Eric Topol (00:06):

Hello, this is Eric Topol from Ground Truths, and I'm delighted to welcome Owen Tripp, who is a CEO of Included Health. And Owen, I'd like to start off if you would, with the story from 2016, because really what I'm interested in is patients and how to get the right doctor. So can you tell us about when you lost your hearing in your right ear back, what, nine years ago or so?

Owen Tripp (00:38):

Yeah, it's amazing to say nine years, Eric, but obviously as your listeners will soon understand a pretty vivid memory in my past. So I had been working as I do and noticed a loss of hearing in my right ear. I had never experienced any hearing loss before, and I went twice actually to a sort of national primary care chain that now owned by Amazon actually. And they described it as eustachian tube dysfunction, which is a pretty benign common thing that basically meant that my tubes were blocked and that I needed to have some drainage. They recommended Sudafed to no effect. And it was only a couple weeks later where I was walking some of the senior medical team at my company down to the San Francisco Giants game. And I was describing this experience of hearing loss and I said I was also losing a little bit of sensation in the right side of my face. And they said, that is not eustachian tube dysfunction. And well, I can let the story unfold from there. But basically my colleagues helped me quickly put together a plan to get this properly diagnosed and treated. The underlying condition is called vestibular schwannoma, even more commonly known as an acoustic neuroma. So a pretty rare benign brain tumor that exists on the vestibular nerve, and it would've cost my life had it not been treated.

Eric Topol (02:28):

So from what I gather, you saw an ENT physician, but that ENT physician was not really well versed in this condition, which is I guess a bit surprising. And then eventually you got to the right ENT physician in San Francisco. Is that right?

Owen Tripp (02:49):

Well, the first doctor was probably an internal medicine doctor, and I think it's fair to say that he had probably not seen many, if any cases. By the time I reached an ENT, they were interested in working me up for what's known as sudden sensorineural hearing loss (SSHL), which is basically a fancy term for you lose hearing for a variety of possible pathologies and reasons, but you go through a process of differential diagnosis to understand what's actually going on. By the time that I reached that ENT, the audio tests had showed that I had significant hearing loss in my right ear. And what an MRI would confirm was this mass that I just described to you, which was quite large. It was already about a centimeter large and growing into the inner ear canal.

Eric Topol (03:49):

Yeah, so I read that your Stanford brain scan suggested it was about size of a plum and that you then got the call that you had this mass in your brainstem tumor. So obviously that's a delicate operation to undergo. So the first thing was getting a diagnosis and then the next thing was getting the right surgeon to work on your brain to resect this. So how did you figure out who was the right person? Because there's only a few thousand of these operations done every year, as I understand.

Owen Tripp (04:27):

That's exactly right. Yeah, very few. And without putting your listeners to sleep too early in our discussion, what I'll say is that there are a lot of ways that you can actually do this. There are very few cases, any approach really requires either shrinking or removing that tumor entirely. My size of tumor meant it was really only going to be a surgical approach, and there I had to decide amongst multiple potential approaches. And this is what's interesting, Eric, you started saying you wanted to talk about the patient experience. You have to understand that I'm somebody, while not a doctor, I lead a very large healthcare company. We provide millions of visits and services per year on very complex medical diagnoses down to more standard day-to-day fare. And so, being in the world of medical complexity was not daunting on the basics, but then I'm the patient and now I have to make a surgical treatment decision amongst many possible choices, and I was able to get multiple opinions.

Owen Tripp (05:42):

I got an opinion from the House clinic, which is closer to you in LA. This is really the place where they invented the surgical approach to treating these things. I also got an approach shared with me from the Mayo Clinic and one from UCSF and one from Stanford, and ultimately, I picked the Stanford team. And these are fascinating and delicate structures as you know that you're dealing with in the brain, but the surgery is a long surgery performed by multiple surgeons. It's such an exhausting surgery that as you're sort of peeling away that tumor that you need relief. And so, after a 13 hour surgery, multiple nights in the hospital and some significant training to learn how to walk and move and not lose my balance, I am as you see me today, but it was possible under one of the surgical approaches that I would've lost the use of the right side of my face, which obviously was not an option given what I given what I do.

Eric Topol (06:51):

Yeah, well, I know there had to be a tough rehab and so glad that you recovered well, and I guess you still don't have hearing in that one ear, right?

Owen Tripp:

That's right.

Eric Topol:

But otherwise, you're walking well, and you've completely recovered from what could have been a very disastrous type of, not just the tumor itself, but also the way it would be operated on. 13 hours is a long time to be in the operating room as a patient.

Owen Tripp (07:22):

You've got a whole team in there. You've got people testing nerve function, you've got people obviously managing the anesthesiology, which is sufficiently complex given what's involved. You've got a specialized ENT called a neurotologist. You've got the neurosurgeon who creates access. So it's quite a team that does these things.

Eric Topol (07:40):

Yeah, wow. Now, the reason I wanted to delve into this from your past is because I get a call or email or whatever contact every week at least one, is can you help me find the right doctor for such and such? And this has been going on throughout my career. I mean, when I was back in 20 years ago at Cleveland Clinic, the people on the board, I said, well, I wrote about it in one of my books. Why did you become a trustee on the board? And he said, so I could get access to the right doctor. And so, this is amazing. We live in an information era supposedly where people can get information about this being the most precious part, which is they want to get the right diagnosis, they want to get the right treatment or prevention, whatever, and they can't get it. And I'm finding this just extraordinary given that we can do deep research through several different AI models and get reports generated on whatever you want, but you can't get the right doctor. So now let's go over to what you're working on. This company Included Health. When did you start that?

Owen Tripp (08:59):

Well, I started the company that was known as Grand Rounds in 2011. And Grand Rounds still to this day, we've rebranded as Included Health had a very simple but powerful idea, one you just obliquely referred to, which is if we get people to higher quality medicine by helping them find the right level and quality of care, that two good things would happen. One, the sort of obvious one, patients would get better, they'd move on with their lives, they'd return to health. But two and critically that we would actually help the system overall with the cost burden of unnecessary, inappropriate and low quality care because the coda to the example you gave of people calling you looking for a physician referral, and you and I both know this, my guess is you've probably had to clean plenty of it up in your career is if you go to the wrong doctor, you don't get out of the problem. The problem just persists. And that patient is likely to bounce around like a ping pong ball until they find what they actually need. And that costs the payers of healthcare in this country a lot of money. So I started the company in 2011 to try to solve that problem.

Eric Topol (10:14):

Yeah, one example, a patient of mine who I've looked after for some 35 years contacted me and said, a very close friend of mine lives in the Palm Springs region and he has this horrible skin condition and he's tortured and he's been to six centers, UCSF, Stanford, Oregon Health Science, Eisenhower, UCLA, and he had a full workup and he can't sleep because he's itching all the time. His whole skin is exfoliating and cellulitis and he had biopsies everywhere. He’s put on all kinds of drugs, monoclonal antibodies. And I said to this patient of mine I said, I don't know, this is way out of my area. I checked at Scripps and turns out there was this kind of the Columbo of dermatology, he can solve any mystery. And the patient went to see him, and he was diagnosed within about a minute that he had scabies, and he was treated and completely recovered after having thousands and thousands of dollars of all these workups at these leading medical centers that you would expect could make a diagnosis of scabies.

Owen Tripp (11:38):

That’s a pretty common diagnosis.

Eric Topol (11:40):

Yeah. I mean you might expect it more in somebody who was homeless perhaps, but that doesn't mean it can't happen in anyone. And within the first few minutes he did a scrape and showed the patient under the microscope and made a definitive diagnosis and the patient to this day is still trying to pay all his bills for all these biopsies and drugs and whatnot, and very upset that he went through all this for over a year and he thought he wanted to die, it was so bad. Now, I had never heard of Included Health and you have now links with a third of the Fortune 100 companies. So what do you do with these companies?

Owen Tripp (12:22):

Yeah, it's pretty cool. These companies, so very large organizations like Walmart and JPMorgan Chase and the rest of the big pioneers of American industry and business put us in as a benefit to help their employees have the same experience that I described to provide almost Eric Topol like guidance service to help people find access to high quality care, which might be referring them into the community or to an academic medical center, but often is also us providing care delivery ourselves through on-demand primary care, urgent care, behavioral health. And now just last year we introduced a couple of our first specialty lines. And the idea, Eric, is that these companies buy this because they know their employees will love it and they do. It is often one of, if not the most highly rated benefits available. But also because in getting their employees better care faster, the employees come back to work, they feel more connected to the company, they're able to do better and safer and higher quality work. And they get more mileage out of their health benefits. And you have to remember that the costs of health benefits in this country are inflating even in this time of hyperinflation. They're inflating faster than anything else, and this is one of most companies, number one pain points for how they are going to control their overall budget. So this is a solution that both give them visibility to controlling cost and can deliver them an excellent patient experience that is not an offer that they've been able to get from the traditional managed care operators.

Eric Topol (14:11):

So I guess there's a kind of multidimensional approach that you're describing. For one, you can help find a doctor that's the right doctor for the right patient. And you're also actually providing medical services too, right?

Owen Tripp (14:27):

That's right.

Eric Topol (14:30):

Are these physicians who are employed by Included Health?

Owen Tripp (14:34):

They are, and we feel very strongly about that. We think that in our model, we want to train people, hire people in a specific way, prepare them for the kind of work that we do. And there's a lot we could spend time talking about there, but one of the key features of that is teamwork. We want people to work in a collaborative model where they understand that while they may be expert in one specific thing that is connected to a service line, they're working in a much broader team in support of the member, in support of that patient. And we talk about the patients being very first here, and you and I had a laugh on this in the past, so many hospitals will say we're patient first. So many managed care companies will say they're patient first, but it is actually hard the way that the system is designed to truly be patient first. At Included Health, we measure whether patients will come back to us, whether they tell their friends about us, whether they have high quality member satisfaction and are they living more healthy days. So everybody gets surveyed for patient reported outcomes, which is highly unusual as you know, to have both the clinical outcomes and the patient reported outcomes as well.

Eric Topol (15:41):

Is that all through virtual visits or are there physical visits as well?

Owen Tripp (15:47):

Today that is all through virtual visits. So we provide 24/7/365 access to urgent care, primary care, behavioral health, the start of the specialty clinic, which we launched last year. And then we provide support for patients who have questions about how these things are going to be billed, what other benefits they have access to. And where appropriate, we send them out to care. So obviously we can't provide all the exams virtually. We can't provide everything that a comprehensive physical would today, but as you and I know that is also changing rapidly. And so, we can do things to put sensors and other observational devices in people's homes to collect that data positively.

Eric Topol (16:32):

Now, how is that different than Teladoc and all these other telehealth based companies? I mean because trying to understand on the one hand you have a service that you can provide that can be extremely helpful and seems to be relatively unique. Whereas the other seems to be shared with other companies that started in this telehealth space.

Owen Tripp (16:57):

I think the easiest way to think about the difference here is how a traditional telemedicine company is paid and how we're paid because I think it'll give you some clue as to why we've designed it the way we've designed it. So the traditional telehealth model is you put a quarter in the jukebox, you listen to a song when the song's over, you got to get out and move on with the rest of your life. And quite literally what I mean is that you're going to see one doctor, one time, you will never see that same doctor again. You are not going to have a connected experience across your visits. I mean, you might have an underlying chart, but there's not going to be a continuity of care and follow up there as you would in an integrated setting. Now by comparison, and that's all derived from the fact that those telehealth companies are paid by the drink, they're paid by the visit.

Owen Tripp (17:49):

In our model, we are committing to a set of experience goals and a set of outcomes to the companies that you refer to that pay our bill. And so, the visits that our members enjoy are all connected. So if you have a primary care visit, that is connected to your behavioral health visit, which is great and is as it should be. If you have a primary care appointment where you identify the need for follow-up cardiology for example. That patient can be followed through that cardiology visit that we circle back, that we make sure that the patient is educated, that he or she has all their questions answered. That's because we know that if the patient actually isn't confident in what they heard and they don't follow through on the plan, then it's all for naught. It's not going to work. And it's a simple sort of observation, but it's how we get paid and why we think it's a really important way to think about medicine.

Eric Topol (18:44):

So these companies, and they're pretty big companies like Google and AT&T and as you said, JPMorgan and the list goes on and on. Any one of the employees can get this. Is that how it works?

Owen Tripp (18:56):

That's right, that's right. And even better, most of what I've described to you today is at a low or zero cost to them. So this is a very affordable, easy way to access care. Thinking about one of our very large airline clients the other day, we're often dealing with their flight crews and ramp agents at very strange hours in very strange places away from home, so that they don't have to wait to get access to care. And you can understand that at a basic humanitarian level why that's great, but you can also understand it from a safety perspective that if there is something that is impeding that person's ability to be functioning at work, that becomes an issue for the corporation itself.

Eric Topol (19:39):

Yeah, so it's interesting you call it included because most of us in the country are excluded. That is, they don't have any way to turn through to get help for a really good referral. Everything's out of network if they are covered and they're not one of the fortunate to be in these companies that you're providing the service for. So do you have any peers or are there any others that are going to come into this space to help a lot of these people that are in a tough situation where they don't really have anyone to turn to?

Owen Tripp (20:21):

Well, I hope so. Because like you, I've dedicated my career to trying to use information and use science and use in my own right to bring along the model. At Included Health, we talk about raising the standard of care for everybody, and what we mean by that is, we actually hope that this becomes a model that others can follow. The same way the Cleveland Clinic did, the same way the Mayo Clinic did. They brought a model into the world that others soon try to replicate, and that was a good thing. So we'd like to see more attempt to do this. The reality is we have not seen that because unfortunately the old system has a lot of incentives in place to function exactly the way that it is designed. The health system is going to maximize the number of patients that correspond to the highest paying procedures and tests, et cetera. The managed care company is going to try to process the highest number of claims, work the most efficient utilization management and prior authorization, but left out in the middle of all of that is the patient. And so, we really wanted to build that model with the patient at the center, and when I started this company now over a decade ago, that was just a dream that we could do that. Now serving over 10 million members, this feels like it's possible and it feels like a model others could follow.

Eric Topol (21:50):

Yeah, well that was what struck me is here you're reaching 10 million people. I'd never heard of it. I was like, wow. I thought I try to keep up with things. But now the other thing I wanted to get into you with is AI. Obviously, that has a lot of promise in many different ways. As you know, there are some 12 million diagnostic serious errors a year in the US. I mean you were one, I've been part of them. Most people have been roughed up one way or another. Then there's 800,000 Americans who have disability or die from these errors a year, according to Johns Hopkins relatively recent study. So one of the ways that AI could help is accuracy. But of course, there's many other ways it can help make the lives of both patients helping to integrate their data and physicians to go through a patient's records and set points of their labs and all sorts of other things. Where do you see AI fitting into the model that you've built?

Owen Tripp (22:58):

Well, I'll give you two that I'm really excited about, that I don't think I hear other people talking about. And again, I'm going to start with that patient, with that member and what he or she wants and needs. One and Eric, bear with me, this is going to sound very banal, but one is just making sense of these very complicated plan documents and explanations of benefits. I'm aware of how well-trained you are and how much you've written. I believe you are the most published in your field. I believe that is a fact. And yet if I showed you a plan description document and an explanation of benefit and I asked you, Eric, could you tell me how much it's going to cost to have an MRI at this facility? I don't think you would've any way of figuring that out. And that is something that people confront every single day in this country. And a lot of people are not like you and me, in that we could probably tolerate a big cost range for that MRI. For some people that might actually be the difference between whether they eat or not, or get their kids prescription or not.

Owen Tripp (24:05):

And so, we want to make the questions about what your benefits cover and how you understand what's available to you in your plan. We want to make that really easy and we want to make it so that you don't have to have a PhD in insurance language to be able to ask the properly formatted question. As you know, the foundation models are terrific at that problem. So that's one.

Eric Topol (24:27):

And that's a good one, that's very practical and very much needed. Yeah.

Owen Tripp (24:32):

The second one I'm really excited about, and I think this will also be near and dear to your heart, is AI has this ability to be sort of nonjudgmental in the best possible way. And so, if we have a patient on a plan to manage hypertension or to manage weight or to manage other elements of a healthy lifestyle. And here we're not talking about deep science, we're just talking about what we've known to work for a long period of time. AI as a coach to help follow through on those goals and passively take data on how you're progressing, but have behind it the world's greatest medical team to be able to jump in when things become more acute or more complex. That's an awesome tool that I think every person needs to be carrying around, so that if my care plan or if my goal is about sleeping better, if my goal is about getting pregnant, if my goal is about reducing my blood pressure, that I can do that in a way that I can have a conversation where I don't feel as a patient that I'm screwing up or letting somebody down, and I can be honest with that AI.

Owen Tripp (25:39):

So I'm really excited about the potential for the AI as an adjunct coach and care team manager to continue to proceed along with that member with medical support behind that when necessary.

Eric Topol (25:55):

Yeah, I mean there's a couple of things I'd say about that. Firstly, the fact that you're thinking it from the patient perspective where most working in AI is thinking it from the clinician perspective, so that's really important. The next is that we get notifications, and you need to not sit every hour or something like that from a ring or from a smartwatch or whatever. That isn't particularly intelligent, although it may be needed. The point is we don't get notifications like, what was your blood pressure? Or can you send a PDF of your heart rhythm or this sort of thing. Now the problem too is that people are generating lots of data just by wearing a smartwatch or a fitness band. You've got your activity, your sleep, your heart rate, and all sorts of things that are derivatives of that. No less, you could have other sensors like a glucose monitoring and on and on. No less your electronic health record, and there's no integration of any of this.

Eric Topol (27:00):

So this idea that we could have a really intelligent AI virtual coach for the patient, which as you said could have connects with a physician as needed, bringing in the data or bringing in some type of issue that the doctor needs to attend to, but it doesn't seem like anything is getting done. We have the AI capabilities, but nothing's getting done. It's frustrating because I wrote about this in 2019 in the Deep Medicine book, and it's just like some of the most sophisticated companies you would think Apple, for the ring Oura and so many others. They have the data, but they don't integrate anything, and they don't really set up notifications for patients. How are we going to get out of this rut?

Owen Tripp (27:51):

We are producing oil tankers of data around personal experience and not actually turning that into positive energy for what patients can do. But I do want to be optimistic on this point because I actually think, and I shared this with you when we last saw each other. Your thinking was ahead of the time, but foundational for people like me to say, we need to go actually make that real. And let me explain to you what I mean by making it real. We need to bring together the insight that you have an elevated heart rate or that your step count is down, or that your sleep schedule is off. We need to bring that together with the possibility of connecting with a medical professional, which these devices do not have the ability to do that today, and nor do those companies really want to get in that business. And also make that context of what you can afford as a patient.

Owen Tripp (28:51):

So we have data that's suggestive of an underlying issue. We have a medical team that's prepared to actually help you on that issue. And then we have financial security to know that whatever is identified actually will be paid for. Now, that's not a hard triangle conceptually, but no one of those companies is actually interested in all the points of the triangle, and you have to be because otherwise it's not going to work for the patient. If your business is in selling devices. Really all I'm thinking about is how do I sell devices and subscriptions. If my business is exclusively in providing care, that's really all I'm thinking about. If my business is in managing risk and writing insurance policies, that's really all I'm thinking about. You have to do all those three things in concert.

Eric Topol (29:34):

Yeah, I mean in many ways it goes back to what we were talking about earlier, which is we're in this phenomenal era of information to the fifth power. But here we are, we have a lot of data from multiple sources, and it doesn't get integrated. So for example, a person has a problem and they don't know what is the root cause of it. Let's say it's poor sleep, or it could be that they're having stress, which would be manifest through their heart rate or heart rate variability or all sorts of other metrics. And there's no intelligence provided for them to interpret their data because it's all siloed and we're just not really doing that for patients. I hope that'll happen. Hopefully, Included Health could be a lead in that. Maybe you can show the way. Anyway, this has been a fun conversation, Owen. It's rare that I've talked in Ground Truths with any person running a company, but I thought yours.

Eric Topol (30:36):

Firstly, I didn't know anything about it and it’s big. And secondly, that it's a kind of a unique model that really I'm hoping that others will get involved in and that someday we'll all be included. Maybe not with Included Health, but with better healthcare in this country, which is certainly not the norm, not the routine. And also, as you aptly pointed out at terrible costs with all sorts of waste, unnecessary tests and that sort of thing. So thanks for what you're doing and I'll be following your future efforts and hopefully we can keep making some strides.

Owen Tripp (31:15):

We will. And I wanted to say thanks for the conversation too and for your thinking on these topics. And look, I want to leave you just with a quick dose of optimism, and you and I both know this. The American system at its best is an extraordinary system, unrivaled in the world, in my opinion. But we do have to have more people included. All the services need to be included in one place. When we get there, we're going to really see what's possible here.

Eric Topol (31:40):

I do want to agree with you that if you can get to the right doctor and if you can afford it, that is ideally covered by your insurance. It is a phenomenal system, but getting there, that's the hard part. And every day people are confronted. I'm sure, thousands and thousands with serious condition either to get the diagnosis or the treatment, and they have a really rough time. So anyway, so thank you and I really appreciate your taking the time to meet with me today.

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Eric Topol (00:05):

Hello, it's Eric Topol from Ground Truths, and I've got some really exciting stuff to talk to you about today. And it's about the announcement for a new Center for pediatric CRISPR Cures. And I'm delight to introduce doctors Jennifer Doudna and Priscilla Chan. And so, first let me say this is amazing to see this thing going forward. It's an outgrowth of a New England Journal paper and monumental report on CRISPR in May. [See the below post for more context]

Let me introduce first, Dr. Doudna. Jennifer is the Li Ka Shing Chancellor's Chair and a Professor in the departments of chemistry and of molecular and cell biology at the University of California Berkeley. She's also the subject of this book, one of my favorite books of all time, the Code Breaker. And as you know, the 2020 Nobel Prize laureate for her work in CRISPR-Cas9 genome editing, and she founded the Innovative Genomics Institute (IGI) back 10 years ago. So Jennifer, welcome.

Jennifer Doudna (01:08):

Thank you, Eric. Great to be here.

Eric Topol (01:10):

And now Dr. Priscilla Chan, who is the co-founder of the Chan Zuckerberg Initiative (CZI) that also was started back in 2015. So here we are, a decade later, these two leaders. She is a pediatrician having trained at UCSF and is committed to the initiative which has as its mission statement, “to make it possible to cure, prevent, and manage all diseases in this century.” So today we're going to talk about a step closer to that. Welcome, Priscilla.

Priscilla Chan (01:44):

Thank you. Thanks for having me.

Eric Topol (01:46):

Alright, so I thought we'd start off by, how did you two get together? Have you known each other for over this past decade since you both got all your things going?

Jennifer Doudna (01:56):

Yes, we have. We've known each other for a while. And of course, I've admired the progress at the CZI on fundamental science. I was an advisor very early on and I think actually that's how we got to know each other. Right, Priscilla?

Priscilla Chan (02:11):

Yeah, that's right. We got to know each other then. And we've been crisscrossing paths. And I personally remember the day you won the Nobel Prize. It was in the heart of the pandemic and a lot of celebrations were happening over Zoom. And I grabbed my then 5-year-old and got onto the UCSF celebration and I was like, look, this is happening. And it was really cool for me and for my daughter.

