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Professor Joseph Allen directs the Healthy Buildings Program at Harvard Chan School of Public Health. His expertise extends far beyond what makes buildings healthy. He has been a leading voice and advocate during the Covid pandemic for air quality and ventilation. He coined the term “Forever Chemicals” and has written extensively on this vital topic, no less other important exposures, which we covered In our wide-ranging conversation. You will see how remarkably articulate and passionate Prof Allen is about these issues, along with his optimism for solutions.

A video snippet of our conversation: buildings as the 1st line of defense vs respiratory pathogens. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with External Links and Links to Audio

Eric Topol (00:00:06):

Well, hello. It's Eric Topol from Ground Truths and I am just delighted to have with me, Joseph Allen from the Harvard School of Public Health, where he directs the Healthy Buildings Program that he founded and does a whole lot more that we're going to get into. So welcome, Joe.

Joseph Allen (00:00:24):

Thanks. It's great to be here. I appreciate the invitation.

Joe Allen’s Background As A Detective

Eric Topol (00:00:28):

Well, you have been, as I've learned, rocking it for many years long before the pandemic. There's quite a background about you having been a son of a homicide detective, private eye agency, and then you were going to become an FBI agent. And the quote from that in the article that's the Air Investigator is truly a classic. Yeah, you have in there, “I guarantee I'm the only public health student ever to fail an FBI lie detector polygraph in the morning and start graduate school a few hours later.” That's amazing. That's amazing.

Joseph Allen (00:01:29):

All right. Well, you've done your deep research apparently. That's good. Yeah, my dad was a homicide detective and I was a private investigator. That's no longer my secret. It's out in the world. And I switched careers and it happened to be the day I took the polygraph at the FBI headquarters in Boston, was the same day I started graduate studies in public health.

Sick vs Healthy Buildings (Pre-Covid)

Eric Topol (00:01:53):

Well, you're still a detective and now you're a detective of everything that can hurt us or help us environmentally and my goodness, how grateful we are that you change your career path. I don't know anyone who's had more impact on buildings, on air, and we're going to get into chemicals as well. So if we go back a bit here, you wrote a book before the pandemic, talk about being prescient. It’s called Healthy Buildings: How Indoor Spaces Can Make You Sick - or Keep You Well with John Macomber, your co-author. What was it that gave you the insight to write a book before there was this thing called Covid?

Joseph Allen (00:02:41):

Yeah, well, thanks for making the connection too, my past career to current career. For many years, I thought there wasn't a connection, but I agree. There's actually a lot of similarities and I also am really appreciative. I am lucky I found the field of Public Health, it's clearly where I belong. I feel like I belong here. It's a place to make an impact that I want to make in my career. So yeah, the Healthy Buildings book, we started writing years before the pandemic and was largely motivated by, I think what you and others and other people in my field have known, is that buildings have an outsized impact on our health. Yet it's not something that comes to the forefront when you ask people about what matters for their health. Right, I often start presentations by asking people that, what constitutes healthy living? They'll say, I can't smoke, I have to eat well.

(00:03:30):

I have to exercise. Maybe they'll say, outdoor pollution’s bad for you. Very few people, if any, will say, well, the air I breathe inside my building matters a lot. And over the years I had started my public health career doing forensic investigations of sick buildings. People really can get sick in buildings. It can be anything from headaches and not being able to concentrate all the way to cancer clusters and people dying because of the building. And I've seen this in my career, and it was quite frustrating because I knew, we all knew how to design and operate buildings in a way that can actually keep people healthy. But I was frustrated like many in my field that it wasn't advancing. In other words, the science was there, but the practice wasn't changing. We were still doing things the wrong way around ventilation, materials we put in our building, and I would lecture over and over and give presentations and I decided I want to try something new.

(00:04:22):

I do peer-reviewed science. That's great. I write pieces like you for the public, and I thought we'd try a longer form piece in a book, and it's published by Harvard Press. John Macomber for those who know is a professor at Harvard Business School who's an expert in real estate finance. So he'd been talking about the economic benefits of healthier buildings and some hand waving as he describes around public health. I've been talking about the public health benefits and trying to wave an economic argument. We teamed up to kind of use both of our strengths to, I hope make a compelling case that buildings are good for health and they're also just good business. In other words, try to break down as many barriers as we can to adoption. And then the book was published right as Covid hit.

Indoor Air Quality and Cognition

Eric Topol (00:05:05):

Yeah. I mean, it's amazing. I know that typically you have to have a book almost a year ahead to have it in print. So you were way, way ahead of this virus. Now, I'm going to come back to it later, but there were two things beyond the book that are pretty striking about your work. One is that you did all these studies to show with people wearing sensors to show that when the levels of CO2 were high by sensors that their cognition indoors was suffering. Maybe you could just tell us a little bit about these sensors and why aren't we all wearing sensors so that we don't lose whatever cognitive power that we have?

Joseph Allen (00:05:56):

Well, yeah. First I think we will start having these air quality sensors. As you know, they're starting to become a lot more popular. But yeah, when I first joined the faculty full-time at Harvard, one of the first studies I conducted with my team was to look at how indoor air quality influences cognitive function. And we performed a double-blind study where we took people, office workers and put them in a typical office setting. And unbeknownst to them, we started changing the air they were breathing in really subtle ways during the day, so they didn't know what we were doing. At the end of the day, we administered an hour and a half long cognitive function battery, and like all studies, we control for things like caffeine intake, baseline cognitive performance, all the other factors we want to account for. And after controlling for those factors in a double-blind study, we see that indoor air quality, minor improvements to indoor air quality led to dramatic increases in cognitive function test scores across domains that people recognize as important for everyday life.

(00:06:59):

How do you seek out and utilize information? How do you make strategic decisions? How do you handle yourself during a crisis and importantly recover after that crisis? I don't mean the world's ending crisis. I mean something happens at work that's stressful. How do you handle that and how do you respond? Well, it turns out that amongst all the factors that influence how we respond there, indoor air quality matters a lot. We call that study the COGfx Study for cognitive function. We replicated it across the US, we replicated it across the world with office workers around the world, and again, always showing these links, the subtle impact of indoor air quality on cognitive function performance. Now, that also then starts to be the basis for some of the economic analysis we perform with my colleague at Harvard Business School. We say, well, look, if you perform this much better related to air quality, what would happen if we implemented this at scale in a business?

(00:07:51):

And we estimate that there are just massive economic gains to be had. On a per person basis, we found and published on this, that's about $6,000 to $7,000 per person per year benefit across a company. It could lead to 10% gains to the bottom line performance of the company. And again, I'm a public health professor. My goal is to improve people's health, but we add a lens, mental health, brain health is part of health, and we add the economic lens to say, look, this is good for a worker of productivity and the costs are downright trivial when you compare it against the benefits, even just including the cognitive function benefits, not even including the respiratory health benefit.

Eric Topol (00:08:33):

And I mean, it's so striking that you did these studies in a time before sensors were, and they still are not widely accepted, and it really helped prove, and when we start to fall asleep in a group session indoors, it may not just be because we didn't have enough sleep the night before, right.

Joseph Allen (00:08:56):

It's funny you say that. I talk about that too. It's like, do we actually need the study to tell us to quantify what we've all experienced these bad conference rooms, you get tired, you can't concentrate, you get sleepy while you're driving your car. Yeah, a whole bunch of other factors. Maybe the speaker's boring, but a key factor is clearly indoor air quality and things like good ventilation, the chemical load in the space are all contributing.

Eric Topol (00:09:20):

Yeah. No, it's pretty darn striking. Now we're going to get into the pandemic, and this of course is when your work finally crystallized that you've been working on this for years, and then finally your collaboration with some of the aerosol experts. It was a transdisciplinary synergy that was truly extraordinary. And when you were on 60 Minutes last October, you said, “Think about the public health gains we've made over the past hundred years. We've made improvements to water quality, outdoor air pollution, our food safety, we've made improvements to sanitation: absolute basics of public health. Where has indoor air been in that conversation?” You brought it to us. I mean, you led the Lancet Commission on this. You've done a White House Summit keynote. You had a lot of influence. Why did it take us to finally wake up to this issue that you've been working on for years?

Covid is Airborne, Denial

Joseph Allen (00:10:31):

Yeah. Well, I appreciate that, but I also liked what you started with. I mean, there's been a lot of us pulling on this, and I think one of the magical moments, if you could say that when the pandemic happened was that it forced these collaborations and forced a lot of us in our field to be a bit more vocal. And even that comment about the gains we made in public health, that comes from an article that we co-authored with 40 plus scientists around the world in science, trying to drive home the point that we've ignored one of the key factors that determines our health. We were all frustrated at the beginning of the pandemic. The first piece I wrote was January 2020, talking about healthy buildings as the first line of defense, airborne spread, ventilation, filtration. I could not get it published. I could not get it published.

(00:11:20):

So I moved it to an international paper. I wrote it in the Financial Times in early February, but it wasn't until mid-March that the Times took my piece on this airborne spread buildings ventilation. At the same time, we know people like Linsey Marr, Rich Corsi, many others, Shelly Miller out there publishing, doing the fundamental research, all trying to elevate, and I think we started to find each other and say, hey, someone's trying to hit the medical journals. We're not landing there. I'm trying to hit the Times, and we’re not landing there. We're trying to get the reporters to pay attention. It's not landing there. Let's team up. Let's write these joint pieces. And I think what happened was you saw the benefit of the collective effort and interdisciplinary expertise, right? We could all start to come together, start instead of having these separate voices, a little bit of a unified voice despite important scientific minor disagreements, but start to say, hey, we started elevate each other and said, this is really important. It's the missing component of the messaging in the early days of the pandemic, and to know how to defend yourself.

Eric Topol (00:12:20):

Well, I think a lot of people think the big miss, and I know you agree, was the lack of recognition of aerosol transmission instead of just liquid droplets. But what you brought to this was really your priors on the buildings themselves and the ventilation systems and air quality that was highly, I mean, critical to it isn't just the aerosol, it's obviously how buildings are set up. Now, there's an amazing piece of course that appeared in the summer of 2021 called the Air Investigator, which profiled you, and in it brings up several things that finally are, we're starting to get our act together. I mean, ultimately there was in May 2023 years later, the CDC says, we're going to do something about this. Can you tell us what was this very distinct new path that the CDC was at least saying? And also couple that with whatever action if or not action has been taken.

Joseph Allen (00:13:33):

Yeah. So there really was a monumental shift that took, it was years in development, but we finally won the argument, collectively that airborne spread was the dominant mode of transmission. Okay, we got that. Then the question is, well, what changes? Do we actually get guidance here? And that took a little bit longer. I give Rochelle Walensky a lot of credit when she came into the CDC, we talked with her about this. That's when you start to first see ventilation starts showing up and the guidance, including guidance for schools. So I think that was a big win, but still no one was willing to set an official target or standard around higher ventilation rates. So that's important. Early in the pandemic, some people started to hear a message, yes, ventilation is important. What's the obvious next question, well, how much, what do I need? So in the summer of 2020, actually Shelly Miller and I collaborated on this.

(00:14:23):

We published some guidance on ventilation targets for schools. We said four to six air changes per hour (ACH) and target that. Well, it wasn't until 2023, spring of 2023 that you mentioned that CDC published target ventilation rates, and they went with five air changes per hour, which is right where we were talking about in summer 2020. It's what the Lancet of COVID-19 Commission adopted, but it's momentous in this way. It's the first time in CDCs history they've ever published a ventilation rate target for health. Now, I know this seems slow at the time, and it was, but if we think about some of the permanent gains that will come out of the pandemic. Pandemic changes society and science and policy and practice this, we are never going back. Now buildings will be a first line of defense for respiratory pathogens going forward that can no longer be ignored. And now we have the published target by CDC. That's a big deal because it's not just a recognition, but there's actually something to shoot for out there. It's a target I happen to like, I think there are differences between different scientists, but ultimately we've lifted the floor and said, look, we actually have to raise ventilation rates and we have something to shoot for. The public needed that kind of guidance a lot earlier, of course, but it was a big deal that it happened. It’s just too bad it took until spring 2023.

Eric Topol (00:15:46):

Yeah, I certainly agree that it was momentous, but a year plus later, has there been any change as a result of this major proclamation, if you will?

Joseph Allen (00:15:59):

Well, I actually see a lot of change from a practitioner level, but I want to talk about it in two aspects. I see a lot of schools, universities, major companies that have made this shift. For example, in the 60 Minutes piece, I talk that I advised Amazon and globally they're measuring indoor air quality with real-time sensors in their buildings. I've worked with hundreds of school districts that have made improvements to indoor air quality. I know companies that have shifted their entire approach to how they design and operate their buildings. So it's happening. But what really needs to happen, Eric, if this movement is going to benefit everyone, is that these targets need to be codified. They need to go into building codes. It can't just be, oh, I've heard about this. So I made the decision. I have the resources and the money to make this improvement.

(00:16:44):

To create a healthy building or a healthy school, we need to be sure this gets built into our code. So it just becomes the way it's done. That is not happening. There are some efforts. There are some bills at the national level. Some states are trying to pass bills, and I have to say, this is why I'm optimistic. It feels very slow. I'm as frustrated as anybody. I wanted this done before the pandemic. As soon as the pandemic hit, we saw it. We knew what we needed to get done. It didn't happen. But if we think about the long arc here and the public health gains we're actually, it's remarkable to me that we actually have bills being introduced around indoor air quality that ASHRAE has set a new health focused target for the first time really in their history. CDC, first time. New buildings going up in New York City designed to these public health targets. That's really different. I've been in this field for 20 plus years. I've never seen anything like it. So the pace is still slow, but it really is happening. But it has to reach everybody, and the only way that's going to happen is really this gets into building codes and performance standards.

The Old Efficient Energy Buildings

Eric Topol (00:17:52):

Yeah. Well, I like your optimistic perspective. I do want to go back for a second, back decades ago there was this big impetus to make these energy efficient buildings and to just change the way the buildings were constructed so that there was no leak and it kind of set up this problem or exacerbated, didn't it?

Joseph Allen (00:18:19):

Yeah. I mean, I've written about this a lot. I write in the book our ventilation standards, they've been a colossal mistake. They have cost the public in terms of its health because in the seventies, we started to really tighten up our building envelopes and lower the ventilation rates. The standards were no longer focused on providing people with a healthy indoor space. As I write in the book, they were targeted towards minimally acceptable indoor air quality, bare minimums. By the way that science is unequivocal, is not protective of health, not protective against respiratory pathogens, doesn't promote good cognitive function, not good for allergies. These levels led to more illness in schools, more absences for teachers and students, an absolute disaster from a public health standpoint. We've been in this, what I call the sick building era since then. Buildings that just don't bring in enough clean outdoor air. And now you take this, you have a building stock for 40 years tighter and tighter and tighter bumps up against a novel virus that spread nearly entirely indoors. Is it any wonder we had, the disaster we had with COVID-19, we built these bills. They were designed intentionally with low ventilation and poor filtration.

Optimal Ventilation and Filtration

Eric Topol (00:19:41):

Yeah. Well, it's extraordinary because now we've got to get a reset and it's going to take a while to get this done. We'll talk a bit about cost of doing this or the investment, if you will, but let's just get some terms metrics straight because these are really important. You already mentioned ACH, the number of air changers per hour, where funny thing you recommended between four and six and the CDC came out with five. There's also the minimum efficiency reporting value (MERV). A lot of places, buildings have MERV 8, which is insufficient. We need MERV 13. Can you tell us about that?

Joseph Allen (00:20:23):

Yeah, sure. So I think when we think about how much, you have two ways to capture these respiratory particles, right? Or get rid of them. One is you dilute them out of the building or you capture them on filters. You can inactivate them through UV and otherwise. But let's just stay on the ventilation and filtration side of this. So the air changing per hour is talking about how often the air is change inside. It's an easy metric. There are some strengths to it, there's some weaknesses, but it's intuitive and I'll you some numbers so you can make sense of this. We recommended four to six air changes per hour. Typical home in the US has half an air change per hour. Typical school designed to three air changes per hour, but they operate usually at one and a half. So we tried to raise this up to four, five, or six or even higher. On the filtration side, you mentioned MERV, right? That's just a rating system for filters, and you can think about it this way. Most of the filters that are in a building are cheap MERV 8 filters, I tend to think of them as filters that protect the equipment. A MERV 13 filter may capture 80 or 90% of particles. That's a filter designed to protect people. The difference in price between a MERV 8 and a MERV 13 is a couple of bucks.

(00:21:30):

And a lot of the pushback we got early in the pandemic, some people said, well, look, there's a greater resistance from the better filter. My fan can't handle it. My HVAC system can't handle it. That was nonsense. You have low pressure drop MERV 13 filters. In other words, there really wasn't a barrier. It was a couple extra bucks for a filter that went from a MERV 8 might capture 20 or 30% to a filter, MERV 13 that captures 80 or 90% with very little, if any impact on energy or mechanical system performance. Absolute no-brainer. We should have been doing this for decades because it also protects against outdoor air pollution and other particles we generate indoors. So that was a no-brainer. So you combine both those ventilation filtration, some of these targets are out there in terms of air change per hour. You can combine the metric if we want to get technical to talk about it, but basically you're trying to create an overall amount of clean air. Either you bring in fresh outdoor air or you filter that air. It really is pretty straightforward, but we just didn't have some of these targets set and the standards we're calling for these minimum acceptable levels, which we're not protective of health.

Eric Topol (00:22:37):

So another way to get better air quality are these portable air cleaners, and you actually just wrote about that with your colleagues in the Royal Society of Chemistry, not a journal that I typically read, but this was an important article. Can you give us, these are not very expensive ways to augment air quality. Can you tell us about these PACs ?

Joseph Allen (00:23:06):

These portable air cleaners (PACs), so the same logic applies if people say, well, I can't upgrade my system. That's not a problem for very low cost, you could have, these devices are essentially a fan and a filter, and the amount of clean air you get depends on how strong the fan is and how good the filter is. Really pretty simple stuff here, and you can put one of these in a room if it's sized right. My Harvard team has built tools to help people size this. If you're not quite sure how to do it, we have a technical explainer. Really, if you size it right, you can get that four, five or six air changes per hour, very cheap and very quickly. So this was a tool I thought would be very valuable. Rich Corsi and I wrote about this all through the summer of 2020 to talk about, hey, a stop gap measure.

(00:23:50):

Let's throw out some of these portable air cleaners. You increase the air changes or clean air delivery pretty effectively for very low cost, and they work. And now the paper we just published in my team a couple of days ago starts to advance this more. We used a CFD model, so computational fluid dynamics. Essentially, you can look at the tracers and the airflow patterns in the room, and we learn a couple things that matter. Placement matters, so we like it in the center of the room if you can or as close as possible. And also the airflow matters. So the air cleaners are cleaning the air, but they're also moving the air, and that helps disperse these kind of clouds or plumes when an infected person is breathing or speaking. So you want to have good ventilation, good filtration. Also a lot of air movement in the space to help dilute and move around some of these respiratory particles so that they do get ventilated out or captured in a filter.

Eric Topol (00:24:40):

Yeah. So let me ask you, since we know outdoors are a lot safer. If you could do all these things indoors with filtration, air changing the quality, can you simulate the outdoors to get rid of the risk or markedly reduce the risk of respiratory viruses like SARS-CoV-2 and others?

Joseph Allen (00:25:04):

Yeah, you can't drop it to zero. There's no such thing as zero risk in any of these environments. But yeah, I think some of the estimates we've seen in my own team has produced in the 60-70% reduction range. I mean, if you do this right with really good ventilation filtration, you can drop that risk even further. Now, things like distancing matter, whether or not somebody's wearing a mask, these things are all going to play into it. But you can really dramatically drop the risk by handling just the basics of ventilation and filtration. And one way to think about it is this, distance to the infector still matters, right? So if you and I are speaking closely and I breathe on you, it's going to be hard to interrupt that flow. But you can reduce it through good ventilation filtration. But really what it's doing also is preventing super spreading events.

(00:25:55):

In other words, if I'm in the corner of a room and I'm infectious and you're on the other side, well if that room is sealed up pretty good, poor ventilation, no filtration, the respiratory aerosols are going to build up and your risk is going to increase and we're in there for an hour or two, like you would be in a room or office and you're exposed to infectious aerosol. With good ventilation filtration, those respiratory particles don't have a chance to reach you, or by the time they do, they're much further diluted. Linsey Marr I think was really great early in the pandemic by talking about this in terms of cigarette smoke. So a small room with no ventilation filtration, someone smoking in the corner, yeah, it's going to fill up over time with smoke you're breathing in that secondhand smoke. In a place with great ventilation filtration, that's going to be a lot further reduced, right? You're not going to get the buildup of the smoke and smoke particles are going to operate similarly to respiratory particles. So I think it's intuitive and it's logical. And if you follow public health guidance of harm reduction, risk reduction, if you drop exposure, you drop risk.

(00:26:58):

The goal is to reduce exposure. How do we do that? Well, we can modify the building which is going to play a key role in exposure reduction.

Eric Topol (00:27:06):

Now, to add to this, if I wear a sensor or have a sensor in the room for CO2, does that help to know that you're doing the right thing?

Joseph Allen (00:27:17):

Yeah, absolutely. So people who are not familiar with these air quality sensors. They're small portal air quality sensors. One of the things they commonly measure is carbon dioxide. We're the main source of CO2 inside. It's a really good indicator of ventilation rate and occupancy. And the idea is pretty simple. If the CO2 is low, you don't have a buildup of particles from the respiratory tract, right? And CO2 is a gas, but it's a good indicator of overall ventilation rate. This room I'm in right now at the Harvard School of Public Health has air quality sensors. We have this at Harvard Business School. We have it at the Harvard Health Clinics. Many other places are doing it, Boston Public schools have real-time air quality monitors. Here's the trick with CO2. So first I'll say we have some guidance on this at the Harvard Healthy Buildings page, if people want to go look it up, how to choose an air quality sensor, how to interpret CO2 levels.

Carbon Dioxide Levels

(00:28:04):

But here's a way to think about it. We generally would like to see CO2 levels less than 800 parts per million. Historically, people in my field have said under 1,000 is okay. We like to see that low. If your CO2 is low, the risk is low. If your CO2 is high, it doesn't necessarily mean your risk is high because that's where filtration can come in. So let me say that a little bit better. If CO2 is low, you're diluting enough of the respiratory particles. If it's high, that means your ventilation is low, but you might have excellent filtration happening. Either those MERV 13 filters we talked about or the portable air cleaners. Those filters don't capture CO2. So high CO2 just means you better have a good filter game in place or the risk is going to be high. So if you CO2 is low, you're in good shape. If it's high, you don't quite know. But if you have bad filtration, then the risk is going to be much higher.

Eric Topol (00:29:01):

I like that 800 number because that's a little lower than some of the other thresholds. And why don't we do as good as we can? The other question about is a particulate matter. So we are worried about the less than 5 microns, less than 2.5 microns. Can you tell us about that and is there a way that you can monitor that directly?

Joseph Allen (00:29:25):

Sure. A lot of these same sensors that measure CO2 also measure PM 2.5 which stands for particular matter. 2.5 microns is smaller, one of the key components of outdoor air pollution and EPA just set new standards, right? WHO has a standard for 5 microgram per cubic meter. EPA just lowered our national outdoor limit from 12 to 9 microgram per cubic meter. So that's a really good indicator of how well your filters are working. Here again, in a place like this or where you are, you should see particle levels really under 5 microgram per cubic meter without any major source happening. What's really interesting about those like the room I'm in now, when the wildfire smoke came through the East coast last year, levels were extraordinary outside 100, 200, 300 microgram per cubic meter. But because we have upgraded our filters, so we use MERV 15 here at Harvard, the indoor levels of particles stayed very low.

(00:30:16):

So it shows you how the power of these filters can actually, they do a really good job of capturing particles, whether it be from our lungs or from some other source. So you can measure this, but I'll tell you what's something interesting, if you want to tie it into our discussion about standards. So we think about particles. We have a lot of standards for outdoor air pollution. So there's a national ambient air quality standard 9 microgram per cubic meter. We don't have standards for indoor air quality. The only legally enforceable standard for indoor particles is OSHA's standard, and it's 5,000 microgram per cubic meter 5,000.

(00:30:59):

And it's absurd, right? It's an absurdity. Here we are EPAs, should it be 12, should it be 9, or should it be 8? And for indoors, the legally enforceable limit for OSHA 5,000. So it points to the big problem here. We talked about earlier about the need for these standards to codify some of this. Yes, we have awareness from the public. We have sensors to measure this. We have CDC now saying what we were saying with the Lancet COVID-19 Commission and elsewhere.

This is big movement, but the standards then need to come up behind it and get into code and new standards that are health focused and health based. And we have momentum, but we can't lose it right now because it's the first time in my career I felt like we're on the cusp of really getting this and we are so close. But of course it's always in danger of slipping through our fingers.

Regulatory Oversight for Air

Eric Topol (00:31:45):

Well, does this have anything to do with the fact that in the US there's no regulatory oversight over air as opposed to let's say Japan or other places?

Joseph Allen (00:31:57):

Yeah, I mean, we have regulatory oversight of outdoor air. That's EPA. There's a new bill that was introduced to give EPA more resources to deal with indoor air. EPA has got a great indoor air environments division, but it doesn't have the legally enforceable mandate or statute that we have for outdoor. So they'd give great guidance and have for a long time. I really like that group at EPA, but there's no teeth behind this. So what we have is worker health protections at OSHA to its own admission, says its standards are out of date. So we need an overhaul of how we think about the standards. I like the market driven approach. I think that's being effective, and I think we can do it from voluntary standards that can get adopted into code at the municipal level. I think that's a real path. I see it happening. I see the influence of all this work hitting legislators. So that's where I think the most promising path is for real change.

The Risks of Outdoor Air Pollution

Eric Topol (00:33:03):

Yeah, I think sidestepping, governmental teeth, that probably is going to be a lot quicker. Now, before we get to the cost issue, I do want to mention, as you know very well, the issue of air pollution in Science

a dedicated issue just a few weeks ago, it brought up, of course, that outdoor air pollution we've been talking about indoor is extraordinary risk for cancer, dementia, diabetes, I mean everything. Just everything. And there is an interaction between outdoor pollution and what goes on indoor. Can you explain basically reaffirm your concern about particulate matter outdoors, and then what about this interaction with what goes on indoors?

Joseph Allen (00:33:59):

Yeah, so it's a great point. I mean, outdoor pollution has been one of the most studied environmental pollutants we know. And there's all of these links, new links between Alzheimer's, dementia, Parkinson's disease, anxiety, depression, cardiovascular health, you named it, right? I've been talking about this and very vocal. It's in the book and elsewhere I called the dirty secret of outdoor air pollution. The reality is outdoor air pollution penetrates indoors, and the amount depends on the building structure, the type of filters you have. But let's take an infiltration value of say 50%. So you have a lot of outdoor air pollution, maybe half of that penetrates inside, so it's lower, the concentration is lower, but 90% of the breaths you take are indoor. And if you do the math on it, it's really straightforward. The majority of outdoor air pollution you breathe happens inside.

(00:34:52):

And people, I think when they hear that think, wait, that can't be right. But that's the reality that outdoor pollution comes inside and we're taking so many breaths inside. Your total daily dose of outdoor air pollution is greater from the time you spend inside. I talk about this all the time. You see any article about outdoor air pollution, what's the cover picture? It's someone outside, maybe they're wearing a mask you can't really see. It's smoky hazy. But actually one of the biggest threats is what's happening inside. The nice thing here, again, the solutions are pretty simple and cost-effective. So again, upgrade from MERV 8 to MERV 13, a portable air cleaner. We are just capturing particles on a filter basic step that can really reduce the threat of outdoor air pollution inside. But it's ignored all the time. When the wildfire smoke hit New York City. New York City's orange, I called colleagues who are in the news business.

(00:35:48):

We have to be talking about the indoor threat because the guidance was good, but incomplete. Talk about Mayor Adams in New York City. Go inside, okay, that's good advice. And go to a place that has good filtration or they should have been giving out these low cost air cleaners. So just going inside isn't going to protect your lungs unless you're actually filtering a lot more of that air coming in. So trying to drive home the point here that actually we talk about these in silos. Well, wildfire smoke and particles, Covid and respiratory particles, we're all talking about these different environmental issues that harm our health, but they're all happening through or mediated by the building performance. And if we just get the building performance right, some basics around good ventilation, good filtration, you start to address multiple threats simultaneously. Outdoor air pollution, wildfire smoke, allergens, COVID-19, influenza, RSV, better cognitive function performance, anxiety. You start addressing the root cause or one of the contributors and buildings we can then start to leverage as a true public health tool. We have not taken advantage of the power of buildings to be a true public health tool.

Eric Topol (00:36:59):

Oh, you say it so well, and in fact your Table on page 44 in Healthy Buildings , we’ll link it because it shows quantitatively what you just described about outdoor and indoor cross fertilization if you will. Now before leaving air pollution outdoors, indoors, in order for us to affect this transformation that would markedly improve our health at the public health individual level, we're talking about a big investment. Can you put that in, you did already in some respects, but if we did this right in every school, I think in California, they're trying to mandate that in schools, in the White House, they're mandating federal buildings. This is just a little piece of what's needed. This would cost whatever trillions or hundreds of billions of dollars. What would it take to do this? Because obviously the health benefits would be so striking.

What’s It Gonna Cost?

Joseph Allen (00:38:04):

Well, I think one of the issues, so we can talk about the cost. A lot of the things I'm talking about are intentionally low cost, right? You look at the Lancet of COVID-19 Commission, our report we wrote a report on the first four healthy building strategies every building should pursue. Number one commission your building that's giving your building a tune-up. Well, guess what? That not only improves air quality, it saves energy and therefore saves money. It actually becomes cost neutral. If not provides an ROI after a couple of years. So that's simple. Increase the amount of outdoor air ventilation coming in that has an energy cost, we've written about this. Improved filtration, that's a couple bucks, really a couple bucks, this is small dollars or portable air cleaners, not that expensive. I think one of the big, and Lawrence Berkeley National Lab has written this famous paper people like to cite that shows there's $20 billion of benefits to the US economy if we do this.

(00:38:59):

And I think it points to one of the problems. And what I try to address in my book too, is that very often when we're having this conversation about what's it going to cost, we don't talk about the full cost benefit. In other words, we say, well, it's going to cost X amount. We can't do that. But we don't talk about what are the costs of sick buildings? What are the costs of kids being out of school for an entire year? What are the costs of hormonal disruption to an entire group of women in their reproductive years due to the material choices we make in our buildings? What are the costs to outdoor air pollution and cardiovascular disease, mental health? Because we don't have good filters in our buildings that cost a couple dollars. So in our book, we do this cost benefit analysis in the proforma in our book, we lay out what the costs are to a company. We calculate energy costs. We say these are the CapEx costs, capital costs for fixed costs and the OpEx costs for operating expenditures. That's a classic business analysis. But we factor in the public health benefits, productivity, reduced absenteeism. And you do that, and I don't care how you model it, you are going to get the same answer that the benefits far outweigh the cost by orders of magnitude.

Eric Topol (00:40:16):

Yeah, I want to emphasize orders of magnitude. Not ten hundred, whatever thousand X, right?

Joseph Allen (00:40:23):

What would be the benefit if we said we could reduce influenza transmission indoors in schools and offices by even a small percent because we improve ventilation and filtration? Think of the hospitalization costs, illness costs, out of work costs, out of school costs. The problem is we haven't always done that full analysis. So the conversation gets quickly to well, that's too much. We can't afford that. I always say healthy buildings are not expensive. Sick buildings are expensive. Totally leave human health out of that cost benefit equation. And then it warps this discussion until you bring human health benefits back in.

Forever Chemicals

Eric Topol (00:40:58):

Well, I couldn't agree more with you and I wanted to frame this by giving this crazy numbers that people think it's going to cost to the reality. I mean, if there ever was an investment for good, this is the one that you've outlined so well. Alright, now I want to turn to this other topic that you have been working on for years long before it kind of came to the fore, and that is forever chemicals. Now, forever chemicals, I had no idea that back in 2018 you coined this term. You coined the term, which is now a forever on forever chemicals. And basically, this is a per- and polyfluoroalkyl substances (PFAS), but no one will remember that. They will remember forever chemicals. So can you tell us about this? Because this of course recently, as you know well in May in the New Yorker, there was an expose of 3M, perhaps the chief offender of these. They're everywhere, but especially they were in 3M products and continue to be in 3M products. Obviously they've been linked with all kinds of bad things. What's the story on forever chemicals?

Joseph Allen (00:42:14):

Yeah, they are a class of chemicals that have been used for decades since the forties. And as consumers, we like them, right? They're the things that make your raincoat repel rain. It makes your non-stick pan, your scrambled eggs don't stick to the pan. We put them on carpets for stain resistance, but they came with a real dark side. These per- and polyfluoroalkyl substances, as I say, a name only a chemist could love have been linked with things like testicular cancer, kidney cancer, interference with lipid metabolism, other hormonal disruption. And they are now a global pollutant. And one of the reasons I wrote the piece to brand them as forever chemicals was because I'm in the field of environmental health. We had been talking about these for a long time and I just didn't hear the public aware or didn't capture their attention. And part of it, I think is how we talk about some of these things.

(00:43:14):

I think a lot about this. Per- and polyfluoroalkyl substances, no one's going to, so the forever chemicals is actually a play on their defining feature. So these chemicals, these stain repellent chemicals are characterized by long chains of the carbon fluorine bond. And when we string these together that imparts this and you put them on top of a product that imparts the property of stain resistance, grease resistance, water resistance, but the carbon fluorine bond is the strongest in all of organic chemistry. And these chains of the carbon fluorine bond never fully break down in the environment. And when we talk in my field about persistent organic pollutants, we talk about chemicals that break down on the order of decades. Forever chemicals don't break down. They break down the order of millennia. That's why we're finding them everywhere. We know they're toxic at very low levels. So the idea of talking about forever chemicals, I wanted to talk about their foreverness.

(00:44:13):

This is permanent. What we're creating and the F and the C are the play on the carbon-fluorine bond and I wrote an article trying to raise awareness about this because some companies that have produced these have known about their toxicity for decades, and it's just starting the past couple of years, we're just starting to pay attention to the scale of environmental pollution. Tens of millions of Americans have forever chemicals in their drinking water above the safe limit, tens of millions. I worked as an expert in a big lawsuit for the plaintiffs that were drinking forever chemicals in their water that was dumped into the drinking water supply by a manufacturing company. I met young men with testicular cancer from drinking forever chemicals in their water. These really has escaped the public's consciousness, it wasn't really talked about. Now of course, we know every water body, we use these things in firefighting foams or every airport has water pollution.

(00:45:17):

Most airports do. Firefighters are really concerned about this, high rates of cancer in the firefighter population. So this is a major problem, and the cleanup is not straightforward or easy because they're now a global pollutant. They persist forever. They're hard to remediate and we're stuck with them. So that's the downside, I can talk about the positives. I try to remain an optimist or things we're doing to try to solve this problem, but that's ultimately the story. And my motivation was I just to have people have language to be able to talk about this that didn't require a degree in organic chemistry to understand what they were.

Eric Topol (00:45:52):

Yeah, I mean their pervasiveness is pretty scary. And I am pretty worried about the fact that we still don't know a lot of what they're doing in terms of clinical sequela. I mean, you mentioned a couple types of cancer, but I don't even know if there is a safe threshold.

Joseph Allen (00:46:16):

Eric, I'll tell you one that'll be really interesting for you. A colleague of mine did a famous study on forever chemicals many years ago now and found that kids with higher levels of forever chemicals had reduced vaccine effectiveness related to these chemicals. So your point is, right, a lot of times we're using these industrial chemicals. We know a couple endpoints for their affecting our bodies, but we don't know all of them. And what we know is certainly alarming enough that we know enough to know we shouldn't be using them.

Eric Topol (00:46:51):

And you wrote another masterful op-ed in the Washington Post, 6 forever chemical just 10,000 to go. Maybe you could just review what that was about.

