The Bioinformatics CRO Podcast

Episode 51 with Adam Freund

Adam Freund, founder and CEO of Arda Therapeutics, discusses how targeted killing of pathogenic cells could be used to treat chronic disease and aging. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

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Adam is founder of and CEO of Arda Therapeutics, a company using single-cell sequencing to characterize and target pathogenic cells to treat chronic diseases and aging.

Transcript of Episode 51: Adam Freund

Disclaimer: Transcripts may contain some errors.

Grant Belgard: [00:00:00] Welcome to the Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Adam Freund, the founder and CEO at Arda Therapeutics. Welcome, Adam.

Adam Freund: [00:00:10] Thanks, Grant. I’m happy to be here.

Grant Belgard: [00:00:12] Yeah, we’re happy to have you. So what can you tell us about Arda?

Adam Freund: [00:00:16] Arda is a seed stage biotech startup. We are based in the Bay Area, and our mission is to find and eliminate pathogenic cells. And we do this in chronic diseases and ultimately in aging itself.

Grant Belgard: [00:00:30] How do you think about pathogenic cells? What in your mind makes a cell pathogenic?

Adam Freund: [00:00:36] It’s a great question. At a high level, cells are the functional units of life. And because of that, we also think that they’re the functional units of disease, which to put it another way is that when a tissue goes wrong, it’s because cells within that tissue have gone wrong. And in some cases, the cases that are particularly relevant to Arda, that means too much of a particular cell type. So think for example, myofibroblasts in fibrosis. They’re an important wound healing cell type, but in certain diseases they become overly abundant and then they don’t resolve or go away when they’re supposed to and so you get disease. And in cancer therapy, targeting and eliminating pathogenic cells is sort of a tautology. That’s what the entire field is based on. But in other diseases, we think that it’s a missed opportunity outside of cancer and maybe a little bit autoimmune disease. There are virtually no drugs that directly eliminate cells. Instead, what most modern pharmacology does is modulate individual pathways or proteins within the cells that drive disease, the pathogenic cells. But cells are complex networks, and because of that their behavior can be difficult to change via the modulation of single targets. And so we believe that it will be more effective to eliminate entire pathogenic networks, that is entire cells.

Grant Belgard: [00:02:02] So once you’ve identified a pathogenic cell for a particular indication, how would you go about eliminating them?

Adam Freund: [00:02:10] So our approach primarily focuses on using cell surface factors to drive biologics, antibodies generally to those cells and deliver cytotoxic activity. We’re really lucky actually that the cancer immunotherapy field over the last ten years or so has done this incredible job of creating a modular, programmable, therapeutic toolbox for eliminating specific cell populations. And unlike previous generations of cancer therapy, which were chemotherapy and were based on the internal metabolism of cells, these new tools are based on cell surface markers, which means they can be used any time you have a unique or enriched cell surface marker. And so that’s what we do. We think that the potential of these immunotherapy tools, multiple different modalities within this space are applicable and have really amazing potential outside of oncology. And that’s what we’re trying to bring to the to patients.

Grant Belgard: [00:03:08] So how would the regulatory pathway look for that?

Adam Freund: [00:03:11] It should look like most other biologics. As with anything, we will be developing the antibodies. We’ll be doing a lot of preclinical testing for efficacy and safety and then we will be registering for an I&D and moving into a phase one where we’ll be looking at ascending dose safety things like that. And then in phase two and three, looking for efficacy. One of the things that we will be focused on from a mechanistic perspective is making sure that the drugs that we’re developing are, one, eliminating the cells that we want them to eliminate, and two, not eliminating a bunch of other cells that we don’t want to eliminate. And then of course, we’ll have biomarkers related to that. And then we’ll also be looking at the efficacy readouts, which is to say, do we actually make the patients better and healthier.

Grant Belgard: [00:03:58] How do you think about animal models in this kind of work?

Adam Freund: [00:04:03] I think about them a lot because I think we all know that they are highly imperfect. A lot of the failures of drugs in the clinic can be attributed to poor preclinical models. And if you think about the way that we develop and evaluate preclinical models as a field, it’s sort of easy to understand why, especially when we’re talking about rodent models, mouse or rat models. What we usually do is based on some general understanding of the human disease, guess at a set of interventions, either genetic or environmental that might cause similar problems in the rodent. And then we assess histopathologically. That is to say, we look at a slice of the tissue and in a usually pretty subjective way, ask whether the tissue now looks like, the tissue from a human disease patient. And the issue with this is that it’s quite qualitative. It’s quite subjective and it usually doesn’t involve a large sample size. And so consequently, when we assume that because an intervention works in a rodent model, it will work in the human model, we’re often disappointed, At Arda, we can’t solve the entire preclinical model problem via biotech. I think this is a real issue and it’s something that I think the field needs to grapple with. But really at the end of the day, there’s no substitute for testing in humans. And that’s not something that we can get around. However, we can I think do a little bit better than the current status quo.