Eric Topol (02:46):

Well, it's pretty remarkable convergence leading up to today's announcement, but I know Priscilla, that you've been active in this rare disease space, you've had at CZI a Rare As One Project. Maybe you could tell us a bit about that.

Priscilla Chan (03:01):

Yeah, so at CZI, we work on basic science research, and I think that often surprises people because they know that I'm a pediatrician. And so, they often think, oh, you must work in healthcare or healthcare delivery. And we've actually chosen very intentionally to work in basic science research. In part because my training as a pediatrician at UCSF. As you both know, UCSF is a tertiary coronary care center where we see very unusual and rare cases of pediatric presentations. And it was there where I learned how little we knew about rare diseases and diseases in general and how powerful patients were. And that research was the pipeline for hope and for new discoveries for these families that often otherwise don't have very much access to treatments or cures. They have a PDF that maybe describes what their child has. And so, I decided to invest in basic science through CZI, but always saw the power of bringing rare disease patient cohorts. One, because if you've ever met a parent of a child with rare disease, they are a force to be reckoned with. Two, they can make research so much better due to their insights as patients and patient advocates. And I think they close the distance between basic science and impact in patients. And so, we've been working on that since 2019 and has been a passion of ours.

Eric Topol (04:40):

Wow, that's great. Now Jennifer, this IGI that you founded a decade ago, it's doing all kinds of things that are even well beyond rare diseases. We recently spoke, I know on Ground Truths about things as diverse as editing the gut microbiome in asthma and potentially someday Alzheimer’s. But here you were very much involved at IGI with the baby KJ Muldoon. Maybe you could take us through this because this is such an extraordinary advance in the whole CRISPR Cures story.

Jennifer Doudna (05:18):

Yes, Eric. It's a very exciting story and we're very, very proud of the teamwork that went into making it possible to cure baby KJ of his very rare disease. And in brief, the story began back in August of last year when he was born with a metabolic disorder that prevented him from digesting protein, it's called a urea cycle disorder and rare, but extremely severe. And to the point where he was in the ICU and facing a very, very difficult prognosis. And so, fortunately his clinical team at Children's Hospital of Philadelphia (CHOP) reached out to Fyodor Urnov, who is the Director of Translational Medicine at the IGI here in the Bay Area. They teamed up and realized that they could quickly diagnose that child because we had an IRB approved here at the IGI that allowed us to collect patient samples and do diagnosis. So that was done.

Jennifer Doudna (06:26):

We created an off-the-shelf CRISPR therapy that would be targeted to the exact mutation that caused that young boy's disease. And then we worked with the FDA in Washington to make sure that we could very safely proceed with testing of that therapy initially in the lab and then ultimately in two different animal models. And then we opened a clinical trial that allowed that boy to be enrolled with, of course his parents' approval and for him to be dosed and the result was spectacular. And in fact, he was released from the hospital recently as a happy, healthy child, gaining lots of weight and looking very chunky. So it's really exciting.

Eric Topol (07:16):

It's so amazing. I don't think people necessarily grasp this. This timeline [see above] that we'll post with this is just mind boggling how you could, as you said Jennifer, in about six months to go from the birth and sequencing through cell specific cultures with the genome mutations through multiple experimental models with non-human primates even, looking at off-target effects, through the multiple FDA reviews and then dosing, cumulatively three dosing to save this baby's life. It really just amazing. Now that is a template. And before we go to this new Center, I just wanted to also mention not just the timeline of compression, which is unimaginable and the partnership that you've had at IGI with I guess Danaher to help manufacture, which is just another part of the story. But also the fact that you're not just even with CRISPR 1.0 as being used in approvals previously for sickle cell and β-thalassemia, but now we're talking about base editing in vivo in the body using mRNA delivery. So maybe you could comment on that, Jennifer.

Jennifer Doudna (08:38):

Yeah, very good point. So yeah, we used a version of CRISPR that was created by David Liu at the Broad Institute and published and available. And so, it was possible to create that, again, targeted to the exact mutation that caused baby KJ’s disease. And fortunately, there was also an off-the-shelf way to deliver it because we had access to lipid nanoparticles that were developed for other purposes including vaccinations. And the type of disease that KJ suffered from is one that is treatable by editing cells in the liver, which is where the lipid nanoparticle naturally goes. So there were definitely some serendipity here, but it was amazing how all of these pieces were available. We just had to pull them together to create this therapy.

Eric Topol (09:30):

Yeah, no, it is amazing. So that I think is a great substrate for starting a new Center. And so, maybe back to you Priscilla, as to what your vision was when working with Jennifer and IGI to go through with this.

Priscilla Chan (09:45):

I think the thing that's incredibly exciting, you mentioned that at CZI our mission is to cure, prevent, and manage all disease. And when we talked about this 10 years ago, it felt like this far off idea, but every day it seems closer and closer. And I think the part that's super exciting about this is the direct connection between the basic science that's happening in CRISPR and the molecular and down to the nucleotide understanding of these mutations and the ability to correct them. And I think many of us, our imaginations have included this possibility, but it's very exciting that it has happened with baby KJ and CHOP. And we need to be able to do the work to understand how we can treat more patients this way, how to understand the obstacles, unblock them, streamline the process, bring down the cost, so that we better understand this pathway for treatment, as well as to increasingly democratize access to this type of platform. And so, our hope is to be able to do that. Take the work and inspiration that IGI and the team at CHOP have done and continue to push forward and to look at more cases, look at more organ systems. We're going to be looking in addition to the liver, at the bone marrow and the immune system.

Priscilla Chan (11:17):

And to be able to really work through more of the steps so that we can bring this to more families and patients.

Eric Topol (11:30):

Yeah, well it's pretty remarkable because here you have incurable ultra-rare diseases. If you can help these babies, just think of what this could do in a much broader context. I mean there a lot of common diseases have their roots with some of these very rare ones. So how do you see going forward, Jennifer, as to where you UC Berkeley, Gladstone, UCSF. I'm envious of you all up there in Northern California I have to say, will pull this off. How will you get the first similar case to KJ Muldoon going forward?

Jennifer Doudna (12:13):

Right. Well, IGI is a joint institute, as you probably know, Eric. So we were founded 10 years ago as a joint institute between UC Berkeley and UCSF. And now we have a third campus partner, UC Davis and we have the Gladstone Institute. So we've got an extraordinary group of clinicians and researchers that are coming together for this project and the Center to make it a success. We are building a clinical team at UCSF. We have several extraordinary leaders including Jennifer Puck and Chris Dvorak, and they are both going to be involved in identifying patients that could be enrolled in this program based on their diagnosis. And we will have a clinical advisory group that will help with that as well. So we'll be vetting patients probably right after we announce this, we're going to be looking to start enrolling people who might need this type of help.

Eric Topol (13:18):

Do you think it's possible to go any faster right now than the six months that it took for KJ?

Jennifer Doudna (13:26):

I think it could be. And here's the reason. There's a very interesting possibility that because of the type of technology that we're talking about with CRISPR, which fundamentally, and you and I have talked about this previously on your other podcast. But we've talked about the fact that it's a programmable technology and that means that we can change one aspect of it, one piece of it, which is a piece of a molecule called RNA that's able to direct CRISPR to the right sequence where we want to do editing and not change anything else about it. The protein, the CRISPR protein stays the same, the delivery vehicle stays the same, everything else stays the same. And so, we're working right now with FDA to get a platform designation for CRISPR that might allow streamlining of the testing process in some cases. So it'll obviously come down to the details of the disease, but we're hopeful that in the end it will be possible. And Priscilla and I have talked about this too, that as AI continues to advance and we get more and more information about rare diseases, we'll be able to predict accurately the effects of editing. And so, in some cases in the future it may be possible to streamline the testing process even further safely.

Eric Topol (14:51):

And I also would note, as you both know, well this administration is really keen on genome editing and they've had a joint announcement regarding their support. And in my discussions with the FDA commissioner, this is something they are very excited about. So the timing of the new Center for pediatric CRISPR Cures is aligned with the current administration, which is good to see. It's not always the case. Now going back, Priscilla, to your point that not just for the liver because delivery has been an issue of course, and we're going to try to get after a lot of these really rare diseases, it's going to go beyond there. So this is also an exciting new dimension of the Center, as you said, to go after the bone marrow for hematopoietic cells, perhaps other organs as well.

Priscilla Chan (15:42):

I mean what the expertise and feasibility, the immune system is going to be the next target. Jennifer Puck has been a pioneer in this work. She's the one who designed the newborn screen that will be the tool that picks up these patients as they are born. And I think the thing that's tremendous is the immune system, first of all is active in many, many diseases, not just these cases of children born with partial or absence of immune systems. And the course right now that these babies are left with is complete isolation and then a very long and arduous course of a bone marrow transplant with high morbidity and mortality. And even if after the transplant you have complications like graft versus host and immunosuppression. And so, the idea of being able to very specifically and with less the conditioning and morbidity and mortality of the treatment, being able to address this is incredible. And the implications for other diseases like blood cancers or other hematopoietic diseases, that's incredible. And that actually has an incredibly broad base of patients that can benefit from the learnings from these babies with severe combined immunodeficiencies.

Eric Topol (17:10):

Yeah, I think that goes back to a point earlier maybe to amplify in that previous CRISPR generation, it required outside the body work and it was extremely laborious and time consuming and obviously added much more to the expense because of hospitalization time. This is different. This is basically doing this inside the affected patient's body. And that is one of the biggest reasons why this is a big step forward and why we're so fortunate that your Center is moving forward. Maybe before we wrap up, you might want to comment, Jennifer on how you were able to bring in to build this platform, the manufacturing arm of it, because that seems to be yet another dimension that's helpful.

Jennifer Doudna (18:01):

Indeed, yes. And we were again fortunate with timing because you mentioned briefly that the IGI had set up a program with the Danaher Corporation back in January of last year. We call it our Beacon project. And it's focused on rare disease. And it's a really interesting kind of a unique partnership because Danaher is a manufacturing conglomerate. So they have companies that make molecules, they make proteins, they make RNA molecules, they make delivery molecules. And so, they were excited to be involved with us because they want to be a provider of these types of therapies in the future. And they can see the future of CRISPR is very exciting. It's expanding, growing area. And so, that agreement was in place already when the baby KJ case came to our attention. And so, what we're hoping to do with Danaher is again, work with them and their scientists to continue to ask, how can we reduce the cost of these therapies by reducing the cost of the molecules that are necessary, how to make them efficiently. We already, it's very interesting, Fyodor Urnov has toured their plant in North Dakota recently, and he found in talking to their engineers, there are a number of things that we can already see will be possible to do that are going to make the process of manufacturing these molecules faster and cheaper by a lot.

Eric Topol (19:28):

Wow.

Jennifer Doudna (19:28):

So it's a win-win for everybody. And so, we're really excited to do that in the context of this new Center.

Eric Topol (19:36):

Oh, that's phenomenal because some of these disorders you don't have that much time to work with before they could be brain or organ or vital tissue damage. So that's great to hear that. What you built here is the significance of it can't be under emphasized, I'll say because we have this May report of baby KJ, which could have been a one-off and it could have been years before we saw another cure of an ultra-rare disorder. And what you're doing here is insurance against that. You're going to have many more cracks at this. And I think this is the excitement about having a new dedicated Center. So just in closing, maybe some remarks from you Priscilla.

Priscilla Chan (20:24):

I just want to emphasize one point that's really exciting as we talk about these ultra-rare cases that they're often like one in a million. All these learnings actually help maximize the impact of lots of research across the sector that impacts actually everyone's health. And so, our learnings here from these patients that have very significant presentations that really can stand to benefit from any treatment is hopefully paving the way for many, many more of us to be able to live healthier, higher quality lives through basic science.

Eric Topol (21:13):

And over to you, Jennifer.

Jennifer Doudna (21:15):

Couldn't agree more. It's a really interesting moment. I think what we hope we are, is we're at sort of an inflection point where, as I mentioned earlier, all the pieces are in place to do this kind of therapeutic and we just need a team that will focus on doing it and pulling it together. And also learning from that process so that as Priscilla just said, we are ultimately able to use the same strategy for other diseases and potentially for diseases that affect lots of people. So it's exciting.

Eric Topol (21:46):

For sure. Now, if I could just sum up, this is now a decade past the origination of your work of CRISPR and how already at the first decade culminated in sickle cell disease treatment and β-thalassemia. Now we're into the second decade of CRISPR. And look what we've seen, something that was unimaginable until it actually happened and was reported just a little over a month ago. Now going back to Priscilla's point, we're talking about thousands of different rare Mendelian genomic disorders, thousands of them. And if you add them all up of rare diseases, we're talking about hundreds of millions of people affected around the world. So this is a foray into something much bigger, no less the fact that some of these rare mutations are shared by common diseases and approaches. So this really big stuff, congratulations to both of you and your organizations, the Innovative Genomics Institute and the Chan Zuckerberg Initiative for taking this on. We'll be following it with very deep interest, thank you.

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“To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.”—Adam Kucharski

My conversation with Professor Kucharski on what constitutes certainty and proof in science (and other domains), with emphasis on many of the learnings from Covid. Given the politicization of science and A.I.’s deepfakes and power for blurring of truth, it’s hard to think of a topic more important right now.

Audio file (Ground Truths can also be downloaded on Apple Podcasts and Spotify)

Eric Topol (00:06):

Hello, it's Eric Topol from Ground Truths and I am really delighted to welcome Adam Kucharski, who is the author of a new book, Proof: The Art and Science of Certainty. He’s a distinguished mathematician, by the way, the first mathematician we've had on Ground Truths and a person who I had the real privilege of getting to know a bit through the Covid pandemic. So welcome, Adam.

Adam Kucharski (00:28):

Thanks for having me.

Eric Topol (00:30):

Yeah, I mean, I think just to let everybody know, you're a Professor at London School of Hygiene and Tropical Medicine and also noteworthy you won the Adams Prize, which is one of the most impressive recognitions in the field of mathematics. This is the book, it's a winner, Proof and there's so much to talk about. So Adam, maybe what I'd start off is the quote in the book that captivates in the beginning, “life is full of situations that can reveal remarkably large gaps in our understanding of what is true and why it's true. This is a book about those gaps.” So what was the motivation when you undertook this very big endeavor?

Adam Kucharski (01:17):

I think a lot of it comes to the work I do at my day job where we have to deal with a lot of evidence under pressure, particularly if you work in outbreaks or emerging health concerns. And often it really pushes the limits, our methodology and how we converge on what's true subject to potential revision in the future. I think particularly having a background in math’s, I think you kind of grow up with this idea that you can get to these concrete, almost immovable truths and then even just looking through the history, realizing that often isn't the case, that there's these kind of very human dynamics that play out around them. And it's something I think that everyone in science can reflect on that sometimes what convinces us doesn't convince other people, and particularly when you have that kind of urgency of time pressure, working out how to navigate that.

Eric Topol (02:05):

Yeah. Well, I mean I think these times of course have really gotten us to appreciate, particularly during Covid, the importance of understanding uncertainty. And I think one of the ways that we can dispel what people assume they know is the famous Monty Hall, which you get into a bit in the book. So I think everybody here is familiar with that show, Let's Make a Deal and maybe you can just take us through what happens with one of the doors are unveiled and how that changes the mathematics.

Adam Kucharski (02:50):

Yeah, sure. So I think it is a problem that's been around for a while and it's based on this game show. So you've got three doors that are closed. Behind two of the doors there is a goat and behind one of the doors is a luxury car. So obviously, you want to win the car. The host asks you to pick a door, so you point to one, maybe door number two, then the host who knows what's behind the doors opens another door to reveal a goat and then ask you, do you want to change your mind? Do you want to switch doors? And a lot of the, I think intuition people have, and certainly when I first came across this problem many years ago is well, you've got two doors left, right? You've picked one, there's another one, it's 50-50. And even some quite well-respected mathematicians.

Adam Kucharski (03:27):

People like Paul Erdős who was really published more papers than almost anyone else, that was their initial gut reaction. But if you work through all of the combinations, if you pick this door and then the host does this, and you switch or not switch and work through all of those options. You actually double your chances if you switch versus sticking with the door. So something that's counterintuitive, but I think one of the things that really struck me and even over the years trying to explain it is convincing myself of the answer, which was when I first came across it as a teenager, I did quite quickly is very different to convincing someone else. And even actually Paul Erdős, one of his colleagues showed him what I call proof by exhaustion. So go through every combination and that didn't really convince him. So then he started to simulate and said, well, let's do a computer simulation of the game a hundred thousand times. And again, switching was this optimal strategy, but Erdős wasn't really convinced because I accept that this is the case, but I'm not really satisfied with it. And I think that encapsulates for a lot of people, their experience of proof and evidence. It's a fact and you have to take it as given, but there's actually quite a big bridge often to really understanding why it's true and feeling convinced by it.

Eric Topol (04:41):

Yeah, I think it's a fabulous example because I think everyone would naturally assume it's 50-50 and it isn't. And I think that gets us to the topic at hand. What I love, there's many things I love about this book. One is that you don't just get into science and medicine, but you cut across all the domains, law, mathematics, AI. So it's a very comprehensive sweep of everything about proof and truth, and it couldn't come at a better time as we'll get into. Maybe just starting off with math, the term I love mathematical monsters. Can you tell us a little bit more about that?

Adam Kucharski (05:25):

Yeah, this was a fascinating situation that emerged in the late 19th century where a lot of math’s, certainly in Europe had been derived from geometry because a lot of the ancient Greek influence on how we shaped things and then Newton and his work on rates of change and calculus, it was really the natural world that provided a lot of inspiration, these kind of tangible objects, tangible movements. And as mathematicians started to build out the theory around rates of change and how we tackle these kinds of situations, they sometimes took that intuition a bit too seriously. And there was some theorems that they said were intuitively obvious, some of these French mathematicians. And so, one for example is this idea of you how things change smoothly over time and how you do those calculations. But what happened was some mathematicians came along and showed that when you have things that can be infinitely small, that intuition didn't necessarily hold in the same way.

Adam Kucharski (06:26):

And they came up with these examples that broke a lot of these theorems and a lot of the establishments at the time called these things monsters. They called them these aberrations against common sense and this idea that if Newton had known about them, he never would've done all of his discovery because they're just nuisances and we just need to get rid of them. And there's this real tension at the core of mathematics in the late 1800s where some people just wanted to disregard this and say, look, it works for most of the time, that's good enough. And then others really weren't happy with this quite vague logic. They wanted to put it on much sturdier ground. And what was remarkable actually is if you trace this then into the 20th century, a lot of these monsters and these particularly in some cases functions which could almost move constantly, this constant motion rather than our intuitive concept of movement as something that's smooth, if you drop an apple, it accelerates at a very smooth rate, would become foundational in our understanding of things like probability, Einstein's work on atomic theory. A lot of these concepts where geometry breaks down would be really important in relativity. So actually, these things that we thought were monsters actually were all around us all the time, and science couldn't advance without them. So I think it's just this remarkable example of this tension within a field that supposedly concrete and the things that were going to be shunned actually turn out to be quite important.

Eric Topol (07:53):

It's great how you convey how nature isn't so neat and tidy and things like Brownian motion, understanding that, I mean, just so many things that I think fit into that general category. In the legal, we won't get into too much because that's not so much the audience of Ground Truths, but the classic things about innocent and until proven guilty and proof beyond reasonable doubt, I mean these are obviously really important parts of that overall sense of proof and truth. We're going to get into one thing I'm fascinated about related to that subsequently and then in science. So before we get into the different types of proof, obviously the pandemic is still fresh in our minds and we're an endemic with Covid now, and there are so many things we got wrong along the way of uncertainty and didn't convey that science isn't always evolving search for what is the truth. There's plenty no shortage of uncertainty at any moment. So can you recap some of the, you did so much work during the pandemic and obviously some of it's in the book. What were some of the major things that you took out of proof and truth from the pandemic?

Adam Kucharski (09:14):

I think it was almost this story of two hearts because on the one hand, science was the thing that got us where we are today. The reason that so much normality could resume and so much risk was reduced was development of vaccines and the understanding of treatments and the understanding of variants as they came to their characteristics. So it was kind of this amazing opportunity to see this happen faster than it ever happened in history. And I think ever in science, it certainly shifted a lot of my thinking about what's possible and even how we should think about these kinds of problems. But also on the other hand, I think where people might have been more familiar with seeing science progress a bit more slowly and reach consensus around some of these health issues, having that emerge very rapidly can present challenges even we found with some of the work we did on Alpha and then the Delta variants, and it was the early quantification of these.

Adam Kucharski (10:08):

So really the big question is, is this thing more transmissible? Because at the time countries were thinking about control measures, thinking about relaxing things, and you've got this just enormous social economic health decision-making based around essentially is it a lot more spreadable or is it not? And you only had these fragments of evidence. So I think for me, that was really an illustration of the sharp end. And I think what we ended up doing with some of those was rather than arguing over a precise number, something like Delta, instead we kind of looked at, well, what's the range that matters? So in the sense of arguing over whether it's 40% or 50% or 30% more transmissible is perhaps less important than being, it's substantially more transmissible and it's going to start going up. Is it going to go up extremely fast or just very fast?

Adam Kucharski (10:59):

That's still a very useful conclusion. I think what often created some of the more challenges, I think the things that on reflection people looking back pick up on are where there was probably overstated certainty. We saw that around some of the airborne spread, for example, stated as a fact by in some cases some organizations, I think in some situations as well, governments had a constraint and presented it as scientific. So the UK, for example, would say testing isn't useful. And what was happening at the time was there wasn't enough tests. So it was more a case of they can't test at that volume. But I think blowing between what the science was saying and what the decision-making, and I think also one thing we found in the UK was we made a lot of the epidemiological evidence available. I think that was really, I think something that was important.

Adam Kucharski (11:51):

I found it a lot easier to communicate if talking to the media to be able to say, look, this is the paper that's out, this is what it means, this is the evidence. I always found it quite uncomfortable having to communicate things where you knew there were reports behind the scenes, but you couldn't actually articulate. But I think what that did is it created this impression that particularly epidemiology was driving the decision-making a lot more than it perhaps was in reality because so much of that was being made public and a lot more of the evidence around education or economics was being done behind the scenes. I think that created this kind of asymmetry in public perception about how that was feeding in. And so, I think there was always that, and it happens, it is really hard as well as a scientist when you've got journalists asking you how to run the country to work out those steps of am I describing the evidence behind what we're seeing? Am I describing the evidence about different interventions or am I proposing to some extent my value system on what we do? And I think all of that in very intense times can be very easy to get blurred together in public communication. I think we saw a few examples of that where things were being the follow the science on policy type angle where actually once you get into what you're prioritizing within a society, quite rightly, you've got other things beyond just the epidemiology driving that.

Eric Topol (13:09):

Yeah, I mean that term that you just use follow the science is such an important term because it tells us about the dynamic aspect. It isn't just a snapshot, it's constantly being revised. But during the pandemic we had things like the six-foot rule that was never supported by data, but yet still today, if I walk around my hospital and there's still the footprints of the six-foot rule and not paying attention to the fact that this was airborne and took years before some of these things were accepted. The flatten the curve stuff with lockdowns, which I never was supportive of that, but perhaps at the worst point, the idea that hospitals would get overrun was an issue, but it got carried away with school shutdowns for prolonged periods and in some parts of the world, especially very stringent lockdowns. But anyway, we learned a lot.

