Transcript of Episode 13: Deepti Gurdasani, Christina Pagel & Nisreen Alwan

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today are three heroes of COVID from the UK. Nisreen, can you please introduce yourself? 

Nisreen: Yeah, sure. Thanks, Grant. So I’m Nisreen Alwan. I’m an associate professor in public health at the University of Southampton and an honorary consultant of public health at University Hospital, South Hampton.

So my research area, before the pandemic, was maternal and child health. So for a few years now, I’ve researched particularly around pregnancy, before pregnancy, between two pregnancies what modifiable factors mothers have or are exposed to, including environmental factors. And how they influence their health and the health of their children, both in the short and the long run.

Now in terms of COVID. So obviously I’m in public health and when the pandemic started in the UK back in February and March, I got very much involved with a group of public health academics and epidemiologists to try and provide some independent public health input to what was going on. So we produced a few outputs and letters looking at the government response and putting some expert input into what was going on in the spring.  While I was doing that, I also got symptoms of COVID-19 in March, which didn’t completely go away. I kept getting relapsing symptoms, so basically what we now know as long COVID.

So I got involved in the advocacy around long COVID because people weren’t really talking about illness from COVID-19 at all. It was very much black and white, death or nothing happens to you. So I started talking about how we need to measure illness and what long COVID looks like. That’s been my heaviest involvement I would say in the COVID-19 pandemic so far, but I also continue to input on things which are very much relevant to public health, particularly in my area of interest, which is around children and families.

Grant: Thank you so much and Deepti? 

Deepti: Hi, I’m Deepti Gurdasani. I’m a clinical epidemiologist and senior lecturer in machine learning at Queen Mary University of London. A lot of my work over the last decade has focused on studying the genetic and clinical determinants of disease in a global context. So particularly in more ethnically diverse populations, more recently my work has focused on trying to understand the impact of different interventions on COVID pandemic trajectories in a global context using machine learning methods. As well as studying long COVID alongside Nisreen to understand what the prominent clusters of patients’ symptoms are and how people are likely to progress over time. 

And a lot of my work is essentially in developing new methods to help us understand what puts people more at risk and what influences how a pandemic starts and continues in different parts of the world.

Grant: Thank you so much. And last but not least, Christina. 

Christina: Hi, I’m Christina Pagel. I’m a professor of operational research at University College, London. And operational research is like a branch of very applied mathematics. I think in the States it’s called operations research or management science, or systems engineering. It has all these different terms, depending on which department you’re in.

But it’s basically motivated by using whatever kind of quantitative methods you can to help solve real problems. And that’s kind of what I do. And part of that has been national policy, so we have worked quite closely with the department of health around pandemic planning, but back around swine flu time, say 2009/2010.

And since then, a lot of my work has been working with clinicians and people living with congenital heart disease and kind of trying to use national data sets to understand outcomes, communicate outcomes, make sure we’re researching what matters to patients, what matters to doctors and how do you explain that really complex interaction with the health system over time?

So that’s kind of what I’ve been doing. And then when the pandemic hit. My projects came to a big hole because we work so closely with doctors and they obviously all pulled off in that effort. And then we worked quite closely with local hospitals, just trying to help them understand what was happening. Helped them think about how to organize their care and what made a difference just because no one really knew what was happening in the first few months. And then in May, I was asked to join Independent Sage, which is like a group of 12-13 scientists from different backgrounds. 

And that kind of was meant to be just one, two hour meeting in May. And it’s ended up taking over my life eight months later. And we’ve kind of ended up serving in both a policy advocacy and public communications role. And that’s what I’ve been doing. Just trying to take all the amazing science that’s happening from people like Nisreen and Deepti and just trying to explain to the public what it means, what’s happening, are things bad, are they good, what do we have to watch out for, where is it going.  And just trying to do that in a way that is accessible and not sensationalist, I suppose, is what I’m trying to do.

Grant: Thank you so much. That is a great segue to our first topic: where things are going. I would love to hear all of your thoughts on the new variants. And maybe for the sake of our listeners: there’s a lot of noise out there. If you can kind of distill down what we know and what remains unresolved.

Christina: Well I can start on that. I’m just a mathematician by background and I work mostly in the more operational side of healthcare. And I don’t have a background in viral and infectious diseases, so I hadn’t really been thinking about variants to be honest.

So I knew that obviously there’s potential for mutation and people talked about it, but over the summer I was hearing, “Oh, don’t worry. It’s mutating slowly.” So I just hadn’t really been thinking about it. And then in November or very early in December, when they first mentioned that there might be a new more transmissible variant in the UK, I was just like, “Oh”, and then I remember very vividly watching the press conference where Matt Hancock said it might be up to 50%, 70% more infectious.

And I thought, “this is really, really bad, really bad.” Just from the basic maths of it. Once you have something that’s that much more infectious, then everything we’re trying to do to keep things down is that much harder. I also, to be honest, felt not relief, but at least it suddenly made sense why things were going wrong in the Southeast of England. Because I just could not understand why cases were going up with the kind of restrictions that we had.

And so that made sense. I was like, “Oh, okay, well that kind of explains it.” But then I just thought, “then we’re in trouble. We’re in real trouble. Unless we do something straight away.” And that’s kind of proven to be the case. We had a terrible, terrible January in the UK and other countries that have had the UK variant had real problems: that’s Ireland, Israel, Portugal.

And then of course there’s the South African variant and then the Brazilian Variant. And you realize now that we have so many millions of people who’ve had COVID. And it has had so many opportunities to mutate. And if it comes to a situation where it can infect people more easily or it can infect people who’ve already had it or who’ve had the vaccine, that gives it a big advantage. 

And I think that is the biggest danger now because we’ve got millions of people who’ve been vaccinated and millions of people with COVID and you’re really giving it a lot of incentive to evolve in a way that’s quite bad for us. And so we’ve ended up in a bit of an arms race, and I’m not particularly happy about it.

Deepti: So while there’s a lot of talk about new variants now with the media, the truth is that the virus has been evolving over a long period of time. So in February itself, we know that there was a variant called D614G that was identified and was circulating. This is a variant that was subsequently found to be about 20 to 30% more transmissible than the previous variant and became dominant globally by June. And since then, there’s several variants that have risen on the spike protein, which is the part of the virus that we know binds to human cells and is necessary for infection that have potentially allowed the virus to escape immune responses directed at previous variants, at least in the laboratory.

And subsequently in September, we heard about many new variants arising from infection to and back from mink farms. So mink were a reservoir for infection. And a large number of mutations were accumulating that were being transferred back to humans. And once again, there were concerns about how these mutations could potentially escape immune responses, directives as previous variants.

So in many ways, this was expected, I guess, given the high levels of transmission that we’re continuing in many countries, but I guess the degree of increase in transmission was quite a shock when that happened. So we heard about the UK variant very close to when we heard about the so-called South Africa variant with both of those potentially increasing transmission by about 50%. And subsequently it also became clear that there were other variants in Manaus as well.

And there were many shared mutations between these variants. So they were particular mutations such as the 501Y mutation, which was potentially associated with increased transmissibility, increased affinity to the ACE2 receptor, which is a receptor required for binding and infecting human cells. And there were also variants such as the mutation such as the E484K, which has been associated with escape against immune responses, directed at previous variants. 

Now we have become aware that unfortunately, these laboratory findings are translating into reduced vaccine effectiveness. We know from the Novavax and Johnson & Johnson trials, and more recently a report from preliminary data from the AstraZeneca-Oxford vaccine, that effectiveness to prevent symptomatic disease at least is likely to be lower against this particular variant for vaccines that were manufactured against previous variants, and this is something that’s really concerning at the moment. 

But what’s really interesting is the way these variants have emerged in different parts of the world, again and again, independently, but converged onto the same mutations, which suggest that these are actual virus adaptations that are favorable to the virus in certain environments.

And there’s no reason to think that adaptation will stop here and not continue given that we’ve seen the virus evolving pretty much since February 2020. So we really need to tackle this in a different way than we have so far. We’re also hearing about new adaptations on top of variants.

So for example, the UK variant now seems to be evolving in the direction of the South African-Manaus variant, developing the same mutations that have been associated with escape and reduced vaccine effectiveness. So unless we really do something to stop this, this is likely to continue. 

Grant: Nisreen, would you like to give input on that? Or should we move on? 

Nisreen: There’s not much more. I can add really to that explanation. I suppose the main, simple truth around the variants to me from a public health point of view, is that it’s obvious that if you give the virus a lot of room to spread you will get these mutations and variants. You could get more of them.

And so I think it’s about how much room we’re giving the virus to spread. And the other thing from a public health point of view is even though there is a difference in the transmissibility of these variants, actually the public health measures that we use are the same and they would work on all the variants.

So that’s good news in a way, in that we don’t have to learn this all over again in terms of what we can do to suppress the virus other than the vaccination, obviously, which will have to adapt to the variants. But in terms of the non-pharmacological interventions, they’re the same. 

Christina: And we’re seeing that in the UK now, like the quite strict lockdown is working to reduce cases, even though the English Variant is now dominant across the country, but we are still having reduction in cases and reduction in hospital admissions.

Grant: And so following on from that, what do you think is a reasonable spectrum of possibilities for where this might go in the future.  Do you think it’s in the realm of possibility that we’ll be back to normal life in the foreseeable future or that we’ll have some kind of a COVID-22 that has 30% fatality or something? Is it just a complete unknown? 

Christina: Define foreseeable. What’s the foreseeable future? Is it a year, five years, 10 years? 

Grant: Can you give us some hope? Like what, what would be a time range? 

Christina: I don’t think we’ll be back to normal this year. That’s why I think, I don’t think we will this year. If we can get as many people vaccinated everywhere, not just in rich countries, it has to be everywhere. Keep some measure of quarantine at borders. Keep really good surveillance in place. Keep cases down until you get kind of local elimination and you’re looking at sporadic outbreaks. Then I can see a return to normal, but doing that is going to take well over a year. 

And if we don’t, if we just say we’ll vaccinate our own country, then you will get new variants rising and you will get variants that evade the vaccines. Not in the least because if they can evade antibodies from people, who’ve had it before, that same evolution might help them if they get the vaccine. So we just can’t risk it and we’ll just end up doing it again. So that is my fear. That we will end up in some kind of COVID Groundhog day, which would be just really, really miserable. But we know there’s a way out. It’s just, will we do it?

Nisreen: I think one of the things, if I may say, which might have hindered the pandemic response is this desperate urge to go back to normal. I think if we had adapted a bit and said, this might be with us for a while, so we have to change our behaviors. We have to change how we do things in a certain way, which is tolerable to us as a society, to our mental health which doesn’t result in severe social isolation. 

For example, if we had adapted a bit from the start and looked at it as a bit of a more of a long-term adaptation, then maybe we would be in a better place now. And also this is not too late to think about. So I think this pandemic has been played and I just see it and it pains me. Everything is black and white. Everything is really bad or we want to go back to completely normal and the same with the mortality. “This virus doesn’t touch you at all, so go and get it.” And every single aspect of the pandemic has been plagued by this black and white picture. And we need a bit of gray in here. It’s a difficult time for us, but it doesn’t have to be black and white. It could be gray if we get the balance right.

Christina: Yeah. I think that’s a really good point about this desire to get back to normal, almost making things worse as people kind of keep trying to push it. And it just slaps you back in the face.

Deepti: To me, the question about when we can get back to normal is completely dependent on government strategy and political wealth. So there are two end points to this. One end point is complete elimination so we don’t have cases anymore, and life can return to a new normal. And the other end point is achieving herd immunity so that we don’t see at least large outbreaks of infections anymore. 

The former probably is faster to achieve because many countries have done this within a period of about six months or so, but requires very strong political will, fixing a lot of systems within the UK that are broken and actually persevering with lockdown, supporting the public through the spirit until the cases reach near zero before lifting restrictions and then having good surveillance systems in place to actually pick up cases and support people, and isolating where needed.

The second strategy, which is pursuing herd immunity primarily through vaccination. It is much harder to predict what the end point of that will be. And the reason for that is that it’s really unclear whether herd immunity can even be reached through vaccination. We’re dealing with a variant that’s more transmissible, which means the herd immediate threshold is likely to be higher.

The vaccine is still not going to be available in many groups, particularly children who do transmit the virus. And we don’t know what the vaccine uptake is going to be across the population. And of course, we don’t know what the vaccine effectiveness is in preventing infection. All of those unknowns make it very hard to understand whether herd immunity can be reached at all. And if it can, when it will be reached.

If we add new variants into the mix, it’s even more complicated because as new variants emerge, they may be able to escape vaccine acquired immunity, to the previous variants, which would make trying to achieve a herd immunity threshold almost impossible and outbreaks could potentially continue for many, many years.

So I definitely favor the former strategy because I think it’s more clear cut. There’s less uncertainty around it, and it can be achieved in a shorter period of time, but it’s all down to political will and strategy. And it’s very clear that many countries are still pursuing the latter, even though that’s a lot less certain and the potential public health costs and the economic cost is much higher.

Grant: Anything else to add on that before we go on to long COVID. 

Christina: I would just add that to me, like the arguments for an elimination strategy, like Deepti was saying, have just got stronger and stronger over the last six or seven months. And I think, especially with the new variants and the new transmissibility. I actually think we probably won’t ever be able to reach the herd immunity threshold. 

And if we did, we still don’t know how long immunity lasts, so that’s the other issue. So an elimination strategy just seems like the one thing that we know we can do because lots of countries have done it. The problem is that if only some countries did it and some countries never do it, then you’re looking at having quite strict border controls for years and years and years. And that has consequences. So I kind of felt like we have to work together as a whole globe. And I’m not sure we have a great track record of doing it. 

Deepti: Thanks for pointing that out, Christine. Those are very, very important points. 

Nisreen: I agree with both Christina and Deepti, so there’s no sensational disagreement on this one. I think an elimination strategy is the way forward.

Christine: It just seems like a no-brainer. And I have to be honest, like whenever I see pictures of people going to concerts and New Zealand or just going out, I’m just so jealous. I’m like, I want that. Or people in a busy market in Thailand or Taiwan and going out to restaurants: why can’t I have that in my life?

Nisreen: And people over there have accepted certain sacrifices to do that in terms of lots of things, but including border control. It’s tough not being able to leave, and they’ve accepted that. And I think what was key to that is a very clear public communication right from the very start. 

Basically the policymakers saying, this is the goal. It’s clear. You know what we’re aiming for. These are the sacrifices that need to be made, but this is what we’ll get if we make these sacrifices and that’s what happened. And we didn’t have that clear strategy and I’m afraid we still don’t have it yet, a year in.

Christine: It’s definitely happened in the UK, that we try to “tech” our way out of it. It was kind of like: we’ve got this great tech technological solution, whatever it happens to be. And it’s kind of been a series of them, none of which have worked. Instead of actually just saying we’re in a global pandemic and there’s nothing about that that’s going to be very fun. 

I mean, sometimes people are like, I don’t want to do this. And I’m like, well, it sucks. Right. It sucks to be in this situation. And we’re kind of the first, really bad pandemic in the modern era when you have a globally connected civilization. So it is really, really difficult, but that honesty of messaging and of saying, if we do these things, we’ll get these payoffs. We just didn’t really get it here. 

Deepti: And yeah, I completely agree with that. And also the fact that we’ve learned a lot about SARS-CoV-2 over the past year, but the fact is we didn’t need any of that information to know how to manage it because the countries who managed it successfully right at the beginning, treated it as it was: a highly infectious respiratory virus.

And it was just public health 101 that they followed and that worked. And we know that’s what works even now. But as Christina said, we tried to find these new technologies, which we’ve not even added to our response, but rather use them to replace basic public health responses. And it’s no surprise that it’s completely failed. 

Grant: Speaking of those technologies, where are we on vaccines? And do you think with all the new variants going around that the mRNA-based vaccines will be able to be updated quickly enough? Do you think there is a technological path out of this? 

Christina: Well, I can’t really talk about vaccines without sounding like a complete and utter idiot. Like I barely know what mRNA is. Seriously, go for it. 

Deepti: Okay. I mean, I’m happy to talk about that. So vaccines can be updated. And while technically it might be an easy thing to do, they still will require a huge amount of testing and the laboratory in people and validation, which will take several months.

And I think the big question is: what are going to be the variants in place by the time that vaccine comes out. So we need to actually take preemptive action to prevent variants from emerging right now. By really curbing transmission and pushing towards elimination. Because unless we do that, we’re constantly going to be behind vaccine manufacturing.

The point is that we can’t wait three to four months to actually vaccinate people because our emergency services were overwhelmed until a few weeks ago. And even now many places are very close to capacity. So the idea that we can maintain huge amounts of transmission alongside vaccination and have a good vaccine response isn’t really grounded in reality.

We need to remember that we have these variants emerging right now, even without huge amounts of immune selection pressure within the population. As we start vaccinating more and more people, there’s going to be much more immune related selection pressure. Potentially speeding up the sort of adaptation we’ve been seeing, and we may see even more escape mutations. So we need to actually keep ahead of this by following elimination and preventing emergence of new variants, rather than constantly having to adapt our vaccines too late to these variants. 

Nisreen: Yeah. I mean, again, I agree. I think it’s that we can see now and everybody, the public is probably seeing there’s a race between the vaccine and the virus adapting and changing.

And I think all the technology in the world will not win the race if the virus has a lot of room to spread and infect more and more people and change. So it’s very clear now that the vaccine can not be our only strategy out of this. Although I am increasingly surprised by the many people who are still reinforcing this method.

Christina: So the fact that we have so many good vaccines within a year, I still think is pretty miraculous. And it does put us in a much better position than we would otherwise have been. Even with all of the caveats and the concerns that Deepti and Nisreen have spoken about. That is pretty amazing to me.

Nisreen: And I agree. 

Deepti: I agree completely with what Christina said. We have a large number of vaccines that are effective against not just the previous variants of the virus, but still highly effective, at least in preventing severe disease for most vaccines, even against current variants of the virus. And if people are offered the vaccine, they should absolutely take them. But I’m more talking about adapting policy to kind of ensure that these resources are protected for our future. 

Grant: Great. Thank you so much for that. And in our last 10 minutes, I think it’d be great to talk about long COVID given that obviously a very large number of people have been infected and realistically it sounds like a very large number will continue to be infected in the foreseeable future. So can you talk about long COVID and what we do know, what we don’t know, what maybe your biggest concerns are long-term for people’s health?

Nisreen: So surprisingly, we still really don’t know a lot about long COVID. What we do know is it is to have long-term illness. Obviously, when I say long-term, that is the age of the pandemic, which is not very long now. And we know that some people who get the infection do not recover quickly.

And that includes two things. People who are severely ill and hospitalized because of the infection. They might get discharged from hospital and still feel unwell for a while. They might suffer complications like heart or lung clotting complications, neurological complications. But also people who were not hospitalized at the start and had a so-called mild illness, might not recover.

So the estimate at the moment is about one in 10 people have not recovered even from a mild illness at about three months from onset. So that’s quite a sizable proportion depending on how many of the population are getting infected. So it’s basically a collection of symptoms that people experience and it’s multi-system, so it could be heart, lung, neurological, skin, general symptoms.

It’s rare that people will have just one system affected. And the severity of it is really variable. We still don’t know the mechanism, and if there’s more than one underlying mechanism of long COVID. Because of the variable clinical picture as well. And the proposed mechanisms include overdrive of the immune system that happens after getting COVID-19 that causes an autoimmune process, or it could also be a persistent virus that flares up from time to time. Or a general inflammatory process. So there are multiple mechanisms proposed. 

My main concern about long COVID is relevant to what we’ve been talking about, which is basically the strategy. The strategy is focused mainly on vaccinations. So if you say that that is the most important thing and our target is to prevent severe disease, which basically means hospitalization and death. If that’s achieved, that’s fine. We’re out of the pandemic. We don’t need to do much about it. 

Then that could actually allow for a lot of long COVID where people don’t get to that threshold of needing to go to the hospital in their initial illness, but then they don’t really recover. And that means huge implications for society in terms of people not being able to work and the sick pay not being able to care for people.

So. I don’t really see this factored in. I don’t see long COVID factored in in all of these pandemic policy decisions that are being taken at the moment. And that’s a big concern. 

Grant: Is there data on the effect of vaccines on the incidence of long COVID?

Christina: No, we just don’t know if it’s effective. 

Nisreen: There is no data. There are anecdotal stories of some people improving after getting the vaccine, some people getting worse symptoms. So really there’s no systematic data on it. And that is probably also because we don’t have a lot of long COVID patients who are already vaccinated or given the priorities for vaccination. Because lots of people with long COVID are in younger age groups and healthcare people who don’t have any underlying medical conditions before getting it.

Deepti: I think that’s a very important point that when we look at severe disease, which is defined as hospitalizations and deaths, the sort of demographics of the age groups we look at are quite different from the ones that are affected by long COVID. And targeting just one group is not going to necessarily impact the other. For example, in the UK, easing of lockdown has been tied in right from the beginning from vaccine targets. 

And that doesn’t really account for any of this at all because the vaccine essentially will prevent severe disease in those who are being vaccinated, but we want to vaccinate enough people to come close to reaching herd immunity. So transmission will very likely continue amongst the younger population who are the majority of cases anyway, if we start easing lock down.

And that will mean many, many more cases of long COVID. So we will probably end up with a pandemic of chronic disease in a few months time or even a few years time. It’s something that we don’t fully understand, but will not take seriously, unfortunately until it’s too late. 

Christina: I mean, it has just been so irresponsible. So at the moment in the UK, we’re trying to vaccinate everybody over the age of 70 plus health care workers by the middle of February, and we’re on track to do that. And that’s amazing. And that will have a massive impact on deaths and the reasonable impacts on hospitalizations. But 90% of cases are under 70 year olds.

They’re not the drivers of transmission and just the idea that somehow that these schools open up and it’s fine for younger people to get it.. It’s just crazy because it’s not fine. Even something like one in seven kids have symptoms after seven weeks from the Office of National Statistics.

It’s just the idea that we would just expose people to potentially long-term problems. Well, we know there are long-term problems, but also we don’t know what could happen in five years time. We don’t know what the long-term impact is at all. How can we, it’s only a year old, right? I mean, after the Spanish Flu in 1918, there was a kind of mini epidemic of Parkinson’s 15 years later.

Because it ended up damaging the brain. We don’t know any of that. So the idea that you would just encourage people to get infected because you can’t be bothered to have an effective public health strategy just drives me crazy as you might be able to tell. 

Nisreen: I think that’s exactly what it is. The uncertainty of it all. And right from the start we saw that. I certainly did. It’s a new virus. You don’t know what it’s going to do. And there was this certainty around saying, No, it’s going to go away and it won’t touch you. And when people were talking about herd immunity and getting young people infected, it was really a horror movie happening in front of my eyes. Because I was thinking Do they know something that I don’t know? Because how do they know this virus won’t give you long-term effects?

And even now you say long COVID, it’s just post viral syndrome. So the two things. First of all, first of all, post-viral syndrome is not “just”. It disables people severely, potentially for a very long time. So if you have loads of people with it, that’s a major problem. But also you don’t know if it’s post-viral so you don’t know if it’s going to go away and you don’t know who’s likely to get worse.

There’s so much we don’t know. And the way we’ve abandoned the precautionary principle and embraced that uncertainty, I find it very astonishing. And we continue to do so. We really need to revisit that in terms of how we communicate that uncertainty and act on it. 

Grant: Great Deepti, Christina, thank you so much for joining us.

Christina: Thank you. 

Deepti: Thanks for having us. 

Nisreen: Thank you so much.

Transcript of Episode 11: Vay Cao

Grant: Welcome to The Bioinformatics CRO podcast. I’m your host Grant Belgard, and joining me today is Vay Cao. Vay, can you introduce yourself please? 

Vay: Sure, well, thank you so much for having me. It’s a pleasure to be here and chat with everyone. So my name is Vay.  I guess, for the audience we have today, I have a PhD in neuroscience. I did my research at the National Institutes of Health and with Brown University. So. I have extended my career outside of academia and actually made it a point to try and bring more fellow academics with me on this journey by founding a small organization called Free the PhD. Recently, I’ve actually kind of put it under an umbrella called Free the Degree.

But you know, for my day job, I actually started off my career as an application scientist at a startup company. And today I find myself actually working more in the healthcare space. I’m on the commercial side of things. So I’m looking forward to our conversation today. And hopefully when we get to talking about careers and sort of looking forward towards the future, my goal is always to help inspire and motivate people to shoot for new things and take some new risks. So thank you again for having me. 

Grant: Thank you for coming on. So tell us about Free the PhD. What is it? 

Vay: Yeah. I mean, you know, it was just an idea kind of was born out of some frustration and personal experience in the challenges of figuring out what options are available for academics who may want to leave the bench, want to get out of, you know, the grant writing cycle are not interested in being faculty, don’t want to teach that whole world of not knowing what’s out there. 

I actually was very privileged to come from an institution that actually offered a lot of resources for people who want to explore alternative careers. However, I think many of your listeners who have this kind of background would agree that much of the challenge is actually psychological and not always necessarily in terms of resource, you know, need.