Adam Freund: [00:05:29] And that’s because when we compare human disease to preclinical models, we do so at the single cell level. So I haven’t really mentioned this up to now, but we’re really focused on using single cell data to identify pathogenic cells. And what that means is that we look at human data at the single cell level, usually single cell RNA-seq data. And we also look at single cell RNA-seq data from rodent models of the disease as well as other types of preclinical models. And in that high dimensional single cell space, we can compare and contrast models to the human disease. So we can look for the overall cellular architecture of the disease, the overall transcriptomic similarity. We can look at whether our specific cell populations are present in the disease, the populations that we find in humans. We can ask whether they are also present in these preclinical models. And then lastly, when we eventually hone in on a specific target, something that’s on the cell surface of the cells that we want to eliminate, we can ask whether the expression pattern of that target is similar in preclinical models as it is in humans, which tells us whether some of the safety and efficacy data will be translatable to humans. And so combined, we actually think that this will allow us to select better models, maybe even develop better models and better models lead to better drugs.

Grant Belgard: [00:06:44] When you talk about pathogenic cells, is it usually the case that a pathogenic cell state is accompanied by an unusual accumulation or depletion in the relative abundance of that cell type or do you see those things as quite unrelated? So I’m thinking here of something like fibrosis, right? I mean, you certainly have a change in the abundance, but then also concomitantly in the state and likewise with Microglial activation proliferation, is that the rule more exceptions that come to mind.

Adam Freund: [00:07:23] So I think that if we’re talking about a state, a cellular state that only exists in the disease context, then sort of by definition that state is accumulating. Now, it might not be accumulating because cells are actively dividing. It might be accumulating because cells are shifting from one healthy phenotype to an aberrant phenotype. And that could change how much we expect cell elimination to work. So one of the things that we think about is that not every disease is a good target for Arda’s strategy. Examples would be sarcopenia or neurodegenerative disease. These are diseases mostly of cell degeneration. And that’s not a context where eliminating more cells is likely to cause benefit. However, there are many diseases, inflammatory diseases, fibrotic diseases, other kinds of local tissue remodeling diseases where we do think that eliminating cells can either help the tissue restore balance, can trigger even in some cases regeneration, or can break a positive feedback loop that is causing that tissue to remain diseased. And so one of the ways that we carefully choose our indications is by assessing based on everything we know about the disease as well as the single cell data, do we think that there’s a cell population here that is causal? And if we have a reasonably strong hypothesis that the answer is yes, then we can go and more deeply characterize and investigate the single cell data.

Grant Belgard: [00:09:03] How many pathogenic cell indication combinations would you say are pretty much off the shelf based on the existing literature, you have pretty high confidence that eliminating the cell type would causally change the course of the disease for the better? And how much do you have to put into novel target identification, which I guess in your case is curiously kind of a combination of cell types by specific surface markers, right?

Adam Freund: [00:09:39] That’s exactly right. When we think of target identification, we think of two layers. There’s the layer of, can we find and identify the cells based on mostly their transcriptional signature and their enrichment in disease. But then can we find specific cell surface targets on those cells that can be used to eliminate them and not healthy cells. So that’s exactly right. And to your question about how many indications is this sort of do we expect this to be a useful strategy? We actually think this will be quite broad. And partly this has been driven by what we’ve seen in the single cell literature over the last five or so years. So as single cell data has accumulated, we’ve seen huge numbers of papers coming out where people will examine a disease, usually from human patients for the first time using single cell data. And they almost always ask the question, do we find cell states that are enriched or unique in the disease state and that appear causal? Correlation is not causation. So just because a cell state is enriched in disease, doesn’t mean it’s bad. It could be adaptive. In the case of if you’re looking at normal wound healing, the presence of myofibroblasts is adaptive. It’s helping that wound heal. But in the case of fibrosis, it’s maladaptive. And so there’s a little bit of biological inference that has to happen here.

Adam Freund: [00:11:00] But by and large, finding a cell state or multiple cell states that are enriched in patients and not really present in controls, and that those cells express genes or factors that are thought to be causally pathogenic based on the understanding of disease, it appears to be the rule rather than the exception across tissues, across cell types, across indications, across groups as well multiple groups from all over the world who are doing this kind of work are finding the same thing again and again. And as we’ve compiled data sets that have been produced by these groups, we find the same signatures again and again. And so we really do think that this is quite a broad space. To be specific, the initial areas that we’re exploring really because we find one, a strong therapeutic hypothesis and two, quite a lot of single cell data already available. And those are fibrotic diseases and as I mentioned, inflammatory diseases. So some autoimmune diseases, other sort of local inflammatory diseases and so that’s where we’re starting. But eventually we plan to expand to multiple other indications and hopefully ultimately aging itself, which we think of as being driven in part by a chronic inflammatory process that has its roots in the accumulation of pathogenic cells.