Eric Topol (14:10):

But perhaps one of the greatest lessons is that people's expectations about science is that it's absolute and somehow you have this truth that's not there. I mean, it's getting revised. It's kind of on the job training, it's on this case on the pandemic revision. But very interesting. And that gets us to, I think the next topic, which I think is a fundamental part of the book distributed throughout the book, which is the different types of proof in biomedicine and of course across all these domains. And so, you take us through things like randomized trials, p-values, 95 percent confidence intervals, counterfactuals, causation and correlation, peer review, the works, which is great because a lot of people have misconceptions of these things. So for example, randomized trials, which is the temple of the randomized trials, they're not as great as a lot of people think, yes, they can help us establish cause and effect, but they're skewed because of the people who come into the trial. So they may not at all be a representative sample. What are your thoughts about over deference to randomized trials?

Adam Kucharski (15:31):

Yeah, I think that the story of how we rank evidence in medicines a fascinating one. I mean even just how long it took for people to think about these elements of randomization. Fundamentally, what we're trying to do when we have evidence here in medicine or science is prevent ourselves from confusing randomness for a signal. I mean, that's fundamentally, we don't want to mistake something, we think it's going on and it's not. And the challenge, particularly with any intervention is you only get to see one version of reality. You can't give someone a drug, follow them, rewind history, not give them the drug and then follow them again. So one of the things that essentially randomization allows us to do is, if you have two groups, one that's been randomized, one that hasn't on average, the difference in outcomes between those groups is going to be down to the treatment effect.

Adam Kucharski (16:20):

So it doesn't necessarily mean in reality that'd be the case, but on average that's the expectation that you'd have. And it's kind of interesting actually that the first modern randomized control trial (RCT) in medicine in 1947, this is for TB and streptomycin. The randomization element actually, it wasn't so much statistical as behavioral, that if you have people coming to hospital, you could to some extent just say, we'll just alternate. We're not going to randomize. We're just going to first patient we'll say is a control, second patient a treatment. But what they found in a lot of previous studies was doctors have bias. Maybe that patient looks a little bit ill or that one maybe is on borderline for eligibility. And often you got these quite striking imbalances when you allowed it for human judgment. So it was really about shielding against those behavioral elements. But I think there's a few situations, it's a really powerful tool for a lot of these questions, but as you mentioned, one is this issue of you have the population you study on and then perhaps in reality how that translates elsewhere.

Adam Kucharski (17:17):

And we see, I mean things like flu vaccines are a good example, which are very dependent on immunity and evolution and what goes on in different populations. Sometimes you've had a result on a vaccine in one place and then the effectiveness doesn't translate in the same way to somewhere else. I think the other really important thing to bear in mind is, as I said, it's the averaging that you're getting an average effect between two different groups. And I think we see certainly a lot of development around things like personalized medicine where actually you're much more interested in the outcome for the individual. And so, what a trial can give you evidence is on average across a group, this is the effect that I can expect this intervention to have. But we've now seen more of the emergence things like N=1 studies where you can actually over the same individual, particularly for chronic conditions, look at those kind of interventions.

Adam Kucharski (18:05):

And also there's just these extreme examples where you're ethically not going to run a trial, there's never been a trial of whether it's a good idea to have intensive care units in hospitals or there's a lot of these kind of historical treatments which are just so overwhelmingly effective that we're not going to run trial. So almost this hierarchy over time, you can see it getting shifted because actually you do have these situations where other forms of evidence can get you either closer to what you need or just more feasibly an answer where it's just not ethical or practical to do an RCT.

Eric Topol (18:37):

And that brings us to the natural experiments I just wrote about recently, the one with shingles, which there's two big natural experiments to suggest that shingles vaccine might reduce the risk of Alzheimer's, an added benefit beyond the shingles that was not anticipated. Your thoughts about natural experiments, because here you're getting a much different type of population assessment, again, not at the individual level, but not necessarily restricted by some potentially skewed enrollment criteria.

Adam Kucharski (19:14):

I think this is as emerged as a really valuable tool. It's kind of interesting, in the book you're talking to economists like Josh Angrist, that a lot of these ideas emerge in epidemiology, but I think were really then taken up by economists, particularly as they wanted to add more credibility to a lot of these policy questions. And ultimately, it comes down to this issue that for a lot of problems, we can't necessarily intervene and randomize, but there might be a situation that's done it to some extent for us, so the classic example is the Vietnam draft where it was kind of random birthdays with drawn out of lottery. And so, there's been a lot of studies subsequently about the effect of serving in the military on different subsequent lifetime outcomes because broadly those people have been randomized. It was for a different reason. But you've got that element of randomization driving that.

Adam Kucharski (20:02):

And so again, with some of the recent shingles data and other studies, you might have a situation for example, where there's been an intervention that's somewhat arbitrary in terms of time. It's a cutoff on a birth date, for example. And under certain assumptions you could think, well, actually there's no real reason for the person on this day and this day to be fundamentally different. I mean, perhaps there might be effects of cohorts if it's school years or this sort of thing. But generally, this isn't the same as having people who are very, very different ages and very different characteristics. It's just nature, or in this case, just a policy intervention for a different reason has given you that randomization, which allows you or pseudo randomization, which allows you to then look at something about the effect of an intervention that you wouldn't as reliably if you were just digging into the data of yes, no who's received a vaccine.

Eric Topol (20:52):

Yeah, no, I think it's really valuable. And now I think increasingly given priority, if you can find these natural experiments and they’re not always so abundant to use to extrapolate from, but when they are, they're phenomenal. The causation correlation is so big. The issue there, I mean Judea Pearl's, the Book of Why, and you give so many great examples throughout the book in Proof. I wonder if you could comment that on that a bit more because this is where associations are confused somehow or other with a direct effect. And we unfortunately make these jumps all too frequently. Perhaps it's the most common problem that's occurring in the way we interpret medical research data.

Adam Kucharski (21:52):

Yeah, I think it's an issue that I think a lot of people get drilled into in their training just because a correlation between things doesn't mean that that thing causes this thing. But it really struck me as I talked to people, researching the book, in practice in research, there's actually a bit more to it in how it's played out. So first of all, if there's a correlation between things, it doesn't tell you much generally that's useful for intervention. If two things are correlated, it doesn't mean that changing that thing's going to have an effect on that thing. There might be something that's influencing both of them. If you have more ice cream sales, it will lead to more heat stroke cases. It doesn't mean that changing ice cream sales is going to have that effect, but it does allow you to make predictions potentially because if you can identify consistent patterns, you can say, okay, if this thing going up, I'm going to make a prediction that this thing's going up.

Adam Kucharski (22:37):

So one thing I found quite striking, actually talking to research in different fields is how many fields choose to focus on prediction because it kind of avoids having to deal with this cause and effect problem. And even in fields like psychology, it was kind of interesting that there's a lot of focus on predicting things like relationship outcomes, but actually for people, you don't want a prediction about your relationship. You want to know, well, how can I do something about it? You don't just want someone to sell you your relationship's going to go downhill. So there's almost part of the challenge is people just got stuck on prediction because it's an easier field of work, whereas actually some of those problems will involve intervention. I think the other thing that really stood out for me is in epidemiology and a lot of other fields, rightly, people are very cautious to not get that mixed up.

Adam Kucharski (23:24):

They don't want to mix up correlations or associations with causation, but you've kind of got this weird situation where a lot of papers go out of their way to not use causal language and say it's an association, it's just an association. It's just an association. You can't say anything about causality. And then the end of the paper, they'll say, well, we should think about introducing more of this thing or restricting this thing. So really the whole paper and its purpose is framed around a causal intervention, but it's extremely careful throughout the paper to not frame it as a causal claim. So I think we almost by skirting that too much, we actually avoid the problems that people sometimes care about. And I think a lot of the nice work that's been going on in causal inference is trying to get people to confront this more head on rather than say, okay, you can just stay in this prediction world and that's fine. And then just later maybe make a policy suggestion off the back of it.

Eric Topol (24:20):

Yeah, I think this is cause and effect is a very alluring concept to support proof as you so nicely go through in the book. But of course, one of the things that we use to help us is the biological mechanism. So here you have, let's say for example, you're trying to get a new drug approved by the Food and Drug Administration (FDA), and the request is, well, we want two trials, randomized trials, independent. We want to have p-values that are significant, and we want to know the biological mechanism ideally with the dose response of the drug. But there are many drugs as you review that have no biological mechanism established. And even when the tobacco problems were mounting, the actual mechanism of how tobacco use caused cancer wasn't known. So how important is the biological mechanism, especially now that we're well into the AI world where explainability is demanded. And so, we don't know the mechanism, but we also don't know the mechanism and lots of things in medicine too, like anesthetics and even things as simple as aspirin, how it works and many others. So how do we deal with this quest for the biological mechanism?

Adam Kucharski (25:42):

I think that's a really good point. It shows almost a lot of the transition I think we're going through currently. I think particularly for things like smoking cancer where it's very hard to run a trial. You can't make people randomly take up smoking. Having those additional pieces of evidence, whether it's an analogy with a similar carcinogen, whether it's a biological mechanism, can help almost give you more supports for that argument that there's a cause and effect going on. But I think what I found quite striking, and I realized actually that it's something that had kind of bothered me a bit and I'd be interested to hear whether it bothers you, but with the emergence of AI, it's almost a bit of the loss of scientific satisfaction. I think you grow up with learning about how the world works and why this is doing what it's doing.

Adam Kucharski (26:26):

And I talked for example of some of the people involved with AlphaFold and some of the subsequent work in installing those predictions about structures. And they'd almost made peace with it, which I found interesting because I think they started off being a bit uncomfortable with like, yeah, you've got these remarkable AI models making these predictions, but we don't understand still biologically what's happening here. But I think they're just settled in saying, well, biology is really complex on some of these problems, and if we can have a tool that can give us this extremely valuable information, maybe that's okay. And it was just interesting that they'd really kind of gone through that kind process, which I think a lot of people are still grappling with and that almost that discomfort of using AI and what's going to convince you that that's a useful reliable prediction whether it’s something like predicting protein folding or getting in a self-driving car. What's the evidence you need to convince you that's reliable?

Eric Topol (27:26):

Yeah, no, I'm so glad you brought that up because when Demis Hassabis and John Jumper won the Nobel Prize, the point I made was maybe there should be an asterisk with AI because they don't know how it works. I mean, they had all the rich data from the protein data bank, and they got the transformer model to do it for 200 million protein structure prediction, but they still to this day don't fully understand how the model really was working. So it reinforces what you're just saying. And of course, it cuts across so many types of AI. It's just that we tend to hold different standards in medicine not realizing that there's lots of lack of explainability for routine medical treatments today. Now one of the things that I found fascinating in your book, because there's different levels of proof, different types of proof, but solid logical systems.

Eric Topol (28:26):

And on page 60 of the book, especially pertinent to the US right now, there is a bit about Kurt Gödel and what he did there was he basically, there was a question about dictatorship in the US could it ever occur? And Gödel says, “oh, yes, I can prove it.” And he's using the constitution itself to prove it, which I found fascinating because of course we're seeing that emerge right now. Can you give us a little bit more about this, because this is fascinating about the Fifth Amendment, and I mean I never thought that the Constitution would allow for a dictatorship to emerge.

Adam Kucharski (29:23):

And this was a fascinating story, Kurt Gödel who is one of the greatest logical minds of the 20th century and did a lot of work, particularly in the early 20th century around system of rules, particularly things like mathematics and whether they can ever be really fully satisfying. So particularly in mathematics, he showed that there were this problem that is very hard to have a set of rules for something like arithmetic that was both complete and covered every situation, but also had no contradictions. And I think a lot of countries, if you go back, things like Napoleonic code and these attempts to almost write down every possible legal situation that could be imaginable, always just ascended into either they needed amendments or they had contradictions. I think Gödel's work really summed it up, and there's a story, this is in the late forties when he had his citizenship interview and Einstein and Oskar Morgenstern went along as witnesses for him.

Adam Kucharski (30:17):

And it's always told as kind of a lighthearted story as this logical mind, this academic just saying something silly in front of the judge. And actually, to my own admission, I've in the past given talks and mentioned it in this slightly kind of lighthearted way, but for the book I got talking to a few people who'd taken it more seriously. I realized actually he's this extremely logically focused mind at the time, and maybe there should have been something more to it. And people who have kind of dug more into possibilities was saying, well, what could he have spotted that bothered him? And a lot of his work that he did about consistency in mass was around particularly self-referential statements. So if I say this sentence is false, it’s self-referential and if it is false, then it's true, but if it's true, then it's false and you get this kind of weird self-referential contradictions.

Adam Kucharski (31:13):

And so, one of the theories about Gödel was that in the Constitution, it wasn't that there was a kind of rule for someone can become a dictator, but rather people can use the mechanisms within the Constitution to make it easier to make further amendments. And he kind of downward cycle of amendment that he had seen happening in Europe and the run up to the war, and again, because this is never fully documented exactly what he thought, but it's one of the theories that it wouldn't just be outright that it would just be this cycle process of weakening and weakening and weakening and making it easier to add. And actually, when I wrote that, it was all the earlier bits of the book that I drafted, I did sort of debate whether including it I thought, is this actually just a bit in the weeds of American history? And here we are. Yeah, it's remarkable.

Eric Topol (32:00):

Yeah, yeah. No, I mean I found, it struck me when I was reading this because here back in 1947, there was somebody predicting that this could happen based on some, if you want to call it loopholes if you will, or the ability to change things, even though you would've thought otherwise that there wasn't any possible capability for that to happen. Now, one of the things I thought was a bit contradictory is two parts here. One is from Angus Deaton, he wrote, “Gold standard thinking is magical thinking.” And then the other is what you basically are concluding in many respects. “To navigate proof, we must reach into a thicket of errors and biases. We must confront monsters and embrace uncertainty, balancing — and rebalancing —our beliefs. We must seek out every useful fragment of data, gather every relevant tool, searching wider and climbing further. Finding the good foundations among the bad. Dodging dogma and falsehoods. Questioning. Measuring. Triangulating. Convincing. Then perhaps, just perhaps, we'll reach the truth in time.” So here you have on the one hand your search for the truth, proof, which I think that little paragraph says it all. In many respects, it sums up somewhat to the work that you review here and on the other you have this Nobel laureate saying, you don't have to go to extremes here. The enemy of good is perfect, perhaps. I mean, how do you reconcile this sense that you shouldn't go so far? Don't search for absolute perfection of proof.

Adam Kucharski (33:58):

Yeah, I think that encapsulates a lot of what the book is about, is that search for certainty and how far do you have to go. I think one of the things, there's a lot of interesting discussion, some fascinating papers around at what point do you use these studies? What are their flaws? But I think one of the things that does stand out is across fields, across science, medicine, even if you going to cover law, AI, having these kind of cookie cutter, this is the definitive way of doing it. And if you just follow this simple rule, if you do your p-value, you'll get there and you'll be fine. And I think that's where a lot of the danger is. And I think that's what we've seen over time. Certain science people chasing certain targets and all the behaviors that come around that or in certain situations disregarding valuable evidence because you've got this kind of gold standard and nothing else will do.

Adam Kucharski (34:56):

And I think particularly in a crisis, it's very dangerous to have that because you might have a low level of evidence that demands a certain action and you almost bias yourself towards inaction if you have these kind of very simple thresholds. So I think for me, across all of these stories and across the whole book, I mean William Gosset who did a lot of pioneering work on statistical experiments at Guinness in the early 20th century, he had this nice question he sort of framed is, how much do we lose? And if we're thinking about the problems, there's always more studies we can do, there's always more confidence we can have, but whether it's a patient we want to treat or crisis we need to deal with, we need to work out actually getting that level of proof that's really appropriate for where we are currently.

Eric Topol (35:49):

I think exceptionally important that there's this kind of spectrum or continuum in following science and search for truth and that distinction, I think really nails it. Now, one of the things that's unique in the book is you don't just go through all the different types of how you would get to proof, but you also talk about how the evidence is acted on. And for example, you quote, “they spent a lot of time misinforming themselves.” This is the whole idea of taking data and torturing it or using it, dredging it however way you want to support either conspiracy theories or alternative facts. Basically, manipulating sometimes even emasculating what evidence and data we have. And one of the sentences, or I guess this is from Sir Francis Bacon, “truth is a daughter of time”, but the added part is not authority. So here we have our president here that repeats things that are wrong, fabricated or wrong, and he keeps repeating to the point that people believe it's true. But on the other hand, you could say truth is a daughter of time because you like to not accept any truth immediately. You like to see it get replicated and further supported, backed up. So in that one sentence, truth is a daughter of time not authority, there's the whole ball of wax here. Can you take us through that? Because I just think that people don't understand that truth being tested over time, but also manipulated by its repetition. This is a part of the big problem that we live in right now.

Adam Kucharski (37:51):

And I think it's something that writing the book and actually just reflecting on it subsequently has made me think about a lot in just how people approach these kinds of problems. I think that there's an idea that conspiracy theorists are just lazy and have maybe just fallen for a random thing, but talking to people, you really think about these things a lot more in the field. And actually, the more I've ended up engaging with people who believe things that are just outright unevidenced around vaccines, around health issues, they often have this mountain of papers and data to hand and a lot of it, often they will be peer reviewed papers. It won't necessarily be supporting the point that they think it's supports.

Adam Kucharski (38:35):

But it's not something that you can just say everything you're saying is false, that there's actually often a lot of things that have been put together and it's just that leap to that conclusion. I think you also see a lot of scientific language borrowed. So I gave a talker early this year and it got posted on YouTube. It had conspiracy theories it, and there was a lot of conspiracy theory supporters who piled in the comments and one of the points they made is skepticism is good. It's the kind of law society, take no one's word for it, you need this. We are the ones that are kind of doing science and people who just assume that science is settled are in the wrong. And again, you also mentioned that repetition. There's this phenomenon, it's the illusory truth problem that if you repeatedly tell someone someone's something's false, it'll increase their belief in it even if it's something quite outrageous.

Adam Kucharski (39:27):

And that mimics that scientific repetition because people kind of say, okay, well if I've heard it again and again, it's almost like if you tweak these as mini experiments, I'm just accumulating evidence that this thing is true. So it made me think a lot about how you've got essentially a lot of mimicry of the scientific method, amount of data and how you present it and this kind of skepticism being good, but I think a lot of it comes down to as well as just looking at theological flaws, but also ability to be wrong in not actually seeking out things that confirm. I think all of us, it's something that I've certainly tried to do a lot working on emergencies, and one of the scientific advisory groups that I worked on almost it became a catchphrase whenever someone presented something, they finished by saying, tell me why I'm wrong.

Adam Kucharski (40:14):

And if you've got a variant that's more transmissible, I don't want to be right about that really. And it is something that is quite hard to do and I found it is particularly for something that's quite high pressure, trying to get a policymaker or someone to write even just non-publicly by themselves, write down what you think's going to happen or write down what would convince you that you are wrong about something. I think particularly on contentious issues where someone's got perhaps a lot of public persona wrapped up in something that's really hard to do, but I think it's those kind of elements that distinguish between getting sucked into a conspiracy theory and really seeking out evidence that supports it and trying to just get your theory stronger and stronger and actually seeking out things that might overturn your belief about the world. And it's often those things that we don't want overturned. I think those are the views that we all have politically or in other ways, and that's often where the problems lie.

Eric Topol (41:11):

Yeah, I think this is perhaps one of, if not the most essential part here is that to try to deal with the different views. We have biases as you emphasized throughout, but if you can use these different types of proof to have a sound discussion, conversation, refutation whereby you don't summarily dismiss another view which may be skewed and maybe spurious or just absolutely wrong, maybe fabricated whatever, but did you can engage and say, here's why these are my proof points, or this is why there's some extent of certainty you can have regarding this view of the data. I think this is so fundamental because unfortunately as we saw during the pandemic, the strident minority, which were the anti-science, anti-vaxxers, they were summarily dismissed as being kooks and adopting conspiracy theories without the right engagement and the right debates. And I think this might've helped along the way, no less the fact that a lot of scientists didn't really want to engage in the first place and adopt this methodical proof that you've advocated in the book so many different ways to support a hypothesis or an assertion. Now, we've covered a lot here, Adam. Have I missed some central parts of the book and the effort because it's really quite extraordinary. I know it's your third book, but it's certainly a standout and it certainly it's a standout not just for your books, but books on this topic.

Adam Kucharski (43:13):

Thanks. And it's much appreciated. It was not an easy book to write. I think at times, I kind of wondered if I should have taken on the topic and I think a core thing, your last point speaks to that. I think a core thing is that gap often between what convinces us and what convinces someone else. I think it's often very tempting as a scientist to say the evidence is clear or the science has proved this. But even on something like the vaccines, you do get the loud minority who perhaps think they're putting microchips in people and outlandish views, but you actually get a lot more people who might just have some skepticism of pharmaceutical companies or they might have, my wife was pregnant actually at the time during Covid and we waited up because there wasn't much data on pregnancy and the vaccine. And I think it's just finding what is convincing. Is it having more studies from other countries? Is it understanding more about the biology? Is it understanding how you evaluate some of those safety signals? And I think that's just really important to not just think what convinces us and it's going to be obvious to other people, but actually think where are they coming from? Because ultimately having proof isn't that good unless it leads to the action that can make lives better.

Eric Topol (44:24):

Yeah. Well, look, you've inculcated my mind with this book, Adam, called Proof. Anytime I think of the word proof, I'm going to be thinking about you. So thank you. Thanks for taking the time to have a conversation about your book, your work, and I know we're going to count on you for the astute mathematics and analysis of outbreaks in the future, which we will see unfortunately. We are seeing now, in fact already in this country with measles and whatnot. So thank you and we'll continue to follow your great work.

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A bit of an update on SUPER AGERS

My book has been selected as a Next Big Idea Club winner for Season 26 by Adam Grant, Malcolm Gladwell, Susan Cain, and Daniel Pink. This club has spotlighted the most groundbreaking nonfiction books for over a decade. As a winning title, my book will be shipped to thousands of thoughtful readers like you, featured alongside a reading guide, a "Book Bite," Next Big Idea Podcast episode as well as a live virtual Q&A with me in the club’s vibrant online community. If you’re interested in joining the club, here’s a promo code SEASON26 for 20% off at the website.

SUPER AGERS reached #3 for all books on Amazon this week. This was in part related to the segment on the book on the TODAY SHOW which you can see here.

Also at Amazon there is a remarkable sale on the hardcover book for $10.l0 at the moment for up to 4 copies. Not sure how long it will last or what prompted it.

The journalist Paul von Zielbauer has a Substack “Aging With Strength” and did an extensive interview with me on the biology of aging and how we can prevent the major age-related diseases. Here’s the link.

Thanks to so many of you who joined our live conversation with Devi Sridhar!

Professor Devi Sridhar is the Chair of Global Public Health at the University of Edinburgh. Over the past 2 decades she has become one of the world’s leading authorities and advisors for promoting global health. Her new book —How No to Die Too Soon—provides a unique outlook for extending healthspan with a global perspective admixed with many personal stories.

We talked about lifestyle factors with lessons from Japan (on diet) and the Netherlands (on physical activity), ultra-processed foods, air pollution and water quality, the prevention model in Finland, guns, inequities, the US situation for biomedical research and public health agency defunding, and much more.

