces many predictions, and then some of them won’t be that accurate.

Jacob Trefethen:

It could be mispredicting the structure. And what a lot of people who work on protein improvements like this want to do next is, to run a check on: hold on, is this going to end up folding up like I thought it was? And I think you probably know how they do that.

Saloni Dattani:

Ooh, is it AlphaFold?

Jacob Trefethen:

It’s AlphaFold.

Saloni Dattani:

That makes sense. Okay, so just recapping. ProteinMPNN is predicting the amino acid sequence from the structure.

Jacob Trefethen:

Yes.

Saloni Dattani:

And I knew that AlphaFold predicts the structure from the sequence. So if you have predicted the sequence and then you’re like: Wait, does this actually fold into that structure that I started out with? You would use AlphaFold then.

Jacob Trefethen:

Exactly. You can kind of validate: does this fold up how I want it?

Saloni Dattani:

Right. But it’s not truly validating, right? Because you would want that to be done in a lab.

Jacob Trefethen:

Absolutely right. And so what I did in this toy project was just the steps we just described. I did not have enough time to validate in the lab whether the tweaked protein molecule was what I thought it was, or was doing what I wanted it to.

But what’s amazing about these tools is that they’ve gotten so good that, in combination- you know, I was talking to PhD students and post-docs there who had sort of been in the lab before the deep learning revolution, been in the lab during and after the deep learning revolution, and they’re like: everything’s changed.

Now, they can generate, say, their fifty favourite protein sequences that are hypotheses, that they’ve validated with AlphaFold, but haven’t truly validated. And they can say, okay, I’m going to order those up online and I’m going to validate them in the lab... next week or the week after.

Saloni Dattani:

You mentioned the change between now and the grad students before.

Jacob Trefethen:

Yeah.

Saloni Dattani:

And that kind of reminded me of what it used to be like to do this. I was recently reading this book called The Codebreaker, and it’s just about Jennifer Doudna and how she developed CRISPR and stuff like that. But actually, the author actually starts kind of much earlier on, and he describes what’s happening in the 1940s and ‘50s, when people are discovering the structure of DNA.

And this is relevant, because I think when Watson and Crick were trying to figure out that the 3D structure that led to DNA, they had to actually use these physical models of atoms in a lab. They’d have these balls-and-stick type figures and then be like: okay, what angles are working, or what is going here? What is going there? What are the specific atoms that is making this DNA?

And they didn’t even know that it was a double helix at first; I think they thought it was a triple helix, and one of them had just misremembered the amount of water that was in the total molecule or something, and that got them confused for a bit.

But that’s sort of what would be happening here before, where if you’re trying to figure out the amino acid sequence of a structure initially, and I think this was maybe pre-2000s or something, people would be actually making physical models of the same thing. Does this work if I put this amino acid here, and then the next one there, and does it fit together? Is one of them negatively charged, and the other one also negatively charged? How does it all work? And that sounds extremely complicated when you have a much bigger protein or yeah, I mean it’s just complicated. And there are so many possibilities.

Jacob Trefethen:

So we no longer have to go physical ball and physical ball.

Saloni Dattani:

I think there were some things between that and now. There were some kind of statistical modelling techniques in the 2000s and 2010s, and then there was some AI tools, and deep learning, and stuff like that that was used as well.

But this is really different because I think it’s trained on data of the structures of proteins. What that means is, over time, people would be working in the lab to figure out exactly what a protein looks like with x-ray crystallography - so you’re crystallising the protein, you’re getting an X-ray image, you’re trying to determine what it actually looks like - or various other techniques.

And then they are trying to figure out, again, what does this look like? And because you would then figure out the coordinates of the different atoms, you then have a lot of data that’s collected over the last few decades that has gone into this database called Protein Data Bank, and I think that’s the biggest one.

Using all of that data – of the coordinates of the atoms, and which amino acid sequences there are, and maybe similar types of proteins, and things like that – you could then make these predictions a lot better. So I think that’s what this is doing. It’s this graph-based deep neural network. So it has data on the coordinates, which you’ve also put into it with the overall structure that you have, and then it’s like: which amino acid sequence is in each part of the chain?

Jacob Trefethen:

Yeah, and that data is- we’re talking over a hundred thousand - I think it was 170,000 - structures that AlphaFold2 trained on.

Saloni Dattani:

So that’s individual structures that people have worked out in the lab through these other methods, like x-ray crystallography and making these physical models-

Jacob Trefethen:

Which is crazy.

Saloni Dattani:

It’s a huge amount.

Jacob Trefethen:

Decades, probably fifty years of work of thousands, tens of thousands, of grad students, postdocs, professors - mostly on public funding.

Saloni Dattani:

I guess I’m imagining in the past, you might just be an individual person who’s like, “Let me try to guess what the amino acid sequence is based on stuff that I’ve learned.” and that’s much harder than if you just are this computer model that’s trained on so much of it. You don’t have to try to remember each of the structures.

Jacob Trefethen:

I can do it, but I know a lot of other people struggle. Yeah, they really did it different.