Grant Belgard: [00:12:20] And how do you see the work that you’re doing now building up to tackling aging more broadly? Do you see those as completely distinctive programs with no overlap, or do you think a lot of what you’re doing now can feed into that?

Adam Freund: [00:12:33] I think a lot of what we’re doing now can feed into it, especially on the inflammatory disease side of things, because that’s our best hypothesis for how this kind of approach might impact aging. And that could change over time, of course, as we learn more. But one of the things I’ve often focused on when thinking about aging and just to clarify, I’ve studied aging for quite some time. I’ve been fascinated by this topic ever since I was in graduate school and I first came across the idea that aging is not just unavoidable wear and tear. It’s not just the accumulation of entropy in some physical law kind of way. It’s actually a process that can and is modulated by genes and biology across the entire animal kingdom. Once you believe that aging can be modified, it’s hard not to get excited about studying it. And so I’ve been fascinated by this for many years. And one of the things that people in the aging field think a lot about including myself is how do we develop a drug for aging. And there’s two kinds of drugs that one could imagine, a drug that slows down aging. Every year you take it, you only age half as much as somebody who’s not taking it. And then a type of drug where you reverse aspects of aging. The first one poses some serious problems from a clinical trial perspective in that if primarily what you’re doing is slowing down the aging process, the earlier that you start dosing, the better.

Adam Freund: [00:13:57] First of all, in terms of a person’s life, which means the healthier they are, which means the more liability any safety signal has, and the longer your clinical trials have to be because you’re effectively waiting for your control group to age. And then you’re hoping that your experimental group doesn’t and that can take years depending on how many people you have in your study. It could take many, many years. In contrast, the idea of reversing certain aspects of aging is a lot more tractable because in this case, you’re now looking for your experimental group to get better and your control group to basically just stay the same. And depending on the mechanism, theoretically this could happen in elderly people who already have experienced some degenerative changes as well as in younger, healthy people and really I think the only reason people aren’t more focused on finding this latter type of intervention, these age reversal processes as opposed to age delaying processes, is that I think there’s an assumption that it’s harder. It’s harder to turn something back than to stop it from happening in the first place. But I don’t actually agree with that. I think that if you imagine a biological process that’s been tuned over millions, billions of years of evolution, the idea that we’re going to be able to tweak it just enough so that it works even more efficiently than it already does and so we age just a little bit slower or maybe even a lot slower than we already do, that we’re just going to be able to tune homeostasis to be perfect such that we never decay.

Adam Freund: [00:15:25] That to me sounds quite challenging. Imagine building a car where it never needs service, right? It just runs perfectly forever as opposed to a car where you recognize it’s going to break down and you just know that every year you’re going to take it into the shop and do a full tune up. The latter approach to me actually seems quite a bit more tractable. And so one of the reasons why I’m excited about Arda’s approach is that it fits squarely into this reversal camp in that what we are trying to do is to identify cells that over time accumulate with age and that cause detrimental tissue changes. Once we find those cells, if we eliminate them, we’ve effectively set the clock back at least for that particular process. And we don’t think that this will cure every aspect of aging. There are some parts of aging that really just probably need to be addressed using joint replacement or other kinds of non-pharmacological interventions. But for many things including those caused by the accumulation of chronically inflamed cells, I think that eliminating those populations could be a really effective path to treatment.

Grant Belgard: [00:16:25] Are there companies in this space taking this approach?

Adam Freund: [00:16:28] So there are companies that overlap with Arda in specific areas, although none that we know of are taking an identical approach. Perhaps one that people in the aging space might be most familiar with are companies that target senescent cells. Essentially senescence is a cell state that different types of cells can enter in response to damage or other external stimuli. It’s traditionally been described as an irreversible cell cycle arrest where the cells can no longer divide. And we found empirically over the years that we’ve been studying this process that in addition to not dividing, they also upregulate certain inflammatory processes and pathways. And when this was discovered, it launched a series of companies that thought that if we eliminated these cells, that these might be the sort of wellspring of age related changes and that eliminating them might reverse some of these changes. And that hypothesis had a lot going for it, and it still does to some extent. However, we’re learning that the senescence state has been mostly characterized through in vitro cell culture experiments and has not translated super well to the in vivo setting. And we’ve seen some clinical trial failures in this space and I suspect we’ll see more before the day is out. Therefore with Arda, we’re trying to do is go the other direction. We’re starting with patient data and letting the data tell us which cells to eliminate and how to eliminate them, rather than starting with a predetermined disease hypothesis about what the state of those cells should be. But from maybe a 30,000 foot view of the general aging space, I think that it sometimes seems like what we’re doing and what some of these companies are doing have some similarities. And to the extent that we’re both trying to find bad cells and eliminate them, there are some similarities. But I think that there’s a really important distinction there as well.