Joseph Allen (00:47:02):

Yeah, I've been talking a lot about this issue I call chemical whack-a-mole. So forever chemical is the perfect example of it. So we finally got people's attention on forever chemicals. EPA just regulated 6 of them. Well, guess what? There are 10,000 if not many more than that. Different variants or what we call chemical cousins. Now that's important for this reason. If you think about how we approach these from a regulatory standpoint, each of the 10,000 plus forever chemicals are treated as different. So by the time EPA regulates 6, that's important. It does free up funding for cleanup and things like this. But already the market had shifted away from those 6. So in other words, in the many thousand products that still use forever chemicals, they're no longer using those 6 because scientists have told people these things are toxic years ago. So they switch one little thing in the chemical, it becomes a new chemical from a regulatory perspective.

(00:47:57):

But to our bodies, it's the same thing. This happens over and over. This has happened with pesticides. It happens with chemicals and nail polish. It happens in chemicals in e-cigarettes. It happens with flame retardant chemicals. I wrote a piece in the Post maybe six years ago talking about chemical whack-a-mole, and this problem that we keep addressing, these one-off, we hit one, it changes just slightly. Chemical cousin pops up, we hit that one. Five years later, scientists say, hey, the next one doesn't look good either. We're doing this for decades. It's really silly. It's ineffective, it's broken, and there are better ways to handle this going forward.

Eric Topol (00:48:31):

And you know what gets me, and it's like in the pharma industry that I've seen the people who run these companies like 3M that was involved in a multi-decade coverup, they're never held accountable. I mean, they know what they're doing and they just play these games that you outlined. They're still using 16,000 products, according to the New Yorker, the employee that exposed them, the whistleblower in the New Yorker article.

Joseph Allen (00:48:58):

That was an amazing article by Sharon Lerner talking to the people who had worked there and she uncovered that they knew the toxicity back in the seventies, and yes, they were still making these products. One of the things that I think has gotten attention of some companies is while the regulations have been behind, the lawsuits are piling up.

Joseph Allen (00:49:21):

The lawsuit I was a part of as an expert for that was about an $800 million settlement in favor of the plaintiffs. A couple months later is another one that was $750 million. So right there, $1.5 billion, there's been several billion dollars. This has caught the attention of companies. This has caught the attention of product manufacturers who are using the forever chemicals, starting to realize they need to reformulate. And so, in a good way now, that's not the way we should be dealing with this, but it has started to get companies to wake up that maybe they had been sleeping on it, that this is a major problem and actually the markets have responded to it.

Eric Topol (00:50:02):

Well, that's good.

Joseph Allen (00:50:03):

Because these are major liabilities on the books.

Eric Topol (00:50:05):

Yeah, I mean, I think what I've seen of course with being the tobacco industry and I was involved with Vioxx of course, is the companies just appeal and appeal and it sounds really good that they've had to pay $800 million, but they never wind up paying anything because they basically just use their muscle and their resources to appeal and put it off forever. So I mean, it's one way to deal with it is a litigation, but it seems like that's not going to be enough to really get this overhauled. I don't know. You may be more sanguine.

Joseph Allen (00:50:44):

No, no, I agree with you. It's the wrong way. I mean, we don't want to, the solution here is not to go after companies after people are sick. We need get in front of this and be proactive. I mentioned it only because I know it has made other companies pay attention how many billion does so-and-so sue for. So that's a good signal that other companies are starting to move away from forever chemicals. But I do want to talk about one of the positive approaches we're doing at Harvard, and we have a lot of other partners in the private sector doing this. We're trying to turn off the spigot of forever chemicals entering the market in the first place. As a faculty advisor to what we call the Harvard Healthier Building Materials Academy, we publish new standards. We no longer buy products that have forever chemicals in them for our spaces.

(00:51:31):

So we buy a chair or carpet. We demand no forever chemicals. What's really neat about this is we also say, we treat them as a whole class. We don't say we don't want PFOA. That's one of the regulated chemicals. We say we don't want any of the 10,000. We are not waiting for the studies to show us they act like the other ones. We've kind of been burned by this for decades. So we're actually telling the suppliers we don't want these chemicals and they're delivering products to us without these chemicals in them. We have 50 projects on our campus built with these new design standards without forever chemicals and other toxic chemicals. We've also done studies that a doctoral student done the study. When we do this, we find lower levels of these chemicals in air and dust, of course. So we're showing that it works.

(00:52:19):

Now, the goal is not to say, hey, we just want to make Harvard a healthier campus and the hell with everybody else. The goal is to show it can be done with no impact to cost, schedule or product performance. We get a healthier environment, products look great, they perform great. We've also now partnered with other big companies in the tech industry in particular to try and grow or influence the market by saying, look how many X amount of purchasing dollars each year? And it's a lot, and we're demanding that our carpets don't have this, that our chairs don't have it, and the supply chain is responding. The goal, of course, is to just make it be the case that we just have healthy materials in the supply chain for everybody. So if you or I, or anybody else goes to buy a chair, it just doesn't have toxic chemicals in it.

Eric Topol (00:53:06):

Right, but these days the public awareness still isn't there, nor are the retailers that are selling whether it's going to buy a rug or a chair or new pots and pans. You can't go in and say, does this have any forever chemicals? They don't even know, right?

Joseph Allen (00:53:24):

Impossible. I study this and it's hard for me when I go out to try and find and make better decisions for myself. This is one of the reasons why we're working, of course, trying to help with the regulatory side, but also trying to change the market. Say, look, you can produce the similar product without these chemicals, save yourself for future lawsuits. Also, there's a market for healthy materials, and we want everybody to be a part of that market and just fundamentally change the supply chain. It's not ideal, but it's what we can do to influence the market. And honestly, we're having a lot of impact. I've been to these manufacturing plants where they have phased out these toxic chemicals.

Eric Topol (00:54:03):

That’s great to hear.

Joseph Allen (00:54:06):

And we see it working on our campus and other companies’ campuses.

Eric Topol (00:54:10):

Well, nobody can ever accuse you of not taking on big projects, okay.

Joseph Allen (00:54:15):

You don't get into public health unless you want to tackle the big ones that are really going to influence.

Micro(nano) Plastics

Eric Topol (00:54:20):

Well, that's true, Joe, but I don't know anybody who's spearheading things like you. So it's phenomenal. Now before we wrap up, there's another major environmental problem which has come to the fore, which are plastics, microplastics, nanoplastics. They're everywhere too, and they're incriminated with all the things that we've been talking about as well. What is your view about that?

Joseph Allen (00:54:48):

Well, I think it's one, well, you see the extent of the pollution. It's a global pollutant. These are petrochemicals. So it's building up, and these are fossil fuel derivatives. So you can link this not just to the direct human health impacts, the ecosystem impacts, but also ecosystem and health impacts through climate change. So we've seen our reliance on plastics grow exponentially over the past several decades, and now we're seeing the price we're paying for that, where we're seeing plastics, but also microplastics kind of everywhere, much like the forever chemicals. Everywhere we look, we find them and we're just starting to scratch a surface on what we know about the environmental impacts. I think there's a lot more that can be done here. Try to be optimistic again, at least if you find a problem, you got to try and point to some kind of solution or at least a pathway towards solutions.

(00:55:41):

But I like some of the stuff from others colleagues at Yale in particular on the principles of green chemistry. I write about them in my book a little bit, but it's this designing for non-permanence or biodegradable materials so that if we're using anything that we're not leaving these permanent and lasting impacts on our ecosystem that then build up and they build up in the environment, then they build up in all of us and in our food systems. So it seems to me that should be part of it. So think about forever chemicals. Should we be using chemicals that never break down in the environment that we know are toxic? How do we do that? As Harvard, one of the motivating things here for forever chemicals too, is how are we ignoring our own science? Everyone's producing this science, but how do we ignore even our own and we feel we have responsibility to the communities next to us and the communities around the world. We're taking action on climate change. How are we not taking action on these chemicals? I put plastics right in there in terms of the environmental pollutants that largely come from our built environment, food products and the products we purchase and use in our homes and in our bodies and in all the materials we use.

Eric Topol (00:56:50):

When you see the plastic show up in our arteries with a three, four-fold increase of heart attacks and strokes, when you see it in our testicles and every other organ in the body, you start to wonder, are we ever going to do something about this plastic crisis? Which is somewhat distinct from the forever chemicals. I mean, this is another dimension of the problem. And tying a lot of this together, you mentioned, we are not going to get into it today, but our climate crisis isn't being addressed fast enough and it's making all these things exacerbating.

Joseph Allen (00:57:27):

Yeah, let me touch on that because I think it is important. It gets to something I said earlier about a lot of these problems we treat as silos, but I think a lot of the problems run through our buildings, and that means buildings are part of the solution set. Buildings consume 40% of global energy.

(00:57:42):

Concrete and steel count for huge percentages of our global CO2 emissions. So if we're going to get climate solved, we're going to have to solve it through our buildings too. So when you start putting this all together, Eric, right, and this is why I talk about buildings as healthy buildings could potentially be one of the greatest public health interventions we have of this century. If we get it right, and I don't mean we get the Covid part, right. We get the forever chemicals part, right. Or the microplastics part, right. If you start getting this all right, good ventilation, better filtration, healthy materials across the board, energy efficient systems, so we're not drawing on the energy demand of our buildings that are contributing to the climate crisis. Buildings that also address climate adaptation and resilience. So they protect us from extreme heat, wildfire smoke, flooding that we know is coming and happening right now.

(00:58:37):

You put that all together and it shows the centrality of buildings on our collective health from our time spent indoors, but also their contribution to environmental health, which is ultimately our collective human health as well. And this is why I'm passionate about healthy buildings as a real good lens to put this all under. If we start getting these right, the decisions we make around our buildings, we can really improve the human condition across all of these dimensions we're talking about. And I actually don't think it's all that hard in all of these. I've seen solutions.

Eric Topol (00:59:12):

I'm with you. I mean, there's innovations that are happening to take the place of concrete, right?

Joseph Allen (00:59:20):

Sure. We have low emission concrete right now that's available. We have energy recovery ventilation available right now. We have real time sensors. We can do demand control ventilation right now. We have better filters right now. We have healthy materials right now.

(00:59:33):

We have this, we have it. And it's not expensive if we quantify the health benefits, the many, many multiple benefits. So it's all within our reach, and it's just about finding these different pathways. Some of its market driven, some of it's regulatory, some of it's at the local level, some of it's about raising awareness, giving people the language to talk about these things. So I do think it's the real beginning of the healthy buildings era. I really, truly believe it. I've never seen change like this in my field. I've been chasing sick buildings for a long time.

Joseph Allen (01:00:11):

And clearly there's pathways to do better.

Eric Topol (01:00:13):

You're a phenom. I mean, really, you not only have all the wisdom, but you articulate it so well. I mean, you’re leading the charge on this, and we're really indebted to you. I'm really grateful for you taking an hour of your busy time to enlighten us on this. I think what you're doing is it's going to keep you busy for your whole career.

Joseph Allen (01:00:44):

Well, the goal here is for me to put myself out of business. We shouldn't have a healthy buildings program. It just should be the way it's done. So I'm looking forward to the time out of business, hopefully have a healthy building future, then I can retire, be happy, and we'll be onto the next big problem.

Eric Topol (01:00:57):

We'll all be following your writings, which are many, and fortunately not just for science publications, but also for the public though, they're so important because the awareness level as I can't emphasize enough, it's just not there yet. And I think this episode is going to help bring that to a higher level. So Joe, thank you so much for everything you're doing.

Joseph Allen (01:01:20):

Well, I appreciate it. Thanks for what you're doing too, and thanks for inviting me on. We can't get the word out unless we start sharing it across our different audiences, so I appreciate it. Thanks so much.

Eric Topol (01:01:28):

You bet.

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Steve Horvath made the seminal discovery of the—Horvath Clock— an epigenetic clock based on DNA methylation, which is now being used extensively in medical research and offered commercially for individuals (←we talk about that!). He was on the faculty at UCLA from 2000-2022 as a Professor of Human Genetics and Biostatistics, and now works on anti-aging research at Altos Labs.

A perspective on the importance of epigenetic clocks this week’s Nature”This insight is crucial for deriving reliable biological markers of ageing in tissues or blood. Such a feat has been accomplished through the ingenious identification of epigenetic clocks in our genome. But these insights are even more important for revealing targets that enable intervention in the ageing process.”

A video snippet on vegetable intake and epigenetic clocks. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to Audio and External Links

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath. He's a geneticist, a statistician, a mathematician. He's got a lot of background that has led to what is a landmark finding in biomedicine, the Horvath clock. So Steve, welcome.

Steve Horvath (00:30):

Thank you for having me.

Eric Topol (00:33):

Well, it's really fascinating. I followed your work for well over a decade since you introduced the pan-tissue clock in 2013, and it's fascinating to go back a bit on that finding, which initially, I guess was in saliva a couple of years prior, and then you found it everywhere you looked, wherever cells had a nucleus and tissues. And what gave you the sense that these markers of methylation on the DNA would give us some clues about the aging process? How did you even come about to make this discovery?

Serendipity

Steve Horvath (01:17):

It was an accidental discovery because before the methylation clock, I had worked very hard on a gene expression clock, a transcriptomic biomarker. I mean, I was at the height of my energy levels. I worked really on weekends, really eight hour days during the week. But all the weekends I had collected a large set of gene expression data and I dredged the data. And for two years and I couldn't get anywhere, there was nothing I could do. But nowadays, of course, you see various publications where people built transcriptomic clocks. But back in the day when we had these arrays, I just couldn't see a signal. And then at some point I got roped into a study of homosexuality where my collaborator at UCLA wanted to see whether there's an epigenetic correlate of sexual orientation in saliva. And so yeah, being a biostatistician, I said, sure, I analyzed the data and I couldn't find any signal for homosexuality.

(02:48):

But then I just looked for an aging signal in the same, and really within an hour of analyzing the data, I knew that I have to completely drop gene expression. I need to go after methylation. And the signal is so profound, and as you said initially we looked at saliva samples and we thought, isn't it curious? You spit in a cup and you can measure someone's age. And we were of course, hoping that this could become a valuable readout of biologic age, but it took, of course, many years to realize that potential. Nowadays, there's several companies that offer a saliva based methylation clock test. But yeah, many years passed, and it was important to fill in the details and to build the case that methylation clocks are predictive of things we care about time to death or time to various forms of morbidity. So it took many, many years to analyze large cohort studies and to accumulate the evidence that it actually works.

Eric Topol (04:16):

Yeah, I mean, it was pretty amazing back almost a decade ago when I would see, we would take tissue or blood sample and look at your clock and it would say, age of the person is 75 years. And then we look at the actual age of the person who is 75 years to say, wait a minute, how can this be? So I mean, the plausibility of this discovery, if you look back, I mean you say, well, this is just kind of the rust of the pipes, or how do you process that the methylation is such a marker potentially of a person's biologic age? Of course, we're going to get into how it could be a way to intervene to change the aging process. But would it be fair to say that its epigenetic clocks are not the same as biologic aging or how do you put all that together?

Epigenetic Age vs Biologic Age

Steve Horvath (05:21):

Yes, for sure. An epigenetic age estimate is certainly not the same as a biologic age estimate. And the reason why I say it is because biologic age is really determined by so many things and by so many organs. And as I mentioned initially, we had a clock for saliva later for blood and so on. And so, if you only have an epigenetic readout of a certain cell type, it's really too limited to assess the whole organismal state. And arguably you would want to measure also proteomics, readouts and many other data modalities. So I typically avoid the terminology biologic age, because to begin with, we don't have a definition of it. Decades of discussions, nobody really has a precise definition of it.

Second Generation Epigenetic Clocks

Eric Topol (06:35):

Well, from the first generation Horvath clock then became this newer second generation, GrimAge, PhenoAge, the DunedinPACE of aging. How has that helped to advance the field? Because as you touched on, they're measuring different things and what is it meant by kind of a second generation clock?

Steve Horvath (07:03):

Yeah, so a second generation clock truly aims to predict mortality or morbidity risk. As opposed to simply chronologic age or what is known as calendar age. And fortunately, there's no doubt that the second generation clocks can do that. I often finish a talk on GrimAge by telling the audience that I give them a money back guarantee, that it will be predictive of mortality in their cohort study. I'm 100% certain that it works if you analyze a hundred people or so. The question is more whether an individual could benefit from such a test. And there are now many providers of various epigenetic clock tests. These biomarkers have different names, but they're quite pricey. A couple of hundred dollars are needed to get such a measurement. And the question is, is it helpful for the individual should you get such a test? And I would say we are not quite there yet for a variety of reasons. The main reason being we don't have good interventions against accelerated epigenetic age. So because when you think about it, why does a doctor order a test for you? For example, cholesterol levels. Well, because they have a drug against elevated cholesterol levels, the statin. And at the moment, we don't have validated interventions against accelerated epigenetic age. So that's kind of missing.

Eric Topol (09:13):

Yeah, we're going to get to that because obviously a lot of things are in the pipeline there, but are you saying then that these people that are getting these consumer tests, that they're getting a test that really wasn't validated at an individual level, so it predicts their mortality that it may be good at a cohort or population level, but maybe it's not so helpful, accurate, or would you say it is accurate? I mean, GrimAge is a good name because since it says when you're going to die. How do you make the differentiation between the individual level or beyond?

Steve Horvath (09:59):

Yeah, I think it's good to compare to other biomarkers. So take glucose levels, hemoglobin A1C, nobody doubts that these levels predict mortality risk when you study couples a hundred people. But how accurate is such a test for an individual? Clearly there is substantial noise associated with a prediction. Two people could have exactly the same hemoglobin A1C levels, but live very different lifespans. And the same holds for epigenetic clocks. They do predict how long you live. In theory, one could arrive at an estimate of age and death. There's a complicated mathematical formula that allows you to do that, but there would be a substantial error bar associated with it, an order of magnitude plus minus five years. And so, for the individual, such an estimate is not that important because the error bar is substantial. But I want to add that these second generation clocks, they do predict mortality risk. There's no question.

Maximal Lifespan

Eric Topol (11:35):

Well, as you know, the longevity space is now very crowded with all sorts of clubs, and it's like a circus out there. And some of these things are being promoted that really don't have the basis or have a false sense to consumers who want to live forever and be healthy forever. But maybe these markers are not really helping guide them so much. Now, you recently published you and your group a fascinating paper, so getting away from the individual for a second, but now at the species level and in Science Advances, and we'll put this diagram with the podcast, but you looked at 348 mammal species for the maximal lifespan with DNA methylation. And it was amazing to see the display from the desert hamster all the way to the humpback whale with somewhere along the way, the humans. So you could predict maximal lifespan pretty well, right?

Steve Horvath (12:43):

Yes. So I collected this very large dataset over seven years, and one of the reasons was to understand the mystery of maximum lifespan. The bowhead whale can live over 211 years, whereas certain mice only three or four years. And my question was, can methylation teach us something about maximum lifespan? And the answer is a resounding, yes. The methylation profiles very much predict the maximum lifespan of a species. And maybe to use a metaphor to explain the patterns. So one can visualize methylation around the DNA molecule, like a landscape. You want that certain regions exhibit high levels of methylation. These regions must be really shut down and other parts of the DNA as opposed to exhibit very low methylation, for example, a transcriptional start sites. And long lived species have a very hilly landscapes, high hills of methylation and steep valleys of low methylation. Where shorter lived species have flatter landscapes. So that was one of the insights of that study. The other perhaps paradoxical insight was that the locations in our DNA that gain methylation with chronologic age, these regions often differ from regions that determine the maximum lifespan of our species. So that's a bit perhaps paradoxical and counterintuitive, but it just shows that the DNA encodes our species characteristics at different locations from our mortality risk.

The Other Clocks

Eric Topol (15:13):

Right. No, and I mean it's fascinating. I can imagine how it could take seven years to pull all that data together. It's amazing. Now, one of the issues of course, is if you're trying to gauge the biologic age, which we already established is somewhat different than epigenetic age or a clock, there are many different ways to do that. And you mentioned transcriptome clocks, which are not as well perhaps developed. Obviously, none of these others are developed like the Horvath clock and newer generation clocks, but there's immuno aging clocks like iAge, there's proteomic clocks, there's organ clocks with high-throughput proteomics, thousands of proteins. Do you see these as complimentary, like orthogonal where they each add to the story? Or do you really see the methylation as distinct?

Steve Horvath (16:20):

Well, I think ideally you measure all of the above to really get a very granular understanding of different facets of aging. And however, scientists always like to find deep connections between different readouts. For example, it would be wonderful if we could use proteomics instead of methylation, or my group has worked on the opposite. So we can actually estimate protein levels in the plasma based on methylation for about 10% of all plasma proteins, you can estimate their levels based on methylation. So yeah, people who are interested in these deeper programs that ideally link everything, some sort of aging program that underlies these different manifestations of aging, they will want to reduce everything. But until we have a deeper understanding, I think let's air on the side of measuring too much.

Eric Topol (17:45):

Well, what's interesting, as you mentioned, I didn't realize you could basically impute the protein story from the methylation, but one of the issues is if you want to do 11,000 plasma proteins, it could cost a thousand dollars. But if you want to do a bisulfite methylation, you might do that for very inexpensively. So there's a practical part of this too, and the immune characterization is even more expensive and difficult from a practical standpoint. So we go back to that initial work that you did and how you got into an area that is practical, inexpensive compared to some of the alternatives. But as you say, they may have features that are also helpful. Now, this is now the craze, this epigenetic clocks, and I want to mention you probably didn't see it because it's not a journal that you would look at, but just yesterday, July 29th, there were 12 papers published in JAMA Network Open.

Modulating Your Epigenetic Clock

(18:51):

Everything from how loss of loved ones changes your epigenetic clock to PTSD, to vegan diets, to inequities. I mean, just incredible. So it is the rage now. It's taken the biomedical community some years to catch up to where you were. And one of the things of course that we know that from your prior work that is an intervention that helps give a less accelerated epigenetic clock is exercise. And in fact, that was highlighted in our Lancet essay in the first week August issue. But can you comment on that and anything else that we know like plant diets and anything that favorably influence our DNA methylation pattern?

Steve Horvath (19:52):

Yes. So interestingly, vegetable intake really has a strong effect on GrimAge and many other epigenetic clocks. And maybe this is obvious to the listener, everybody knows that vegetable intake is healthy. However, it's very surprising to me as a scientist to contemplate how is it that vegetable intake affects the methylation levels of your blood? How does it affect the hematopoietic stem cells? I just don't understand the mechanism behind it, and however, the effect is very strong. So we studied postmenopausal women in the women's health initiative, and for these women, we had blood measures of carotenoid levels. So this is an objective measure of vegetable intake, and the correlations were substantial. So that's one intervention I'm quite certain about. Other intervention that have a strong effect relate to metabolic syndrome, anything that relates to type 2 diabetes such as obesity, high glucose levels, that part of the biology very much affects our epigenetic clocks. So disturbed metabolism has a strong effect.

Eric Topol (21:37):

Has these findings changed your diet or made you exercise more or anything like that?

Steve Horvath (21:44):

. So I eat a lot of frozen vegetables. My freezer as full frozen vegetables.

Eric Topol (21:56):

That's great. Well, there's a lot of uses today as we touched on in the Lancet piece as we're waiting for more benchmarking and more work on this. But for example, we have a shortage of donor organs, and there are people who might be of calendar age advanced, but their epigenetic clock might put them at a much younger age. Is that ready for use in the transplant world as one application?

Steve Horvath (22:37):

I haven't seen that yet. I've seen several studies that have explored that idea. The idea is rather obvious, but I haven't seen it implemented in practicum.

Eric Topol (22:53):

Another one is that we don't, as you've seen from some of these studies on organ clocks, our organs age at different paces and some people are accelerated heart agers or brain agers. If you had access to tissue to get methylation, would you see the same thing or this is of course of interest because we're trying to understand high risk individuals for age related diseases, whether it's dementia or heart disease or cancer. So is the second generation clocks like PhenoAge just good enough, or would you think that the organ clocks would give you some added insight?

Steve Horvath (23:47):

Yeah, I would say this is literally the frontier of research. Several groups attempt to use blood methylation or saliva or skin or fat adipose as surrogates for various other organs. And I've seen very encouraging results. So I do think this idea makes scientific sense, and which comes back to one of the miracles of methylation that this is even possible because if you had written a grant 10 years ago where you said, I will measure blood methylation to assess cognitive functioning, for example, you wouldn't have received any score, not in no funding, but however, interestingly, blood methylation does relate to cognitive functioning and many other organ functions. And so, the proof of concepts have been established. Blood methylation relates to fatty liver disease, kidney disease, lung disease. It has all been done in epidemiological studies. However, the question is how much could a blood methylation measurement help an individual? Should I measure my blood methylation to learn about my liver? And I would say we are not there yet because arguably there are wonderful plasma biomarkers to assess organ functions. And in certain ways, one needs to provide evidence that a methylation measurement is superior or compliments plasma based biomarker. And that's a hard hurdle to take.

Eric Topol (26:02):

Right. I imagine someday it may become the norm of assessing people's risk, but as you say, we're not there yet because it's a tough bar to meet, for sure. Now, you were a Professor from year 2000 at UCLA in multiple departments in genetics and biostats, and then in more recent times you joined the Cambridge unit of Altos, which is one of the companies that has gotten the most attention for its diverse efforts towards modulating, rejuvenating the aging process. So you and many top scientists around the world were recruited to Altos. I know some here at the San Diego campus. Was this thinking that it could help accelerate the whole idea of modulating aging in a favorably way or where do you see that the biotech world can play a role?

Can We Change the Pace of Aging?

Steve Horvath (27:15):

Yes. I mean, speaking for myself, I was getting tired of writing scientific papers and not affecting clinical care. I felt I needed to help identify or validate rejuvenating interventions because of the great promise, and this is perhaps best done in the setting of a biotech that is focused on translation. And that's why I joined. I'm moving away from biomarker development towards finding interventions that move the needle and ideally rejuvenate multiple organs and cell types at the same time.

Eric Topol (28:09):

Right. Now, there's lots of ideas of how we could do that from senolytics that would get rid of specific senescent cells that are bad actors to epigenetic reprogramming or chemical reprogramming or so many anti-inflammatory, like the recent paper of IL-11 that I'm sure you saw in Nature just a couple of weeks ago and many, many other ways to get there. What are you thinking? Is this going to be possible? Obviously, there's lots of naysayers. Is it going to be possible body wide or only for specific ways? For example, maybe we could bring back the thymus from its involution or we could stop ovarian failure in women so that their loss of advantage is delayed many years. Or do you think we're going to get to body wide anti-aging?

Steve Horvath (29:13):

Yeah, I think of it as divide and conquer. So ultimately I do believe that we can rejuvenate most cell types and tissues. The question is how do you roll out this program? Do you look for this one silver bullet that does it? For example, this idea of interrupted reprogramming based on Yamanaka factor combinations that looks of course very promising and rodent models. But then such silver bullet treatments could be risky for patient keyword malignant transformation, cancer risk, and it could be far safer to focus on one organ system or one tissue. For example, David Sinclair's company Life Biosciences looks at optic nerve regeneration for a reason. It could be safer. And so yeah, I'm very happy that companies explore different strategies. Certain companies focus on one condition, fatty liver disease or NASH. Other companies focus on immune system restoration. But I think many people think of one condition as really a first step to establish safety and efficacy, and then hopefully they could translate it to other body systems and organ systems.

Eric Topol (31:02):

But is it fair to say you're optimistic that we will be able to change the aging pace in people?

Steve Horvath (31:10):

Yes, I think yes. I'm very optimistic and there are several reasons for this optimism. The first is that dramatic results can be achieved in mice and rats. So we and others have published studies that show that you can reduce the epigenetic age by 30% or so and you can extend the lifespan, and you cited this very exciting paper by Stuart Cook on IL-11 inhibition that just came out in Nature. So I keep seeing these kinds of headlines, and then I want to think that one of these will actually work for humans. So the second thing that makes me optimistic is really this combination of artificial intelligence and biomedical research. Then going forward, robotics. So I can see several ways of accelerating biomedical research. So I'm quite optimistic.

The Role of A.I.

Eric Topol (32:24):

Maybe go a little deeper on the AI potential to help here. How does AI come into play?

Steve Horvath (32:33):

So AI can help in so many different ways. The first topic is biomarker development. I of course spent 10 years on a certain statistical model for building biomarkers, which is known as penalized regression. It works well, but AI allows the community to build imaging based biomarkers. So for example, based on MRI images, but also cells growing in a dish, we can say this treatment aged the cells growing in the dish or rejuvenated them. So that's one topic, biomarker discovery. The second is, of course, to design small molecules, keyword, these protein design where it has greatly accelerated drug discovery. And there are several companies working in this space, and again, there's wonderful case studies that look very convincing to me. And the third aspect of AI is another obvious one. AI can read many papers. I mean, you could be a 50-year-old professor who has read papers their entire life, but an AI can really read far better and summarize insights better.

Eric Topol (34:27):

Yeah, the complimentary in terms of the reasoning of that information. So absolutely right now, one of the problems we have here is that aging is not seen as a disease. Of course, we can remember when obesity was not considered a disease and then there was a drug and everything changed. But here we don't have a classification it's a disease. It's considered a natural process that is highly variable in people. But the question is, we can't do studies that are going to wait 20, 30 years to find out if we promoted health span and lifespan. And so, we have to rely on these clocks. So how do you see this playing out? Do you think that we might see a regulatory approval on a surrogate proxy, like an advanced Horvath clock, or do you think that's not going to cut it, that you're going to have to show more to get a anti-aging treatment across the regulatory threshold?

Steve Horvath (35:42):

Yeah, that's a very good question. So I believe that the biomarker community has already assembled enough evidence to offer a battery of tests that could be used as surrogate endpoints of interventional study. And we could discuss the components of this battery. But I would say we already have biomarkers beyond just methylation. One could have the readouts of walking speed or muscle function, many readouts, and they could be aggregated into an index to summarize the biologic age, perhaps, of the individual. So that already exists. At the same time, this field is undergoing explosive growth. You mentioned every day new papers come out in the relatively small field of epigenetic clocks. There's so many papers that it's hard to keep track, but I embrace it. I think it's wonderful because clocks get ever more powerful.

(37:11):

So yeah, I would say there should be different versions. Ideally, a regulatory agency would make an executive decision and say, for the next three years, use the following five biomarkers. Then a few years later, as the science advances, they could come up with an updated version, but even a 90% solution would very much accelerate progress in the whole field of rejuvenating interventions. So I would very much embrace a top down decision on which biomarkers should be made, because the bottom up approach, by the way, simply doesn't work. The minute you put three professors in the room to come up with a decision, which biomarker is best, there will be three different opinions. We need impartial arbiter that makes a decision.

GLP-1 Drugs and Aging

Eric Topol (38:23):

Now, the drug class that's come on the scene, of course it was in incubating for decades for diabetes, but now obesity and so of the obesity related. But now we're seeing the GLP-1 drugs that are showing potential effects in Parkinson's and Alzheimer's and cardiovascular disease, and even in obesity related cancers. And I mean across the board. And you mentioned metabolic derangement as one of the things that accelerate aging. Do you think these class of drugs that has greatly passed our expectations already and it's being tested of course, with even more potent drugs or triple receptors and pills and whatnot, will that be a candidate as one of the anti-aging interventions in the future?

Steve Horvath (39:19):

Yeah, for sure. A couple of months ago, I participated in a conference and one of the speakers showed unpublished results from a study, and they looked good to me. I mean, they registered on epigenetic clocks. This is all unpublished, but it made perfect sense to me because I mentioned the clocks do relate to metabolic health. So I was quite pleased that they registered that intervention.

Eric Topol (39:56):

It's fascinating because we could all be taking GLP-1 drugs someday, not for obesity or not for sleep apnea, but for things that are more far reaching. I didn't know about that unpublished result. That's fascinating.

Steve Horvath (40:15):

Yeah, I have a joke, which is I wish I was chubby because I would be using these drugs, but I'm relatively slender, so I don't have any good reason to take them.

Eric Topol (40:28):

That says a lot. I don't know anybody who knows more about this process than you and is very candid and frank about it. So Steve, this has been terrific to have your insights, the body of work that you should be so proud of that extends over many years and many great years and more contributions to come undoubtedly. So thank you for joining us today, and we will follow this continued evolution of our ability, not just to track the aging process, but also to modulate. So thanks very much.

Steve Horvath (41:06):

Thank you. I really like your podcast Ground Truths, it’s very informative. So thank you for this.

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After finishing her training in neurology at Mayo Clinic, Dr. Svetlana Blitshteyn started a Dysautonomia Clinic in 2009. Little did she know what was in store many years later when Covid hit!

Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube

Transcript with audio and external links

Eric Topol (00:07):

Well, hello, it's Eric Topol from Ground Truths, and I have with me a really great authority on dysautonomia and POTS. We will get into what that is for those who aren't following this closely. And it's Svetlana Blitshteyn who is a faculty member at University of Buffalo and a neurologist who long before there was such a thing as Covid was already onto one of the most important pathways of the body, the autonomic nervous system and how it can go off track. So welcome, Svetlana.

Svetlana Blitshteyn (00:40):

Thank you so much, Eric for having me. And I want to say it's a great honor for me to be here and just to be on the list with your other guests. It's remarkable and I'm very grateful and congratulations on being on the TIME100 Health list for influential people in 2024. And I am grateful for everything that you've done. As I mentioned earlier, I'm a big fan of your work before the pandemic and of course with Covid I followed your podcast and posts because you became the best science communicator and I'm very happy to see you being a strong advocate and thank you for everything you've done.

Eric Topol (01:27):

Well, that's so kind to you. And I think talking about getting things going before the pandemic, back in 2011, you published a book with Jodi Epstein Rhum called POTS - Together We Stand: Riding the Waves of Dysautonomia. And you probably didn't have an idea that there would be an epidemic of that more than a decade later, I guess, right?

Svetlana Blitshteyn (01:54):

Yeah, absolutely. Of course, SARS-CoV-2 is a new virus and we can technically say that Long Covid and post Covid complications could be viewed as a new entity. But practically speaking, we know that post-infectious syndromes have been happening for many decades. And so, the most common trigger for POTS happened to be infection, whether it was influenza or mononucleosis or Lyme or enterovirus. We knew this was happening. So I think it didn't take long for me and my colleagues to realize that we're going to be seeing a lot of patients with autonomic dysfunction after Covid.

On the Front Line

Eric Topol (02:40):

Well, one of the things that's important for having you on is you're in the front lines taking care of lots of patients with Long Covid and this postural orthostatic tachycardia syndrome (POTS). And I wonder if you could tell us what it's care for these patients because so many of them are incapacitated. As a cardiologist, I see of course some because of the cardiovascular aspects, but you are dealing with this on a day-to-day basis.

Svetlana Blitshteyn (03:14):

Yeah, absolutely. As early as April 2020 when everything was closed, I got a call from a young doctor in New York City saying that he had Covid and he couldn't recover, he couldn't return to the hospital. And his colleagues and cardiology attendants also had the same symptoms and the symptoms were palpitations, orthostatic intolerance, tachycardia, fatigue. Now, how he knew to contact me is that his sister was my patient with POTS before Covid pandemic. So he kind of figured this looked like my sister, let me check this out. And it didn't take long for me to have a lot of patience from the early wave. And then fairly soon, I think within months I was thinking, we have to write this up because this is important. And to some of us it was not news, but I was sure that to many physicians and public health officials, this would be something new.

Svetlana Blitshteyn (04:18):

So because I'm a busy clinician and don't have a lot of time for publications, I had to recruit a graduate student from McMasters and together we had this paper out, which was the first and largest case series on post Covid POTS and other autonomic disorders. And interestingly, even though it came out I think in 2021, by the time it was published, it became the most citable paper for me. And so I think from then on organizations and societies became interested in the work that I do because prior to that, I must say in the kind of a niche specialty was I don't think it was very popular or of interest to me.

How Did You Get Interested in Dysautonomia?

Eric Topol (05:06):

Yeah, so that's why I wanted to just take a step back with you Svetlana, because you had the foresight to be the founder and director of the Dysautonomia Clinic when a lot of people weren't in touch with this as an important entity. What prompted you as a neurologist to really zoom in on dysautonomia when you started this clinic?