Saloni Dattani:

Okay, so quick recap. We have this protein in the streptococcus bacteria that we want to make and we want to change a bit. What was the change that you mentioned that we’re doing here?

Jacob Trefethen:

We’re going to make it smaller and just use a couple of its domains.

Saloni Dattani:

So we’re only using a few domains, and that should be enough for us to recognise it without causing other problems for us.

Jacob Trefethen:

That’s the hope.

Saloni Dattani:

Right. Alright. And then what’s next?

Jacob Trefethen:

Well, what you would do next – that I didn’t get to do because I had to move on, would be trying to validate what ProteinMPNN and AlphaFold have helped you get to as hypotheses, but validate them in the lab.

Saloni Dattani:

So we’ve got the domains that we want, we know the structure that we want, we then have figured out the amino acid sequence that leads to that, and then we want to check: does that sequence actually create that structure?

Jacob Trefethen:

Yes. And the asterisk I’d give though is that we have hypotheses of multiple sequences where they’re a little bit different because ProteinMPNN is giving us some hypothesis.

Saloni Dattani:

Right, and some of them might be wrong.

Jacob Trefethen:

Some of them might be wrong, some might be better than others, and a lot can change with just one amino acid change here or there.

Saloni Dattani:

Right. As you said before, that one amino acid change means that it can no longer cut up our signalling proteins.

Jacob Trefethen:

So what you would do here- and as a spoiler, after I’d done all this thinking and computational stuff, I was like, you know, before I order these up, let me just check if someone has done this. And you’ll be amazed to hear that a group at Shandong University in China had already done all these experiments three years ago.

Saloni Dattani:

Aww.

Jacob Trefethen:

It’s terrible to have an idea.

Saloni Dattani:

So they had already come up with this slightly adjusted version of this protein with just those domains, and they had tested that it worked in a lab... and? Did they?

Jacob Trefethen:

They had done the subdomain analysis. I’m not sure which alterations they’ve made, and if they used AI for those alterations. But the basic punchline of what they did was- Let me walk through what validation would look like here.

At the very end of the day, the validation we care about is: if you inject this, or inhale it, or something, as a vaccine: will it protect you as a human being against a future Strep A infection? We’re not going to test that.

We’re going to test some earlier things to get an initial idea. So what you can do is, you can order - let’s say we had 50 hypothesis amino acid strings - you can order up the DNA that would code for each of those amino acid strings. Remember from our first episode, we go DNA to RNA to proteins, so we want the DNA sequence.

Saloni Dattani:

So we’ve basically made each of the sequences that ProteinMPNN has spit out.

Jacob Trefethen:

Yes.

Saloni Dattani:

We’ve made a bunch of them.

Jacob Trefethen:

We’ve made a bunch of them, say, fifty, and we’re getting the DNA strings. You can order those online or from your favourite provider, Twist Biosciences and IDT- you can get them delivered to the lab, maybe next week, it’ll be pretty quick. You can then take those DNA strings, and try and grow up the proteins by putting the DNA inside a living system - so say bacteria like E. coli or yeast or-

Saloni Dattani:

What is grow-? Why are you telling this protein to grow up?

Jacob Trefethen:

I’m telling YOU to grow up, Saloni! I am telling the protein to grow up because I need to use it in experiments myself.

Saloni Dattani:

But what is growing up? What is-

Jacob Trefethen:

Growing up is-

Saloni Dattani:

What is a baby protein?

Jacob Trefethen:

A baby protein is, well- more like a butterfly situation here, where I want the butterfly, so the caterpillar is a string of DNA, and I’m going to have to get a little cocoon going. That cocoon, in this case, is a bunch of yeast cells that I’ve put in a little vat, and I’m going to feed nutrients to and I’m going to jostle around for two days, and they’re going to have a good time in that.

Saloni Dattani:

Aww! That’s really cute.

Jacob Trefethen:

And by the end, I’m going to slice ‘em up! And I’m going to take the protein-

Saloni Dattani:

This got really violent.

Jacob Trefethen:

I would never hurt a butterfly, but a yeast cell... watch out.

Saloni Dattani:

Okay, so you are trying to recreate this protein by growing it up in a bacterium.

Jacob Trefethen:

Yes.

Saloni Dattani:

From the DNA?

Jacob Trefethen:

Yes, exactly.

Saloni Dattani:

And you have now made lots of them.

Jacob Trefethen:

Now I’ve made lots of them, and here’s what I might do with them. Experiment number one: I would probably take a bunch of those proteins and inject them - as if they’re a vaccine - into some mice. And the mice are going to have some sort of response to that, or maybe they won’t.

The response I’ll first check is: take a sample of blood from them, I might say a month later and see, okay, did any antibodies get produced? And if so, sign number one we’re headed in the right direction. If not, uh-uh, this ain’t so good.

Saloni Dattani:

Uh oh. And well, you’re not just testing: does it produce any antibodies? You’re like: does it produce antibodies against the specific protein?

Jacob Trefethen:

Absolutely. Well, the next experiment I would do is against the specific- what I would do is: take a ‘wild type’ of the protein, so don’t take this thing I just grew up, get some Strep A bacteria and take this isolate somehow.