My conversation with Matthew Walker, PhD on faculty at UC Berkeley where he is a professor of neuroscience and psychology, the founder and director of the Center for Human Sleep Science, and has a long history of seminal contributions on sleep science and health.

Audio File (also downloadable at Apple Podcast and Spotify)

“Sleep is a non-negotiable

biological state required for the maintenance of human life . . . our needs

for sleep parallel those for air, food, and water.”—Grandner and Fernandez

Eric Topol (00:07):

Hello, it's Eric Topol with Ground Truths, and I am really delighted to welcome Matt Walker, who I believe has had more impact on sleep health than anyone I know. It's reflected by the fact that he is a Professor at UC Berkeley, heads up the center that he originated for Human Sleep Science. He wrote a remarkable book back in 2017, Why We Sleep, and also we'll link to that as well as the TED Talk of 2019. Sleep is Your Superpower with 24 million views. That's a lot of views here.

Matt Walker:

Striking, isn't it?

Eric Topol:

Wow. I think does reflect the kind of impact, you were onto the sleep story sooner, earlier than anyone I know. And what I wanted to do today was get to the updates because you taught us a lot back then and a lot of things have been happening in these years since. You're on it, of course, I think you have a podcast Sleep Diplomat, and you're obviously continued working on the science of sleep. But maybe the first thing I'd ask you about is in the last few years, what do you think has been, are there been any real changes or breakthroughs in the field?

What Is New?

Matt Walker (01:27):

Yeah, I think there has been changes, and maybe we'll speak about one of them, which is the emergence of this brain cleansing system called the glymphatic system, but spreading that aside for potential future discussion. I would say that there are maybe at least two fascinating areas. The first is the broader impact of sleep on much more complex human social interactions. We think of sleep at maybe the level of the cell or systems or whole scale biology or even the entire organism. We forget that a lack of sleep, or at least the evidence suggests a lack of sleep will dislocate each other, one from the other. And there's been some great work by Dr. Eti Ben Simon for example, demonstrating that when you are sleep deprived, you become more asocial. So you basically become socially repellent. You want to withdraw, you become lonely. And what's also fascinating is that other people, even they don't know that you sleep deprived, they rate you as being less socially sort of attractive to engage with.

Matt Walker (02:35):

And after interacting with you, the sleep deprived individual, even though they don't know you're sleep deprived, they themselves walk away feeling more lonely themselves. So there is a social loneliness contagion that happens that a sleep deprived lonely individual can have almost a viral knock on effect that causes loneliness in another well-rested individual. And then that work spanned out and it started to demonstrate that another impact of a lack of sleep socially is that we stop wanting to help other people. And you think, well, helping behavior that's not really very impactful. Try to tell me of any major civilization that has not risen up through human cooperation and helping. There just isn't one. Human cooperative behavior is one of our innate traits as homo sapiens. And what they discovered is that when you are insufficiently slept, firstly, you don't wish to help other people. And you can see that at the individual level.

Matt Walker (03:41):

You can see it in groups. And then there was a great study again by Dr. Eti Ben Simon that demonstrated this at a national level because what she did was she looked at this wonderful manipulation of one hour of sleep that happens twice a year to 1.6 billion people. It's called daylight savings time at spring. Yeah, when you lose one hour of sleep opportunity. She looked at donations across the nation and sure enough, there was this big dent in donation giving in the sleepy Monday and Tuesday after the clock change. Because of that sleep, we become less willing to empathetically and selflessly help other individuals. And so, to me I think it's just a fascinating area. And then the other area I think is great, and I'm sorry I'm racing forward because I get so excited. But this work now looking at what we call genetic short sleepers and sort of idiots like me have been out there touting the importance of somewhere between seven to nine hours of sleep.

Matt Walker (04:48):

And once you get less than that, and we'll perhaps speak about that, you can see biological changes. But there is a subset of individuals who, and we've identified at least two different genes. One of them is what we call the DEC2 gene. And it seems to allow individuals to sleep about five hours, maybe even a little bit less and show no impairment whatsoever. Now we haven't tracked these individuals across the lifespan to truly understand does it lead to a higher mortality risk. But so far, they don't implode like you perhaps or I would do when you are limited to this anemic diet of five hours of sleep. They hang in there just fine. And I think philosophically what that tells me, and by the way, for people who are listening thinking, gosh, I think I'm probably one of those people. Statistically, I think you are more likely to be struck by lightning in your lifetime than you are to have the DEC2 gene. Think about what tells us, Eric. It tells us that there is a moment in biology in the evolution of this thing called the sleep physiological need that has changed such that mother nature has found a genetic way to ZIP file sleep.

Matt Walker (06:14):

You can essentially compress sleep from seven to nine hour need, down to five to six hour need. To me, that is absolutely fascinating. So now the race is on, what are the mechanisms that control this? How do we understand them? I'm sure much to my chagrin, society would like to then say, okay, is there a pill that I can take to basically ZIP file my own sleep and then it becomes an arms race in my mind, which is then all of a sudden six hours becomes the new eight hours and then everyone is saying, well, six hours is my need. Well I'll go to four hours and then it's this arms race of de-escalation of sleep. Anyway, I'm going on and on, does that help give you a sense of two of the what I feel the more fascinating areas?

Eric Topol (07:01):

Absolutely. When I saw the other recent report on the short sleep gene variant and thought about what the potential of that would be with respect to potential drug development or could you imagine genome editing early in life that you don't need any sleep? I mean crazy stuff.

Matt Walker (07:19):

It was amazing.

Glymphatics and Deep Sleep

for more, see previous Ground Truths on this topic

Eric Topol (07:22):

No, the mechanism of course we have to work out and also what you mentioned regarding the social and the behavior engagement, all that sort of thing, it was just fascinating stuff. Now we touched on one thing early on to come back to the glymphatics these channels to get rid of the waste metabolites from the brain each night that might be considered toxic metabolites. We've learned a lot about those and of course there's some controversy about it. What are your thoughts?

Matt Walker (07:55):

Yeah, I think there's really quite comprehensive evidence suggesting that the brain has this cleansing system like the body has one the lymphatic system, the brain has one the glymphatic system named after these glial cells that make it up. And I think there's been evidence from multiple groups across multiple different species types, from mouse models all the way up to human models suggesting that there is a state dependent control of the brain cleansing system, which is a fancy way of saying if you are awake in light NREM, deep NREM or perhaps you're just quiet and you are resting in your wakefulness, the glymphatic system is not switched on at the same rate across all of those different brain states. And I think the overwhelming evidence so far using different techniques in different species from different groups is that sleep is a preferential time. It's not an exclusive time, it's a preferential time when that brain cleansing system kicks into gear because as some people have, I think argued, and you could say it's hyperbolic, but wakefulness is low level from a biochemicals perspective, it's low level brain damage and sleep is therefore your sanitary salvation that combat that biochemical cascade.

Matt Walker (09:15):

So in other words, a better way of putting it would be, sleep is the price that you pay for wakefulness in some ways. And I think there was a recent controversial study that came out in 2022 or 2023, and they actually suggested quite the opposite. They said using their specific imaging methods, they found that the sort of clearance, the amount of cerebral spinal fluid, which is what washes through the brain to cleanse the toxins, the rate of that flow of cerebral spinal fluid was highest during wakefulness and lowest during deep NREM sleep, the exact opposite of what others have found. Now, I think the defendants of the glymphatic sleep dependent hypothesis pushed back and said, well, if you look at the imaging methods. Firstly, they’re nonstandard. Secondly, they were measuring the cerebral spinal flow in an artificial way because they were actually perfusing solutions through the brain rather than naturally letting it flow and therefore the artificial forcing of fluid changed the prototypical result you would get.

Matt Walker (10:27):

And they also argued that the essentially kind of the sampling rate, so how quickly are you taking snapshots of the cerebral spinal fluid flow. Those were different and they were probably missing some of the sleep dependent slow oscillations that seemed to sort of drive that pulsatile flow. Honestly, I think that paper was still very well done, and I still think there is right now, I would still cleave to the majority of overwhelming evidence considering it's not just from one group in one species, but across multiple species, multiple groups. And I think it's nevertheless a weight of burden that has pushed back. And my sense right now, I used to think and cleaves to the notion that it was a sleep expressly selective process. Now I don't think that that's the case anymore. I think that the glymphatic system is a dynamic system, but it's always looking for the opportunity to go into cleansing mode. And you can kind of go into almost like a low battery mode when you are awake, but in quiet rest. And I think that can drive some already early clearance from the brain and then when you go into sleep, it's like powering your phone off entirely. It truly gets the chance to cleanse and reboot the biochemical system. But I think it's really interesting. I think there's a lot of work still yet to be done. It's not quite as case closed as we used to think.

Eric Topol (12:03):

Yeah, I mean first of all, it's great that you straighten out the controversy because that's exactly what I was referring to. And secondly, as you also pointed out, the weight of the evidence is that it's a sleep dependent phenomena, particularly during flow wave deep sleep is at least what I've seen.

Matt Walker (12:21):

Yes.

Eric Topol (12:22):

What's also interesting, your point about it being dynamic, which fascinating, there was a paper in my field of cardiology, people with atrial fibrillation had less active glymphatic, less clearance which was really interesting. And then the other finding that's also noteworthy was that Ambien made things worse. What do you think about that?

We Are An Embodied Organism

Matt Walker (12:45):

I think it's really interesting, and just to come back to your point about the AFib paper, what we know is that this cleansing system in the brain does seem to track the big slow brainwaves of deep slow wave sleep, but it's not only tracking the big slow brainwaves. If anything, there's something to do with the cardiorespiratory cycle, the respiration rate and the cardiac signal that may actually sink with the brainwaves. And it's essentially a cardiorespiratory neurophysiological coupling, which is a lot of ways, which is to say heart, lungs and brain coupled together. And it's the coupling of the cardiorespiratory slow oscillations that drive these pulsatile fluid mechanical, it's literally a hydro mechanical, hydro meaning cerebral spinal fluid push and pull in and out of the system drawing those metabolites out. So ago, if you have a disrupted either cardiac or respiratory or neurophysiological signal, no wonder the glymphatic system isn't going to work as efficiently.

Matt Walker (14:00):

I think that's a beautiful demonstration of the hemineglect that people like me who study sleep largely from the neck upwards would miss. But if you think about sleep is not just for the brain, it's for the body and it's not just for the body, it's for the brain. And we're an embodied organism. We study the organism in silos, neurology, psychiatry, cardiology, respiratory, but they all interact. And so, I think what's lovely about your example is the reminder that if you don't study the body in this study of the glymphatic system, you could miss out a profound explanation that possibly accounts for the head scratching, I don't know why we're getting this result. So that's a long way to come back to it. But the same group that was the pioneer in the discovery of the glymphatic system led by Maiken Nedergaard at the University of Rochester.

In SUPER AGERS, p. 57. SRI-sleep regulatory index

Sleep Medications

Matt Walker (15:01):

She has gone on to then look to say, well, if this is a sleep dependent process of brain cleansing during deep sleep, what about sleeping pills because so many people are either taking or are addicted to sleeping pills. And we've gone through, we’re in the era of web 3.0 with sleeping pills, we started off web 1.0 which were the benzos, the kind of temazepam, diazepam, lorazepam. Then we went to web 2.0, which was sort of the Ambien (zolpidem), Lunesta, Sonata. And what was common about those two classes of drugs is that they both went after something called the GABA receptor in the brain, which is this major inhibitory receptor in the brain. And essentially, they were called sedative hypnotics because they sedated your cortex. And when you take an Ambien and not going to argue you're awake. You're clearly not awake, but to argue you're a naturalistic sleep, if you look at this, physiology is an equal fallacy.

Matt Walker (16:01):

They made this interesting experimental hypothesis that when you take Ambien, you sleep longer and based on how you score deep sleep, it would seem as though Ambien increases the amount of minutes that you spend in deep sleep. But if you look at the electrical signature during that “increased deep sleep” it's not the same. Ambien takes a big bite. There's a big dent out of the very slowest of the slow brainwaves, and it's the slowest of the slow brainwaves that drive the glymphatic system. So what they found was that when you take Ambien or you give mice Ambien. Yes, they sleep longer, they seem to have more deep sleep, but the brain cleansing mechanism seem to be reduced by anywhere between 30-40%, which is counterintuitive. If you are sleeping more and you're getting more deep sleep and the glymphatic system is active during deep sleep, you should get greater cleansing of the brain.

Matt Walker (17:05):

Here they found, yes, the drug increased sleep, particularly deep sleep, but it empowered the cleansing of the brain system. Now, have we got evidence of that in humans yet? No, we don't. I don't think it's far away though, because there was a counter study that brings us onto web 3.0. There's a new class of sleep medications. It's the first class of medications that have actually been publicly advocating for, they're called the DORAs drugs, and they are a class of drugs and there's three of them that are FDA approved right now. DORA stands for dual orexin receptor antagonists, which means that these drugs go in there and they block the action of a chemical called orexin. What is orexin? Orexin is the volume button for wakefulness. It dials at wakefulness, but these drugs come into your system and unlike the sedative sort of baseball bat to the cortex, which is Ambien, these drugs are much more elegant.

Matt Walker (18:11):

They go down towards the brainstem and they just dial down the volume on wakefulness and then they step back, and they allow the antithesis of wakefulness to come in its place, which is this thing called naturalistic sleep. And people sleep longer. So as a scientist, you and I perhaps skeptics would then say, well, so you increase sleep, and I have four words for you. Yes, and so what. Just because you increase sleep, it doesn't mean that it's functional sleep. It could just be like the old notion of junk DNA, that it's epiphenomenal sleep. It's not functional sleep. There was a study out of WashU and they took 85-year olds and above and they gave them one of these DORAs drugs. It's a drug called Belsomra, it’s a play on good sleep or beautiful sleep, chemical named suvorexant and randomized placebo control. What they found is that when they took the drug, yes, these older adults slept longer, they had more deep sleep, but then what they did was clever. Before and after the night of sleep, they drew blood because we can now measure markers of β-amyloid and tau protein circulating in the bloodstream, which are these two markers of Alzheimer's disease.

Matt Walker (19:28):

Why is that relevant to the glymphatic system? It's relevant because two of the pieces of metabolic detritus that the cleansing system washes away at night, β-amyloid and tau. I'm sure enough of what they found was that not only did the adults sleep longer with these sleeping medications, they also had a greater clearance of β-amyloid and tau within the bloodstream. So this was the exact opposite of the Ambien study, which was where they were seeing an impairment in the glymphatic activity. Here in humans was a study with the web 3.0 sleep medications. Suvorexant, not only did it increase sleep, but it seemed to increase. Well, the assumption was that it was increasing glymphatic clearance because at least as the end outcome product, there was greater clearance of β-amyloid and tau protein in the blood. It wasn't just junk sleep, it was functional sleep. So for the first time I'd seen a sleeping medication that increased sleep more naturalistically, but that increased sleep made you the organism function better the next day as a consequence. Does that make any sense?

Eric Topol (20:38):

Absolutely. And it's interesting that we may have a sleep medicine finally or a class that actually is doing what is desired. This is one of the other things I was going to ask you about is that as you pointed out, this is an interaction throughout the organism, throughout the human being, and we've seen studies about how sleep disrupts metabolism and through that of course, and even separately, can take down our immune system or disrupt that as well. And so, one of the questions I guess is your thoughts about these other effects because you mentioned of course the potential of looking at things like p-Tau217 markers or other markers that would denote the status of your ultimate risk for moving on to Alzheimer's disease. But there's these other factors that also play a role with lack of adequate sleep and perhaps particularly sleep quality. I wonder if you could just comment about this because there's so many different systems of the body that are integrated here, and so the sanitary effect that you just described with the ability to potentially see less, at least biomarkers for what would be considered risks to ultimately develop Alzheimer's, there's also these other very important effects when we talk about high quality sleep, I guess, right? And maybe you could comment about that.

Matt Walker (22:21):

Yeah, I think quantity is what we've been talking about in some ways, but quality has also come onto the radar as absolutely essential. And what we find is that the quality of your sleep is as if not more predictive of both all-cause mortality, cardiovascular mortality, metabolic mortality, and in some regards, cancer mortality as well. And when I say quality of sleep, what we're really referring to here is at least one of two things. One is the continuity of your sleep. So you could be sleeping for eight and a half hours according to your sleep tracker, but maybe you are getting eight and a half hours by spending ten hours of time in bed because you are awake so much throughout the night and your sleep is very sort of punctured and littered with all of these awakenings across the night. That's sufficient quantity of sleep eight and a half hours, but it's poor quality of sleep because you are spending too much time awake.

Matt Walker (23:30):

And so, our measure of quality of sleep typically is what we call sleep efficiency. Of the time that you are in bed, what percent of that time are you asleep? And we like to see some measure of at least 85% or above because once you get less than 85% in terms of your sleep quality or your sleep efficiency, then you start to see many of these unfolding system-wide impairments. You seem to have high risk of diabetes, high risk obesity, high risk, as we said, cardiovascular disease. Also, hormonal changes both in men and in women. We see upstairs in the brain with poor quality of sleep, much more so than quantity of sleep. Poor quality of sleep is a more powerful predictor of mood disturbances and psychiatric conditions. And in fact, I think if you look at the data, at least in my center in the past 23 years, we've not been able to discover a single psychiatric condition in which sleep is normal, which to me is a stunning revelation. And what that tells us is that in many of those conditions they do seem to be getting not too bad of quantity of sleep. What is the marker of psychiatric sleep disturbance is not short quantity, it's poor quality of sleep. So I think it's a wonderful important point that I don't think we pay enough attention to, which is the quality.

Eric Topol (25:05):

Yes. And the other thing that you've emphasized, and I just want to reiterate to people listening or watching that is the regularity story, just like you said with quality. The data and I'll put the figure in that shows the link between regularity and cardiovascular, neurodegenerative, cancer, that regularity thing. A lot of people don't understand how important that is as well.

Matt Walker (25:30):

Stunning study from data from the UK Biobank, and this is across thousands and thousands of individuals and they tracked quantity and they tracked regularity and they split people up into the quartiles, those who were most regular and those who were least regular. And as you'll see in those sort of the figure that you flash up, those people who were in the upper quartile of regularity, de-risk all-cause mortality, cancer mortality, cardiovascular mortality, it was stunning. And then they did a cute little experiment of a statistical test where they took quantity because they had it in these individuals and regularity and they kind of put them in the same statistical bucket and did a sort of a Coke Pepsi challenge to see which one won out. And what it seemed to be was that regularity almost beat out quantity in terms of predicting all-cause mortality. Now that's not to say that you can get away with saying, well, I sleep four hours a night, but I sleep very regularly, consistently four hours a night. No, you need both, but regularity. I was someone who based on my remarkably vanilla and pedestrian personality, I've always been quite regular in my regard. But goodness me, even when I read that paper, I thought I'm doubling down on regularity. It's so important. That tells us, I think something that is in some ways a story not about sleep. It's a story about your circadian rhythm.

Matt Walker (27:02):

We speak a lot, or I speak a lot about sleep, and I think I've probably done a mis service to the other aspect of the sleep wake rhythmicity, which is called your 24 hours circadian rhythm. Now your sleep pressure, the drive to sleep is independent of your circadian rhythm, but they often work beautifully in harmony with each other, and you fall asleep, and you stay asleep. But I think the circadian system is critical because, excuse me, and what the circadian rhythm also regulates, sneezing right at the inopportune moment when you are recording a podcast. But nevertheless, what that tells me is that when you feed your brain signals of wake sleep consistency, which is to say wake, sleep, timing, regularity, there is something about feeding the brain signals of regularity that anchor your 24-hour circadian rhythm and as a consequence, it improves the quantity and the quality of your sleep. They're intertwined.

What About Sleep Trackers?

Eric Topol (28:09):

That's a terrific explanation for what I think a lot of people don't appreciate it's importance. Now, last topic about tracking. Now we understand how important sleep is. It is the superpower I am with you on that really brought that to light in so many ways. But of course, now we can track it with rings with smart watches and we get these readouts things like efficiency as part of the Oura score and other rings and deep sleep or NREM, REM, the works, you can see your awake times that you didn't know you're awake and the whole bit. Do you recommend for people that aren't getting great sleep quality beyond that they should try to establish a regular schedule that they should track to try to improve it and of course how would they improve it? Or are these things like having a cold mattress temperature that is controlled? What are the tricks that you would suggest for trying to improve your sleep through tracking? Or do you think tracking shouldn't be done?

Matt Walker (29:16):

Oh gosh, it's such a wonderful question and as with wonderful questions, the answer is usually it's complicated and I have to be careful because for someone who's currently wearing three different sleep trackers, it's going to be hard for me to answer this question completely in the negative. And there are three different sleep trackers. But I would say that for the most part, I like the idea of sleep tracking if you are sleeping well, meaning if as long as you're not suffering from insomnia. The reason is because sleep unlike those two other critical of health, which is diet and exercise, is very difficult to subjectively estimate. So if I were to ask you, Eric, how many times have you worked out in the past week, you'd be able to tell me how cleanly or how poorly have you been eating in the past week. You could tell me.

Matt Walker (30:09):

But if I was to say to you, Eric, how much deep sleep did you get last Tuesday? And if you don't have a sleep tracker, you'd say, I don't know. And so, there's something useful about tracking, especially a non-conscious process that I think is meaningful to many. And often medicine we say what gets measured gets managed, and there is that trite sort of statement. I do think that that's still true for sleep. So many people I've spoken to have, for example, markedly reduced the amount of alcohol consumption because they've been seeing the huge impact that the alcohol consumption in the evening has on their ring smart ring data as a consequence. So overall, I think they're pretty good. When people ask me what's the best sleep tracker, I usually say it's the one that you wear most frequently because if I come up with a band, headband, chest straps, all sorts of different things and it's a hundred percent accurate, but after three uses of it, you stop using it, that's a useless sleep tracker. So I like to think about sleep trackers that are low friction and no friction. When we go to sleep, we take things off, we don't put things on. That's why I liked things like the ring. For example, I think that's a non-intrusive way. I think the mattress may be as if not better because it's a completely friction less device. You don't have to remember to charge it. You don't have to put it on, you just fall into bed, and it tracks your sleep.

Matt Walker (31:40):

One form factor, I like to think about sleep trackers is the form factor itself. But then the other is accuracy. And I think right now if you look at the data, probably Oura is winning the ring kind of wars. If you look at all wristband wars, I think it's probably the most accurate relative to something like Fitbit or Apple Watch or the Whoop Band. But they're all pretty close. I think Oura is probably the leader in class right now at least. Keep in mind that I used to be an advisor for Oura. I want to make that very clear. So take what I say with a grain of salt in that regard. I think to your question, well, I'll come back to mattresses in just one second.

Matt Walker (32:34):

For people who are struggling with sleep, I think you've got to be very, very careful with sleep trackers because they can have the counterproductive effect where I gave you the example of alcohol or eating too late. And these sleep trackers help you modify your behaviors to improve your sleep. Well, there are places where these trackers can actually do you a disservice. When you get so hyper focused on your data and your data not looking good each and every day, it becomes a self-fulfilling prophecy of a negative spiral. And we now have a condition in sleep medicine called orthosomnia. So ortho in medicine typically means straightened. So you've heard of orthodontic straightening teeth, orthopedic straightening bones, orthosomnia is about getting so obsessed with getting your sleep perfect and your sleep straight that it causes an insomnia like syndrome. Now, I don't know, I think the press has made more of this than there is.