So when I did eventually make my jump out of academia into this startup, I had so many worries and concerns. And I didn’t really have any role models, at least in my own personal life to model this sudden change in career path after. Pretty much everyone in my life went the academic route, of course, including my own PhD mentors.

So when I finally was on my way across the country to this new state, new job, new life, I just remember telling myself this was a lot of work. It was very stressful to get to this point. And it almost seems like a waste of this experience to just kind of let it pass. I was really looking forward to the future, but I’m like, it would be nice if I could take this experience that I went through and help make it less stressful for the next person and the next person.

And I’m not alone, certainly, in this desire to give back to the academic community and encourage more people to explore their options. So Free the PhD came about basically from this desire to kind of give back and not waste that experience that I went through in transitioning. So it started off actually as a very typical document editing service.

I think it was like my second or third year of working when I figured out it is a real job and I’m not going to be fired or the company is not going to go under right away. So I feel like I have a foot to stand on and kind of reach back to the community. So from kind of just editing people’s documents, I realized, as I mentioned already, there’s so much of this emotional turmoil. And a lot of the things that people are really looking for I think are ways to increase their confidence in themselves and feel justified in trying something different. 

So I started adding more of these like personalized conversations with people. Some consult calls and it sort of started to evolve into this larger program and essentially started putting down a lot of FAQ’s. People ask the same questions I give the same answers, and I started putting together a course.

So basically today Free the PhD is actually an online community. It’s a digital course. Kind of one-on-one career coaching, all done digitally. And it’s really allowed me to continue to build on this passion of mine for career advocacy for academics, while I’m still holding on to a full-time job. 

And as I mentioned Free the Degree is actually just an umbrella now that Free the PhD lives under. So it’s been super rewarding. I’ve learned so much. It’s like my own mini entrepreneurial project. And, you know, even just today, I had someone tell me that they got their verbal offer, and now they’re just saying a few months ago, they didn’t think it was possible. And, you know, I told them everyone is capable of it and sometimes we just need some support to get there.

Grant: And what’s the scope of, for the PhD? Is it pretty focused on biomedical research? 

Vay: I mean, so we actually have never turned anyone away. We have actually had people come from the humanities, social sciences. I think people tend to look for a background similar to their own when they’re seeking advice. So we do have a majority of our members and people who subscribed to our courses coming from STEM. That’s simply because many of us who are acting as advisors have that kind of background.

But yeah, we’ve worked with people from all different kinds of backgrounds. Clearly people’s skill sets and experiences will dictate some of the options that they have, which are low hanging fruit. But at the end of the day, I really think a lot of the emotional and psychological support that we provide is universal.

Grant: What’s the scope of Free the Degree? 

Vay: That’s really just something that I think is a placeholder for the moment. I think the kinds of advice and the sort of step-by-step programs that we’ve laid out, they kind of apply to almost anyone who has been in a niche for a long time. 

So, academics, we have our own unique problems and benefits to society, but so does everyone else who might’ve been in one particular focused field for a really long time and all of a sudden realizes they want to leave that place. Right. So I think for me, I just decided to kind of pick an umbrella term that can encompass future directions that we could go in.

Cause clearly, I mean, PhDs, we’re just one kind of academic demographic, right. And there are so many other people who might also feel stuck where they are today, but might actually really benefit from exploring new fields. 

Grant: How’s the landscape changing for fresh biomedical PhD graduates? 

Vay: Oh, that’s a great question. I mean, right now we’re in a very unique time in history, I suppose. I think there’s been a lot of people who probably have discovered that the faculty openings have been harder to find. They’re certainly more competitive since we always are seeing an increasing number of PhDs being graduated every year without that same increase in number of faculty positions.

Right. So it’s simply a numbers game. I think the landscape has been doing this for a long time, actually. So it’s not even a particularly fast change in the landscape. But I do think perhaps some of the assumptions that are made in academia haven’t changed as quickly as the landscape. And I think that’s probably where the challenges lie.

There’s a disconnect between what people think about their job opportunities within academia versus the reality. So, you know, I think especially for fresh graduates, or–better yet, which is always our philosophy–people who are not yet graduated but are getting there and are thinking about where they want to go.

You want to be realistic and you need to get the data. I think, especially for biomedical PhDs, but really any academic right. Your research skills, you got to apply them to yourself too. You have to figure out what’s really out there. You need to take the skills you use in your research projects and go out and actually talk to people who are either new faculty on the faculty hunt–if that’s something that you really want to do. And just kind of be more honest with yourself about how likely it is that you can go for option A versus option B versus option C. And that’s how you’re going to really lower your stress levels. Right? 

I think the landscape overall, especially right now, I think the educational world is struggling a lot under the current pandemic situations. We don’t know how the economy is going to fare in the next few years. So if you’re kind of eyeing the job market soon, it makes sense to not lock yourself into only one option. 

Grant: Who do you think should and who shouldn’t do a PhD in these days?

Vay: Oh, man. That’s a hard question. Cause you know, if you had asked me if I should have done a PhD, like looking back, I’m not sure. Maybe I should have done business school instead, but now at the same time doing the PhD has given me so many opportunities and has really both broadened and giving me depth in a field that I think is really interesting and has a lot of applications to real life.

So I think there are always the people who are very certain and have always known they need to get that depth of knowledge. And that’s awesome. I think it’s great. They are also people that–and I would certainly put myself into this category–who are not sure what they want to do after undergrad. Not in love with my job options coming straight out of this particular degree, it could be family pressure, could be social pressure could just be, I don’t know, I want to try it and they do a PhD. 

And now sometimes I hear people saying, that’s a mistake. Like you really need to know you want to do it. But I also think that’s not really realistic. Right? You don’t know what you want to do until you actually do it. So I think there’s really no wrong answer.

I think for people who are not necessarily sure about what precisely they want to do. Just make sure that as soon as you say, start your program, that you don’t lose track of what you care about as a person. Right. It gets really, really easy to do that. I think, especially in academia, but at the end of the day, you are earning the degree and you’ve got to figure out what to do with it.

When you’ve finally got it–it may seem like forever, but you will graduate most likely–as long as you always are looking at your options, talking to other people, exploring as you go. You can get a PhD without even wanting to be in academia. And actually, I think there are plenty of examples of people who go for the degree for reasons other than wanting to enter the faculty path.

There are people who want to go into entrepreneurship and they want to lead biomedical companies. Or maybe you like a think tank in the future. They may want to go into policy or even politics or right. So there’s so many different ways that you can leverage a terminal degree, like a PhD and the technical skills and research skills you gained through this program anywhere in society.

So as long as you’re kind of trying to figure that out actively, purposefully, step-by-step, I don’t think it’s wrong for anyone to pursue a PhD. I think what people will perhaps regret a little bit is if you kind of fall into it and then just let it take you wherever the internal pressure makes you go.

Cause then you might find yourself way off the path away from what really matters to you. And then to come back to what matters can take a lot more effort and be more stressful. So, you know, I think a lot of advice out there will say, if you’re not sure don’t do it. But I think that also is not a very scientific way to think.

Cause you kind of got to get your hands on that data on that experience to really know if it’s right for you. But then again, if you know in the first two years that this is not for you, you should move on. Right? Don’t feel like you should be trapped in something. I think taking ownership of what you want to do with your life is a very, very difficult thing to do. But usually people benefit when they do that. 

Grant: What translatable skills do you think are best honed in a PhD program, as opposed to spending that time in the workforce? Or spending that time partially getting a master’s and the rest in the workforce?

Vay: You know, I think one of the things that you really do a lot in a PhD, just simply because of the time and sort of the expectations of the body of work you’re supposed to accomplish during the PhD, you know, it’s kind of like figuring out how you flounder for a really long time and hopefully not fall apart through that process. That, you know, a lot of people talk about resilience or grit. And I really think if you’re not kind of put through some sort of a ringer, it’s harder to develop that type of perseverance. Just kind of being beaten down, but getting up all the time.

It can get abusive at a certain point and it’s not for everyone for sure. But I do think this is one of those like squishy soft skills that it’s hard to articulate on a resume. Like you don’t put grit on your resume as something you bring to the table. But people can typically tell that. I think, especially when you’re hiring, especially companies that are going through a lot of change or they’re moving really fast or they’re in a very demanding or challenging field. They are looking for people who can handle that type of environment. 

And I think in many ways, although a PhD doesn’t necessarily have the same time crunch for deadlines, the kind of ambiguity of am I doing the right thing? Is this, you know, on the right path? Having to deal with that for many years–again, people in master’s programs do amazing work–but the length of time you’re kind of thrown into this ambiguity in a PhD, I think almost forces that type of skill set to become part of who you are. 

And I actually think that’s a very valuable trait. And the reason why I think many PhDs are amazing candidates for many of the jobs that exist out there is because everyone will benefit from being able to leverage their grit and their perseverance and sort of just being able to get back up. 

Grant: So, in what fields do you see the strongest evidence of a glut of PhDs? And do you even agree with that framing? And where do you think we don’t have enough? 

Vay: It’s a good question. I think there is a lot of talk about there being too many PhDs being produced. I think it depends on what perspective you’re taking. So if we’re talking about, we want to only educate enough PhDs to fill the open faculty positions we have, well, clearly there’s a glut, right? But I also think that as a society and especially here in the US where our taxpayer dollars are paying for research. I think you need people who are like the folks that enter PhD programs like highly ambitious, motivated, you know, really driven by finding the truth or truth and quotations here, but just to pursue an intellectual direction.

I think society benefits from supporting people in these types of endeavors. I think in a society where otherwise you’re driven by other priorities that may never at all touch the level of intellectual rigor and depth that you can go into in various parts of academia. So I would say if we’re talking about filling faculty positions, yes. We definitely have a glut.

And I think that is going to be worse where there are the fewest faculty openings. That’s just a practical answer if we’re talking more philosophical though, and perhaps on the larger scale of society, I don’t know. I mean, even though I didn’t continue into the faculty path myself, I did leave behind a body of research. I did publish some protocols. I published a paper. People cite my paper. I’m still pleasantly surprised it happens, but you know, you’ve contributed something that will last in the field. I think there’s value in that. Now, whether or not that should be the reason that we have the kind of programs that we have today for doctorates, I think that’s up for debate.

Exactly how do you want to kind of shepherd people to contribute to different fields and does a PhD have to have all the things that it requires today? But I actually think we should continue to support people who want to contribute to research endeavors. I think the PhD is the only degree that supports the kind of depth and scope of projects that we have today.

So again, I’m kind of a, I’m kind of mixed on this one, right? Cause I think especially being where I am today, outside of academia and seeing so many PhDs being very successful and happy and fulfilled after leaving the PhD and contributing to society outside of academia. They still did that PhD though.

They still contribute to their field. And perhaps I think many people will say that their PhD experience helped them in their life outside of academia. So I think there is mutual benefit, both to the individual and to society. I think we can make the system better. I think we can make it more efficient.

I think we can better prepare people who go through PhD programs to be successful either in or outside of academia. So I think I’m still very supportive of our society funding and encouraging people to go into research. They may not be for their entire career, but I think there’s value in this idea of fostering individual research programs. I just think we can shape and tailor them to meet the needs of our modern society. 

Grant: How do we go about that? What reforms do you think are warranted? 

Vay: Many of these things are starting to happen now. In terms of say preparing people for even thinking about careers outside academia, changing the conversation within academia, perhaps adjusting some of the expectations of what you’re supposed to do as both a PI as a mentor and as a trainee, maybe the length of time you’re funded, the kind of scope of projects and then taking a look at the different kinds of fields that maybe require a little bit more investment in terms of personnel and resources.

And then there’s also a need in terms of the workforce, right. And conditioning the workforce to understand the value of people who have this kind of training. It’s becoming more common simply because there’s more of us out there. So, right. We’re representing people who have this academic background and have shown practically what it can do when we are given, you know, whatever problem we’re solving in the workplace.

So, it’s definitely kind of a three body problem. That, at least as part of Free the PhD, I’ve been trying to help to address. So it’s in the individual who is entering PhDs, knowing and thinking and preparing ahead. It’s in the educational institutions that are developing and changing and modifying their programs and recruiting people in how they choose to prepare their trainees for the future. And that’s also in greater society in terms of employers and recruiters and hiring managers. Understanding that maybe this type of so-called untraditional candidate is actually going to be a huge benefit to our team and our company or organization. So it’s definitely an ecosystem that needs to change together.

It’s probably not all going to change at the same time. Clearly I think us as academics, pushing into the world, we are the first people because it’s our necessity to get out there and make a living somehow. But again, I think all of these changes are happening. And I’m actually really glad to see that because I think it’s going to be sort of a win-win for everyone. 

Grant: Is academia a cult? Or more softly, are there some cult-like characteristics that commonly need to be deprogrammed as people leave? 

Vay: That’s a loaded question. I mentioned niches earlier about anyone who has been, I won’t say isolated per se, but perhaps sequestered or siloed in one particular environment for a long time. I mean, I wouldn’t necessarily call it a cult per se, but I certainly will say that the environment is very self-fulfilling in some ways. Or almost like an echo chamber sometimes just because of how tight knit the community is and how much they pretty much only interact with each other.

So even when we go out and talk at conferences or we present talks, typically we’re presenting to other fellow academics if we’re still in academia. So it’s actually one of the reasons why, when we’re helping someone to edit their resume for industry and we’re talking about presentation skills, communication skills, I want to see something outside of scientific presentations. Cause again, you’re just, you’re talking to the same audience. Show me some variability. Show me some diversity and how you can tailor your message to different people. 

And yeah sure, students versus postdocs versus PIs are kind of different audiences, but really they’re all academics, right? So when I can see examples of people tutoring children, and then going out and giving public talks at a bar, immediately that adds more depth to their communication skills. So academia, I think, is the same type of silo that many other types of insular workplaces or cultures are.

And I think it has the same challenges. So like you said: there can be some deprogramming necessary, but really it’s just what we were used to. And now all of a sudden we’re exposed to people with different priorities, people who care about completely different things who are using very different acronyms and jargon and all of a sudden it’s like: Oh my God, what is this world? 

And really it’s been out there this whole time. So as trainees, as people who are being brought up in this type of a more insular environment, I think it’s our responsibility as individuals to realize this is not the whole world. We know this, I think intellectually, but emotionally, sometimes we don’t. And it is up to each one of us to reach out and keep those connections outside of any type of insular environment, if we want to have an easier time breaking through in the future. 

I also clearly think that the academic world itself would benefit from integrating external conversations and interactions more often, not for the benefit of trainees, but actually just because cross-communication and cross-fertilization of ideas is always beneficial. So I think this is going to vary. I think I’ve found certainly between different fields, there’s different amounts of baseline communication between academics and others. So in some fields, for example in neuroscience, being a newer field, there’s not that much industry.

There’s not that many applied applications of this technology or this research topic. So we don’t have many industry or other people to really interact with. Whereas if you’re going to look at computer science or even chemical engineering, a lot of people are very used to interacting with people in industry and companies and, you know, entrepreneurship. It’s just part of what they do. 

So they will have less “deprogramming” to do because they’re consistently taking in other priorities outside of academia. So at the end of the day, I think if you don’t like the insular environment you’re in, then take some effort to break out of it, especially if it’s not kind of handed to you. Again, it can be a little bit difficult on the psychological side of things, but really the world is out there and you have every right to reach through the walls of academia and get to know other people outside.

Grant: How did you get to where you are today? What influenced your own choices and decisions? 

Well, I’ve always loved biology and I love behavior in particular. So I think if I was someone who had grown up decades and decades ago, I’d probably be one of those naturalists, trekking through the woods and documenting the life cycles of different animals. I think that would be one of my dream jobs if only it made money, of course. 

But when I went to college, I knew I wanted to study something biological in nature, just cause I’ve always been interested in that. I actually picked microbiology without honestly knowing all that much about it. And then when I discovered what it was all about, I mean clearly it’s about microbes, but the specifics of the work, right, what you learn about and what you do in the lab was really not at the scale that excited me.  

And again, I didn’t really know that until I did it. So like pipetting and running gels and purifying proteins and growing bacteria is just not my cup of tea. I’m much more interested in larger organisms and that’s actually what influenced me. It was also partly family reasons:  my family is big into intellectual endeavors and wanted me to go for a PhD. But it was also from my own interests: I was not interested in doing the kind of jobs that I could easily get with a microbiology bachelor’s degree. So it was like, all right, if I don’t want those kinds of jobs, I guess I could try for this PhD program.

And I actually did end up going straight from undergrad into a PhD program. So I know a lot of other people will do a post-bac. You’ll take some time off, maybe work or do a masters degree. It just so happened that I got into a program straight out of undergrad, so that’s what I did.

And then as far as the field goes, I did focus more on the types of topics I felt would be more personally rewarding. So I applied to psychology programs, cognitive neuroscience, and then actually a kinesiology program as well because again, these all have to do with behavior, more oriented towards entire organisms, rather than cells and molecules. 

So yeah, that’s kind of how I got into the PhD program. And then again, as I mentioned at the beginning, I ended up in a pretty unique program, which was a joint one between a university and a national lab. So I spent the first year through the book knowledge comps, and then actually moving to the national lab at the NIH to do the bulk of the research. 

So yeah, through that process, I think I also confirmed to myself that academic research, the process, the findings, the things that are very interesting to me–I love neuroscience. It’s amazing as a field–I just don’t like doing it. I don’t find the research process personally rewarding. And again, you could argue that maybe I shouldn’t have done the PhD, but I really wouldn’t have known that without actually doing the PhD. 

So in order to help me keep my sanity through this process–which I think happens to a lot of us, even if we are die-hard fans of research–I started doing other things in grad school. So I took on some social activities, leadership activities, creative activities, because that’s also just a big part of who I am. And at the end of the PhD, I had somehow racked up a lot of other experiences outside of the PhD thesis research that I conveniently could talk about on my resume and during interviews.

And again, it just made me stand out as a job candidate. This is definitely advice that we give every day to people at Free the PhD, because, again, it just shows the diversity and flexibility of you as an academic to fulfill the needs of different audiences. And I think really it’s that gap that is lacking and holds a lot of us back from getting job opportunities, especially straight out of academia.

People are like, you only know how to think about academic things and academic people with academic audiences and it’s up to us unfortunately or not to show them that no, that’s not true. I have done things for this audience and that audience, and I know how to think about your problems and blah, blah, blah.

So I really think that that long period in a PhD of ambiguity, of not knowing if this is the right direction also gave me some room to pursue small side projects that I mentioned: writing, leadership, creative endeavors. I taught myself video editing because I was bored. Our graduate student council had a talent show during one of their events and they’re like: can you like sing something? So I made a little music video to go with that. And these skill sets that seem a little bit frivolous compared to our thesis or postdoc project, actually add a lot of depth to you as an individual and as a future job candidate. 

I don’t know if I would have pursued these things, if I didn’t have this kind of against space within the PhD program to really work on things that I liked. And again, I know in many environments, people don’t feel like they have that space, and I’m here to say that you should take it for yourself because it’s important to both grow yourself as a person, manage stress and mental health, and also help you open more doors once you think you want to leave academia. Because one of the things that’s common when people come to us for help trying to find a job or figure out what they should do next is we’ll look at their resume and all their work and there’s nothing that we can easily apply to something outside of academia. So it’s so much easier if you’ve already done some other things. 

Grant: Would you say that’s the most common mistake people make in grad school? 

Vay: Yeah, I wouldn’t say it’s a mistake per se. In many ways I think we pride ourselves as a demographic on being focused on really diving deep into research. So I won’t say it’s a mistake, but I will say that it is a challenge. If you want to pursue a job after a PhD that really relies on those skills that you developed in the lab, you’re fine. That’s not the issue. 

The issue is if you want to run away from the lab after you graduate, but all you have to show is lab work. Clearly there’s going to be a mismatch there. So that’s when people have a problem, especially if they want to leave right away. It’s probably not going to happen that easily.

For example, if a grad student wants to go and do something more business related and they have done nothing related to that during their PhD, I know people who have ended up doing a postdoc sort of as a foothold. So they have something to pay the bills to survive on and to give themselves space to pursue some of these other things. But you can do that as early as your first few years in grad school, if you really want to. It’s really just about keeping your eye on the future, even as you’re working on what you’re doing today. 

You don’t have to kill yourself over it. Just like, make sure you don’t get complete tunnel vision. There is a horizon out there. You want to make sure you’re kind of pointed in that direction that you want to go. And if you’re not, then just taking a few hours a week, talking to different people, taking an online course, working at a small project can actually really pay dividends down the line.

So yeah, I wouldn’t call them mistake, but I will say it’s a very common kind of habit that people fall into just saying, Oh, my research will speak for itself. And yeah it might. It just kinda depends on where you want to go after. Cause if you go somewhere where your research is sort of irrelevant, your research is not going to speak for you. So something else has to speak for you and you can start building that foundation today. 

Grant: And how about your own path after the PhD? 

Vay: I applied to so many jobs, starting from my third year and it wasn’t even serious because I knew I wanted to finish out the PhD. I think at that point, my experiments were finally sort of working, which is so common, like third year blues are just the worst. Even in my third year I started putting out applications just to see what would work and what was out there. Of course I made the classic mistake of applying to everything with pretty much the same documents, which is definitely not likely to work.

So yeah, I didn’t get an actual hit in terms of interviews. And actually, I think I interviewed for one. But the job that I got came pretty suddenly, and it was also because I was looking. I didn’t technically have an internal referral, but I did have experience with the company’s products, which I’m actually always looking for when people are looking for industry jobs.

I’m like: what have you used before? Have you thought about actually working for one of these companies, because you have an advantage over other people who haven’t. So yeah, it was posted on a scientific society website, on their jobs page. And I looked at the job description. Most of the time when I was looking for jobs, I was looking for things that did not say postdoc. Of course that’s usually the job postings you’re going to find the most of on scientific society websites. And this one was different. It was for an application scientist. And I was like, what does that mean? But when you looked at the job description, and what they wanted from the person, I was like, Oh this sounds cool. I could do this. And I knew their product. I just actually started working with one of the earliest prototypes that they had developed. There’s always a little bit of luck or a little bit of serendipity in everyone’s journey.

My PI just happened to have their product. So that wasn’t something that I had meant to happen, but I did work on it. And those skills that I mentioned, I developed through the PhD, the speaking skills, the teaching skills, creative skill, even the video editing skills, all things that I ended up mentioning in my cover letter and talked about during the interview.

I really sat down to think about: Why do they need this person? And they weren’t even looking for a PhD. They want a bachelor’s level person, but companies, especially good companies, good hiring managers, are looking for someone who can fulfill the need that they have, whether or not the person looks exactly like what they thought that a candidate should look like. So you have to sell yourself. And I think I was fortunate that I was able to do that, having hands-on experience with the product. And I could point to the tutoring that I had done, the protocols that I had written, the videos that I had made and say, I can now apply all of these things to help your customers be successful.

And I think that was a very compelling argument because they gave me the job. So that’s how I made that first leap out of academia. I was really taking these routes that I had started to grow in different directions and apply them to the needs of this new audience of this company. 

It was a startup company and I really enjoyed my time there. I think startups are amazing opportunities for any academic. They typically move fast. They’re cutting edge. People are collaborative. You’re working on new things that no one else has done before. So a lot of those transferable skills that we develop in a PhD applied really nicely to this kind of a work environment.

And then yeah, I was there for almost six years and transitioned through different teams. And I really let myself use my own interests to drive that direction. Application scientists are typically the support team in any company that has a highly technical product. So it was a great fit. And actually, I think application scientists roles are a fantastic first step for many highly technical academics.

There’s just a lot of stuff that you bring to the table right away, that you can use immediately. And then for myself, I mentioned I have a creative streak. So I started working on marketing materials for the company. They had a very small marketing team and I really enjoyed communicating with people and being creative about it.

And that’s a lot of what large parts of marketing can be focused on. So, I love that part. And then, sort of serendipitously sort of purposefully I ended up moving into much more of the commercial side and into the sales world. Partly because my boss actually asked me, did you ever think about sales? And I was like, no. And he basically said I should think about it. A lot of what happens to us in life is about keeping an open mind. Just keeping those feelers out there for new opportunities. I had never thought about sales ever. I had my own preconceived notions and biases about what sales means.

I think that’s very common in academia, but I got to see for myself exactly what this type of job in this type of industry is like. And I have to say I enjoyed it. There’s always going to be pros and cons. Perks and problems in any type of role. So it’s really just about letting yourself be honest about what you want.

What do you want in your career? What do you want in your life? Does salary matter? Does travel matter? Does personal time and flexibility matter? These are things that we don’t typically think about when we’re training in academia, but it becomes more and more important as we get further along in both our career and our life.

I actually ended my time at the startup company in the sales team. And then I was recruited to my current position where I’m still technically on a sales team, but I’m playing a more educational role than the entire sort of commercial life cycle or sales cycle for a client. So it’s a little bit like an MSL (medical science liaison) position where you’re supporting sales efforts through technical education essentially. 

Did I ever think I’d end up in a role like this? No. Did I ever think I’d jump from very, very basic neuroscience to now thinking about patients and disease and lab tests? No, I didn’t. So there’s a lot that’s going to happen to each and every one of us that we can’t predict, but all you can do is try to be true to what you have strengths in naturally. And then people recognize that and they’ll try and draw you for those types of things. And that’s just a win-win for everyone because you’ll be happier and they will benefit from someone who really likes to do what they do. 