Saloni Dattani:

Right. So, does the mouse now create antibodies against the protein that I was trying to use as the target?

Jacob Trefethen:

Exactly. So I would take the serum, blood, and say, are the antibodies produced? Let’s say there were some produced, so it’s immunogenic. Are they cross-reactive to the wild type of the protein?

Saloni Dattani:

Right.

Jacob Trefethen:

So do they bind it tightly? Do they bind it a lot?

Saloni Dattani:

Yeah. So did your little protein vaccine actually help the mouse protect itself with antibodies against the natural protein?

Jacob Trefethen:

Yes, but I would go even one step further, which is, so that’s the next experiment, but to protect the mouse, you actually care about: is it protected against the bacteria? Not just the protein. The third experiment’s still in a dish.

I’m still taking my serum that has antibodies in, I’m putting it against the bacteria and I’m saying, does it bind the bacteria or neutralise bacteria? The best thing after that would be: okay, we injected this mouse and now we’re going to challenge it with some sort of - I know, I know - with a bacteria. And then some of them-

Saloni Dattani:

-are probably going to die.

Jacob Trefethen:

Are probably going to die, yeah.

Saloni Dattani:

How dangerous is this infection?

Jacob Trefethen:

For most humans? Not super dangerous. So probably true for most mice.

Saloni Dattani:

Okay. So you’ve now checked that a different research group has made this and validated it. And now you know that that protein that you improved could have worked.

Jacob Trefethen:

Yeah, basically it turns out that, of the five domains that make up that protein, if you take some subsets, it’s not looking good. You take some other subsets, it’s looking pretty good.

So it actually looks like it’s probably worth doing. If you take those subsets that look like maybe they work, you can then create a vaccine involving maybe just that, or involving other subsections of other proteins that might be antigens.

Saloni Dattani:

That’s really cool.

Jacob Trefethen:

It’s really cool. It’s really cool.

Saloni Dattani:

But this would still just be the start of the whole process of developing a new vaccine. You would then need to test it in human clinical trials like- phase one, phase two, phase three- that could take another eight years, ten years, something like that.

Jacob Trefethen:

That’s right, and AI has not sped up that yet, so there is more to do.

Saloni Dattani:

Has this been done before? Are there drugs and vaccines already that have been improved with AI?

Jacob Trefethen:

There is a drug which was made during the COVID pandemic by Neil King, at the Institute for Protein Design, and David Veesler and students there, that use predecessor tools to create a vaccine using just the RBD region receptor binding domain of the spike protein.

So the vaccines that I got, at least, I got the- what did I get? I got Pfizer followed by Moderna maybe? And those are using the full spike protein and that was pretty good. But if you can get away with not using the full one, you might be able to do even better.

And sure enough, Neil King managed to say, I’m going to use the really important domain - the receptor binding domain only - and I’m going to encode it on a nanoparticle, but like a soccer ball, and I’m going to stick out a lot of them, and it’s going to generate really good antibody response-

Saloni Dattani:

That’s very cool.

Jacob Trefethen:

- and that happened within the last five years!

Saloni Dattani:

Wow.

Jacob Trefethen:

It’s wild. Yeah.

Saloni Dattani:

I’m also thinking, it’s kind of reminded me of the RSV vaccines. Were they also improved with AI, I think?

Jacob Trefethen:

The story I remember there is Jason McLellan at the VRC in 2013. My guess is that that main breakthrough for RSV pre-dated the deep learning revolution, but I don’t know the story of it. Probably did involve cryo-electron microscopy, which probably was helped by ML?

Saloni Dattani:

So I think what I remember is RSV - which is respiratory syncytial virus; it’s a lung infection that’s one of the most common reasons that infants go to hospital in the US. In the 1960s and ‘70s, people tried to make RSV vaccines, and there was this protein on the surface of the virus that they used as a target, but it wasn’t working very well. It had a lot of side effects, and in some cases, it was actually making the infection much worse when people got infected.

So a lot of people just gave up at that point, and it was just seen as this- this is this unsolvable challenge: “We’re not going to be able to develop vaccines against RSV, sorry.”

Then something changed in the 2010s, which was that this type of electron microscopy became much better, and I think it was a software improvement that- you could then figure out, at a much higher resolution, what these proteins looked like. So what they figured out was: at the surface of the virus, the protein looks a certain way before it infects the cells, but a different way after it infects the cells, because it uses this protein to fuse to the cell and enter it. Unfortunately, the previous vaccines were using the ‘after’ version of the protein.

Jacob Trefethen:

The post-fusion, yeah.

Saloni Dattani:

But that doesn’t work. Because if the virus is swimming around in your blood, and your immune cells only look know what it looks like after it’s entered, well, that’s too late. You need to figure out what it looks like before.

So they figured out what it looked like before it entered the cell, and that is called the ‘pre-fusion’ version of the protein. In order to remake vaccines with that version, I think they used AI to introduce stabilizing mutations and keep it that way.