Matt Walker (33:30):

It probably is about 5-7% of the population. I would say at that moment in time, do one of two things. Either take the ring off entirely and just say, I'm going to get my sea legs back underneath me, get some cognitive behavioral therapy for insomnia. And when I'm confident I'll put the ring back on. Or don't throw the baby out with the bath water, keep wearing the ring. Try to say to yourself only on let's say a Sunday afternoon, will I open up the app and look historically what's been happening during the past week so that you keep getting your data, but you don't get the angiogenic daily sort of repetition of reinforcement of I'm not sleeping well. I should also note by the way that I think sleep trackers are not a substitute for either a sleep recording laboratory, but also, they're not a substitute for ultimately telling you entirely how good your sleep is.

Matt Walker (34:24):

Don't forget, you should always keep in mind how do I feel the next day? Because I think a lot of people will see their readiness score as 92 and they feel miserable. They just feel rough. And then another day, my readiness score was 62 and I just went out and I just ran my fastest five mile that I've done in the past six months. So don't forget that subjective sense of sleep is just as important as objective measures of sleep. The final thing I would say to your point about the mattresses, I actually do think that they are a really great vehicle for sleep augmentation because these smart mattresses, they're filled with sensors, things like Eight Sleep, and they will assess your physiology, they will track your sleep just like a sleep tracking ring. But what's also good is that because they can manipulate temperature and your sleep is so thermoregulatory sensitive that they create this kind, it's almost like this bent arc of thermal story throughout the night because you have to warm up at the surface to cool down at the core to fall asleep, then you have to stay cool to stay asleep, then you have to warm up to wake up and they take you through that natural change.

Matt Walker (35:41):

But they do it intelligently because they're measuring your sleep minute to minute. And then they're saying, I'm tweaking temperature a little bit. Has sleep improved? Has it become worse? Oh, it's become better. Let's lean into that. Let's get them even colder. Oh, wait a second, it's getting worse. Let's warm it back up a little bit. It's like a staircase method, like a Richter shock. And gradually they find your sweet spot and I think that is a really elegant system. And now they're measuring snoring. Snoring perturbations, and they can augment the bed and raise the angle of the bed up just a little bit so that the gravity doesn't have as much of a hold on your airway because when you're lying on your back, the airway wants to collapse down to gravity, and when you raise back up again, it will change that. And so, I think that there's lots of new advantages in, I think mattress technology that we'll see coming out into the future. I think it's a great vehicle for sleep augmentation.

Eric Topol (36:37):

That's terrific. Well, this has been for me, very educational, as I would've predicted, if anybody's up on everything in this area, it would be you. So thank you, Matt. It's a really brilliant discussion, really enlightening. We could talk some more hours, but I think we've encapsulated some of the big things. And before we finish up, is there anything else you wanted to say?

Matt Walker (37:05):

No, I think just to thank you for both your work in general in terms of science communication, your offer here specifically to allow me to try to be a very poorly communicated voice of sleep, and also just what you've done in general for I think the accuracy of science communication out into the public. Please never stop, continue to be a shining light for all of us. You are remarkable. Thank you, Eric.

Eric Topol (37:31):

Oh, you're very kind. And I look forward to the next chance we get to visit in person. It's been too long, Matt. And all the best to you. Thanks for joining today.

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

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The UK is the world leader in human genomics, and laid the foundation for advancing medicine with the UK Biobank, Genomes England and now Our Future Health (w/ 5 million participants). Sir John Bell is a major force in driving and advising these and many other initiatives. After 22 years as the Regius Professor of Medicine at the University of Oxford he left in 2024 to be President of the Ellison Institute of Technology.

Professor Bell has been duly recognized in the UK: knighted in 2015 and appointed Companion of Honor in 2023.

In our conversation, you will get a sense for how EIT will be transformational for using A.I. and life science for promoting human health.

Transcript with audio links

Eric Topol (00:06):

Hello, this is Eric Topol from Ground Truths. And I'm really delighted to welcome today, Sir John Bell who had an extraordinary career as a geneticist, immunologist, we'll talk about several initiatives he's been involved with during his long tenure at University of Oxford, recently became head of the Ellison Institute of Technology (EIT) in the UK. So welcome, John.

Sir John Bell (00:30):

Thanks, Eric. Thanks very much for having me.

Eric Topol (00:34):

Well, I think it's just extraordinary the contributions that you have made and continue to make to advance medicine, and I thought what we could do is get into that. I mean, what's interesting, you have had some notable migrations over your career, I think starting in Canada, at Stanford, then over as Rhodes Scholar in Oxford. And then you of course had a couple of decades in a very prestigious position, which as I understand was started in 1546 by King Henry VII, and served as the Regius Professor of Medicine at the University of Oxford. Do I have that right?

Sir John Bell (01:11):

It was actually Henry VIII, but you were close.

Eric Topol (01:14):

Henry VIII, that's great. Yeah. Okay, good. Well, that's a pretty notable professorship. And then of course in recent times you left to head up this pretty formidable new institute, which is something that's a big trend going on around the world, particularly in the US and we'll talk about. So maybe we can start with the new thing. Tell us more about the Ellison Institute of Technology (EIT), if you will.

Sir John Bell (01:47):

Yeah. So as you know, Larry Ellison has been one of the great tech entrepreneurs focused really on developing terrific databases over his career and through Oracle, which is the company that he founded. And Larry is really keen to try and give back something substantial to the world, which is based on science and technology. So he and I did quite a bit together over the Covid pandemic. He and I talked a lot about what we're doing and so on. He came to visit afterwards and he had, I think he decided that the right way to make his contributions would be to set up an institute that would be using the state-of-the-art science and technology with a lot of AI and machine learning, but also some of the other modern tools to address the major problems in healthcare, in food security, in green energy and climate change and in global governance.

Sir John Bell (02:49):

So anyway, he launched this about 18 months ago. He approached me to ask whether I would run it. He wanted to set it up outside Oxford, and he wanted to do something which is a bit different than others. And that is his view was that we needed to try and create solutions to these problems which are commercially viable and not all the solutions are going to be commercially viable, but where you can create those, you make them sustainable. So the idea is to make sure that we create solutions that people want to buy, and then if they buy them, you can create a sustainable solution to those issues. So we are actually a company, but we are addressing many of the same problems that the big foundations are addressing. And the big issues that you and I talk about in health, for example, are all on our list. So we're kind of optimistic as to where this will go and Larry's supporting the project and we're going to build out an institute here which will have about 5,000 people in it, and we'll be, I think a pretty exciting new addition to the science and technology ecosystem globally.

Eric Topol (04:02):

Well, I know the reverberations and the excitement is palpable and some of the colleagues I've spoken to, not just in England, but of course all over the world. So congratulations on that. It was a big move for you to leave the hardcore academics. And the other thing I wanted to ask you, John, is you had distinguished your career in immunology, in genetics, type 1 diabetes and other conditions, autoimmune conditions, and now you've really diversified, as you described with these different areas of emphasis at the new institute. Is that more fun to do it or do you have deputies that you can assign to things like climate change in other areas?

Sir John Bell (04:50):

Trust me, Eric, I'm not making any definitive decisions about areas I know nothing about, but part of this is about how do you set up leadership, run a team, get the right people in. And I have to say one of the really interesting things about the institute is we've been able to recruit some outstanding people across all those domains. And as you know, success is almost all dependent on people. So we're really pretty optimistic we're going to have a significant impact. And of course, we also want to take risks because not a lot of point in us doing stuff that everybody else is doing. So we're going to be doing some things that are pretty way out there and some of them will fail, so we are just going to get used to trying to make sure we get a few of them across the finish line. But the other thing is that, and you've experienced this too, you never get too old to learn. I mean, I'm sucking up stuff that I never thought I would ever learn about, which is fun actually, and really marvel.

Eric Topol (05:55):

It's fantastic. I mean, you've really broadened and it's great that you have the runway to get these people on board and I think you're having a big building that's under construction?

Sir John Bell (06:07):

Yeah, we've got the original building that Larry committed to is about 330,000 square feet of space. I mean, this is completely amazing, but we are of course to accommodate up to 5,000 people, we're going to need more than that. So we are looking at a much wider campus here that'll involve more than just that building. I think we'll end up with several million square feet of space by the time we're finished. So mean, it's a really big project, but we've already made progress in some domains to try and get projects and the beginnings of companies on the road to try and solve some of the big problems. So we're quite excited about it.

Eric Topol (06:49):

Now you, I assume it's pretty close to Oxford, and will you have some kind of inter interactions that are substantial?

Sir John Bell (06:58):

Yeah, so the university's been terrific about this actually, because of course most universities would say, well, why don't you do it inside the university and just give us the money and it'll all be fine. So of course Larry. Larry wasn't born yesterday, so I said, well, thank you very much, but I think we'll probably do this nearby. But the university also realized this is a really exciting opportunity for them and we've got a really good relationship with them. We've signed an agreement with them as to who will work where. We've agreed not to steal a lot of their staff. We're going to be bringing new people into the ecosystem. Some of the university people will spend some time with us and sometime in the university, so that will help. But we're also bringing quite a few new people into the setting. So the university has been really positive. And I think one of the things that's attractive to the university, and you'll be familiar with this problem in the UK, is that we're quite good. The discovery science here is pretty good.

Sir John Bell (08:06):

And we do startups now at scale. So Oxford does lots of little startup companies in the biotech space and all the rest of it, but we never scale any of these companies because there isn't the depth of capital for scaling capital to get these things scaled. And so, in a way what we're trying to do here at Ellison actually avoids that problem because Larry knows how to scale companies, and we've got the financial support now. If we have things that are really successful, we can build the full stack solution to some of these problems. So I think the university is really intrigued as to how we might do that. We're going to have to bring some people in that know how to do that and build billion dollar companies, but it's sufficiently attractive. We've already started to recruit some really outstanding people. So as a way to change the UK system broadly, it's actually quite a good disruptive influence on the way the thing works to try and fix some of the fundamental problems.

Eric Topol (09:07):

I love that model and the ability that you can go from small startups to really transformative companies have any impact. It fits in well with the overall objectives, I can see that. The thing that also is intriguing regarding this whole effort is that in parallel we've learned your influence. The UK is a genomics world leader without any question and no coincidence that that's been your area of emphasis in your career. So we've watched these three initiatives that I think you were involved in the UK Biobank, which has had more impact than any cohort ever assembled. Every day there's another paper using that data that's coming out. There's Genomes England, and then now Our Future Health, which a lot of people don't know about here, which is well into the 5 million people enrollment. Can you tell us about, this is now 15 years ago plus when these were started, and of course now with a new one that's the biggest ever. What was your thinking and involvement and how you built the UK to be a world leader in this space?

Sir John Bell (10:26):

So if you turn the clock back 20 years, or actually slightly more than 25 years ago, it was clear that genomics was going to have a play. And I think many of us believed that there was going to be a genetic element to most of the major common disease turn out to be true. But at the time, there were a few skeptics, but it seemed to us that there was going to be a genetic story that underpinned an awful lot of human disease and medicine. And we were fortunate because in Oxford as you know, one of my predecessors in the Regius job was Richard Doll, and he built up this fantastic epidemiology capability in Oxford around Richard Peto, Rory Collins, and those folks, and they really knew how to do large scale epidemiology. And one of the things that they'd observed, which is it turns out to be true with genetics as well, is a lot of the effects are relatively small, but they're still quite significant. So you do need large scale cohorts to understand what you're doing. And it was really Richard that pioneered the whole thinking behind that. So when we had another element in the formula, which was the ability to detect genetic variation and put that into the formula, it seemed to me that we could move into an era where you could set up, again, large cohorts, but build into the ability to have DNA, interrogate the DNA, and also ultimately interrogate things like proteomics and metabolomics, which were just in their infancy at that stage.

Sir John Bell (12:04):

Very early on I got together because I was at that stage at the Nuffield Chair of Medicine, and I got together, Rory and Richard and a couple of others, and we talked a little bit about what it would look like, and we agreed that a half a million people late to middle age, 45 and above would probably over time when you did the power calculations, give you a pretty good insight in most of the major diseases. And then it was really a question of collecting them and storing the samples. So in order to get it funded at the time I was on the council of the MRC and George Radda, who you may remember, was quite a distinguished NMR physiologist here. He was the chief executive of the MRC. So I approached him and I said, look, George, this would be a great thing for us to do in the UK because we have all the clinical records of these people going back for a decade, and will continue to do that.

Sir John Bell (13:01):

Of course, we immediately sent it out to a peer review committee in the MRC who completely trashed the idea and said, you got to be joking. So I thought, okay, that’s how that lasted. And I did say to George, I said, that must mean this is a really good idea because if it had gone straight through peer review, you would've known you were toast. So anyway, I think we had one more swing at peer review and decided in the end that wasn't going to work. In the end, George to his credit, took it to MRC council and we pitched it and everybody thought, what a great idea, let's just get on and do it. And then the Wellcome came in. Mark Walport was at the Wellcome at the time, great guy, and did a really good job at bringing the Wellcome on board.

Sir John Bell (13:45):

And people forget the quantum of money we had to do this at the time was about 60 million pounds. I mean, it wasn’t astonishly small. And then of course we had a couple of wise people who came in to give us advice, and the first thing they said, well, if you ever thought you were really going to be able to do genetics on 500,000 people, forget it. That'll never work. So I thought, okay, I'll just mark that one out. And then they said, and by the way, you shouldn't assume you can get any data from the health service because you'll never be able to collect clinical data on any of these people. So I said, yeah, yeah, okay, I get it. Just give us the money and let us get on. So anyway, it's quite an interesting story. It does show how conservative the community actually is for new ideas.

Sir John Bell (14:39):

Then I chaired the first science committee, and we decided about a year into it that we really needed the chief executive. So we got Rory Collins to lead it and done it. I sat on the board then for the next 10 years, but well look, it was a great success. And as you say, it is kind of the paradigm for now, large genetic epidemiology cohorts. So then, as you know, I advise government for many years, and David Cameron had just been elected as Prime Minister. This was in about 2010. And at the time I'd been tracking because we had quite a strong genomics program in the Wellcome Trust center, which I'd set up in the university, and we were really interested in the genetics of common disease. It became clear that the price of sequencing and Illumina was now the clear leader in the sequencing space.

Sir John Bell (15:39):

But it was also clear that Illumina was making significant advances in the price of sequencing because as you remember, the days when it cost $5,000 to do a genome. Anyway, it became clear that they actually had technology that gets you down to a much more sensible price, something like $500 a genome. So I approached David and I said, we are now pretty sure that for many of the rare diseases that you see in clinical practice, there is a genetic answer that can be detected if you sequenced a whole genome. So why don't we set something up in the NHS to provide what was essentially the beginnings of a clinical service to help the parents of kids with various disabilities work out what's going on, what's wrong with their children. And David had had a child with Ohtahara syndrome, which as you know is again, and so David was very, he said, oh God, I'll tell you the story about how awful it was for me and for my wife Samantha.

Sir John Bell (16:41):

And nobody could tell us anything about what was going on, and we weren't looking for a cure, but it would've really helped if somebody said, we know what it is, we know what the cause is, we'll chip away and maybe there will be something we can do, but at least you know the answer. So anyway, he gave us very strong support and said to the NHS, can you please get on and do it? Again massive resistance, Eric as you can imagine, all the clinical geneticists said, oh my God, what are they doing? It's complete disaster, dah, dah, dah. So anyway, we put on our tin hats and went out and got the thing going. And again, they did a really good job. They got to, their idea was to get a hundred thousand genomes done in a reasonable timeframe. I think five years we set ourselves and the technology advance, people often underestimate the parallel development of technology, which is always going on. And so, that really enabled us to get that done, and it still continues. They're doing a big neonatal program at the moment, which is really exciting. And then I was asked by Theresa May to build a life science strategy because the UK, we do this stuff not as big and broad as America, but for a small country we do life sciences pretty well.

Eric Topol (18:02):

That’s an understatement, by the way. A big understatement.

Sir John Bell (18:04):

Anyway, so I wrote the strategies in 2017 for Theresa about what we would do as a nation to support life sciences. And it was interesting because I brought a group of pharma companies together to say, look, this is for you guys, so tell us what you want done. We had a series of meetings and what became clear is that they were really interested in where healthcare was going to end up in the next 20 years. And they said, you guys should try and get ahead of that wave. And so, we agreed that one of the domains that really hadn't been explored properly, it was the whole concept of prevention.

Sir John Bell (18:45):

Early diagnosis and prevention, which they were smart enough to realize that the kind of current paradigm of treating everybody in the last six months of life, you can make money doing that, there's no doubt, but it doesn't really fix the problem. And so, they said, look, we would love it if you created a cohort from the age of 18 that was big enough that we could actually track the trajectories of people with these diseases, identify them at a presymptomatic stage, intervene with preventative therapies, diagnose diseases earlier, and see if we could fundamentally change the whole approach to public health. So we anyway, went back and did the numbers because of course at much wider age group, a lot of people don't get at all sick, but we thought if we collected 5 million people, we would probably have enough. That's 10% of the UK adult population.

Sir John Bell (19:37):

So anyway, amazingly the government said, off you go. We then had Covid, which as you know, kept you and I busy for a few years before we could get back to it. But then we got at it, and we hired a great guy who had done a bit of this in the UAE, and he came across and we set up a population health recruitment structure, which was community-based. And we rapidly started to recruit people. So we've now got 2.9 million people registered, 2.3 million people consented, and we've got blood in the bank and all the necessary data including questionnaire data for 1.5 million people growing up. So we will get to 5 million and it's amazing.

Eric Topol (20:29):

It is. It really is, and I’m just blown away by the progress you've made. And what was interesting too, besides you all weren't complacent about, oh, we got this UK Biobank and you just kept forging ahead. And by the way, I really share this importance of finally what has been a fantasy of primary prevention, which never really achieved. It's always, oh, after a heart attack. But that's what I wrote about in the Super Agers book, and I'll get you a copy.

Sir John Bell (21:02):

No, I know you're a passionate believer in this and we need to do a lot of things. So we need to work out what's the trial protocol for primary prevention. We need to get the regulators on board. We've got to get them to understand that we need diagnostics that define risk, not disease, because that's going to be a key bit of what we're going to try and do. And we need to understand that for a lot of these diseases, you have to intervene quite early to flatten that morbidity curve.

Eric Topol (21:32):

Yeah, absolutely. What we've learned, for example, from the UK Biobank is not just, of course the genomics that you touched on, but the proteomics, the organ clocks and all these other layers of data. So that gets me to my next topic, which I know you're all over it, which is AI.

Eric Topol (21:51):

So when I did the NHS review back in 2018, 2019, the group of people which were amazing that I had to work with no doubt why the UK punches well beyond its weight. I had about 50 people, and they just said, you know what? Yeah, we are the world leaders in genomics. We want to be the world leader in AI. Now these days you only hear about US and China, which is ridiculous. And you have perhaps one of the, I would say most formidable groups there with Demis and Google DeepMind, it’s just extraordinary. So all the things that the main foci of the Ellison Institute intersect with AI.

Sir John Bell (22:36):

They do. And we, we've got two underpinning platforms, well actually three underpinning platforms that go across all those domains. Larry was really keen that we became a real leader in AI. So he's funded that with a massive compute capacity. And remember, most universities these days have a hard time competing on compute because it's expensive.

Eric Topol (22:57):

Oh yeah.

Sir John Bell (22:58):

So that is a real advantage to us. He's also funded a great team. We've recruited some people from Demis's shop who are obviously outstanding, but also others from around Europe. So we really, we've recruited now about 15 really outstanding machine learning and AI people. And of course, we're now thinking about the other asset that the UK has got, and particularly in the healthcare space is data. So we do have some really unique data sets because those are the three bits of this that you need if you're going to make this work. So we're pretty excited about that as an underpinning bit of the whole Ellison Institute strategy is to fundamentally underpin it with very strong AI. Then the second platform is generative biology or synthetic biology, because this is a field which is sort of, I hesitate to say limped along, but it's lacked a real focus.

Sir John Bell (23:59):

But we've been able to recruit Jason Chin from the LMB in Cambridge, and he is one of the real dramatic innovators in that space. And we see there's a real opportunity now to synthesize large bits of DNA, introduce them into cells, microbes, use it for a whole variety of different purposes, try and transform plants at a level that people haven't done before. So with AI and synthetic biology, we think we can feed all the main domains above us, and that's another exciting concept to what we're trying to do. But your report on AI was a bit of a turning point for the UK because you did point out to us that we did have a massive opportunity if we got our skates, and we do have talent, but you can't just do it with talent these days, you need compute, and you need data. So we're trying to assemble those things. So we think we'll be a big addition to that globally, hopefully.

Eric Topol (25:00):

Yeah. Well that's another reason why I am so excited to talk to you and know more about this Ellison Institute just because it's unique. I mean, there are other institutes as like Chan Zuckerberg, the Arc Institute. This is kind of a worldwide trend that we're seeing where great philanthropy investments are being seen outside of government, but none have the computing resources that are being made available nor the ability to recruit the AI scientists that'll help drive this forward. Now, the last topic I want to get into with you today is one that is where you're really grounded in, and that's the immune response.

Eric Topol (25:43):

So it's pretty darn clear now that, well, in medicine we have nothing. We have the white cell neutrophil to lymphocyte ratio, what a joke. And then on the other hand, we can do T and B cell sequencing repertoires, and we can do all this stuff, autoantibody screens, and the list goes on and on. How are we ever going to make a big dent in health where we know the immune system is such a vital part of this without the ability to check one's immune status at any point in time in a comprehensive way? What are your thoughts about that?

Sir John Bell (26:21):

Yeah, so you seem to be reading my mind there. We need to recruit you over here because I mean, this is exactly, this is one of our big projects that we've got that we're leaning into, and that is that, and we all experienced in Covid the ins and outs of vaccines, what works, what doesn't work. But what very clear is that we don't really know anything about vaccines. We basically, you put something together and you hope the trial works, you've got no intermediate steps. So we're building a really substantial immunophenotyping capability that will start to interrogate the different arms of the immune response at a molecular level so that we can use a combination of human challenge models. So we've got a big human challenge model facility here, use human challenge models with pathogens and with associated vaccines to try and interrogate which bits of the immune response are responsible for protection or therapy of particular immunologically mediated diseases or infectious diseases.

Sir John Bell (27:30):

And a crucial bit to that. And one of the reasons people have tried this before, but first of all, the depth at which you can interrogate the immune system has changed a lot recently, you can get a lot more data. But secondly, this is again, where the AI becomes important because it isn't going to be a simple, oh, it's the T-cell, it's going to be, well, it's a bit of the T cells, but it's also a bit of the innate immune response and don't forget mate cells and don't forget a bit of this and that. So we think that if we can assemble the right data set from these structured environments, we can start to predict and anticipate which type of immune response you need to stimulate both for therapy and for protection against disease. And hopefully that will actually create a whole scientific foundation for vaccine development, but also other kinds of immune therapy and things like cancer and potentially autoimmune disease as well. So that's a big push for us. We're just busy. The lab isn't set up. We've got somebody to run the lab now. We've got the human challenge model set up with Andy Pollard and colleagues. So we're building that out. And within six months, I think we'll be starting to collect data. So I'm just kind of hoping we can get the immune system in a bit more structured, because you’re absolutely right. It's a bit pin the tail on the donkey at the moment. You have no idea what's actually causing what.