Grant Belgard: [00:18:21] So tell us about your path to founding Arda, because you’re a solo founder, right?

Adam Freund: [00:18:28] That’s correct, yeah. I left my previous role and started Arda as a solo founder.

Grant Belgard: [00:18:34] So what? It’s obviously a pretty huge leap to take. I’m sure you had a lot of scientific confidence that had been building over over the years that led to that. So can you tell us about how that journey looked?

Adam Freund: [00:18:48] I would be happy to. I really was thinking about Arda and ideas related to cell elimination for a long time before I ended up starting the company, almost going all the way back to my graduate work. So did my graduate work with Judy Campisi, studying of all things cell senescence, and it was in that lab that I was first exposed to the idea that eliminating these cells might be a good thing. And in fact Unity, the first senolytic company was founded essentially right in Judy’s lab while I was there and grew after I left. And that was really exciting thing to see. After the cell senescence work in my graduate school, I went to Stanford where I did my postdoc studying telomerase biochemistry. So also trying to understand more about the processes related to aging. And again, I continued to think about telomere shortening is one of the major causes of cell senescence, at least in vitro.

[00:19:43] And so I continued to think about this idea of are these cells accumulating? If so, what are the processes? And then when I got to Calico, which was my role after my postdoc, there I was a principal investigator and a scientific lead on a few drug development programs and in my lab, we spent a lot of time thinking about how to represent organism state and cell state from high dimensional data. And I kept abreast of the cell senescence literature, watched how that field evolved and kept thinking about how we might be able to use high dimensional data to develop new therapies and eventually came around and saw a few other interesting proof of concept papers coming out from different academic groups where instead of targeting senescent cells, they were targeting fibroblasts in certain kinds of fibrosis or microglia in neuroinflammation. And this idea just developed that maybe we could do this in a more systematic and deliberate way and ride this wave of single cell data, because it really is. Single cell data allows this to happen in a way that we never were able to do it before. So for decades right since the advent of microarrays really, we’ve been able to measure the expression of every gene in the genome at a bulk tissue level, and that let us find which genes were most upregulated in disease. And it turned out that this was a pretty good place to start for finding targets. As mentioned earlier, correlation is not causation, but if you pick the top 15 most upregulated genes in a diseased tissue, chances are some of those are causally pathogenic.

Adam Freund: [00:21:18] But we didn’t have something like this for cells. If we wanted to understand which cell populations were enriched in disease, we had to do this very biased search using a handful of markers and then assessing via [] FACs. And consequently the resolution we had was extremely limited. We could talk about fibroblasts versus Myofibroblasts or we could talk about M1 and M2 macrophages, but that was really about it. And now we can see right with the advent of single cell data and the amount of data being generated, we have cellular catalogs of disease at unprecedented resolution. So although I had been thinking about this idea of eliminating pathogenic cells for a long time, it was only really when the technology reached the stage we’re at now that this became actionable. And so when that happened, to me it didn’t really seem like a big leap to say, well let’s combine single cell analysis with the tools of immunotherapy and let’s see what we can do. And that’s when I finally decided to take the leap and start Arda.

Grant Belgard: [00:22:21] Since Arda was your first biotech startup, what surprises did you encounter along the way?

Adam Freund: [00:22:30] With any origin story, there were some fits and starts along the way. As you mentioned when I started Arda, I was starting as a solo founder and I was leaving my current role at a biotech company and starting, and that meant that really I started with very little. And so it was just sort of me and a pitch deck really. I mean, there was no data that I owned because I couldn’t take anything from my previous role, nor was there anything really relevant from my previous role for the company. I didn’t have an academic lab where I was spinning something out of, and I don’t think I appreciated at the time how rare that was in the biotech world. It turns out that most biotechs start in one of two ways. They’re either spinning out of an academic institution. And so there’s a whole body of data and there are papers that are directly relevant, and there’s perhaps even a scientific advisory board already in place, or they come out of these venture creation firms where in a similar way they’ve been incubated with a team that has generated a bunch of data supporting a hypothesis.