Svetlana Blitshteyn (05:28):

Sure. So the reasons are how I ended up in this field is kind of a convoluted road and the reasons are many, but one, I will say that I trained at Mayo Clinic where we received very good training on autonomic disorders and EMG and coming back to returning back to Buffalo, I began working at the large multiple sclerosis clinic because Western New York has a high incidence MS. And so, what they quickly realized in that clinic is that there was a subset of women who did not qualify for the diagnostic criteria of multiple sclerosis, yet they had a lot of the same symptoms and they were certainly very disabled. Now I recognize that these women had autonomic disorders of all sorts and small fiber neuropathy, and I think this population sort of grew and eventually I realized there is no one not only in Buffalo but the entire Western New York who is doing this work.

Svetlana Blitshteyn (06:34):

So I kind of fell into that. But another reason is actually more personal that I haven’t talked about. So years ago I was traveling to Toronto, Canada for a neurology meeting to present my big study on meningioma and hormone replacement therapy using Mayo Clinic database. And so, in that year, the study received top 10 noteworthy studies of the year award from the Society of Neuro-Oncology, and it was profiled in Reuters Health. Now, on the way back from the conference, I had the flu, and when they returned I could no longer walk the same hallways of the hospital where I walked previously. And no matter how hard I try to push my body, we all do this in medicine, we push through, I just couldn’t do it. No amount of wishing or positive thinking. And so, I think that’s how I came to know personally the post-infectious syndromes. And I think it almost became a duality of experiencing this and also practicing it.

Eric Topol (07:52):

No, that’s really striking and it wasn’t so common to hear about this post flu, but certainly it changed in 2020. So how does a person with POTS typically present to you?

Clinical Presentation

Svetlana Blitshteyn (08:08):

So these are very important questions because what I want to stress is though POTS is one of the most common autonomic disorders. Even if you don’t have POTS by the diagnostic criteria, you may still have autonomic dysfunction and significant autonomic symptoms. How do they present? Well, they present like most Long Covid patients, the most common symptoms are orthostatic intolerance, fatigue, exercise intolerance, post exertional malaise, dizziness, tachycardia, brain fog. And these are common themes across the board in Long Covid patients, but also in pre-Covid post-acute infection syndrome patients. And you have to recognize because I think what I tell my colleagues is that oftentimes patients are not going to present to you saying, I have orthostatic intolerance. Many times they will say, I’m very tired. I can no longer go to the gym or when I go to the store, I have to be out of there in 15 minutes because the orthostatic intolerance symptoms come up.

Svetlana Blitshteyn (09:22):

So sometimes the patients themselves don’t recognize that and it’s up to us physicians to ask the right questions to get the information down. History is very important, knowing the pattern. And then of course, as I always say in all of my papers and lectures, you have to do a 10-minute stand test by measuring supine and standing blood pressure and heart rate on every Long Covid patients. And that’s how you spot those that have excessive postural tachycardia or their blood pressure dropping or so forth. So we have the tools. We don’t need fancy autonomic labs. We don’t even need a tilt table test. The diagnostic criteria for POTS is that you need to have either a 10-minute stand test or a tilt table test to get the diagnosis for POTS, orthostatic hypotension or even neurocardiogenic syncope. Now I think it's important to stress that even if a patient doesn't qualify, and let's say many patients with Long Covid will not elevate their heart rate by at least 30 beats per minute, it could be 20, it could be 25. These criteria are of course essential when we do research studies. But I think practically speaking, in patient care where everything is gray and nothing is black or white, especially in autonomic disorders, you really have to make a diagnosis saying, this sounds like autonomic dysfunction. Let me treat the patient for this problem.

Eric Topol (11:07):

Well, you brought up something that’s really important because doctors don’t have much time and they’re inpatient. They don’t wait 10 minutes to do a test to check your blood pressure. They send the patients for a tilt table, which nobody likes to have that test done, and it’s unnecessary added appointment and expense and whatnot. So that’s a good tip right there that you can get the same information just by checking the blood pressure and heart rate on standing for an extended period of time, which 10 minutes is a long time in the clinic of course. Now, what is the mechanism, what do you think is going on with the SARS-CoV-2 virus and its predilection to affect the autonomic nervous system? As you know, so many studies have questioned whether you even actually infect neurons or alternatively, which is more likely this an inflammation of the neural tissue. But what do you think is going on here?

Underpinnings

Svetlana Blitshteyn (12:10):

Right, so I think it’s important to say we don’t have exact pathophysiology of what exactly is going on. I think we can only extrapolate that what’s going on in Long Covid is possibly what’s going on in any post infectious onset dysautonomia. And so there are many hypothesis and there are many suggestions, and we share this disorder with cardiologist and immunologist and rheumatologist. The way I view this is what I described in my paper from a few years ago is that this is likely a central nervous system disorder with multisystemic involvement and it involves the cardiovascular system, immunologic, metabolic, possibly prothrombotic. The pathophysiology of all POTS closely parallels to pathophysiology of Long Covid. Now we don’t know if it’s the same thing and certainly I see that there may be more complications in Long Covid patients in the realm of cardiovascular manifestations in the realm of blood clots and things like that.

Svetlana Blitshteyn (13:21):

So we can’t say it’s the same, but it very closely resembles and I think at the core is going to be inflammation, autoimmunity and immunologic dysfunction. Now there are also other things that are very important and that would be mitochondrial dysfunction, that would be hypercoagulable state, it would be endothelial dysfunction. And I think the silver lining of Long Covid and having so many people invested in research and so many funds is that by uncovering what Long Covid is, we’re now going to be uncovering what POTS and other autonomic disorders are. And I think we also need to mention a couple of other things. One is small fiber neuropathy, small fiber neuropathy and POTS are very much comorbid conditions. And similarly, small fiber neuropathy frequently occurs in patients with Long Covid, so that’s a substrate with the damaged small nerve fibers that they're everywhere in our bodies and also innervate the organs as well.

Svetlana Blitshteyn (14:34):

The second big thing is that needs to be mentioned is hyperactive mast cells. So mast cells, small nerve fibers and capillaries are very much located in proximity. And what I have usually is a slide from an old paper in oral biology that gives you a specimen where you see a capillary vessel, a stain small nerve fiber, and in between them there is a mass cell with tryptase in it stained in black. And so there is a close communication between small nerve fibers between endothelial wall and between mast cells, and that’s what we commonly see as a triad. We see this as a triad in Long Covid patients. We see that as a triad in patients with joint hypermobility syndrome and hypermobile EDS, and you also see this in many of the autoimmune disorders where people develop new allergies and new sensitivities concurrent or preceding the onset of autoimmune disease.

Small Fiber Neuropathy

Eric Topol (15:49):

Yeah, no, it’s fascinating. And I know you’ve worked with this in Ehlers-Danlos syndrome (EDS) as you mentioned, the hypermobility, but just to go back on this, when you want to entertain the involvement of small fiber neuropathy, is that diagnosable? I mean it’s obvious that you can get the tachycardia, the change in position blood pressure, but do you have to do other tests to say there is indeed a small fiber neuropathy or is that a clinical diagnosis?

Svetlana Blitshteyn (16:20):

Absolutely. We have the testing and the testing is skin biopsy. That is simply a punch biopsy that you can do in your clinic and it takes about 15 minutes. You have the free kit that the company of, there are many companies, I don’t want to name specific ones, but there are several companies that do this kind of work. You send the biopsy back to them, they look under the microscope, they stain it. You can also stain it with amyloid stain to rule out amyloidosis, which we do in neurology, and I think that’s quite accessible to many clinicians everywhere. Now we also have another test called QSART (quantitative sudomotor axon reflex test), and that’s a test part of autonomic lab. Mayo Clinic has it, Cleveland Clinic has it, other big labs have it, and it’s hard to get there because the wait time is big.

Svetlana Blitshteyn (17:15):

Patients need to travel. Insurance doesn’t always authorize, so access is a big problem, but more accessible is the skin biopsy. And so, by doing skin biopsy and then correlating with neurologic exam findings, which oftentimes involved reduce pain and temperature sensation in the feet, sometimes in the hands you can conclude that the patient has small fiber neuropathy and that's a very tangible and objective diagnosis. There again, with everything related to diagnostics, some neuropathy is very patchy and the patchy neuropathy is the one that may not be in your feet where you do the skin biopsy. It may be in the torso, it may be in the face, and we don't have biopsy there. So you can totally miss it. The results can come back as normal, but you can have patchy type of small fiber neuropathy and there are also diagnostic tests that might be not sensitive to pick up issues. So I think in everything Long Covid, it highlights the fact that many tests that we use in medicine are outdated perhaps and not targeted towards these patients with Long Covid. Therefore we say, well, we did the workup, everything looks good. MRI looks good, cardiac echo looks great, and yet the patient is very sick with all kinds of Long Covid complications.

Pure Post-Viral POTS?

Eric Topol (18:55):

Right. Now, before we get into the treatments, I want to just segment this a bit. Can you get pure POTS that is no Long Covid just POTS, or as you implied that usually there's some coalescence of symptoms with the usual Long Covid symptoms and POTS added to that?

Svetlana Blitshteyn (19:21):

So the studies have shown for us that about 40% of patients with POTS have post-infectious onset, which means more than a half doesn’t. And so of course you can have POTS from other causes and the most common is puberty, hormonal change, the most common age of onset is about 13, 14 years old and 80% of women of childbearing age and other triggers or pregnancy, hormonal change again, surgery, trauma like concussion, post-concussion, autonomic dysfunction is quite common.

Eric Topol (20:05):

So these are pure POTS without the other symptoms. Is that what you're saying in these examples?

Svetlana Blitshteyn (20:12):

Well, it's a very good question. It depends what you mean by pure POTS, and I have seen especially cardiologists cling to this notion that there is pure POTS and then there is POTS plus. Now I think majority of people don't have pure POTS and by pure POTS I think you mean those who have postural tachycardia and nothing else. And so most patients, I think 80% have a number of symptoms. So in my clinic I almost never see someone who is otherwise well and all they have is postural tachycardia and then they're having a great time. Some patients do exist like that, they tend to be athletic, they can still function in their life, but majority of patients come to us with symptoms like dizziness, like fatigue, like exercise intolerance, decline in functioning. So I think there is this notion that while there is pure POTS, let me just fix the postural tachycardia and the patient will be great and we all want that. Certainly sometimes I get lucky and when I give the patient a beta blocker or ivabradine or a calcium channel blocker, sometimes we use it, certainly they get better, but most patients don't have that because the disability that drives POTS isn't actually postural tachycardia, it's all that other stuff and a lot of it's neurologic, which is why I put this as a central nervous system disorder.

Treatments

Eric Topol (21:58):

Yeah, that's so important. Now you mentioned the treatments. These are drug treatments, largely beta blockers, and can you tell us what's the success rate with the various treatments that you use in your clinic?

Svetlana Blitshteyn (22:13):

So the first thing we'll have to mention is that there are no FDA approved therapies for POTS, just like there are no FDA approved therapies for Long Covid. And so, everything we use is off label. Now, oftentimes people think that because it wasn't evidence-based and there are no big trials. We do have trials, we do have trials for beta blockers and we know they work. We have trials for Midodrine and we know that's working. We also have fludrocortisone, which is a medication that improves sodium and water resorption. So we know that there are certain things we've used for decades that have been working, and I think that's what I was trying to convey in this paper of post Covid autonomic dysfunction assessment and treatment is that when you see these patients, and you can be of any specialty, you can be in primary care, you can be a physiatrist, a cardiologist, there are things to do, there are medications to use.

Svetlana Blitshteyn (23:20):

Oftentimes colleagues would say, well, you diagnose them and then what do you treat them with? And then I can refer them to table six in that paper and say, look at this list. You have a lot of options to try. We have the first line treatment options, which are your beta blockers and Midodrine and Florinef and Mestinon. And then we have the second line therapies you can choose from the stimulants are there Provigil, Nuvigil, Wellbutrin, Droxidopa is FDA approved for neurogenic orthostatic hypotension. Now we don't use it commonly, but it can still be tried in people whose blood pressures are falling on your exam. So we have a number of medications to choose from in addition to non-pharmacologic therapies.

Eric Topol (24:14):

Right now, I'm going to get to the non-pharmacologic in a moment, but the beta blocker, which is kind of the first one to give, it's a little bit paradoxical. It makes people tired, and these people already are, don't have much energy. Is the success rate of beta blocker good enough that that should be the first thing to try?

Svetlana Blitshteyn (24:35):

Absolutely. The first line medication treatment options are beta blockers. Why? Okay, why are they working? They're not only working to reduce heart rate, but they may also decrease sympathetic overactivity, which is the driving mechanism of autonomic dysfunction. And when you reduce that overactivity, even your energy level can improve. Now, the key here is to use a low dose. A lot of the time I see this mistake being done where the doctor is just prescribing 25 milligrams of metoprolol twice a day. Well, this is too high. And so, the key is to use very low doses and to use them and then increase them as needed. We have a bunch of beta blockers to choose from. We have the non-selective propranolol that you can use when someone maybe has a migraine headache or significant anxiety, they penetrate the brain, and we have non-selected beta blockers like atenolol, metoprolol and others that you can use at half a tablet. Sometimes I start my patients at quarter of tablet and then go from there. So low doses will block tachycardia, decrease sympathetic overactivity, and in many cases will allow the patient to remain upright for longer periods of time.

Eric Topol (26:09):

That's really helpful. Now, one of the other things, I believe it's approved in Canada, not in the US, is a vagal neuromodulation device. And I wonder, it seems like it would be nice to avoid drugs if there was a device that worked really well. Is there anything that is in the hopper for that?

Svetlana Blitshteyn (26:32):

Yeah, absolutely. Non-invasive vagus nerve stimulator is in clinical trials for POTS and other autonomic disorders, but we have it FDA for treatment of migraine and cluster headaches, so it's already approved here and it can also be helpful for chronic pain and gastroparesis. So there are studies on mice that show that with the application of noninvasive vagus nerve stimulator, there is reduction of pro-inflammatory cytokines. So here is this very important connection that comes from Kevin Tracey's work that showed inflammatory reflex, and that's a reflex between the vagus nerve and the immune system. So when we talk about sympathetic overactivity, we need to also think about that. That's a mechanism for pro-inflammatory state and possibly prothrombotic state. So anything that decreases sympathetic overactivity and enhancing parasympathetic tone is going to be good for you.

Eric Topol (27:51):

Now, let's go over to, I mean, I'm going to get into this body brain axis in a moment because there's another part of the story here that's becoming more interesting, fascinating, in fact every day. But before I do that, you mentioned the small fiber neuropathy. Is there a specific treatment for that or is that just something that is just an added dimension of the problem without a specific treatment available?

Svetlana Blitshteyn (28:21):

Yeah, we certainly have treatment for small fiber neuropathy. We have symptomatic treatment for neuropathic pain, and these medications are gabapentin, pregabalin, amitriptyline and low dose naltrexone that have been gaining popularity. We used that before the pandemic. We used low dose naltrexone for people with chronic pain related to joint hypermobility. And so, we have symptomatic, we also have patches and creams and all kinds of topical applications for people with neuropathic pain. Then we also have, we try to go for the root cause, right? So the number one cause of small fiber neuropathy in the United States is diabetes. And certainly, you need to control hyperglycemia and in some patients you only need a pre-diabetic state, not even full diabetes to already have peripheral neuropathy. So you want to control blood glucose level first and foremost. Now then we have a big category of autoimmune and immune mediated causes, and that's where it gets very interesting because practical experience from many institutions and many neurologists worldwide have shown that when you give a subset of patients with autoimmune small fiber neuropathy, immunotherapy like IVIG, a lot of patients feel significantly better. And so, I think paralleling our field in dysautonomia and POTS, we are looking forward to immunotherapy being more mainstream rather than exception from the rule because access and insurance coverage is a huge barrier for clinicians and patients, but that may be a very effective treatment options for treatment refractory patients whose symptoms do not improve with symptomatic treatment.

Eric Topol (30:38):

Now, with all these treatments that are on the potential menu to try, and of course sometimes it really is a trial and error to get one that hopefully works for Covid, Long Covid, what is the natural history? Does this persist over years, or can it be completely resolved?

Svetlana Blitshteyn (31:00):

That’s a great question. Everyday Long Covid patients ask me, and I think what we are seeing is that there is a good subset of patients for whom Long Covid is going to be temporary and they will improve and even recover close to normal. Now remember that original case series of patients that I reported in early 2021 based on my 2020 experience in that 20 patient case series, very few recovered, three patients recovered back to normal. Most patients had lingering ongoing chronic symptoms. So of course mine is a kind of a referral bias where I get to see the sickest patients and it looks to be like it’s a problem of chronic illness variety. But I also think there is going to be a subset of patients and then we have to study them. We need to study who got better and who didn’t. And people improve significantly and some even recover close to normal. But I think certain symptoms like maybe fatigue and heat intolerance could persist because those are very heavily rooted in autonomic dysfunction.

Vaccination and POTS

Eric Topol (32:26):

Yeah, well, that’s something that’s sobering and why we need trials and to go after this in much more intensity and priority. Now the other issue here is while with Covid, this is almost always the virus infection, there have been reports of the vaccine inducing POTS and Long Covid, and so what does that tell us?

Svetlana Blitshteyn (32:54):

Well, that’s a big, big topic. Years ago, I was the first one to report a patient with POTS that was developed after HPV vaccine Gardasil. Now, at that time I was a young neurologist. Then the patient came to me saying she was an athlete saying two weeks after Gardasil vaccine, she developed these very disabling symptoms. And I thought it was very interesting and unique and I thought, well, I’ll just publish it. I never knew that this would be the start of a whole different discussion and debate on HPV vaccines. There were multiple reports from numerous countries, Denmark, Mexico, Japan. Japan actually suspended their mass HPV vaccination program. So somehow it became a big deal. Now many people, including my colleagues didn’t agree that POTS can begin POTS, small fiber neuropathy, other adverse neurologic events can begin after vaccination in general. And so, this was a topic that was widely debated and the European medical agencies came back saying, we don't have enough evidence.

Svetlana Blitshteyn (34:20):

Of course, we all want to have a good cancer vaccine. And it was amazing to watch this Covid vaccine issue unfolding where more than one study now have shown that indeed you can develop POTS after Covid vaccines and that the rate of POTS after Covid vaccines is actually slightly higher than before vaccination. So I think it was kind of interesting to see this unfold where I was now invited by Nature Journal to write an editorial on this very topic. So I think it's important to mention that sometimes POTS can begin after vaccination and however, I've always advised my patients to be vaccinated even now. Even now, I have patients who are unvaccinated and I say, I'm worried about you getting a second Covid or third without these vaccines, so please get vaccinated. Vaccines are very important public health measure, but we also have to acknowledge that sometimes people develop POTS, small fiber neuropathy and other complications after Covid vaccines.

Prominence of the Vagus Nerve

Eric Topol (35:44):

Yeah, I think this is important to emphasize here because of all vaccinations can lead to neurologic sequelae. I mean look at Guillain-Barre, which is even more worrisome and that brings in the autoimmune component I think. And of course, the Covid vaccines and boosters have a liability in a small, very small percentage of people to do this. And that can't be discounted because it's a small risk and it's always this kind of risk benefit story when you're getting vaccinated that you are again spotlighting. Now gets us to the biggest thing of all besides the practical pearls you've been coming up with to help everyone in patients and clinicians. In recent weeks, there's been explosion of these intra body circuits. There was a paper from Columbia last week that taught us about the body-brain circuits between the vagus nerve and the caudal Nucleus of the Solitary Tract (cNST) of the brain and how this is basically a master switch for the immune system. And so, the vagus nerve there and then you have this gut to brain story, which is the whole gut microbiome is talking to the brain through the vagus nerve. I mean, everything comes down to the vagus nerve. So you've been working all your career and now everything's coming into this vagus nerve kind of final common pathway that's connecting all sorts of parts of the body that we didn't truly understand before. So could you comment about this because it's pretty striking.

Svetlana Blitshteyn (37:34):

Absolutely. I think this pandemic is highlighting the pitfalls of everything we didn't know but should have in the past. And I think this is one of them. How important is the autonomic nervous system and how important is the vagus nerve that is the longest nerve in the body and carries the parasympathetic outflow. And I think this is a very important point that we have to move forward. We cannot stop at the autonomic knowledge that we've gained thus far. Autonomic neurology and autonomic medicine has always been the field with fellowship, and we have American Autonomic Society as well. But I think now is a great time to move forward and study how the autonomic nervous system communicates with the immunologic system. And again, Kevin Tracey's work was groundbreaking in the sense that he connected the dots and realized that if you stimulate the vagus nerve and the parasympathetic outflow, then you can reduce pro-inflammatory cytokines and that he has shown that you can also improve or significantly such disorders like rheumatoid arthritis and other autoimmune inflammatory conditions.

Svetlana Blitshteyn (39:03):

Now we have the invasive vagus nerve stimulation procedures, and quite honestly, we don't want that to be the mainstream because you don't want to have a neurosurgery as you go to treatment. Of course, you want the non-invasive vagus nerve stimulation being the mainstream therapy. But I think a lot of research needs to happen and it's going to be a very much a multidisciplinary field where we'll have immunology, translational sciences, we'll have neurosurgeons like Kevin Tracey, we'll have rheumatologists, neurologists, cardiologists. We'll have a multidisciplinary collaborative group to further understand what's going on in these autoimmune inflammatory disorders, including those of post-infectious origin.

Eric Topol (40:02):

I certainly agree with all of your points there. I mean, I'm really struck now because the immune system is front and center with so much of what we're seeing with of course Long Covid, but also things like Alzheimer's and Parkinson's and across the board with metabolic diseases. And here we have this connection with your sweet spot of the autonomic nervous system, and we have these pathways that had not been delineated before. I didn't know too much about the cNST of the brain to be such an important connect point for this. And I wonder, so here's another example. Concurrently the glucagon-like peptide 1 (GLP-1) drugs have this pronounced effect on reducing inflammation in the body before the weight loss and in the brain through the gut-brain axis, as we recently discussed with Dan Drucker, have you ever tried a GLP-1 drug or noticed that GLP-1 drugs help people with Long Covid or the POTS problem?

Svetlana Blitshteyn (41:12):

So I have heard anecdotally people with Long Covid using these drugs for other reasons, saying I feel much better. In fact, I recently had a woman who said, I have never been more productive than I am now on this medication. And she used the word productive, which is important because non-productive implies so many things. It's the brain fog, it's the physical fatigue, it's the mental fatigue. So I think we are, first of all, I want to say, I always said that the brain is not separate from the body. And neurologic manifestations of systemic disease is a very big untapped area. And I think it's not going to be surprising for me to see that these drugs can improve many brain parameters and possibly even neuroinflammation. We don't know, but we certainly need to study this.

Eric Topol (42:15):

Yeah, it's interesting because statins had been tried for multiple sclerosis, I think maybe not with very clear cut benefit effects, but here you have a new class of drugs which eventually are going to be in pills and not just one receptor but triple receptor, much more potent than what we're seeing in the clinic today. And you wonder if we're onto an anti-inflammatory for the brain and body that could help in this. I mean, we have a crisis here with Long Covid in POTS without a remedy, without adequate resources that are being dedicated to the clinical trials that are so vital to execute and find treatments. And that's just one candidate of many. I mean, obviously there's so many possible ones on the list. So if you could design studies now based on your extraordinary rich experience with Long Covid and POTS, what would you go after right now? What do you think is the thing that's, would it be to evaluate more of these noninvasive, non-pharmacologic treatments like the vagal nerve stimulation, or are there particular drugs that you find intriguing?

Svetlana Blitshteyn (43:33):

Well, a few years ago we published a case series of patients with severe POTS and nothing helped them, but they improved significantly and some even made close to recovery improvement and were able to return to their careers because they were treated with immunotherapy. So the paper is a subcutaneous immunoglobulin and plasmapheresis and the improvement was remarkable. I say there was one physician there who could not start her residency. She got sick in medical school and could not start her residency due to severe POTS and no amount of beta blockers, Midodrine or Florinef helped her get out the house and out of bed. And therefore, sheer luck, she was able to get subcutaneous immunoglobulin and she improved significantly, finished her residency and is now a practicing physician. So I think when we have these cases, it's important to bring them to scientific community. And I think I'm very excited that hopefully soon we're going to have trials of immunotherapy and immunomodulating treatment options for patients with Long Covid and hopefully POTS in general, I believe in novel, but also repurposed, repurposed treatment.

Svetlana Blitshteyn (45:01):

IVIG has been used for decades, so it's not a new medication. And contrary to popular belief, it's actually quite safe. It is expensive, it's a blood product, but we are very familiar with it in medicine and neurology. So I think we have to look forward to everything. And as I tell my patients, I'm always aggressive with medications when they come to me and their doctor said something like, well, let's see, it's going to go away on its own or keep doing your salt and fluids intake or wear compression sucks. Well, they're already doing it. It's not helping. And now it's a good time to try everything we have. And I would like to have more. I would like to have immunotherapy available. I would like to have immunosuppressants even tried potentially, and maybe we'll be able to try medication for possible viral persistence. Let's see how that works out. We have other inflammatory modalities out there that can potentially give us the tools. You see, I think being that it's a multifactorial disorder, that I don't think it's going to be one thing for everyone. We need to have a toolbox where we're going to choose what's best for your specific case because when we talk about Long Covid, we have to remember there are many different phenotypes under that umbrella.

A Serious Matter

Eric Topol (46:40):

Now, before we wrap up, I mean I guess I wanted to emphasize how there are clinicians out there who discount Long Covid in POTS. They think it's something that is a figment of imagination. Now, on the other hand, you and I especially, you know that people are totally disabled. Certain days they can't even get out of bed, they can't get back to their work, their life. And this can go on and on as we've been discussing. So can you set it straight about, I mean, you are seeing these people every day. What do you have to say to our fellow colleague physicians who tend to minimize and say, this is extremely rare, if it even exists, and that these people have some type of psychiatric problem. And it's really, it's distressing of course, but could you speak to that?

Svetlana Blitshteyn (47:39):

Absolutely. So as I always say, Long Covid is not a psychiatric or psychological disorder, and it's also not a functional neurologic disorder. Now, having said that, as I just mentioned, brain is not separate from the body. And neurologic manifestations of systemic disease are numerous. We just had a paper out on neurologic manifestations of mast cell activation syndrome. So certainly some patients will develop psychiatric manifestations and some patients will develop major depression, anxiety, OCD or functional neurologic disorder. But those are complications of systemic disease, meaning that you cannot diagnose a patient with anxiety and send them off to a psychologist or a psychiatrist without diagnosing POTS and treating it. And in many cases, when you approach an underlying systemic disorder with the right medications, like dysautonomia for example, all of the symptoms including psychological and psychiatric, tend to improve as well. And certainly, there is going to be a small subset of Long Covid patients whose primary problem is psychiatric.

Svetlana Blitshteyn (49:01):

And I think that's totally fine. That is not to say that all Long Covid is psychiatric. Some will have significant psychiatric manifestations. I mean, there are cases of post Covid psychosis and autoimmune encephalitis and all kinds of psychiatric problems that people may develop, but I think we can't really stratify well, this is physiologic and this word functional that I'm not a fan of. This is physiologic as we see it on MRI. But here, because we don't see anything on MRI, it means you are fine and can just exercise your way out of it. So I think with this Long Covid, hopefully we'll get answers as to the pathophysiology, but also most importantly, hopefully we'll get these therapies that millions of people before Covid pandemic were looking for.

Eric Topol (50:02):

Well, I just want to thank you because you were onto this well over 10, 15 years before there was such a thing as Covid, you've dedicated your career to this. These are some of the most challenging patients to try to help and has to be vexing, that you can't get their symptoms resolved no less the underlying problem. And we're indebted to you, Svetlana, because you've really been ahead of the curve here. You were writing a patient book before there were such things as patient activists in Long Covid, as we've seen, which have been so many of the heroes of this whole problem. But thank you for all the work you do. We'll continue to follow. We learned from you about POTS and Long Covid from your work and really appreciate everything you've done. Thank you.

Svetlana Blitshteyn (50:58):

Thank you so much, Eric, for having me. As I said, it's a great honor for me to be here. Remarkable, amazing. And thank you for all this work that you're doing and being an advocate for our field because we always need great champions to help us move forward in these complicated disorders.

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“We haven't invested this much money into an infrastructure like this really until you go back to the pyramids”—Kate Crawford

Transcript with links to audio and external links. Ground Truths podcasts are on Apple and Spotify. The video interviews are on YouTube

Eric Topol (00:06):

Well, hello, this is Eric Topol with Ground Truths, and I'm really delighted today to welcome Kate Crawford, who we're very lucky to have as an Australian here in the United States. And she's multidimensional, as I've learned, not just a scholar of AI, all the dimensions of AI, but also an artist, a musician. We're going to get into all this today, so welcome Kate.

Kate Crawford (00:31):

Thank you so much, Eric. It's a pleasure to be here.

Eric Topol (00:34):

Well, I knew of your work coming out of the University of Southern California (USC) as a professor there and at Microsoft Research, and I'm only now learning about all these other things that you've been up to including being recognized in TIME 2023 as one of 100 most influential people in AI and it's really fascinating to see all the things that you've been doing. But I guess I'd start off with one of your recent publications in Nature. It was a world view, and it was about generative AI is guzzling water and energy. And in that you wrote about how these large AI systems, which are getting larger seemingly every day are needing as much energy as entire nations and the water consumption is rampant. So maybe we can just start off with that. You wrote a really compelling piece expressing concerns, and obviously this is not just the beginning of all the different aspects you've been tackling with AI.

Exponential Growth, Exponential Concerns

Kate Crawford (01:39):

Well, we're in a really interesting moment. What I've done as a researcher in this space for a very long time now is really introduce a material analysis of artificial intelligence. So we are often told that AI is a very immaterial technology. It's algorithms in the cloud, it's objective mathematics, but in actual fact, it comes with an enormous material infrastructure. And this is something that I took five years to research for my last book, Atlas of AI. It meant going to the mines where lithium and cobalt are being extracted. It meant going into the Amazon fulfillment warehouses to see how humans collaborate with robotic and AI systems. And it also meant looking at the large-scale labs where training data is being gathered and then labeled by crowd workers. And for me, this really changed my thinking. It meant that going from being a professor for 15 years focusing on AI from a very traditional perspective where we write papers, we're sitting in our offices behind desks, that I really had to go and do these journeys, these field trips, to understand that full extractive infrastructure that is needed to run AI at a planetary scale.

(02:58):

So I've been keeping a very close eye on what would change with generative AI and what we've seen particularly in the last two years has been an extraordinary expansion of the three core elements that I really write about in Atlas, so the extraction of data of non-renewable resources, and of course hidden labor. So what we've seen, particularly on the resources side, is a gigantic spike both in terms of energy and water and that's often the story that we don't hear. We're not aware that when we're told about the fact that there gigantic hundred billion computers that are now being developed for the next stage of generative AI that has an enormous energy and water footprint. So I've been researching that along with many others who are now increasingly concerned about how we might think about AI more holistically.

Eric Topol (03:52):

Well, let's go back to your book, which is an extraordinary book, the AI Atlas and how you dissected not just the well power of politics and planetary costs, but that has won awards and it was a few years back, and I wonder so much has changed since then. I mean ChatGPT in late 2022 caught everybody off guard who wasn't into this knowing that this has been incubating for a number of years, and as you said, these base models are just extraordinary in every parameter you can think about, particularly the computing resource and consumption. So your concerns were of course registered then, have they gone to exponential growth now?

Kate Crawford (04:45):

I love the way you put that. I think you're right. I think my concerns have grown exponentially with the models. But I was like everybody else, even though I've been doing this for a long time and I had something of a heads up in terms of where we were moving with transformer models, I was also quite taken aback at the extraordinary uptake of ChatGPT back in November 2022 in fact, gosh, it still feels like yesterday it's been such an extraordinary timescale. But looking at that shift to a hundred million users in two months and then the sort of rapid competition that was emerging from the major tech companies that I think really took me by surprise, the degree to which everybody was jumping on the bandwagon, applying some form of large language model to everything and anything suddenly the hammer was being applied to every single nail.

(05:42):

And in all of that sound and fury and excitement, I think there will be some really useful applications of these tools. But I also think there's a risk that we apply it in spaces where it's really not well suited that we are not looking at the societal and political risks that come along with these approaches, particularly next token prediction as a way of generating knowledge. And then finally this bigger set of questions around what is it really costing the planet to build these infrastructures that are really gargantuan? I mean, as a species, we haven't invested this much money into an infrastructure like this really until you go back to the pyramids, you really got to go very far back to say that type of just gargantuan spending in terms of capital, in terms of labor, in terms of all of the things are required to really build these kinds of systems. So for me, that's the moment that we're in right now and perhaps here together in 2024, we can take a breath from that extraordinary 18 month period and hopefully be a little more reflective on what we're building and why and where will it be best used.

Propagation of Biases

Eric Topol (06:57):

Yeah. Well, there's so many aspects of this that I'd like to get into with you. I mean, one of course, you're as a keen observer and activist in this whole space, you've made I think a very clear point about how our culture is mirrored in our AI that is our biases, and people are of course very quick to blame AI per se, but it seems like it's a bigger problem than just that. Maybe you could comment about, obviously biases are a profound concern about propagation of them, and where do you see where the problem is and how it can be attacked?

Kate Crawford (07:43):

Well, it is an enormous problem, and it has been for many years. I was first really interested in this question in the era that was known as the big data era. So we can think about the mid-2000s, and I really started studying large scale uses of data in scientific applications, but also in what you call social scientific settings using things like social media to detect and predict opinion, movement, the way that people were assessing key issues. And time and time again, I saw the same problem, which is that we have this tendency to assume that with scale comes greater accuracy without looking at the skews from the data sources. Where is that data coming from? What are the potential skews there? Is there a population that's overrepresented compared to others? And so, I began very early on looking at those questions. And then when we had very large-scale data sets start to emerge, like ImageNet, which was really perhaps the most influential dataset behind computer vision that was released in 2009, it was used widely, it was freely available.

(09:00):

That version was available for over a decade and no one had really looked inside it. And so, working with Trevor Paglen and others, we analyzed how people were being represented in this data set. And it was really quite extraordinary because initially people are labeled with terms that might seem relatively unsurprising, like this is a picture of a nurse, or this is a picture of a doctor, or this is a picture of a CEO. But then you look to see who is the archetypical CEO, and it's all pictures of white men, or if it's a basketball player, it's all pictures of black men. And then the labeling became more and more extreme, and there are terms like, this is an alcoholic, this is a corrupt politician, this is a kleptomaniac, this is a bad person. And then a whole series of labels that are simply not repeatable on your podcast.

(09:54):

So in finding this, we were absolutely horrified. And again, to know that so many AI models had trained on this as a way of doing visual recognition was so concerning because of course, very few people had even traced who was using this model. So trying to do the reverse engineering of where these really problematic assumptions were being built in hardcoded into how AI models see and interpret the world, that was a giant unknown and remains to this day quite problematic. We did a recent study that just came out a couple of months ago looking at one of the biggest data sets behind generative AI systems that are doing text to image generation. It's called LAION-5B, which stands for 5 billion. It has 5 billion images and text captions drawn from the internet. And you might think, as you said, this will just mirror societal biases, but it's actually far more weird than you might imagine.

(10:55):

It's not a representative sample even of the internet because particularly for these data sets that are now trying to use the ALT tags that are used around images, who uses ALT tags the most on the internet? Well, it's e-commerce sites and it's often stock image sites. So what you'll see and what we discovered in our study was that the vast majority of images and labels are coming from sites like Shopify and Pinterest, these kind of shopping aspirational collection sites. And that is a very specific way of seeing the world, so it's by no means even a perfect mirror. It's a skewed mirror in multiple ways. And that's something that we need to think of particularly when we turn to more targeted models that might be working in say healthcare or in education or even in criminal justice, where we see all sorts of problems emerge.

Exploiting Humans for RLHF

Eric Topol (11:51):

Well, that's really interesting. I wonder to extend that a bit about the human labor side of this. Base models are tweaked, fine-tuned, and one of the ways to do that, of course is getting people to weigh in. And this has been written about quite a bit about how the people that are doing this can be exploited, getting wages that are ridiculously weak. And I wonder if you could comment about that because in the ethics of AI, this seems to be one of the many things that a lot of people don't realize about reinforcement learning.