Eric Topol (29:02):

Yeah. Well, I didn't know about your efforts there, and I applaud that because it seems to me the big miss, the hole and the whole story about how we're going to advanced human health and with the recent breakthroughs in lupus and these various autoimmune diseases by just targeting CD19 B cells and resetting like a Ctrl-Alt-Delete of their immune system.

Sir John Bell (29:27):

No, it's amazing. And you wouldn't have predicted a lot of this stuff. I think that means that we haven't really got under the skin of the mechanistic events here, and we need to do more to try and get there, but there's steady advance in this field. So I'm pretty optimistic we'll make some headway in this space over the course of the next few years. So we're really excited about that. It's an important piece of the puzzle.

Eric Topol (29:53):

Yeah. Well, I am really impressed that you got all the bases covered here, and what a really exhilarating chance to kind of peek at what you're doing there. And we're going to be following it. I know I'm going to be following it very closely because I know all the other things that you've been involved with in your colleagues, big impact stuff. You don't take the little swings here. The last thing, maybe to get your comment, we're in a state of profound disruption here where science is getting gutted by a madman and his henchmen, whatever you want to call it, which is really obviously a very serious state. I'm hoping this is a short term hit, but worried that this will have a long, perhaps profound. Any words of encouragement that we're going to get through this from the other side of the pond?

Sir John Bell (30:52):

Well, I think regardless of the tariffs, the scientific community are a global community. And I think we need to remember that because our mission is a global mission, and we need to lean into that together. First of all, America is such a powerhouse of everything that's been done scientifically in the human health domain. But not only that, but across all the other domains that we work in, we can't really make the kind of progress that we need to without America being part of the agenda. So first of all, a lot of sympathy for you and your colleagues. I know it must be massively destabilizing for you, not be confident that the things that work are there to help you. But I'm pretty confident that this will settle down. Most of the science is for, well, all the science is really for public good, and I think the public recognizes it and they'll notice if it's not being prosecuted in the way that it has to be. And the global science community cannot survive without you. So we're all leaning in behind you, and I hope it will settle. One of my worries is that these things take years to set up and literally hours or minutes to destroy. So we can't afford to take years to set them back up again. So we do need to be a bit careful about that, but I still have huge confidence in what you guys can achieve and we're all behind you.

Eric Topol (32:37):

Well, that's really helpful getting some words of wisdom from you there, John. So this has been terrific. Thanks so much for joining, getting your perspective on what you're doing, what's important is so essential. And we’ll stay tuned for sure.

Sir John Bell (32:59):

And come and visit us at the EIT, Eric. We'd be glad to see you.

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

Some of the topics that John and I discussed—immunology, A.I., genomics, and prevention—are emphasized in my new book SUPER AGERS. A quick update: It will have a new cover after making the New York Times Bestseller list and is currently ranked #25 for all books on Amazon. Thanks to so many of you for supporting the book!

Here are a few recent podcasts:

Dax Shepard:

Dr. Mike

Sanjay Gupta

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

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Audio file, also on Apple and Spotify

Tyler Cowen, Ph.D, is the Holbert L. Harris Professor of Economics at George Mason University. He is the author of 17 books, most recently Talent.: How to Identify Energizers, Creatives, and Winners Around the World. Tyler has been recognized as one of the most influential economists of the past decade. He initiated and directs the philanthropic project Emergent Ventures, writes a blog Marginal Revolution, and a podcast Conversations With Tyler, and also writes columns for The Free Press." He is writing a new book (and perhaps his last) on Mentors.

“Maybe AGI [Artificial General Intelligence] is like porn — I know it when I see it. And I’ve seen it.”—Tyler Cowen

Our conversation on acquiring information, A.I., A.G.I., the NIH, the assault on science, the role of doctors in the A.I. era,, the meaning of life, books of the future, and much more.

Transcript with links

Eric Topol (00:06):

Well, hello. This is Eric Topol with Ground Truths, and I am really thrilled today to have the chance to have a conversation with Tyler Cowen, who is, when you look up polymath in the dictionary, you might see a picture of him. He is into everything. And recently in the Economist magazine 1843, John Phipps wrote a great piece profile, the man who wants to know everything. And actually, I think there's a lot to that.

Tyler Cowen (00:36):

That's why we need longevity work, right?

Eric Topol (00:39):

Right. So he's written a number of books. How many books now, Tyler?

Tyler Cowen:

17, I'm not sure.

Eric Topol:

Only 17? And he also has a blog that's been going on for over 20 years, Marginal Revolution that he does with Alex Tabarrok.

Tyler Cowen (00:57):

Correct.

Eric Topol (00:57):

And yeah, and then Conversations with Tyler, a podcast, which I think an awful lot of people are tuned into that. So with that, I'm just thrilled to get a chance to talk with you because I used to think I read a lot, but then I learned about you.

“Cowen calls himself “hyperlexic”. On a good day, he claims to read four or five

books. Secretly, I timed him at 30 seconds per page reading a dense tract by

Martin Luther. “—John Phipps, The Economist’s 1843

I've been reading more from the AIs lately and less from books. So I'll get one good book and ask the AI a lot of questions.

Eric Topol (01:24):

Yeah. Well, do you use NotebookLM for that?

Tyler Cowen (01:28):

No, just o3 from OpenAI at the moment, but a lot of the models are very good. Claude, there's others.

Eric Topol (01:35):

Yeah, yeah. No, I see how that's a whole different way to interrogate a book and it's great. And in fact, that gets me to a topic I was going to get to later, but I'll do it now. You're soon or you have already started writing for the Free Press with Barri Weiss.

Tyler Cowen (01:54):

That’s right, yes. I have a piece coming out later today. It's been about two weeks. It's been great so far.

“Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James”—Bari Weiss

Yeah, so that's interesting. I hadn't heard of it until I saw the announcement from Barri and I thought what was great about it is she introduced it. She said, “Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James. Now who could do that, right. So I thought, well, you know what? I need independent confirmation of that, that is as being a polymath. And then I saw Patrick Collison, who I know at Stripe and Arc Institute, “you can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.” So you're an information acquirer and one of the books you wrote, I love the title Infovore.

Tyler Cowen (03:09):

The Age of the Infovore, that’s right.

Eric Topol (03:11):

I mean, have people been using that term because you are emblematic of it?

“You can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.”—Patrick Collison

It was used on the internet at some obscure site, and I saw it and I fell in love with that word, and I thought I should try to popularize it, but it doesn't come from me, but I think I am the popularizer of it.

Yeah, well, if anybody was ingesting more information and being able to work with it. That's what I didn't realize about you, Tyler, is restaurants and basketball and all these other fine arts, very impressive. Now, one of the topics I wanted to get into you is I guess related to a topic you've written about fair amount, which is the great stagnation, and right now we're seeing issues like an attack on science. And in the past, you've written about how you want to raise the social status of scientists. So how do you see this current, I would even characterize as a frontal assault on science?

Tyler Cowen (04:16):

Well, I'm very worried about current Trump administration policies. They change so frequently and so unpredictably, it's a little hard to even describe what they always are. So in that sense, it's a little hard to criticize them, but I think they're scaring away talent. They might scare away funding and especially the biomedical sciences, the fixed costs behind a lot of lab work, clinical trials, they're so high that if you scare money away, it does not come back very readily or very quickly. So I think the problem is biggest perhaps for a lot of the biomedical sciences. I do think a lot of reform there has been needed, and I hope somehow the Trump policies evolve to that sort of reform. So I think the NIH has become too high bound and far too conservative, and they take too long to give grants, and I don't like how the overhead system has been done. So there's plenty of room for improvement, but I don't see so far at least that the efforts have been constructive. They've been mostly destructive.

Eric Topol (05:18):

Yeah, I totally agree. Rather than creative destruction it’s just destruction and it's unfortunate because it seems to be haphazard and reckless to me at least. We of course, like so many institutions rely on NIH funding for the work, but I agree that reform is fine as long as it's done in a very thought out, careful way, so we can eke out the most productivity for the best investment. Now along with that, you started Emergent Ventures where you're funding young talent.

Tyler Cowen (05:57):

That's right. That's a philanthropic fund. And we now have slightly over 1000 winners. They're not all young, I'd say they're mostly young and a great number of them want to go into the biomedical sciences or have done so. And this is part of what made me realize what an incredible influx of talent we're seeing into those areas. I'm not sure this is widely appreciated by the world. I'm sure you see it. I also see how much of that talent actually is coming from Canada, from Ontario in particular, and I've just become far more optimistic about computational biology and progress in biology and medical cures, fixes, whatever you want to call it, extending lives. 10 years ago, I was like, yeah, who knows? A lot of things looked pretty stuck. Then we had a number of years where life expectancy was falling, and now I think we're on the verge of a true golden age.

Eric Topol (06:52):

I couldn't agree with you more on that. And I know some of the people that you funded like Anne Wylie who developed a saliva test for Covid out of Yale. But as you say, there's so many great young and maybe not so young scientists all over, Canada being one great reservoir. And now of course I'm worried that we're seeing emigration rather than more immigration of this talent. Any thoughts about that?

Tyler Cowen (07:21):

Well, the good news is this, I'm in contact with young people almost every day, often from other countries. They still want to come to the United States. I would say I sign an O-1 letter for someone about once a week, and at least not yet has the magic been dissipated. So I'm less pessimistic than some people are, but I absolutely do see the dangers. We’re just the biggest market, the freest place we have by far the most ambitious people. I think that's actually the most significant factor. And young people sense that, and they just want to come here and there's not really another place they can go that will fit them.

Eric Topol (08:04):

Yeah, I mean one of the things as you've probably noted is there's these new forces that are taking on big shouldering. In fact, Patrick Collison with Arc Institute and Chan Zuckerberg for their institute and others like that, where the work you're doing with Emergent Ventures, you're supporting important projects, talents, and if this whole freefall in NIH funding and other agency funding continues, it looks like we may have to rely more on that, especially if we're going to attract some talent from outside. I don't know how else we're going to make. You're absolutely right about how we are such a great destination and great collaborations and mentors and all that history, but I'm worried that it could be in kind of a threatened mode, if you will.

Tyler Cowen (08:59):

I hope AI lowers costs. As you probably know at Arc, they had Greg Brockman come in for some number of months and he's one of the people, well, he helped build up Stripe, but he also was highly significant in OpenAI behind the GPT-4 model. And to have Greg Brockman at your institute doing AI for what, six months, that's a massive acceleration that actually no university had the wisdom to do, and Arc did. So I think we're seeing just more entrepreneurial thinking in the area. There's still this problem of bottlenecks. So let's say AI is great for drug discovery as it may be. Well, clinical trials then become a bigger bottleneck. The FDA becomes a bigger bottleneck. So rapid improvement in only one area while great is actually not good enough.

Eric Topol (09:46):

Yeah, I'm glad you brought up that effect in Arc Institute because we both know Patrick Hsu, who's a brilliant young guy who works there and has published some incredible large language models applied to life science in recent months, and it is impressive how they used AI in almost a singular way as compared to as you said, many other leading institutions. So that is I think, a really important thing to emphasize.

Tyler Cowen (10:18):

Arc can move very quickly. I think that's not really appreciated. So if Patrick Hsu decides Silvana Konermann, Patrick Collison, if they decide something ought to be bought or purchased or set in motion, it can happen in less than a day. And it does happen basically immediately. And it's not only that it's quicker, I think when you have quicker decisions, they're better and it's infectious to the people you're working with. And there's an understanding that the core environment is not a bureaucratic one. So it has a kind of multiplier effect through the institution.

Eric Topol (10:54):

Yeah, I totally agree with you. It's always been a philosophy in your mind to get stuff done, get s**t done, whatever you want to call it. They're getting it done. And that's what's so impressive. And not just that they've got some new funds available, but rather they're executing in a way that's parallel to the way the world's evolving in the AI front, which is I think faster than most people would ever have expected, anticipated. Now that gets me to a post you had on Marginal Revolution just last week, which one of the things I love about Marginal Revolution is you don't have to read a whole lot of stuff. You just give the bullets, the juice, if you will. Here you wrote o3 and AGI, is April 16th AGI day? And everybody's talking about artificial general intelligence is here. It's going to be here five years, it's going to be seven years.

Eric Topol (11:50):

It certainly seems to be getting closer. And in this you wrote, “I think it is AGI, seriously. Try asking it lots of questions, and then ask yourself: just how much smarter was I expecting AGI to be? As I’ve argued in the past, AGI, however you define it, is not much of a social event per se. It still will take us a long time to use it properly. Benchmarks, benchmarks, blah blah blah. Maybe AGI is like porn — I know it when I see it. And I’ve seen it.” I thought that was really well done, Tyler. Anything you want to amplify on that?

Tyler Cowen (12:29):

Look, if I ask at economics questions and I'm trained as an economist, it beats me. So I don't care if other people don't call it AGI, but one of the original definitions of AGI was that it would beat most experts most of the time on most matters, say 90% or above, and we're there. So people keep on shifting the goalposts. They'll say, well, sometimes it hallucinates or it's not very good at playing tic-tac toe, or there's always another complaint. Those are not irrelevant, but I'll just say, sit down, have someone write at a test of 20 questions, you're a PhD, you take the test, let o3 take the test, then have someone grade, see how you've done, then form your opinion. That's my suggestion.

Eric Topol (13:16):

I think it's pretty practical. I mean, enough with the Turing test, I mean, we've had that Turing test for decades, and I think the way you described it is a little more practical and meaningful these days. But its capabilities to me at least, are still beyond belief eke out of current, not just the large language models, but large reasoning models. And so, it's just gotten to a point where and it's accelerating, every week there's so many other, the competition is good for taking it to the next level.

Tyler Cowen (13:50):

It can do tasks and it self improves. So o3-pro will be out in a few weeks. It may be out by the time you're hearing this. I think that's obviously going to be better than just pure o3. And then GPT-5 people have said it will be this summer. So every few months there are major advances and there's no sign of those stopping.

Eric Topol (14:12):

Absolutely. Now, of course, you've been likened to “Treat Tyler like a really good GPT” that is because you're this information meister. What do you ask the man who you can ask anything? That's kind of what we have when we can go to any one of these sites and start our prompts, whatever. So it's kind of funny in some ways you might've annotated this with your quest for knowledge.

Tyler Cowen (14:44):

Well, I feel I understand the thing better than most people do for that reason, but it's not entirely encouraging to me personally, selfishly to be described that way, whether or not it's accurate. It just means I have a lot more new competition.

Eric Topol (14:59):

Well, I love this one. “I'm not very interested in the meaning of life, but I'm very interested in collecting information on what other people think is the meaning of life. And it's not entirely a joke” and that's also what you wrote about in the Free Press thing, that most of the things that are going to be written are going to be better AI in the media and that we should be writing books for the AI that's going to ingest them. How do you see this human AI interface growing or moving?

Tyler Cowen (15:30):

The AI is your smartest reader. It's your most sympathetic reader. It will remember what you tell it. So I think humans should sit down and ask, what does the AI need to know? And also, what is it that I know that's not on the historical record anywhere? That's not just repetition if I put it down, say on the internet. So there's no point in writing repetitions anymore because the AI already knows those things. So the value of what you'd call broadly, memoir, biography, anecdote, you could say secrets. It's now much higher. And the value of repeating basic truths, which by the way, I love as an economist, to be clear, like free trade, tariffs are usually bad, those are basic truths. But just repeating that people will be going to the AI and saying it again won't make the AI any better. So everything you write or podcast, you should have this point in mind.

Eric Topol (16:26):

So you obviously have all throughout your life in reading lots of books. Will your practice still be to do the primary reading of the book, or will you then go to o3 or whatever or the other way around?

Tyler Cowen (16:42):

I've become fussier about my reading. So I'll pick up a book and start and then start asking o3 or other models questions about the book. So it's like I get a customized version of the book I want, but I'm also reading somewhat more fiction. Now, AI might in time become very good at fiction, but we're not there now. So fiction is more special. It's becoming more human, and I should read more of it, and I'm doing that.

Eric Topol (17:10):

Yeah, no, that's great. Now, over the weekend, there was a lot of hubbub about Bill Gates saying that we won't need doctors in the next 10 years because of AI. What are your thoughts about that?

Tyler Cowen (17:22):

Well, that's wrong as stated, but he may have put it in a more complex way. He's a very smart guy of course. AI already does better diagnosis on humans than medical doctors. Not by a lot, but by somewhat. And that's free and that's great, but if you need brain surgery for some while, you still need the human doctor. So human doctors will need to adjust. And if someone imagines that at some point robots do the brain surgery better, well fine. But I'm not convinced that's within the next 10 years. That would surprise me.

Eric Topol (17:55):

So to that point, recently, a colleague of mine wrote an op-ed in the New York Times about six studies comparing AI alone versus doctors with AI. And in all six studies, the AI did better than the doctors who had access to AI. Now, you could interpret that as, well they don't know how to use AI. They have automation bias or that is true. What do you think?

Tyler Cowen (18:27):

It's probably true, but I would add as an interpretation, the value of meta rationality has gone up. So to date, we have not selected doctors for their ability to work with AI, obviously, but some doctors have the personal quality, it's quite distinct from intelligence, but if just knowing when they should defer to someone or something else, and those doctors and researchers will become much more valuable. They're sufficiently modest to defer to the AI and have some judgment as to when they should do that. That's now a super important quality. Over time, I hope our doctors have much more of that. They are selected on that basis, and then that result won't be true anymore.

Eric Topol (19:07):

So obviously you would qualify. There's a spectrum here. The AI enthusiasts, you and I are both in that group, and then there's the doomsayers and there's somewhere middle ground, of course, where people are trying to see the right balance. Are there concerns about AI, I mean anything about that, how it's moving forward that you're worried about?

Tyler Cowen (19:39):

Well, any change that big one should have very real concerns. Maybe our biggest concern is that we're not sure what our biggest concern should be. One simple effect that I see coming soon is it will devalue the status of a lot of our intellectuals and what's called our chattering class. A lot of its people like us, we won't seem so impressive anymore. Now, that's not the end of the world for everyone as a whole, but if you ask, what does it mean for society to have the status of its elites so punctured? At a time when we have some, I would say very negative forces attacking those elites in other ways, that to me is very concerning.

Eric Topol (20:25):

Do you think that although we've seen what's happening with the current administration with respect to the tariffs, and we've already talked about the effects on science funding, do you see this as a short-term hit that will eventually prevail? Do you see them selectively supporting AI efforts and finding the right balance with the tech companies to support them and the competition that exists globally with China and whatnot? How are we going to get forward and what some people consider pretty dark times, which is of course, so seemingly at odds with the most extraordinary times of human support with AI?

Tyler Cowen (21:16):

Well, the Trump people are very pro AI. I think that's one of the good things about the administration, much pro AI and more interested than were the Biden people. The Biden people, you could say they were interested, but they feared it would destroy the whole world, and they wanted to choke and throttle it in a variety of ways. So I think there's a great number of issues where the Trump people have gone very badly wrong, but at least so far AI's not one of them. I'd give them there like an A or A+ so far. We'll see, right?

Eric Topol (21:44):

Yeah. As you've seen, we still have some of these companies in some kind of a hot seat like Meta and Google regarding their monopolies, and we saw how some of the tech leaders, not all of them, became very supportive, potentially you could interpret that for their own interests. They wanted to give money to the inauguration and also get favor curry some political favor. But I haven't yet seen the commitment to support AI, talk about a golden age for the United States because so much of this is really centered here and some of the great minds that are helping to drive the AI and these models. But I wonder if there's more that can be done so that we continue to lead in this space.

Tyler Cowen (22:45):

There's a number of issues here. The first is Trump administration policy toward the FTC, I think has not been wonderful. They appointed someone who seems like would be more appropriate for a democratic or more left-leaning administration. But if you look at the people in the Office of Science and Technology Policy in the White House, they're excellent, and there's always different forces in any administration. But again, so far so good. I don't think they should continue the antitrust suit against Google that is looking like it's going against Google, but that's not really the Trump administration, that's the judiciary, and that's been underway for quite some while. So with Trump, it's always very hard to predict. The lack of predictability, I would say, is itself a big problem. But again, if you're looking for one area where it's good, that would be my pick.

Eric Topol (23:35):

Yeah, well, I would agree with that for sure. I just want to see more evidence that we capitalize on the opportunities here and don't let down. I mean, do you think outlawing selling the Nvidia chips to China is the way to do this? It seems like that hurts Nvidia and isn't China going to get whatever they want anyway?

Tyler Cowen (24:02):

That restriction, I favored when it was put in. I'm now of the view that it has not proved useful. And if you look at how many of those chips get sold, say to Malaysia, which is not a top AI performer, one strongly suspects, they end up going to China. China is incentivized to develop its own high-quality chips and be fully independent of Western supply lines. So I think it's not worked out well.

Eric Topol (24:29):

Yeah, no, I see that since you've written so much about this, it's good to get your views because I share those views and you know a lot more about this than I would, but it seems like whether it's Malaysia or other channels, they're going to get the Blackwell chips that they want. And it seems like this is almost like during Covid, how you would close down foreign travel. It's like it doesn't really work that well. There's a big world out there, right?

Tyler Cowen (25:01):

It’s an interesting question. What kind of timing do you want for when both America and China get super powerful AI? And I don't think you actually want only America to have it. It's a bit like nuclear weapons, but you don't want China to have it first. So you want some kind of staggered sequence where we're always a bit ahead of them, but they also maybe are constraining us a bit. I hope we're on track to get that, but I really, really don't want China to have it first.

Eric Topol (25:31):

Yeah, I mean I think there's, as you're pointing out aptly is a healthy managed competition and that if we can keep that lead there, it is good for both and it's good for the world ideally. But getting back, is there anything you're worried about in AI? I mean because I know you're upbeat about its net effective, and we've already talked about amazing potential for efficiency, productivity. It basically upends a lot of economic models of the past, right?

Tyler Cowen (26:04):

Yes. I think it changes or will change so many parts of life. Again, it's a bit difficult to specify worries, but how we think of ourselves as humans, how we think of our gods, our religions, I feel all that will be different. If you imagine trying to predict the effects of the printing press after Gutenberg, that would've been nearly impossible to do. I think we're all very glad we got the printing press, but you would not say all of it went well. It's not that you would blame the printing press for those subsequent wars, but it was disruptive to the earlier political equilibrium. I think we need to take great care to do it better this time. AI in different forms will be weaponized. There's great potential for destruction there and evil people will use it. So of course, we need to be very much concerned.

Eric Topol (26:54):

And there's obviously many of these companies have ways to try to have efforts to anticipate that. That is alignments and various safety type parallel efforts like Ilya did when he moved out of OpenAI and others. Is that an important part of each of these big efforts, whether it's OpenAI, Google, or the rest of them anthropic that they put in resources to keep things from going off the tracks?

Tyler Cowen (27:34):

That's good and it's important, but I think it's also of limited value because the more we learn how to control AI systems directly, the bad guys will have similar lessons, and they will use alignment possibly to make their AIs bad and worse and that it obeys them. So yeah, I'd rather the good guys make progress on what they're trying to do, but don't think it's going to solve the problem. It creates new problems as well.