Grant: So on the psychological side, what would be your top three pieces of advice for grad students or postdocs who are looking to leave academia?

Vay: Three pieces of advice? So I’d say, first of all, try to always have an open mind. I’ve already mentioned kind of keeping your eye on the horizon, even as you’re kind of diving into a tunnel into the middle of the art in your inner research topic. There are many, many different ways to think about where you could end up. Having an open mind about it, and not judging yourself or putting too much pressure on yourself to achieve only X. Success is not just one thing. It’s going to evolve. The definitions will evolve. You’re going to change your mind about what’s important to you. And that’s all really normal. So it’s very important not to lock yourself into this is the only right way to do something.

And especially as researchers, we should typically know that that’s not necessarily the case. Just because we want this protocol to work doesn’t mean it’s going to. Just because we want this pathway to be a certain way doesn’t mean it’s going to be that way. So it’s the exact same thing about trying to plot out your life.

You can certainly set yourself a path that’s desired, but if you end up taking a left turn or a right turn somewhere, that’s fine. Just reorient yourself. There’s nothing wrong with it. People do it all the time. We just don’t see it as much. I think people just don’t talk about it that much. Most people’s paths are not straightforward.

It’s not one line. You rarely work for one company forever. It just doesn’t happen anymore. Be open-minded and sort of along the lines of being open-minded, make sure you reserve time and energy to do hands-on things that are not your research. Like this is just very practical advice.

No one can help you do this. It’s why I’ve always been trying to get to earlier and earlier stage college students to do seminars and talks and workshops because we want inspire more people to get the experience that could help open more doors later on as early as possible. If you have like three months before you graduate, that’s usually not enough time to show a complete project in something else that would help attract an employer or a hiring manager. But if you started in even your fourth year and you graduate in your fifth, that’s one year of experience you can talk about. You don’t have to be officially anointed or paid by someone to claim that experience because it’s really that learning process, a process of prioritizing execution of a project. 

It’s that experience people want. That’s what they mean when they say they want one or two years of experience in X. So you can give that to yourself. Gift it to yourself. You deserve it, as early as possible. So just stay open and try some other things because really you’re not going to know if you like sales or don’t like sales or like business or don’t like business, or are going to be great at consulting or are going to suck at it, if you haven’t even tried something related to the work. And it shouldn’t be that difficult, especially now with the internet.

There are plenty of relatively cheap or free ways to get exposure. And I would say, don’t feel like you need a certificate. Don’t feel like you need to pay a few thousand dollars to get some sort of official degree to show you can do something. In many ways it is actually the self-driven side projects that are the most impressive because those are the ones that you’ve taken the initiative to do and figure out for yourself. And that is what people are going to be judging your capabilities on. So, make sure you save time in your day, in your week, in your month to work on your own projects.

And nothing can substitute for this. And I can say this from personal experience, a certificate is great if you can show like, Oh, I’ve completed a course. But that’s not going to substitute for actual experience. I did this project and this was the result. And here are the people who benefited and whatever.

So, I mean, you can make the same argument. Like you can say someone has a particular degree or a certification in an instrumentation in your lab versus maybe you’ve been using it day to day troubleshooting, figuring it out, finding all the quirks, teaching other people. I would hire you in a heartbeat over someone who just has book knowledge about something.

And so this is exactly the same for us, if we’re interested in a particular field in the workplace. Do you need an MBA to get a job on the business side of things? No, you do not, but you probably do need to work on some sort of business related experience, some project or internship if you can.

Because it’s being immersed in that environment, facing those challenges and floundering your way to success. It’s that process that people are looking for. So give that chance to yourself to experience it.  And then you can figure out how you want to leverage it in the future. So I’d say that’s sort of two things, and then I’d say the third one is one that I have repeated many times to different people.

But I think it still bears repeating. It’s the fact that you are not going to waste your PhD. You no matter what you do. And I think this is one of those very, very deep seated, psychological fears that everyone has. We invest a lot of time and energy into this degree, into this time in our life. And I think everyone would like to make use of it in some way in the career they pursue.

And sometimes there might be this great direction that people are hesitant to go into because they feel like they’re not doing their degree justice in some manner. And I just want to encourage you to think about it differently. You’re not defined by the degree you define the degree, right?

The degree follows your name. So you are whatever kind of PhD you want to be. And to any one who says otherwise you’ve contributed already to your field in your degree training. So you don’t necessarily owe the field anything more. You did what you’re supposed to do to get that degree. Now you can take it and do what you want with it.

Right. So, I mean, that was certainly one of the fears that went through my head: Oh, I’m going to go be a customer service representative. It’s not the way you should be thinking about it. But I admit it happened to me too. And I was like, is this what I did a PhD for? But then looking back, you realize at the end of the day, it doesn’t matter.

I still have the PhD. It’s mine. No one can take it away from me. No one can redefine what I did in that period. Papers are still out there. People are still reading my protocols. So I have done myself justice in getting the degree. I’ve done my field justice in finishing it and contributing to the field. And now it is your turn to have ownership over where you want to take the degree, where you want to take yourself. So believe that you have the rights to define how a PhD should be seen, how it should be valued and what you want to do with it. So that would be my advice. 

Grant: Thank you so much for those words of wisdom, it’s been really great having you.

Vay: Yeah. It’s been wonderful to be here. I hope that people enjoy our conversation. I think it’s great that you’re getting out there and telling people more about what you guys do. And then also sharing the thoughts of fellow academics out here in the real world. Anyone who might be struggling a little bit right now with the direction that they want to go in, we welcome you to check out our website, freethephd.com. We have an open forum that people post in and have actually very similar kinds of conversations with different PhDs in different career paths, just to introduce people to different options. And then as I mentioned, we have developed over the last few years.

A pretty comprehensive step-by-step online program. And that comes with personalized guidance. When people have questions ranging from: Is this LinkedIn message okay to send to someone? To hey, you know, I’m not sure how to answer this interview question. How should I go about it?

We’ve really tried to put together the best of both worlds, the sort of digital self-serve plus that personalized attention. Cause again, I really, really believe having that emotional, psychological support through the process is absolutely vital. You really have to feel like it’s okay to do what you need to do. And then people do great. They do amazing things. So it’s been such a pleasure and honor to help people get to that point. And so we hope to continue helping more fellow academics. 

Grant: Thank you so much. 

Vay: Thank you.

Transcript of Episode 10: Mark DePristo

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Mark DePristo. Mark, can you introduce yourself please? 

Mark: Delighted to be here, Grant. Thank you for the invitation. I’m Mark DePristo. I’m the CEO of Big Hat Biosciences, a San Francisco based AI for drug discovery startup. My background in that space has really been at the intersection between bio and tech for about 20 years. I was an undergrad in computer science and math. And I won a Marshall Fellowship that sent me to England and I got a PhD in biochemistry, and I really got the bio bug after that. 

I actually went to Harvard and was an experimentalist for three years. So I learned how to pipette, though pretty poorly. I saw the full stack of that. And from there I went and was at the Broad Institute and most recently at Google applying AI to bio in the broadest way possible. 

Grant: Fantastic.  So, can you tell us more about what you’re doing at Big Hat?  

Mark: Big Hat Biosciences is really focused on radically improving the design of antibody therapeutics. And we’re doing that to enable a next generation of even more sophisticated therapeutic molecules, which I’m sure we’ll get into in more detail. And how we’re doing that is we’re really leveraging recent advances in AI and machine learning as well as synthetic biology techniques to build a new type of wet lab, that’s very high cycle time. So it can do a lot of work quickly. And it’s coupled at every part to AI and machine learning technologies to guide that. So Big Hat is really a close-loop antibody engineering shop. And we drive that technology to basically do data-driven or rational antibody design. 

Grant: And can you talk a bit more about what the ideal Big Hat antibodies would be able to do? 

Mark: Yeah.  So to really answer that question, I think we have to sort of talk a little bit about the history of antibodies. So,  the first wave of antibody therapeutics were all based on monoclonal antibody technologies. And what that means is you’re basically repurposing an immune system–it could be mice, it could be human–to give you an antibody that the body can produce. It’s a natural product of the body. And those molecules combine to all sorts of different things. You can make them bind to surface receptors. And this is really the origin of the top drugs today that are biologics that are all antibodies. 

The challenge with monoclonal antibodies is not that there’s something fundamentally wrong with them–they’re very good molecules. It’s just that they’re very limited in what they can do. They’re the natural product of the immune system. 

So they intrinsically do all the things that the immune system wants them to do. Right. They interact with the immune system, they activate inflammation pathways. They have a very specific way of binding interacting with targets. And they’re really, really big. I mean, these are massive molecules and all of those issues limit what you can do with them. 

And so after about 20 years and monoclonal antibodies, there was this huge explosion of so-called next generation formats. And these are antibodies that are engineered to not be natural products. They’re now,  more designed around the needs of therapeutics. So there’s all sorts of things like molecular glues that use parts of antibodies to stick things together. 

You can stick molecules together, you can stick cells to molecules. You can stick cells to cells with these bridges. You know, we want to make small antibodies. We want to make antibodies that are environment sensitive, so change their behavior and pH. Change their behavior in the presence of other molecules. 

Of course, those don’t come out of human immune systems. Don’t come out of mice. We own the engineering of that. And I think Big Hat’s really founded on the problem, that gives rise to Big Hat, the commercial entity, is designing these so-called Frankenstein antibodies is just incredibly difficult. And it’s difficult along a whole bunch of dimensions that we’ve never been particularly good at. It’s really challenging to do rational drug design. No matter whether you’re working on biologics or small molecules, it’s just very difficult. 

Two, you have this enormous space of possible antibodies, right? You have this sort of combinatorial search problem on which amino acids to put in the antibodies. That caused the search space to get really big. It can be very hard to find good molecules. 

And finally,  because most monoclonal antibodies are coming out of organisms–human, mice etc.–they’re pretty good molecules. Like they have to be tolerable in the body. Once you start to engineer those molecules and make Frankenstein versions of them, they don’t really have to function particularly well. 

They can be deeply unstable. They can aggregate. They can do all sorts of things you don’t want them to do. And removing those things, stabilizing the molecules, removing its aggregation propensity, this is really hard to do rationally. And so we have these amazingly exciting next generation antibodies, things that are transforming cancer therapies, immune therapies,  even infectious disease, but we have an unbelievably difficult time creating them because the processes we built for natural product discovery that worked great for monoclonal antibodies don’t really help you on that engineered molecule because they’re not coming out of organisms. And so the problem is fundamentally different and that challenge is sort of what Big Hat is focused on addressing. And the modern technologies we use to address this is sort of why we’re able to do this now. That’s really what’s changed about the world. 

It’s not that people didn’t want to do this 20 years ago. They were all very excited. It’s just that 20 years ago, when I was doing this work, making a couple of mutations to an antibody, it would be weeks of work. And today this is something that Big Hat does routinely. 

Grant: That’s incredible. So, what kind of balance do you have on your team between ML people, structural biologists? Is it usually people who came out of PhD programs, where they did machine learning for structural biology? Do you get people who trained up through very different fields and then they apply that to another field at Big Hat? 

Mark: That’s a great question. So it has to do a lot with sort of what is the structure of the teams and the organization at Big Hat. You know, when we were really forming Big Hat, there was this open question of like, how should we organize the teams? Should we have a computational biology co-organization on one side and an experimental biology work on the other?

And that’s what you typically see if you look at most companies: you’ll see that kind of computational versus experimental division. Often from the very start of the org structure. We really were nervous about going down that route. We really saw the value of Big Hat in part through the integration of the wet lab and dry lab tech. 

And we really didn’t want people to be thinking about it being somebody else’s problem to do data analysis or somebody else’s problem to produce high quality experimental work. And so we actually have totally not gone down that route and we’ve really structured Big Hat more around the projects we’re working on. 

So in a matrix, we’ve pivoted the matrix to another dimension, and organized the company that way. And what that means is that it allows us to produce teams that are what you could imagine, skill complete. Like all the things that the team needs to be able to do has at least some representative expertise on it. And it’s manifested in,  three or four people, that have eight or nine skills that are required. So everyone is multifunctional, but the team isn’t complete unless you have all the people. 

It means that we mostly hire people for two attributes: they have some number of skills that they can bring to that team and they work really well with the other people on that team. It’s hard to be the Atlas and push up all the problems at Big Hat on your own. It’s just not going to work because nobody has the expertise across all the tech that we work on. We work on everything from DNA synthesis to active learning technologies on the cloud. That’s just not reasonable to ask for. So we composed Big Hat out of a bunch of puzzle pieces that all fit together to give a picture of what we need to do.  

Grant: So I noticed on your website, you talk about being a team oriented, inclusive, remote friendly, and family centric culture. Were you remote friendly from the beginning? Was that accelerated by COVID or is that by design?  

Mark: It was both by design and by necessity. Actually, we were remote from our first hire. Our first hire is a guy named Eddie Abrams, who’s the VP of engineering who I worked with at SynapDx before Google. Eddie, he’s a fabulous guy, and Eddie lives in Arizona. 

So the choice was simply: do we want Eddie to join us and be remote in Arizona at the start? Or do we not want to do that? And we made the right choice. We took Eddie on. It’s been a fabulous journey with him as employee number one, but that happened a few months before COVID started. So we built Big Hat from the ground up to make sure that our first employee could be productive in a remote-oriented culture. 

And so that’s turned out. Obviously we didn’t anticipate how important that decision would be, but it proved great. And now Big Hat is highly distributed all over the country. In fact, I don’t believe that the center of mass of Big Hat is even close to San Francisco at this point. Most of the company is actually stationed on the East coast. 

That’s the remote answer. So the remote answer is definitely very remote friendly. I mean, we were just fundamentally remote by definition. I was actually at Big Hat yesterday for the first time in months. So it was great to see everyone again and see what the lab looks like now. But we try to do that because you know you want to eat your own dog food. So I try to stay remote, so I’m sure that the company is remote friendly.  

Really inclusive is just this vision, that two things: we want to be flexible. You know, not everyone wants to work the same hours in the same time zone in the same way as everybody else.  And in particular, it’s clear that the people who need the flexible hours are often people who have more complicated lives through any number of things. I mean, I have three young kids and this introduces so much logistical complexity in my life. You know, I need to work in an environment that’s flexible and I have a lot of benefits to being able to do that. 

So. Big Hat has structured itself around this idea that people should work when they need to work. We’re going to be remote across many time zones. Like we can’t say the business hours are 9 to 5 Pacific time and demand everyone be there. So we really are a more results oriented place. We try to make sure that it’s very clear what everyone’s working on. 

We all work on it together. It’s very collaborative. And we care not about face time or how many hours, but really are you contributing to the mission and the goals of Big Hat. That focus on results and flexibility means that we’re able to recruit lots of people in a much more inclusive way than if you’re physically located in one place with very specific timelines and when you should work. 

And family centric is just as simple as basically Big Hat forces working hours on people. So, what we mean by that is we don’t want people to be getting emails at midnight. Like if people are sending emails on the weekend, this is strongly discouraged. The expectation is that everyone is working a 40 hour week, and they’re not killing themselves to put in an 80 hour week because we don’t want you to do that. 

We don’t want people to burn out. We want to make sure that people who have other obligations in their lives can fulfill those obligations without worrying that Big Hat is somehow unhappy with that. And what it really forces honestly is two things: one is it forces real prioritization, right? Like we’re not going to work. 

Thursday night, just because we want to do a little bit extra work that week, right? Like, unless that’s critically important. For example if we have a deadline on that Friday, in which case we do this. But that can’t be your MO because ultimately Big Hat is really a collection of people. And that’s why we’re successful is that we’re able to have really good people who like to work and are very productive in the environment we’ve created. 

And in some sense, the worst possible outcome for Big Hat would be to set up a culture where we scour the earth to find all the right people to work with us and then bring them into an environment that’s such a pressure cooker that they burn out almost instantly, and then they’re not productive. And that’s a huge failure mode for tons of startups. 

And it’s one of the biggest questions we get. I mean, people will come and say, you guys are such an early startup. Are you expecting me to be here at 9:00 PM every day? And it’s really a pleasure to be able to tell people that no, that’s not at all what the culture is like. I stopped working at 5:30.

Like that’s it. My kids are home. Dinner is happening. I’m going to go do that. So, that’s forces real prioritization, real focus on the company. And actually it turns out to be the right answer for the company as well. You want to be a focused company that only does the critical stuff. 

So drawing an arbitrary line in the sand that says 40 hours is okay and beyond 40 hours is not okay. Actually it turns out to be an excellent prioritization mechanism and everyone works on the things that are super, super important because you got a fixed amount of time to work on it.  

Grant: So I saw a tweet the other day: “A paradoxical thing about people who consistently choose the most high leverage activity is their efforts have a rough-edged, half-assed quality. Because polishing things to perfection is a low leverage activity.”

Mark: That’s interesting. Who said that? 

Grant: Tiago Forte. I’m not sure if I pronounced his name correctly.  

Mark: I like it.  

Grant: I was wondering if maybe we could go back to the beginning to understand how you got here. Let’s go all the way back to childhood. What got you interested in science? Were you interested in science as a child? 

Going way back, I was not a very academic kid at all. I was definitely the problem kid all the way up until about high school. It wasn’t really until eighth grade. In fact, when I really got engaged in academics at all. And that was because I managed to get placed out of my remedial mathematics by taking the pre-algebra aptitude test that Iowa gives out to everyone every year to see if they should be in the algebra class. 

In eighth grade, it turned out I should have been in that class, which was really the start of like, Hey, maybe I can do math and maybe I can do this other stuff. But yeah, I grew up in Iowa, in a small town of 50,000 people, roughly half students and half full-time residents. It was a great place. You know, it was super interesting to grow up in the Midwest. 

I mean, it would have been easier had the internet been there at the time I grew up, which sort of came in right at the moment I left. But it was a pretty ideal childhood. It was very peaceful. It was very easy. There was no crime, nothing to be concerned about. I could just wander around the town even from a very early age. 

But I never really was academic at all. It wasn’t until high school when I really got interested in some things. I went into Northwestern University as a declared English and history double major because those were the best teachers I had by far in high school. I mean, I loved literature. I loved history. I used to love art history of all things. So when I went to Europe at one point, like I just traveled around to all the museums. And honestly it wasn’t until I got to Northwestern that I even saw anything that was sort of interesting on the science side. It was always presented in the most dry imaginable manner. 

You know, you’d read these physics books and you’d be memorizing the equations as though the equations and your ability to solve those equations was the thing that was interesting scientifically. It’s only now that I’ve been reading The Feynman Lectures for fun over the last few weeks, and it’s so enjoyable to approach science from this perspective of understanding, as opposed to manipulating equations. 

That was really my big journey when I was at Northwestern: the transformation from an English history double major into a computer science and math double major. And I kind of went from English and history, and I got into some cognitive science, which of course I never saw in high school. 

Maybe now there’s cognitive science in high school. I actually don’t know what the curriculum looks like these days, but I’d never seen anything like this. This was amazing, this class about how people behave and how to understand the brain. And from there, I was sucked into the computer science classes and the math classes and yeah, I came out really being a tech person, but not a scientist yet. I mean, I was really into math and computer science. 

But I had the good fortune to win a Marshall scholarship. You know, this was really a transformative experience for me. One, it was amazingly empowering. I mean, they give it out to 40 people in the United States every year. So really that made you feel like, okay, I have really accomplished something. Then, I am on a trajectory where I can contribute. And it sent me to England with kind of a blank check. They really didn’t care what I did at all. And I tried to be an undergrad again at Cambridge University. 

I was originally a natural sciences undergrad, and that was clearly not for me. And I spent a couple of weeks just doing nothing at Cambridge, trying to figure out what I was going to do with myself. And I read an article by a professor at UCSF named Ken Dill, which substantially changed my view on what I should do. He was talking about the problem of protein folding, and the complexity of proteins and computation. This was 2001. And I was like I should do this. This is an area that seems exciting. Like, why don’t I go talk to the biochemistry professors at Cambridge. It seems to be a pretty good place. So I literally walked into my future PhD advisors office. That was a guy named Tom Blundell, who was the chair of biochemistry at Cambridge. I said, I have my own money. Do you want somebody to hang out in your lab? I don’t know anything. And he said, that’s fine. You should talk to this guy, Paul Walker.  He’s a great friend of mine now. And it’s funny, we’ve been bouncing around in the same field for many, many years. And he was nice enough to let me in the lab. 

And then I suddenly was a biochemistry PhD student, and I had a lot to learn and I spent a lot of time reading a lot of books about science. I mean I really had almost no idea about any of it. So it was incredibly exciting to learn. And after three and a half years of doing that, I popped out with a PhD in biochemistry and mostly focused around methods for solving crystal structures. 

So I mean it makes sense. I had this technical skill. I could help with the hard technical problem of interpreting all these spots that you get from spraying crystals of proteins, which was my cup of tea. But after that I had really gotten the bio bug–to be totally frank–and I had this realization that if I wanted to be a serious life sciences researcher, I had to go and do experiments. 

So I signed on to a lab to do experiments at Harvard. I joined Daniel Hartl’s lab. I joined originally with a guy named Shamil Sunyaev, but ultimately was more split between Jim Collin’s group and Dan Hartl’s group. And in Dan’s group I became an actual biochemist. Like I actually purified proteins and made Newtons to proteins and we published a great paper with–actually hardly anybody’s on this paper. There’s only four of us on it. One of the guys is still an incredibly good friend of mine. He lives literally down the road from me here in California, Nigel Delaney, and another guy, whose name is Dan Weinreich and Dan Hartl were on the paper where we made 32 mutants of beta lactamase. 

So we created the plasma and we went in and made 25 mutations of all combinations of five mutations that include antibiotic resistance in bacteria. And we just asked this very simple question, like how resistant is each possible trajectory from no mutations to all five? And that was a major science paper in 2006, because it was so hard to make mutations.

It was so hard to do that experiment. It took us a year or more to just make 32 mutants and measure their MIC’s. But it was just transformative because you make data. Like you could understand it.  It wasn’t just analyzing the data in somebody else’s database. I could make the data and understand what was happening. 

And that was totally amazing. I mean, it was great to be in that environment and to work on things like that. But by that point I was really convinced that I didn’t want to be an academic. You know, I think academia is a great place for some people. And I have to say, I continue to be disappointed by the narrative you see in the academic community that the pinnacle of success is to become a professor like the person who’s educating you or mentoring you through the process. And like, do you really don’t want that? 

I was pretty happy to leave and joined a consulting firm called LEK Consulting to learn about business. So I spent many, many months really trying to understand–suddenly–the business side of biotech. Like how much should we buy this company for? I don’t know, Hey, how do you value somebody’s pipeline? 

Like, I don’t know they got one asset in phase two. Like how much money is a company worth that has this asset, but can’t even sell it. So I was super fascinated by it. It was a great place to work because of all these questions. Ultimately I spent a lot of my time thinking about those questions, 

But I didn’t actually remain too long because I got pulled into the Broad Institute, which was at that point a very small place. My friend who I had actually walked into the room with on the first day of biochemistry in Cambridge was at the Broad Institute. He had moved to Boston a year before me and was basically saying, Hey, these sequencers from a company called Solexa slash Illumina have just arrived here. 

And we have no idea what to do with them. We’ve got this project called A Thousand Genomes that we’re trying to start up and like barely understand what’s happening here. There’s way too much data. And it’s just chaotic and you do want to come help. And that was really, I think the start of what I would think of as my serious professional career. The Broad Institute was the first place that ever gave me an opportunity to manage a team, to think about a product that isn’t a paper. 

I had grown up in the sciences. So the end result of everything was the write up, the paper. Maybe you gave a talk, but that was it. Like suddenly I was at a genomics Institute. Oh my God. I mean, I could publish papers, but there was also like, we just have to sequence organisms and like make software that you could use to do this. 

And so I really built out a team there that created the GATK, which is called the genome analysis toolkit, which is now a pretty widely used piece of software in genomics. And I grew that team. It was originally very small, maybe one or two people, and they grew up. At the end when I left there were about 20, but it’s huge now. I think there’s a hundred people on that team, owning all of the analytics at The Broad Institute. 

And I would say that was just a fabulous experience. You know, I got to build software that was high scale. And in general, genome sequencing covers a lot of data. So that was fascinating. You know, it was super hard, statistically: I learned everything about stats and machine learning, really not from all the theoretical stuff that I’d done before, but there’s nothing like banging your head over and over into the error modes that are on the next gen sequencer to make you really appreciate all the different ways that you can build statistical models and all sorts of machine learning things. That’s really hard. And it’s the most nightmarish environment for all of this stuff. Systematic errors and all sorts of complex structures to do them. 

And that’s hard, but at the same time, you can sequence the genome of an organism, at low costs. So it’s totally worth every hour you could put into solving that problem. I spent five years thinking about that problem at the Broad. And it was great. It was so satisfying to create GATK. 