Adam Freund: [00:23:36] And only once they’ve reached a certain level of empirical confidence in the overall idea have they launched. I didn’t have either of those things. And one of the pieces of pushback I got a lot when I was initially raising was just why do you think any of this is going to work? I mean, my pitch deck was full of examples from the literature, but it wasn’t my data. It was other people’s data. I had a few computational analyses I had run that sort of suggest that this could work. But by and large it was pretty bare bones and it really took a lot of talking to different investors before I found investors who were willing to take both on the idea, but also really, frankly, on me because there wasn’t much else to bet on at that point. I’m really fortunate to have the investors that I have. And so the round did eventually come together at the sort of end of 2021. But it was scary for a while there. It did not come together as fast as I had hoped it would.

Grant Belgard: [00:24:32] And you joined the On Deck Longevity Biotech Fellowship in the fall of 2021. How have you found that?

Adam Freund: [00:24:40] The On Deck Longevity Biotech Fellowship? That’s right. So how did I find that? Nathan Cheng, who runs on the On Deck Longevity Biotech Group, reached out to me. I guess he found me through, boy I don’t even know, maybe some of my papers or maybe a contact. And it seemed like a great opportunity to meet other people who were at the stage of starting companies or considering starting companies that were focused on extending healthy longevity. And I think I joined that in 2021. It’s been a great opportunity and a great way to meet other people in the field and learn from other people who are further along in their startup entrepreneurial journey than I am. And that really has been powerful. Also I should say that that’s been powerful in a lot of other contexts as well. So On Deck is one group that I’m a part of, but I’m also through my investors and through other organizations and part of other groups of scientists, mostly who have started companies or who are early at biotech companies. And it’s really helpful to be able to learn from other people’s experience. There’s so much to know about starting and developing companies that I didn’t know, and there’s so much I still don’t know. But being able to ask questions and get advice allows you to shortcut a lot of things, even something as simple as, Hey, what law firm should I use? How do I pay my employees? Or What’s a reasonable benefits package? I mean, you can go on the Internet and research this for two weeks, or you can ask the three founders you respect most and guess that if it’s good for them, it’s probably good for you too. There’s so many decisions like that. It’s incredibly helpful to be able to shortcut some of that stuff.

Grant Belgard: [00:26:20] What are other groups that you found helpful in that respect?

Adam Freund: [00:26:22] So let me look at my Slack workspace, because they’re all right there. So I found that the On Deck Longevity Biotech is one, Andreessen Horowitz, our lead seed investor, has Biohealth community. They also have email lists that are incredibly helpful that bring together, by the way, people who aren’t just in bio. There’s also a lot of people who are in tech and health tech and crypto, and it’s really interesting to see which lessons they’ve learned apply to bio and which don’t because they don’t. Bio is a different animal in a lot of ways, but there are also some consistent patterns and ideas around starting and growing companies that cross sectors. There’s also the Village global community, which is another one of our investors. They provide a space for founders and entrepreneurs to discuss ideas like this. And then there’s a few longevity groups like Longevity SF, which I’ve recently joined and again is more focused on the aging side of things, which always fascinated me. So it’s just helpful to have these communities because it can be when you’re just starting out, your company is very small. There’s just there’s not a lot of people to talk to so having these communities in place can be very, very helpful, both professionally because it allows you to sort of network, but also just psychologically. Because it can give you the feeling of a community which it’s otherwise hard to get from a company of just a couple of people.

Grant Belgard: [00:27:38] If you could go back to send a message to yourself in the summer 2021, what would that message be?

Adam Freund: [00:27:44] I think I would send a message to set my expectations appropriately. I would say it’s going to be hard to fundraise. And then when you eventually do fundraise, it’s going to be even harder to hire because once you know you’ve got the money, then you have to convince people to join you on your crazy mission. And it’s been a journey. And now we’re of course building traction and things are really moving. But it does take a certain degree of stoicism and staying the course and developing thick skin. I will say that too. I think one of the things that I have changed the most in the last year and a half, I’ve been doing this is that more people have said no to me in the last 18 months than in like the rest of my life combined. So I can’t tell you. And it’s not that anyone’s rude about it. I mean really honestly, venture capitalists sometimes get a bad rap, but found that the vast majority were professional and they were polite, but they were politely saying no. And that’s hard when you go to someone and you say, okay, so here’s the idea I have. It’s literally my best idea.

Adam Freund: [00:28:46] Like of all the things I think in this world, this is my best one and I want to spend the next X number of years of my life solely dedicated to making this work. And the person across the table from you looks at it and says, Yeah, I don’t think that’s a very good idea. That’s hard to hear, even if they’re being nice about it. And the first ten times it really hurts. And then you sort of grow calluses on your soul and it starts to hurt less and that sort of sounds like you go dead inside. And it’s not totally true, but it’s also not totally false because you have to be able to weather the nose. You have to be able to just smile, say thanks and move on and in a way where you’re not ignoring useful feedback. Because one of the most important things is you go through this journey is adapting to the feedback that you receive, but also not letting no cause you to stop moving. Because if you do that, you’ll never get to port. So you have to be able to weather the nose and I think that at this point, I’ve developed some pretty thick calluses.