Kate Crawford (12:39):

Oh, I completely agree. It's quite an extraordinary story. And of course now we have a new category of crowd labor that's called reinforcement learning with human feedback or RLHF. And what was discovered by multiple investigations was that these laborers are in many cases paid less than $2 an hour in very exploitative conditions, looking at results that in many cases are really quite horrifying. They could be accounts of murder, suicide, trauma, this can be visual material, it can be text-based material. And again, the workers in these working for these companies, and again, it's often contract labor, it's not directly within a tech company, it's contracted out. It's very hidden, it's very hard to research and find. But these laborers have been experiencing trauma and are really now in many cases bringing lawsuits, but also trying to unionize and say, these are not acceptable conditions for people to be working under.

(13:44):

So in the case of OpenAI, it was found that it was Kenyan workers who were doing this work for just poverty wages, but it's really across the board. It's so common now that humans are doing the hard work behind the scenes to make these systems appear autonomous. And that's the real trap that we're being told that this is the artificial intelligence. But in actual fact, what Jeff Bezos calls Mechanical Turk is that it's artificial, artificial intelligence otherwise known as human beings. So that is a very significant layer in terms of how these systems work that is often unacknowledged. And clearly these workers in many cases are muzzled from speaking, they're not allowed to talk about what they do, they can't even tell their families. They're certainly prevented from collective action, which is why we've seen this push towards unionization. And finally, of course, they're not sharing in any of the profits that are being generated by these extraordinary new systems that are making a very small number of people, very wealthy indeed.

Eric Topol (14:51):

And do you know if that's improving or is it still just as bad as it has been reported? It's really deeply concerning to see human exploitation, and we all know well about sweatshops and all that, but here's another version, and it's really quite distressing.

Kate Crawford (15:09):

It really is. And in fact, there have been several people now working to create really almost like fair work guidelines. So Oxford has the sort of fair work initiative looking specifically at crowd work. They also have a rating system where they rate all of the major technology companies for how well they're treating their crowd laborers. And I have to say the numbers aren't looking good in the last 12 months, so I would love to see much more improvement there. We are also starting to see legislation be tabled specifically on this topic. In fact, Germany was one of the most recent to start to explore how they would create a strong legislative backing to make sure that there's fair labor conditions. Also, Chile was actually one of the first to legislate in this space, but you can imagine it's very difficult to do because it's a system that is operating under the radar through sort of multiple contracted chains. And even some of the people within tech companies will tell me it's really hard to know if they're working with a company that's doing this in the right way and paying people well. But frankly, I'd like to see far greater scrutiny otherwise, as you say, we're building on this system, which looks like AI sweatshops.

Eric Topol (16:24):

Yeah, no, I think people just have this illusion that these machines are doing everything by themselves, and that couldn't be further from the truth, especially when you're trying to take it to the next level. And there's only so much human content you can scrape from the internet, and obviously it needs additional input to take it to that more refined performance. Now, besides your writing and being much of a conscience for AI, you're also a builder. I mean, I first got to know some of your efforts through when you started the AI Now Institute. Maybe you can tell us a bit about that. Now you're onto the Knowing Machines Project and I don't know how many other projects you're working on, so maybe you can tell us about what it's like not just to be a keen observer, but also one to actually get initiatives going.

Kate Crawford (17:22):

Well, I think it's incredibly important that we start to build interdisciplinary coalitions of researchers, but sometimes even beyond the academic field, which is where I really initially trained in this space, and really thinking about how do we involve journalists, how do we involve filmmakers, how do we involve people who will look at these issues in really different ways and tell these stories more widely? Because clearly this really powerful shift that we're making as a society towards using AI in all sorts of domains is also a public issue. It's a democratic issue and it's an issue where we should all be able to really see into how these systems are working and have a say in how they'll be impacting our lives. So one of the things that I've done is really create research groups that are interdisciplinary, starting at Microsoft Research as one of the co-founders of FATE, a group that stands for fairness, accountability, transparency and ethics, and then the AI Now Institute, which was originally at NYU, and now with Knowing Machines, which is an international group, which I've been really delighted to build, rather than just purely focusing on those in the US because of course these systems are inherently transnational, they will be affecting global populations.

(18:42):

So we really need to think about how do you bring people from very different perspectives with different training to ask this question around how are these systems being built, who is benefiting and who might be harmed, and how can we address those issues now in order to actually prevent some of those harms and prevent the greatest risks that I see that are possible with this enormous turn to artificial intelligence everywhere?

Eric Topol (19:07):

Yeah, and it's interesting how you over the years are a key advisor, whether it's the White House, the UN or the European Parliament. And I'm curious about your experience because I didn't know much about the Paris ENS. Can you tell us about you were Visiting Chair, this is AI and Justice at the École Normale Supérieure (ENS), I don’t know if I pronounce that right. My French is horrible, but this sounds like something really interesting.

Kate Crawford (19:42):

Well, it was really fascinating because this was the first time that ENS, which is really one of the top research institutions in Europe, had turned to this focus of how do we contend with artificial intelligence, not just as a technical question, but as a sort of a profound question of justice of society of ethics. And so, I was invited to be the first visiting chair, but tragically this corresponded with the start of the pandemic in 2020. And so, it ended up being a two-year virtual professorship, which is really a tragedy when you’re thinking about spending time in Paris to be spending it on Zoom. It’s not quite the same thing, but I had the great fortune of using that time to assemble a group of scholars around the world who were looking at these questions from very different disciplines. Some were historians of science, others were sociologists, some were philosophers, some were machine learners.

(20:39):

And really essentially assembled this group to think through some of the leading challenges in terms the potential social impacts and current social impacts of these systems. And so, we just recently published that through the academies of Science and Engineering, and it’s been almost like a template for thinking about here are core domains that need more research. And interestingly, we’re at that moment, I think now where we can say we have to look in a much more granular fashion beyond the hype cycles, beyond the sense of potential, the enormous potential upside that we’re always hearing about to look at, okay, how do these systems actually work now? What kinds of questions can we bring into the research space so that we’re really connecting the ideas that come traditionally from the social sciences and the humanistic disciplines into the world of machine learning and AI design. That’s where I see the enormous upside that we can no longer stay in these very rigorously patrolled silos and to really use that interdisciplinary awareness to build systems differently and hopefully more sustainably as well.

Is Working At Microsoft A Conflict?

Eric Topol (21:55):

Yeah, no, that’s what I especially like about your work is that you’re not a doomsday person or force. You’re always just trying to make it better, but now that's what gets me to this really interesting question because you are a senior principal researcher at Microsoft and Microsoft might not like some of these things that you're advocating, how does that potential conflict work out?

Kate Crawford (22:23):

It's interesting. I mean, people often ask me, am I a technology optimist or a technology pessimist? And I always say I'm a technology realist, and we're looking at these systems being used. I think we are not benefited by discourses of AI doomerism nor by AI boosterism. We have to assess the real politic and the political economies into which these systems flow. So obviously part of the way that I've got to know what I know about how systems are designed and how they work at scale is through being at Microsoft Research where I'm working alongside extraordinary colleagues and all of whom come from, in many cases, professorial backgrounds who are deep experts in their fields. And we have this opportunity to work together and to look at these questions very early on in the kinds of production cycles and enormous shifts in the way that we use technology.

(23:20):

But it is interesting of course that at the moment Microsoft is absolutely at the leading edge of this change, and I've always thought that it's incredibly important for researchers and academics who are in industrial spaces to be able to speak freely, to be able to share what they see and to use that as a way that the industry can, well hopefully keep itself honest, but also share between what it knows and what everybody else knows because there's a giant risk in having those spaces be heavily demarcated and having researchers really be muzzled. I think that's where we see real problems emerge. Of course, one of the great concerns a couple of years ago was when Timnit Gebru and others were fired from Google for speaking openly about the concerns they had about the first-generation large language models. And my hope is that there's been a lesson through that really unfortunate set of decisions made at Google that we need people speaking from the inside about these questions in order to actually make these systems better, as you say, over the medium and long term.

Eric Topol (24:26):

Yeah, no, that brings me to thought of Peter Lee, who I'm sure because he wrote a book about GPT-4 and healthcare and was very candid about its potential, real benefits and the liabilities, and he's a very humble kind of guy. He's not one that has any bravado that I know of, so it speaks well to at least another colleague of yours there at Microsoft and their ability to see all the different sides here, not just what we'll talk about in a minute the arms race both across companies and countries. But before I get to that, there's this other part of you and I wonder if there's really two or three of you that is as a composer of music and art, I looked at your Anatomy of an AI System, I guess, which is on exhibit at the Museum of Modern Art (MoMA) in New York, and that in itself is amazing, but how do you get into all these other parts, are these hobbies or is this part of a main part of your creative work or where does it fit in?

Kate Crawford (25:40):

Eric, didn't I mention the cloning program that I participated in early and that there are many Kate’s and it's fantastic we all work together. Yeah, that explains it. Look, it's interesting. Way back as a teenager, I was fascinated with technology. Of course, it was the early stages of the web at that moment, and I could see clearly that this was, the internet was going to completely change everything from my generation in terms of what we would do in terms of the way that we would experience the world. And as I was also at that time an electronic musician in bands, I was like, this was a really fantastic combination of bringing together creative practice with a set of much larger concerns and interests around at a systems level, how technology and society are co-constituted, how they evolve together and shape each other. And that’s really been the map of how I’ve always worked across my life.

(26:48):

And it’s interesting, I've always collaborated with artists and Vladan Joler who I worked with on anatomy of an AI system. We actually met at a conference on voice enabled AI systems, and it was really looking at the ethics of could it be possible to build an open source, publicly accessible version of say Alexa rather than purely a private model owned by a corporation, and could that be done in a more public open source way? And we asked a different question, we looked at each other and we're like, oh, I haven't met you yet, but I can see that there are some problems here. One of them is it's not just about the data and it's not just about the technical pipelines, it's about where the components come from. It's about the mining structures that needed to make all of these systems. It's about the entire end of life what happens when we throw these devices out from generally between three to four years of use and how they go into these giant e-waste tips.

(27:51):

And we basically started looking at this as an enormous sort of life and death of a single AI system, which for us started out by drawing these things on large pieces of butcher's paper, which just expanded and expanded until we had this enormous systems level analysis of what it takes just to ask Alexa what the weather is today. And in doing that, it taught me a couple of things. One that people really want to understand all of the things that go into making an AI system work. This piece has had a very long life. It's been in over a hundred museums around the world. It's traveled further than I have, but it's also very much about that broader political economy that AI systems aren't neutral, they don't just exist to serve us. They are often sort of fed into corporate structures that are using them to generate profits, and that means that they're used in very particular ways and that there are these externalities in terms of how they produced that linger in our environments that have really quite detrimental impacts on systems of labor and how people are recompensed and a whole range of relationships to how data is seen and used as though it's a natural resource that doesn't actually come from people's lives, that doesn't come with risks attached to it.

(29:13):

So that project was really quite profound for me. So we've continued to do these kinds of, I would call them research art projects, and we just released a new one called Calculating Empires, which looks at a 500 year history of technology and power looking specifically at how empires over time have used new technologies to centralize their power and expand and grow, which of course is part of what we're seeing at the moment in the empires of AI.

Eric Topol (29:43):

And what about the music side?

Kate Crawford (29:45):

Well, I have to say I've been a little bit slack on the music side. Things have been busy in AI Eric, I have to say it's kept me away from the music studio, but I always intend to get back there. Fortunately, I have a kid who's very musical and he's always luring me away from my desk and my research saying, let’s write some music. And so, he'll keep me honest.

Geopolitics and the Arms Races

Eric Topol (30:06):

Well, I think it's striking just because you have this blend of the humanities and you're so deep into trying to understand and improve our approaches in technology. And it seems like a very unusual, I don't know, too many techies that have these different dimensions, so that's impressive. Now let's get back to the arms race. You just were talking about tracing history over hundreds of years and empires, but right now we have a little problem. We have the big tech titans that are going after each other on a daily basis, and of course you know the group very well. And then you have China and the US that are vying to be the dominant force and problems with China accessing NVIDIA chips and Taiwan sitting there in a potentially very dangerous position, not just for Taiwan, but also for the US. And I wonder if you could just give us your sense about the tensions here. They're US based as well of course, because that's some of the major forces in companies, but then they're also globally. So we have a lot of stuff in the background that people don't like to think about, but it's actually happening right now.

Kate Crawford (31:35):

I think it's one of the most important things that we can focus on, in fact. I mean and again, this is why I think a materialist analysis of artificial intelligence is so important because not only does it force you to look at the raw components, where does the energy come from? Where does the water come from? But it means you're looking at where the chipsets come from. And you can see that in many cases there are these infrastructural choke points where we are highly dependent on specific components that sit within geopolitical flashpoints. And Taiwan is really the exemplar of this sort of choke point at the moment. And again, several companies are trying to address this by spinning up new factories to build these components, but this takes a lot of time and an enormous amount of resources yet again. So what we're seeing is I think a very difficult moment in the geopolitics of artificial intelligence.

(32:31):

What we've had certainly for the last decade has been almost a geopolitical duopoly. We've had the US and China not only having enormous power and influence in this space, but also goading each other into producing the most extreme forms of both data extractive and surveillance technologies. And unfortunately, this is just as true in the United States that I commonly hear this in rooms in DC where you'll hear advisors say, well, having any type of guardrails or ethical considerations for our AI systems is a problem if it means that China's going to do it anyway. And that creates this race to the bottom dynamic of do as much of whatever you can do regardless of the ethical and in some cases legal problems that will create. And I think that's been the dynamic that we've seen for some time. And of course the last 18 months to two years, we've seen that really extraordinary AI war happening internally in the United States where again, this race dynamic I think does create unfortunately this tendency to just go as fast as possible without thinking about potential downsides.

(33:53):

And I think we're seeing the legacy of that right now. And of course, a lot of the conversations from people designing these systems are now starting to say, look, being first is great, but we don’t want to be in a situation as we saw recently with Google’s Gemini where you have to pull an entire model off the shelves and you have to say, this is not ready. We actually have to remove it and start again. So this is the result I think of that high pressure, high speed dynamic that we’ve been seeing both inside the US but between the US and China. And of course, what that does to the rest of the world is create this kind of client states where we've got the EU trying to say, alright, well we'll export a regulatory model if we're not going to be treated as an equivalent player here. And then of course, so many other countries who are just seen as spaces to extract low paid labor or the mineralogical layer. So that is the big problem that I see is that that dynamic has only intensified in recent years.

A.I. and Medicine

Eric Topol (34:54):

Yeah, I know it's really another level of concern and it seems like it could be pretty volatile if for example, if the US China relations takes another dive and the tensions there go to levels that haven't been seen so far. I guess the other thing, there's so much that is I think controversial, unsettled in this space and so much excitement. I mean, just yesterday for example, was the first AI randomized trial to show that you could save lives. When I wrote that up, it was about the four other studies that showed how it wasn't working. Different studies of course, but there's so much excitement at the same time, there's deep concerns. You've been a master at articulating these deep concerns. What have we missed in our discussion today, I mean we've covered a lot of ground, but what do you see are other things that should be mentioned?

Kate Crawford (36:04):

Well, one of the things that I've loved in terms of following your work, Eric, is that you very carefully walk that line between allowing the excitement when we see really wonderful studies come out that say, look, there's great potential here, but also articulating concerns where you see them. So I think I'd love to hear, I mean take this opportunity to ask you a question and say what's exciting you about the way that this particularly new generation AI is being used in the medical context and what are the biggest concerns you have there?

Eric Topol (36:35):

Yeah, and it's interesting because the biggest advance so far in research and medicine was the study yesterday using deep learning without any transformer large language model effort. And that's where that multiplicative of opportunity or potential is still very iffy, it's wobbly. I mean, it needs much more refinement than where we are right now. It's exciting because it is multimodal and it brings in the ability to bring all the layers of a human being to understand our uniqueness and then do much better in terms of, I got a piece coming out soon in Science about medical forecasting and how we could really get to prevention of conditions that people are at high risk. I mean like for example today the US preventive task force said that all women age 40 should have mammograms, 40.

Kate Crawford (37:30):

I saw that.

Eric Topol (37:30):

Yeah, and this is just crazy Looney Tunes because here we have the potential to know pretty precisely who are those 12%, only 12% of women who would ever get breast cancer in their lifetime, and why should we put the other 88% through all this no less the fact that there are some women even younger than age 40 that have significantly high risk that are not picked up. But I do think eventually when we get these large language models to actualize their potential, we'll do really great forecasting and we'll be able to not just prevent or forestall cancer, Alzheimer’s and so many things. It's quite exciting, but it's the earliest, we're not even at first base yet, but I think I can see our way to get there eventually. And it's interesting because the discussion I had previously with Geoffrey Hinton, and I wonder if you think this as well, that he sees the health medical space as the only really safe space. He thinks most everything else has got more concerns about the downsides is the sweet spot as he called it. But I know that's not particularly an area that you are into, but I wonder if you share that the excitement about your health could be improved in the future with AI.

Kate Crawford (38:52):

Well, I think it's a space of enormous potential, but again, enormous risk for the same reasons that we discussed earlier, which is we have to look at the training data and where it's coming from. Do we have truly representative sources of data? And this of course has been a consistent problem certainly for the last hundred years and longer. When we look at who are the medical patients whose data is being collected, are we seeing skews? And that has created all sorts of problems, particularly in the last 50 years in terms of misdiagnosing women, people of color, missing and not taking seriously the health complaints of people who are already seen as marginalized populations, thus then further skewing the data that is then used to train AI models. So this is something that we have to take very seriously, and I had the great fortune of being invited by Francis Collins to work with the NIH on their AI advisory board.

(39:50):

They produced a board to look just at these questions around how can this moment in AI be harnessed in such a way that we can think about the data layer, think about the quality of data and how we train models. And it was a really fascinating sort of year long discussion because in the room we had people who were just technologists who just wanted as much data as possible and just give us all that data and then we'll do something, but we'll figure it out later. Then there were people who had been part of the Human Genome Project and had worked with Francis on questions around the legal and ethical and social questions, which he had really centered in that project very early on. And they said, no, we have to learn these lessons. We have to learn that data comes from somewhere. It's not divorced of context, and we have to think about who's being represented there and also who's not being represented there because that will then be intensified in any model that we train on that data.

Humans and Automation Bias

(40:48):

And then also thinking about what would happen in terms of if those models are only held by a few companies who can profit from them and not more publicly and widely shared. These were the sorts of conversations that I think at the absolute forefront in terms of how we're going to navigate this moment. But if we get that right, if we center those questions, then I think we have far greater potential here than we might imagine. But I'm also really cognizant of the fact that even if you have a perfect AI model, you are always going to have imperfect people applying it. And I'm sure you saw that same study that came out in JAMA back in December last year, which was looking at how AI bias, even slightly biased models can worsen human medical diagnosis. I don’t know if you saw this study, but I thought it was really extraordinary.

(41:38):

It was sort of 450 doctors and physician's assistants and they were really being shown a handful of cases of patients with acute respiratory failure and they really needed come up with some sort of diagnosis and they were getting suggestions from an AI model. One model was trained very carefully with highly accurate data, and the other was a fairly shoddy, shall we say, AI model with quite biased data. And what was interesting is that the clinicians when they were working with very well-trained AI model, we're actually producing a better diagnosis across the board in terms of the cases they were looking at. I think their accuracy went up by almost 4.5 percentage points, but when they were working with the less accurate model, their capacity actually dropped well below their usual diagnostic baseline, something like almost 12 percentage points below their usual diagnostic quality. And so, this really makes me think of the kind of core problem that's been really studied for 40 years by social scientists, which is called automation bias, which is when even an expert, a technical system which is giving a recommendation, our tendency is to believe it and to discard our own knowledge, our own predictions, our own sense.

(42:58):

And it's been tested with fighter pilots, it's been tested with doctors, it's been tested with judges, and it's the same phenomenon across the board. So one of the things that we're going to need to do collectively, but particularly in the space of medicine and healthcare, is retaining that skepticism, retaining that ability to ask questions of where did this recommendation come from with this AI system and should I trust it? What was it trained on? Where did the data come from? What might those gaps be? Because we're going to need that skepticism if we're going to get through particularly this, as you say, this sort of early stage one period where in many cases these models just haven't had a lot of testing yet and people are going to tend to believe them out of the box.

The Large Language Model Copyright Issue

Eric Topol (43:45):

No, it's so true. And one of the key points is that almost every study that's been published in large language models in medicine are contrived. They're using patient actors or they're using case studies, but they're not in the real world. And that's where you have to really learn, as you know, that's a much more complex and messy world than the in silico world of course. Now, before wrapping up, one of the things that's controversial we didn't yet hit is the fact that in order for these base models to get trained, they basically ingest all human content. So they've ingested everything you've ever written, your books, your articles, my books, my articles, and you have the likes of the New York Times suing OpenAI, and soon it's going to run out of human content and just use synthetic content, I guess. But what's your sense about this? Do you feel that that's trespassing or is this another example of exploiting content and people, or is this really what has to be done in order to really make all this work?

Kate Crawford (44:59):

Well, isn't it a fascinating moment to see this mass grabbing of data, everything that is possibly extractable. I actually just recently published an article in Grey Room with the legal scholar, Jason Schultz, looking at how this is producing a crisis in copyright law because in many ways, copyright law just cannot contend with generative AI in particular because all of the ways in which copyright law and intellectual property more broadly has been understood, has been premised around human ideas of providing an incentive and thus a limited time monopoly based on really inspiring people to create more things. Well, this doesn't apply to algorithms, they don't respond to incentives in this way. The fact that, again, it's a longstanding tradition in copyright that we do not give copyright to non-human authors. So you might remember that there was a very famous monkey selfie case where a monkey had actually stepped on a camera and it had triggered a photograph of the monkey, and could this actually be a copyright image that could be given to the monkey?

(46:12):

Absolutely not, is what the court's decided. And the same has now happened, of course, for all generative AI systems. So right now, everything that you produce be that in GPT or in Midjourney or in Stable Diffusion, you name it, that does not have copyright protections. So we're in the biggest experiment of production after copyright in world history, and I don't think it's going to last very long. To be clear, I think we're going to start to see some real shifts, I think really in the next 6 to 12 months. But it has been this moment of seeing this gigantic gap in what our legal structures can do that they just haven't been able to contend with this moment. The same thing is true, I think, of ingestion, of this capturing of human content without consent. Clearly, many artists, many writers, many publishing houses like the New York Times are very concerned about this, but the difficulty that they're presented with is this idea of fair use, that you can collect large amounts of data if you are doing something with that, which is sufficiently transformative.

(47:17):

I'm really interested in the question of whether or not this does constitute sufficiently transformative uses. Certainly if you looked at the way that large language models a year ago, you could really prompt them into sharing their training data, spitting out entire New York Times articles or entire book chapters. That is no longer the case. All of the major companies building these systems have really safeguarded against that now but nonetheless, you have this question of should we be moving towards a system that is based on licensing, where we're really asking people if we can use their data and paying them a license fee? You can see how that could absolutely work and would address a lot of these concerns, but ultimately it will rely on this question of fair use. And I think with the current legal structures that we have in the current case law, that is unlikely to be seen as something that's actionable.

(48:10):

But I expect what we'll look at is what really happened in the early 20th century around the player piano, which was that I'm sure you remember this extraordinary technology of the player piano. That was one of the first systems that automated the playing of music and you'd have a piano that had a wax cylinder that almost like code had imprinted on a song or a piece of music, and it could be played in the public square or in a bar or in a saloon without having to pay a single artist and artists were terrified. They were furious, they were public hearings, there were sort of congressional hearings and even a Supreme Court case that decided that this was not a copyright infringement. This was a sufficiently transformative use of a piece of music that it could stand. And in the end, it was actually Congress that acted.

(49:01):

And we from that got the 1908 Copyright Act and from that we got this idea of royalties. And that has become the basis of the music industry itself for a very long time. And now we're facing another moment where I think we have a legislative challenge. How would you actually create a different paradigm for AI that would recognize a new licensing system that would reward artists, writers, musicians, all of the people whose work has been ingested into training data for AI so that they are recognized and in some ways, recompensed by this massive at scale extraction?

Eric Topol (49:48):

Wow, this has been an exhilarating conversation, Kate. I've learned so much from you over the years, but especially even just our chance to talk today. You articulate these problems so well, and I know you're working on solutions to almost everything, and you're so young, you could probably make a difference in the decades ahead. This is great, so I want to thank you not just for the chance to visit today, but all the work that you've been doing, you and your colleagues to make AI better, make it fulfill the great promise that it has. It is so extraordinary, and hopefully it'll deliver on some of the things that we have big unmet needs, so thanks to you. This has really been fun.

Kate Crawford (50:35):

This has been wonderful. And likewise, Eric, your work has just been a fantastic influence and I've been delighted to get to know you over the years and let's see what happens. It's going to be a wild ride from now to who knows when.

Eric Topol (50:48):

No question, but you'll keep us straight, I know that. Thank you so much.

Kate Crawford (50:52):

Thanks so much, Eric.

*******************************

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If there’s one person you’d want to talk to about immunology, the immune system and Covid, holes in our knowledge base about the complex immune system, and where the field is headed, it would be Professor Iwasaki. And add to that the topic of Women in Science. Here’s our wide-ranging conversation.

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Transcript with many external link and links to the audio, recorded 30 April 2024

Eric Topol (00:06):

Hello, it's Eric Topol and I'm really thrilled to have my friend Akiko Iwasaki from Yale, and before I start talking with Akiko, I just want to mention there aren't too many silver linings of the pandemic, but one for me was getting to know Professor Iwasaki. She is my go-to immunologist. I've learned so much from her over the last four years and she's amazing. She just, as you may know, she was just recently named one of the most influential people in the world by TIME100. [and also recognized this week in TIME 100 Health]. And besides that, she's been elected to the National Academy of Medicine, National Academy of Sciences. She's the president of the American Association of Immunologists and she's a Howard Hughes principal investigator. So Akiko, it's wonderful to have you to join into an extended discussion of things that we have of mutual interest.

Akiko Iwasaki (01:04):

Thank you so much, Eric, for having me. I equally appreciate all of what you do, and I follow your blog and tweets and everything. So thank you Eric.

Eric Topol (01:14):

Well, you are a phenom. I mean just, that's all I can say because I think it was so appropriate that TIME recognize your contributions, not just over the pandemic, but of course throughout your career, a brilliant career in immunology. I thought we'd start out with our topic of great interest on Long Covid. You've done seminal work here and this is an evolving topic obviously. I wonder what your latest thoughts are on the pathogenesis and where things are headed.

Long Covid

Akiko Iwasaki (01:55):

Yeah, so as I have been saying throughout the pandemic, I think that Long Covid is not one disease. It's a collection of multiple diseases and that are sort of ending up in similar sets of symptoms. Obviously, there are over 200 symptoms and not everyone has the same set of symptoms, but what we are going for is trying to understand the disease drivers, so persistent viral infection is one of them. There are overwhelming evidence for that theory now, all the way from autopsy and biopsy studies to looking at peripheral blood RNA signatures as well as circulating spike protein and nucleocapsid proteins that are detected in people with Long Covid. Now whether that persistent virus or remnants of virus is driving the disease itself is unclear still. And that's why trials like the one that we are engaging with Harlan Krumholz on Paxlovid should tell us what percentage of the people are suffering from that type of driver and whether antivirals like Paxlovid might be able to mitigate those. If I may, I'd like to talk about three other hypotheses.

Eric Topol (03:15):

Yeah, I'd love for you to do that.

Akiko Iwasaki (03:18):

Okay, great. So the second hypothesis that we've been working on is autoimmune disease. And so, this is clearly happening in a subset of people, again, it's a heterogeneous disease, but we can actually not only look at reactogenicity of antibodies from people with Long Covid where we can transfer IgG from patients with Long Covid into an animal, a healthy animal, and really measure outcomes of a pathogenesis. So that's a functional evidence that antibodies in some people with Long Covid is really actually causing some of the damages that are occurring in vivo. And the third hypothesis is the reactivation of herpes viruses. So many of us adults have multiple latent herpes virus family members that are just dormant and are not really causing any pathologies. But in people with Long Covid, we're seeing elevated reactivation of viruses like Epstein-Barr virus (EBV) or Varicella-zoster virus (VZV) and that may again be just a signature of Long Covid, but it may also be driving some of the symptoms that people are suffering from.

(04:32):

So that's again, we see the signature over and over, not just our group, but multiple other groups, Michael Peluso's group, Jim Heath, and many others. So that's also an emerging evidence from multiple groups showing that. And finally, we think that inflammation that occurs during the acute phase can sort of chronically change some tissue tone. For instance, in the brain with Michelle Monje’s team, we developed a sort of localized mild Covid model of infection and showed that changes in microglia can be seen seven weeks post infection even though the virus is completely gone. So that means that inflammation that's established as a result of this initial infection can have prolonged sequence and sequela within the person and that may also be driving disease. And Eric, the reason we need to understand these diseases separately is because not only for diagnostic purposes, but for therapeutic purposes because to target a persistent virus is very different approach from targeting autoantibodies, for example.

Eric Topol (05:49):

Well, that's great. There's a lot to unpack there as you laid out four distinct paths that could result in the clinical syndrome and sequelae. I think you know I had the chance to have a really fun conversation with Michelle about their joint work that you've done, and she reminded me how she made a cold call to you to start as a collaboration, which I thought was fantastic. Look what that yielded. But yeah, this is fascinating because as I think you're getting at is that it may not be the same pathogenesis in any given individual so that all these, and even others might be operative. I guess maybe I first delve into the antibody story as you're well aware, we see after people get Covid a higher rate of autoimmune diseases crop up, which is really interesting because it seems to rev up self-directed immune response. And this I think many people haven't really noted yet, although obviously you're well aware of this, it's across all the different autoimmune diseases, connective tissue disease, not just one in particular. And it's, as you say, the idea that you could take the blood from a person suffering from Long Covid and give it to an experimental animal model and be able to recapitulate some of the abnormalities, it's really pretty striking. So the question I guess is if you were to do plasmapheresis and try to basically expunge these autoantibodies, wouldn't you expect people to have some symptomatic benefit pretty rapidly or is it just that the process is already far from the initiating step?

Akiko Iwasaki (07:54):

That's a great question. Plasmapheresis may be able to transiently improve the person if they're suffering from these autoantibody mediated diseases. People have reported, for example, IVIG treatment has dramatically improved their symptoms, but not in everybody. So it's really critical to understand who's suffering from this particular driver and appropriately treat those people. And there are many other very effective therapies in autoimmune disease field that can be repurposed for treating these patients as well.

Eric Topol (08:34):

The only clinical trial that has clicked so far, interestingly, came out of Hong Kong with different types of ways to manipulate the gut microbiome, which again, you know better than me is a major modulator of our immune system response. What are your thoughts about taking advantage of that way to somehow modulate this untoward immune response in people with this condition?

Akiko Iwasaki (09:07):

Yeah, so that is an exciting sort of development, and I don't mean to discount the importance of microbiome at all. It's just the drivers that are mentioning are something that can be directly linked to disease, but certainly dysbiosis and translocation of metabolites and microbiome itself could trigger Long Covid as well. So it's something that we're definitely keeping our eyes on. And as you say, Eric, the immune system is in intimate contact with the gut microbiome and also the gut is intimate contact with the brain. So there's a lot of connections that we really need to be paying attention to. So yeah, absolutely. This is a very exciting development.

Eric Topol (09:57):

And it is intriguing of course, the reactivation of viruses. I mean, we’ve learned in recent years how important EBV is in multiple sclerosis (MS). The question I have for you on that pathway, is this just an epiphenomena or do you actually think that could be a driving force in some people?

Akiko Iwasaki (10:19):

Yeah, so that's really hard to untangle in people. I mean, David Putrino and my team we're planning a clinical trial using Truvada. Truvada obviously is an HIV drug, but it has reported antiviral activity to Epstein-Barr virus (EBV) and others. So potentially we can try to interrogate that in people, but we're also developing mouse models that can sort of recapitulate EBV like viral reactivation and to see whether there's any sort of causal link between the reactivation and disease process.

Eric Topol (10:57):

Right now, recently there's been a bunch of anecdotes of people who get the glucagon-like peptide one (GLP-1) drugs which have a potent anti-inflammatory, both systemic and in the brain. I'd love to test these drugs, but of course these companies that make them or have other interests outside of Long Covid, do you think there's potential for a drug like that?

Akiko Iwasaki (11:23):

Yeah, so those drugs seem to have a lot of miraculous effects on every disease. So obviously it has to be used carefully because many people with Long Covid have issues with liver functions and other existing conditions that may or may not be conducive to taking those types of GLP-1 agonists. But in subset of people, maybe this can be tried, especially due to the anti-inflammatory properties, it may benefit again, a subset of people. I don't expect a single drug to cure everyone. That would be pretty amazing, but unlikely.

Eric Topol (12:09):

Absolutely. And it's unfortunate we are not further along in this whole story of clinical trials, testing treatments and applauding your efforts with my friend Harlan there to get into the testing which we had hoped RECOVER was going to do with their more than billion dollars or allocation, which didn't get us too far in that. Now before we leave Long Covid, which we could speak about for hours, I mean it's so darn important because so many people are really out there disabled or suffering on a daily basis or periodically they get better and then get worse again. There's been this whole idea that, oh, it's going away and that reinfections don't pose a threat. Maybe you could straighten that story out because I think there seems to be some miscues about the risk of Long Covid even as we go along with the continued circulating virus.

Akiko Iwasaki (13:11):

Right, so when you look at the epidemiological evidence of Long Covid, clearly in the beginning when we had no vaccines, no antivirals, no real good measure against Covid, the incident of developing Long Covid per infection was higher than a current date where we do have vaccines and Omicron may have changed its property significantly. So if you compare, let's say the Delta period versus Omicron period, there seems to be a reduced risk per infection of Long Covid. However, Omicron is super infectious. It's infected millions of people, and if you look at the total number of people suffering from Long Covid, we're not seeing a huge decline there at all because of the transmissibility of Omicron. So I think it's too early for us to say, okay, the rates are declining, we don't need to worry about it. Not at all, I think we still have to be vigilant.

(14:14):

We need to be up to date on vaccines and boosters because those seem to reduce the risk for Long Covid and whether Paxlovid can reduce the rate of Long Covid at the acute phase for the high risk individual, it seems to be yes, but for people who are not at high risk may or may not be very effective. So again, we just need to be very cautious. It's difficult obviously, to be completely avoiding virus at this time point, but I think masking and anything you can do, vaccination boosters is going to be helpful. And a reinfection does carry risk for developing Long Covid. So that prior infection is not going to prevent Long Covid altogether, even though the risk may be slightly reduced in the first infection. So when you think about these risks, again we need to be cognizant that reinfection and some people have multiple infections and then eventually get Long Covid, so we're just not safe from Long Covid yet.

Nasal Vaccines and Mucosal Immunity

Eric Topol (15:24):

Right. No, I think that's the problem is that people have not acknowledged that there's an ongoing risk and that we should continue to keep our guard up. I want to applaud you and your colleagues. You recently put out [Yale School of Public Health] this multi-panel about Covid, which we'll post with this podcast that gave a lot of the facts straight and simple diagrams, and I think this is what you need is this is kind of like all your threads on Twitter. . They're always such great educational ways to get across important information. So now let's go onto a second topic of great mutual interest where you've also been a leader and that's in the mucosal nasal vaccine story. I had the privilege of writing with you a nice article in Science Immunology back in 2022 about Operation Nasal Vaccine, and unfortunately we don't have a nasal vaccine. We need a nasal vaccine against Covid. Where do we stand with this now?