But after five years there it was clear that I didn’t want to become a professor. That was sort of the only out I had. I was super senior by that point. I just didn’t want to do that. You know, I didn’t want to write grants and papers for the rest of my life. I found releasing GATK software updates, like a thousand times more satisfying than writing a Nature paper. I had to figure out what to do next. And I knew I wanted to go back to business. I loved being on the business side at LEK, but I knew that I didn’t want to be a comp consultant again. 

I mean, I liked building stuff. And so I joined a guy named Stan Lapidus at his startup called SynapDx, which was a little bit outside of Boston in a place called Lexington. And he was running an amazing startup. I mean, they were looking for biomarkers of autism in the blood. And so I got to join this company and for two years ran a multi-omics trial, trying to look for any possible way of diagnosing earlier risk for autism. 

And it was a great experience. I mean, I did every kind of imaginable blood base sample you could find. Unfortunately there is a signal in the blood–I mean, we know that it’s in the DNA, it’s in the small molecules–but there’s not enough to be clinically useful. So at the end of SynapDx, which was really, I have to say, fabulous. Despite the negative outcome, it was among the most important experiences I’ve had.

One of the guys who was on the board there was Andy Conrad, who was starting this Google life sciences thing inside of Google X and asked if I wanted to go there. So we packed up everything and moved out to San Francisco and were suddenly at Google X. And then that became Verily. And I ultimately moved to Google Brain. 

So I was in Google brain from 2015 to 2019. So really at the heart of the AI revolution inside of Google. It was a shocking place to find myself, but it was totally an amazing experience. That’s my story. In a nutshell, like I can dive into any one of the pieces in any more detail that you’d like to hear about. 

Grant: Yeah. I’m just wondering for the last hop from Google to Big Hat what prompted you to start your own company? 

Mark: I think there were two major drivers there. One was, I loved being at SynapDx. It was amazing. All my colleagues were amazing, great people. And we had a small company. It was only 20 people and we did amazing things. 

We had a whole giant clinical trial. Created these amazing machine learning systems in the cloud to analyze tons of data. It was just fabulous. And it really convinced me that there’s a lot of things that you could do with 20 people and you can move the needle on really important problems. 

And I’ve always liked the commercial side of things. When I was young, I did all sorts of commercial stuff. Like really I loved mowing lawns and such. Actually the worst investment I ever made in my life is that I was really into Magic the Gathering cards. Way before they were popular. So I had everything, I had hundreds of these cards, they’re all worth thousands and thousands of dollars. 

Given that I was like a baseball card collector. It’s kind of embarrassing that I didn’t see that, but I loved it. I mean, when I was a high school student, I would go to the local university and trade Magic the Gathering cards all weekend. I actually never really liked playing. 

All I really liked to do was trade. So I would find out who had what cards and liked what cards. And I would just arbitrage card values all day long to collect more and more of the stuff. So that was really, I think, the start of my real interest in commerce, knowing what other people wanted and who had what you could really collect up some amazing stuff. 

So I always had that bug and I saw how much you could do in a startup with Stan. And it was great being in Google, but Google is big. I mean, that’s just the truth of it. It was a startup, but it’s a hundred thousand person company now. And so no matter how happy I was in Google, I mean, my colleagues were amazing. We had unbelievable support. It’s still too big for me, it’s hard. I don’t like spending all day sort of talking to all the different people, trying to convince them about what we’re doing. You’ve got to like that if you are in a big company. Because you got all your peers and all the people up in the org above you, who are going to want to talk to you about what you’re doing. And that I just found too, too much overhead. 

Especially in Google. You consider that at Google you’re talking to people who don’t even know about life science. You’re saying, Hey, look, life science is important. You could end up in that situation. So if you really want to stay focused on moving the needle on life sciences stuff to sort of bottom out into conversations about why life sciences even work could be a little demoralizing. 

But it was a great place despite that. And ultimately I think what really convinced me that I had to leave was when I was at Google, I had the opportunity to sort of apply machine learning at arbitrary scale to any public data set I wanted. So we pulled in everything. I mean, all I did all day long when I was at Google was think about how I could hit some life sciences problem with the deep learning hammer. 

And it was really like can I transform this kind of data problem? And can I bring ImageNet or Transformers or whatever kind of system we have in place to solve it? And so we did that all day, every day for years. And what was really clear toward the end was that we made the most progress when we could compare to some kind of reality or experiment that was much more sophisticated than the raw data for your models. And that’s why DeepVariant, which was the flagship effort of the team, which is interpreting genome sequencing data and in some sense did so well, because you could reference these gold standard datasets to generate tons of training data points. 

But as soon as you left that, you didn’t know what truth was in almost all of biology. So it was very hard to train models. So you would make some progress, but you could just forever be frustrated by the inability to have the right data to validate your models. 

And I don’t mean trivial, like let’s split the data. Like that’s not what I meant. I mean, everybody does that. That’s table stakes of being able to do splits. It’s more like, how do I know my model generalizes, if I’m going to use it in the future? And the truth of the matter is: there’s no amount of cross validation that’s going to make you convinced that the data you’re going to collect today is going to be similar to the data you already collected. 

And so Big Hat was simply a product that I wanted to do something next, where the lab was integrated from the start. It wasn’t bolted on to the AI systems after the fact. And that was fundamentally never going to happen at Google. Google was in silico in the first place. From a life sciences perspective, doing experiments was incredibly difficult for many reasons, but at the end of the day, like I wanted to have experiments integrated into all this stuff. And so that observation that there was so much that could be done if you just had an integrated lab with the AI was why I left. 

And I spent months and months once I left saying, okay, out of all these areas, where would you get the biggest bang for your buck if you could integrate a lab? And we want a lab that doesn’t look like everybody else’s lab. I want a high throughput lab. I want a high cycle time lab. I want my lab to be measured in like Hertz, right? Not number of data points. Like I want a totally different scale. And so that really led toward Big Hat. 

And of course I met my co-founders at both SynapDx and then really at Google. And so we all sort of had a similar issue, which was: we need better experiments. Experiments designed for the needs of the AIs.  

Grant: That’s fantastic. So other than antibody engineering what else do you think will be the most transformative applications of machine learning and biology in the coming few years? 

I think there’s four big areas that I would expect to see major advances. One is the space that Big Hat is in which we broadly think of as molecular engineering. Most of the technologies we have for creating molecules, small molecules, big molecules, material science, all this stuff, they’re like they’re screening technologies, right? 

Like I can generate diversity from some natural thing. I can go grab the fungi from the forest of the Amazon and screen it for anti-microbial properties. And that story is everywhere. Right? Like I can do that for material science. I can do that for biologic molecules. And nobody really likes that. You know, it’s just totally disempowering because we want to engineer, like we do cars, not screen for random molecules that we don’t fully understand. 

And so AI tech is key. To break through that transition from screening to rational design, you need technologies that look like the things that Big Hat is heavily in, predicting the properties of molecules. So given a molecule, what do you think it’s going to do? What are all the properties? And then the other side of it is, given I made a whole bunch of molecules already, what’s the right next set to make that we think would be even better than what we’ve made before? And those are really the two AI techs that Big Hat cares a lot about. 

And I think that’s going to play out everywhere. Material science, small molecules, big molecules. I think you’ll see huge advances in target selection with AI in the next few years. The argument there is simple: we’re very good at target selection today from genetics, right? So at the Broad, it was just like unbelievably sophisticated GWAS stuff happening everywhere. 

And it’s usually like that’s table stakes. Like you already have unbelievably sophisticated statistical machinery that has nothing to do with machine learning to define your genetic variants associated with disease. What’s been difficult, really is integration of data, right? It’s hard to take the genetic variance and expression levels and cell state stuff and all these sort of heterogeneous data types that actually could tell you really more specifically about how to think about drug design, and really whether this is a good target, what exactly you want to do with the target. 

And I think AI is big there. It’s really not clear how to do data integration without going into some sort of AI like tech. The statistical model is unclear. So I think you’ll see a huge boom because you’ll be able to integrate data. You’ll be able to leverage subtler signals to make more informed decisions about the targets. 

I think the flip side of that, the other really complicated thing on the drug design side, is not the targets, but the people. So, which drugs are likely to work on which people? It’s a huge problem. I think you’ll see a lot of more sophisticated patient selection approaches arising from machine learning, where you’re saying, yeah, we can give this drug to everybody, but we think that this subset is going to be most enriched. Let’s do that trial on the subset because it’s faster, cheaper, better. We’ll have better signal and all this stuff. 

Really if you could imagine this world where you could perfectly choose which patients will respond to your drug, but this just means that you don’t have to have giant trials. You can have small trials that have big effect sizes. So the pressure there to figure that out is so huge. I think you’ll see tons of AI applications. And again, I’m not that this is not a novel insight. Lots of people are out there doing that. 

And finally, there’s the biomarker problem, right? I want to predict who’s going to get the most benefit from this drug. And I have two drugs, which of the two are most likely to work for you? You didn’t respond to this. What’s the next best choice? Like all of that kind of stuff. It’s going to be transformed by AI. 

You’ll do imaging based and biomarker based assessments systematically to help you make decisions. And my prediction is that it’ll reach the patients last in some sense, which is disappointing, but not unreasonable. Because I think what you’ll see is that’ll come out in patients that are in trials or patients that are really already in a medical situation. 

As opposed to personalized medicine, right? What you’re going to see is things like smarter cancer drug selection, and that’s already going on now. And then maybe one day 20 years from now, somebody will tell you to take Motrin instead of Aspirin because of your genetics. 

But I think we’re still a ways away from that world. But if you’re in a clinical trial, I’m sure that you’ll be routed soon. Based on the AI deck. 

What lessons do you wish you’d learned earlier in your career? 

There are many, many things that I wish I had known. You know, I was reflecting on this when you sent me this question earlier, it’s not obvious to me that you can learn these things just by listening.  Like, I can tell you what I wish I had known. And in some sense, intellectually, I probably did know it, but there’s a world of difference between reading in the book and knowing it in your bones. I think there’s a couple of takeaways. One is: technical skill isn’t everything. Most things actually look more like a threshold than a continuous scale of return. 

It’s like, there are very few areas–I think maybe pure math is like this–where raw skill translates to success all the way up into whatever quintile you happen to be in. If you’re in the top 50 in the world, you could tell the difference between the top 50 and the top five. You could do that in sports, but I don’t think you could do that in software engineering. 

There’s a table stakes. Like you’re just productive enough. And granted, some people are super productive and others are a little bit less, but my experience is that you can do it or you can’t. It’s very binary. Like, do you have the skills to do that? So it’s really important not to think that the world is only optimizing along these one trajectories. 

It’s like most things are like, you want enough competence that you can function in that environment and now you’re good. So I think that was a surprising thing in retrospect. And many people actually fall into the trap of thinking that technical skill is everything in some sense, like academics it leads in this direction, right? 

Like you’re the world expert on like one protein and all its mutants. And your technical skill is maximized in this tiny little area, but no one wants to work with you because you didn’t spend any time learning how to manage your people. Right? So that’s a good way to think about it. 

So that was one observation that I’ve had. What was surprising to me too.  Similar to that is: being right isn’t always so important. There’s a lot of time where you should lose the battle to win the war. And that’s not so easy to do because you have to really understand what the wars are so that you can lose the battles on purpose. 

It’s like, if you have really interesting ideas that are undermining previous people’s work in your field, how do you handle that? Like, it’s very easy when you’re new to these areas to be like, everyone else is wrong. I’m right. Look here. I will rub everyone’s nose in it. That’s not ideal. The outcomes that you get, if you have consensus, as opposed to battling it out so that you seem like “I’m right and they’re wrong.” It’s better to not be so focused on making sure everyone knows you are the right one. And that’s again an easy trap. 

Another thing that I thought was interesting is being smart is like a table stake. You’ve got to have at least some amount of smarts to play at a certain level in academics or business, but success in those areas are determined by other factors once you have enough. 

You know, and you could see this go off some people, right? Like the smartest person, the person with the best idea, isn’t necessarily in charge, isn’t necessarily the leader in the field, isn’t necessarily the most influential. And it’s not always obvious when you’re young, at least it wasn’t obvious to me. Like why is that habit? Like, what does that mean? Like, is it not a meritocracy? It’s just a multi objective. There’s things that matter a lot for success in real endeavors that aren’t just about being smart. Just being the smartest guy in the room doesn’t mean that you should be in charge. You might actually have major deficits in other things that actually matter more for success. 

And that’s a hard lesson to learn, and it’s not always obvious where you learn that. Those were sort of early things. I think there’s things that I’ve learned more recently that I think are useful. One is: I would be very careful about becoming a trophy collector. In some sense, getting a Marshall Scholarship was one of the best things that ever happened to me because I remember thinking to myself at one point, I’m not sure I’m ever going to win anything as prestigious as this again. This is it. I was like, Okay, I don’t want to collect anything else. 

I can focus on something else. I could focus on my ability to attract the right people who want to work with me and like, they don’t give prizes for that. That was really an important lesson that I had the benefit of learning early, not through sophistication on my end. I just got lucky to see that up close.

But you can see a lot of people over time chasing those trophies. They start to become obsessed with: I want to be on the 30, under 30 list. I want to be on the 40, under 40. I want to make sure I’m at the hottest, coolest startup with the coolest logos. 

And you can really end up in a situation where you’ve obviously chased the trophies instead of caring about the race and that’s not good. Like you can really see that people get very unhappy because they’ve pursued the awards at the expense of the fields that they’re in. In the field, where they happen to get the awards, but they don’t really care about advancing. 

And I think that the flip side of that is you’ve got to know yourself, know what you want to do. It’s easy to be scared of what you want to do. It doesn’t really matter. If it’s what you want to do, if it’s what’s going to make you happy, maybe you should just do it anyway. Even if it’s not gonna lead to a trophy at the end, I’ve had many people I respect say, wow, they’ve done amazing things. Rick Moran is a good example of this. I like thinking about these examples, he had kids and he decided that he wanted them to have a life where their father was around. So he paused his acting career at the height of his fame and spent 20 years doing this. 

I think a lot of people would look at that and go, man, that’s crazy. I’m like, that takes real commitment. That is a person who knows what they want and I have nothing but respect for that. And that’s what you should really aim for is the confidence to make decisions that are like that. And that’s really hard and you’ve got to not be scared. 

I mean, I’m sure he was terrified to make such a decision. And that happens all the time. I mean, I’ve sat back and I’m like, I’m going to walk away from Google? As the head of this AI division? That’s unbelievable. I get paid tons of money to hang out and do whatever I want. But at the end of the day, it’s like, I’m never going to be happy here. Like I can’t be here for another five years because this isn’t what I want to do. 

So even though it was painful in many, many ways and super scary, you’ve just got to try it. There’s tons of famous quotes along this, but it’s ultimately like what failure modes are you trying to avoid? The failure mode, I think you’ve really got to ultimately avoid is that you don’t do anything with your life, right? Like you had the opportunity and you didn’t do it. And it’s amazing how fast it goes. 

Grant: I think that’s one of the most common regrets people have on their deathbed.

Mark: Totally. And you can see how it’s so easy. It goes super fast and you get more and more comfortable and the choices to go do things that you think are interesting and important to do get harder and harder, but you’ve got to do it. Otherwise you could end up in a terrible place. So my biggest advice for everyone is you’ve just got to do it. It’s scary, but you’ve got to do it. 

Grant: The Nike slogan. 

Mark: Yeah. There’s a reason for this. Like being an athlete, it’s scary. You’re going to stand up and compete in a stadium of 30,000 people, half of whom don’t even like you. They just want you to do terrible. But you’ve got to walk out on the field and participate. 

That’s really hard. And I’ve often now been jealous of the athletes, who have grown up in environments that challenged them to do that. Cause I have to do that now on the business side and pitch Big Hat. I’ve got to raise money. Like if I don’t raise the money, the company, it’s going to go under. This is very stressful. 

You have to know how to approach that, to know that it’s going to be okay. Sometimes you lose it. Sometimes it doesn’t work. Sometimes you’re humiliated. Like it just happens. There’s a really beautiful quote from Michael Jordan. It’s just a litany of his failures and he’s like: 70 times I’ve been passed the ball to take the game winning shot and missed.

And you’re like seventy times? That’s a lot, but it’s what’s going to happen. If you want to play in positions where you might make the game-winning shot. You’ve got to be prepared to lose. And I did mine. It’s funny. The most valuable thing that happened to me was to join SynapDx and have it totally blow up. Because it’s not so bad. It was terrible because I had to lay people off. I’m not minimizing how in the moment, this was terrible, but still it’s okay. A couple of years later I’m like that was bad. That was a learning experience. 

Grant: Life goes on.

Mark: Exactly. But like the bad’s aren’t as bad as you think they’re going to be. And in many ways, that was a key thing I could tell people. Look, I’ve been in a failed startup. At Big Hat, we’re trying our best, but don’t be so afraid of the failure mode to not go after the success because it’s not that bad. If you fail, you fail. It’ll be okay.  

Grant: I totally agree. This has been really fun. 

Mark: Yeah it’s been a total pleasure. Thank you so much for the invitation. I’ve been increasingly impressed with what I’ve learned from these podcasts and being able to really contribute is so satisfying. So thank you for the invitation and it’s really great to be here. 

Grant: Thanks Mark. Really appreciate it. 

Transcript of Episode 8: Peter Joyce

Grant: Welcome to The Bioinformatics CRO podcast. I’m Grant Belgard, and joining me today is Peter Joyce. Peter, can you introduce yourself please? 

Peter: Hi, Grant. Good to be on. I’m Pete Joyce, CEO of Grey Wolf and co-founder of the company back in 2017. Good to be on the podcast. 

Grant: Great. We’re glad to have you. So, can you tell us about Grey Wolf? What are you doing? 

Peter: So Grey Wolf is a company developing small molecule inhibitors of two enzymes called ERAP1 and ERAP2, and by inhibiting these enzymes, we’re aiming to increase tumor visibility to the immune system so that we can promote the attack and destruction of cancers and act as a novel immuno-oncology therapy. So the company closed series A funding in April of 2018 and excitingly in the last few months just nominated our first molecule that’s now making its way through formal, nonclinical development against ERAP1. And so we aim to be in the clinic in the next couple of years. 

Grant: And if successful, where would this fit in the menagerie of oncology drugs?

Peter: Yeah, it’s a good question because immuno-oncology is certainly a very exciting and hot space, and the anti-PD1’s and anti-CTLA4’s have been game-changing in the area. We see an awful lot of very interesting approaches following up after the anti-PD1 and anti-CTLA4, going after various other checkpoints, for example, or driving activation of the immune system in different ways as well as potential vaccine therapies.

Peter: And whilst, no doubt, some of those will be successful, there isn’t really any therapy looking specifically at the tumor visibility problem. What’s apparent from these checkpoint trials that have been running with anti-PD1 and anti-CTLA4 is there’s a good proportion of patients that aren’t responding to them. Apparently the tumor is not as visible to the immune system, and that’s where we think ERAP1 and ERAP2 sit, is the ability to modulate how the tumor is perceived so that you can promote their recognition to the immune system. And so it dovetails quite nicely with something like a checkpoint, and really gets at that visibility issue. 

Grant: And, how large is that patient population, approximately? If it works amazingly?

Peter: Yeah. So the promise of any ERAP1 inhibitor or the ERAP2 inhibitor in theory is relatively broad. Even in cancer types such as metastatic melanoma or lung cancer where these checkpoint inhibitors have worked particularly well, there’s still a lot of patients that unfortunately aren’t seeing that prolonged benefit in excess of 50% in those diseases where we are seeing really high, high benefit. And then as you move down the visibility spectrum into cancer types that maybe are seeing more like a 20% durable response rate, as you can imagine, there’s a lot of patients there that unfortunately don’t see that survival out beyond one to two years that 10-20% of patients are seeing. So it’s very large. It’s quite hard to calculate, but that’s why there’s so much interest and excitement in this space, because whilst there’s a lot of exciting work that’s happened, there’s a lot still to be done.

Grant: Great. Can you tell us a little bit more about Grey Wolf as a company? How the company works, why you decided to organize it in that way, and how it was affected by all this madness around COVID? 

Peter: Yeah, happy to. So maybe just to introduce the company itself, it’s a company that I started with Tom McCarthy in Oxford, in the UK. We started it in late 2017. I was previously at Vertex Pharmaceuticals as a drug discovery scientist there, and the company itself is, I guess, a virtual company. We don’t have any labs of our own. We operate with a number of different specialists, academic and contract research organizations, and really we coordinate and direct the research. Because you know, drug discovery and development is so multifaceted, and requires an awful lot of expertise.

Peter: That’s a hard thing for a very small company to have all in-house. And we run the company as if it were a project team inside a larger organization, really making sure that all of these different parties, of which The Bioinformatics CRO is one, are all working together with the with the clear aim of trying to deliver these novel therapeutics into the clinic and testing these hypotheses out clinically. So that’s how the company operates on a day-to-day basis. In the context of COVID, we’ve been remarkably lucky, I think. Because of that virtual nature, we do have our own offices because face-to-face contact continues to be very important. We do make a lot of effort to go and see all of the various different groups we work with in terms of trying to build up that project atmosphere.

Peter: So COVID hit us in the way that obviously we couldn’t meet face to face anymore, but we were all very well set up virtually to be able to continue to operate. And we’re very lucky in the sense that it hasn’t really affected any of our timelines this year, apart from things like Zoom fatigue and the usual things I’m sure we’re all suffering from, but we can count ourselves lucky in that. We’ve not had to lay any staff off. The major objectives of the companies overall continue to be met throughout the course of the year, and we’re excited as we move into 2021 in terms of the next phase of the company. 

Grant: Can you talk a little bit about the genesis of Grey Wolf? When you were at Vertex, what led you to jump ship and do your own thing?

Peter: Yeah, it’s a good question. I was pondering this earlier today. I initially came into Vertex as a bench scientist working on actually amyotrophic lateral sclerosis, so motor neuron disease, then moved into more of the oncology space working in immuno-oncology as well as DNA damage repair. Then laterally I was working in rare diseases, which is the often genetically validated diseases, which is the major focus of Vertex now. And through that process, I think I was exposed to a number of other podcasts and avenues that talked about this idea of setting up your own company. I always thought that was something I would like to try in terms of the ability to mold that company’s direction and drive it in the way in which you think it potentially could work.

Peter: Also the idea that a small biotech has that potential to be very nimble, operate very quickly, have very quick and efficient decision-making, and so that had always been percolating over the last couple of years at Vertex. Then Vertex exited oncology in 2017, and there were some interesting ideas in the literature. One of which was ERAP1 that I thought was particularly interesting and worthy of following up, because it was altogether quite different. So the two things really came together, and obviously never set up a company before and it didn’t know how to do it really. I spent my evenings and weekends trying to figure out how you set up a small biotech company. It was during that journey- I think it was in 2018. I was lucky enough to have been introduced to Tom McCarthy who’s the exec chair of Grey Wolf and been very successful in the biotech space, taking a small molecule all the way through to proof of concept in neuropathic pain and then partnering that with Novartis.

Peter: And he was looking for his next challenge in the space, and we decided to join forces. He actually made an initial seed investment in the company, and that’s what allowed me to step out of Vertex, get the company going. Then we essentially became a double act, raising series A or raising seed investment from signatures in addition to the money Tom invested. Then raising series A in April of 2018 and really leveraging all of the excellent contacts and skill sets that Tom has in setting up companies to get the company going, and we’d been working together ever since. 

Grant: Fantastic. What have been your biggest surprises on this journey?

Peter: Probably the biggest surprise is how almost addictive it is running a biotech. As scientists, as we all know, waiting for that next data point is always really exciting. That’s always something that feeds us and is why we do what we do. And then you mix that with the premise from which you decided to go off and set up a company and an ability to finance and get investment into that company. The two come together. Those two worlds come together, and as a result, it’s very hard to switch your brain off because you love what you’re doing. You’re really wanting to push to the next stage of development and investigate whether the idea is a good idea. And if it is, to really try and capitalize on it and push it into patients and ultimately test the hypothesis out.

Peter: So I would say yeah, on a day-to-day basis, that’s great because there’s nothing like loving what you do. The other element is learning. Learning more about my leadership style, learning how to leverage deep expertise across the Grey Wolf team in the various different facets of drug discovery, drug development, and operations to enable us to get the outcomes we want. And that’s certainly a growth within me as I set up the company with Tom and continues to be an active area of development for me, something I think you can always get better at and learn from. So probably those are the two key things.

Grant: That’s great. So Pete, tell us where the name for Grey Wolf came from.

Peter: Yeah, happy to. So as I do enjoy a bit of fly fishing, which I do with my father-in-law. I’ve had many fishless days, unfortunately, but there is one fly that seems to be lucky. It’s called a grey wolf fly. It’s a mayfly mimic, and I thought it was setting up the first biotech company, you know, it’s a bit of a lucky charm, so decided and it was also a bit of a different name. I decided to call Grey Wolf Grey Wolf therapeutics as a result after that fly, although the spelling is different for those who do Google it afterwards.

Grant: That is really interesting. I would not have guessed that. So let’s go back to the very beginning. Where did you grow up? Were you interested in science as a kid? If so, what got you interested in it?