Grant Belgard: [00:29:45] Yeah, well the good news I think on the hiring front is, at least from what I’ve seen as you grow and get better established and so on, that becomes easier and easier, right? It’s a lot harder to hire your first or second employee than it is to hire your 20th or especially you’re like 200th by that point. When people Google your company name, they won’t just find your company page. There will be articles about it. They can send it to their friends and family and have them not try to talk them out of joining such a young, seemingly unstable company, even though I think that’s often an unfair characterization. But it’s certainly a common perception nonetheless.

Adam Freund: [00:30:34] It’s so true. One of the things that I feel like we spend the most time doing as early founders and early employees of companies is building credibility. That’s all it is, right. Most people when you tell them about your idea for your company, they’re not judging the scientific merits of your idea. Not really. I mean, that’s part of it. But what they’re really doing is watching you and saying, does this person, are they a charlatan? Because most other people unless you’re talking to someone who’s also an expert in the exact same things that you’re claiming to be an expert in, they know that they don’t have the same expertise you have. And so they’re not going to sit there and say, well I’m going to argue with you about X, Y and Z in terms of the scientific details. What they’re trying to establish is, are the scientific details that this person is telling me reliable. Is this person trustworthy? Does this person have credibility? And there’s a million ways that we signal or don’t signal that we have that credibility. I was I think fortunate in that when I went out to do my raise. I had spent seven years at Calico, which had a great team from Genentech, Art Levinson, Hal Baron, Cynthia Kenyon on the Longevity space, and others who had created a company with a lot of credibility. And then before that was institutions that carried some credibility.

[00:31:43] So I was able to go into rooms and say, Look, I don’t have any data. I just have this pitch deck and this smile and somehow still at least give some indication that I was a real scientist who knew what I was talking about. This is one of the reasons why it’s so hard to hire early employees. I was very fortunate in that my first hire was a person who I had worked with actually. He was a postdoc in the same lab when I was a graduate student and he had gone on to actually work at Unity. He was there for eight years, was the first employee and understood the space really well, understood it probably better than me frankly and knew me. And I knew him and we knew we worked well together. So the first hire was reasonably straightforward. And that’s Remi-Martin Laberge, who is our vice president of research. But then after that, exactly as you said you’re meeting people who don’t know you and you’re trying to convince them that this is the thing they should bet their life in their career on for the next couple of years at least. And that can be hard. But as soon as you see that, oh, half a dozen people have made that same choice, it starts to seem a little bit less crazy.

Adam Freund: [00:32:48] And so then it gets easier. And by the time 20 people have made that choice, you’re like, oh, okay, this isn’t totally crazy. But what people don’t realize sometimes is that it’s sometimes useful to fight against that tendency because earlier that you get into a startup, the much more impact you can have and the much bigger upside there can be both professionally and financially if you ever look at a curve of the way equity dies off by employee number, it’s pretty substantial. And the probability of success doesn’t necessarily go up at the same rate that equity and options for career growth go down. In fact in many ways, if you’re joining a company that just raised a round of financing and they haven’t hired anybody so they haven’t spent it, that company is in better shape than a company that has just hired 15 new employees, spent the bulk of their A round and now needs to turn their attention to a B round within six months. So I understand why it happens, but I actually think there’s a lot of reasons to if being in the startup world is something that excites you, really try to pick companies not based on the credibility signals being sent by others, but based on your own assessment of whether that company is a good fit for you.

Grant Belgard: [00:33:59] And what factors would you weigh heavily in that? Because I think sometimes the kind of go to is there may not necessarily be the most predictive.

Adam Freund: [00:34:12] This is hard. This is a hard question. There are so many unknowns. Just like when you’re hiring for a role, you’re trying to learn as much as you can about this incredibly complex person who’s sitting across the table from you in 30 minutes. There’s only so much information that you can gather. And the same thing is true when it’s in the other direction. And here you are a candidate trying to decide which startup or which company you’re going to join. So if you are a scientist, of course the science. As much as you can dig into the science, pressure test it, ask the questions, and if you get any sort of defensiveness as a response or anything like that, that’s very concerning. If you get a lot of, well, we can’t talk about it because it’s super secret. Personally I think that’s also concerning. I think that generally people secret sauce is not as secret or as delicious as they think it is. It’s more about like execution ideas are cheap. So definitely on the science side. But then also I would look for how thoughtful the people at the company are, especially the leadership about building a culture and an incentive structure that are carefully designed. I think sometimes there’s this idea that startups can just be these places where it’s a little bit of the Wild West and everything just sort of works and clicks together.