Akiko Iwasaki (16:31):

Yeah, so you're right. I mean nasal vaccines, I don't really know what the barrier is because I think the preclinical models all support the effectiveness against transmission and infection and obviously disease. And there is a White House initiative to support rapid development of next generation vaccine, which includes mucosal vaccine, so perhaps that's sort of pushing some of these vaccine candidates forward. You're probably more familiar than me about those kinds of events that are happening. But yeah, it's unfortunate that we don't have an approved mucosal booster vaccine yet, and our research has shown that as simple as a spray of recombinant spike protein without any adjuvants are able to restimulate immune response and then establish mucosal immunity in the nasal cavity, which goes a long way in preventing infection as well as transmission. So yeah, I mean I'm equally frustrated that things like that don't exist yet.

The Neomycin and Neosporin Surprise

Eric Topol (17:52):

Well, I mean the work that you and many other groups around the world have published on this is so compelling and this is the main thing that we don't have now, which is a way to prevent infection. And I think most of us would be very happy to have a spray that every three or four months and gave us much higher levels of protection than we're ever going to get from shots. And your whole concept of prime and spike, I mean this is something that we could have had years ago if there was a priority, and unfortunately there never has been. Now, the other day you came with a surprise in a paper on Neomycin as an alternate or Neosporin ointment. Can you tell us about that? Because that one wasn't expected. This was to use an antibiotic in a way to reduce Covid and other respiratory virus.

Akiko Iwasaki (18:50):

Right. So yeah, that's a little known fact. I mean, of course widespread use of antibiotics has caused some significant issues with resistance and so on. However, when you look at the literature of different types of antibiotics, we have reported in 2018 that certain types of antibiotics known as aminoglycoside, which includes Neosporin or neomycin, has this sort of unintended antiviral property by triggering Toll-like receptor 3 in specialized cell types known as conventional dendritic cell type 1. And we published that for a genital herpes model that we were working on at the time. But because it's acting on the host, the Toll-like receptor 3 on the host cell to induce interferon and interferon stimulated genes to prevent the replication of the virus, we knew that it could be pan-viral. It doesn't really matter what the virus is. So we basically leverage that discovery that was made by a postdoc Smita Gopinath when she was in the lab to see if we can use that in the nasal cavity.

(20:07):

And that's what Tianyang Mao, a former graduate student did, in fact. And yeah, little spray of neomycin in the nose of the mice reduce this infection as well as disease and can even be used to treat shortly after the infection disease progress and using hamster models we also showed that hamsters that are pretreated with neomycin when they were caged with infected hamsters, the transmission rate was much reduced. And we also did with Dr. Charles Dela Cruz, a small clinical trial, randomized though into placebo and Neosporin arms of healthy volunteers. We asked them to put in a pea size amount of Neosporin on a cotton swab into the nose, and they were doing that twice a day for seven days. We measured the RNA from the nose of these people and indeed see that more than half the participants in the Neosporin group had elevated interferon stimulated genes, whereas the control group, which were given Vaseline had no response. So this sort of shows the promise of using something as generic and cheap as Neosporin to trigger antiviral state in the nose. Now it does require a much larger trial making sure that the safety profiles there and effectiveness against viral infection, but it's just a beginning of a story that could develop into something useful.

New Frontiers in Immunology and Tx Cells

Eric Topol (21:51):

Yeah, I thought it was fascinating, and it does bring up, which I think has also been underdeveloped, is our approaches for interferon a frontline defense where augmenting that, just getting that exploiting the nasal mucosa, the entry site, whether it be through that means or of course through even more potent a nasal vaccine, it's like a missing, it's a hole in our whole defense of against this virus that's led to millions of people not just dying, but of course also sick and also with Long Covid around the world. So I hope that we'll see some progress, but I thought that was a really fascinating hint of something to come that could be very helpful in the meantime while we're waiting for specific nasal vaccines. Now added to all these things recently, like last week you published a paper in Cell with your husband who's in the same department, I think at Yale. Is that right? Can you tell us about that and this paper about the whole new perspectives in immunology?

Akiko Iwasaki (23:05):

Yeah, so my husband Ruslan Medzhitov is a very famous immunologist who's in the same department, and we've written four or five review and opinion pieces together over the years. This new one is in Cell and it's really exploring new perspectives in immunology. We were asked by the editors to celebrate the 50th anniversary of the Cell journal with a perspective on the immune system. And the immune response is just a beautiful system that is triggered in response to specific pathogens and can really provide long-term or even sometimes lifelong immunity and resistance against pathogens and it really saves our lives. Much has been learned throughout the last 20, 30 years about the innate and adaptive immune system and how they're linked. In this new perspective, we are trying to raise some issues that the current paradigm cannot explain properly, some of the mysteries that are still remaining in the immune system.

(24:22):

And we try to come up with new concepts about even the role of the immune system in general. For instance, is the immune system only good for fighting pathogens or can it be repurposed for conducting normal physiology in the host? And we came up with a new subset of T-cells known as, or we call it Tx cells, which basically is an interoceptive type of T-cells that monitor homeostasis in different tissues and are helping with the normal process of biology as opposed to fighting viruses or bacteria or fungi. But these cells, when they are not appropriately regulated, they are also the source of autoimmune diseases because they are by design reactive against auto antigens. And so, this is a whole new framework to think about, a different arm of the immune function, which is really looking inside of our body and not really fighting against pathogens, but we believe these cells exist, and we know that the counterpart of Tx cells, which is the T regulatory cells, are indeed well known for its physiological functions. So we're hoping that this new perspective will trigger a new set of approaches in the field to try to understand this interceptive property of T-cells.

Eric Topol (25:59):

Yeah, well, I thought it was fascinating, of course, and I wanted to get into that more because I think what we're learning is this immune system not only obviously is for cancer whole. We're only starting to get warmed up with immunotherapy where checkpoint inhibitors were just the beginning and now obviously with vaccines and all these different ways that we can take the CAR-T cells, engineered T-cells, take the immune system to fight cancer and potentially to even use it as a way to prevent cancer. If you have these, whether it's Tx or Tregs or whatever T-cells can do this. But even bigger than that is the idea that it's tied in with the aging process. So as you know, again, much more than I do, our senescent immune cells are not good for us. And the whole idea is that we could build immune resilience if we could somehow figure out these mysteries that you're getting at, whereby we get vulnerable just as we were with Covid. And as we get older, we get vulnerable to not just infections, but everything going wrong, whether it's the walls of our arteries or whether it's the cancer or the immunity that's going on in our brain for Alzheimer's and neurodegenerative diseases. How can we fix the immune system so that we age more healthily

The Immune System and Healthy Aging

Akiko Iwasaki (27:37):

Oh yeah. A lot of billionaires are also interested in that question and are pouring money into this question. It's interesting, but when you think about the sort of evolutionary perspective, we humans are only living so long. In the very recent decades, our life expectancy used to be much shorter and all we had to survive was to reproduce and generate the next progeny. But nowadays, because of this amazing wealth and health interventions and food and everything else, we're just living so much longer than even our grandparents. The immune system didn't evolve to deal with such one to begin with. So we were doing fine living up to 30 years of age or whatever. But now that we're living up to a hundred years, the immune system isn't really designed to keep up with this kind of stressors. But I think you're getting at a very important kind of more engineering questions of how do we manipulate the immune system or rejuvenate it so that we can remain healthy into the later decades? And it is well known that the immune system itself ages and that our ability to produce new lymphocytes, for example, decline over time and thymus that is important for T-cell development shrinks over time. And so anatomically it's impossible to help stop that process. However, is there a way of, for example, transferring some factors or engineering the immune cells to remain healthy and even like hematopoiesis itself can be manipulated to perhaps rejuvenate the whole immune system in their recent papers showing that. So this is a new frontier.

Eric Topol (29:50):

Do you think that some point in the future, we'll ex vivo inject Yamanaka factors into these cell lines and instead of this idea that you know get young plasma to old folks, and I mean since we don't know what's in there and it doesn't specifically have an effect on immune cells, who knows how it's working, but do you foresee that that might be a potential avenue going forward or even an in vivo delivery of this?

Akiko Iwasaki (30:22):

Yeah, it's not impossible, right? There are really rapidly evolving technologies and gene therapies that are becoming online. So it's not impossible to think about engineering in situ as you're suggesting, but we also have to be certain that we are living longer, but also healthy. So we do have to not only just deal with the aging immune system, but preventing neurodegenerative diseases and so on. And the immune system may have a role to play there as well. So there's a lot of, I mean, I can't think of a non-genetically mediated disease that doesn't involve the immune system.

Eric Topol (31:03):

Sure. No, I mean, it's just, when I think about this, people keep talking about the digital era of digital biology, but I actually think of it more as digital immunobiology, which is driving this because it's center stage and in more and more over time. And the idea that I'm concerned about is that we could rejuvenate the relevant immune cells or the whole immune response, but then it's such a delicate balance that we could actually wind up with untoward, whether it's autoimmune or overly stimulated immune system. It's not such a simple matter, as I'm sure you would agree. Now, this gets me to a broader thing which you've done, which is a profound contribution in life science and medicine, which is being an advocate for women in science. And I wonder if you could speak to that because you have been such a phenomenal force propelling the importance of women in science and not just doing that passively, but also standing up for women, which is being an activist is how you get things to change. So can you tell us about your thoughts there?

An Activist for Women in Science

Akiko Iwasaki (32:22):

Yeah, so I grew up in Japan, and part of the reason I left Japan at the age of 16 was that I felt very stifled because of the societal norm and expectation of what a woman should be. And I felt like I didn't have the opportunity to develop my skills as a scientist remaining in Japan. And maybe things have changed over the years, but at the time when I was growing up, that's how I felt. And so, I was very cognizant of biases in society. And so, in the US and in Canada where I also trained, there's a lot less barrier to success, and we are able to do pretty much anything we want, which is wonderful, and that's why I think I'm here. But at the same time, the inequity still exists, even in pay gaps and things like that that are easy to fix but are still kind of insidious and it's there.

(33:32):

And Yale School of Medicine has done a great job partly because of the efforts of women who spoke up and who actually started to collect evidence for pay gap. And now there's very little pay gap because there's active sort of involvement of the dean and everyone else to ensure equity in the medical school. But it's just a small segment of the society. We really need to expand this to other schools and making sure that women are getting paid equally as men in the same ranks. And also, I see still some sexual harassment or more just toxic environment for people in general in academia. Some PIs get away with a lot of behavior that's not conducive to a healthy environment, so I have written about that as well and how we can have antidotes for such toxic environments. And it really does require the whole village to act on it. It's not just one person speaking up. And there should be measures placed to make sure that those people who does have this tendency of abusive behavior that they can get training and just being aware of these situations and corrective behavior. So I think there's still a lot of work left in academia, but things have obviously improved dramatically over the last few decades, and we are in a very, very good place, but we just have to keep working to achieve true equity.

Why Don’t We Have Immunome Check-Ups?

Eric Topol (35:25):

Well applauding your efforts for that, and I'm still in touch with that. We got a ways to go, and I hope that we'll see steady and even more accelerated and improvement to get to parity, which is what it should be. And I really think you've been a model for doing this. It isn't like you aren't busy with everything else, so to fit that in is wonderful. In closing up, one of the things that I wonder about is our ability to assess back to the immune system for a moment isn't what it should be. That is we do a CBC and we have how many lymphocytes, how many this, why don't we have an immunome, why doesn’t everybody serially have an immune system checkup? Because that would tell us if we’re starting to go haywire and then maybe hunt for reactivated viruses or what’s going on. Do you foresee that we could ever get to a practical immunome as we go forward? Because it seems like it’s a big missing link right now.

Akiko Iwasaki (36:33):

Yeah, I think that’s a great idea. I mean, I’ll be the first one to sign up for the immunome.

Eric Topol (36:40):

But I’m depending on you to make it happen.

Akiko Iwasaki (36:44):

Well, interestingly, Eric, there are lots of amazing technologies that are developed even during the pandemic, which is monitoring everything from antibody reactivity to reactivated viruses to the cytokines to every cell marker you can imagine. So the technologies out there, it’s just I think a matter of having the right set of panels that are relatively affordable because some of these things are thousands of dollars per sample to analyze, and then of course clinical validation, something that’s CLIA approved, and then we can start to, I guess the insurance company needs to also cover this, right? So we need to demonstrate the benefit to health in the long run to be able to afford this kind of immunome analysis. But I think that very wealthy people can already get this done.

Eric Topol (37:43):

Yeah, well, we want to make it so it's a health equity story, not of course, only for the crazy ones that are out there that are taking 112 supplements a day and whatnot. But it's intriguing because I think we might be able to get ahead of things if we had such an easy means. And as you said during the pandemic, for example, my friends here in La Jolla at La Jolla Immunology did all kinds of T-cell studies that were really insightful and of course done with you and others around the country and elsewhere to give us insights that you didn't get just from neutralizing antibodies. But it isn't something that you can get done easily. Now, I think this immunome hopefully will get us to another level in the future. One of the most striking things I've seen in our space clinically before wrapping up is to take the CD19 CAR T therapies to deplete the B cells of people with lupus, systemic sclerosis and other conditions, and completely stop their autoimmune condition. And when the B cells come back, they're not fighting themselves. They're not self-directed anymore. Would you have predicted this? This seems really striking and it may be a clue to the kind of mastering approaches to autoimmune diseases in the future.

Akiko Iwasaki (39:19):

Yeah, absolutely. So for multiple sclerosis, for example, where B cells weren't thought to be a key player by doing anti-CD20 depletion, there's this remarkable clinical effects. So I think we can only find the answer experimentally in people when they do these clinical trials and show this remarkable effects. That's when we say, aha, we don't really understand immunology. You know what I mean? That's when we have to be humble about what we think we understand. We really don't know until we try it. So that's a really good lesson learned. And these may be also applicable to people with autoimmune phenotype in Long Covid, right? We may be able to benefit from similar kinds of depletion therapy. So I think we have a lot to learn still.

Eric Topol (40:14):

Yeah, that's why, again, going back to the paper you just had in Cell about the mysteries and about some new ideas and challenging the dogma is so important. I still consider the immune system most complex one in the body by far, and I'm depending on you Akiko to unravel it, not to put any weight on your shoulders. Anyway, this has been so much fun. You are such a gem and always learning from you, and I can't thank you enough for all the work. And the fact is that you've got decades ahead of you to keep building on this. You've already done enough for many people, many scientists in your career, and I know you'll keep going. So we're all going to be following you with great interest in learning from you on a frequent basis. And I hope we'll build on some of the things we've talked about like a Long Covid treatment, treatments that are effective nasal vaccines, maybe even some dab of Neosporin, and keep on the momentum we’ve had with the understanding of the immune system, and finally, someday achieving the true parity of gender and science. And so, thank you for all that you do.

Akiko Iwasaki (41:35):

Thank you so much, Eric.

************************

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“Where do I think the next amazing revolution is going to come? … There’s no question that digital biology is going to be it. For the very first time in our history, in human history, biology has the opportunity to be engineering, not science.” —Jensen Huang, NVIDIA CEO

Aviv Regev is one of the leading life scientists of our time. In this conversation, we cover the ongoing revolution in digital biology that has been enabled by new deep knowledge on cells, proteins and genes, and the use of generative A.I .

Transcript with audio and external links

Eric Topol (00:05):

Hello, it's Eric Topol with Ground Truths and with me today I've really got the pleasure of welcoming Aviv Regev, who is the Executive Vice President of Research and Early Development at Genentech, having been 14 years a leader at the Broad Institute and who I view as one of the leading life scientists in the world. So Aviv, thanks so much for joining.

Aviv Regev (00:33):

Thank you for having me and for the very kind introduction.

The Human Cell Atlas

Eric Topol (00:36):

Well, it is no question in my view that is the truth and I wanted to have a chance to visit a few of the principal areas that you have been nurturing over many years. First of all, the Human Cell Atlas (HCA), the 37 trillion cells in our body approximately a little affected by size and gender and whatnot, but you founded the human cell atlas and maybe you can give us a little background on what you were thinking forward thinking of course when you and your colleagues initiated that big, big project.

Aviv Regev (01:18):

Thanks. Co-founded together with my very good friend and colleague, Sarah Teichmann, who was at the Sanger and just moved to Cambridge. I think our community at the time, which was still small at the time, really had the vision that has been playing out in the last several years, which is a huge gratification that if we had a systematic map of the cells of the body, we would be able both to understand biology better as well as to provide insight that would be meaningful in trying to diagnose and to treat disease. The basic idea behind that was that cells are the basic unit of life. They're often the first level at which you understand disease as well as in which you understand health and that in the human body, given the very large number of individual cells, 37.2 trillion give or take, and there are many different characteristics.

(02:16):

Even though biologists have been spending decades and centuries trying to characterize cells, they still had a haphazard view of them and that the advancing technology at the time – it was mostly single cell genomics, it was the beginnings also of spatial genomics – suggested that now there would be a systematic way, like a shared way of doing it across all cells in the human body rather than in ways that were niche and bespoke and as a result didn't unify together. I will also say, and if you go back to our old white paper, you will see some of it that we had this feeling because many of us were computational scientists by training, including both myself and Sarah Teichmann, that having a map like this, an atlas as we call it, a data set of this magnitude and scale, would really allow us to build a model to understand cells. Today, we call them foundational models or foundation models. We knew that machine learning is hungry for these kinds of data and that once you give it to machine learning, you get amazing things in return. We didn't know exactly what those things would be, and that has been playing out in front of our eyes as well in the last couple of years.

Spatial Omics

Eric Topol (03:30):

Well, that gets us to the topic you touched on the second area I wanted to get into, which is extraordinary, which is the spatial omics, which is related to the ability to the single cell sequencing of cells and nuclei and not just RNA and DNA and methylation and chromatin. I mean, this is incredible that you can track the evolution of cancer, that the old word that we would say is a tumor is heterogeneous, is obsolete because you can map every cell. I mean, this is just changing insights about so much of disease health mechanisms, so this is one of the hottest areas of all of life science. It's an outgrowth of knowing about cells. How do you summarize this whole era of spatial omics?

Aviv Regev (04:26):

Yeah, so there's a beautiful sentence in the search for lost time from Marcel Proust that I'm going to mess up in paraphrasing, but it is roughly that going on new journeys is not about actually going somewhere physically but looking with new eyes and I butchered the quote completely.[See below for actual quote.] I think that is actually what single cells and then spatial genomics or spatial omics more broadly has given us. It's the ability to look at the same phenomenon that we looked at all along, be it cancer or animal development or homeostasis in the lung or the way our brain works, but having new eyes in looking and because these new eyes are not just seeing more of something we've seen before, but actually seeing things that we couldn't realize were there before. It starts with finding cells we didn't know existed, but it's also the processes that these cells undergo, the mechanisms that actually control that, the causal mechanisms that control that, and especially in the case of spatial genomics, the ways in which cells come together.

(05:43):

And so we often like to think about the cell because it's the unit of life, but in a multicellular organism we just as much have to think about tissues and after that organs and systems and so on. In a tissue, you have this amazing orchestration of the interactions between different kinds of cells, and this happens in space and in time and as we're able to look at this in biology often structure is tightly associated to function. So the structure of the protein to the function of the protein in the same way, the way in which things are structured in tissue, which cells are next to each other, what molecules are they expressing, how are they physically interacting, really tells us how they conduct the business of the tissue. When the tissue functions well, it is this multicellular circuit that performs this amazing thing known as homeostasis.

(06:36):

Everything changes and yet the tissue stays the same and functions, and in disease, of course, when these connections break, they're not done in the right way you end up with pathology, which is of course something that even historically we have always looked at in the level of the tissue. So now we can see it in a much better way, and as we see it in a better way, we resolve better things. Yes, we can understand better the mechanisms that underlie the resistance to therapeutics. We can follow a temporal process like cancer as it unfortunately evolves. We can understand how autoimmune disease plays out with many cells that are actually bent out of shape in their interactions. We can also follow magnificent things like how we start from a single cell, the fertilized egg, and we become 37.2 trillion cell marvel. These are all things that this ability to look in a different way allows us to do.

Eric Topol (07:34):

It's just extraordinary. I wrote at Ground Truths about this. I gave all the examples at that time, and now there's about 50 more in the cardiovascular arena, knowing with single cell of the pineal gland that the explanation of why people with heart failure have sleep disturbances. I mean that's just one of the things of so many now these new insights it's really just so remarkable. Now we get to the current revolution, and I wanted to read to you a quote that I have.

Digital Biology

Aviv Regev (08:16):

I should have prepared mine. I did it off the top of my head.

Eric Topol (08:20):

It's actually from Jensen Huang at NVIDIA about the digital biology [at top of the transcript] and how it changes the world and how you're changing the world with AI and lab in the loop and all these things going on in three years that you've been at Genentech. So maybe you can tell us about this revolution of AI and how you're embracing it to have AI get into positive feedbacks as to what experiment to do next from all the data that is generated.

Aviv Regev (08:55):

Yeah, so Jensen and NVIDIA are actually great partners for us in Genentech, so it's fun to contemplate any quote that comes from there. I'll actually say this has been in the making since the early 2010s. 2012 I like to reflect on because I think it was a remarkable year for what we're seeing right now in biology, specifically in biology and medicine. In 2012, we had the beginnings of really robust protocols for single cell genomics, the first generation of those, we had CRISPR happen as a method to actually edit cells, so we had the ability to manipulate systems at a much better way than we had before, and deep learning happened in the same year as well. Wasn't that a nice year? But sometimes people only realize the magnitude of the year that happened years later. I think the deep learning impact people realized  first, then the single cells, and then the CRISPR, then the single cells.

(09:49):

So in order maybe a little bit, but now we're really living through what that promise can deliver for us. It's still the early days of that, of the delivery, but we are really seeing it. The thing to realize there is that for many, many of the problems that we try to solve in biomedicine, the problem is bigger than we would ever be able to perform experiments or collect data. Even if we had the genomes of all the people in the world, all billions and billions of them, that's just a smidge compared to all of the ways in which their common variants could combine in the next person. Even if we can perturb and perturb and perturb, we cannot do all of the combinations of perturbations even in one cell type, let alone the many different cell types that are out there. So even if we searched for all the small molecules that are out there, there are 10 to the 60 that have drug-like properties, we can't assess all of them, even computationally, we can't assess numbers like that.

(10:52):

And so we have to somehow find a way around problems that are as big as that and this is where the lab in the loop idea comes in and why AI is so material. AI is great, taking worlds, universes like that, that appear extremely big, nominally, like in basic numbers, but in fact have a lot of structure and constraint in them so you can reduce them and in this reduced latent space, they actually become doable. You can search them, you can compute on them, you can do all sorts of things on them, and you can predict things that you wouldn't actually do in the real world. Biology is exceptionally good, exceptionally good at lab sciences, where you actually have the ability to manipulate, and in biology in particular, you can manipulate at the causes because you have genetics. So when you put these two worlds together, you can actually go after these problems that appear too big that are so important to understanding the causes of disease or devising the next drug.

(11:51):

You can iterate. So you start, say, with an experimental system or with all the data that you have already, I don't know from an initiative like the human cell atlas, and from this you generate your original model of how you think the world works. This you do with machine learning applied to previous data. Based on this model, you can make predictions, those predictions suggest the next set of experiments and you can ask the model to make the most optimized set of predictions for what you're trying to learn. Instead of just stopping there, that's a critical point. You go back and you actually do an experiment and you set up your experiments to be scaled like that to be big rather than small. Sometimes it means you actually have to compromise on the quality of any individual part of the experiment, but you more than make up for that with quantity.

The A.I. Lab-in-the-Loop

(12:38):

So now you generate the next data from which you can tell both how well did your algorithm actually predict? Maybe the model didn’t predict so well, but you know that because you have lab results and you have more data in order to repeat the loop, train the model again, fit it again, make the new next set of predictions and iterate like this until you're satisfied. Not that you've tried all options, because that's not achievable, but that you can predict all the interesting options. That is really the basis of the idea and it applies whether you're solving a general basic question in biology or you're interested in understanding the mechanism of the disease or you're trying to develop a therapeutic like a small molecule or a large molecule or a cell therapy. In all of these contexts, you can apply this virtual loop, but to apply it, you have to change how you do things. You need algorithms that solve problems that are a little different than the ones they solved before and you need lab experiments that are conducted differently than they were conducted before and that's actually what we're trying to do.

Eric Topol (13:39):

Now I did find the quote, I just want to read it so we have it, “biology has the opportunity to be engineering, not science. When something becomes engineering, not science, it becomes exponentially improving. It can compound on the benefits of previous years.” Which is kind of a nice summary of what you just described. Now as we go forward, you mentioned the deep learning origin back at the same time of CRISPR and so many things happening and this convergence continues transformer models obviously one that's very well known, AlphaFold, AlphaFold2, but you work especially in antibodies and if I remember correctly from one of your presentations, there's 20 to the 32nd power of antibody sequences, something like that, so it's right up there with the 10 to the 60th number of small molecules. How do transformer models enhance your work, your discovery efforts?

Aviv Regev (14:46):

And not just in antibodies, I'll give you three brief examples. So absolutely in antibodies it's an example where you have a very large space and you can treat it as a language and transformers are one component of it. There's other related and unrelated models that you would use. For example, diffusion based models are very useful. They're the kind that people are used to when you do things, you use DALL-E or Midjourney and so on makes these weird pictures, think about that picture and not as a picture and now you're thinking about a three-dimensional object which is actually an antibody, a molecule. You also mentioned AlphaFold and AlphaFold 2, which are great advances with some components related to transformers and some otherwise, but those were done as general purpose machines for proteins and antibodies are actually not general purpose proteins. They're antibodies and therapeutic antibodies are even further constrained.

(15:37):

Antibodies also really thrive, especially for therapeutics and also in our body, they need diversity and many of these first models that were done for protein structure really focused on using conservation as an evolutionary signal comparison across species in order to learn the model that predicts the structure, but with antibodies you have these regions of course that don't repeat ever. They're special, they're diverse, and so you need to do a lot of things in the process in order to make the model fit in the best possible way. And then again, this loop really comes in. You have data from many, many historical antibodies. You use that to train the model. You use that model in order to make particular predictions for antibodies that you either want to generate de novo or that you want to optimize for particular properties. You make those actually in the lab and in this way gradually your models become better and better at this task with antibodies.

(16:36):

I do want to say this is not just about antibodies. So for example, we develop cancer vaccines. These are personalized vaccines and there is a component in making a personalized cancer vaccine, which is choosing which antigens you would actually encode into the vaccine and transformers play a crucial role in actually making this prediction today of what are good neoantigens that will get presented to the immune system. You sometimes want to generate a regulatory sequence because you want to generate a better AAV-like molecule or to engineer something in a cell therapy, so you want to put a cis-regulatory sequence that controls gene expression. Actually personally for me, this was the first project where I used a transformer, which we started years ago. It was published a couple of years ago where we learned a general model that can predict in a particular system. Literally you throw a sequence at that model now and it will predict how much expression it would drive. So these models are very powerful. They are not the be all and end all of all problems that we have, but they are fantastically useful, especially for molecular therapeutics.

Good Trouble: Hallucinations

Eric Topol (17:48):

Well, one of the that has been an outgrowth of this is to actually take advantage of the hallucinations or confabulation of molecules. For example, the work of David Baker, who I'm sure you know well at University of Washington, the protein design institute. We are seeing now molecules, antibodies, proteins that don't exist in nature from actually, and all the things that are dubbed bad in GPT-4 and ChatGPT may actually help in the discovery in life science and biomedicine. Can you comment about that?

Aviv Regev (18:29):

Yeah, I think much more broadly about hallucinations and what you want to think about is something that's like constrained hallucination is how we're creative, right? Often people talk about hallucinations and they shudder at it. It sounds to them insane because if you think about your, say a large language model as a search tool and it starts inventing papers that don't exist. You might be like, I don't like that, but in reality, if it invents something meaningful that doesn't exist, I love that. So that constrained hallucination, I'm just using that colloquially, is a great property if it's constrained and harnessed in the right way. That's creativity, and creativity is very material for what we do. So yes, absolutely in what we call the de novo domain making new things that don't exist. This generative process is the heart of drug discovery. We make molecules that didn't exist before.

(19:22):

They have to be imagined out of something. They can't just be a thing that was there already and that's true for many different kinds of therapeutic molecules and for other purposes as well, but of course they still have to function in an effective way in the real world. So that's where you want them to be constrained in some way and that's what you want out of the model. I also want to say one of the areas that personally, and I think for the field as a whole, I find the most exciting and still underused is the capacity of these models to hallucinate for us or help us with the creative endeavors of identifying the causes of processes, which is very different than the generative process of making molecules. Thinking about the web of interactions that exist inside a cell and between cells that drives disease processes that is very hard for us to reason through and to collect all the bits of information and to fill in blanks, those fillings of the blanks, that's our creativity, that's what generates the next hypothesis for us. I'm very excited about that process and about that prospect, and I think that's where the hallucination of models might end up proving to be particularly impressive.

A.I. Accelerated Drug Discovery

Eric Topol (20:35):

Yeah. Now obviously the field of using AI to accelerate drug discovery is extremely hot, just as we were talking about with spatial omics. Do you think that is warranted? I mean you've made a big bet on that you and your folks there at Genentech of course, and so many others, and it's a very crowded space with so many big pharma partnering with AI. What do you see about this acceleration? Is it really going to reap? Is it going to bear fruit? Are we going to see, we've already seen some drugs of course, that are outgrowths, like Baricitinib in the pandemic and others, but what are your expectations? I know you're not one to get into any hyperbole, so I'm really curious as to what you think is the future path.

Aviv Regev (21:33):

So definitely my hypothesis is that this will be highly, highly impactful. I think it has the potential to be as impactful as molecular biology has been for drug discovery in the 1970s and 1980s. We still live that impact. We now take it for granted. But, of course that's a hypothesis. I also believe that this is a long game and it's a deep investment, meaning decorating what you currently do with some additions from right and left is not going to be enough. This lab in the loop requires deep work working at the heart of how you do science, not as an add-on or in addition to or yet another variant on what has become a pretty established approach to how things are done. That is where I think the main distinction would be and that requires both the length of the investment, the effort to invest in, and also the willingness to really go all out, all in and all out.

(22:36):

And that takes time. The real risk is the hype. It's actually the enthusiasm now compared to say 2020 is risky for us because people get very enthusiastic and then it doesn't pay off immediately. No, these iterations of a lab in the loop, they take time and they take effort and they take a lot of changes and at first, algorithms often fail before they succeed. You have to iterate them and so that is actually one of the biggest risks that people would be like, but I tried it. It didn't work. This was just some over-hyped thing. I'm walking away and doing it the old way. So that's where we actually have to keep at it, but also keep our expectations not low in magnitude. I think that it would actually deliver, but understanding that it's actually a long investment and that unless you do it deeply, it's not going to deliver the goods.

Eric Topol (23:32):

I think this point warrants emphasis because the success already we've seen has not been in necessarily discovery and in preliminary validation of new molecules, but rather data mining repurposing, which is a much easier route to go quicker, but also there's so many nodes on past whereby AI can make a difference even in clinical trials, in synthetic efforts to project how a clinical trial will turn out and being able to do toxic screens without preclinical animal work. There's just so many aspects of this that are AI suited to rev it up, but the one that you're working on, of course is the kind of main agenda and I think you framed it so carefully that we have to be patient here, that it has a chance to be so transformative. Now, you touched on the parallels to things like DALL-E and Midjourney and large language models. A lot of our listeners will be thinking only of ChatGPT or GPT-4 or others. This is what you work on, the language of life. This is not text of having a conversation with a chatbot. Do you think that as we go forward, that we have to rename these models because they're known today as language models? Or do you think that, hey, you know what, this is another language. This is a language that life science and biomedicine works with. How do you frame it all?

Large Non-Human Language Models

Aviv Regev (25:18):

First of all, they absolutely can remain large language models because these are languages, and that's not even a new insight. People have treated biological sequences, for example, in the past too, using language models. The language models were just not as great as the ones that we have right now and the data that were available to train models in the past were not as amazing as what we have right now. So often these are really the shifts. We also actually should pay respect to human language. Human language encodes a tremendous amount of our current scientific knowledge and even language models of human language are tremendously important for this scientific endeavor that I've just described. On top of them come language models of non-human language such as the language of DNA or the language of protein sequences, which are also tremendously important as well as many other generative models, representation learning, and other approaches for machine learning that are material for handling the different kinds of data and questions that we have.

(26:25):

It is not a single thing. What large language models and especially ChatGPT, this is an enormous favor for which I am very grateful, is that I think it actually convinced people of the power. That conviction is extremely important when you're solving a difficult problem. If you feel that there's a way to get there, you're going to behave differently than if you're like, nothing will ever come out of it. When people experience ChatGPT actually in their daily lives in basic things, doing things that felt to them so human, this feeling overrides all the intellectual part of things. It's better than the thinking and then they're like, in that case, this could actually play out in my other things as well. That, I think, was actually materially important and was a substantial moment and we could really feel it. I could feel it in my interactions with people before and after how their thinking shifted. Even though we were on this journey from before.

Aviv Regev (27:30):

We were. It felt different.

Eric Topol (27:32):

Right, the awareness of hundreds of millions of people suddenly in end of November 2022 and then you were of course going to Genentech years before that, a couple few years before that, and you already knew this was on the move and you were redesigning the research at Genentech.

Aviv Regev (27:55):

Yes, we changed things well before, but it definitely helps in how people embrace and engage feels different because they've seen something like that demonstrated in front of them in a way that felt very personal, that wasn't about work. It's also about work, but it's about everything. That was very material actually and I am very grateful for that as well as for the tool itself and the many other things that this allows us to do but we have, as you said, we have been by then well on our way, and it was actually a fun moment for that reason as well.

Eric Topol (28:32):

So one of the things I'm curious about is we don't think about the humans enough, and we're talking about the models and the automation, but you have undoubtedly a large team of computer scientists and life scientists. How do you get them to interact? They're of course, in many respects, in different orbits, and the more they interact, the more synergy will come out of that. What is your recipe for fostering their crosstalk?

Aviv Regev (29:09):

Yeah, this is a fantastic question. I think the future is in figuring out the human question always above all and usually when I draw it, like on the slide, you can draw the loop, but we always put the people in the center of that loop. It's very material to us and I will highlight a few points. One crucial thing that we've done is that we made sure that we have enough critical mass across the board, and it played out in different ways. For example, we built a new computational organization, gRED Computational Sciences, from what was before many different parts rather than one consolidated whole. Of course within that we also built a very strong AI machine learning team, which we didn't have as much before, so some of it was new people that we didn't have before, but some of it was also putting it with its own identity.

(29:56):

So it is just as much, not more, but also not less just as much of a pillar, just as much of a driver as our biology is, as our chemistry and molecule making is, as our clinical work is. This equal footing is essential and extremely important. The second important point is you really have to think about how you do your project. For example, when we acquired Prescient, at the time they were three people, tiny, tiny company became our machine learning for drug discovery. It's not tiny anymore, but when we acquired them, we also invested in our antibody engineering so that we could do antibody engineering in a lab in the loop, which is not how we did it before, which meant we invested in our experiments in a different way. We built a department for cell and tissue genomics so we can conduct biology experiments also in a different way.

(30:46):

So we changed our experiments, not just our computation. The third point that I think is really material, I often say that when I'm getting asked, everyone should feel very comfortable talking with an accent. We don't expect our computational scientists to start behaving like they were actually biology trained in a typical way all along, or chemists trained in a typical way all along and by the same token, we don't actually expect our biologists to just embrace wholeheartedly and relinquish completely one way of thinking for another way of thinking, not at all. To the contrary, we actually think all these accents, that's a huge strength because the computer scientist thinks about biology or about chemistry or about medical work differently than a medical doctor or a chemist or a biologist would because a biologist thinks about a model differently and sometimes that is the moment of brilliance that defines the problem and the model in the most impactful way.

(31:48):

We want all of that and that requires both this equal footing and this willingness to think beyond your domain, not just hand over things, but actually also be there in this other area where you're not the expert but you're weird. Talking with an accent can actually be super beneficial. Plus it's a lot of fun. We're all scientists, we all love learning new things. So that's some of the features of how we try to build that world and you kind of do it in the same way. You iterate, you try it out, you see how it works, and you change things. It's not all fixed and set in stone because no one actually wrote a recipe, or at least I didn't find that cookbook yet. You kind of invent it as you go on.