Peter: Yeah. So, I don’t think, during what we call primary school in the UK, I was particularly interested or not interested in science. My grandfather was a keen bird watcher. I do remember being fascinated by birds at a young age and just interested and a bit of a twitcher back in the day, as you say. I think probably my interest in science probably really stemmed in secondary school, and I spent a portion of my time in high school in the US in Atlanta. I had a chemistry teacher, Tony Locke, who was probably really the person who got me very motivated initially. 

Peter: I was probably best at science out of all of the disciplines, and he put it across in a way that I found it quite fascinating and interesting, and ultimately encouraged me to go down the biochemistry route, which is why I then jumped off and did my degree back in the UK. And I think from there, obviously I stepped into the PhD. I fell into doing a PhD in the sense I didn’t really know what else to do, if I’m honest with myself. Then as I started working through that PhD, and as everyone waited till the third before you start generating the specific results or data. It’s two years of no results. That’s when the bug probably started to hit in a way, in the sense of the idea that you’re finding out something entirely novel here that people haven’t really discovered before, and you get to frame the questions that you want to ask. Probably that’s how it started, and then moving into my degree.

Grant: And, on a bit of a side note, what brought your family to Atlanta? 

Peter: So my father worked in the cement business. He’s a chemical engineer by background, always had moved around quite a lot because managing different cement plants and ultimately being responsible for more and more plants, there was an opportunity for him in the States. We actually almost moved to the Philippines before that, but that fell through for a couple of reasons. But then we went, moved to Atlanta and I moved at the age of 15 and came back at the age of 19. 

Grant: Nice. I guess Atlanta is our nearest big city, along with New Orleans. 

Peter: Yes. I guess you’re not too far, are you, actually from Atlanta?

Grant: It’s all relative by American standards. 

Peter: Yeah, exactly. By our standards, this is a long way.

Grant: So what did you do after your PhD? 

Peter: I actually took a bit of a detour. I worked at Wyeth pharmaceuticals in the UK doing something very different, so not research at all. I worked in the regulatory affairs group, working on post marketing. Sorry, post-approval marketing regulatory affairs. I think, probably if I’m honest at the end of the PhD, there’s always ups and downs during that process, and I wanted to explore what else there was potentially out there. I wasn’t really sure of what the next step would be. Yeah, I took that job on, and I think I knew very quickly within the first four months that this was not what I wanted to do. It was really useful and interesting learning about that side of a pharma business and actually probably useful as we move in closer to the development phase of Grey Wolf.

Peter: But yeah, I quickly realized I wanted to get back into research and science, and so from there I went and worked at the MRC in Oxfordshire at the animal unit. I was working on- that’s when I stepped into motor neuron disease, working in a highly collaborative group between the MRC and UCL at the Institute of Neurology and in London. Working at developing novel in vitro and in vivo models of ALS disease was the aim of the project, and so did that for three and a half years. 

Grant: What kind of models?

Peter: Cellular systems, but predominantly it was mouse model systems, and it was using a mutagenesis screen. So the place where I work was a place with Harwell and they used chemical mutagen ENU to essentially drive a whole number of point mutations throughout the mouse genome. Then the job was really to go in and find genes that were interesting in the context of ALS that we knew cause familial forms of the disease. And I try and identify intriguing mutations that have been created through this mutagenesis.

Peter: We found some interesting ones. One gene, we found some in the first gene, so two genes that we know cause the familial forms of the disease. And then the job was to rederive those animals and then phenotype and characterize them, as well as start to understand what are the potential mechanisms by which ALS could be caused in those model systems. The whole premise here was there’s a number of mouse model systems that rely on transgenic overexpression. The fear in this field is always that they could potentially lead to artifactual effects because you’re just driving such high levels of expression. Whereas this way was looking at the endogenous expression of these genes to see. Can you look into a more physiologically relevant environment to see if the pathology is the same as what we see in these other transgenic systems? 

Grant: What about after Harwell? 

Peter: So after Harwell, there was a job advertised at Vertex. I knew I wanted to get back into pharmaceutical research. I really enjoyed the postdoc at MRC, but I knew I wanted to get into that applied research in the pharmaceutical space, and lucky enough that there was a job advertised at Vertex. So Vertex is a Boston based company. They have research sites in Boston and at the time they had one in Canada. They also have a center in San Diego, and they have one in Oxfordshire. And so there was a research site there that was advertising for an ALS scientist to come in and help with the ALS program they were doing. So I applied, got the job, and was working on developing complex in vitro systems initially to try and help with some of the drug discovery programs, as well as driving new target ideas for the ALS programs.

Peter: ALS is a tough area for drug discovery in all honesty, and I think you can see that by the challenging trials and things that are going on. There was a slight refocus, and that’s when I moved into oncology immuno-oncology and DNA damage repair. That site in the UK was really strong in that space, both in IO but also in DNA damage repair, having put the first ATR inhibitor into clinical trials, which continues to do well now. So then, we started learning an awful lot about immuno-oncology, which I found fascinating and worked and led some programs in that space, before then transitioning into rare disease because of the transition of partnering those programs to Merck. Then, I was responsible for helping build the early programs in the rare disease space at the Oxfordshire site and leading some discovery projects there. 

Grant: So looking back across all your experiences and education, what experiences do you think prepared you the most for what you’re doing now and why?

Peter: Good question. I think all scientists go through that period where nothing is really working or you’re faced constantly with negative data. Certainly the first couple of years in my PhD were certainly like that. So I think that level of resilience is really important because you go through a lot of ups and downs in setting up and running a biotech. That is certainly from a personal perspective, it stands you in good stead. I think that also came through- I did a lot of sport growing up, and the same getting used to winning, but more importantly, getting used to losing and being a good loser. No one likes to lose, but I think you’ve got to do it. You gotta get accustomed to it and pick yourself up and get on with it. So, yeah, I think that from a personality perspective is really important. I think that the other key thing that was- it’s a bit of a corny sporting analogy, but it’s also very, very true in drug discovery.

Peter: And I learned quite early on at Vertex was you can never know everything. It’s so highly specialized it’s like being on a basketball team and thinking that you can’t be a point guard, a guard, and a forward. It’s just physically, that’s not possible. So you learn what you can do, but you learn enough about the areas that you can’t do. You know what the questions are you need to ask or where you need to rely on other people. And I think there’s someone stepping out to set up a biotech, particularly in the discovery phase, I was aware of what I didn’t know, what the skill sets I needed to bring in, and that continues now, as we start to move through the development phase. There are people who know more about development than I do. It’s about harnessing everyone’s capability and skills to achieve the outcome, so those are probably two key aspects of spring to mind. 

Grant: And as you build a virtual biotech, how have you balanced what you bring in-house and what you do externally?

Peter: So as we are purely virtual, the emphasis for me initially was trying to build that on the R&D side, was trying to build that project team as would exist in a pharma organization and what we had in Vertex. And so that means you need expertise across each of the scientific disciplines- biology, chemistry, DMPK, safety, toxicology, formulation, CMC at the appropriate points. In the virtual space, you don’t need full time employees across all of those things. You need to have expert people that can fill the various different roles as and when you need them to. A lot of the people we work with work on a consultancy basis, and then we have some more full-time employees or consultants working with us. Then the other aspects, which is really where Kirsty, who’s our chief operating officer, and actually is married to Tom, but has got a deep experience in running tech companies actually over the last 20 years. We needed to be able to operate the company.

Peter: One of the key aspects of having a small biotech was the ability to be nimble, was the ability to decision-make quickly. We make good decisions and if the decisions weren’t good, we can change the decision quickly, and that relies on having an operations area that operates incredibly efficiently. Because you’ve got to go through legal paperwork, you’ve got to go through the financing aspects, all of the investment agreements and everything. They all need to be done efficiently, but to the right level of detail. And so really we lean heavily on Kirsty, and she’s built up that aspect of the organization as we’ve gone along. The two work in parallel, and that allows us to operate efficiently as we go forward.

Grant: Do you think you’ll ever stop? Post Grey Wolf, what do you think you’ll do?

Peter: We’ll probably do it again. It’s the aim. I always really enjoyed time in Vertex coming up with novel or different ways of approaching drug discovery problems or coming up with therapeutic hypotheses that you think worthy of testing and really spending a fair amount of time on that target identification and what is a good target. Stewart Hughes who runs Pathios Therapeutics as a company. I’m also involved with them, we used to work together a lot on that aspect because getting that bit right is so important to everything else that follows.

Peter: And so, yeah, I’ve always got an eye out to the literature and academic groups that we work with and talk to about what could be an interesting possibility of a target idea. Not for prosecuting now, because I think we’ve all got our plates full, but in the future. Because ultimately, it’s not a chore getting out of bed, doing what we do. We’re ultimately trying to make a difference, but at the same time we get to do something that’s very, very interesting. So no, probably I don’t think I’ll probably ever stop.

Grant: Good answer. So, what would your advice be to current industry bench scientists who might be considering doing what you’re doing now? What kinds of skills should they build? What kinds of co-founders should they look for? 

Peter: The number one thing is to be a bit of a sponge and always continue to be a bit of a sponge. You can always do things better. You can always learn. The adage of growth mindset, and whilst it gets overused now, is really, really important. There’s always areas you can be interested in. There’s always things you can find out more about. And it’s not always with a view to you becoming an expert in everything, it’s with a view to understanding how everything fits together. So I would say, yeah, have that level of intrigue and interest. Be curious and absorb as much as you can on the specifics of setting up a company. At least in this iteration of Grey Wolf, one piece of advice I got quite early on from a family member actually was to identify someone who’d been through the drill, been successful because there will be tough times. There will be difficulties. There will be things that don’t go right, and we will know that as scientists, but even on the company and business level, there’s always going to be challenges you’re going to face. So having someone alongside you that has been through all of that before is a great source of help and benefit.

Peter: Tom and Kirsty both have lived that, so that’s a real source of help. A problem shared is a problem halved and all that. So, those are two definitely key things I think you should think about. And then to be honest, the third I would say is the “why”. Make sure you’ve got the “why” right, because there’s times where things are difficult or challenging. You’ve got to really understand why you’re doing what you’re doing. Otherwise, you’ll find it very hard to motivate yourself and get yourself out of any issues and things that you’re doing. You know, I think in our space, the “why” is quite easy. If you’re trying to develop a novel therapy that ultimately is going to help patients, but you can lose track of that at times when things are maybe a bit more challenging or you’re on your 15th Zoom call of the day. But yeah, I think that’s another important thing.

Grant: I guess on that note- you don’t necessarily need to go into specifics if you can’t- but what’s been one of the hardest things that’s happened with Grey Wolf since you started. 

Peter: Probably the hardest thing was, even though I was very, very excited about the next step, the hardest thing was finally saying and actually having those initial conversations with Vertex because it was a company I hugely admired. I had a lot of colleagues there that I really enjoyed working with, and I believe the science was really interesting. So it wasn’t like I was leaving a job that I didn’t enjoy or potentially wouldn’t have a good future in. I had two kids at the time. I have three kids now. At the time, my wife wasn’t working because we just had our second child.

Peter: And so the hardest thing was making the jump and the step, and some people thought I was probably a bit crazy, a bit mad. But it was always something I wanted to do and give it a go. So that was probably the hardest thing from a personal perspective. There’ve been hard things along the way. I think we’ve been very lucky at Grey Wolf. Particularly the ERAP1 project so far has gone quicker than any other project that I’ve been involved in, and the science, for the most part, continues to prove out the idea founded in the literature. So there’s been ups and downs. There always will be, but I think we’ve been quite lucky in the way that’s operated.

Grant: Great. What do you think is some common advice or conventional wisdom that’s wrong or at least that has been wrong in your career? 

Peter: I think probably one thing, and it’s prompted by a book I read recently actually called Range, was in the sector of science, you can get caught up with being a hyperspecialist with really knowing everything there is to know about one thing. And I think that gets drilled into you during your education, really. You get more and more specialized as you go to the point. Though if you start to follow an academic career, you get really stuck down into a niche. And I think the beauty of drug discovery development space is you have to have to lift out of that.

Peter: Although even within that, you’ve got this idea, this notion of a specialist always. You’ve always got this notion of a specialist, and I think in the experience I’ve had today, you no doubt need the specialists. You need them for the particular areas and the particular depth of experience on a particular problem, but you absolutely need the people that range. So people who are able to see the larger picture, can take something from over here and apply it to something all the way over here, which you wouldn’t connect if you’re stuck down in a specialism. And so I think conventional wisdom certainly pushes specialism, but I think we should be also mindful that there’s an equal, if not bigger place for people who can see the bigger picture.

Peter: And I would suggest that that needs to come down further into education in the early phases of education, because you see it with the top academic scientists. Those are the ones who actually are seeing the larger picture and can connect these amazing collaborative networks together and do and ask some pretty fundamental questions because they’ve not gotten down, drilled into this tiny little niche.

Grant: So how do you think we accomplish that? Project based learning? 

Peter: Yeah. Project based learning’s one idea. Broader education for longer, problem solving as opposed to just learning lots of different facts, learning the skills from which to do that. Education’s going that way in a lot of respects. Good question. It is interesting. They talk about the Roger Federer example or the Tiger Woods example, and the Tiger Woods example- being a two year old that picks up a golf club. He was fantastic and was amazing at it, but all he did was play golf. 

Peter: That’s all he did. Roger Federer was pretty good at about four or five sports, apparently up until his teenage years and continues to be a pretty impressive athlete and only really focusing on tennis later on. He exposed himself- or his family exposed him- to multiple different facets for as long as they possibly could until he just chose to specialize.That really does apply to our sector. Those people who are really successful and develop game changing therapies are the ones that can see the bigger picture. 

Grant: So I guess that goes back to being a sponge. What do you think are some of the most effective ways to be a sponge? Some reading on the side on the weekends or trying to get very different work experiences? 

Peter: I think the first thing is figuring out the best way in which you learn. You know, some people learn by reading. Some people learn by talking to people, because that will be the fastest route to which you can be a sponge. You know, obviously we all read the scientists. We have to read publications and everything. I’d probably learn faster by actually having conversations with people and asking questions. I think I was lucky at the time in Vertex because I was able to attend meetings covering a broad range of topics and things. Being a little bit pushy in a nice way to expose yourself to the different facets of whatever sector you are in to broaden your horizons. I also think you can get a bit workaholic. I think giving yourself some downtime, letting your brain reboot, whether that be exercise or whatever it may be, I think is important.

Peter: I think we all talk about it. Certainly there was a period during the first lockdown over here in the UK where I wasn’t getting an awful lot of headspace at all. And then it becomes quite hard to have that “sponge mindset” of trying to absorb and learn lots of things because you’re not giving your head, your brain, the time to download all of the things it’s picked up.

Peter: And you mentioned Pathios earlier. I was just wondering outside of Grey Wolf, what other kinds of things are you involved in and why? 

Peter: Grey Wolf takes up almost all of my time. I do some advisory work with Pathios. I think it’s a really cool idea and interesting idea of targeting macrophage conditioning and modulating the way in which they can go from being immunosuppressive to a more immunostimulatory or pathogenic macrophage. So I’m involved somewhat in that. Outside of that from a work perspective, I’m just really focusing on making sure Grey Wolf delivers, is my primary aim. I think in the future, I’d like to take on some non-exec type roles or, as we were talking about, thinking about what is that next company down the track, but at the moment, yeah, very much focused on the job in hand.

Grant: Makes sense. It’s all consuming. I mentioned this in my email. I don’t know if you’ve had a chance to think about an area in which you think most people are wrong. Or was that the range answer?

Peter: I mean that was what I was thinking with respect to that. There’s this focus on that type of learning. I only think about that with my children now, not just pushing them down one particular avenue, like being a chess champion. It’s exposing them to multiple different things so that they can start to pick and choose what they enjoy and what they’re good at. Often the two tend to coincide. But I did have an episode interestingly during lockdown. I think one of your other questions in the email is what hobbies you have and enjoy. Most sports. I used to play a lot of table tennis growing up because my dad built a table for my old brother in the garage.

Peter: And so I spend most of my weekends doing that. About a year ago we’ve got a garage- or garage, as you’d say in the US- at home and we bought a table tennis table for it. During lockdown, I got quite excited because Will, who’s my oldest, him and I would play probably most evenings, actually. It was a nice way of having a bit of downtime, and he was getting pretty good. He was actually having some very, very good long rallies, but I realized whilst I was really enjoying it, he was trying to not enjoy it so much because he started to get a little bit competitive. I think he got a bit repetitious for him, actually, to be honest, as much as anything else. A part of me is a bit disappointed because you know I love table tennis, could probably play at 10 hours a day every day, but I had to acknowledge that it’s not something that he loves as much as I do quite yet.

Grant: I guess it goes back to letting kids try lots of different things and finding what they like. Hopefully it’s not just some social media. 

Peter: Yeah. Or video games, it’s  constantly video games in our household.

Grant: Cool. Well, hey, thank you so much for joining us today, Peter. It was really great. 

Peter: Thank you. Really enjoyed it. Good to speak to you.

Transcript of Episode 7: Adam Marblestone

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Adam Marblestone. Adam, can you introduce yourself please? 

Adam: Sure. Hi Grant, I’m a Schmidt Futures Innovation fellow working on trying to roadmap and galvanize funding for new medium scale moonshot science projects. And previously I’ve done a bunch of research in a few different fields, many of them connected to neuroscience in some way: a bit on neuroscience-inspired AI, a bit on brain computer interfaces, and earlier some work on molecular biology tools and imaging tools for the brain. 

Grant: Thanks. I’d really like to get to some of your older work, but first let’s start out with FRO’s. What are they?

Adam: The basic observation behind FRO is that there’s a class of problem that is not a great fit either for academia or for startups. The reason why academia struggles with this type of problem is if it requires a certain level of concerted organization and scale of an effort beyond a, let’s say a handful of people tightly working together.

So an example of this is recently a deep DeepMind’s recent work on protein folding, where you have 18 co-first authors on one paper. And that would be very unusual in academia because in the end, everybody has to have their own thesis or their own postdoc or so on. So you do have people in academia who very much want to collaborate and can be very good at it, but from an incentive perspective, it’s hard to get say 20 or 30 people working in a tight-knit engineering organization for let’s say five or seven years to build something, a really complicated integrated system. On the other hand in startups, it is very possible to do that, but by the time you’d be five or seven years, and you really need a very clear path to revenue and product market fit and often for a fundamental, if you want basic science platform, that wouldn’t be a good fit. So FRO’s are simply an attempt to get both the government and philanthropists to fund dedicated nonprofit organizations to tackle this class.

Grant: How do you see this as different from what came before?

Adam: Well, I think there are definitely hints of this, and in some ways we’re just drawing attention to the need for more of this. I would say projects like Janelia Farm Research Campus doing a fly connectome, or the Allen Mouse Brain Atlas, some projects at institutes like the Broad and Sanger Institute.

There are certainly concerted projects, and there’s also examples that are much larger like the Genome Project was maybe $3 billion or so. The way we envisioned focus research organizations is 30 to 50 times smaller than that, roughly. So tens to hundreds of millions, rather than billions. Enough that you would meaningfully change the incentives and organizational structure and path of a field, but not necessarily have to put billions into a single project. So I think that really the observation is just that there’s more need for this type of model than is being satisfied by the existing mechanisms, and that it is possible to systematically go and try to identify what those are and push on them.

Grant: And what fields and projects do you think would be the best candidates for an FRO?

Adam: Well, that’s part of what we’re trying to figure out now. We’re going around to the scientific community pretty broadly and asking people, “what would you do if you were not only not limited by funding, but also not limited by structural affordances?” Imagine that you could take your research program that you want to do and spend four or five years doing that as a FRO with the optimal resources, and then spin off several companies, and then incorporate that back into universities or government labs. So we’re going around pretty widely. I think there are a few different categories of FRO that do emerge from that. One is where you have to build a prototype system that just involves a lot of complicated working parts, but it’s directed toward some kind of basic science problem.

The classic example I always use of this is next gen connectomic brain mapping. So normally with brain mapping, at the finest resolution where you could actually see connections between individual neurons, you are dealing with the electron microscope, which is the highest resolution microscope that there is.

You need that because the way you’re labeling the neurons is you’re just saying any given pixel is either black or white, basically a gray scale. Is this the membrane of the cell, or is this not the membrane of the cell? And then you have to image that very, very finely and then try to reconstruct the shapes and connections of all the neurons from that very high resolution, but at some sense, limited data, which is just membrane or non-members. Now with optical microscopes, you can get much more information out of any given pixel.

You can, first of all, have several colors, but more importantly, you can image the same spot many times because the light is not destructive and you could flow on different chemicals and reagents. So you can get a huge amount of information out of any one spot, but the resolution of optical microscopes is slightly lower.

You can overcome that with new chemistries, like expansion microscopy, which moves the molecules apart from each other, so that they’re more easy to resolve and with fancy microscopes and instrumentation that can boost that a bit. If you wanted to make a brain mapping technology or generally a biological tissue mapping technology based on this, where you can see lots of molecules and adapt this, to be able to actually see neural connections, you just have to put a lot of pieces together.

You have to put multiple types of chemistry, multiple types of biology, multiple types of engineering and instrumentation, and it’s just complex, building out that prototype system. Once you have that, scaling it is also challenging, but it’s a slightly different problem. So that’s an example where you’re building a prototype, and you also see things like that in completely different fields.

You can imagine making the first drill that can drill down into very high temperature rock for fine geothermal energy sources or something like that. It’s a completely different field. Similarly needs to integrate high temperature electronics from NASA with drills from the oil industry and geochemists or things like that.

So that’s a prototyping FRO.I think another category is something that’s more like a foundry where in general, there is some ability to do something already, to produce some kind of artifact already, but it’s not reliable enough and systematic enough that end users have access to it. So this is maybe closer to things that companies would do, but in some cases, the resource that you want to produce should be some kind of public good, and/or it’s deeply pre-commercial in the sense of the application space is not yet known.

So we see this in several areas of nanotechnology or nanofabrication where, for example, with the next generation of 3D integrated chips. Conventional chips, you basically have layers of silicon metal insulator on a flat surface, but what you might want to do in the future is have things like layers of carbon nanotubes, and very deep three-dimensional interconnects between these different layers, so very densely integrated in 3D with lower energy switches. And that’s something that requires you to go beyond the kind of foundry environment that Intel or TSMC or these other companies where you can order chips from already have. So you need to build a foundry for that. At the same time, it’s not clear exactly what the application is yet, because you would have to figure out what kind of software compilers and all sorts of other things you would make for that.

So you actually need a foundry just to be able to discover applications, let alone actually sell something. So that’s an example of FRO for a foundry, and we see that with a bunch of different areas of nanofabrication. Then I think a final category of, although this is not an exhaustive list, would be something like an observatory, which is a little bit more similar to the Human Genome Project, where you just need to collect a data set in a very unified way. Maybe you have existing- 

Grant: Maybe like the Allen Institute. 

Adam: So yes, I think the Allen Institute, many existing institutes, I think do something that’s more like this. They take a relatively mature set of technologies and they just integrate the data. They just create a more unified way of collecting and accessing that data. I see a lot of need for that in some fields of disease biology and in-vivo biology where a lot of the existing institutes that do the data collection at this scale actually are focusing mostly on the normal state.

One example of this would be human brain tissue, doing really deep proteomics, maybe even single cell level proteomics of human brain tissue samples. Another example would be in the field of aging or geroscience. There are a bunch of putative interventions that have emerged, including one that was recently on the cover of Nature, claiming to have ways of basically reversing aspects of the aging process.

And this is pretty amazing, but often different papers and labs in this field measured completely different things when they apply those interventions. So, one lab will have a relatively small number of mice and they’ll measure a few properties, but maybe not the lifespan or maybe not methylation clocks in the blood that are supposed to be predictive of the rate of aging. Another lab will measure the meth methylation clock but not measure cardiac function or something like that. And so how do you have a really unified basis on which to compare and combine these anti-aging type interventions? That would be an example of the kind of observatory I think you could build where you would really systematically look at the phenotypic effects of aging and interventions against aging. 

Grant: Very cool. So, if you could direct a $100 million to a specific FRO today on this podcast, you have to choose in the next minute, what would the topic be? 

Adam: Yeah, it’s hard to choose one. That’s part of why I think we want to create a- 

Grant: Wait, which of your children do you love the most?

Adam: You can’t answer it. I mean, I think that there are different ones that are appealing for different reasons. I think some are appealing because they so clearly fit the FRO category. They’re so clearly not doable in some other way. Others are perhaps more appealing because of the near-term impact that they would have, or the kind of clarity of the path to impact. I think we want to really add to the vocabulary of government and philanthropy that this is a more repeatable model to be used and complimentary to lots of other ones, including the ARPA or DARPA model, which is a bit more discovery oriented and yet also aims for a certain degree of scale or concertedness of efforts.

Personally, you know I have some long term interest in the brain mapping when I mentioned it, but that’s just one example. Yeah. 

Grant: So what would be the ideal outcome of your current position? What would constitute success? 

Adam: Well, I think if we could get philanthropists excited about essentially wanting to own topics this way. So if you have someone who’s really interested in antibiotics, then perhaps in addition to supporting a diverse set of research about antibiotics or investing in companies that have antibiotics near to the markets. The go-to thing would be to say, well, let’s figure out what the FRO would be for accelerating the field of antibiotics discovery.