Eric Topol (28:04):

So because of AI, do you think you'll write any more books in the future?

Tyler Cowen (28:11):

I'm writing a book right now. I suspect it will be my last. That book, its title is Mentors. It's about how to mentor individuals and what do the social sciences know about mentoring. My view is that even if the AI could write the book better than I can, that people actually want to read a book like that from a human. I could be wrong, but I think we should in the future, restrict ourselves to books that are better by a human. I will write every day for the rest of my life, but I'm not sure that books make sense at the current moment.

Eric Topol (28:41):

Yeah, that's a really important point, and I understand that completely. Now, when you write for the Free Press, which will be besides the Conversations with Tyler podcast and the Marginal Revolution, what kind of things will you be writing about in the Free Press?

Tyler Cowen (28:56):

Well, I just submitted a piece. It's a defense of elitism. So the problem with our elites is that they have not been elitist enough and have not adhered strictly enough to the scientific method. So it's a very simple point. I think to you it would be pretty obvious, but it needs to be said. It's not out there enough in the debate that yes, sometimes the elites have truly and badly let us down, but the answer is not to reject elitism per se, but to impose higher elitist standards on our sometimes supposed elites. So that's the piece I just sent in. It's coming out soon and should be out by the time anyone hears this.

Eric Topol (29:33):

Well, I look forward to reading that. So besides a polymath, you might be my favorite polymath, Tyler you didn't know that. Also, you're a futurist because when you have that much information ingested, and now of course with a super performance of AI to help, it really does help to try to predict where we're headed. Have I missed anything in this short conversation that you think we should touch on?

Tyler Cowen (30:07):

Well, I'll touch on a great interest of yours. I like your new book very much. I think over the course of the next 40 years working with AI, we will beat back essentially every malady that kills people. It doesn't mean you live forever. Many, many more people will simply die of what we now call old age. There's different theories as to what that means. I don't have a lot of expertise in that, but the actual things people are dying from will be greatly postponed. And if you have a kid today to think that kid might expect to live to be 97 or even older, that to me is extremely plausible.

Tyler Cowen (30:45):

I won't be around to see it, but that's a phenomenal development for human beings.

Eric Topol (30:50):

I share that with you. I'm sad that I won't be around to see it, but exactly as you've outlined, the fact that we're going to be able to have a huge impact on particularly the age-related diseases, but also as you touched on the genetic diseases with genome editing and many other, I think, abilities that we have now controlling the immune system, I mean a central part of how we get into trouble with diseases. So I couldn't agree with you more, and that's a really good note to finish on because so many of the things that we have discussed today, we share similar views and we come at it from totally different worlds. The economist that has a very wide-angle lens, and I guess you'd say the physician who has a more narrow lens aperture. But thank you so much, Tyler for joining me today.

Tyler Cowen (31:48):

My pleasure. Let me close by telling you some good news. I have AI friends who think you and I, I’m 63 will be around to see that, I don't agree with them they don't convince me, but there are smart people who think the benefits from this will come quite soon.

Eric Topol (32:03):

I sure hope they're right.

Tyler Cowen (32:05):

Yes.

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

SUPER AGERS, my new book, was released on May 6th. It’s about extending our healthspan, and I introduce 2 of my patients (one below, Mrs. L.R.) as exemplars to learn from. This potential to prevent the 3 major age-related diseases would not be possible without the jumps in the science of aging and multimodal A.I.

My op-ed preview of the book was published in The NY Times last week. Here’s a gift link. I did a podcast with Mel Robbins on the book here. Here’s my publisher ‘s (Simon and Schuster) site for the book. If you’re interested in the audio book, I am the reader (first time I have done this, quite an experience!)

The book was reviewed in WSJ. Here’s a gift link

There have been many pieces written about it. Here’s a gift link to the one in the Wall Street Journal and here for the one in the New York Times .

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

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In our divided world we face or avoid conflicts on a frequent basis. I turned to Bob Bordone and Joel Salinas to find out the best strategies to deal with these, including having them take on a mock conflict between each other on the merits of Covid research.

Audio file

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Transcript with Audio Links

Eric Topol (00:06):

Well, hello. It's Eric Topol with Ground Truths, and we're going to get into a new book called Conflict Resilience: Negotiating Disagreement Without Giving Up or Giving In, and we're lucky to have its two authors, Bob Bordone, who is a Senior Fellow at Harvard Law School, and Joel Salinas, who is a physician, neurologist, a clinician scientist at NYU. So welcome both Bob and Joel.

Bob Bordone and Joel Salinas (00:34):

Thank you for having us. Yeah, looking forward to the conversation.

Eric Topol (00:37):

Yeah. So first, how did you guys get together? This is a pretty diverse, you got law and medicine, usually they don't talk to each other very much.

Bob Bordone (00:46):

Well, we were very fortunate. I mean, we basically were friends, but part of that friendship, I think emerged from work that I do around conflict issues in the Mass General system and then just the larger, bigger Mass General, Harvard community. Yeah, so this began really as a friendship where we were each swimming in very different waters, but then as we would start to talk, we realized there was a lot of connection and maybe the possibility to bring two different disciplines together in a way that might be practically useful and make an impact. And even when we started writing this, which was during Covid, what seemed to be some pretty polarizing times that were unlikely to resolve by the time the book would come out.

Eric Topol (01:44):

Yeah, well you sure hit it with the divisiveness and the polarized world that we live in is perhaps worse than ever, certainly in all my years, and probably long before then as well. So this topic of resilience, it's a very interesting concept because some people might think of resilience as just being tough. So go into a conflict and just go heavy tough. That obviously is not what you're writing about. And I guess maybe we can start off, what was the goal here? Obviously, there's other books that have addressed this topic, I'm sure, but yours is somewhat unique in many respects because it brings in the science of it and many strategies perhaps that have never been developed. But when you got together, what was the mission that you set out to do?

Joel Salinas (02:38):

Yeah, well maybe I can start out and then you can add on. So my research has been all around understanding how social relationships influenced brain health, and one of the things that I was seeing was social isolation and loneliness had been steadily increasing. Want to figure out what kind of interventions or what are the factors that are involved here? And I think one of the things that has stood out is just the difficulty with being able to navigate conflict in different contexts. And so, the idea around conflict resilience is really, even though there's been lots of books on what to say and what specific tactics to use, I think that there was this skillset around just being able to sit with the discomfort of that disagreement, which will ultimately help make it much more useful to take on those tactics. One way to think about it, if it's like all these tactics are like learning how to cook with a set of recipes in the kitchen, what we're really proposing here is that you also need to be able to stand the heat of the kitchen to even be able to cook.

Eric Topol (03:47):

Okay. Go ahead, Bob.

Bob Bordone (03:49):

Yeah, and I would say I was starting to write about my first kind of piece on this topic where I use the word conflict resilience was in 2018, and it really came from an observed dynamic that I was seeing in my teaching of Harvard Law School students. I was on the admissions committee, I'd been on the admissions committee for many years. I knew that we worked very hard and were quite successful in fact, at bringing together a very diverse student body, including politically. And people sometimes maybe think of elite law schools as being very progressive. But Harvard Law School, the biggest student organization is actually the Federalists, which is the conservative students. And despite that effort, what I noticed in the classroom was a reduction in conversation, diversity of viewpoint across the board, interesting classrooms became boring. And even though I was teaching around conflict and negotiation and difficult conversations, I would read in students' journals things like, I want to avoid conflict or I don't want to get into it.

Bob Bordone (04:59):

And so, it occurred to me that quite a part, as Joel said, from any skills, if we don't develop this capacity to sit with disagreement, then we will never get to problem solving. I'm in favor of problem solving. But this paper on conflict resilience, its original title was called Against Problem Solving. Mostly because I thought that if we had opened the possibility of problem solving as a precondition for entering the room, then we might never enter the room, particularly if we've told the demonized and dehumanized story about them. And so, that somehow we had to make the case that sitting with the discomfort of the disagreement, even if it didn't mean problem solving, although we hope for that, even if we didn't mean that it was worthwhile and it was important. And so, part of what was really attractive to me about joining up with Joel is that he just brought all of this brain science aspect to it that I had this kind of teaching and kind of academic in the negotiation and dispute resolution research experience, but couldn't bring to bear the kind of brain science parts of, well, what is going on in our brain when we do want to run or when we get into that really unproductive battle.

Eric Topol (06:27):

Yeah, I agree that the unique part here is that whole scaffolding with the neuroscience, the behavioral science, and those five Fs that you mentioned. You alluded to fight, flight, freeze, fawn, or fester. Yeah, so avoidance of conflict has kind of been the default for many people now because we have political divides, we have anti-science versus pro-science divides and on and on. There's a quote in the book that I thought we'd start off with because it really lays the groundwork from you both. “The biggest hidden barrier to being conflict resilient stems from the inability or unwillingness to face and sit with our own internal conflicts - the negotiations between our divided and sometimes contradictory “selves.” Even more surprising is that although there are dozens of self-help books on negotiation and conflict resolution, almost none of them spend any meaningful time on this critical intrapersonal barrier to handing conflict.” So maybe Joel, maybe start you off here. I guess you were bullied as a kid, and maybe that gives you a little background here. Joel, tell us about that if you would.

Bob Bordone (07:46):

Hey, Eric. On our bad days sometimes I probably inadvertently bully Joel still today, but he's pretty resilient now.

Joel Salinas (07:53):

Yeah, I'm a Teflon. So I think I am generally conflict of what an individual, and I think a lot of listeners and viewers can relate with that experience. And I think that also kind of speaks to some of the neuroscience that comes into this, which is that our brain has really evolved to be a fortune telling machine. It takes all of our past experiences, turns them into memories, and then makes projections about what's going to happen. And this projection or prediction of what's going to happen might as well be reality for our brain's sake. And so, if we had really negative experiences with conflict in the past growing up, whether through our families or the schoolyard or others, there'll be likely a very negative charge of negative emotional charge that comes with that. And what that does is that it increases the chances that you'll trigger this system for salience and arousal, which then sets off the alarms essentially in your body that then creates these fight or flight type responses where you're more likely to fall back on these really reflexive behaviors to make the bad thing less bad.

Joel Salinas (09:08):

And when you do that, whether it's through avoiding or to blowing through conflict like a battering ram that then trains your brain to assign some kind of a reward using the orbital frontal cortex, a system that kind of keeps tabs over how much reward you get for a behavior, it makes it much more likely that you'll do it again. And so, we from a very young age, develop a propensity to either avoid conflict or tackle it. And it varies depending on the context and how you're feeling, but it just makes it much, much harder to be able to bring on a much more thoughtful and deliberative approach to conflict.

Eric Topol (09:49):

Yeah, I mean, I think one of the salient points is that avoiding the conflict can make things worse. And as you described that it's not, I would've thought that there are some people who are just innately gifted to being diplomatic and artful about having to deal with the conflict issue and others, there's just no hope. But in fact, it can be acquired. And you alluded to this kind of neuroplasticity, the brain and you advocate for chair work. Can you tell us about chair work, because that's something I wouldn't have thought would help in this manner.

Bob Bordone (10:30):

Sure. I mean, I'll say a little bit work about that. A big part of this chair work idea, frankly, is influenced by work in internal family systems. And I was very fortunate early in my career, even though I was at teaching at law school to start partnering with some folks who did IFS work, they call it peace work often. But the chair work is really identifying some of these conflicted sides of ourself, right? The side of ourself that maybe feels like it's important and okay to raise this issue because it's something that matters to me and maybe the side of ourself that feels like it's pointless and it will hurt the relationship and maybe the side of ourself that's fearful and to name each of them. And then to actually give each in preparation a physical chair where we sit in that chair and give voice to each of those sides.

Bob Bordone (11:32):

And I'm imagining that at least some people listening to this will say, this sounds very hokey, and does he really mean going to the chairs? And the answer is, yes, I do mean that because there is something about the physicality of it that forces you to give voice to something that is true and real in you. And the chair work is very helpful to set up what an opening might be into a hard conversation, meaning that all of the chairs are real and authentic and okay, they're worthy of getting some voice. So as someone who teaches in a law school, it's all about advocacy. And you would find students who would be very good at advocating on behalf of a client would be incredibly poor at advocating on behalf of themself. And so, separating out the side that maybe has a little bit of feeling, it's selfish, but actually giving it a legitimate voice, help them when they get to the table to be able to say, I'm worried about this, or I realize I may be wrong about this, or it might be upsetting. And also, it's important and deserves to be heard because one of the things around avoidance is we often do avoidance in service of preserving the relationship or not disrupting. And we do maybe preserve the relationship for the time being of the person across the table, except we go home and there's still the side of us that is not feeling good about it, and the person we're not preserving the relationship with is that side, then we just get to have a sleepless night. And so, that's really the kind of idea behind the chair work.

Eric Topol (13:22):

That's helpful, Bob. I guess managing conflict, of course, I think we know you don't get emotional. Okay, sure. But yeah, there's three parts of that, three components, self-awareness. We've been talking about that deep listening, which of course when you're engaging in a discussion that's potentially leading to escalation of a conflict or the amplification that is really important. And then effective assertion. Now, that's where it seems to me things fall apart. If you're making effective assertion, then everything kind of blows up. So tell us about how you can be assertive and still, you're not trying to win the argument. I get that, but how can you be assertive and still come out in a positive way?

Joel Salinas (14:16):

Maybe I can start, Bob.

Joel Salinas (14:19):

I think one of the things that really is a good predictor of how effective you'll be at effective assertion is how good you were at the deep listening part. So the more genuine you are and curious you are about the perspective of the other person, really understanding what are the set of facts, experiences, beliefs that eventually lead up to that headline of what their position is or what their interests are. The better you'll be able articulate your own perspective while still engaging in the conversation. And the other thing that's really important here is that in that listening piece, it's really essential to be able to bring in tenets of really great listening that includes eliminating distractions, both external and internal. It involves having a nonjudgmental position toward the other person and being able to reflect an understanding of what the other person is saying. But all of that does not mean that you are endorsing their point of view. And I think that's really essential. It's really about getting as clear as you can about where the other person is coming from. So that way when you have an opportunity to share your perspective, you're able to really speak to the concerns of the other person and your own.

Eric Topol (15:46):

Yeah. Well, in reading the book, it took me, interestingly to an evening discussion I had with a very close friend.

Eric Topol (15:56):

And he was saying, we do need a randomized trial of the measles vaccine, MMR for autism. And I said, what? And I started thinking about, well, I'm going to hear him out because there's so much evidence now that you would think this has been totally debunked. And his view is, well, it can't hurt. And I'm thinking, well, so in that discussion, a lot of these points that you've been raising help me to come not to a point where basically I was trying to put a bow on it, as you said, or trying to externalize or abstract it. But to have a happy ending with him about this saying, okay, well it's never going to get done, but if you want to get it, I'm supportive of that. We don't do enough of this. I had to listen to what he had to say. I had to deal with my own confirmation biases and not get emotional and all that stuff, right. Now, I'd like the two of you to role play on something like that if you would. And let me just give you an example. Maybe you can run with it. Let's go to Covid, okay?

Eric Topol (17:14):

So one of you will take the side that we shouldn't do any more Covid research because the pandemic is over and we need to be efficient and not use these funds for other things. Covid is over, Long Covid is a hoax, and the other person will take the side that, no, this is a really big deal because Covid has not gone away and there's still a endemic of the virus, Long Covid in millions of people. Who wants to take away the funds? Would that be you, Bob?

Bob Bordone (17:52):

As a lawyer, I am happy to take any side.

Eric Topol (17:55):

Okay. You are the one to be on that side. Okay. And Joel, you are going to be the pro science side, if you will. Can you start that argument?

Bob Bordone (18:05):

Eric, can I make a suggestion? Yeah, but I'm happy to. It might be fun if one of us tries to be a person who hasn't read the book and the other person maybe tries to actually model the skills. What do you think about that?

Eric Topol (18:18):

Sure. Yeah, that’s fine.

Joel Salinas (18:19):

Bob, I'll take on the unskilled position.

Bob Bordone (18:22):

Okay, fine.

Joel Salinas (18:25):

All right. So Bob, you know what? I keep hearing about people wanting to cut Covid funding and just really, I just can't believe it. It just makes me want to throw up because there's such an important need to do this research. It's just critical to understand the long-term effects of it, and Covid even gone yet. So I just can't believe that people would even want to cut this research at all.

Bob Bordone (18:50):

Well, first of all, it sounds like you're stunned and surprised by this. Am I right about that?

Joel Salinas (18:56):

Yeah, I'm beyond stunned. I'm revolted by it.

Bob Bordone (19:01):

So you're pretty angry about it. And I'm curious if I can ask you, you said that the disease is still going on, and of course Covid still exists. I am curious from your perspective, what do you think the benefits of spending lots and lots of money on the diseases at this point, since it's not at that level where it's killing a lot of people?

Joel Salinas (19:30):

Well, I think that it is killing a lot of people. Still, the disease hasn't gone away and it has a huge impact on health. I think we're still feeling the impacts on that. So I think that being able to understand what the impact does require funding to be able to do the research. And if we don't do that research, then we don't understand what interventions there can be.

Bob Bordone (19:51):

And what are the impacts? I mean, clearly there's impacts of the pandemic broadly in our society, but what are the kinds of health impacts from your perspective that research would be helpful to from a medical perspective?

Joel Salinas (20:05):

Well, for sure it impacts cognition. We have people talking about brain fog and Long Covid, and that has a real societal impact on productivity and people's ability to engage in life. It affects people's mood. And then you've got the people who have respiratory symptoms from Covid that have continued to gone on, and that decreases their ability to do their day-to-day things. It's a real societal impact.

Bob Bordone (20:28):

And how would you think about balancing whatever impact Covid has from all of the other funding choices that need to be made given a shrinking research pool for funds?

Joel Salinas (20:44):

I don't know. I mean, I think it's an important priority, and I know that there's a lot of other priorities. I think it needs to be weighed against a lot of other big programs that are out there. I just want to make sure that it doesn't go away because it needs to happen.

Bob Bordone (20:56):

Yeah. No, it's helpful to hear that. And if we had more time, I'd ask you some more questions. I mean, one thing that, as I think about this is given just the number of priorities out there, I worry that because Covid was in the press so much and is so politicized that we overweight the importance of money in that direction. And I would say that there's probably other things if we have a fixed set of money that kills a lot more people and has a lot more health impact. And so, I'd rather see the funds get placed there than just satisfy some kind of highly salient political issue.

Joel Salinas (21:40):

And I just want to make sure that the funding happens. I mean, it should be to a level that it makes sense to continue the funding so that we get good results from it, that it can be applied. But yeah, I guess you're right that it needs to be weighed against other research priorities. I mean, that's a whole other topic that gets me upset, but I think I just want to make sure that this funding doesn't go away.

Bob Bordone (22:03):

Yeah. So it sounds like for you, the concern is less about reduction and more about moving it to zero?

Joel Salinas (22:12):

I think so, yeah.

Bob Bordone (22:13):

And if it did move to zero, what is the thing you'd be most worried about?

Joel Salinas (22:18):

I think we would lose out on this really unique opportunity after all these people had been affected by this condition to understand the long-term effects. So that way, if there's another resurgence, we'll understand what can we do about it to mitigate those effects. I mean, we're still trying to figure out what the effects of a lockdown were on people. I think that's something that needs to be better understood.

Bob Bordone (22:40):

So for you, the research is very forward looking about future pandemics that might come up.

Joel Salinas (22:46):

Absolutely.

Bob Bordone (22:47):

And that might be something that I'd be more interested in than how can we prevent future pandemics than I would worrying about. I mean, it's very regrettable what has happened to this set of people who have Long Covid, of course. I just think that that has happened, and I would almost rather see the funds move in the direction of how do we prevent another pandemic than how do we worry about a relatively small set of people, although it's tragic on them, a relatively small set of people who may still suffer those benefits.

Joel Salinas (23:26):

Yeah, I think we do want to focus on the prevention, definitely. I still just don't want to lose sight of making sure that we're getting the research done that needs to happen.

Bob Bordone (23:38):

Should we cut?

Eric Topol (23:39):

That's helpful. These are two experts in conflict resilience here. I mean, the only thing I'd add is that Long Covid is affecting millions of Americans, perhaps as many as 60 million people around the world, and we have no treatment for it. So it's a big deal.

Bob Bordone (23:56):

I just want to say for the record, I was just being an actor there.

Eric Topol (24:03):

Yeah, that’s okay.

Bob Bordone (24:04):

I don't even know if my arguments on the other side were making sense, but I was trying.

Eric Topol (24:08):

I think you did a good job. I think both of you did a good job. I think the point here is that you were able to have a civil discussion, make your points, I forced you into it. You couldn't avoid it. You're in touch, obviously with your own innate issues. You kind of really emphasize that throughout the book, which is you got to be in touch with yourself, not just about your priors, but also your current, what you're feeling, your posture, your heart rate, all these other physical things. So you really got us queued into what's important when you're having a discussion that could lead to, it could exacerbate the conflict rather than help come to a happy mid stance or where both people feel that they've expressed themselves adequately. I really love the Frederick Douglass quote in your book, “if there is no struggle, there is no progress. Those who profess to favor freedom and yet depreciate agitation…want crops without plowing up the ground. They want rain without thunder and lightning. They want the ocean without the awful roar of its many waters.” I think that is so rich. And before we wrap up, I just want to get your overall thoughts. What haven't we touched on in our brief conversation about the topic, about the book that we should before we close today? Maybe start with you, Bob.

Bob Bordone (25:53):

Yeah, I mean, in some sense, I think it connects to exactly that quote, which is that without conflict, we are not going to get the kind of changes and dynamism we would want in our organizations, whether it's a medical center, a country, a family, but also without the conflict, we don't get the deeper connection that is possible because it's not until the first, no, that all of the yeses actually have the meaning that they should. And so, even though it seems scary to go into conflict, what I would say is it offers opportunities maybe for agreement, but if not for agreement, for a deeper kind of more authentic and real relationship. And I would just say for me, part of this is inviting people to reframe the way they think about what conflict can do in their lives.

Joel Salinas (26:58):

Yeah. I think if there's one thing that listeners or viewers take from this is awareness is more than half the battle. So just really taking the time to become more aware of how you react to different disagreements with different conflicts, how you're responding to it physically and mentally, and what specific patterns might emerge in terms of whether it's with colleagues, with people with authority, with family members. And I think that alone begins to get you to pay more attention about how you can be more deliberate in your responses. And ideally, you can try out some of the skills from the book with those disagreements that are a little less stressful for you. Just like when you go to the gym, you don't start out by lifting the heaviest weights. You start out by getting the reps down with the good form, and then you build that muscle. And similar with building the brain programming wiring around it is to start low and build up from there.

Eric Topol (27:57):

Yeah. Well, I think what you have put forth in the book will go down anchoring such an important problem. It's magnified now than more than ever. People are socially isolated, not just in the pandemic, but post pandemic and the divisiveness is profound. So hopefully the tips that you've provided, the science behind it, the practical ways to navigate and deal with this will help people as we go forward. So thank you both for the work you did in putting together the book, and hopefully some of our listeners or viewers will use these tools in the future and will have much better exchanges with others who have different views, different what might be considered adversarial perspective, whatever. So thank you very much for joining today.

Joel Salinas (28:58):

Well, thank you.