Eric Topol (32:28):

That's terrific. Well, there's so much excitement in this convergence of life science and the digital biology we've been talking about, have I missed anything? We covered human cell atlas, the spatial omics, the lab in the loop. Is there anything that I didn't touch on that you find important?

Aviv Regev (32:49):

There's something we didn't mention and is the reason I come to work every day and everyone I work with here, and I actually think also the people of the human cell atlas, we didn't really talk about the patients.

(33:00):

There's so much, I think you and I share this perspective, there's so much trepidation around some of these new methods and we understand why and also we all saw that technology sometimes can play out in ways that are really with unintended consequences, but there's also so much hope for patients. This is what drives people to do this work every day, this really difficult work that tends not to work out much more frequently than it works out now that we're trying to move that needle in a substantial way. It's the patients, and that gives this human side to all of it. I think it's really important to remember. It also makes us very responsible. We look at things very responsibly when we do this work, but it also gives us this feeling in our hearts that is really unbeatable, that you're doing it for something good.

Eric Topol (33:52):

I think that emphasis couldn't be more appropriate. One of the things I think about all the time is that because we're moving into this, if you will, hyper accelerated phase of discovery over the years ahead with this just unparallel convergence of tools to work with, that somebody could be cured of a condition, somebody could have an autoimmune disease that we will be able to promote tolerogenicity and they wouldn't have the autoimmune disease and if they could just sit tight and wait a few years before this comes, as opposed to just missing out because it takes time to get this all to gel. So I'm glad you brought that up, Aviv, because I do think that's what it's all about and that's why we're cheering for your work and so many others to get it done, get across the goal line because there's these 10,000 diseases out there and there's so many unmet needs across them where we don't have treatments that are very effective or have all sorts of horrible side effects. We don't have cures, and we've got all the things now, as we've mentioned here in this conversation, whether it's genome editing and ability to process massive scale data in a way that never could be conceived some years ago. Let's hope that we help the patients, and go ahead.

Aviv Regev (35:25):

I found the Proust quote, if you want it recorded correctly.

Eric Topol (35:29):

Yeah, good.

Aviv Regev (35:30):

It's much longer than what I did. It says, “the only true voyage, the only bath in the Fountain of Youth would be not to visit strange lands but to possess other eyes, to see the universe through the eyes of another, of a hundred others, to see the hundred universes that each of them sees, that each of them is; and this we do, with great artists; with artists like these we do fly from star to star.”—Marcel Proust

Eric Topol (35:57):

I love that and what a wonderful way to close our conversation today. Aviv, I look forward to more conversations with you. You are an unbelievable gem. Thanks so much for joining today.

Aviv Regev (36:10):

Thank you so much.

*************************************

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Professor Doudna was awarded the 2020 Nobel Prize in Chemistry with Professor Emmanuelle Charpentier for their pioneering work in CRISPR genome editing. The first genome editing therapy (Casgevy) was just FDA approved, only a decade after the CRISPR-Cas9 editing system discovery. But It’s just the beginning of a much bigger impact story for medicine and life science.

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Transcript with links to audio and relevant external links

Eric Topol (00:06):

This is Eric Topol with Ground Truths, and I'm really excited today to have with me Professor Jennifer Doudna, who heads up the Innovative Genomics Institute (IGI) at UC Berkeley, along with other academic appointments, and as everybody knows, was the Nobel laureate for her extraordinary discovery efforts with CRISPR genome editing. So welcome, Jennifer.

Jennifer Doudna (00:31):

Hello, Eric. Great to be here.

Eric Topol (00:34):

Well, you know we hadn't met before, but I felt like I know you so well because this is one of my favorite books, The Code Breaker. And Walter Isaacson did such a wonderful job to tell your story. What did you think of the book?

My interview with Walter Isaacson on The Code Breaker, a book I highly recommend

Jennifer Doudna (00:48):

I thought Walter did a great job. He's a good storyteller, and as you know from probably from reading it or maybe talking to others about it, he wrote a page turner. He actually really dug into the science and all the different aspects of it that I think created a great tale.

Eric Topol (01:07):

Yeah, I recommended highly. It was my favorite book when it came out a couple years ago, and it is a page turner. In fact, I just want to read one, there's so many quotes out of it, but in the early part of the book, he says, “the invention of CRISPR and the plague of Covid will hasten our transition to the third great revolution of modern times. These revolutions arose from the discovery beginning just over a century ago, of the three fundamental kernels of our existence, the atom, the bit, and the gene.” That kind of tells a big story just in one sentence, but I thought I’d start with the IGI, the institute that you have set up at Berkeley and what its overall goals are.

Jennifer Doudna (01:58):

Right. Well, let's just go back a few years maybe to the origins of this institute and my thinking around it, because in the early days of CRISPR, it was clear that we were really at a moment that was quite unique in the sense that there was a transformative technology. It was going to intersect with lots of other discoveries and technologies. And I work at a public institution and my question to myself was, how can I make sure that this powerful tool is first of all used responsibly and secondly, that it's used in a way that benefits as many people as possible, and it's a tall order, but clearly we needed to have some kind of a structure that would allow people to work together towards those goals. And that was really the mission behind the IGI, which was started as a partnership between UC Berkeley and UCSF and now actually includes UC Davis as well.

The First FDA Approved Genome Editing

Eric Topol (02:57):

I didn't realize that. That's terrific. Well, this is a pretty big time because 10 years or so, I guess starting to be 11 when you got this thing going, now we're starting to see, well, hundreds of patients have been treated and in December the FDA approved the first CRISPR therapy for sickle cell disease, Casgevy. Is that the way you say it?

Jennifer Doudna (03:23):

Casgevy, yeah.

Eric Topol (03:24):

That must have felt pretty good to see if you go from the molecules to the bench all the way now to actually treating diseases and getting approval, which is no easy task.

Jennifer Doudna (03:39):

Well, Eric, for me, I'm a biochemist and somebody who has always worked on the fundamentals of biology, and so it's really been extraordinary to see the pace at which the CRISPR technology has been adopted, and not just for fundamental research, but also for real applications. And Casgevy is sort of the crowning example of that so far, is that it's really a technology that we can already see how it's being used to, I think it's fair to say, effectively cure a genetic disease for the first time. Really amazing.

Genome Editing is Not the Same as Gene Therapy

Eric Topol (04:17):

Yeah. Now I want to get back to that. I know there's going to be refinements about that. And of course, there's beta thalassemia, so we've got two already, and our mutual friend Fyodor Urnov would say two down 5,000 to go. But I think before I get to the actual repair of the sickle cell defect molecular defect, I think one of the questions I think that people listeners may not know is the differentiation of genome editing with gene therapy. I mean, as you know, there was recently a gene therapy approval for something like $4.25 million for metachromatic leukodystrophy. So maybe you could give us kind of skinny on how these two fundamental therapies are different.

Jennifer Doudna (05:07):

Right. Well, it's a great question because the terminology sounds kind of the same, and so it could be confusing. Gene therapy goes back decades, I can remember gene therapy being discussed as an exciting new at the time, direction back when I was a graduate student. That was little while ago. And it refers to the idea that we can use a genetic approach for disease treatment or even for a cure. However, it fundamentally requires some mechanism of integrating new information into a genome. And traditionally that's been done using viruses, which are great at doing that. It's just that they do it wherever they want to do it, not necessarily where we want that information to go. And this is where CRISPR comes in. It's a technology allows precision in that kind of genetic manipulation. So it allows the scientist or the clinician to decide where to make a genetic change. And that gives us tremendous opportunity to do things with a kind of accuracy that hasn't been possible before.

Eric Topol (06:12):

Yeah, no question. That's just a footnote. My thesis in college at University of Virginia, 1975, I'm an old dog, was prospects for gene therapy in man. So it took a while, didn't it? But it's a lot better now with what you've been working on, you and your colleagues now and for the last decade for sure. Now, what I was really surprised about is it's not just of course, these hemoglobin disorders, but now already in phase two trials, you've got hereditary angioedema, which is a life-threatening condition, amyloidosis, cancer ex vivo, and also chronic urinary tract infections. And of course, there's six more others like autoimmune diseases like lupus and type 1 diabetes. So this is really blossoming. It's really extraordinary.

Eric Topol (07:11):

I mean, wow. So one of the questions I had about phages, because this is kind of going back to this original work and discovery, antimicrobial resistance is really a big problem and it's a global health crisis, and there's only two routes there coming up with new drugs, which has been slow and not really supported by the life science industry. And the other promising area is with phages. And I wonder, since this is an area you know so well, why haven't we put more, we're starting to see more trials in phages. Why haven't we doubled down or tripled down on this to help the antimicrobial resistance problem?

Jennifer Doudna (08:00):

Well, it's a really interesting area, and as you said, it's kind of one of those areas of science where I think there was interest a while ago and some effort was made for reasons that are not entirely clear to me, at least it fizzled out as a real focused field for a long time. But then more recently, people have realized that there's an opportunity here to take advantage of some natural biology in which viruses can infect and destroy microbes. Why aren't we taking better advantage of that for our own health purposes? So I personally am very excited about this area. I think there's a lot of fundamental work still to be done, but I think there's a tremendous opportunity there as well.

CRISPR 2.0

Eric Topol (08:48):

Yeah, I sure think we need to invest in that. Now, getting back to this sickle cell story, which is so extraordinary. This is kind of a workaround plan of getting fetal hemoglobin built up, but what about actually repairing, getting to fixing the lesion, if you will?

Eric Topol (09:11):

Yeah. Is that needed?

Jennifer Doudna (09:13):

Well, maybe it's worth saying a little bit about how Casgevy works, and you alluded to this. It's not a direct cure. It's a mechanism that allows activation of a second protein called fetal hemoglobin that can suppress the effect of the sickle cell mutation. And it's great, and I think for patients, it offers a really interesting opportunity with their disease that hasn't been available in the past, but at the same time, it's not a true cure. And so the question is could we use a CRISPR type technology to actually make a correction to the genetic defect that directly causes the disease? And I think the answer is yes. The field isn't there quite yet. It's still relatively difficult to control the exact way that DNA editing is occurring, especially if we're doing it in vivo in the body. But boy, many people are working on this, as you probably know. And I really think that's on the horizon.

Eric Topol (10:19):

Yeah. Well, I think we want to get into the in vivo story as well because that, I think right now it's so complicated for a person to have to go through the procedure to get ultimately this treatment currently for sickle cell, whereas if you could do this in vivo and you could actually get the cure, that would be of the objective. Now, you published just earlier this month in PNAS a wonderful paper about the EDVs and the lipid nanoparticles that are ways that we could get to a better precision editing. These EDVs I guess if I have it right, enveloped virus-like particles. It could be different types, it could be extracellular vesicles or whatnot. But do you think that's going to be important? Because right now we're limited for delivery, we're limited to achieve the right kind of editing to do this highly precise. Is that a big step for the future?

Jennifer Doudna (11:27):

Really big. I think that's gating at the moment. Right now, as you mentioned, somebody that might want to get the drug Casgevy for sickle cell disease or thalassemia, they have to go through a bone marrow transplant to get it. And that means that it's very expensive. It's time consuming. It's obviously not pleasant to have to go through that. And so that automatically means that right now that therapy is quite restricted in the patients that it can benefit. But we imagine a day when you could get this type of therapy into the body with a one-time injection. Maybe someday it's a pill that could be taken where the gene editors target the right cells in the body. In diseases like that, it would be the stem cells in the bone marrow and carry out gene editing that can have a therapeutic benefit. And again, it's one of those ideas that sounds like science fiction, and yet already there's tremendous advance in that direction. And I think over the next, I don't know, I'm guessing 5 to 10 years we're going to see that coming online.

Editing RNA, the Epigenome, and the Microbiome

Eric Topol (12:35):

Yeah, I'm guessing just because there's so much work on the lipid nanoparticles to tweak them. And there's four different components that could easily be made so much better. And then all these virus-like proteins, I mean, it may happen even sooner. And it's really exciting. And I love that diagram in that paper. You have basically every organ of the body that isn't accessible now, potentially that would become accessible. And that's exciting because whatever blossoming we're seeing right now with these phase two trials ongoing, then you basically have no limits. And that I think is really important. So in vivo editing big. Now, the other thing that's cropped up in recent times is we've just been focused on DNA, but now there's RNA editing, there's epigenetic or epigenomic editing. What are your thoughts about that?

Jennifer Doudna (13:26):

Very exciting as well. It's kind of a parallel strategy. The idea there would be to, rather than making a permanent change in the DNA of a cell, you could change just the genetic output of the cell and or even make a change to DNA that would alter its ability to be expressed and to produce proteins in the cell. So these are strategies that are accessible, again, using CRISPR tools. And the question is now how to use them in ways that will be therapeutically beneficial. Again, topics that are under very active investigation in both academic labs and at companies.

Eric Topol (14:13):

Yeah. Now speaking of that, this whole idea of rejuvenation, this is Altos. You may I'm sure know my friend here, Juan Carlos Belmonte, who's been pushing on this for some time at Altos now formerly at Salk. And I know you helped advise Altos, but this idea of basically epigenetic, well using the four Yamanaka factors and basically getting cells that go to a state that are rejuvenated and all these animal models that show that it really happens, are you thinking that really could become a therapy in the times ahead in patients for aging or particular ideas that you have of how to use that?

Jennifer Doudna (15:02):

Well, you mentioned the company Altos. I mean, Altos and a number of other groups are actively investigating this. Not I would say specifically regarding genome editing, although being able to monitor and probably change gene functions that might affect the aging process could be attractive in the future. I think the hard question there is which genes do we tweak and how do we make sure that it's safe? And better than me I mean, that's a very difficult thing to study clinically because it takes time for one thing, and we probably don't have the best models either. So I think there are challenges there for sure. But along the way, I feel very excited about the kind of fundamental knowledge that will come from those studies. And in particular, this question of how tissues rejuvenate I think is absolutely fascinating. And some organisms do this better than others. And so, understanding how that works in organisms that are able to say regrow a limb, I think can be very interesting.

Eric Topol (16:10):

And that gets me to that recent study. Well, as you well know, there's a company Verve that's working on the familial hypercholesterolemia and using editing with the PCSK9 through the liver and having some initial, at least a dozen patients have been treated. But then this epigenetic study of editing in mice for PCSK9 also showed results. Of course, that's much further behind actually treating patients with base editing. But it's really intriguing that you can do some of these things without having to go through DNA isn't it?

Jennifer Doudna (16:51):

Amazing, right? Yeah, it's very interesting.

Reducing the Cost of Genome Editing

Eric Topol (16:54):

Wild. Now, one of the things of course that people bring up is, well, this is so darn expensive and it's great. It's a science triumph, but then who can get these treatments? And recently in January, you announced a Danaher-IGI Beacon, and maybe you can tell us a bit about that, because again, here's a chance to really markedly reduce the cost, right?

Jennifer Doudna (17:25):

That's right. That's the vision there. And huge kudos to my colleague Fyodor Urnov, who really spearheaded that effort and leads the team on the IGI side. But the vision there was to partner with a company that has the ability to manufacture molecules in ways that are very, very hard, of course, for academic labs and even for most companies to do. And so the idea was to bring together the best of genome editing technology, the best of clinical medicine, especially focused on rare human diseases. And this is with our partners at UCSF and with the folks in the Danaher team who are experts at downstream issues of manufacturing. And so the hope there is that we can bring those pieces together to create ways of using CRISPR that will be cost effective for patients. And frankly, we'll also create a kind of roadmap for how to do this, how to do this more efficiently. And we're kind of building the plane while we're flying it, if you know what I mean. But we're trying to really work creatively with organizations like the FDA to come up with strategies for clinical trials that will maintain safety, but also speed up the timeline.

Eric Topol (18:44):

And I think it's really exciting. We need that and I'm on the scientific advisory board of Danaher, a new commitment for me. And when Fyodor presented that recently, I said, wow, this is exciting. We haven't really had a path to how to get these therapies down to a much lower cost. Now, another thing that's exciting that you're involved in, which I think crosses the whole genome editing, the two most important things that I've seen in my lifetime are genome editing and AI, and they also work together. So maybe before we get into AI for drug discovery, how does AI come into play when you're thinking about doing genome editing?

Jennifer Doudna (19:34):

Well, the thing about CRISPR is that as a tool, it's powerful not only as a one and done kind of an approach, but it's also very powerful genomically, meaning that you can make large libraries of these guide RNAs that allow interrogation of many genes at once. And so that's great on the one hand, but it's also daunting because it generates large collections of data that are difficult to manually inspect. And in some cases, I believe really very, very difficult to analyze in traditional ways. But imagine that we have ways of training models that can look at genetic intersections, ways that genes might be affecting the behavior of not only other genes, but also how a person responds to drugs, how a person responds to their environment and allows us to make predictions about genetic outcomes based on that information. I think that's extremely exciting, and I definitely think that over the next few years we'll see that kind of analysis coming online more and more.

Eric Topol (20:45):

Yeah, the convergence, I think is going to be, it's already being done now, but it's just going to keep building. Now, Demis Hassabis, who one of the brilliant people in the field of AI leads the whole Google Deep Mind AI efforts now, but he formed after AlphaFold2 behaving to predict proteins, 200 million proteins of the universe. He started a company Isomorphic Labs as a way to accelerate using AI drug discovery. What can you tell us about that?

Jennifer Doudna (21:23):

It's exciting, isn't it? I'm on the SAB for that company, and I think it's very interesting to see their approach to drug discovery. It's different from what I've been familiar with at other companies because they're really taking a computational lens to this challenge. The idea there is can we actually predict things like the way a small molecule might interact with a particular protein or even how it might interact with a large protein complex. And increasingly because of AlphaFold and programs like that, that allow accurate prediction of structures, it's possible to do that kind of work extremely quickly. A lot of it can be done in silico rather than in the laboratory. And when you do get around to doing experiments in the lab, you can get away with many fewer experiments because you know the right ones to do. Now, will this actually accelerate the rate at which we get to approved therapeutics? I wonder about your opinion about that. I remain unsure.

Editing Out Alzheimer’s Risk Alleles

Eric Topol (22:32):

Yeah. I mean, we have one great success story so far during the pandemic Baricitinib, a drug that repurposed here, a drug that was for rheumatoid arthritis, found by data mining that have a high prospects for Covid and now saves lives in Covid. So at least that's one down, but we got a lot more here too. But it, it's great that Demis recruited you on the SAB for Isomorphic because it brings in a great mind in a different field. And it goes back to one of the things you mentioned earlier is how can we get some of this genome editing into a pill someday? Wow. Now, one of the things that for personal interest, as an APOE4 carrier, I'm looking to you to fix my APOE4 and give me APOE2. How can I expect to get that done in the near future?

Jennifer Doudna (23:30):

Oh boy. Okay, we'll have to roll up our sleeves on that one. But it is appealing, isn't it? I think about it too. It's a fascinating idea. Could we get to a point someday where we can use genome editing as a prophylactic, not as a treatment after the fact, but as a way to actually protect ourselves from disease? And the APOE4 example is a really interesting one because there's really good evidence that by changing the type of allele that one has for the APOE gene, you can actually affect a person's likelihood of developing Alzheimer's in later life. But how do we get there? I think one thing to point out is that right now doing genome editing in the brain is, well, it's hard. I mean, it's very hard.

Eric Topol (24:18):

It a little bit's been done in cerebral spinal fluid to show that you can get the APOE2 switch. But I don't know that I want to sign up for an LP to have that done.

Jennifer Doudna (24:30):

Not quite yet.

Eric Topol (24:31):

But someday it's wild. It's totally wild. And that actually gets me back to that program for coronary heart disease and heart attacks, because when you're treating people with familial hypercholesterolemia, this extreme phenotype. Someday and this goes for many of these rare diseases that you and others are working on, it can have much broader applicability if you have a one-off treatment to prevent coronary disease and heart attacks and you might use that for people well beyond those who have an LDL cholesterol that are in the thousands. So that's what I think a lot of people don't realize that this editing potential isn't just for these monogenic and rare diseases. So we just wanted to emphasize that. Well, this has been a kind of wild ride through so much going on in this field. I mean, it is extraordinary. What am I missing that you're excited about?

Jennifer Doudna (25:32):

Well, we didn't talk about the microbiome. I'll just very briefly mention that one of our latest initiatives at the IGI is editing the microbiome. And you probably know there are more and more connections that are being made between our microbiome and all kinds of health and disease states. So we think that being able to manipulate the microbiome precisely is going to open up another whole opportunity to impact our health.

Can Editing Slow the Aging Process?

Eric Topol (26:03):

Yeah, I should have realized that when I only mentioned two layers of biology, there's another one that's active. Extraordinary, just going back to aging for a second today, there was a really interesting paper from Irv Weissman Stanford, who I'm sure you know and colleagues, where they basically depleted the myeloid stem cells in aged mice. And they rejuvenated the immune system. I mean, it really brought it back to life as a young malice. Now, there probably are ways to do that with editing without having to deplete stem cells. And the thought about other ways to approach the aging process now that we're learning so much about science and about the immune system, which is one of the most complex ones to work in. Do you have ideas about that are already out there that we could influence the aging process, especially for those of us who are getting old?

Jennifer Doudna (27:07):

We're all on that path, Eric. Well, I guess the way that I think about it is I like to think that genome editing is going to pave the way to make those kinds of fundamental discoveries. I still feel that there's a lot of our genetics that we don't understand. And so, by being able to manipulate genes precisely and increasingly to look at how genes interact with each other, I think one fundamental question it relates to aging actually is why do some of us age at a seemingly faster pace than others? And it must have to do at least in part with our genetic makeup and how we respond to our environment. So I definitely think there are big opportunities there, really in fundamental research initially, but maybe later to actually change those kinds of things.

Eric Topol (28:03):

Yeah, I'm very impressed in recent times how much the advances are being made at basic science level and experimental models. A lot of promise there. Now, is there anything about this field that you worry about that keeps you up at night that you think, besides, we talked about that we got to get the cost down, we have to bridge health inequities for sure, but is there anything else that you're concerned about right now?

Jennifer Doudna (28:33):

Well, I think anytime a new technology goes into clinical trials, you worry that things may get out ahead of their skis, and there may be some overreach that happens. I think we haven't really seen that so far in the CRISPR field, which is great. But I guess I remain cautious. I think that we all saw what happened in the field of gene therapy now decades ago, but that really put a poll on that field for a long time. And so, I definitely think that we need to continue to be very cautious as gene editing continues to advance.

Eric Topol (29:10):

Yeah, no question. I think the momentum now is getting past that point where you would be concerned about known unknowns, if you will, things that going back to the days of the Gelsinger crisis. But it's really extraordinary. I am so thrilled to have this conversation with you and to get a chance to review where the field is and where it's going. I mean, it's exploding with promise and potential well beyond and faster. I mean, it takes a drug 17 years, and you've already gotten this into two treatments. I mean, I'm struck when you were working on this, how you could have thought that within a 10-year time span you'd already have FDA approvals. It's extraordinary.

Jennifer Doudna (30:09):

Yeah, we hardly dared hope. Of course, we're all thrilled that it went that fast, but I think it would've been hard to imagine it at the time.

Eric Topol (30:17):

Yeah. Well, when that gets simplified and doesn't require hospitalizations and bone marrow, and then you'll know you're off to the races. But look, what a great start. Phenomenal. So congratulations. I'm so thrilled to have the chance to have this conversation. And obviously we're all going to be following your work because what a beacon of science and progress and changing medicine. So thanks and give my best to my friend there at IGI, Fyodor, who's a character. He's a real character. I love the guy, and he's a good friend.

Jennifer Doudna (30:55):

I certainly will Eric, and thank you so much. It's been great talking with you.

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Note: This podcast is a companion to the Ground Truths newsletter “A Big Week for GLP-1 Drugs”

Eric Topol (00:06):

It is Eric Topol with Ground Truths, and with me today is Dr. Daniel Drucker from the University of Toronto, who is one of the leading endocrinologists in the world, and he along with Joel Habener and Jens Juul Holst from the University of Copenhagen and Denmark, have been credited with numerous prizes of their discovery work of glucagon-like peptide-1 (GLP-1) as we get to know these family of drugs and he's a true pioneer. He's been working on this for decades. So welcome, Daniel.

Daniel Drucker (00:43):

Thank you.

Eric Topol (00:45):

Yeah, it's great to have you and to get the perspective, one of the true pioneers in this field, because to say it's blossom would be an understatement, don't you think?

Daniel Drucker (00:57):

Yeah, it's been a bit of a hectic three years. We had a good quiet 30 plus years of solid science and then it's just exploded over the last few years.

Eric Topol (01:06):

Yeah, back in 30 years ago, did you have any sense that this was coming?

Daniel Drucker (01:14):

Not what we're experiencing today, I think there was a vision for the diabetes story. The first experiments were demonstrating insulin secretion and patents were followed around the use for the treatment of GLP-1 for diabetes. The food intake story was much more gradual and the weight loss story was quite slow. And in fact, as you know, we've had a GLP-1 drug approved for people with obesity since 2014, so it's 10 years since liraglutide was approved, but it didn't really catch the public's attention. The weight loss was good, but it wasn't as spectacular as what we're seeing today. So this really has taken off just over the last three, four years.

Eric Topol (01:58):

Yeah, no, it's actually, I've never seen a drug class like this in my life, Daniel. I mean, I've obviously witnessed the statins, but this one in terms of pleiotropy of having diverse effects, and I want to get to the brain here in just a minute because that seems to be quite a big factor. But one thing just before we get too deep into this, I think you have been great to recognize one of your colleagues who you work with at Harvard, Svetlana Mojsov. And the question I guess is over the years, as you said, there was a real kind of incremental path and I guess was in 1996 when you said, well, this drug likely will inhibit food intake, but then there were gaps of many years since then, as you mentioned about getting into the obesity side. Was that because there wasn't much weight loss in the people with diabetes or was it related to the dose of the drugs that were being tested?

Why Did It Take So Long to Get to Obesity?

Daniel Drucker (03:11):

Well, really both. So the initial doses we tested for type 2 diabetes did not produce a lot of weight loss, maybe 2-3%. And then when we got semaglutide for type 2 diabetes, maybe we were getting 4-5% mean weight loss. And so that was really good and that was much better than we achieved before with any glucose lowering drug. But a lot of credit goes to Novo Nordisk because they looked at the dose for liraglutide and diabetes, which was 1.8 milligrams once daily for people with type 2 diabetes. And they asked a simple question, what if we increase the dose for weight loss? And the answer was, we get better weight loss with 3 milligrams once a day. So they learn that. And when they introduced semaglutide for type 2 diabetes, the doses were 0.5 and 1 milligrams. But in the back of their minds was the same question, what if we increased the dose and they landed on 2.4 milligrams once a week. And that's when we really started to see that the unexpected spectacular weight loss that we're now quite familiar with.

Eric Topol (04:16):

Was there also something too that diabetics don't lose as much weight if you were to have match dose?

Daniel Drucker (04:22):

Yeah, that's a general phenomenon. If one goes from either diet to bariatric surgery, and certainly with weight loss medicines, we tend to see maybe two thirds to three quarters of the amount of weight loss in people with type 2 diabetes. We don't really understand it. The brain pathways are probably resistant to some of the pathways that are activated that lead to weight loss, and it's really an interesting observation that needs further study.

The Brain Effect

Eric Topol (04:50):

Yeah, it's fascinating really. And it might've at least in part, held up this progress that has been truly remarkable. Now, recently you published a paper among many, you're a very prolific scientist, of course, physician scientist, but back in December in Cell Metabolism was a very important paper that explored the brain gut axis, the ability to inhibit inflammation and the mechanism through Toll-like receptors that you were seeing that. So maybe you could summarize the fact that you saw this, you were quoted in this Atlantic piece by Sarah Zhang, the science behind Ozempic was wrong. The weight loss effects of GLP-1 drugs have little to do with the gut and basically claiming that it's related to the effects on the brain, which of course could be reduced inflammation, reduced or inhibiting centers of addiction craving, that sort of thing. So how do you interpret your recent results and ongoing studies regarding GLP-1's effect on the brain?

Daniel Drucker (06:02):

Sure, so to be clear, I don't think that was a quote. I never would've said the science behind Ozempic was wrong. I think that was a headline writer doing what they do best, which is catching people's attention. I think what I was trying to say is that where this field started with insulin secretion first and then weight loss second, those are clearly very important pharmacological attributes of GLP-1. But physiologically, if we take GLP-1 away or we take the receptor away, you don't really develop diabetes without GLP-1. You don't really gain a lot of weight without GLP-1. So physiologically it's not that important. Why do we have GLP-1 in the distal gut? I think physiologically it's there to defend against infection and reduce gut inflammation. But we noticed that GLP-1 reduces inflammation in many different places in the heart and blood vessels and in the liver and many organs where you don't see a lot of GLP-1 receptors and you don't see a lot of GLP-1 receptors on immune cells.

Daniel Drucker (07:04):

So that really led us to the question, well, how does it work and affect all these organs where we don't see a lot of the receptors? And that's where we landed on the brain. Obviously the nervous system can communicate with many different cell types in almost every organ. And we identified neurons that expressed the GLP-1 receptor, which when blocked abrogated or completely eliminated the ability of GLP-1 to reduce inflammation in the periphery in white cells or in lungs. So it's been known for some time that the brain can control the immune system. So this is just the latest piece in the puzzle of how GLP-1 might reduce inflammation.

Eric Topol (07:49):

And just to be clear, I was quoting the Atlantic headline, not you that you were quoted within that article, but this is something that's really interesting because obviously GLP-1 is made in the brain in certain parts of the brain, it's transient in terms of its half-life made from the gut. But when we give these drugs, these agonists, how does it get in the brain? Because isn't there a problem with the blood brain barrier?

Daniel Drucker (08:22):

So I don't think the drugs get into the brain very well. We have a lot of data on this, so people have done the classic experiments, they either make radioactive ligands or fluorescent ligands, and they look how much gets in it and not very much gets in beyond the blood-brain barrier. And we also have big drugs that are immunoglobulin based and they work really well, so they don't get into the brain very much at all. And so, the way I describe this is that GLP-1 talks to the brain, but it doesn't directly get into the brain to meaningful extent, it does communicate somewhat there are areas obviously that are accessible in the area of the stream and circumventricular organs, but most of the time we have this communication that's not well understood that results in the magic that we see. And there are some discussions around for the neurodegenerative disease story where GLP-1 is being looked at in Parkinson's disease and in people with Alzheimer's disease. Would you be able to get more benefit if you could get the drugs into the brain to a greater extent, or would you simply increase the adverse event profile and the adverse response? So really important area for study as we begin to go beyond diabetes and obesity.

Eric Topol (09:41):

Yeah, I mean as you're pointing out, there's two ongoing trials, pretty large trials in Alzheimer's, early Alzheimer's, which may be a little bit too late, but at any rate, testing GLP-1 to see whether or not it could help prevent progression of the disease. And as you also mentioned, diseases and Parkinson's. But I guess, so the magic as you referred to it, the gut -brain axis so that when you give the GLP-1 family of drugs, we'll talk more about the double and triple receptor in a moment, but when you give these drugs, how does the message you get from the gut to the brain would you say?

Daniel Drucker (10:27):

So pharmacologically, we can give someone or an animal the drug, it does reach some of the accessible neurons that have GLP-1 receptors, and they probably transmit signals deeper into the brain and then activate signal transduction. So one way to look at it, if you use c-fos, the protein, which is an immediate early gene, which is increased when we activate neurons, we see rapid activation of c-fos in many regions that are deep within the brain within minutes. And we know that GLP-1 is not getting directly to those neurons, but it's activating pathways that turn on those neurons. And so, there's probably a very intricate set of pathways that sense the GLP-1 and the accessible neurons and then transmit those signals deeper into the brain.

Double and Triple Receptor Agonists

Eric Topol (11:18):

Okay, well that makes sense. Now, as this has been moving along in obesity from semaglutide to tirzepatide and beyond, we're seeing even more potency it appears, and we have now double and triple receptors adding into glucagon itself and the gastric inhibitory polypeptide, and there's mixed data. So for example, the Amgen drug has the opposite effect on GIP as does the dual receptor, but comes out with the same weight loss I guess. How do we understand, I mean you know these gut hormones inside and out, how do we get such disparate results when you're either blocking or revving up a peptide effect?

Daniel Drucker (12:13):

Yeah, it's a mystery. I always sort of joke that you've invited the wrong person because I don't fully understand how to reconcile this honestly. There are some theories you could say that tirzepatide may possibly desensitize the GIP receptor, and that would align with what the GIP receptor blocking component is. And so, I think we need a lot of research, we may actually never know in humans how to reconcile these observations. I think we can do the experiments in animals, we're doing them, other people are doing them to look at the gain and loss of function and use best genetics. But in humans, you'd have to block or activate these receptors in very specific populations for a long period of time with tools that we probably don't have. So we may not reconstruct. We may end up with Maritide from Amgen that's producing 15-20% plus weight loss and tirzepatide from Lilly, that's spectacular, that's producing more than 20% weight loss. And yet as you mentioned at the GIP level, they have opposite effect. So I don't think we fully understand. Maybe your next guest will explain it to you and invite me on. I'd be happy to listen.

Eric Topol (13:27):

Well, I don't know. I don't think anybody can explain it. You've done it as well as I think as possible right now. But then we have the triple receptor, which it seems like if you take that drug, you could just go kind of skeletal. It seems like there's no plateau and its effect, that is I guess is it retatrutide, is that the name of it?

Daniel Drucker (13:47):

Retatrutide, yeah.

Eric Topol (13:48):

Retatrutide, okay. And then of course we're going on with potentially oral drugs or drugs that last for a year. And where do you see all that headed?

Daniel Drucker (14:00):

So I think the way I describe innovation in this field is there are two buckets that we've talked about today. So one bucket is the new molecule, so we're going to have all kinds of different combinations that will be peptides, that will be small molecule orals, the NIH is funding innovative programs to see if we can develop cell-based factories that produce GLP-1. There are gene editing and gene therapy approaches. So there are going to be multiple different molecular approaches to delivering molecules that are better and hopefully easier to take maybe once monthly, maybe every six months. So that's really exciting. And the other obvious bucket is the disease that we're targeting, so we started off with type 2 diabetes. We're now firmly established in the obesity field. In your field, we've seen consistently positive cardiovascular outcome trials. We had a press release a few months ago in October - November saying that semaglutide reduces chronic kidney disease. We have trials underway with peripheral artery disease with Parkinson's disease, with Alzheimer's and a number of neuropsychiatric conditions. So I think we're going to see both innovation on the molecule side as well as expanding if the trials are positive, expanding clinical indication. So it's going to be a pretty exciting next couple of years.

Eric Topol (15:21):

Right, no question. And as you well know, just in the past week, the FDA gave the green light for using these drugs for heart failure with preserved ejection fraction, which was an important randomized trial that showed that. Now there's got to be some downsides of course there's no drug that's perfect. And I wanted to get your comments about muscle loss, potentially bone density reduction. What are the downsides that we should be thinking about with these drugs?

Side Effects

Daniel Drucker (15:54):

Sure, so the known side effects are predominantly gastrointestinal. So we have nausea, diarrhea, constipation and vomiting. And very importantly, if those side effects are severe enough that someone can't eat and drink for 24 hours, we need to tell them you have to seek medical attention because some people will get dehydrated and rarely get acute kidney injury. This is rare, but it's described in many of the outcome trials, and we definitely want to avoid that. Gallbladder events are probably one in several hundred to one in a thousand, and that can be anywhere from gallbladder inflammation to gallbladder stones to biliary obstruction. Don't fully understand that although GLP-1 does reduce gallbladder motility, so that may contribute. And then very rarely we're seeing reports of small bowel obstruction in some people difficult to sort out. We don't really see that in the large clinical trials, but we have to take people at there were, we haven't seen an imbalance in pancreatitis, we haven't seen an imbalance of cancer.

Daniel Drucker (17:01):

There is no evidence for clinically significant bone disease either at the level of reduced bone densities or more importantly at the level of fractures. And we have a lot of real world data that's looked at that. Now muscle losses is really interesting. So when the initial drugs were approved, they didn't produce much weight loss. We didn't think about it. Now that we're getting the 15 20% plus, the question is, will we see clinically significant sarcopenia? And I use the word clinically significant carefully. So we definitely see muscle lean mass loss on a DEXA scan, for example. But what we're not seeing so far are people who are saying, you know what my grip strength is weak. I can't get up off the chair. I have trouble reaching up into the cupboard. My exercise or walking capacity is limited. We’re not seeing that. In fact, we’re seeing the opposite.