And then it’s a big ticket item compared to too many things, but for some of the larger scale philanthropists out there, tens of millions of dollars is not a completely unreasonable amount to spend. You might spend that much on having a building or something. How about instead, just nail antibiotics discovery platforms or something like that. That would be on the philanthropic side. On the government side, while there are multiple potential governments, but on the US government side, what we wrote in this white paper that we’re circulating in the policy community through this entity called the Day One Project- day one referring to the first day of a new presidential administration.

The idea there is there’s an entity called the Office of Science and Technology Policy, which is part of the White House, and it helps to coordinate the priorities that the president and the White House have with what the individual agencies like the National Institutes of Health or the Department of Energy are doing.

I think perhaps an ideal outcome on the government side would be the Office of Science and Technology Policy (OSTP) coordinated initiatives where multiple different agencies each have their own. If you want interpretation of how to push more research into these kinds of concerted moonshot projects, you could therefore have FRO’s in multiple domains that way.

Grant: So going way, way back, I want to go to your PhD, but I’m just curious to go back even further. Tell us about your childhood, where you grew up. Were you already interested in science or did that come later? 

Adam: Yeah. So I grew up in Western Massachusetts, and as a young kid I wasn’t necessarily the most scientific. I had some of my elementary school colleagues were much more scientifically advanced than I was.

I was doing a bunch of sports, karate, and gymnastics and horse riding, and eventually got really into gymnastics for a while. But my dad had a telescope. He was an amateur. He was an economist but an amateur astronomer. I got exposed to astronomy that way, so I knew that was cool. I had supportive parents that would buy books and stuff for me.

So I’d sometimes end up in the Barnes and Noble. I don’t know what you have local to you, but when I was a kid, the Barnes and Noble bookstore was where you would go. I sort of use it as a library cause you could basically read stuff that was on the shelf, so I would go to the science section of Barnes and Noble, which presumably was a consequence of my dad helping me get excited about both stars, galaxies, and robots or so on. So that seemed like the natural place to go, and eventually in doing that, I stumbled across books that got me interested enough. One was about nanotechnology and introduced me to the idea that you could have general purpose technologies that would very broadly impact multiple key areas of human life.

Grant: Do you remember the title of the book?

Adam: Oh, that was Drexler’s book called Engines of Creation. 

Grant: That was one of my formative books too, actually.

Adam: Wow. Yeah. It’s funny how it doesn’t necessarily have to be a successful academic field for something to be absolutely transformational for generations of people.

So that was one, there was another popular science book at the time that I was a kid called The Elegant Universe by Brian Green, which was about string theory and cosmology. So between those two, I knew that there was a lot of cool things you could do with biotech and a component to getting at that through physics, and so that set my path. So eventually gymnastics was taking too much time away from my reading physics books, and I eventually got to the Feynman books and stuff. Once gymnastics was in the way of the physics books, then it was clear what the choice was.

Grant: Great, and maybe we can jump forward several years. Tell us about what you did during your PhD. 

Adam: Well, PhD was a pretty crazy ride because I had a high freedom graduate program called the biophysics program at Harvard, which was really a kind of wild card where you could basically work in any department or lab in the broader biomedical sphere at both Harvard and MIT. It’s kind of an underappreciated program in how much freedom it gives you.

And then I ended up with a very high freedom advisor, George Church, who is first of all, very creative and open-minded, but second of all has, I don’t know, fifty to a hundred people in the lab at any given time. I had a lot of freedom there, and I also ended up with a high freedom fellowship. So I really did have this free phrase. Enough rope to hang yourself, and I definitely had that many times over. As a result, basically as a PhD student, I was trying to pursue somewhat perhaps unrealistic moonshot projects, very much of the nature of what we’re now trying to do with FRO’s, and it was only along the way that we managed to publish a few kind of basic papers to actually do the PhD.

But I started out interested mostly in biomolecular self-assembly and trying to make the so-called DNA origami structures, which self-assemble in a programmable way, I wanted to make those much bigger so that we could integrate those onto silicon chips and make a kind of bio chip that would have nanometer to centimeter levels of control all the way in the single platform.

With that one, I think we made a little bit of progress. We did make some unusually large DNA origami, but I also realized somewhere in the middle of that I didn’t have a very clear path of what the applications would really be of that. So midway through, I sort of switched to neuroscience and had also a very unambitious goal of trying to record simultaneously all neurons in the mouse brain.

Which in the Church lab, which was mostly focused on DNA based technologies, the natural way to think about doing that was to try to record neural activity into DNA molecules inside your cell, so we did a lot of ideation and kind of team building around that. A little bit of preliminary experiments, where we were exploring whether you can get these DNA polymerases which copy DNA to encode information about the ion concentrations in their environment, which would be reflective of neural activity, into patterns of errors in copying DNA. And then we sort of branched out from there, both to collaborating with a number of other labs on applications of DNA barcoding and DNA encoding to structural brain mapping, and on trying to understand more from a physics perspective a number of different ways you could record neural activity and super large scale. Including but not limited to these kinds of molecular methods, so that was a pretty weird PhD. 

But it definitely was the breeding ground for thinking about these kinds of focus research organizations and what would it actually take to properly execute on some of those ideas that as a grad student, I was nowhere close to being able to do on my own or with the few people I was collaborating with.

Grant: And somehow in the middle of all that you co-founded BioBright. 

Adam: Yeah. Well,  I met another person who was interning in the Church lab at the time named Charles Fracchia, and one way to put it, maybe unflatteringly to both of us, is that neither of us were that great at wet lab experimentation. Charles was maybe a little better than I was. He had had an undergrad in biology and mine was in theoretical physics, but we were interested in whether there was a way to make it easier to keep track of what was going on in your wet lab experiments, and there was a popular and exciting notion just emerging around that same time of cloud labs.

What if you were to basically have a web interface to an external lab that would do the experiments for you with companies like Emerald and Transcriptic popping up, and our feeling was that that was part of the story. But the other part of the story would be, how do you augment the scientist in situ in their own lab and make it easier for them to take notes about what’s happening and to compare what happened in the experiment to what the protocol should have been, or even simply to gather and centralize data from all the different experiments and equipment in the lab into a central way of looking at what was happening.

So eventually Charles did some more work on this in the MIT media lab as a master’s student, and then went off and became CEO of his company. I was a co-founder and helped it get some grant funding and recruit some initial people, but Charles has really been leading the charge on that.

And that has just evolved a lot by interactions with customers and contracts and understanding what different commercial entities need in terms of tracking and analysis of their experiments. But that’s also been a really interesting thing to see happen. Yeah.

Grant: And so after you finished your PhD, you went to work with Ed Boyden at MIT. Can you tell us about what you did there? 

Adam: I developed a lot of ideas around these issues of large-scale brain mapping in the PhD, as I mentioned. The idea there was that teaming up with Ed and others, we could increase the scale beyond individual experiments that I was doing. And so how can we launch larger initiatives and projects?

It’s just seeing the line from where I was towards doing focus research organizations. This is where we started to think about how to move in that direction. So a lot of the work I was doing with Ed, I wasn’t doing experiments in the lab, but I was doing a lot of grant writing and a lot of coordinating of teams and developing strategies and collaborations to try to particularly develop some of these next generation structural brain mapping methods.

The main thing that was exciting at the time and continues to be really exciting to me is in 2014 or so between the Church lab and Tony Zador’s lab at Cold Spring Harbor which had been leading the development of these so-called DNA barcodes for identifying individual neurons in the brain. With Ed having some input into that as well, we were thinking about how do you combine all these pieces together into an ultimate brain mapping technology for the structural and molecular end of brain mapping, not for the living state.

It seemed really promising, but there was this one missing piece, which is that you have to really fancy microscopy in order to make this work. You’d have to slice the tissue really thin, and it was just challenging. And the church lab had just come out with this idea of fluorescent in-situ sequencing which was kind of working, but it was a bit hard to get it working in actual intact slices of tissue, as opposed to just cultured cells. The tissue processing and the microscopy were just hard. But Ed at the time took me into his office and said, “You know, we have this cool thing that we’ve been working on, which is a new way of doing microscopy that solves all the problems that you’ve identified.”

And I was like, wow, that’s pretty crazy, and this was this expansion microscopy thing where you physically swell the brain tissue with the hydro gel to move the molecules apart from each other in an isotropic uniform fashion, 

Grant: Like everyone in the neuroscience world did a journal club on that.

Adam: So what we were trying to launch, maybe we were a little ahead of ourselves, we were trying to launch big initiatives to move this into really developing a strategy and workflow for doing a very integrated form of brain mapping. In practice, it took several years just to be able to get any kind of grants at all about this, for example because it just seems so crazy to people. Even though they had done a really great job and validating this, so it took some time just to gain basic acceptance by the community, and what we ended up accomplishing and Ed’s lab and collaborators with me kind of helping coordinate a bit of it.

What they ended up accomplishing is really demonstrating a bunch of different ways in which this can be used, including so-called double expansion or iterative expansion, where you can expand twice and get twentyfold expansion instead of fourfold expansion. This integration of the chemistry of the in-situ sequencing, or physique, so-called multiplexing methods with the expansion, which is now in a pre-printed bioRxiv and going to be published pretty soon, expansion microscopy where you’re staining the lipids much in the way you would stain with the electron microscope to make it more relevant for connectomic brain mapping in the more traditional sense.

We also just pushed forward a bunch of other ideas, including a still-theoretical approach to how you do single molecule protein sequencing, a bunch of things that sort of related to microscopy and molecular multiplexing, and it was a really amazing time with lots of people. Just seeing a lot of possibilities emerge.

Grant: And after that you went to work with Brian Johnson at Kernel, yeah? 

Adam: Right. Seeing the difficulty at the time of having anything quite close to a focus research organization for this brain mapping, I decided that maybe my best bet would be to use that same strategic roadmapping approach that I’ve applied to these different areas and try to push it in the context where we really did have a very scalable team that was commercial driven, but also thinking long-term because Brian Johnson had been making a big commitment of his own funds in his own time to run Kernel.

And at the same time Elon Musk was starting Neurolink. I had some discussions with those people as well, and there was kind of this burgeoning brain interface mini industrial boom around late 2016. All of a sudden people are like, we should start gigantic Brayton computer interface companies.

Whereas they hadn’t been saying that before weirdly. So Kernel was a great experience, and we basically went through and tried to figure out all the possible things that Kernel could do, ranging from deeply invasive medical devices to super next gen physics of how you would do noninvasive things, and then sort of met in the middle with what they’re doing now, which is a set of relatively practical but still quite new approaches to non-invasive brain activity, mapping headsets basically. They recently released a headset that does what’s called functional near-infrared spectroscopy, fNIRS, which is an optical way of measuring brain activity.

They’ve basically released a much faster, cheaper, better, more portable fNIRS headset and are starting to give that to a bunch of collaborators to discover what can you actually do with that. Can you decode speech? Can you decode mental imagery? Can you use it to help train AI? Can you use it to detect if a patient with a coma is conscious? A bunch of different things.

If you have more accessible imaging technology that you could potentially do and yeah, it’s super exciting to see that progress. 

Grant: And then you went to DeepMind. I don’t know how much you can tell us about that. 

Adam: Yeah, yeah, DeepMind was cool. I was on the neuroscience team. That was a really great experience, basically at the point where Kernel identified its product direction.

My crazy scientific roadmapping I think was a little bit less relevant. I chose to let them push on the commercial execution of that and the engineering of that, which is not really my strength, but it’s definitely the strength of some other people they have there and scratch my AI itch. Because in all of this time, thinking about how we could map brain circuits or brain activity, I had of course been reading a huge amount about the neuroscience literature and what do people think these things actually do? What’s the actual functional interpretation of brain circuits? So that’s what I tried to learn about in the time that I spent at DeepMind, which was amazing. I was on the neuroscience team there, and we explored a bunch of ideas, a lot of them having to do with how memory works. They’re on the AI side. It’s not directly possible to connect it to circuits yet. You know, so there’s nothing we can say, well, okay, we mapped this thing over here with in-situ sequencing or electromicroscopy, and then here’s the AI algorithm. Although we I spent a lot of time learning about exactly how close we are or are not to that in different systems.

And there are some systems where it’s much closer, like the way that the songbird does reinforcement learning for learning how to sing its song. That is something where we have both an algorithm understanding and a circuit understanding. Some of the higher level questions about how to get memory to work have a flexible way of accessing working memory and short-term memory in much the way we think about thinking is sort of “I remembered this and I combined this idea with that idea in some compositional way.” It seems to rely on having memory buffers basically, and we were thinking a lot about how that works in a way that’s inspired by what we know about neuroscience, but it’s still a little bit loose. But it was really cool.

I got to learn about how AI researchers think and write a bunch of AI code to try to test out these types of models of how memory might work. It’s a completely different perspective than conventional circuit neuroscience, but I think that I remain super optimistic actually, that these things are going to converge. But of course it might take some time. Yeah. 

Grant: So what areas of science and technology are you most excited about and do you expect might have the highest likelihood of affecting a major transformation to people’s lives and in society?

Adam: Lots of them, lots of them. Yeah. I’m excited about a number. I would say that with my initial push on nanotechnology as a teenager, right.

As I was mentioning, I still want to see a path to make that work. I see that still as having sort of fallen behind biotech. Lots of things we want to do with the material world we can do either with chemistry or with biology right now. And so general purpose atomically precise fabrication I think is one of these things that is really great, but it has a limited foothold in what the research community is doing right now to actually bootstrap that.

So in terms of things that seem on the cusp of really exciting developments and that I’m particularly interested in, looking at with focus research organizations,I would point to a few. One is aging and age-related diseases, and I think the aging field is really starting to take off. You can always debate this.

Have we actually understood anything fundamental about how aging happens and until you see results in humans, extending a mouse lifespan could mean something completely different. You know, mice die for very different reasons than humans do. Mice apparently mostly die from getting cancer. There’s not as much let’s say heart disease or various other things or Alzheimer’s or things like that, but I’m really interested in the idea that we can, including with animal models, identify common roots fundamental drivers of age-related diseases. 

I’m really interested also in neuropsychiatric diseases where many pharma companies have basically killed their neuro R&D divisions, but I’m optimistic that some of the brain mapping and proteomics and related types of technologies we’ve been thinking about now for a years, can circle back and tell you more mechanistic insights with what’s going on with brain diseases.

I’m weirdly really interested in geothermal energy these days, as a possibly underappreciated source of clean energy that requires its own moonshot to figure out how to drill deep in the earth. 

I’m interested a bit in applications of AI to social technologies and discourse. Can you make recommender systems that would be more genuinely helpful to people rather than just recommending what you will be most likely to click on? Can you recommend what will be most helpful to you as judged a month later or a year later? Or can you use that to sort of do fact checking or improve people’s reasoning ability with all these new AI based natural language and prediction technologies? So, I mean, I’m just excited about so many things and that’s, that’s why I’m trying to focus on organizational enablement right now. It partly means I don’t have to choose one. 

Grant: So since you’ve been investigating these areas, can you maybe give kind of the take home points of your understanding of where the aging field is today? What we know, what we don’t know, what maybe we think we know that we may not actually know. 

Adam: Hmm. I can try, but that’s a hard one, but I can try.

There was maybe a basis many decades ago for things to be kind of exciting just in the realization that different species age at very different rates, you have things like naked mole rats that seem somewhat similar to other rodents, and yet live much longer.

Things started to get exciting when people like Cynthia Kenyon were doing genetic screens in C elegans where they could pass for low lifespan and they could identify genes in a somewhat random way that would have a dramatic effect on the sea elegance lifespan, empirically. 

And then that started to hone in on metabolic regulation of how much we are doing something like repair versus how much we are just consuming as much as we can and growing as fast as we can. So sort of growth versus repair lead to the discovery of rapamycin and Metformin and stuff sort of emerge on that axis.

That was one stage. And then I think there’s been a more recent stage that in some significant part comes out of both this revolution of induced pluripotent STEM cells and the Yamanaka Nobel prize for work that I think was done around 2006 era whereby you can by dumping a few transcription factors on differentiated and aged cells, you can “reprogram” them back to a polypotent state, but also one in which a number of aspects of their physiology seem to revert back to a younger configuration. For cells in a dish, this was already exciting. 

One of the things that they measured in that Yamanaka paper was DNA methylation. And they said, look we know that as cells differentiate their methylation patterns change, and this procedure has actually reversed that methylation change. Perhaps, partly as a result of that, or just as a result of the growth of DNA array technologies and sequencing technologies, others have started to develop these ideas of epigenetic clocks that measure aspects of the rate of aging, that seemed to be somehow related to this differentiation versus reprogramming spectrum.

In that same era, there was also the discovery that if you want non-cell autonomous or sort of circulating factors that operate in the blood–this is not clear how exactly it relates to this metabolic access of the first Kenyon kind of discoveries–but in some as yet not very well understood way seems to restore the kind of regenerative proliferative capacity of cells through circulating factors. For example, famously if you take the blood from young mice and put it into old mice.

But what is really going on there? And just in the past year or so, there’ve been a number of advances along that, including one from UC Berkeley, where they simply rather than put blood of young mice, they simply dilute the blood of old mice. And they just put back one protein called albumin and saline. And that seems to have some of the same effects, although this is just a sort of preliminary study.  

And another paper where they have an as yet undisclosed fraction of a young blood proteome. They put that into old rats and they measure a bunch of physiology. They measure methylation clocks and things like that, and they see a bunch of seemingly kind of coordinated effects from these circulating factors. 

And meanwhile, all of these lines have proliferated and developed, and there’ve been a number of different compounds discovered that have some kind of effect in enhancing the re regeneration or blocking cellular killing senescent cells, understanding the interactions between how things like senescent cells drive systemic inflammation and how inflammation can in turn cause a bunch of other aspects of aging related problems like atherosclerotic heart disease and things like that. 

So I think there’s a lot of progress in understanding the physiology of different levers. It’s still not a unified picture where you can completely say, these are the core drivers versus these are the effects.

And there’s a so-called set of hallmarks of aging and, at the same time, there was this other paper about the pillars of aging, each of which I think identified eight or nine different underlying features. But none of those are completely understood. Is that really the right list of seven? Could you instead parcellate that in a different way that would reflect cause and effect more closely?

That’s still not really known, but I think it’s a great time to push towards these concerted FRO-style moonshots to really measure everything about what’s happening in those processes. You can never really measure everything, but to have a really systematic kind of genome project style attack on, on understanding how each one of these levers affects all of the others for more data-driven hallmarks of aging.

Grant: It’s really exciting. I love the optimism here. I’m a big fan of that. I was wondering if before we wrap up, if we could change tack a bit, and as you think about these various scientific and technological moonshots, what do you think is the greatest, realistic dystopian threat that could come out of this? And what can we do to mitigate it?

Adam: Yeah. I spent some time. I’m not really an expert on this, but I spent some time trying to ascertain what people are thinking about more versus less in these areas. I think sometimes there’s a lot of emphasis on risks from artificial intelligence. And in particular, sometimes people say that if you do neuro-inspired AI, that would actually be even more risky because if you sort of try to copy the brain for AI, it will be harder to prove theorems about that and basically prove bounds of sort of controllability or correctness because you never derived it from math in the first place. You’re just taking heuristic hints and then seeing what happens. 

I’m not sure I agree with that. I think that potentially, the brain may have some understandable, relatively unified set of learning principles that it uses. And that if we actually understand that better, we may be able to design AI safety or alignment mechanisms that are actually just more reflective of how the algorithm actually works. It doesn’t have to be totally inscrutable.

I also think that there’s potentially some software engineering ways that you can make really capable AI systems that are not agents in the sense of optimizing some single objective function that you’re worried about getting out of control, but rather just a very tightly sort of supervised sets of processes that are more like Microsoft word than they are a person. I think there’s a lot to be understood there, broadening out the research in AI safety. 

I generally feel that the obvious one is pandemics both natural and engineered. And I think we need to have ubiquitous DNA sequencing everywhere. Apparently it’s actually possible to see so-called viral chatter. So if, if a virus is starting to make its way to the point where it could start spreading in the human population, before that happens, you would start to see in patient samples, just a little bit of an uptake of this virus. So even if you aren’t going and sequencing the rivers or something like that, if you’re just sequencing people that come in for primary care visits or physicals or whatever, if you’re just sequencing the population, it should be able to track the emergence of viral pathogens. 

But I don’t think we have anything like that in place, let alone, globally to be able to anticipate. But we should also have something like ready-made vaccine candidates for each of the 20 or so major categories of viruses, so that you really know whether an mRNA or a peptide or what is going to work and maybe you have to customize it a little bit. 

So I think that we are still very under-prepared for biological risks and hazards. And then I think we’re very sociologically at risk of mass scale misunderstanding and vindictiveness. As we see playing out on the internet and I hope that particularly AI technologies can be used to actually sort of mitigate some of that rather than amplify it. But right now it might be on balance just by optimizing for attention or engagement. It might be on balance actually increasing the infighting of humans that need to be focused on moving forward as a species. 

Grant: Yeah, I think that’s still overall, relatively optimistic answered a dystopian question. So unfortunately we’re out of time. I wish we had more. 

Adam: Thanks a lot for this very wide and thought provoking discussion Grant.

Grant: Yeah. I really appreciate you coming on. Thanks Adam.

Transcript of Episode 6: Tony Altar

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Tony Altar. Tony, would you like to introduce yourself?

Tony: Hi Grant. Yeah, it’s great to be here on the podcast. My name is Tony Altar. I have a PhD degree in neuroscience. I’ve spent the major part of my career working on neuropharmacology, neuroscience and helping to develop drugs for people with psychiatric conditions. More recently, I’ve been involved in the neurological side of brain disorders. And as a result, I’ve moved my career more from the pharmacological approach to gene therapy and genetic approaches, because I think neurological disorders are more caused by genetic problems and problems at the early expression of genes, as opposed to drug mechanisms for psychiatric disorders. So I’ve really moved my career quite a bit, just in the last 10 years.

Grant: Great. Let’s talk about your career. Maybe start from the beginning because I think it’s a pretty interesting path, but maybe if we can go way back to child Tony, what kinds of things were you into? How did you end up in science? What possibilities did you entertain?

Tony: I love that question, Grant, because I think for all of us, understanding the origins of our own careers, our own interests are really informative and important. I think those are some of the enduring parts of our personality that help us through the tough times, as well as the good times in our career. So for me, that’s true as well. My interest in science started with my own father who was a fairly successful theoretical physicist, and extended to- I guess I would call him my second father- a man named Art Yuwiler, who ran a neurobiochemistry laboratory. Which, at the age of 17 when I started working there, I don’t think I could even pronounce neurobiochemistry, nor did I really understand much of what these guys in the lab were saying, but it was a fascinating experience. Between my upbringing from my mother and father’s side and love of science and then the ability to actually conduct science at such an early age, that helps set a path for me.

Grant: Where did you grow up?

Tony: I grew up in West Los Angeles. enjoyed the area near UCLA, near the ocean. It was a great place to be. We moved there in the late fifties and lived there. I did my schooling at UCLA, UC Santa Barbara, UC Irvine. So a good dose of California through my entire education.

Grant: What pulled you away from California in the end?

Tony: Well, having luckily married my wife Kristen, I needed to start paying the bills. I also did want to work in the pharmacology industry, especially helping discover and develop drugs for the CNS. So I moved to the East coast to come work at a company called Ciba-Geigy. Now it’s known as Novartis, so that was a great opportunity after my postdoc at UC Irvine to really put into play the creative process that I always dreamed about doing. In fact, it was back in Dr. Yuwiler’s laboratory where I had a small desk, and right next to my desk, there was a little placard from Upjohn Pharmaceuticals and it said, “The best way to create new therapies for brain disorders is to understand the basis of those brain disorders.” And that always stayed with me. Understanding the cause of disease, if you know that you can actually have a real opportunity to come up with a new approach and come up with therapies. And I believe that today more than ever.

Grant: What got you into the brain?

Tony: I got interested in the brain- actually another figure in my life, a good friend of mine Doc Renneker, who is now a very famous surfing guy, Mark Renneker and I used to skateboard every weekend at UCLA. That’s where we got pretty good at skateboarding in the very earliest days of the sport. Mark’s father was a psychiatrist, and we used to chat about the brain and his patients to some extent. I was always fascinated about the whole possibility that you could help people with psychiatric problems by talking with them and by giving them drugs. There was also around that time in the middle of the late sixties, as you can imagine with the psychedelic revolution that came along.

Tony: We got really interested in this whole idea that a single molecule completely changes someone’s personality. It could completely change their insights into the world. To me, that hasn’t gone away as an interest. It’s coming full circle now. So that was really interesting to me because I realized that a single molecule like an LSD or psilocybin can have such profound effects when given as a pill, then our own neurochemistry when subtly changed could explain things like depression, like schizophrenia. So that was kind of an epiphany that I added up at a pretty early age, and working in the neuro biochemistry lab reinforced that thinking as well. So I think that was part of it, the cultural milieu of Los Angeles at that time and the whole country, really. Being able to address it from a scientific perspective and then use it, those were all great recipes for my career.