And now, we have at least three RSV vaccines that have been approved in the last two or three years. This breakthrough, in the 2010s with microscopy, meant suddenly we know how to design an RSV vaccine now. So there were multiple people who were like: “Oh, well, now we can do it.” And so it wasn’t just one person who made the breakthrough, but I think this was the big breakthrough- was the microscopy-

Jacob Trefethen:

And that big breakthrough-

Saloni Dattani:

-and AI.

Jacob Trefethen:

And the microscopy breakthrough happened on public funding at the NIH Vaccine Research Center.

Saloni Dattani:

Whoa.

Jacob Trefethen:

And then the vaccines that- there are now many vaccines-

Saloni Dattani:

Saving a lot of babies.

Jacob Trefethen:

And saving a lot of babies. So that’s my pitch, implicit pitch, that often you need a breakthrough. Often, it takes researchers who aren’t trying to go after a product, they’re actually trying to understand something.

Saloni Dattani:

And something might look like an unsolvable challenge for decades and then something changes and now three people can do it.

Jacob Trefethen:

So, stepping back to summarise the whole story here. We started with an invader we want to make a vaccine against. We took a protein that might be an antigen, and we started tweaking that protein with the help of a couple of AI systems - ProteinMPNN and AlphaFold - to come up with some hypotheses of sub-units of that protein, smaller versions of it, that could be vaccines. The initial results are that maybe some of them actually look promising, and should be taken further and explored further.

Saloni Dattani:

Right, and so where is the one that you talked about, the ScpA? Is that in clinical trials right now, what’s going on?

Jacob Trefethen:

Not yet, because everything is slower than it should be in vaccine design, especially for global health. But I would say it’s one of probably the top 10 antigens that people are exploring pretty seriously. People are looking at combinations of those antigens in four or five different proteins, in combination with other adjuvants that help prompt an even stronger immune response. And I’m cautiously hopeful that one of those combinations will actually prove to work.

Saloni Dattani:

Just to indulge you a bit on Strep A: what is the reason that- what’s going on with the field? Why don’t we have much more interest in this? And how far away are we from a vaccine?

Jacob Trefethen:

I think that there are a couple answers to why there’s not more interest. The really predominant one is that most of the deaths that Strep A leads to - so the biggest forms of harm - occur in countries that are not wealthy, so there’s not as much of an incentive as there could be, for pharmaceutical companies to prioritise it.

That said, there’s a pretty decent incentive because a lot of parents with young kids are not exactly fans of Strep and would be probably quite excited if there were an available vaccine just to prevent Strep throat, or sore throat pharyngitis, and scarlet fever, and all of that.

I think there are other reasons too. What you really are making me want to do is a whole episode on Strep, because it’s so interesting. To answer the last part of your question, I am actually fairly hopeful that this should be a solvable issue. I think you should be able, with modern techniques, to design around some of previous problems, and you should be able to test whether these things work, and get an answer. What we care about most is figuring out how they work in children, so that you can prevent these repeated infections that lead to problems.

Saloni Dattani:

And so, we talked about AI being used to: one, improve or cut up this protein into a specific domain, and then see if this domain is still soluble, and if not, improve that solubility. Then I also mentioned RSV vaccines, where AI has been used to stabilize a particular protein that’s used in the vaccine. What other uses of AI is there, when you’re applying it to proteins and improving them?

Jacob Trefethen:

Two more come to mind, of properties that are often really nice for vaccines. One is thermo-stability. Human proteins have evolved to behave very well at human body temperature. If you’re shipping a vaccine around the world, sometimes you’re in colder and sometimes you’re in hotter temperatures than the human body. So if you can make sure that that won’t denature your protein, then you’re going to still have a useful vaccine out the other end. So you might want to make some tweaks for that.

Another one is, we talked about immunogenicity a bit earlier, of- does this prompt any antibodies, for example. You also care about what’s called “immuno-focusing” sometimes, where: can you present the parts of a given protein, say, the epitopes that are most reactive, as much as possible, or in the right geometry, so that you can target the response to the most productive bits. So it’s not just, are you prompting any antibodies, but are you prompting the right ones? Are you prompting it as frequently as possible and binding as tight as possible?

Saloni Dattani:

Right. So these are all ways of making sure that our immune system recognises the protein or the whole pathogen well, how well it’s doing that, and then also maybe optimizing the way that it’s doing that.

I think you mentioned this at some point, when we were talking earlier, about how sometimes if you are recognising a pathogen, that might confuse your immune system, because parts of a pathogen might look like other parts of your body, and then you could develop an autoimmune reaction to other parts of your body, because your immune cells have treated that as “foreign” as well, after seeing the pathogen. So we’re trying to do all of these things, potentially.

Jacob Trefethen:

All of these things.

Saloni Dattani:

Is there anything else? Are there other uses that we would have for AI?

Jacob Trefethen:

I bet you there are, and I can’t wait for listeners to write in and tell us or start working in their basement on some of these applications.

Saloni Dattani:

Cool, right, but this is also only the beginning. There has to be a lot of testing and stuff before things get to the market, so that it can be used for people.

And also, there’s lots of stuff that happens before this process – like collecting all of the data in the first place, doing the laboratory research, figuring out these structures, making sure that there is data for AI models to train on and help us make these improvements.