Adam Freund: [00:35:31] And that might be true up to something like ten people. But once you get above that, the laws of human interaction assert themselves in ways that are difficult to predict and often not what’s best for the overall growth of the company unless there are processes and systems and a culture in place to realign people. And it’s amazing when this goes right, because startups do provide this incredible opportunity to have everyone rowing in the same direction. One of the things that that I think a lot about is for any person in any organization, there’s this question of how they should prioritize their activities. And in many cases, what’s best for that person is not what’s best for the organization. Think about middle manager at some large pharma company who’s they have stock and they have an individual performance bonus, but they don’t really have the ability to impact in a huge way the outcome of the company. They can do it in little ways. And if the company is wildly successful, it doesn’t directly impact them by a lot. What matters more to them is their individual base salary, their bonus and their title. And so you get a lot of internal politicking and people working on these sort of local optimization problems.

Adam Freund: [00:36:45] And if every person at the company is doing that, it’s only by chance that that comes up to a point where it’s actually benefiting the organization as a whole. And it’s almost certainly not the most efficient way one could do that. And the alternative is at a startup. The best way for every person at a small startup to succeed both professionally and financially is for the company to succeed. It doesn’t make any sense to work your way up the corporate ladder of a six person company to become the vice president of nothing. And then the company tanks and you have to go get another job. It makes a lot more sense is to say, I’m going to spend every waking minute trying to make this idea, this company actually work. Because if that happens by virtue of me being early, I will have so much potential. I’ll have so much experience. My equity will be worth so much. There’s all this stuff and that creates alignment. And alignment is so hard to create otherwise. And so to me, that’s really important. But you have to put these other processes into place. You think carefully about this because the bigger you get, the harder it is to maintain that alignment.

Grant Belgard: [00:37:45] And once lost, it’s hard to regain.

Adam Freund: [00:37:47] Oh, completely. Absolutely right. And I mean like one or two people can act as dominant negatives and people take their cues from others about what’s appropriate and acceptable in an organization so it’s really important. And this makes or breaks companies. And so if I were evaluating different startups or different companies, I would look really carefully at the incentive structures and how they’ve been designed and how thoughtful people are about them.

Grant Belgard: [00:38:12] It’s really insightful. Thank you so much for joining. It’s been a really great conversation.

States Ranked by Age adjusted COVID Deaths - Updated April 20 - See table for more details

States Ranked by Age-Adjusted COVID Deaths

Data updated on December 7, 2022

We’re inundated with statistics on how US states have fared relative to one another throughout the pandemic. Sometimes these can appear contradictory because the data can be cut to support a variety of narratives. We wanted an updated source of cumulative age-adjusted COVID-19 deaths by state, as death is an important measure of the impact of a pandemic, and states have adopted widely divergent policies.

While COVID-19 deaths are usually adjusted for state population (deaths per 100,000), they are usually not adjusted for the age distribution of a state. It’s important to adjust for age when considering state-to-state differences in outcomes as age is the dominant risk factor for death provided someone is infected with COVID, and state age distributions vary considerably. In the following analysis, we present age-adjusted cumulative COVID-19 deaths and rank each state plus Puerto Rico and the District of Columbia accordingly. We generated the plot and table using the CDC’s Provisional COVID-19 Death Counts by Sex, Age, and State database, which sourced its data from death certificates. These numbers are more consistently processed across states, though they may differ slightly from other sources. 


Bubble areas are proportional to state populations and the horizontal arrangement is arbitrary to reduce overlap.

Here we see dramatic state-to-state differences in cumulative age-adjusted COVID deaths per capita to date, spanning a range of over five fold. In the end, some states that adopted dramatically divergent policies had comparable outcomes (Florida and California, for example).

Mississippi is exceptionally high. A few regional clusters have fared markedly better than the rest: Vermont, New Hampshire & Maine, Oregon & Washington, and Hawaii & Puerto Rico.

Why do COVID deaths vary by state? 

Explore the relationships between age-adjusted COVID deaths and several state-level metrics including: vaccination coverage, obesity rate, the strictness of COVID policy and more. 