Bob Bordone (28:59):

Thank you for having us. It's been a delight.

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

As you can imagine, I’m excited to get my new book out on May 6th. It’s about extended our healthspan, and I introduce 2 of my patients (one below, Mrs. L.R.) as exemplars to learn from.

My op-ed preview of the book was published in The NY Times last week. Here’s a gift link. I did a podcast with Mel Robbins on the book here. Here’s my publisher ‘s (Simon and Schuster) site for the book. If you’re interested in the audio book, I am the reader (first time I have done this, quite an experience!)

Here’s the back cover to give you an idea of what some people had to say about it.

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“Eventually, my dream would be to simulate a virtual cell.”—Demis Hassabis

The aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative

The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.

TRANSCRIPT WITH LINKS TO AUDIO

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford’s Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve.

Steve Quake (00:42):

Thanks, Eric. It's great to be here.

Charlotte Bunne:

Thanks for having me.

Eric Topol (00:45):

Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper?

Steve Quake (01:33):

We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article.

Eric Topol (02:02):

And Charlotte, how did you get to be drafting the paper?

Charlotte Bunne (02:09):

So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started.

Eric Topol (02:29):

And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there.

Steve Quake (03:19):

Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that.

Eric Topol (04:09):

And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell?

Charlotte Bunne (05:41):

So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect.

Charlotte Bunne (06:34):

Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection.

Eric Topol (07:41):

Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this?

Steve Quake (09:06):

Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there.

Steve Quake (10:14):

And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological information there, and it learned a lot about the relationships between cell type and lineage. And that emerged from that high dimensional structure, which was super pleasing to us and really, I mean for me personally gave me the confidence to say this stuff is going to work out. There is a future for the virtual cell. It's not some made up thing. There is real substance there and this is worth investing an enormous amount of CZIs resources in going forward and trying to rally the community around as a project.

Eric Topol (11:04):

Well yeah, the premise here is that there is a language of life, and you just made a good case that there is if you can predict, if you can query, if you can generate like that. It is reminiscent of the famous Go game of Lee Sedol, that world champion and how the machine came up with a move (Move 37) many, many years ago that no human would've anticipated and I think that's what you're getting at. And the ability for inference and reason now to add to this. So Charlotte, one of the things of course is about, well there's two terms in here that are unfamiliar to many of the listeners or viewers of this podcast, universal representations (UR) and virtual instrument (VIs) that you make a pretty significant part of how you are going about this virtual cell model. So could you describe that and also the embeddings as part of the universal representation (UR) because I think embeddings, or these meaningful relationships are key to what Steve was just talking about.

Charlotte Bunne (12:25):

Yes. So in order to somewhat leverage very different modalities in order to leverage basically modalities that will take measurements across different scales, like the idea is that we have large, may it be transformer models that might be very different. If I have imaging data, I have a vision transformer, if I have a text data, I have large language models that are designed of course for DNA then they have a very wide context and so on and so forth. But the idea is somewhat that we have models that are connected through the scales of biology because those scales we know. We know which components are somewhat involved or in measurements that are happening upstream. So we have the somewhat interconnection or very large model that will be trained on many different data and we have this internal model representation that somewhat capture everything they've seen. And so, this is what we call those universal representation (UR) that will exist across the scales of biology.

Charlotte Bunne (13:22):

And what is great about AI, and so I think this is a bit like a history of AI in short is the ability to predict the last years, the ability to generate, we can generate new hypothesis, we can generate modalities that we are missing. We can potentially generate certain cellular state, molecular state have a certain property, but I think what's really coming is this ability to reason. So we see this in those very large language models, the ability to reason about a hypothesis, how we can test it. So this is what those instruments ultimately need to do. So we need to be able to simulate the change of a perturbation on a cellular phenotype. So on the internal representation, the universal representation of a cell state, we need to simulate the fact the mutation has downstream and how this would propagate in our representations upstream. And we need to build many different type of virtual instruments that allow us to basically design and build all those capabilities that ultimately the AI virtual cell needs to possess that will then allow us to reason, to generate hypothesis, to basically predict the next experiment to conduct to predict the outcome of a perturbation experiment to in silico design, cellular states, molecular states, things like that. And this is why we make the separation between internal representation as well as those instruments that operate on those representations.

Eric Topol (14:47):

Yeah, that's what I really liked is that you basically described the architecture, how you're going to do this. By putting these URs into the VIs, having a decoder and a manipulator and you basically got the idea if you can bring all these different integrations about which of course is pending. Now there are obviously many naysayers here that this is impossible. One of them is this guy, Philip Ball. I don't know if you read the language, How Life Works. Now he's a science journalist and he's a prolific writer. He says, “Comparing life to a machine, a robot, a computer, sells it short. Life is a cascade of processes, each with a distinct integrity and autonomy, the logic of which has no parallel outside the living world.” Is he right? There's no way to model this. It's silly, it's too complex.

Steve Quake (15:50):

We don't know, alright. And it's great that there's naysayers. If everyone agreed this was doable, would it be worth doing? I mean the whole point is to take risks and get out and do something really challenging in the frontier where you don't know the answer. If we knew that it was doable, I wouldn't be interested in doing it. So I personally am happy that there's not a consensus.

Eric Topol (16:16):

Well, I mean to capture people's imagination here, if you're successful and you marshal a global effort, I don't know who's going to pay for it because it's a lot of work coming here going forward. But if you can do it, the question here is right today we talk about, oh let's make an organoid so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever. And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and in silico and you have this virtual twin of a person's cells and their tissue and whatnot. So the opportunity here is, I don't know if people get, this is just extraordinary and quick and cheap if you can get there. And it's such a bold initiative idea, who will pay for this do you think?

Steve Quake (17:08):

Well, CZI is putting an enormous amount of resources into it and it's a major project for us. We have been laying the groundwork for it. We recently put together what I think is if not the largest, one of the largest GPU supercomputer clusters for nonprofit basic science research that came online at the end of last year. And in fact in December we put out an RFA for the scientific community to propose using it to build models. And so we're sharing that resource within the scientific community as I think you appreciate, one of the real challenges in the field has been access to compute resources and industry has it academia at a much lower level. We are able to be somewhere in between, not quite at the level of a private company but the tech company but at a level beyond what most universities are being able to do and we're trying to use that to drive the field forward. We're also planning on launching RFAs we this year to help drive this project forward and funding people globally on that. And we are building a substantial internal effort within CZI to help drive this project forward.

Eric Topol (18:17):

I think it has the looks of the human genome project, which at time as you know when it was originally launched that people thought, oh, this is impossible. And then look what happened. It got done. And now the sequence of genome is just a commodity, very relatively, very inexpensive compared to what it used to be.

Steve Quake (18:36):

I think a lot about those parallels. And I will say one thing, Philip Ball, I will concede him the point, the cells are very complicated. The genome project, I mean the sort of genius there was to turn it from a biology problem to a chemistry problem, there is a test tube with a chemical and it work out the structure of that chemical. And if you can do that, the problem is solved. I think what it means to have the virtual cell is much more complex and ambiguous in terms of defining what it's going to do and when you're done. And so, we have our work cut out for us there to try to do that. And that's why a little bit, I established our North Star and CZI for the next decade as understanding the mysteries of the cell and that word mystery is very important to me. I think the molecules, as you pointed out earlier are understood, genome sequenced, protein structure solved or predicted, we know a lot about the molecules. Those are if not solved problems, pretty close to being solved. And the real mystery is how do they work together to create life in the cell? And that's what we're trying to answer with this virtual cell project.

Eric Topol (19:43):

Yeah, I think another thing that of course is happening concurrently to add the likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks and months. Never. I mean, I have a paper in Science tomorrow coming out summarizing the progress about not just RNA, DNA, ligands. I mean the whole idea, AlphaFold3, but now Boltz and so many others. It's just amazing how fast the torrent of new foundation models. So Charlotte, what do you think accounts for this? This is unprecedented in life science to see foundation models coming out at this clip on evolution on, I mean you name it, design of every different molecule of life or of course in cells included in that. What do you think is going on here?

Charlotte Bunne (20:47):

So on the one hand, of course we benefit profits and inherit from all the tremendous efforts that have been made in the last decades on assembling those data sets that are very, very standardized. CELLxGENE is very somehow AI friendly, as you can say, it is somewhat a platform that is easy to feed into algorithms, but at the same time we actually also see really new building mechanisms, design principles of AI algorithms in itself. So I think we have understood that in order to really make progress, build those systems that work well, we need to build AI tools that are designed for biological data. So to give you an easy example, if I use a large language model on text, it's not going to work out of the box for DNA because we have different reading directions, different context lens and many, many, many, many more.

Charlotte Bunne (21:40):

And if I look at standard computer vision where we can say AI really excels and I'm applying standard computer vision, vision transformers on multiplex images, they're not going to work because normal computer vision architectures, they always expect the same three inputs, RGB, right? In multiplex images, I'm measuring up to 150 proteins potentially in a single experiment, but every study will measure different proteins. So I deal with many different scales like larger scales and I used to attention mechanisms that we have in usual computer vision. Transformers are not going to work anymore, they're not going to scale. And at the same time, I need to be completely flexible in whatever input combination of channel I'm just going to face in this experiment. So this is what we right now did for example, in our very first work, inheriting the design principle that we laid out in the paper AI virtual cell and then come up with new AI architectures that are dealing with these very special requirements that biological data have.

Charlotte Bunne (22:46):

So we have now a lot of computer scientists that work very, very closely have a very good understanding of biologists. Biologists that are getting much and much more into the computer science. So people who are fluent in both languages somewhat, that are able to now build models that are adopted and designed for biological data. And we don't just take basically computer vision architectures that work well on street scenes and try to apply them on biological data. So it's just a very different way of thinking about it, starting constructing basically specialized architectures, besides of course the tremendous data efforts that have happened in the past.

Eric Topol (23:24):

Yeah, and we're not even talking about just sequence because we've also got imaging which has gone through a revolution, be able to image subcellular without having to use any types of stains that would disrupt cells. That's another part of the deep learning era that came along. One thing I thought was fascinating in the paper in Cell you wrote, “For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the dataset used to train ChatGPT.” I mean that's a lot of tokens, that's a lot of stuff, compute resources. It's almost like you're going to need a DeepSeek type of way to get this. I mean not that DeepSeek as its claim to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the human language. That is our language, wouldn't you say?

Steve Quake (24:35):

So Eric, that brings to mind one of my favorite quotes from Sydney Brenner who is such a wit. And in 2000 at the sort of early first flush of success in genomics, he said, biology is drowning in a sea of data and starving for knowledge. A very deep statement, right? And that's a little bit what the motivation was for putting the Short Read Archive statistic into the paper there. And again, for me, part of the value of this endeavor of creating a virtual cell is it's a tool to help us translate data into knowledge.

Eric Topol (25:14):

Yeah, well there's two, I think phenomenal figures in your Cell paper. The first one that kicks across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell. And we'll link that with this in the transcript. And the other thing we'll link is there's a nice Atlantic article, “A Virtual Cell Is a ‘Holy Grail’ of Science. It's Getting Closer.” That might not be quite close as next week or year, but it's getting close and that's good for people who are not well grounded in this because it's much more taken out of the technical realm. This is really exciting. I mean what you're onto here and what's interesting, Steve, since I've known you for so many years earlier in your career you really worked on omics that is being DNA and RNA and in recent times you've made this switch to cells. Is that just because you're trying to anticipate the field or tell us a little bit about your migration.

Steve Quake (26:23):

Yeah, so a big part of my career has been trying to develop new measurement technologies that'll provide insight into biology. And decades ago that was understanding molecules. Now it's understanding more complex biological things like cells and it was like a natural progression. I mean we built the sequencers, sequenced the genomes, done. And it was clear that people were just going to do that at scale then and create lots of data. Hopefully knowledge would get out of that. But for me as an academic, I never thought I'd be in the position I'm in now was put it that way. I just wanted to keep running a small research group. So I realized I would have to get out of the genome thing and find the next frontier and it became this intersection of microfluidics and genomics, which as you know, I spent a lot of time developing microfluidic tools to analyze cells and try to do single cell biology to understand their heterogeneity. And that through a winding path led me to all these cell atlases and to where we are now.

Eric Topol (27:26):

Well, we're fortunate for that and also with your work with CZI to help propel that forward and I think it sounds like we're going to need a lot of help to get this thing done. Now Charlotte, as a computer scientist now at EPFL, what are you going to do to keep working on this and what's your career advice for people in computer science who have an interest in digital biology?

Charlotte Bunne (27:51):

So I work in particular on the prospect of using this to build diagnostic tools and to make diagnostics in the clinic easier because ultimately we have somewhat limited capabilities in the hospital to run deep omics, but the idea of being able to somewhat map with a cheaper and lighter modality or somewhat diagnostic test into something much richer because a model has been seeing all those different data and can basically contextualize it. It's very interesting. We've seen all those pathology foundation models. If I can always run an H&E, but then decide when to run deeper diagnostics to have a better or more accurate prediction, that is very powerful and it's ultimately reducing the costs, but the precision that we have in hospitals. So my faculty position right now is co-located between the School of Life Sciences, School of Computer Science. So I have a dual affiliation and I'm affiliated to the hospitals to actually make this possible and as a career advice, I think don't be shy and stick to your discipline.

Charlotte Bunne (28:56):

I have a bachelor's in biology, but I never only did biology. I have a PhD in computer science, which you would think a bachelor in biology not necessarily qualifies you through. So I think this interdisciplinarity also requires you to be very fluent, very comfortable in reading many different styles of papers and publications because a publication in a computer science venue will be very, very different from the way we write in biology. So don't stick to your study program, but just be free in selecting whatever course gets you closer to the knowledge you need in order to do the research or whatever task you are building and working on.

Eric Topol (29:39):

Well, Charlotte, the way you're set up there with this coalescence of life science and computer science is so ideal and so unusual here in the US, so that's fantastic. That's what we need and that's really the underpinning of how you're going to get to the virtual cells, getting these two communities together. And Steve, likewise, you were an engineer and somehow you became one of the pioneers of digital biology way back before it had that term, this interdisciplinary, transdisciplinary. We need so much of that in order for you all to be successful, right?

Steve Quake (30:20):

Absolutely. I mean there's so much great discovery to be done on the boundary between fields. I trained as a physicist and kind of made my career this boundary between physics and biology and technology development and it's just sort of been a gift that keeps on giving. You've got a new way to measure something, you discover something new scientifically and it just all suggests new things to measure. It's very self-reinforcing.

Eric Topol (30:50):

Now, a couple of people who you know well have made some pretty big statements about this whole era of digital biology and I think the virtual cell is perhaps the biggest initiative of all the digital biology ongoing efforts, but Jensen Huang wrote, “for the first time in human history, biology has the opportunity to be engineering, not science.” And Demis Hassabis wrote or said, ‘we're seeing engineering science, you have to build the artifact of interest first, and then once you have it, you can use the scientific method to reduce it down and understand its components.’ Well here there's a lot to do to understand its components and if we can do that, for example, right now as both of AI drug discoveries and high gear and there's umpteen numbers of companies working on it, but it doesn't account for the cell. I mean it basically is protein, protein ligand interactions. What if we had drug discovery that was cell based? Could you comment about that? Because that doesn't even exist right now.

Steve Quake (32:02):

Yeah, I mean I can say something first, Charlotte, if you've got thoughts, I'm curious to hear them. So I do think AI approaches are going to be very useful designing molecules. And so, from the perspective of designing new therapeutics, whether they're small molecules or antibodies, yeah, I mean there's a ton of investment in that area that is a near term fruit, perfect thing for venture people to invest in and there's opportunity there. There's been enough proof of principle. However, I do agree with you that if you want to really understand what happens when you drug a target, you're going to want to have some model of the cell and maybe not just the cell, but all the different cell types of the body to understand where toxicity will come from if you have on-target toxicity and whether you get efficacy on the thing you're trying to do.

Steve Quake (32:55):

And so, we really hope that people will use the virtual cell models we're going to build as part of the drug discovery development process, I agree with you in a little of a blind spot and we think if we make something useful, people will be using it. The other thing I'll say on that point is I'm very enthusiastic about the future of cellular therapies and one of our big bets at CZI has been starting the New York Biohub, which is aimed at really being very ambitious about establishing the engineering and scientific foundations of how to engineer completely, radically more powerful cellular therapies. And the virtual cell is going to help them do that, right? It's going to be essential for them to achieve that mission.

Eric Topol (33:39):

I think you're pointing out one of the most important things going on in medicine today is how we didn't anticipate that live cell therapy, engineered cells and ideally off the shelf or in vivo, not just having to take them out and work on them outside the body, is a revolution ongoing, and it's not just in cancer, it's in autoimmune diseases and many others. So it's part of the virtual cell need. We need this. One of the things that's a misnomer, I want you both to comment on, we keep talking about single cell, single cell. And there's a paper spatial multi-omics this week, five different single cell scales all integrated. It's great, but we don't get to single cell. We're basically looking at 50 cells, 100 cells. We're not doing single cell because we're not going deep enough. Is that just a matter of time when we actually are doing, and of course the more we do get down to the single or a few cells, the more insights we're going to get. Would you comment about that? Because we have all this literature on single cell comes out every day, but we're not really there yet.

Steve Quake (34:53):

Charlotte, do you want to take a first pass at that and then I can say something?

Charlotte Bunne (34:56):

Yes. So it depends. So I think if we look at certain spatial proteomics, we still have subcellular resolutions. So of course, we always measure many different cells, but we are able to somewhat get down to resolution where we can look at certain colocalization of proteins. This also goes back to the point just made before having this very good environment to study drugs. If I want to build a new drug, if I want to build a new protein, the idea of building this multiscale model allows us to actually simulate different, somehow binding changes and binding because we simulate the effect of a drug. Ultimately, the redouts we have they are subcellular. So of course, we often in the spatial biology, we often have a bit like methods that are rather coarse they have a spot that averages over certain some cells like hundreds of cells or few cells.

Charlotte Bunne (35:50):

But I think we also have more and more technologies that are zooming in that are subcellular where we can actually tag or have those probe-based methods that allow us to zoom in. There's microscopy of individual cells to really capture them in 3D. They are of course not very high throughput yet, but it gives us also an idea of the morphology and how ultimately morphology determine certain somehow cellular properties or cellular phenotype. So I think there's lots of progress also on the experimental and that ultimately will back feed into the AI virtual cell, those models that will be fed by those data. Similarly, looking at dynamics, right, looking at live imaging of individual cells of their morphological changes. Also, this ultimately is data that we'll need to get a better understanding of disease mechanisms, cellular phenotypes functions, perturbation responses.

Eric Topol (36:47):

Right. Yes, Steve, you can comment on that and the amazing progress that we have made with space and time, spatial temporal resolution, spatial omics over these years, but that we still could go deeper in terms of getting to individual cells, right?

Steve Quake (37:06):

So, what can we do with a single cell? I'd say we are very mature in our ability to amplify and sequence the genome of a single cell, amplify and sequence the transcriptome of a single cell. You can ask is one cell enough to make a biological conclusion? And maybe I think what you're referring to is people want to see replicates and so you can ask how many cells do you need to see to have confidence in any given biological conclusion, which is a reasonable thing. It's a statistical question in good science. I think I've been very impressed with how the mass spec people have been doing recently. I think they've finally cracked the ability to look at proteins from single cells and they can look at a couple thousand proteins. That was I think one of these Nature method of the year things at the end of last year and deep visual proteomics.

Eric Topol (37:59):

Deep visual proteomics, yes.

Steve Quake (38:00):

Yeah, they are over the hump. Yeah, they are over the hump with single cell measurements. Part of what's missing right now I think is the ability to reliably do all of that on the same cell. So this is what Charlotte was referring to be able to do sort of multi-modal measurements on single cells. That's kind of in its infancy and there's a few examples, but there's a lot more work to be done on that. And I think also the fact that these measurements are all destructive right now, and so you're losing the ability to look how the cells evolve over time. You've got to say this time point, I'm going to dissect this thing and look at a state and I don't get to see what happens further down the road. So that's another future I think measurement challenge to be addressed.

Eric Topol (38:42):

And I think I'm just trying to identify some of the multitude of challenges in this extraordinarily bold initiative because there are no shortage and that's good about it. It is given people lots of work to do to overcome, override some of these challenges. Now before we wrap up, besides the fact that you point out that all the work has to be done and be validated in real experiments, not just live in a virtual AI world, but you also comment about the safety and ethics of this work and assuming you're going to gradually get there and be successful. So could either or both of you comment about that because it's very thoughtful that you're thinking already about that.

Steve Quake (41:10):

As scientists and members of the larger community, we want to be careful and ensure that we're interacting with people who said policy in a way that ensures that these tools are being used to advance the cause of science and not do things that are detrimental to human health and are used in a way that respects patient privacy. And so, the ethics around how you use all this with respect to individuals is going to be important to be thoughtful about from the beginning. And I also think there's an ethical question around what it means to be publishing papers and you don't want people to be forging papers using data from the virtual cell without being clear about where that came from and pretending that it was a real experiment. So there's issues around those sorts of ethics as well that need to be considered.

Eric Topol (42:07):

And of those 40 some authors, do you around the world, do you have the sense that you all work together to achieve this goal? Is there kind of a global bonding here that's going to collaborate?

Steve Quake (42:23):

I think this effort is going to go way beyond those 40 authors. It's going to include a much larger set of people and I'm really excited to see that evolve with time.

Eric Topol (42:31):

Yeah, no, it's really quite extraordinary how you kick this thing off and the paper is the blueprint for something that we are all going to anticipate that could change a lot of science and medicine. I mean we saw, as you mentioned, Steve, how that deep visual proteomics (DVP) saved lives. It was what I wrote a spatial medicine, no longer spatial biology. And so, the way that this can change the future of medicine, I think a lot of people just have to have a little bit of imagination that once we get there with this AIVC, that there's a lot in store that's really quite exciting. Well, I think this has been an invigorating review of that paper and some of the issues surrounding it. I couldn't be more enthusiastic for your success and ultimately where this could take us. Did I miss anything during the discussion that we should touch on before we wrap up?

Steve Quake (43:31):

Not from my perspective. It was a pleasure as always Eric, and a fun discussion.

Charlotte Bunne (43:38):

Thanks so much.

Eric Topol (43:39):

Well thank you both and all the co-authors of this paper. We're going to be following this with the great interest, and I think for most people listening, they may not know that this is in store for the future. Someday we will get there. I think one of the things to point out right now is the models we have today that large language models based on transformer architecture, they're going to continue to evolve. We're already seeing so much in inference and ability for reasoning to be exploited and not asking for prompts with immediate answers, but waiting for days to get back. A lot more work from a lot more computing resources. But we're going to get models in the future to fold this together. I think that's one of the things that you've touched on the paper so that whatever we have today in concert with what you've laid out, AI is just going to keep getting better.

Eric Topol (44:39):

The biology that these foundation models are going to get broader and more compelling as to their use cases. So that's why I believe in this. I don't see this as a static situation right now. I just think that you're anticipating the future, and we will have better models to be able to integrate this massive amount of what some people would consider disparate data sources. So thank you both and all your colleagues for writing this paper. I don't know how you got the 42 authors to agree to it all, which is great, and it's just a beginning of something that's a new frontier. So thanks very much.

Steve Quake (45:19):

Thank you, Eric.

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