Jacob Trefethen:

Okay, so we’re improving proteins found in nature. What about if... we could... design... entirely new ones... never seen before?

Saloni Dattani:

[gasps] I want to do that!

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter:

https://scientificdiscovery.dev

Jacob’s blog:

https://blog.jacobtrefethen.com

Books:

• Genentech: The beginnings of biotech by Sally Smith Hughes

Articles:

• FDA (2007). Celebrating a Milestone: FDA's Approval of First Genetically-Engineered Product https://fda.report/media/110447/Celebrating-a-Milestone--FDA%27s-Approval-of-the-First-Genetircally-Engineered-Product.pdf

• Genentech (2016). Cloning Insulin https://www.gene.com/stories/cloning-insulin

• Arthur Riggs (2020). Making, Cloning, and the Expression of Human Insulin Genes in Bacteria: The Path to Humulin https://academic.oup.com/edrv/article/42/3/374/6042201

Podcasts:

• Novo Nordisk (Ozempic) by the Acquired podcast https://www.acquired.fm/episodes/novo-nordisk-ozempic

Acknowledgements:

• Aria Babu, editor at Works in Progress

• Adrian Bradley, on-site producer

• Anna Magpie, fact-checking

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Works in Progress & Open Philanthropy

Transcript

Saloni Dattani:

I hope you're excited for this episode because we're going to talk about the history of insulin in 15 minutes. That sounds tough considering our first episode about the history of HIV treatment was almost five hours. But I am confident that we can get through this.

Jacob Trefethen:

We can do it together.

Saloni Dattani:

We can do it. We'll start in the 1920s, and at this point, scientists were trying to develop treatments for diabetes and they were trying to study the pancreas. They knew the pancreas was involved and it seemed to produce something that was reducing sugar levels.

The pancreas is a small organ behind your stomach, and they knew it was involved because if you destroyed parts of the pancreas in animals, they would have problems controlling their blood sugar. They developed symptoms that were diabetic, so they would have frequent urination and they would produce sweet urine and soon they'd die.

There was a possibility that something in the pancreas was protecting people regularly from diabetes and diabetic symptoms. And in the 1920s, scientists at the University of Toronto figured out how to extract cells from the pancreases of animals, and injected insulin from these cells into a 14-year-old boy with diabetes. In 1922, he was essentially on his deathbed, and he was the first person in history to receive an injection of insulin.

Jacob Trefethen:

Whoa. A hundred years ago.

Saloni Dattani:

A hundred years ago, and his symptoms improved dramatically. And this was a breakthrough. I thought it was just so interesting to think of how much has happened in that a hundred years, and they received many requests to basically share this insulin, and the process they had developed. There were two companies that scaled that up. In the US, there was Eli Lilly, and in Europe, there were two Danish researchers who brought this treatment to Europe and developed the company that became Novo Nordisk and produced insulin as well.

But at that time, it was still a very inefficient process: they had to extract insulin from dead animals, like pigs and cows, and within the pancreas, only around 1% of cells produce insulin at all, so it's extremely inefficient. This means that you would require a lot of pancreases from dead animals to produce any insulin. Even in the 1970s, it took 8,000 pounds of pancreas glands from 23,500 animals just to make one pound of insulin.

Jacob Trefethen:

No, that's horrible.

Saloni Dattani:

It's horrible. And it's a lot, right? And it was still a breakthrough. Before that time, one common treatment for diabetes was a "starvation diet". There was another diet where just people had to just stop eating entire food categories because they would have a spike in sugar levels in their blood, and that could eventually lead to coma and death. Gradually, over time, just slowly, this process became more efficient; people figured out slightly better ways of doing it. But it was still limited by the amount of animal that was supplied by the meat industry. As diabetes patients were treated, more of them survived for longer, and the demand for insulin grew, and people worried that it would be really hard to meet all of that demand with a process that required extracting it from animals.

Another problem was even in the 1940s, '50s and '60s even, people with diabetes would have to dose themselves at home. So they would have to inject themselves with a thick needle that they would sharpen themselves with a razor stone. And they had to boil the glass syringes that they used to inject themselves and then reuse those syringes. This was surprising to me as well. I didn't realise this, but disposable needles didn't become common until decades later. So it was both painful and difficult and could lead to contamination.

Jacob Trefethen:

This reminds me of some horrible scenes in the film "Killers of the Fire Moon", which-

Saloni Dattani:

I haven't seen it!

Jacob Trefethen:

-thankfully I don't have time to get into. Well, you're going to have to watch it after because I can't spoil it for our audience.

Saloni Dattani:

What's the- oh. Well, I really want to know now.

Jacob Trefethen:

If you're diabetic at home in the first half of the 20th century and you are married to someone who doesn't have your best interests at heart, it's not such a good combination, I'll leave it there.

Saloni Dattani:

I'll try to remember that.

Yeah, so where were we? I guess we were sort of in the 1940s to '60s at this point. What was just about to happen was major breakthrough. So in the 1970s, scientists were about to develop something called "recombinant DNA technology", and that would change everything — especially with diabetes — but it would also change biology.