Grant: So you went into biotech, and one thing I’ve always found interesting about your career, Tony, is you spent almost your entire career postdoc in industry, but you’ve published like a successful academic. How have you done that? How have you managed to keep that up? 

Tony: It’s an interesting question because the general idea is if someone’s in academia, they have to publish or perish, and if they’re in a drug company, they don’t want to tell anybody anything about what they’re doing. I was somehow able to do both. I’m really not sure the answer to that, because I did a lot of publishing at the university of California. I think I published three or four papers before I even got my PhD, but the bulk of my publications was at places like Genentech, Regeneron, out of Ciba-Geigy. Actually, every company I’ve ever worked at. I don’t know, maybe they were a little more permissive for us, but at most of those companies, there was also a high priority placed on excellent science. Publications showing the world what was being done, that was a great recruiting tool.

Tony: I think the companies I worked in always put a high premium on that kind of academic excellence as well as developing compounds. So maybe part of it, to answer your question, was being able to be at places that fostered that kind of environment. The resources at those companies to this very day were always very good. Yeah. We had lots of opportunities to do the work and didn’t have to worry about writing grants. We could spend our time writing papers and stuff. That’s another reason I was always attracted to the pharmaceutical industry, because you really did have that emphasis on research and production rather than scrounging for money and trying to placate reviewers and play the academic game. I was pretty turned off about by the time I was done with my postdoc.

Grant: So how can one as a founder and/or manager create an environment that’s conducive to that?

Tony: That’s a very important question, and I’m faced with that now as I start my own new company with a co-founder. A company that’s focused on gene therapy that we’re really very excited about. This is an important question. You want to attract the best and the brightest, which was the mantra at Genentech and other companies I’ve been at. It was really important to hire the best people, but you have to retain them, too. We have to like our boss. That’s the number one factor for why people leave. They don’t get along with their boss. They need to have a scientific career that’s successful and really productive. That’s another reason that you retain people, because they love the environment they’re in and make great results and are rewarded with publications. You have to see an end product in a company.

Tony: Most people that work in companies are there for some of those reasons I mentioned. They have resources, the pay is good, the environment can be outstanding with other colleagues, but we’re also there because we want to make a difference for human health. We want to really create something like a pill that makes a schizophrenic patient better, or a gene therapy product that helps with a neurological decision, or a pharmacogenomic product that gets patients on the right medications for their ADHD. I’ve been allowed to succeed in all of those spheres. So the best way to retain people has a lot to do with how you recruit them because the people coming into a company see the people who were there. A big part of the decision is to the boss but also the colleagues. Who am I going to work with in this new environment?

Tony: If they’re excited, they’re doing high quality work, and they’re hitting all of those criteria I just mentioned, those interviews are going to go great. I remember an interview I had at Regeneron when I decided to go there and work with Len Schleifer, George Yancopoulos, and the team. The best part of that whole interview process was we went out to dinner one night and there were ten people who came to dinner. We had this fantastic discussion around a big table about all the science that was going on, things I’d been doing at Genentech at the time. I think after that dinner, I pretty much decided I’m coming here. So you recruit with the people who are there, you retain by allowing the new people who’ve just come in to also succeed, and you have a high ethical standard.

Tony: You run a company where there’s no smoke and mirrors, where there’s real results, where the science is first rate, and you can publish in the best journals. As long as you can manage scientists to achieve that way, you’re a good scientific manager. And the rest will just fall.

My expression for managing scientists is you manage by letting them do good science, and then the other problems, which will always arise- competition, maybe some jealousies that come along, frictions that develop, uncertainties. These always will come up in any lab environment, but as long as the results are these high level achievements, everybody will benefit. One thing to really pay attention to here is that- and in retrospect, I’ve become very aware of this- when you look back at the companies you’ve been working at, or universities too, it’s the team you worked with and the treatments you got as a team that you talk about.

Tony: I really don’t talk much about my own individual accomplishments. I talk about what we did at Otsuka and discovering and developing Abilify as a huge team. Eventually, it was a hundred people along with another company, Bristol Myers Squibb, and more than a hundred to do the clinical work. We as a team developed this best in class drug for schizophrenia, depression, and bipolar disorder. No one person does that, but it’s the team that you’re working with. It really makes the difference. I do believe in that. The best management and the best way to retain people is you encourage that team feeling. You have the team succeed.

Grant: I don’t know if you’re allowed to talk about that, but can you talk about the development of Abilify and what the thinking was around that? How it went down?

Tony: Sure. Oh, I’m allowed to talk because it’s mostly been published. And in fact, Otsuka is another example of an outstanding company. They’re a Japanese pharmaceutical company. It really started back in the 1980s when I was working along with some other people on trying to come up with a partial dopamine D2 agonist. D2, meaning a dopamine D2-type of receptor. The theory was actually promoted by Dr. Arvid Carlsson. Arvid won the Nobel prize in 2000 because he discovered dopamine. Not a shabby thing. Leading to theories about schizophrenia and Parkinson’s disease, and Arvid proposed very early that perhaps instead of blocking a dopamine receptor to treat schizophrenia, which was already known, you could actually put in a partial agonist, which didn’t block the receptor, but it quieted the receptors activity to an intermediate level.

Tony: Whereas a receptor blocker will stop all activity. That’ll treat schizophrenia, but it also creates lots of side effects. So Dr. Carlsson proposed that a partial dopamine D2 receptor agonist would lower excessive tone, but bring lower tone up to a sort of a middle level. I worked on that in the early 1980s, and at Ciba-Geigy, we brought some drugs to the clinic. Unfortunately, those drugs have some liver metabolism problems and the company was really quite conservative and didn’t pursue it any further than that. So when I got the opportunity to interview at Otsuka to head up their global neuroscience department, I looked at this early clinical data and some of the ideas about the mechanism, I realized this was very similar to the work I’d already done.

Tony: But now we were 15 years later. I was able to take that job and manage the team in Japan and in the US. We further profiled the compound. One of the important things about Abilify is, turns out, it’s not only a partial dopamine receptor agonist. We discovered that it’s a partial serotonin agonist as well. We realized it could not only treat schizophrenia, but depression and bipolar disorder because of what Arvid Carlsson, who now was one of our consultants, called a serotonin-dopamine system stabilizer. I thought that was a brilliant concept. That went on to be a marketing tool to describe how Abilify works. There are ten times more people with depression than schizophrenia and two to three times more people with bipolar than schizophrenia. The result was, Abilify was the largest grossing drug worldwide in 2014, and I think to this day has treated millions and millions of patients. I’ve been blessed that I was able to work with Dr. Carlsson on this project and with this fantastic team, both at Otsuka and Bristol Myers Squibb to get this really important drug made. All this is publicly known. There’ve been many imitators since.

Grant: Yeah, that’s a huge accomplishment.

Tony: Well, there’s an important term here for that accomplishment and it’s persistence. Persistence is probably the most important single attribute that we as scientists have to embody, because we’re not going to succeed the first time. If we persist, however, it will eventually succeed. Sometimes we have to come full circle. So I had to go from my work at Ciba-Geigy to coming back to a new company to finally get that project finished. That’s a really important thing, is to persist.

Grant: So what, what most excites you in the field of neuropsychiatry? What do you think will have the biggest impact for patients in the next 20 years?

Tony: I think for psychiatry, it’s maybe a little harder to say than for neurology, where I was hoping you were going with the question. In psychiatry, the reason I’m a little more hesitant is because a lot of our psychiatric knowledge has come from how drugs work and which drugs work. By luck, we happened to come across an antidepressant and that we elaborate on those kinds of drugs without really understanding why people are depressed in the first place. The targets in psychiatry are not as clear. There have been some significant breakthroughs with a class of drugs called muscarinic agonists, which for schizophrenia have been proven now to be very effective.

Tony: That may only be the second receptor target that has never been validated for treating schizophrenia. Luckily in the early 2005, my team at a company called Psychiatric Genomics discovered that very approach. It’s been finally capitalized by a company called Karuna. They made a drug that is actually a muscarinic agonist that’s coupled with another drug, and that has proven to be very effective against schizophrenia.

Tony: That may be only the second target after dopamine receptors to treat schizophrenia. I’ve luckily been involved in both the partial dopamine agonist innovation and now maybe this muscarinic agonist, if indeed further trials continue to show that it’s so effective. I think those are the most critical approaches, perhaps one of the most important breakthroughs in psychiatry today, because I don’t think it’s only going to be useful in schizophrenia. I think it may also be useful for Alzheimer’s disease because a very similar story about muscarinic receptor sub activation has been made for Alzheimer’s disease as well. So it could well be that this very same drug, which was made originally at Eli Lilly, could be useful in treating Alzheimer’s disease. Of course, that would be a huge breakthrough.

Grant: What if you were to answer the same question for neurology? Would it be the same answer with the muscarinic receptor?

Tony: Well, maybe for Alzheimer’s it actually could. I mean, Alzheimer’s patients do have a deficiency of muscarinic receptors, signaling, and metabolism of brain neurons as a result. So that could be, but I don’t think that’s going to be the final answer because I think that kind of approach treats some of the symptoms of the disease but not the cause. Therein lies the real difference between neurology and psychiatry. So for neurology, I think a lot of these causes are at the epigenetic level. We know that’s true for many disorders. We know that for Alzheimer’s, we know it about ALS, we know it for Parkinson’s disease, we know it from Huntington’s disease, and the list goes on and on. For all of these diseases, there’s always a subgroup or often a subgroup of patients for whom the genetic mutation that they inherited is the cause of the disorder.

Tony: You don’t see that in psychiatry. That there’s very few of any chains that have been linked to psychiatric conditions, let alone with the penetrance that you see for the neurological mutations. We have a huge opportunity now in neurology to know that at least for a subset of patients- sometimes it’s all of them like Huntington’s disease- where there’s a genetic mutation that is the cause. We know the targets, which is quite different to what you have in psychiatry where we hardly have a clue about what the target is. So that’s where I see it as a huge advantage. Because it’s a genetic problem, the nucleus and the gene expression of nuclear DNA is often the cause of why those patients go on to develop these neurological conditions.

Tony: I think for neurological conditions, gene therapy is a much more viable option, and we’ve already seen that proven in just the last few years with the approval of Spinraza for spinal muscular atrophy and Luxturna for a form of blindness, both of which are due to inherited mutations in genes important for those systems that are diseased.

Grant: Cool. Shifting gears a bit, can you tell us about your skateboarding?

Tony: Ah, skateboarding. Sure.

Grant: When did you get into it?

Tony: Well, I was very lucky. I got into it very early on, and I mentioned I got all of my training in Los Angeles. I went through the Los Angeles Unified School District System at places like Paul Revere Junior High School and Palisades High. I was involved from the very earliest days. Some of my buddies like John Fries, there was a first national skateboard champion. And the year after John, I was able to compete in the same tournament. I did pretty well. Part of that is because we lived in West LA where, I think as far as we can tell, the sport originated. If it didn’t certainly originate there, we kind of did a lot of the improvements. So for me, skateboarding is- today it’s a way for me to keep my brain in shape. I don’t do it just for the fun of it. It’s a great aerobic sport. I know it requires a lot of balance, coordination, persistence. It’s tiring to do it right.

Tony: It’s a full workout. I partly do it for that. There’s a lot of similarities with science and what we did in the early days. We innovated, we had to make our own skateboards where we would cut roller skates in half and then put them on either end of the board. We needed to figure out new materials to make the boards from. At the same time, it was fun. Got chased away by dogs and angry adults. I was eventually able to compete at the highest level. There’s a lot of similarities with scientific achievement. It was only later that my buddies and I, looking back, realized that we were on the forefront of a brand new sport. Totally in awe with what people are doing nowadays on a skateboard.

Tony: But we were part of that early, very early phase. I think it was exciting at the time. I see science in a similar way. I still like to do science because it helps keep my brain in shape too, to think a lot. I have to innovate, I have to plan new stuff, work in an area that no one else has been working on, and carve out new territory. There’s kind of a similar process going on there.

Grant: Tell us about the polyurethane wheels.

Tony: Because my best friend at the time, Mark Renneker, and I did a lot of skateboarding and because Hobie, which is a manufacturer of skateboards and sponsored a lot of skateboard tournaments in Santa Monica, I was able to get into the tournament scene. Luckily in 1966, I was on a skateboard team. At that time, we were skateboarding on a rubbery cork wheel. It was okay, but if you have a little pebble, the skateboard would just stop dead, and you’d go flying. So, it made you very cautious. One day our skateboard team, we were sitting around a table and our captain of the team. He came in with his paper bag, and he poured out this bunch of wheels on the table. They were all these white polyurethane wheels which we’d never seen before.

Tony: In fact, most of us had been skateboarding on metal wheels just a few years before. And we looked at these wheels. We realized immediately what this might mean, and we put them on our boards and sure enough, the boards were much easier to navigate. You could go over little rocks and not get stopped. You had a lot more contact with the surface. And luckily I think I was able to continue to compete at various tournaments, including the national tournament on those new wheels. That really helped. It was a nice example of where an innovative tool really helped you jump ahead.

Tony: Funny to think about these polyurethane wheels. It turns out they weren’t the absolute first polyurethane wheels never made for a skateboard. I mean, that bag came from one batch that had just been made. No other skateboard wheels had ever been made. When I talked to the experts who really follow skateboard lore, if I asked them when they thought the first polyurethane wheels came out, they’d say mid 1970s. We were almost ten years earlier with these wheels. I still have them, of course. Nice collector piece.

Grant: Find all the people who’ve written on the history of skateboarding and set them straight. 

Tony: Yeah, there’s this one guy. We had a big rally at the Smithsonian museum about five years ago. Tony Hawk showed up, Rodney Mullen, and all the great guys came. We already knew some of them when one gentleman who was The Professor, they call him because he professes to have all this knowledge about skateboarding, and he’s a good skateboarder himself, but he was wearing a white lab coat. He was a skateboard expert, and he was the guy that bought the first polyurethane wheels, out in the mid-1970s. Actually, the Smithsonian wanted the board that I have with those wheels on it, but I wouldn’t give it to them.

Grant: Nice. So how did you come to live a stone’s throw from NIH?

Tony: Oh, when Otsuka hired me to head up global neuroscience, the Otsuka lab was here in Rockville, Maryland, so we moved out at that time. 

Grant: What changes have you seen in the biotech scene on the 270 corridor?

Tony: I think the biotech scene along the 270 corridor is really starting to come on to its own. It’s always been a great promise, and there’s always been biotech companies. As I mentioned, I headed up one of them called Psychiatric Genomics in Gaithersburg. It never really became a Mecca like Boston, and it still isn’t quite to that level, but in the last five years, I think things have really been picking up. I know having found new laboratory space ourselves in just the last few months, lab space is still a premium and very large laboratory facilities are now being created and moved into very quickly. Companies like Novavax, Regenexx Bio, MedImmune, and AstraZeneca that took over the MedImmune site.

Tony: There’s a lot of activity that’s building now from some very successful companies. I think it’s starting to really pick up. Having NIH here, having the FDA here, having a lot of other pharma and other organizations that are associated with our industry certainly doesn’t hurt. There’s a lot of really talented people in this area. I’m just surprised it’s taken as long as it has. It’s a great area to live in, and housing’s affordable here. A lot of talent, a lot of universities. I think it’s just going to continue to build out.

Grant: if you were a young biomedical scientist just leaving academia, what geographic area would you head towards? Obviously there are many biotech ecosystems around the world. Which do you think might have the most promise if you’re looking out over the next generation or two?

Tony: Geography is important, but I define geography more than by the laboratory geography. I’d rather be in a great lab that’s in a good institution almost anywhere in the country, compared to being in, let’s say a San Francisco area, in not so good a lab or not, let’s say a more competitive and tight situation. I think the local environment is more important if the lab is fostering excellent research, and people are productive, much like I described for companies themselves. I think that’s the most important thing for a person to choose. The other thing though, besides geography is the opportunity that you find in that first job after a postdoc.

Tony: So, are there innovative techniques and new methods that are coming out of that group that you’re working with? Are they giving you projects that allow you to see your field of science in a way that no one else has been able to look at before? I’m a big proponent on new methods. I think new methods are almost always the key. Look at the CRISPR Nobel Prize that was just awarded. It’s a new method that was realized first for basic biology, but then exploited for its potential therapeutic use. That creates a field day for you. If you come into a lab with new techniques, new ways of seeing science, new ways of exploiting science, everything you do is going to be innovative and important. I would encourage people to think about doing stuff that’s on the cutting edge, as opposed to coming into a lab to put the dot the I’s and cross the T’s on the principal investigator’s work. You don’t really want to be there because that’s going to be stuff where many other people have already been.

Tony: When you go into the marketplace, let’s say your next job, you want to be able to be in a position to say, “I’ve carved out this whole new area. I see that you’re expanding in that area. I’d love to work with your team.” I’ve seen that in the gene therapy field. For example, about five years ago, there was a whole big breakthrough in gene therapy delivery, ways of getting gene therapy products into the brain or into specific tissues. That was a huge area, and it’s continuing to build, it’s continuing to show promise. I noticed that all the young investigators that were working in that field were getting jobs and moving to new places, new companies. All these guys are getting great offers to move, and that’s because the field was seeing the growth of this area. I wanted people with those skills to help those other concerns move to where they needed to be. That’s why the innovative stuff where you can see a real application in the commercial world, I think is the best single geography to consider, not so much where that lab happens to be.

Grant: What do you think will be the big changes coming to biotech as an industry in the coming years?

Tony: I think one of the big changes is in the field of bioinformatics. I’ve actually been a bit skeptical about big data and bioinformatics for a while. Partly I think that’s because bioinformaticians sometimes don’t have a good handle on the biology that they’re trying to uncover through their methods. At the same time, I can give biologists a little bit of grief too, because I think we as biologists need to do a better job knowing about statistics and bioinformatic databases and what they can and can’t provide. Getting quality data has been a real limitation. Just RNA-seq data, for example, can take a whole variety of forms. There are many different ways to measure RNAs, and how you measure it has a huge impact on the interpretations you make. I see a lot of people just blindly measuring the RNA, and they don’t really know what they’re actually measuring. Is it nuclear? Is it cytoplasmic?

Tony: Is it both? Is it cell-type specific? The questions go on and on, and these are important questions. But to answer your question about where I think things are going on in biotechnology, I think bioinformatics will come to play a bigger and bigger role as quality data is provided, as tools are created to analyze that, as people understand better on both sides of the equation what we’re looking at and how we’re interpreting it. I think there’ll be quite a revolution in how we can target disease therapies through what we learned from the bioinformatic analysis.

Grant: That’s good news for us.

Tony: Well, partly working with you, Grant. You’ve helped us actually understand some important properties along the way. I think it’s just going to get better as these methods continue to unravel what’s going on and in gene expression and gene processing for these diseases that are clearly genetic in nature. I think that’s the second level of great excitement for the biotechnology field is using these targets and these mechanisms that we’re learning about to come up with therapies. We were a bit lucky. Well, maybe I shouldn’t say lucky. We worked very hard to find this muscarinic receptor target for schizophrenia.

Tony: That was basically a four year odyssey, but it worked. The reason I say we were lucky is the cells that we used in our in-vitro assay happened to have muscarinic receptors. We knew that was always going to be a limitation. Do the cells even have the receptor mechanisms that you’re going to be evaluating? Often, people don’t even ask that question. We did a whole profiling, so we knew what those cell lines express, so we knew what candidates could come up because they were there, and the other receptor candidates we’d never learned about cause they weren’t expressed. But those are the kinds of questions that we have to ask.

Tony: I think the good thing for bioinformatics experts to keep in mind is to learn the biology about what you’re being asked to analyze. You have to be aware of the experimental design and really know about the material that you’re analyzing, whether it’s RNA-seq data or cell-based data. What are the constraints? What don’t you never learn about because the cells don’t express those players, or you’re looking at the wrong cell type, so you can never learn about the mechanism of disease because the disease is due to another cell. You have to learn about the biology, and it’s not just one sided. The biologists also have to learn about what they’re asking the bioinformatics person to analyze, what the limitations or possibilities are from that data.

Grant: So, you’ve worked remotely a pretty good chunk of your recent career, at least. Can you comment on what you’ve learned from that? And do you have any advice in this time of COVID where everyone’s working remotely, most everyone’s managing remotely. How do you pull it off?

Tony: Well, I’m actually about not to pull it off. I’m about to be back in the lab. I’m really looking forward to that. Learning molecular biology skills and applying what I’ve been learning over the last few years of genetics. I’m not a big fan of remote work. Certainly, it can be done. I’m a bigger fan of remote meetings. I don’t think people need to get on a plane and travel across the country for a one-hour meeting. I’ve even heard of a person once. He flew all the way from here to Asia for dinner. When dinner was over, he was put on a plane and he came back. I mean, this is crazy, and it’s not very helpful to our environment as well.

Tony: I think we have a real responsibility to preserve and improve our environment. I think remote meetings are fantastic. I’m seeing that as a real advantage, but the workplace is different. I think people really should be, when possible, in a similar physical proximity to one another. Especially in a laboratory environment where you have to be there to do the actual work. Hopefully when our COVID-19 vaccines come along and people start wearing masks and behaving properly, we can abruptly put an end to this pandemic. People will look at the best of all these worlds. We’ll have remote meetings. We’ll be back in the workplace. Our commutes may be five days a week, maybe there’ll be three and four. We mix it up a bit and just work to our own better advantage for all of these things.

Grant: Do you care to prognosticate about what will happen with COVID? When will things start to get back to normal?

Tony: I can’t. I’m not an expert, but one thing is really clear. If we all behave ourselves in a concerted way and show discipline about wearing masks, about keeping our social distance, about not having these large gatherings, and the vaccine becomes available, which it looks like it will. We do all of those things. As of January 20th, I would give it then another four months before we see a real improvement. And by the end of the year, we’ll be back almost to normal, but that’s the most optimistic scenario. The harsh reality is there are a lot of people who still will refuse to wear masks and won’t want to get vaccinated, and that won’t stop the process, but it will slow it down quite a bit.

Grant: What surprised you the most about this pandemic and the response?

Tony: I guess what surprised me the most about this pandemic is how so many people will refuse to wear masks. We ask our soldiers who go to war, to wear 50 pounds of gear and to go into battle wearing that kind of gear every day and literally risking their life to fight an enemy. Here we have COVID-19 as an enemy, and I’m shocked when I see people who aren’t willing to wear a one ounce mask and actually defeat an enemy that’s killed more people than these wars. I really don’t know what’s up about that, except that maybe it has become a political statement for people who just don’t like to be told what to do. We live in a society where we have to drive the speed limit. We have to follow certain laws, and this to me seems like a great example of just another minor adjustment that people should be making.

Tony: And most do. In many areas, the compliance is 90% and above, but it isn’t everywhere. If we all pull together and make these small adjustments, our economy can recover much more quickly in the long run. That surprises me that I even have to say things like this or parenting what other people say. That’s the big surprise, which seemed like this should just happen as a matter of common sense.

Grant: It’s a bit depressing.

Tony: Yeah, it is, but I think we’ll get through this, and the vaccines, I think will make a huge difference. 

Grant: As a final question, maybe a bit of a humdinger. What do you think most scientists today have wrong?

Tony: Well, let me rephrase the question a little bit. What do I think scientists nowadays, especially the younger group of scientists coming into industry really need to pay particular attention to? We all need to pay attention to the limitations of the data that we simply download from the internet. You know, I remember the first scientist who said, “Well, I’m going to Google that term and look up the answer.” I thought, what are you crazy? Aren’t you going to go to the library stacks and pull out the journal articles or read the articles and figure it all out. Both methods are clearly limited, but nowadays people do go to Google and they type in pretty much anything they want to learn about science. You certainly can learn a lot. There’s no question. It’s a fantastic tool, but the problem and what people get wrong is that’s kind of where it stops. They go, “Well, I read, I saw on the web that you can use this app. That’s what we’re going to do.” 

Tony: What they don’t do then, they don’t say, “Well, gee, is that assay really the right one for me? Or can I compare it with two others that I learned about? What I have to do empirically in the lab to answer for myself that what I’m reading and learning from the internet it’s true.” That’s what I see as a real problem. I hear this from other people who train students and see the new crops coming and going. There doesn’t seem to be as much willingness to run the empirical foundational studies that are needed to convince you that you’re on the right path. And so scientists often can move into the wrong direction, and they’re doomed to fail from the get go. You have to do the experiments in the lab to convince yourself that you’re on the right path.

Tony: Is your analytical tool sensitive enough? Does it give you the kind of information that you need? Is it analyzing the right materials to even answer the question? All of these kinds of things. I’m a big believer of positive controls and negative controls. You want to run your assay with outcomes you fully expect. We did an RNA-seq study recently where the database that was provided by Grant’s company. The first experiment I did when Grant provided the data to me was I said, “Okay, if this data is correctly provided from Grant’s team, I should be able to recreate the figure from which the data was deposited.” And sure enough, we exactly recreated the data that was in the publication for one gene.