Daniel Drucker (17:53):

As you might expect, people are losing weight, they’re less achy, they can move more, they can exercise more. So the question is buried within that data, are there some individuals with real clinical sarcopenia? And as we get to 25% weight loss, it’s very reasonable to expect that maybe we will see some individuals with clinical sarcopenia. So you’re very familiar. There are half a dozen companies developing medicines to promote fat mass loss and spare muscle with or without semaglutide or tirzepatide. And this is a really interesting area to follow, and I don’t know how it’s going to turn out. We really have to see if we are going to see enough clinically significant muscle loss and sarcopenia to merit a new drug category emerge, so fascinating to follow us.

Eric Topol (18:46):

No, I’m so glad you reviewed that because the muscle loss, it could be heterogeneous and there could be some people that really have some substantial sarcopenia. We’ll learn more about that. Now that gets me to what do we do with lifelong therapy here, Daniel, where are we going? Because it seems as though when you stop these drugs, much of the benefit can be not potentially all, but a substantial amount could be lost over time. Is this something that you would view as an insulin and other hormonal treatments or how do you see it?

The Question of Rebound

Daniel Drucker (19:26):

Yeah, so it’s fascinating. I think that traditional view is the one that you just espoused. That is you stop the drug, you regain the weight, and people are concerned about the rebound weight and maybe gaining more fat and having less favorable body composition. But if you look at the data, and it’s coming very fast and furious. A few months ago, we saw data for a tirzepatide trial, one of the surmount obesity trials, the first author was Louis Aronne in New York and they gave people tirzepatide or placebo for 38 weeks. And then they either continue the tirzepatide or stop the tirzepatide. One year later, so no tirzepatide for one year, more than 40% of the people still managed to keep at least 10% of their weight off, which is more than enough in many people to bestow considerable metabolic health. So I think there are going to be people that don't need to take the medicines all the time for weight loss, but we must remember that when we're excited about heart attacks and strokes and chronic kidney disease, there's no evidence that you can stop the medicines and still get the benefits to reduce those chronic complications.

Daniel Drucker (20:46):

So we're going to have to get much more sophisticated in terms of a personalized and precision medicine approach and ask what are the goals? And if the goals are to reduce heart attack strokes and death, you probably need to stay on the medicine if the goals are to achieve weight loss so that you can be metabolically healthy, there may be a lot of people who can come off the medicine for considerable amounts of time. So we're just learning about this. It's very new and it's really exciting.

Suppressing Inflammation as the Common Thread

Eric Topol (21:11):

Yeah, no question. And just going back to the inflammation story in heart disease, it was notable that there were biomarkers of reduced inflammation in the intervention trial before there was any evidence of weight loss. So the anti-inflammatory effects here seem to be quite important, especially with various end organ benefits. Would you say that's true?

Daniel Drucker (21:35):

Yeah, I think that's one of my favorite sort of unifying theories. If we step back for a minute and we come into this and we say, well, here's a drug that improves heart disease and improves liver inflammation and reduces chronic kidney disease and may have some effect on atherosclerosis and is being studied with promising results and neurodegenerative disease, how do we unify all that? And one way is to say all of these chronic disorders are characterized by a component of chronic inflammation. And Eric, it's fascinating. I get reports from random strangers, people who've been on tirzepatide or people who have been on semaglutide, and they tell me, and you'll be fascinated with this, they tell me, my post Covid brain fog is better since I started the drug. They send me pictures of their hands. These are people with chronic arthritis. And they say, my hands have never looked better since I started the drug. And they tell me they've had ulcerative colitis for years on biologics and all of a sudden it's in remission on these drugs. So these are case reports, they're anecdotes, but they're fascinating and quite consistent with the fact that some people may be experiencing an anti-inflammatory effect of these medicines.

Eric Topol (22:55):

And I think it's notable that this is a much more potent anti-inflammatory effect than we saw from statins. I mean, as you know, well they have an effect, but it's not in the same league, I don't think. And also the point you made regarding this is a very good candidate drug class for Long Covid and for a variety of conditions characterized by chronic inflammation. In fact, so many of our chronic diseases fit into that category. Well, this is fascinating, and by the way, I don't know if you know this, but we were both at Johns Hopkins at the same time when you were there in the early eighties. I was there as a cardiology fellow, but we never had a chance to meet back then.

Daniel Drucker (23:41):

So were you just ahead of Cricket Seidman and the whole team there, or what year was that?

Eric Topol (23:46):

Just before them, that's right. You were there doing, was it your internship?

Daniel Drucker (23:50):

I was doing an Osler internship. I think Victor McKusick loved to have a Canadian every year to recognize Osler, one of the great Canadians, and I was just lucky to get the slot that year.

Eric Topol (24:04):

Yeah, it's wild to have watched your efforts, your career and your colleagues and how much of a profound impact. If you were to look back though, and you were to put this into perspective because there were obviously many other hormones along the way, like leptin and so many others that were candidates to achieve what this has. Do you think there's serendipity that play out here or how do you kind of factor it all together?

Daniel Drucker (24:38):

Well, there there's always serendipity. I mean, for decades when people would write review articles on the neuropeptides that were important for control of hunger and satiety and appetite circuits, I would open the article, read it, and I'd say, darn, there's no GLP-1 on the figure. There's no GLP-1 or receptor on the figure, but there's leptin and agouti and the POMC peptides and all the melanocortin and so on and so forth, because physiologically, these systems are not important. As I mentioned, you don't see childhood obesity or genetic forms of obesity in people with loss of function mutations in the GLP-1 sequence or in the GLP-1 receptor. You just don't see a physiologically important effect for having low GLP-1 or having no GLP-1. And that's of course not the case for mutations in NPY or the melanocortin or leptin, et cetera.

Other Effects

Daniel Drucker (25:36):

But pharmacologically, it's been extraordinarily difficult to make drugs out of these other peptides and pathways that we talked about. But fortuitously or serendipitously, as you point out, these drugs seem to work and amazingly GPCRs are notoriously prone to desensitization. We use that in clinical medicine to turn off entire circuits. And thankfully what goes away with GLP-1 are the adverse effects. So nausea, vomiting, diarrhea, constipation, we see those during the first few weeks and then there’s tachyphylaxis, and they generally go away in most people, but what doesn't go away through good fortune are the ability of GLP-1 to talk to those brain circuits and say, you know what? You're not hungry. You don't need to eat. You don't need to think about food. And that's just good luck. Obviously pharmacologically that's benefited all of us working in this area.

Eric Topol (26:31):

It's extraordinary to be able to get desensitized on the adverse effects and not lose the power of the benefit. What about addiction that is, whether it's alcohol, cigarettes, gambling, addictive behavior, do you see that that's ultimately going to be one of the principal uses of these drugs over time?

Daniel Drucker (26:55):

The liver docs, when I give a talk at a metabolic liver disease meeting, they say we love GLP-1 because not only might it take care of liver disease, but there are still some people that we see that are having problems with alcohol use disorders and it might also reduce that. And obviously there are tons of anecdotes that we see. If you go on social media, and you'll see lots of discussion about this, and there's a hundred or so animal paper showing that addiction related dependence behaviors are improved in the context of these medicines. But we don't have the clinical data. So we have a couple of randomized clinical trials, small ones in people with alcohol use disorder, very unimpressive data. We had a trial in people with smoking, didn't really see much, although interestingly, they noted that people drank less alcohol than they did the smoking trial. So there are dozens and dozens of trials underway now, many investigator initiated trials looking at whether it's nicotine or cocaine or cannabinoids or all kinds of compulsive behaviors. I think in the next 12 to 24 months, we're going to start to learn are these real bonafide effects that are seen in large numbers of people or are these just the anecdotes that we won't get a very good complete response. So it's really exciting neuroscience and we're going to learn a lot over the next couple of years.

Eric Topol (28:20):

Yeah, no, it's a fascinating area which just extends the things that we've been discussing. Now, let's say over time, over the years ahead that these drugs become because of the competition and various factors, perhaps in pill form or infrequent dosing, they become very inexpensive, not like they are today.

Daniel Drucker (28:44):

That'd be great speaking as a non-pharmaceutical physician.

Eric Topol (28:48):

Yeah, yeah, no, these companies, which of course as you well know, it accounts for the number one economy in Denmark and is having a big impact in Europe. And obviously Eli Lilly is now the most valued biopharma company in the world from all these effects are coming from this drug class, but let's just say eventually it's not expensive and the drug companies are not gouging and pleasing their investors, and we're in a different world. With all these things that we've been discussing, do you foresee a future where most people will be taking one form or another of this family of drugs to prevent all these chronic conditions that we've just been discussing independent of obesity, type 2 diabetes, the initial frontier? Do you think that's possible?

Daniel Drucker (29:42):

Yeah, I'm a very conservative data-driven person. So today we don't have the data. So if I was in charge of the drinking water supply in your neighborhood and I had unlimited free cheap GLP-1, I wouldn't dump it in there just yet. I don't think we have the data, but we have trials underway, as you noted for Alzheimer's disease, a challenging condition for our society with a huge unmet need if like fingers crossed, if semaglutide does show a benefit for people living with early Alzheimer's disease, if it helps for Parkinson's, if it helps for metabolic liver disease, there are also studies looking at aging, et cetera. So it's possible one day if we have a lot more data that we will begin to think, okay, maybe this is actually a useful medicine that should deserve much more exposure, but today we just don't have the data.

Eric Topol (30:38):

Absolutely. I couldn't agree more, but just wanted to get you kind of speculate on that a bit off script if you will, but what your thoughts were, because this will take a long time, get to that point, but you just kind of wonder when you have an absence of chronic significant side effects overall with these diverse and relatively potent benefits that cut across many organ systems and as you just mentioned, might even influence the aging process, the biologic process.

Worsening Inequities

Daniel Drucker (31:10):

There's another related sort of angle to this, which is that the accessibility of these medicines is very challenging even in well-developed countries, the United States, Europe, et cetera, and we have hundreds of millions of people in the global south and less well-developed economies that are also challenged by heart disease and diabetes and obesity and chronic kidney disease and liver disease. And I think we need to start having conversations and I think they are happening just like we did for HIV and just like we did for hepatitis and certainly we did very quickly for the Covid vaccines. We need to think out of the box and say we need to help people in other parts of the world who may not have access to the medicines in their current form and at their current pricing. And I think these are really important moral and ethical discussions that need to be happening now because soon we will have small molecules and the price will come down and we need to make sure it's not just people in well-developed countries that can afford access to these medicines. I think this is a great opportunity for pharmaceutical companies and the World Health Organization and other foundations to really think broadly about how we can benefit many more people.

Eric Topol (32:29):

I couldn't agree with you more and I'm so glad you emphasize that because we can't wait for these prices to come down and we need creative ways to bridge, to reduce inequities in a vital drug class that's emerged to have far more applicability and benefit than it was initially envisioned, certainly even 5, 10 years ago, no less 30 years ago when you got on it. So Daniel, I can't thank you enough for this discussion. Really a candid discussion reviewing a lot of the things we do know, don't know will know someday perhaps. I just want to note, I know so many people are cheering for you and your colleagues to get recognized further like by the Nobel folks in the years ahead. I think it's pretty darn likely and hopefully when we get a chance to visit again in the years ahead, we'll unravel some of the things that we discussed today that we didn't know the answers and that you as a really an authority and pioneer in the field. Also, I could admit that there's a ways to go to really understand the boundaries if there are boundaries here for how these drugs are going to be used in the years ahead.

Daniel Drucker (33:51):

Yeah, it's another great story for basic science and bench to bedside, and it's just another story where none of us could have predicted the outcomes that we're talking about today to their full extent. And so to the extent that we can convince our governments and our funding agencies to really fund discovery science, the benefits are never apparent immediately. But boy, do they ever come in spades later on in an unpredictable manner. And this is just a great example.

Eric Topol (34:20):

Yeah, I also would say that this work cracking the case of obesity, which has been a stumbling block, I ran a big trial with Rimonabant, which was a failure with the neuropsychiatric side effects and suicidal ideation that had to get dropped. And there's many others like that as you know, very well Fen-Phen, and a long list. And the fact that this could do what it's doing and well beyond just obesity is just spectacular. And what I think it does, what you just mentioned, Daniel, is the basic science work that led to this is I think an exemplar of why we should put in these efforts and not expect immediate benefits, dividends of those efforts. Because look what's happened here. If you can break through with obesity, imagine what lies ahead. So thanks so much for joining and we'll look forward to continuing to follow your work. I know you're publishing the same pace, exceptional prolific pace over many, many years, and I'm sure that's going to continue.

Daniel Drucker (35:34):

Well, I have a great team and so it's a pleasure me to go into work and talk to them every day.

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Siddhartha Mukherjee is a Professor at Columbia University, oncologist, and extraordinary author of Emperor of All Maladies (which was awarded a Pulitzer Prize), The Gene, and The Song of the Cell, along with outstanding pieces in the New Yorker. He is one of the top thought leaders in medicine of our era.

“I have begun to imagine, think about what it would be to be a digital human..”—Sid Mukherjee

Eric Topol (00:06):

Well, hello, this is Eric Topol with Ground Truths, and I am delighted to have my friend Sid Mukherjee, to have a conversation about all sorts of interesting things. Sid, his most recent book, SONG OF THE CELL is extraordinary. And I understand, Sid, you're working on another book that may be cell related. Is that right?

Sid Mukherjee  (00:30):

Eric, it's not cell related, I would say, but it's AI and death related, and it covers, broadly speaking, it covers AI, longevity and death and memory —topics that I think are universal, but also particularly medicine.

Eric Topol (00:57):

Well, good, and we'll get into that. I had somehow someone steered me that your next book was going to be something building on the last one, but that sounds even more interesting. You're going in another direction. You've covered cancer gene cells, so I think covering this new topic is of particularly interest. So let's get into the AI story and maybe we'll start off with your views on the healthcare side. Where do you think this is headed now?

A.I. and Drug Discovery

Sid Mukherjee  (01:29):

So I think Eric, there are two very broad ways of dividing where AI can enter healthcare, and there may be more, I'm just going to give you two, but there may be more. One is on what I would call the deep science aspect of it, and by that I mean AI-based drug discovery, AI-based antibody discovery, AI-based modeling. All of which use AI tools but are using tools that have to do with machine learning, but may have to do less directly with the kind of large language models. These tools have been in development for a long time. You and I are familiar with them. They are tools. Very simply put, you can imagine that the docking of a drug to a protein, so imagine every drug, every medicine as a small spaceship that docks onto a large spaceship, the large spaceship being the target.

(02:57):

So if you think of it that way, there are fundamental rules. If anyone's watched Star Wars or any of these sci-fi films, there are fundamental rules by which that govern the way that the small spaceship in this case, a molecule like aspirin fits into a pocket of its target, and those are principles that are determined entirely by chemistry and physics, but they can be taught, you can learn what kind of spaceship or molecule is likely to fit into what kind of pocket of the mothership, in this case, the target. And if they can be learned, they're amenable to AI-based discovery.

Eric Topol (03:57):

Right. Well, that's, isn't that what you'd call the fancy term structure-based discovery, where you're using such tools like what AlphaFold2 for proteins and then eventually for antibodies, small molecules, et cetera, that you can really rev up the whole discovery of new molecules, right?

Sid Mukherjee  (04:21):

That's correct, and that's one of the efforts that I'm very heavily involved in. We have created proprietary algorithms that allow us to enable this. Ultimately, of course, there has to be a method by which you start from these AI based methods, then move to physical real chemistry, then move to real biology, then move to obviously human biology and ultimately to human studies. It's a long process, but it's an incredibly fruitful process.

Eric Topol (04:57):

Well, yeah, as an example that recently we had Jim Collins on the podcast and he talked about the first new drug class of antibiotics in two decades that bind to staph aureus methicillin resistant, and now in clinical trials. So it’s happening. There’s 20 AI drugs in clinical trials out there.

Sid Mukherjee  (05:18):

It’s bound to happen. It is an unstoppable bound to happen systematology of drug discovery. This is just bound to happen. It is unstoppable. There are kinks in it in the road, but those will be ironed out, but it’s bound to happen.

(05:41):

So that’s on the very discovery oriented end, which is more related to learning algorithms that have to do with AI and less to do with what we see in day-to-day life, the ChatGPT kind of day-to-day life of the world. On the very other end of the spectrum, just to move along on the very other end of the spectrum are what I would call patient informatics. So by patient informatics, I mean questions like who responds to a particular drug? What genes do they have? What environment are they in? Have they had other drug interactions in the past? What is it about their medical record that will allow us to understand better why or why they're not responding to a medicine?

(06:51):

Those are also AI, can also be really powered by AI, but are much more dependent and much more sensitive to our understanding of these current models, the large language models. So just to give you an example, let's say you wanted to enroll a clinical trial for patients with diabetes to take a new drug. You could go into the electronic medical record, which right now is a text file, and ask the question, have they or have they not responded to the standard agents? And what has their response been? Should they be on glucose monitoring? How bad is their diabetes based on some laboratory parameters, et cetera, et cetera. So that's a very different information rich, electronic medical record rich mechanism to understand how to develop medicines. One lies, the first lies way in the discovery end of the spectrum. The second lies way in the clinical trials and human drug exposure end of the spectrum. And of course, there are things in the middle that I haven't iterated, but those are the two really broad categories where one can imagine AI making a difference and to be fair through various efforts, I'm working on both of those, the two end spectrum.

A.I. and Cancer

Eric Topol (08:34):

Well, let's drill down a bit more on the person individual informatics for a moment, since you're an oncologist, and the way we screen for cancer today is completely ridiculous by age only. But if you had a person's genome sequence, polygenic risk scores for cancers and all the other known data that, for example, the integrity of their immune system response, environmental exposures, which we'll talk about in a moment more, wouldn't we do far better for being able to identify high risk people and even preventing cancer in the future?

Sid Mukherjee  (09:21):

So I have no doubt whatsoever that more information that we can analyze using intelligent platforms. And I'm saying all of these words are relevant, more information analyzed through intelligent platforms. More information by itself is often useless. Intelligent platforms without information by themselves are often useless, but more information with intelligent platforms, that combination can be very useful. And so, one use case of that is just to give you one example, there are several patients, women who have a family history of breast cancer, but who have no mutations in the known single monogenic breast cancer risk genes, BRCA1, BRCA2, and a couple of others. Those patients can be at a high a risk of breast cancer as patients who have BRCA1 and BRCA2. It's just that their risk is spread out through not one gene but thousands of genes. And those patients, of course have to be monitored and their risk is high, and they need to understand what the risk is and how to manage it.

(10:57):

And that's where AI can, and first of all, informatics and then AI can play a big difference because we can understand how to manage those patients. They used to be called, this is kind of, I don't mean this lightly, but they used to be called BRCA3 because they didn't have BRCA1, they didn't have BRCA2, but they had a constellation of genes, not one, not two, but thousands of genes that would increase their risk of breast cancer just a little bit. I often describe these as nudge genes as opposed to shove genes. BRCA1 and BRCA2 are shoved genes. They shove you into having a high risk of breast cancer. But you can imagine that there are nudge genes as well in which they, in which a constellation of not one, not two, not three, but a thousand genetic variations, give a little push each one, a little push towards having a higher risk of breast cancer.

(12:09):

Now, the only way to find these nudge genes is by doing very clever informatic studies, some of which have been done in breast cancer, ovarian cancer, cardiovascular diseases, other diseases where you see these nudge effects, small effects of a single gene, but accumulated across a thousand, 2000, 3000 genes, an effect that's large enough that it's meaningful. And I think that we need to understand those. And once we understand them, I think we need to understand what to do with these patients. Do we screen them more assertively? Do we recommend therapies? You can get more aggressive, less aggressive, but of course that demands clinical trials and a deeper understanding of the biology of what happens.

A.I. And Longevity

Eric Topol (13:10):

Right, so your point about the cumulative effects of small variants, hundreds and hundreds of these variants being equivalent potentially, as we've seen across many diseases, it's really important and you're absolutely right about that. And I've been pushing for trying to get these polygenic risk scores into clinical routine use, and hopefully we're getting closer to that. And that's just as you say, just one layer of this information to add to the intelligence platform. Now, the next thing that you haven't yet touched on connecting the dots is, can AI and informatics be used to promote longevity?

Sid Mukherjee  (13:55):

Yeah, so that's a very interesting question. Let me attack that question in two ways. One biological and one digital. The biological one is to understand, again, the biological one has to do with informatics. So we could use AI so that, imagine that there are thousands, perhaps tens of thousands of variables. You happen to live on a Mediterranean island, you happen to walk five miles a day, you happen to have a particular diet, you happen to have a particular genetic makeup, you happen to have a particular immunological makeup, et cetera, et cetera, et cetera. All of those you happen to have, you happen to have, you happen to have. Now, if we could collect all of this data across hundreds of thousands of individuals, we'd need a system to deconvolute the data and ask the question, what is it about these 750,000 individuals that predicted longevity? Was it the fact that they walked five miles a day? Was it  their genetic makeup? Was it their diet? Was it their insulin level? Was it their, so you can imagine an n-dimensional diagram, as it were, and to deconvolute that n-dimensional diagram and to figure out what was the driving force of their longevity, you would need much more than conventional information analysis. You need AI.

(15:58):

So that's one direction that one could use. Again, informatics to figure out longevity. A second direction, completely independent of the first is to ask the question, what are the biological determinants of longevity in other animals? Is it insulin levels? Is it chronic? Is it the immune system? Is it the lack of, and we'll come back to this question, is it as you very well know, people with extreme longevity, the so-called supercentenarians. Interestingly, the supercentenarians don't generally die of cancer and heart disease, which are the two most common killers of people in their 70s and 80s in most countries of the western world. They die typically of what I would call regenerative failure. Their immune systems collapse. Their stem cells can't make enough skin, so they get skin infections, their skin collapses, they get bone defects, and they die of fractures. They get neurological defects, they die of neurodegenerative diseases and so forth. So they die of true degenerative diseases as opposed to cancer and heart disease, which have been the plagues of human biology since the beginning of time.

(17:49):

Again, I'm talking about the western world, of course, a different story with infectious diseases elsewhere. So a different way to approach the problem would be to say, what are the regenerative blockades that prevent regeneration at a biological level for these patients? And ask the question whether we can overcome these regenerative blockades using, again, the systems that I described before. What are they? What are the checkpoints? What are the mechanisms? And could we encourage the body to override those mechanisms? We still have to deal with heart disease and cancer, but once we had dealt with heart disease and cancer, we would have to ask the question. Okay, now we've dealt with those two things. What are the regenerative blockades that prevent people from having longevity once we've overcome those two big humps, heart disease and cancer?

Eric Topol (19:00):

Yeah, no, I think you're bringing up a really fascinating topic. And as you know, there's been many different ideas for how to achieve that, whether that's the senolytic drugs or getting rid of dead cells or using the transcription factors of cells instead of going into induced pluripotent stem cells, but rather to go to a rejuvenation of cells. Are you optimistic that eventually we're going to crack this case of better approach to regeneration?

Sid Mukherjee  (19:33):

Oh, I'm extremely optimistic. I'm optimistic, but I'm optimistic to a point. And that brings me to the third place, which is I'm optimistic to a point, which is that you conquer in some, hopefully you conquer a major part of heart disease and cancer, and now you're up against cellular regeneration. You then conquer cellular regeneration. And I don't know what the next problem is going to be. It's going to be some new hurdle. So I think there are two solutions to that hurdle. One solution is to say, okay, there's a new hurdle. We'll solve that new hurdle and it's bit by bit extending longevity year by year, by year by year as it were. But a completely second solution occurs to me, and here I'm going completely off script, Eric, which is what I do in my life.

Going Off Script: Being A Digital Human

(20:45):

I have begun to imagine, think about what it would be to be a digital human and by a digital human I mean, it began with my father's death. My father passed away a few years ago, and I would sometimes enter a kind of psychic space, what I would call a psychomanteum, in which I would imagine myself asking him questions about critical moments in my life, make a critical decision. I would rely on my father to make that decision for me. He would give me advice. That advice had some stereotypical qualities about it. Think about this, think about that. My experience has been this. My life has been this. My life has been that. But of course, times change. And I began to wonder whether with the use of digital technologies and digital AI technologies in particular, what could create a simulacrum of a psychomanteum?

(22:06):

So in other words, your physical body would pass, but somehow your digital body, all the memories, the experiences, the learning, all of that, that you had, the emotional connections that you had formed in your lifetime would somehow remain and would remain in a kind of psychomanteum in which you could go into a room. And again, I'm not talking voodoo science here. I'm talking very particular ways of extracting information from a person's decision making, extracting information about a person's ideas about the word their sort of their schema, or as psychologists describe it, the schemata. So that in some universe, if my father downloaded passively or actively the kind of decision making, not the actual decisions, the form of decision making and the form of communication that he liked, that I could go back to him eternally. My grandchildren could go back to him eternally and ask the question, great grandpa, what would you do under these circumstances? And what's amazing about it is that this is not completely science fiction.

Eric Topol (23:45):

Not at all.

Sid Mukherjee  (23:46):

It is within the realms of reality in the sense of there's no digital limitation to it. The main limitation to it is information. So Eric Topol, you make decisions I would imagine with some kind of stereotypical wisdom, you have accumulated wisdom in your life. You think about things in a particular critical way. When you read a book, you read a book in a particular way, it's whatever it might be. And Eric Topol psychomanteum would be, I would go into a space and see you and ask you a question, Eric, you read this book, what did you think about it? You found this piece of evidence. Read this scientific paper. What do you think about it? And so forth.

(24:49):

So again, let me just go back to my first point, which is number one, I think that regenerative medicine will have a regenerative moment itself, and we will discover new medicines, new mechanisms by which we can extend lifespan. Number two, that will involve getting over two big humps that we have right now, cancer and heart disease. Hopefully we'll get over both of those at some point of time. And number three, that in parallel, we will find a way to create digital selves that even when our physical bodies decay and die, that we will have a sense of eternal longevity based on digital selves, which is accessible or readily accessible through AI mechanisms. Yeah, this spectrum, I think will change our ideas of what longevity means.

The Environmental Factors

Eric Topol (26:10):

Well, I think your idea about the digital human and the brain and the decision making and that sort of thing is really well founded by the progress being made in the brain machine interface, as you know, with basically the mind is being digitized and you can get cells to talk, to speak to a person, and all sorts of things that are happening right now that are basically deconvoluting brain function at the cellular, even molecular neural level. So I don't think it's farfetched at all. I'm glad you went off script, Sid. That's great. Now this, I want to get back to something you brought up earlier because there are a lot of obstacles as you will acknowledge. And one of them is that we have in our environment horrible issues about pollution, about carcinogens, the focus of your recent New Yorker piece, plastics, microplastics, nanoplastics, now found in our arteries and brains and causing more, as we just recently saw, more heart attack, strokes and death, and of course the climate crisis. So with all this great science that we've just been discussing, our environment's going to hell, and I want to get your comments because you had a very insightful piece as always in the New Yorker in December about this, and I know you've been thinking about it, that the obstacles are getting worse to override the problems that we have today, don't you think?

Sid Mukherjee  (27:55):

So you're absolutely right. If we go down this path, we are going to go to hell in eye baskets. What we haven't discounted for is really decades, if not possibly a century of research that shows that there are certain kinds of inflammatory agents that cause both cancer, heart disease, and inflammation that have to do with their capacity to be so foreign to the human body that they're recognized as alien objects and so alien that our immune systems can't handle them. And essentially send off what I would call a five-bell alarm, saying that here's something that the immune system can't handle. It's beyond the capacity. And that five-bell alarm, as we now know, unfortunately, causes a systemic inflammatory response. And that systemic inflammatory response can potentiate heart disease, cancer, and maybe many other diseases that we don't know about because we haven't looked.

Eric Topol (29:28):

Absolutely.

Sid Mukherjee  (29:29):

So to connect this back to climate change, pollution is one of them. Air pollution is absolutely one of them. Microplastics, undegradable sort of forever plastics are one of them, or some of them. I think that there is no way around it except to really find a systematic way of assessing them. Look, it is wonderful to have new materials in the world. I'm wearing a jacket made out of God knows what, it's not cloth. I don't know what you are wearing, Eric, but it may not be cloth. These are great materials. This keeps the rain away. But on the other hand, it may be shedding something that I don't know. We need to find scientific ways of assessing the safety and the validity of some new materials that we bring into the world. And the way that we do that is to ask the question, is it inflammatory? Is there something that we are missing? Is there something about it that we should be thinking about that we haven't thought about?

Eric Topol (31:02):

Well, and to that end, you've been a very, I think, astute observer about diet as it relates to cancer. And we know similarly, as we just talked about with our environment, there's the issue of ultra-processed foods, and we've got big food, we got big plastics, big tobacco. I mean, we have all these counter forces to what the science is showing.

Sid Mukherjee  (31:29):

Too many bigs.

Eric Topol (31:31):

Yeah, yeah. But I guess the net of it is, Sid, if I get it right, you think that the progress we're making in science, and that includes the things we've talked about and genome editing and accelerated drug discovery, these sorts of programs, the informatics, the AI can override this chasing of our tail with basically unchecked issues that are, whether it's from our nutrition, our air, what we ingest and breathe, these are some serious problems.

Preventing Diseases

Sid Mukherjee  (32:06):

No, I don't think that. I don't think that cancer and cardiovascular disease prevention, as you very well know, Eric, because you've been in the forefront of it, is a pyramid. The base of the pyramid is prevention. Prevention is the most effective. It's the most difficult. It's the hardest to understand, the most difficult trials to incorporate, but it is the base of the pyramid. And so let it be said that I don't think that we're going to solve cancer, cardiovascular disease by better treatment using CRISPR. My laboratory, and one of my companies before I happened to be wearing the jacket, but was one of the first to use CRISPR and transplant CRISPR. CRISPR, human beings with or CRISPR bone marrow into human beings long before anyone else, we were actually among the first. These human beings, thankfully, and astonishingly remain completely alive. We deleted a gene from their bone marrow. They engrafted with no problem. They're still alive today, and we are treating them for cancer. Astonishing fact, there are 12 of them in the world.

(33:49):

And again, astonishing fact, wonderful, beautiful news, beautiful science. But there are 12, if we want to make a big change in the universe, we need to get not to 12, but to 12 million, potentially 120 million. And that's not going to happen because we're going to CRISPR their bone marrow. It's going to happen because we change their environments, their diets, their lifestyles, their exposures, we understand their risks, their genetics, et cetera, et cetera, et cetera, et cetera. It's not going to happen because we give them CRISPR bone marrow transplants that enable them to change their risk of cancer. So I'm very clear about this or clear eyed about it, I would say, which is to say that great progress in medicine is being made. There's no doubt about it. I'm happy about it. I'm happy to be part of it. I'm happy to be in the forefront of it.

(35:00):

We have now delivered one of the first cellular therapies for cancer in India at a price point that really challenges the price point of the west. We are now producing this commercially and or about to produce this commercially, so for lymphomas and leukemias, I'm so excited about the progress in science. But all of that said, let me be very clear, the real progress in cancer and cardiovascular disease is going to come from prevention. And if that's where we're going, we need to really rethink at a very fundamental level as you have Eric, at a very fundamental level, how do we rethink prevention, cancer prevention, cardiovascular disease prevention, and as a correlate, regenerative disease, regeneration, cancer prevention, cardiovascular disease prevention. The fundamentals are how do we find things that are in our exposome, things that we're exposed to environments, gene environment interactions that increase the risk of cancer and cardiovascular disease, and how do we take them out? And how do we do this without running 15-year trials so that we can get the results now? And that's what I'm really interested in in terms of information.

Eric Topol (36:55):

Yeah. Well, I'm with you there. And just to go along with those 12 patients you mentioned, as you know recently it was reported there were 15 patients with serious autoimmune diseases, and they got a therapy to knock out all their B cells. And when their B cells came back, they didn't make autoantibodies anymore. And this was dermatomyositis and lupus and systemic sclerosis, and it was pretty magical. If it can be extended, like you said, okay, 15 people, just like your 12, if you can do that in millions, well, you can get rid of autoimmune diseases, which would be a nice contribution. I mean, there's so many exciting things going on right now that we've touched on, but as you get to it, you've already approached this inequity issue by bringing potentially very expensive treatments that are exciting to costs that would be applicable in India and many countries that are not in the rich income category. So this is a unique time it seems like Sid, in our advances, in the cutting edge progress that's being made, wouldn't you say?

The Why on Cancer in the Young

Sid Mukherjee  (38:14):

Well, I would say that the two advances have to go hand in hand. There will be patients who are recalcitrant to the standard therapies, your patients with severe lupus dermatitis, et cetera. Those patients will require cutting edge therapy, and we will find ways to deliver it to them. There are other patients, hundreds of not 12, not 15, but hundreds of thousands if not millions, who will require an understanding of why there is an increase, for instance, in asthmatics disease in India. Why is that increasing? Why is there an increase in non-smoking related lung cancer in some parts of the world? Why? What's driving that? Why is there an increase in young patients with cancers in the United States? Of all things that stand out, there is a striking increase in colorectal cancer in young men and women. There's an increase in esophageal cancer in young men and women. Why?

Eric Topol (39:58):

Yeah, why, why?

Sid Mukherjee  (40:00):

Why? And so, the answer to that question lies in understanding the science, getting deeper information informatics, and then potentially understanding the why. So again, I draw the distinction between two broad classes of spaces where information science can make a big difference. On one hand, on the very left hand of the picture, an understanding of how to make new medicines for patients who happen to have these diseases. And on the way right hand of finding out why these patients are there in the first place, and asking the question, why is it that there are more patients, young men and women with colorectal cancer, are we eating something? Is it our diet? Is it our diet plus our environment? Is it the diet plus environment plus genetics? But why? There must be a why. When you have a trend like this, there's always a why. And if there's a why, there's always an answer. Why? And we have the best tools, and this is the positive piece of this. The positive piece of this is that we now have among the best tools that we've ever had to answer that why? And that's what makes me optimistic. Not a drug, not a medicine, not a fancy program, but the collective set of tools that we have that allow us to answer the question why? Because that is of course the question that every patient with esophageal and colorectal cancer is asking why.

Eric Topol (42:01):

I'm with you. What you're bringing up is fundamental. We have the tools, but we've noted this increase in colon cancer in the young for several years, and we're not any closer to understanding the why yet, right?

Sid Mukherjee  (42:18):

Yes. We're not any closer to understanding the why yet. Part of the answer is that we haven't delved into the why properly enough. These are studies that take time. They have longitude because these are studies that have to do with prevention. They take time, they take patients. So the quick answer to your question is, I don't think we've made the effort and we haven't made the effort, especially with the technological advances that we have today. So imagine for a second that we launched a project in which, again, like the Manhattan project, the Apollo project, we advanced a project which said the colorectal cancer in young project in the United States, we brought the best science minds together and ask the question, go into a room, lock yourself up, and don't come out of the room until you have the answer to figuring out how and then why we have young men and women with colorectal cancer increasing. I would imagine you could nominate, I could nominate 10 people to that committee and they would willingly serve. They'd be willing to be locked up in a room and ask the question why? Because they want to answer that question. That why is extraordinarily important.

Eric Topol (44:14):

I'm with you on that too, because we have the tools, like you said, we can assess the gut microbiome, their genome, their diets, their environmental exposures and figure this out. But as you say, there hasn't been a commitment to doing it.

Sid Mukherjee  (44:30):

And that commitment has to come centrally, right? That commitment has to come from the NIH, that has to come from the NCI, the National Cancer Institute, the National Institute of Health. It has to come as a mechanism that says, listen, let's solve this problem. So identifying the problem, there's an increase in colorectal cancer in young people. Important. Yes. Let's, let's figure out the answer why, and let's collect all the information for the next five years, seven years, whatever it might take to answer that question.

Eric Topol (45:18):

And as you said, the intelligent platforms will help analyze it.