So what is recombinant DNA technology? It's when you combine DNA from two organisms. In this case, what's happening is scientists figured out how to introduce the genes to produce insulin into bacteria — which means that bacteria would be producing the insulin and they could be used as little factories to produce it. So we wouldn't have to rely on the pancreases from animals.

These breakthroughs came from researchers at UC San Francisco, like Herbert Boyer, who had developed this method to clone a gene. And there were other researchers at the City of Hope Medical Research Centre like Arthur Riggs, Keichii Itakura and David Goeddel. They applied this method, to clone a gene, to insulin. So essentially they're using the same amino acid sequence that produces insulin, but making the bacteria produce that instead.

Jacob Trefethen:

I see. So before the last fifty years, say, people needed insulin. Insulin's a protein, but we couldn't really make proteins in a systematic way, so we're taking 'em from animals. And then, with these breakthroughs starting about 50 years ago, there were some biotechnology innovations that allowed us to be more systematic about how we make 'em.

Saloni Dattani:

Right. But actually, you've kind of alluded to something very interesting, which is that this turned into the first- the kind of birth of the biotechnology industry as a whole. Because these researchers were able to develop, or modify, genetic material and get bacteria to produce it, and they could kind of engineer that. They spurred this whole industry of people doing that for different uses, but also a different type of model where researchers would not just be academics — they wouldn't just be working with other private companies or with the government to develop products — but they would actually spin out their research into something that could be a commercial product.

Jacob Trefethen:

Yeah.

Saloni Dattani:

So, Boyer spun out his research into a new company called "Genentech", with a venture capitalist called Robert Swanson. And they raised private funding for this project with this promise that they would be able to develop insulin from bacteria, and make it a much more efficient process.

But at the time, there was a big problem with doing this research. Recombinant DNA technology was seen as really dangerous. We were basically "meddling with the genetic code of life". Maybe we would be producing harmful proteins, and if bacteria were producing them, maybe they would produce too much of them, and we wouldn't know how to stop them, or control this process. So, in 1975, scientists came together for this conference and they recommended that there would be restrictions on the technology of recombinant DNA. They recommended high containment labs and a ban on some methods for a few years. But Genentech actually managed to avoid this, and one reason for that is that they actually didn't work with human genes. They didn't know the genetic code for human insulin at the time. All they knew was its amino acid sequence.

Jacob Trefethen:

Whoa. That's so strange to think. I'm so used to so much genetic information now. Wow.

Saloni Dattani:

Right. It's so easy for us to sequence the code for some genetic material now, but at the time it was difficult. All they knew was the amino acids. We have DNA or genetic code — the A, C, T, G — that gets turned into RNA and then gets turned into a chain of amino acids, which then folds up into a much larger structure, which could be a blobby shape of a protein.

In insulin, there are two of these chains of amino acid proteins that are linked up. What the researchers did was they produced each of these protein chains, based on their amino acid sequence in bacteria, separately. They basically thought in reverse: we have the amino acid sequence, what DNA would produce that? And specifically, what DNA would produce that in bacteria? And they got bacteria to produce these different chains, and then they joined them together in the lab to make the final insulin molecule.

These processes as well meant that they were avoiding the potential dangers that people associated with recombinant DNA technology. And it took a lot of hard work, but the researchers at Genentech, this startup, managed to produce some proteins. They produced somatostatin, which is a tiny hormone that regulates growth hormone and insulin in bacteria, in 1977, and that was a huge breakthrough, and they kind of used it as a pilot test. And then in 1978, they applied the same method to produce insulin in bacteria.

Jacob Trefethen:

So we have insulin from a bioreactor, not from an animal.

Saloni Dattani:

Right. We're basically churning out insulin proteins in a tank that's filled with bacteria. This is so interesting as well, because when I talked about how you would get insulin from the pancreases of animals — within the pancreas, only some 1% of cells produce insulin — but in bacteria in a tank that you've engineered, almost a hundred percent of the bacteria would be producing insulin.

Jacob Trefethen:

Right? Yeah, for sure. Yield!

Saloni Dattani:

It took them a while to figure out how to make that process efficient. But the fraction of bacteria producing insulin is much larger, and also, each bacterium was producing so much insulin. There was this amazing description that I read that each bacterium was producing so much insulin that they would kind of swell up like an olive or a tiny stuffed sack that was just full of insulin, which I found very funny.

Jacob Trefethen:

Very good.

Saloni Dattani:

So this breakthrough changed everything. Getting insulin to be produced by bacteria — having recombinant insulin — meant a much larger scope for production. Eli Lilly signed an agreement with Genentech to take this technology, and they asked them to find more efficient ways to scale up this whole process, and they eventually did. In 1982, insulin produced by bacteria, was taken through clinical trials and approved, and it was called Humulin. It was the first genetically engineered drug. And as I said, it was also the birth of the biotech industry, where academic researchers spin out their discoveries into startups, get venture capital to develop them into products, and then partner with big pharmaceutical companies to bring them to market.

Jacob Trefethen:

What an amazing drug. I am so grateful that we have insulin now, that you can make and that people who need it can take.