Tony: And that gave me confidence that the other genes are going to give probably accurate results. So that’s the kind of stuff you need to do. You need to convince yourself in the lab empirically that you’re on the right track, and not just assume that because you found a method somewhere that that’s going to work. We all know most methods that you just snag from somewhere don’t work. You’ve got to tweak them, and then sadly enough, many of the results that we get in papers can’t be replicated. So the way to get it right is to have the right methods, and show that you can produce reproducible results that convinced herself that the methods are right. I mentioned persistence is important. Replication is my other favorite word in science. We have to be able to replicate what we’ve done and what other people have done with similar methods before we have any confidence that we’re on the right track.

Grant: Couldn’t agree more. Well, thank you so much for joining us today, Tony. It was great. 

Tony: Great pleasure to be with you, Grant, and I look forward to seeing great things out of your own organization and from all of us who really are committed to putting all of this that we’ve been talking about together, so that we can help people get over inherited diseases, acquired diseases, and even those that are spontaneous. There’s a lot to be learned. There’s really no end to what medical science is going to be able to achieve, and t’s just exciting to be part of this. 

Grant: What will science do next?

Transcript of Episode 5: Quin Wills

Grant: Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard, and joining me today is Quin Wills. Quin would you like to introduce yourself? 

Quin: Hey, Grant, thanks for having me. I’m Quinn wills. I’m the co-founder and CSO of Ochre Bio. We’re a sort of deep-phenotyping, tissue genomics company, that’s trying to understand liver metabolism and how that maps to liver diseases. For example NASH, which is a type of liver disease that is becoming really big in Western society right now, and liver transplants, which is kind of cool. 

Grant: So what’s the origin story behind Ochre?

Quin: Oh, okay. Well, that goes back quite a bit. You know I started off life on the dark side, so I started off life as a medical doctor and geneticist before I moved into comp bio. And I was always interested in liver metabolism and metabolic stress in those days, more around alcohol metabolism and fetal alcohol syndrome. But when I moved down from South Africa to the UK, I think like a lot of us, I became very intrigued with big genetics and big genomics.

Quin: I could see what was coming on the horizon of the human genome project and wanted to be more directly involved with that. I didn’t like the idea of handing over data for other people to think about and engage with. I really just retrained. Did a few extra degrees in mathematics and comp bio, and then my PhD in systems genomics at Oxford, really focused on technology, but constantly thinking about liver and liver metabolism all the way through. 

Quin: I started my first biotech company before I finished my PhD using, in those days, gene expression arrays. Can you believe it? I’m sort of dating myself now. I was trying to compete with high content imaging to really understand drug toxicities of target effects for the pharma industry. Moved that out East and carried on my academic career, doing single cell genomics and spatial genomics as we do now, these kinds of things. 

Quin: And I pitched the idea to a large pharma company that was coming on to the Oxford campus that we’d like to use these technologies to improve target discovery in NASH or NAFLD rather. I prefer to call it NAFLD, non-alcoholic fatty liver disease. So in other words, too much fat in your liver, which one in four of us have these days with obesity. And they loved it.

Quin: And of course we got going into some big industry-type pipelines and great technologies at a functional genomics level. And I loved it. I genuinely loved it. I think the clinician in me was a little bit frustrated with the struggle to translate this problem. 

Quin: It’s a massive problem. We are realizing it’s so connected to cardiovascular disease, diabetes. Too much fat in the liver now is becoming the main reason for needing a liver transplant in many countries. It’s not alcohol, it’s not viral hepatitis anymore in many places in the world. And it’s also becoming the main reason for the shortage of donor livers. Because fatty livers don’t transplant very well. 

Quin: I just really wanted to solve this and I do really want to solve this still. And I figured that it was one of those sort of moments where we have to be honest with yourself and say, well, “Genomics is great. And all the computational work is great, but we really need more to take this to the next level.”

Quin: And really the idea that came together was when I started engaging the transplant surgeons in Oxford, and we chatted about this problem in the transplant space. And they mentioned to me that they’re about to publish on this incredible liver perfusion machine, effectively a machine that keeps donor livers alive outside the body so you can assess the liver, keep it warm, keep it nourished before you transplant it. And would I want to study these fatty livers, these discarded or donor fatty livers on the machine and do really interesting temporal analysis with gene expression. 

Quin: And I realized very quickly that this was an incredible alternative to a preclinical model. It was so much more satisfying going from sequencing lots of livers or studying liver physiology at a tissue level to then testing out your hypothesis in a living human liver on a machine and thinking about what’s going on rather than going to a high-fat diet mouse, for example.

Quin: But I think for me, there was more to this, and I decided that it deserves to be its own company. And the moral to this is that by really focusing on the transplant space rather than diabetes, which I’ve sort of been for many years, this really allowed us to not only study livers on machines, but it allowed us to find new targets by studying the right spectrum of disease, which is a big issue in the space.

Quin: Right now, people are focused on biopsies of very inflamed, fatty livers, whereas we’re sequencing. We’re building the largest genomic atlas of the human liver, using a thousand livers from one of the biobanks in Oxford, going from healthy level all the way through to the early stages of disease, which we love.

Quin: And I can tell you a little bit about exactly how we’re building up this atlas of what’s going on in the liver. And then finally–and this is more clinical twist rather than a comp bio twist–we wanted to get around the biomarkers problem. You and I both know this is a massive problem in our field and a massive problem in clinical trials.

Quin: And fatty liver disease, NAFLD, is like a lot of chronic diseases: silent for many, many years before it presents. And they are just no good biomarkers, so there are no ways to do good clinical trials, particularly for the early stages of disease. So everybody’s very focused at the moment on very late stage disease endpoints, crude endpoints, and molecules and experimental drugs that frankly do fall too little too late. And this is why we still don’t have a therapy on the market. Not to over simplify: there are many reasons. But it’s a difficulty in clinical trials that is frustrating the space right now.

Quin: Of course we love biomarkers, like any good computational biologists, and we were thinking about it, but we don’t want to bank on it. And so rather than trying to do these very difficult clinical trials in the fatty liver space, in terms of NASH and NAFLD, we rather want to think about this in the transplant space. So treat fatty donor livers with the same kind of therapy we would use for patients living with the disease and improve outcomes in the transplant space. And those trials are much simpler. I mean, they, like all clinical trials, are a big gamble, but compared to the biomarker perspective, have much clearer endpoints.

Quin: And then what we will do as part of that, is look for improved long-term outcomes in these livers, and we’re doing this using these new GalNAc siRNAs. So they are liver-specific siRNAs that can last, with effects for many, many months. And so we can treat these livers entirely ex vivo on these machines before they’re transplanted. And so it never really goes into the patient. It’s entirely a liver specific treatment, but we can see the effects play out over the first six months of the transplant life. And, that’s where we’re at. And that’s what we’re doing as a company. 

Grant: That’s amazing. So tell us a bit more about the lasing efforts.

Quin: Our first big target discovery project is this human liver atlas, which takes a thousand livers from the QUOD biobank in Oxford. So this is a collaboration with transplant surgeons and the QUOD scientist at Oxford, where we are genotyping these livers. We are bulk RNA sequencing these livers. We are spatially sequencing these livers. So in fact, all of these livers get sequenced up to 4 times. So there’s really a lot of data at an RNA sequencing point of view generated on these. 

Quin: We also do imaging AI. And that is really to standardize the histopath. So we have about seven images taken from each liver so we can understand what’s going on. The imaging AI also identifies the key regions that are interesting to us. For example, there’s this cell phenotype called ballooning degeneration that happens into the cells that we think is really important, and we’re trying to understand as something that connects too much fat and then inflammation and fibrosis cirrhosis in the liver.

Quin: And then we have all the bloods from the donors so we can understand cardio-metabolic parameters. And we have about 150 clinical variables from the donors and the recipients. Because most of these livers that we’re working on ultimately exist in somebody out there somewhere. And so we can really start asking questions both around disease progression and particularly the early stages of disease, which we’re quite keen on and then how that maps to transplant outcomes. So can we think of targets that are applicable to both?

Grant: So what would be your ideal outcome in say five years, if everything goes perfectly?

Quin: Yeah. I think like all biotechs, we have what we’re doing now, but there’s a grander vision and unlike a lot of computational biologists, I believe that this type of work is what we call deep phenotyping or cellular genomics is going to be such a big part of tying this together in our space. You know, GWAS and associating SNPs with clinical disease is a great first step, and pharma is beginning to embrace this in many ways. But in particular, I would argue for chronic diseases where there’s so many factors and so many steps in the progression, this has to come into play.

Quin: And so we’re using this to understand liver metabolism and particularly fat metabolism and how that plays out in the liver and also how that plays out systemically. But I think we consider ourselves a cardio-metabolic company, and we’re really looking at more fundamental health span drivers that we think we can be targeting in the liver to improve outcomes and really improve human health span.

Grant: Great. So you guys went through YC. Can you tell us a bit about your experience there?

Quin: Loved it. My co-founder and I, Jack O’Meara, had an unusual meeting. I left my job with this pharma company. I decided to go build my dream treehouse in Costa Rica and rethink how I want to do this and where I want to be based. Do I want to be European in terms of biotech or do I want to be on the US side. And of course, they are very different cultures. Even in the US, West coast biotech is very different from East coast biotech which is very different from UK/ Europe biotech. Very different funding, cultures, VC styles, trajectories in terms of how the companies grow.

Quin: And I figured I possibly want to be on the US side, even though I have such strong roots in Oxford and the transplant scene and the liver scene and the metabolism genomic scene. But then I was introduced to Jack. I called Jack. And at the time I called Jack, he was building huts in Tanzania.

Quin: We immediately realized we were kindred spirits and equally crazy. Jack is an individual who also comes up through biology, more tissue engineering and is really focused on clinical progression. So getting therapies into markets. He really was my natural counterpart and shared a lot of my philosophies. We both share a lot of philosophies on how to really innovate in biotech and what that really requires. So we started off in London. I came back, Jack was in London, but it was a strange one. We went out to California for a conference, met the YC folks on the day that particular cohort was beginning, chatted with them. Within two, three hours I said, “We like you guys, stick around.”

Quin: And we canceled our tickets back and never went back to London until COVID forced us to come back, take our money and get set up. We’re very much a Californian company, but with a sort of subsidiary in the UK and very, very international operational. Oxford lab is what handles a lot of the target validation and screening work after we’d done with the target discovery and before we put it into profused livers, which we do with transplant centers here in the UK, particularly Birmingham.

Grant: So, what is your philosophy of innovation?

Quin: Well, now you’re going to get me to step onto my podium. Yeah, I’ve learnt a lot. I’ve been humbled a lot over the last 15 years of doing comp bio. I’m seeing a lot of different cultures all over the world on how to do things. For me, there are three things that seemed to be common to a lot of very successful genomics companies.

Quin: So maybe we call ourselves a genomics company rather than a biotech company. One is speed. I mention this because this is something that I think a lot of academic computational biologists struggle with. It’s such a culture shock to them particularly if you come from big academic centers. There’s this whole culture of “Oh, it’s okay. Get it a hundred percent right six weeks from now, Eighty percent now is probably not fine.” Whereas in our world it’s very different. 

Quin: The centers in academia are very different. There’s this “publish or perish” sort of notion, whereas in our world it’s “deliver or die”. There’s just no substitute for speed. A biotech company with half as good an idea and twice the amount of money will probably get there faster, unless you are super quick. And there really just is no getting around that. Building that culture in a comp bio company is extremely tough. I am far from perfecting it, but we try our best every time and hopefully get there with the teams.

Quin: I think the other important thing is value based innovation. I can’t think of a better way to describe it. Again, in academia there’s very often this idea of blue sky, go be a brilliant computationist and come up with new algorithms. It doesn’t matter if they’re relevant or not. Just over-engineer, go wild. For us, innovation by definition has to be better, cheaper or faster than what somebody else is doing, because if it’s not, don’t waste your time. Get on, do it the way other people are doing and focus your intelligence and creative mind on other problems where we can be better, cheaper, faster, and it really must be valuable. As a biotech, you have so many priorities and you’re moving so fast that even if something does seem like it could be better, cheaper or faster, right now, it’s just not a priority for you. And being able to balance that, especially if you’re naturally a creative person as so many computational biologists are, it is a difficult balancing trick.

Quin: And I think companies that get that right tend to be more successful. And I think one final one, and this is where I do believe US companies are winning hands-down, is a general can do positive attitude with a positive risk-based culture. We in Europe struggle with us. We are still very risk averse. We still always find ten reasons for why something won’t work. And it amazes me how much of general positivity, which you see both on the East coast and the West coast, can really transform innovation within a company. And those are the three things. 

Grant: What do you think American biotechs can learn from European biotechs culturally?

Quin: That’s a toughie because I’m always raising it the other way around. Let me rephrase that question a certain way. There are certain attitudes in American biotech that won’t play out in European biotech simply because the funding structures are very different. A lot of what I’ve just discussed works extremely well when you’re also chucking a lot of money at the problem.

Quin: As I hinted at early on, there is no substitute for just chucking a lot of money at the problem. That is part of the high risk game that we play when we take venture capital, and we try to move quickly. A lot of biotech in Europe tends to follow a trajectory of incubating within a university center, which perhaps is not so different from other biotechs, but then taking on grant structures, which have a longer timelines. These are all great. There’s no right or wrong way. People will argue whether it’s right or wrong, and there are lots of opinions around those. I do believe fundamentally there’s no right or wrong way to do that, but that is a much longer trajectory, and so certain attitudes won’t play out to that.

Grant: Great. What about the other way around? What can European biotechs learn from American biotechs under the constraints of differences in availability funding?

Quin: Stop overthinking everything is the one simple way of explaining what I see when I look at a lot of European biotechs. Again, it’s really about focus on speed, focus on value-based innovation and focus on creating a can-do culture. Really those three things for me are so, so important. I’m South African, even though sort of the UK is my home now. Even after all these years after making the UK my home, it still amazes me at how difficult it is to get a high five kind of culture going. It just doesn’t always work. It just doesn’t work with the culture. It doesn’t work with people, but finding ways to implement a more positive can-do culture needs to happen in a lot of biotechs to emulate what’s happening in the US. But maybe reappropriated the way we do things on this side of the pond.

Grant: So changing tack here, if you weren’t a scientist or a physician, what would you be doing?

Quin: It would be building tree houses in Costa Rica nonstop.

Grant: Talk a bit more about that. I’m curious about this. What’s the story here?

Quin: It’s easy to make up stories to defend things you’ve said when you were young. I want you to be careful about causality, but I remember in my first year of school when your teacher asks you what you want to be and what do you want to do? I suppose I was a bit of a precocious kid. Probably still a precocious kid now, even though I’m in my forties. I said to my teacher, “I want to be a medical doctor that does research.” So that was my career choice then and there and that one played out.

Quin: Maybe I was creating a self-fulfilling prophecy. I always said I wanted to live in a wooden house. So growing up in South Africa, that had a very particular idea attached to it. But as I learned to love traveling all over the world as a scientist, as so many of us do, I just fell in love with Central America. I really fell in love with Costa Rica and the life out there. And so together with a really good friend of mine, who’s in many ways the Jane Goodall of Central America, put some money down for some jungle and started building my dream treehouse out there next to a beautiful river with gorgeous waterfalls. And I will keep doing this as long as the money is available and I can do it. Build my little community and friends and retire out there one day. 

Grant: When is the other South African internet enabled satellite service coming available? 

Quin: Yes. Soon, please. 

Grant: You can just work from your tree house, right?

Quin: Yes, absolutely. You joke, but right now I’m trying to solve this problem from a distance because one of the first things I had to do out there was set up some solar powered WiFi. Well, 4G to WiFi routers. The solar panels are still working and the repeaters still seem to be working, but the main router is sitting on a hill somewhere. It seems to be giving in. With my poor Spanish and others’ poor English, it’s difficult trying to fix this. So yes, please. Somebody needs to sort out internet to the jungle very soon.

Grant: Pretty cool. Tell us a bit more about when you were a kid. Why did you say you wanted to be a medical doctor who did research? Did you have a family background in this or read some book? Came to you in a dream?

Quin: Honestly, I still don’t know where that came from. Something must’ve sparked that. I come from a long line of engineers and tinkerers and inventors, so that there is the invention streak in my family. That’s for sure. But it was bizarre. I said I wanted to be a doctor. My brother said he wanted to be a lawyer. He did the next best thing and became a banker. 

Grant: I knew it. Where are they living? 

Quin: My brother lives out in the UK, too now. Enjoying the London highlife as a banker.

Grant: Fancy schmancy, huh?

Quin: I hope to make as much money as he does one day.

Grant: So tell us about the COVID situation. I think you two had maybe even a more interesting than average experience with the timing.

Quin: I did a quick trip out to Costa Rica. A few days into the trip I just realized that the world was shutting down with this epidemic which became pandemic and very quickly flew back to California to watch California shut down and everyone panic. I was there when everyone’s doing the panic purchases and toilet paper was disappearing and all the rest. We have to make a call. Do we hang around in the US and try to continue our fundraising out there? Or do we assume that the whole world is going to go virtual and we need to be right back in the UK so once the money is in, we can set up and continue in the UK. It was again one of those things where Jack and I just looked at each other and said, “Right, tonight we’re going home.” and we did and got there.

Quin: Thankfully, everybody adjusted to the new normal very quickly. Thankfully, biotech funding only improved. Not that I would wish for COVID, but I think it focused individuals on healthcare and investing in companies with longer term goals rather than immediate profits. And so that played out very well for us. And thankfully again, we’re able to get set up in an academic center so that the lab can continue on rota. Because a lot of this initial target discovery was in collaboration with QUOD and the transplant scientists as being processing tissues, that could continue. Not without its problems. It’s cost a lot more money to keep the show on the road. I think a lot of international couriers have made a lot of money out of us getting samples sent all over the world, But again, count our blessings and thankfully, so far, so good.

Grant: Great. So what advice would you have for biotech entrepreneurs? 

Quin: So the common question you get asked is, do you need to have a brilliant idea? You do need to recognize your unfair advantage as an individual. What is it that you’re not only particularly passionate about, but that you’re probably better than average with, and that you could really trade on?

Quin: I think in many ways that’s more important, but of course, a fantastic idea doesn’t hurt. Most important, and I’ve learned this painfully over the years and growing up in the space, is that your co-founder is everything. I was really adamant that I didn’t want to have another very academic scientific co-founder. It’s kind of like finding a life partner and getting married. Somebody who just naturally compliments you. You match each other, when you’re both down, you can fill in for each other, it’s you two against the world, that kind of thing. I found that in my co-founder, and I know that no matter what, we will keep assisting together and we’ll find a way to make this successful. And that is just so important to do. 

Grant: So other than serendipity, how can people maximize their chances of finding a good co-founder? How can you increase the likelihood of a serendipitous event?

Quin: That honestly, I think the rise of these accelerator programs internationally has been a fantastic way to do this. Yeah, I could have done things up through the Oxford ecosystem, but one of the reasons why I went for these accelerators is because of this. Something perhaps that we are better at, says he controversially, in the UK versus the US is how accelerator programs are done here. There’s a truly excellent accelerator program here in London that we started off with called Entrepreneur First. They have many philosophies on how one could be competitive as a UK company. 

Quin: And right at the top of that is getting the right co-founder. Versus a YC where it’s “come with an idea and your co-founder, and we’ll give you a good chunk of money just to move very quickly with it”. EF’s approach is “find the right partner”, and they give you money and time to play with the right individual and really test out if this fits together. Both are great models. Both worked for us, but really first and foremost, it began with Entrepreneur First, here in London. 

Grant: Cool. So long-term, looking out when you retire whenever that is, what would you like to be known for? What would you want to have done?

Quin: I think really fundamentally, I am driven by this concept of improving human health span as somebody who started off life with a condition. When I look at health care, it doesn’t matter which health care model you believe in on either side of the pond. When you look at how as a species we’ve doubled our lifespan and are progressing to tripling it, and what that means in terms of crippling healthcare in the later stages of life. I think one of the best ways to flatten that curve, no matter which side of the funding equation you are on in healthcare, is that we need to start moving away just from disease endpoints and to start focusing on fundamental health span endpoints. Some people would call this aging myths and anti-ageing myths.

Quin: I’m careful of using those terms because it does come with a lot of traditional baggage around that, but really focused on the fundamentals of health span and how we tackle that as add-ons to other therapies or even fundamentally just to improve your health span. And when you’re in the cardiometabolic space like this- diabetes, liver metabolism, NAFLD, it’s a really juicy problem to be thinking about. I hope one day I can, I can just make a little bit of a dent in that problem and improve health care for humanity.

Grant: And what do you think are the most sensitive levers there that we can pull?

Quin: That’s a very, very big question. I think perhaps I will answer this in an overly simplistic way. I love to listen to this debate, the healthcare debate. And for me, the obvious debate is always the side of the pond, the US verses universal healthcare. And how does that pay out? It’s interesting because if you look at big data and how it’s driving health care, a lot of trends are non-obvious. For example, a lot of preventative health, which overlaps a lot with how I think about health span, has been driven by private medicine. Insurance companies are rewarding you for your apps and staying healthy and taking so many steps, which is light years ahead of perhaps how we do it in the NHS here in the UK.

Quin: And I say that very carefully so that I don’t get myself into too much trouble. So there are a lot of non-obvious things happening in the space, but I think what is universal to all of us is that we spend an extortionate amount of money in the later years of life to stay healthy. You know, we’ve all now in the COVID era, become familiar with this concept of flatten the curve. Flattening the curve in healthcare I think needs to come from therapies that are fundamentally focused on health span and health span modifiers, whether that’s more preventative medicine or that’s attached to other therapies.

Quin: Even more simply put, one way I like to say this to people who think a lot about obesity, because of course, obesity is a very big area that I think about too, in the space. We really need to stop preaching to people and telling them to move more and eat less. It doesn’t work. It really doesn’t work. And if we could solve obesity as an issue, we would make massive strides towards flattening that curve in healthcare. 

Grant: Well, it’s impressive how strongly heritable obesity is in the context of environment. But in terms of comparing across different phenotypes, it’s very heritable.

Quin: It’s incredible. And it’s just incredible seeing what we know about obesity and body fat distributions and how that affects different diseases. Why are we still so moralistic about this in general health care? I can only hope that future generations will look back at and shake their heads at how silly we were at really just forcing this. Body shaming people and making them feel bad about how much they eat and the sedentary lifestyle. Of course, these things are important.

Quin: Of course, we should do our best to exercise as much as we can and not consume too many calories. but we need to start thinking more smartly about how we modify these phenotypes like obesity and fat distribution. There are the three distributions, and in many ways as a company we focus on one, there’s fats around on the outside of a body. And many of us in research know there’s fat around your internal organs, but now we know various organs also build up fat inside of the organs and inside the cells of the organs as you age, and the liver is one of them. We just need to think about how we treat this problem.

Grant: So on a completely unrelated topic, what surprised you the most about the COVID pandemic? 

Quin: I love non-obvious things playing out, and especially when they come out in your favor. And I think the thing that has amazed me is how well biotech, investment is happening right now. And again, like a lot of complex systems, you should be very careful of coming up with overly simplistic explanations. But it seems to me with COVID that it has really highlighted to people how fragile we are as a species in terms of healthcare. We can fly to the moon. And yet, a simple virus can wipe out an economy very quickly.

Quin: It seems to have really made venture capitalists more eager to focus on companies like biotech, where you’re not expecting to turn out a massive profit in three years’ time. And that’s fantastic and power to all of us, because that is where I believe the future is in good innovation, and venture capitalists need to be taking bigger risks.

Grant: Many people who maybe weren’t in the infectious disease space weren’t seriously anticipating something like this. What things do you think are underestimated? What things do you think in the next decade or so could really come out and change things in a way that most people aren’t at all prepared for. 

Quin: Quite honestly, I think it’s worth pointing again to infectious disease. You know, it’s really incredible. If you’re interested in aging medicine and aging drivers and nutrient sensing and how these things play out as we age, you look at some of the great, popular books that have been written recently. Particularly coming out of East coast, West coast, US, and I think almost all of these books, by various academic authors, except for one or two, perhaps have all said that. Of course the future of humanity is so dependent on understanding the fundamentals of aging metabolism, et cetera, et cetera, with a one exception. That there could be a massive viral or disease pandemic that can wipe us out. And there you go. This is what we live in right now. We are a very fragile species.

Grant: This is the little league version here, right? If it’s at the fatality rate of SARS or MERS or something, we’d be in a deep shit.

Quin: No, absolutely. Let’s hope we learn from this, but history dictates that humans need a few things to go. You need it to go wrong a few times before we learn. Fingers crossed, it is not the case this time round, for sure.

Grant: Great. Well, thank you so much for joining us today. I really, really appreciate it. And also, is there anything else you want to say before we go? Anything else about the company, a shameless plug, some hobby of yours, whatever. 

Quin: No shameless plugs, only to just thank my co-founder and thank my team. It’s such an honor and a privilege as a modern human being to be able to chase your passions, to be financed to chase your passions and have, even if it’s just a small chance of being able to make a real difference to human health. And I’m very lucky to have the people I have around me. So thank you. And, thank you for having me on, man.

Grant: Well, thanks for joining us. It was great.