Sid Mukherjee  (45:23):

Yes. I mean, we have the tools. So if you have the tools and if you collect the information, the tools will analyze that information.

Eric Topol (45:36):

Right. Well, this has been inspiring and daunting at the same time, this discussion. What I love about you, Sid, is you're a big thinker. You're one of the great thinkers in medicine of our era, and you also of course are such an extraordinary writer. So we're going to look forward to your next book and your rejuvenation of the cancer Emperor of All Maladies book but I want to thank you. I always enjoy our discussions. They always get to areas that highlight where we're missing the opportunities that we have that we're not actualizing. That's one of the many things I really love about you and your work, so keep up the good stuff and I look forward to the next chance we get to visit and discuss all this stuff.

Sid Mukherjee  (46:31):

And it's been a great pleasure knowing you for so many years, Eric. And then whenever we have dinner together, the dinner always begins with my asking you why. And so, the why question is the first question. The how question is a harder question. We can always answer the how question, but the why question is the first question. So the next time I have dinner with you, wherever it might be, San Diego, New York, Los Angeles, I'm going to ask you another why question. And you're going to answer the how question, because that's what you're good at. And it's been such a pleasure interacting with you for so many years.

Eric Topol (47:12):

Oh, thank you so much. What a great friend.

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Eric Topol (00:00:05):

This is Eric Topol from Ground Truths, and I am delighted to have with me Holden Thorp, who is the Editor-in-Chief of the Science journals. We're going to talk about Science, not just the magazine journal, but also science in general. This is especially appropriate today because Holden was just recognized by STAT as one of the leaders for 2024 because of his extraordinary efforts to promote science integrity, so welcome Holden.

Holden Thorp (00:00:36):

Thanks Eric, and if I remember correctly, you were recognized by STAT in 2022, so it's an honor to join a group that you're in anytime, that's for sure, and great to be on here with you.

Eric Topol (00:00:47):

Well, that's really kind to you. Let's start off, I think with the journal, because I know that consumes a lot of your efforts and you have five journals within science.

Holden Thorp (00:01:02):

Oh, we have six.

Eric Topol (00:01:03):

Oh six, I'm sorry, six. There's Science, the original, and then five others. Can you tell us what it's like to oversee all these journals?

Overseeing the Science Journals

Holden Thorp (00:01:16):

Yeah, we're a relatively small family compared to our commercial competitors. I know you had Magdalena [Skipper]on and Nature has I think almost ninety journals, so six is pretty small. In addition to Science, which most people are familiar with, we have Science Advances, which also covers all areas of science and is larger and is a gold open access journal and also is overseen by academic editors, not professional editors. All of our other journals are overseen by professional editors. And then the other four are relatively small and specialized areas, and probably people who listen to you and follow you would know about Science Translational Medicine, Science Immunology, Science Signaling and then we also have a journal, Science Robotics which is something I knew nothing about and I learned a lot. I've learned a lot about robotics and the culture of people who work there interacting with them.

Holden Thorp (00:02:22):

So we have a relatively small family. There's only 160 people who work for me, which is manageable. I mean that sounds like a lot, but in my previous jobs I was a provost and a chancellor, and I had tens of thousands of people, so it's really fun for me to have a group where I at least have met everybody who works for me. We're an outstanding set of journals, so we attract an outstanding group of professionals who do all the things that are involved in all this, and it's really, really fun to work with them. At Science, we don't just do research papers, although that's a big, and probably for your listeners the biggest part of what we do. But we also have a news and commentary section and the news section is 30 full-time and many freelancers around the world really running the biggest general news operation for science that there is. And then in the commentary section, which you're a regular contributor for us in expert voices, we attempt to be the best place in the world for scientists to talk to each other. All three of those missions are just really, really fun for me. It's the best job I've ever had, and it's one I hope to do for many years into the future.

Eric Topol (00:03:55):

Well, it's extraordinary because in the four and a half years I think it's been since you took the helm, you've changed the face of Science in many ways. Of course, I think the other distinction from the Nature Journals is that it's a nonprofit entity, which shows it isn't like you're trying to proliferate to all sorts of added journals, but in addition, what you've done, at least the science advisor and the science news and all these things that come out on a daily basis is quite extraordinary as we saw throughout the pandemic. I mean, just reporting that was unparalleled from, as you say, all points around the world about really critically relevant topics. Obviously it extends well beyond the concerns of the pandemic. It has a lot of different functions, but what I think you have done two major things, Holden. One is you medicalized it to some extent.

Eric Topol (00:04:55):

A lot of people saw the journal, particularly Science per se, as a truly basic science journal. Not so much applied in a medical sphere, but these days there's more and more that would be particularly relevant to the practice of medicine, so that's one thing. And the other thing I wanted you to comment on is you're not afraid to speak out and as opposed to many other prior editors who I followed throughout my career at Science, there were pretty much the politically correct type and they weren't going to really express themselves, which you are particularly not afraid of. Maybe you could comment about if you do perceive this medicalization of science to some extent, and also your sense of being able to express yourself freely.

Capturing the Breakthroughs in Structural Biology

Holden Thorp (00:05:48):

Yeah, well, you're kind to say both of those things are certainly things we have worked at. I mean, I do come from a background, even though I'm trained as a chemist, most of what I did towards the latter end of my career, I mean, I did very basic biochemistry when I was a researcher, but the last part of my research career I worked in on development of a drug called Vivjoa, which is an alternative to the fluconazole family that doesn't have the same toxicity and is currently on the market for chronic yeast infection and hopefully some other things in the future when we can get some more clinical trials done.

Holden Thorp (00:06:35):

And I've hung around biotech startups and drug development, so it is part of the business that I knew. I think the pandemic really gave us an opening because Valda Vinson, who's now the Executive Editor and runs all of life sciences for us and policies for the journal, she was so well known in structural biology that most of the first important structures in Covid, including the spike protein, all came to us. I mean, I remember crystal clear February of 2020, she came in my office and she said, I got the structure of the spike protein. And I said, great, what's the spike protein? Turned out later became the most famous protein in the world, at least temporarily. Insulin may be back to being the most famous protein now, but spike protein was up there. And then that kind of cascaded into all the main protease and many of the structures that we got.

Holden Thorp (00:07:45):

And we seized on that for sure, to kind of broaden our focus. We had the Regeneron antibodies, we had the Paxlovid paper, and all of that kind of opened doors for us. And we've also, now we have two clinical editors at Science, Priscilla Kelly and Yevgeniya Nusinovich, and then the Insights section, somebody that you work with closely, Gemma Alderton, she is very fluent in clinical matters. And then of course we've had Science Translational Medicine and we seek continue to strengthen that. Science Immunology was very much boosted by Covid and actually Science Immunology is now, I think probably if you care about impact factors, the second highest specialized immunology journal after Immunity. I've put some emphasis on it for sure, but I think the pandemic also really helped us. As far as me speaking out, a lot of people maybe don't remember, but Don Kennedy, who was the editor in the early 2000s who had been the Stanford president, he was similarly outspoken.

Confronting Controversies

Holden Thorp (00:09:15):

It's funny, sometimes people who disagree with me say, well, Don Kennedy would never say anything like that. And then I can dig up something that Don Kennedy said that's just as aggressive as what I might've said. But you're right, Bruce Alberts was very focused on education, and each one of us has had our own different way of doing things. When Alan Leshner hired me and Sudip Parikh reinforced this when he came on, I mean, he wanted me to liven up the editorial page. He explicitly told me to do that. I may have done more of it than he was expecting, but Alan and Sudip both still remain very supportive of that. I couldn't do what I do without them and also couldn't do it without Lisa Chong, who makes all my words sound so much better than they are when I start. And yeah, it kind of fed on itself.

Holden Thorp (00:10:21):

It started with the pandemic. I think there was an inflection when Trump first said that Covid was just the flu, and when he said some really ridiculous things about the vaccine, and that's where it started. I guess my philosophy was I was thinking about people who, they've got a spouse at home whose job might be disrupted. They got children they've got who are out of school, and somehow they managed to get themselves to the lab to work on our vaccine or some other aspect of the pandemic to try to help the world. What would those people want their journal to say when they came home and turned the news on and saw all these politicians saying all this ridiculous stuff? That was really the sort of mantra that I had in my head, and that kind of drove it. And now I think we've sort of established the fact that it's okay to comment on things that are going on in the world. We're editorially independent, Sudip and the AAAS board, treat us as being editorially independent. I don't take that for granted and it's a privilege to, as I sometimes tell people, my apartment's four blocks from the White House, sometimes I'm over there typing things that they don't like. And that tradition is still alive in this country, at least for the time being, and I try to make the most of it.

Eric Topol (00:12:11):

Well, and especially as you already touched on Holden, when there's a time when the intersection of politics and science really came to a head and still we're dealing with that, and that's why it's been so essential to get your views as the leader of such an important journal that is publishing some of the leading science in the world on a weekly basis. Now, one of the things I do want to get into this other track that you also alluded to. You went from a chemist, and you eventually rose to Dean and chancellor of University of North Carolina (UNC) and also the provost of Washington University, two of our best institutions academically in the country. I would imagine your parents who were both UNC grads would've been especially proud of you being the chancellor.

Holden Thorp (00:13:05):

It's true. Yeah. Unfortunately, my father wasn't there to see it, but my mother, as I always tell people, my mother very much enjoyed being the queen mother of her alma mater.

On Stanford University’s President Resignation

Eric Topol (00:13:16):

Yeah, I would think so, oh my goodness. That gives you another perspective that's unique having been in the senior management of two really prestigious institutions, and this past year a lot has been going on in higher education, and you have again come to the fore about that. Let's just first discuss the Stanford debacle, the president there. Could you kind of give us synopsis, you did some really important writing about that, and what are your thoughts looking back on the student who happens to be Peter Baker's and Susan's son, two incredible journalists at the New York Times and New Yorker, who broke the story at the Stanford Daily as a student, and then it led to eventually the President's resignation. So, what were your thoughts about that?

Holden Thorp (00:14:16):

Yeah, so it's a complicated and sad story in some ways, but it's also fascinating and very instructive. Two of the papers were in Science, two of the three main ones, the other one was in Cell. And we had made an error along the way because Marc had sent a correction in which for some reason never got posted. We searched every email server we had everything we had trying to find exactly what happened, but we think we have a website run by humans and there was something that happened when the corrections were transmitted into our operations group, and they didn't end up on the website. So, one of the things I had to do was to say repeatedly to every reporter who wanted to ask me, including some Pulitzer Prize winners, that we had looked everywhere and couldn't find any reason why somebody would've intentionally stopped those corrections from posting.

Holden Thorp (00:15:36):

And one thing about it was I didn't want, Marc had enough problems, he didn't need to be blamed for the fact that we botched that. So I think people were maybe impressed that we just came out and admitted we made a mistake, but that's really what this area needs. And those things happened before I became the editor in chief, but I was satisfied that where that error happened was done by people who had no idea who Marc Tessier-Lavigne even was, but because of all that, and because we had to decide what to do with these papers, I talked to him extensively at the beginning of this, maybe as much as anybody, now that I look back on it. And I think that for him, the error that happened is very common one. You have a PI with a big lab.

Holden Thorp (00:16:33):

There are many, many incentives for his coworkers and yours to want to get high profile publications. And what we see is mostly at the end when you kind of know what's happening, some corners get cut doing all the controls and all of the last things that have to be done to go into the paper. And someone in his lab did that, and he didn't notice when the jails were sent in. The committee that investigated it later found something that I was certain at the beginning was going to be true, which is he didn't have any direct involvement in and making the problematic images or know that they were there. Every time we see one of these, that's almost always the story.

Holden Thorp (00:17:32):

And if he hadn't been the president of Stanford, he probably would've, I mean, a couple of the papers that were attracted might even could have been just big corrections. That's another topic we can talk about in terms of whether that's the right thing to do but because he was the president of Stanford, it triggered all these things at the university, which made the story much, much more complicated. And it is similar to what we see in a lot of these, that it's the institution that does the most to make these things bigger than they need to be. And in this case, the first thing was that young Theo Baker who I've talked on the phone extensively with, and I just had a long lunch with him in Palo Alto a couple weeks ago, it's the first time we ever met in person. He's finishing up his book, which has been optioned for a movie, and I've told him that I want Mark Hamill to play me in the movie because I don't know if you saw this last thing he did, Fall of the House of Usher but he was a very funny curmudgeonly.

Holden Thorp (00:18:46):

And so, I think he would be a lot like me dealing with Theo, but Theo did great work. Did everything that Theo write add up precisely. I mean, he was teaching himself a lot of this biochemistry as he went along, so you could always find little holes in it, but the general strokes of what he had were correct. And in my opinion, and Marc would've been better served by talking to Theo and answering his questions or talking to other reporters who are covering this and there are many excellent ones. This is something I learned the hard way when I was at North Carolina. It's always better for the President to just face the music and answer the questions instead of doing what they did, which is stand up this long and complicated investigation. And when the institutions do these long investigations, the outcome is always unsatisfying for everybody because the investigation, it found precisely what I think anybody who understands our world would've expected that Marc didn't know about the fraud directly, but that he could have done more to create a culture in his laboratory where these things were picked up, whether that's making his lab smaller or him having fewer other things to do, or precisely what it is, people could speculate.

Managing a Crisis at a University

Holden Thorp (00:20:37):

But of course, that's what always happens in these. So the report produced exactly what any reporter who's covered this their whole lives would've expected it to produce, but the people who don't know the intimate details of how this works, were not satisfied by that. And he ended up having to step down and we'll never know what would've happened if instead of doing all of that, he just said, wow, I really screwed this up. I'm responsible for the fact that these images are in here and I'm going to do everything I can to straighten it out. I'd be happy to take your questions. That's always what I encourage people to do because I was in a similar situation at North Carolina with a scandal involved in athletics and an academic department, and we did umpteen investigations instead of me just saying, hey, everybody, we cheated for 30 years. It started when I was in middle school, but I'm still going to try to clean it up and I'll be happy to answer your questions. And instead, we get lawyers and PR people and all these carefully worded statements, and it's all prolonged. And we see that in every research integrity matter we deal with and there are a lot of other things in higher education that are being weighed down by all of that right now.

Eric Topol (00:22:06):

Yeah. One of the things that is typical when a university faces a crisis, and we're going to get into a couple others in a moment, is that they get a PR firm, and the PR firm says, just say you're going to do an investigation because that'll just pull it out of the news, take it out of the news. It doesn't work that way. And what's amazing is that the universities pay a lot of money to these PR companies for crisis management. And being forthright may indeed be the answer, but that doesn't happen as best as we can see. I think you're suggesting a new path that might be not just relevant, but the way to get this on the right course quickly.

Holden Thorp (00:22:58):

Just on that, there's a person in that PR space who I really like. There are a few of them that are really good, and he's the person who helped me the most. And he used to refer doing the investigation as putting it on the credit card.

Eric Topol (00:23:16):

Yeah. Yeah, exactly.

Holden Thorp (00:23:17):

Okay, because you still have to pay the credit card bill after you charge something.

Eric Topol (00:23:25):

Yeah, better to write a check.

Holden Thorp (00:23:27):

It's better to write a check. Yes, because that 18% interest can add up pretty quickly.

Resignations of the Presidents at Harvard and Penn

Eric Topol (00:23:32):

I like that metaphor entirely appropriate. That's a good one. Now, in the midst of all this, there's been two other leading institutions besides Stanford where the president resigned for different reasons, at least in part one was at Harvard and one at Penn. And this is just a crisis in our top universities in the country. I mean three of the very top universities. So, could you comment about the differences at Harvard and Penn related to what we just discussed at Stanford?

Holden Thorp (00:24:09):

Yeah, so I don't know Claudine Gay, but I've exchanged emails with her, and I do know Liz Magill and I know Sally Kornbluth even better. Our kids went to middle school together because she was at Duke. And I think Sally is in good shape, and she did a little bit better in the hearings because I think she was a little more forthcoming than Liz and Dr. Gay were but I think also Liz was in a pretty weakened state already when she went in there. And I think that what happened that day, and it was a devastating day for higher education. I cleared my calendar, and I watched the whole thing and I couldn't sleep that night. And it was, I thought, oh my goodness, my way of making a living has just taken a death blow. I just felt so much compassion for the three of them, two of whom I knew, one of whom I could imagine having been through similar things myself.

Holden Thorp (00:25:20):

And I think what my take on the whole thing about free speech and the war and all this stuff is that higher education has got a problem, which is that we have promised to deliver a product that we can't really deliver, and that is to provide individualized experiences for students. So, I'm back on the faculty now at GW. I have 16 people in my class, I know every single one of them. I was teaching during the fall, last fall. I teach on Monday nights, which Yom Kippur was on a Monday night, which was before October 7th. And so, I knew precisely how many Jewish kids I had in my class because they had to make up class for that Monday night.

Holden Thorp (00:26:18):

I was basically able to talk to each one of them and make sure. And then GW is a very liberal university, so I had a whole bunch that were all the way on the other side also. I was just able to talk to each of them and make sure they had what they needed from the university. But the institutions don't really have luxury. They don't have somebody who's been doing this for 35 years teaching 16 people who can make sure they're getting what they need, but they write letters to all their students saying, you're going to join a diverse student body where we're going to give you a chance to express yourself and explore everything, but there's too many of them to actually deliver that. And none of them want to say that out loud. And so, what happens in a situation like this?

Holden Thorp (00:27:19):

And everybody says, well, don't send out the statements, don't send out the statements, but how else are you going to communicate with all those people? I mean, because the truth is education is a hands-on individualized deal. And so, the students who are experiencing antisemitism at Harvard or Penn or anywhere else, were feeling distress. And the university wasn't doing what they promised and attending to that, and similarly to the students who wanted to express themselves in the other direction. And so, what really needs to happen is that universities need to put more emphasis on what goes on in the classroom so that these students are getting the attention that they've been promised. But universities are trying to do a lot of research and you're at a place that's got a little simpler mission but some of these big complicated ones are doing urban development and they're trying to win athletics competitions, and they're running hotels and fire departments and police departments, and it's really hard to do all and multi, multi-billion dollar investment vehicles.

Holden Thorp (00:28:47):

It's really hard to do all that and keep the welfare of a bunch of teenagers up at the top of the list. And so, I think really what we need around this topic in general is a reckoning about this very point. Now as far as how to gotten through the hearing a little better, I mean what they said was technically correct, no question about that. But where they struggled was in saying things that would cause them to admit that they had failed at doing what they promised for the people who are feeling distressed. And again, that's kind of my mantra on all these things, whether it's student affairs or research integrity or anything else, the universities have made massive commitments to do probably more things than they can, and rather than fessing up to that, they just bury the whole thing in legalistic bureaucracy, and it's time for us to cut through a lot of that stuff.

Eric Topol (00:30:09):

I couldn't agree more on that.

Holden Thorp (00:30:10):

And in Claudine's case, I think the plagiarism thing, I wrote a piece in the Chronicle that just kind of tried to remind people that the kinds of plagiarism that she was punished for, in my opinion, too much of a punishment is stuff that we routinely pick up now with authenticate and other tools in scholarly publishing, and people just get a report that says, hey, maybe you want to reward this, and that's it. If it doesn't change the academic content of the paper, we hardly ever even pay attention to that. She was being subjected to a modern tool that didn't exist when she wrote the stuff that she wrote. And it's same thing with image analysis, right? When Marc Tessier-Lavigne made his papers, Elisabeth Bik wasn't studying images, and we didn't have proof fig and image twin to pick these things up, so we're taking today's tools and applying them to something that's 20 years old that was produced when those tools didn't exist. You can debate whether that matters or not, but in my opinion it does.

Generative A.I. and Publishing Science

Eric Topol (00:31:31):

Yeah, that's bringing us to the next topic I wanted to get into you with, which is AI. You've already mentioned about the AI detection of image, which we used to rely on Elisabeth as a human to do that, and now it can be done through AI.

Holden Thorp (00:31:51):

Well, it doesn't get everything, so I keep telling Elisabeth she doesn't have to worry about being put out of business.

Eric Topol (00:31:58):

But then there's also, as you said about text detection, and then there's also, as you've written in Science, the overall submission of papers where a GPT may have had significant input to the writing, not just to check the spelling or check minor things. And so, I want to get your views because this is a moving target of course. I mean, it's just the capabilities of AI have just been outpacing, I think a lot of expectations. Where do you see the intersection of AI and Science publishing now? Because as you said, it changes the ground rules for picking up even minor unintended errors or self-plagiarism or whatever, and now it changes the whole landscape considerably.

Holden Thorp (00:32:54):

Yeah. So, I think you said the most important thing, which is that it's a moving target, and you've been writing about this for medicine for longer than just about anybody, so you've been watching that moving target. We started off with a very restrictive stance, and the reason we did that was because we knew it would keep moving. And so, we wanted to start from the most restrictive possible place and then sort of titrate in the things that we allowed because we didn't want to go through the same thing we went through with Photoshop when it first came along. Like all these altered images that we keep talking about by far the most papers that surface are from the period between when Photoshop became a tool and when we finally had sort of a consensus as a community in terms of what was okay and what wasn't okay to do with your gels when you process the images.

Holden Thorp (00:33:55):

And we didn't want the same thing with words where we allowed people to use ChatGPT to write, and then a few years later decided, oh, this thing wasn't permissible, and then we have to go back and re-litigate all those papers. We didn't want to do that again. So, we started off with a pretty restrictive stance, which we've loosened once and we'll probably loosen more as we see how things evolve. What we keep looking for is for entities that don't have a financial interest to issue guidelines, so if it's another journal, especially a commercial journal that makes money on the papers, well, you can imagine that these tools are going to give us even more papers. And for a lot of these entities that charge by the paper, they have a financial incentive for people to use ChatGPT to write papers. We look for societies and coalitions of academics who have come together and said these things are okay.

Holden Thorp (00:35:04):

And the first one of those was when we decided that it was okay, for example, if you are not an English speaker natively to have ChatGPT work on your prose. Now there are lots of people who disagree about that ChatGPT is good at that. That's a separate matter, but we felt we got to a point, I forgot when it was a couple months ago, where we could amend our policies and say that we were going to be more tolerant of text that had been done by ChatGPT. As long as the people who signed the author forms realize that if it makes one of these hallucinating errors that it makes and it gets into the paper that's on them, whether that actually saves you time or not, I don't know.

Holden Thorp (00:36:03):

I also have my doubts about that, but that's kind of where we're going. We're watching these things as they go. We're still very restrictive on images and there was this debacle in this Frontiers paper a couple of weeks ago with a ridiculous image that got through. So right now, we're still not allowing illustrations that were generated by the visual counterparts of ChatGPT. Will we loosen that in the future? Maybe, as things evolve, so when we did our first amendment, some of the reporters, they're just doing their jobs saying, well, you can't make your mind up about this. And I'm like, no, you don't want us to make up our mind once and for all. And by the way, science is something that changes over time also. So, we're watching this develop and we expect everybody jokes about how we spend too much time talking about this, but I think everybody's gotten to the point now where they're realizing we're going to talk about it for years to come.

Eric Topol (00:37:17):

Oh my goodness, yes because we're talking about truth versus fake and this is big stuff. I mean, it affects whether it's the elections, whether it's every sector of our lives are affected by this. And obviously publishing in the leading peer review journal, it couldn't be more important as to get this right and to adjust, as you said, as more evidence, performance and other issues are addressed systematically. That does get me to self-correcting science, something else you've written about, which is kind of self-correcting as to how we will understand the use of large language models and generative AI. But this, you get into science in many different ways, whether it's through the celebrity idea, how it has to adapt and correct that there's a miscue from the public about when evolves and it's actually that science. So maybe you could kind of give us your perspective about you are continuing to reassess what is science as we'll get into more about that in a moment. Where are you at right now on that?

Holden Thorp (00:38:40):

Yeah, so my general sort of shtick about science is to remind people that it's done by human beings. Human beings who have all different kinds of different brains who come from different backgrounds, who have all the human foibles that you see in any other profession. And I think that unfortunately a lot of, and we brought some of this on ourselves, we've kind of taken on an air of infallibility from time to time or as having the final answer when, if you go back just to the simplest Karl Popper and Thomas Kuhn early writings in the philosophy of science, it's crystal clear that science is something that evolves. It's something done by sometimes thousands or even hundreds of thousands of millions of people depending on the topic. And it's not the contributions of any individual person hardly ever.

Holden Thorp (00:39:54):

But yet we continue to give Nobel prizes and hold up various individual scientific figures as being representative. They're usually representative of many, many people. And it's a process that continues to change. And as always point out, if you want to get a paper in science, it's not good to say, hey, here's something everybody thought and we tested it and it's still correct. That's usually not a good way to get a science paper. The right thing to do is to say, hey, the W boson might weigh more than we expected it to, or it turns out that evolution occurs in ways that we didn't expect, or that's how you get a science paper and that's how you get on the cover of Science. Those are the things that we look for, things that change the way people think about science. And so that's what we're all actively looking for, but yet we sometimes portray to the public that we always have everything completely figured out, and the journalists sometimes don't help us because they like to write crisp stories that people can get something out of. And we like to go on TV and say, hey, I got the answer.

Holden Thorp (00:41:23):

Don't wear a mask. Do wear a mask. This is how much the temperature is going to go up next year. Oh, we refined our, and it turns out it's another 10th of a degree this way or that way. I mean, that's what makes what we do interesting and embedded in that is also human error, right? Because we make errors in interpretation. We might see a set of data that we think mean one thing, but then somebody else will do something that helps us interpret it another way. In my opinion, that's certainly not misconduct. We hardly ever publish corrections or retractions over interpretation. We just publish more papers about that unless it's some very egregious thing. And then we also have greed and ambition and ego and lots of other things that cause people to make intentional errors that get most of the attention. And we have errors that are unintentional, but still may relate to fundamental data in the paper.

Holden Thorp (00:42:36):

So when you put all this together, the answer isn't to try to catch everything because there's no way in the world we're going to catch everything and we wouldn't want to, even if we could for some of it, because as John Maddox, who ran my competitor journal for many years in a brilliant way at Nature, someone once asked him how many papers in Nature were wrong? And he said, all of them, because all of them are going to be replaced by new information. And so, what we'd be better off trying to convince the public that this is how science works, which is much harder than just going to them with facts. I mean, that takes a lot of work and doing a better job of telling each other that it's okay when we have to change the record because the biggest thing that erodes trust in science is not the fact that we make mistakes, is that when it turns into a drama over whether we are going to correct the record or not, that's what all these, the Stanford case is probably the biggest in people's minds. But if you look at, we've had this behavioral economic stuff at Harvard, I have this superconductivity at Rochester, Dana Farber's having a big event right now. All of these things don't have to be this dramatic if we would do a better job of collaborating with each other on maintaining an accurate scientific record rather than letting ambition and greed and ego get in the way of all of it.

Who Is A Scientist?

Eric Topol (00:44:21):

Well, you got some important threads in there. The one thing I just would also comment on is my favorite thing in Science is challenging dogma because there's so much dogma, and that's obviously part of what you were getting into and many other aspects as well. But that's the story of Science, that nothing stands. If it does, then you're not doing a good job of really interrogating and following up on whatever is accepted at any particular moment in time. But your writings, whether it's in Science and editorials or science forever, your Substack, which are always insightful but I think one of the most recent ones was about, who is a scientist? And I really love that one because I'll let you explain. There are some people who have a very narrow view and others who see it quite differently. And maybe you could summarize it.

Holden Thorp (00:45:23):

Well, I had the privilege to moderate a panel at the AAAS meeting that included Keith Yamamoto, who was our outgoing president, Willie May, who was our incoming president, Peggy Hamburg, who ran the FDA and many, many other things. Kaye Husbands Fealing who was a social scientist, and Michael Crow, who was the president of Arizona State. These are all extraordinary people. And I just asked him a simple question, so who was the scientist? Because I think one thing that I see in my work, and you probably see in the communication work and writing that you do, that not all of our colleagues who work in the laboratory think that the rest of this stuff is science.

Holden Thorp (00:46:17):

And the place that breaks my heart the most is when somebody says, one of our professional editors isn't qualified to reject their paper because they don't have their own lab. Alright, well you've interacted with a lot of our editors, they read more papers than either one of us. They know more about what's going on in these papers than anybody. They are absolute scholars in every sense of the word and if someone thinks they're not scientists, I don't know who a scientist is. And so, then you can extend that to science communicators. I mean, those are obviously the problems we've been talking about, the people we need the most great teachers. If someone's a great science teacher and they have a PhD and they worked in lab and they're teaching at a university, are they still a scientist even if they don't have a lab anymore?

Holden Thorp (00:47:11):

So in my opinion, an expansive definition of this is the best because we want all these people to be contributing. In fact, many of the problems we have aren't because we're not good in the laboratory. We seem to be able to do a good job generating that. It's more about all these other pieces that we're not nearly as good at. And part of what we need to do is value the people who are good at those things, so I pose this to the panel, and I hope people go on and watch the video. It is worth watching. Keith Yamamoto was in the group that said, it's only if you're doing and planning research that you're a scientist. He knew he was going to be outnumbered before we went out there. We talked about that. I said, Keith, you're my boss. If you don't want me to ask that question, I won't. But to his credit, he wanted to talk about this and then Michael Crow was probably the furthest on the other side who said, what makes humans different from other species is that we're all scientists. We all seek to explain things. So somewhere in the middle and the others were kind of scattered around the middle, although I would say closer to Michael than they were to Keith.

Holden Thorp (00:48:33):

But I think this is important for us to work out because we want everybody who contributes to the scientific enterprise to feel valued. And if they would feel more valued if we called them scientists, that suits me but it doesn't suit all of our academic colleagues apparently.

Eric Topol (00:48:54):

Well, I mean, I think just to weigh in a bit on that, I'm a big proponent of citizen scientists, and we've seen how it has transformed projects like folded for structural biology and so many things, All of Us program that's ongoing right now to try to get a million participants, at least half of whom are underrepresented to be citizen scientists learning about themselves through their genome and other layers of data. And that I think may help us to fight the misinformation, disinformation, the people that do their own research with a purpose that can be sometimes nefarious. The last type of topic I wanted to get to with you was the University of Florida and the state of Florida and the Surgeon General there. And again, we are kind of circling back to a few things that we've discussed today about higher education institutions as well as politics and I wonder if we get some comments about that scenario.

What’s Happening in Florida?

Holden Thorp (00:49:59):

Yeah. Well, I'm coming to you from Orlando, Florida where I have a home that I've had ever since I moved to a cold climate, and I spent the whole pandemic down here. I observed a lot of things going on in the state of Florida firsthand. And I think in a way it's two different worlds because Florida does make a massive investment in higher education more than many other states and that has really not changed that much under Governor DeSantis despite his performative views that seem to be to the contrary. And so, I think it's important to acknowledge that Florida State and Florida and UCF and USF, these are excellent places and many of them have thrived in terms of their budgets even in this weird climate, but the political performance is very much in the other direction. This is where the Stop WOKE Act happened. This is where, again, I live in Orlando. This is a company town that Ron DeSantis decided to take on the Walt Disney Corporation is the second biggest city in Orlando, and it's a company town, and he took on the employer.

Holden Thorp (00:51:32):

It doesn't make a whole lot of political sense, but I think it was all part of his national political ambitions. And down at the base of this was this all strange anti-vax stuff. Now I got my first vaccines down here. I went to public places that were organized by the Army Corps of Engineers that were at public properties. It was at a community college here in Orlando, was extremely well organized. I had no problem. I was there 10 minutes, got my vaccines. It was extremely well organized but at the same time, the guys on TV saying the vaccine's not any good. And he hires this person, Joseph Ladapo, to be his Surgeon General, who I think we would both say is an anti-vaxxer. I mean he just recently said that you didn't need to get a measles vaccine and then in the last couple of days said, if you're unvaccinated and you have measles, you don't have to quarantine for 21 days. Now really would be disastrous if measles came back. You know a lot more about that than I do but I'm a generation that had a measles vaccine and never worried about measles.

Holden Thorp (00:52:59):

So the part of it that I worry about the most is that this person, the Surgeon General, also has a faculty appointment at the University of Florida. And you can see how he got it because his academic resume has been circulated as a result of all of Florida's public records laws and he has a very strong, credible resume that would probably cause him to get tenure at a lot of places. The medical faculty at Florida have tried to assert themselves and say, we really need to distance ourselves from him, but the administration at the University of Florida has not really engaged them. Now, I did ask them last week about the measles thing. I was going to write about it again, and I wrote to them and I said, if you guys aren't going to say anything about what he is saying about the measles, then I'm going to have another editorial.

Holden Thorp (00:54:05):

And they sent me a statement, which I posted that you probably saw that they still didn't condemn him personally, but they did say that measles vaccination was very important, and it was a fairly direct statement. I don't know if that will portend more stronger words from the University of Florida. Maybe now that their president is somebody who's close to the governor, they'll feel a little more comfortable saying things like that. But I think the bigger issue for all of us is when we have academic colleagues who say things that we know are scientifically invalid, and this always gets to the whole free speech thing, but in my opinion, free speech, it is within free speech to say, yes, all these things about vaccines are true, but I still don't think people should be compelled to get vaccinated. That's an opinion. That's fine. But what's not an opinion is to say that vaccines are unsafe if they've been tested over and over again and proven to be effective.

Academic Freedom

Holden Thorp (00:55:24):

That's not an opinion. And I personally don't think that that deserves certainly to be weighted equally with the totality of medical evidence. I think that it's within bounds for academic colleagues and even institutions to call out their colleagues who are not expressing an opinion, but are challenging scientific facts without doing experiments and submitting papers and having lots of people look at it and doing all the stuff that we require in order to change scientific consensus. And this happens in climate change in a very parallel way. I mean, it's an opinion to say the climate is changing, humans are causing it, but I still don't think we should have government regulations about carbon. I think we should wait for the private sector to solve it, or I don't think it's going to have as bad of an effect as people say. Those are policy debates that you can have.

Holden Thorp (00:56:28):

But alleging that climate scientists are falsifying their projection somehow when they're not is in my opinion, not covered by free speech. And I think the best evidence we had of this is this recent verdict with Michael Mann, where it was the people who were criticizing him were found to be defamatory when they said that he committed research fraud. They could say he's exaggerating the threat. They could say they could dislike his style. He does have a very bombastic style. They can say all kinds of things about their opinions about him personally but if you accuse him of committing research fraud, and the paper that was in question was one of the most highly litigated papers of all time. It's been investigated more times than you can count. That's not something that's protected by free speech because it's defamatory to say that, and the jury found that. I think we have a lot of work to do to get within our own world, our colleagues, to get their arms around these two forms of debate.

Eric Topol (00:57:51):

Right. Well, I think this is, again, another really important point you're making during the pandemic parallel to the Michael Mann climate change case is that leading universities, as we recently reviewed in a podcast with Jonathan Howard, who wrote a book about this leading universities like Stanford, UCSF, Johns Hopkins and many others, didn't come out about the people that were doing things, saying things that were truly potential public harm. Not like you're saying, expressing an opinion with the truth, but rather negating evidence that was important to keep people protected from Covid. This is a problem which is thematic in our discussion I think Holden, is that universities have to get with it. They have to be able to help not put things on the credit card, be very transparent, direct quick respond, and not hide behind worried about social media or journalists or whatever else. This has been an incredible discussion, Holden, I got into even more than I thought we would.

Eric Topol (00:59:15):

You're a phenom to defend the whole science landscape that is challenging right now. I think you would agree for many reasons that we've discussed, and it affects education in a very dramatic, serious way. I want to thank you all that you're doing at Science with your team there to lead the charge and stand up for things and not being afraid to stimulate some controversies here and there. It's good for the field. And so, I hope I didn't miss anything and this exhaustive, this is the longest podcast I've done on Ground Truths, I want you to know that.

Holden Thorp (00:59:59):

Well, I'm flattered by that because you've had some great people on, that's for sure. And thank you for all you're doing, not just in science, but to spread the word about all these things and bring people together. It means a lot to all of us.

Eric Topol (01:00:15):

Oh, much appreciated. And we'll convene again soon to discuss so many dimensions of what we just have been reviewing and new ones to come. Thanks very much.

Holden Thorp (01:00:25):

Okay. Always good talking to you.

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