Saloni Dattani:

It's crazy to think about the difference, right? Because we now have this- that I guess I would not have even thought about what came before it. I don't know anyone who would've taken the treatment before — injecting themselves with these huge glass needles. There's this amazing book about the story that you recommended it to me a while ago and I read it and now I highly recommend it as well. It's called "Genentech: the Beginnings of Biotech" by Sally Smith Hughes and-

Jacob Trefethen:

Sally Smith Hughes.

Saloni Dattani:

-I actually have it right here. It's very, very good.

Jacob Trefethen:

Very good.

Saloni Dattani:

This wasn't even the end of the story, right? Okay, we now have insulin that's being produced by bacteria much more efficiently. But just a few years later, in 1985, Novo would develop insulin pens, which were a much easier way of taking insulin and getting immediate effects from it, than the older syringes. And if you think to now, maybe you know people with diabetes, sometimes they have these continuous insulin pumps that are attached to their skin and that deliver insulin to them throughout the day, whenever they need it, and there are also continuous glucose monitors.

I remember when I was young seeing my dad prick his finger to get a drop of his blood to be tested. But now there are continuous monitors for glucose that are also attached to your skin, and you can have new products that combine both of them together. So it's both monitoring the level of glucose in your blood and then also releasing insulin in response to that when you have food, to make these real-time adjustments into controlling blood sugar levels.

Jacob Trefethen:

So cool.

Saloni Dattani:

I think it's amazing, yeah. And even recombinant DNA technology didn't stop there. It's been used to make proteins and hormones for other kinds of diseases. One of them is to treat growth failure in kids. So you could use an enzyme or a hormone called "human growth hormone" and produce that in bacteria, and it was really important because before that the only way to get growth hormone was to extract it from the brains of cadavers. And that was dangerous because cadavers were sometimes contaminated with degenerative proteins that cause diseases like Creutzfeldt-Jakob disease, which is very scary.

Jacob Trefethen:

Well, you might be dealing with... 50% chance you're dealing with zombies at that point.

Saloni Dattani:

I've heard that recombinant DNA is now a very common research tool, used in almost all kinds of biology.

Jacob Trefethen:

It also has completely changed what research you can do. You can just 'grow up' a protein pretty easily in small batches. You don't even need a big bioreactor, and you can test out some stuff with it.

Saloni Dattani:

So, yes, there we have it: a century of insulin in 15 minutes. I hope you enjoyed this and I hope you like and subscribe and share this with all of your friends.

Jacob Trefethen:

Because next episode, we're going to talk about taking proteins nature has designed, like insulin and others, and improving upon them using new methods with artificial intelligence.

Saloni Dattani:

Nice.

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s substack newsletter: https://www.scientificdiscovery.dev

Jacob’s blog:

https://blog.jacobtrefethen.com

Books:

• Ron Milo and Rob Phillips. Biology by the numbers https://book.bionumbers.org/

• Carl Ivar Branden and John Tooze (1999) Introduction to protein structure https://www.taylorfrancis.com/books/mono/10.1201/9781136969898/introduction-protein-structure-john-tooze-carl-ivar-branden

Articles:

• Niko McCarty (2023). Biology is a burrito. https://www.asimov.press/p/burrito-biology

• Rhiannon Morris, Katrina Black, and Elliott Stollar (2022) Uncovering protein function: from classification to complexes. https://pmc.ncbi.nlm.nih.gov/articles/PMC9400073/

• Victor Muñoz and Michele Cerminara (2016) When fast is better: protein folding fundamentals and mechanisms from ultrafast approaches https://portlandpress.com/biochemj/article/473/17/2545/49248/When-fast-is-better-protein-folding-fundamentals

Image credits:

• Chang et al. (2012) Egg white in organic electronics. https://spie.org/news/4149-egg-white-in-organic-electronics [diagram of egg white denaturing and cross-linking]

• John Kendrew’s model of myoglobin’s structure; via Carl Ivar Branden and John Tooze (1999) Introduction to protein structure.

• Carl Ivar Branden and John Tooze (1999) Introduction to protein structure. [diagram of amino acids and protein structure]

• Ron Milo and Rob Phillips. Which is bigger, mRNA or the protein it codes for? https://book.bionumbers.org/which-is-bigger-mrna-or-the-protein-it-codes-for/ [diagram of myoglobin mRNA vs protein]

• Scitable (2014). Microtubules and Filaments. https://www.nature.com/scitable/topicpage/microtubules-and-filaments-14052932/ [diagram of microtubules]

Hard Drugs is a new podcast from Works in Progress and Open Philanthropy about medical innovation presented by Saloni Dattani and Jacob Trefethen.

You can watch or listen on YouTube, Spotify, or Apple Podcasts.

Saloni’s Substack:

Scientific Discovery

Tracing the steps towards scientific progress and how to improve our understanding of the world.

By Saloni Dattani

Jacob’s blog: https://blog.jacobtrefethen.com/

Books:

• How to Survive a Plague — by David France (2016). [Mentioned as a history of the science and activism against the AIDS epidemic, and the protease-inhibitor breakthrough.] https://surviveaplague.com/

• And the Band Played On — Randy Shilts (1987). [Mentioned as an account of the early years of AIDS.] https://us.macmillan.com/books/9780312374631/andthebandplayedon/

• Drug development stories: From bench to bedside — Elsevier (2024). [Mentioned as containing a history of the development of lenacapavir] https://shop.elsevier.com/books/drug-discovery-stories/yu/978-0-443-23932-8

Retrospectives:

• The development of antiretroviral therapy and its impact on the HIV-1/AIDS pandemic — Samuel Broder (2015). https://doi.org/10.1016/j.antiviral.2009.10.002

• History of the discoveries of the first human retroviruses: HTLV-1 and HTLV-2 — Robert Gallo (2005). https://doi.org/10.1038/sj.onc.1208980

• A Look at Long Acting Drugs — Anne de Bruyn Kops for Open Philanthropy (2025). https://bit.ly/long-acting-drugs-op

• How To Save Twenty Million Lives, with Dr Mark Dybul — Statecraft (2023)

• The Road to Fortovase. A History of Saquinavir, the First Human Immunodeficiency Virus Protease Inhibitor — Redshaw et al. (2000) https://link.springer.com/chapter/10.1007/978-3-642-57092-6_1

Articles:

• The origin of genetic diversity in HIV-1 — Smyth et al. (2012). [Mentioned as a review about HIV’s recombination, which described it as “a primitive form of sexual reproduction”] https://www.sciencedirect.com/science/article/pii/S0168170212002122

• PF74 Reinforces the HIV-1 Capsid To Impair Reverse Transcription-Induced Uncoating — Rankovic et al. (2018) https://doi.org/10.1128/JVI.00845-18

• Twice-Yearly Lenacapavir for HIV Prevention in Men and Gender-Diverse Persons — Kelley et al. (2024) https://www.nejm.org/doi/full/10.1056/NEJMoa2411858

• Twice-Yearly Lenacapavir or Daily F/TAF for HIV Prevention in Cisgender Women — Bekker et al. (2024) https://www.nejm.org/doi/10.1056/NEJMoa2407001

• The evolution of HIV-1 and the origin of AIDS — Sharp and Hahn (2010) https://doi.org/10.1098/rstb.2010.0031

• Pathogenic mechanisms of HIV disease — Moir et al. (2011) https://doi.org/10.1146/annurev-pathol-011110-130254

• Estimating per-act HIV transmission risk: a systematic review — Patel et al. (2012) https://journals.lww.com/aidsonline/fulltext/2014/06190/Estimating_per_act_HIV_transmission_risk__a.14.aspx

• The structural biology of HIV-1: mechanistic and therapeutic insights — Engelman and Cherepanov (2012) https://doi.org/10.1038/nrmicro2747

• Challenges and opportunities in the development of complex generic long-acting injectable drug products — O’Brien et al. (2021) https://doi.org/10.1016/j.jconrel.2021.06.017

• Making a “Miracle” HIV Medicine — Nahas (2025) https://press.asimov.com/articles/hiv-medicine

• Highly active antiretroviral therapy transformed the lives of people with HIV — Dattani (2024) https://ourworldindata.org/data-insights/highly-active-antiretroviral-therapy-transformed-the-lives-of-people-with-hiv

Videos:

• Mini-Lecture Series: HIV Capsid Inhibitors: Mechanism of Action — David Spach, National HIV Curriculum (2024)

Image credits:

• Mini-Lecture Series: HIV Capsid Inhibitors: Mechanism of Action — David Spach, National HIV Curriculum (2024) [Multiple diagrams of HIV capsid and lenacapavir’s effect.]

• Saloni Dattani; Our World in Data (2024) Highly active antiretroviral therapy transformed the lives of people with HIV. [Graph of decline in HIV/AIDS mortality after HAART was introduced.]

• Engelman and Cherepanov (2012). The structural biology of HIV-1: mechanistic and therapeutic insights. [Diagram of HIV’s entry into the cell.]

• Susan Moir, Tae-Wook Chun, Anthony S Fauci (2011). Pathogenic mechanisms of HIV disease. [Diagram of HIV replication rates over time, contrasting acute and chronic infection.]

• Saloni Dattani, adapted from Patel et al. (2014). Estimating per-act HIV transmission risk: a systematic review. [Bar chart of risks of contracting HIV from different sources when unprotected.]

• Thomas Splettstoesser under CC-BY. [Diagram of HIV’s internal structure.]

• Twice-Yearly Lenacapavir or Daily F/TAF for HIV Prevention in Cisgender Women — Bekker et al. (2024) [Chart of lenacapavir’s efficacy.]

• Our World in Data based on Joint United Nations Programme on HIV/AIDS (2024). [Chart of global HIV deaths over time.]

Acknowledgements:

• Douglas Chukwu, researcher at Open Philanthropy

• Sanela Rankovic, Acting Instructor at the Institute for Protein Design, University of Washington

• Aria Babu, editor at Works in Progress

• Adrian Bradley, on-site producer

• Anna Magpie, fact-checking

• Abhishaike Mahajan, cover art

• Atalanta Arden-Miller, art direction

• David Hackett, composer

Hard Drugs is produced by Works in Progress & Open Philanthropy.