Explore the Data


Age-Adjusted COVID Deaths Ranking


COVID-19 Deaths per 100,000

Age-Adjusted COVID-19 Deaths per 100,000

1 Mississippi 474 486
2 Oklahoma 438 447
3 Kentucky 416 420
3 Tennessee 415 420
5 Texas 344 412
6 Alabama 417 410
7 Nevada 379 403
8 Arkansas 397 385
8 New Mexico 399 385
10 Indiana 377 384
11 Ohio 399 380
12 Louisiana 363 378
13 North Dakota 388 377
13 West Virginia 430 377
15 Arizona 394 375
16 Georgia 325 371
17 New York 391 370
18 South Carolina 378 369
19 New Jersey 381 367
20 District of Columbia 300 354
21 South Dakota 371 351
22 Missouri 359 343
23 Pennsylvania 389 342
24 Michigan 349 333
25 Montana 353 324
25 Rhode Island 363 324
27 Kansas 327 321
28 Idaho 299 315
29 North Carolina 303 308
30 Wyoming 301 302
31 Florida 357 299
32 Iowa 327 298
33 Delaware 326 297
34 Connecticut 330 295
35 Illinois 289 289
36 Maryland 278 286
37 Colorado 250 285
38 California 258 275
39 Nebraska 276 272
40 Massachusetts 287 270
41 Virginia 251 262
42 Wisconsin 273 260
43 Alaska 192 253
44 Minnesota 250 246
45 Utah 173 231
46 Oregon 197 190
46 Washington 179 190
48 New Hampshire 205 189
49 Maine 215 180
50 Puerto Rico 167 143
51 Vermont 130 114
52 Hawaii 120 106


We determined the age adjusted mortality per 100,000 people (maa) for each state using the formula:

m_aa = SUM (D_x * P_x / (N_x * 100,000))

Where Dx is the total deaths in age group x in the state, Nx is the total population in age group x in the state, and Px is the percent of the population in age group x in the United States.


COVID deaths from CDC: “Provisional COVID-19 Death Counts by Sex, Age, and State” (Updated on December 7, 2022)

Population data from U.S. Census Bureau: “State Population by Characteristics: 2010-2019”

The Bioinformatics CRO Podcast

Episode 50 with Alfredo Andere

Alfredo Andere, co-founder and CEO of Latch Bio, discusses the unique challenges facing young entrepreneurs and the future of cloud computing in biology. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Alfredo is co-founder of and CEO of Latch Bio, a cloud bioinformatics platform that enables collaboration between computational biologists and wet lab researchers.

Transcript of Episode 50: Alfredo Andere

Disclaimer: Transcripts may contain errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Alfredo Andere. Alfredo, can you introduce yourself, please?

Alfredo Andere: [00:00:09] Totally. Thank you so much for having me, Grant. I’m Alfredo Andere. I’m the co founder and CEO at LatchBio.

Grant Belgard: [00:00:17] Welcome. So tell me about Latch.

Alfredo Andere: [00:00:20] Totally. I mean, we’re a young startup here in San Francisco creating data infrastructure for biotech companies. And specifically, we’re helping scientists analyze their data and get insights from it without having to know how to code and without having to build any infrastructure in-house. So what that means is as you know very well from your bioinformatics experience and from all your experience in bio, the data generated from a biological experiment has been annexing every two years. That means that over the last ten years it’s 100,000 X, which means that on the ground floor for biologists, we’ve gone from finishing an experiment and visualizing it with the human eye to now, ten years later, finishing an experiment and getting back a file with 100,000 lines of ACGG or 10,000 amino acid sequences or 100,000 proteomics images or microscopy images. What do you do with that? You’re not going to look at that with the human eye. You have to run it through algorithms and workflows that you’re then going to boil down into statistics, plots, graphs that you can actually look at. The problem with that is that these plots are really hard to generate. It’s not super intuitive. You need to use Python or command line or different algorithmic interfaces that biologists are not really familiar with that. So that’s problem one. And so you need a new interface to do this. So biologists sensitive to bioinformatician, to bio computational biologist, they process the data for them and then send the results back to them.

[00:01:56] Okay, that’s just a new way of doing things. It’s not, per se problematic. The real problem that we found is that these computational people are really hard to find. These bio developers, these bioinformaticians like you Grant, that have the ability to transform this data into visualizable formats. They’re really hard to find. I’m talking like 20 biologists to one Bioinformatician or one bio developer is what we usually see. And so what ends up happening in this new dynamic is biologists finishes their experiment and they send it over for analysis, and then they wait and they wait for a day. They wait for a week. They wait for a month just to get back the results of their experiment that ten years ago they got back instantly. And so I’m looking at this from a perspective coming into this from a software engineering side. I was working at Google and Facebook doing software engineering and one data engineering and the other and just getting to see the world’s best data infrastructure, the state of the art for processing terabytes of data and most of it being used for what? For optimizing advertisements, right. For getting you to click on stuff you don’t really want. And on the other hand, you have these biologists, these scientists trying to cure cancer, trying to cure heart disease, trying to cure aging, global warming. I mean, you name it.

[00:03:20] And they have some of the worst data infrastructure that we’ve seen and it’s slowing down their iteration cycles. It’s really like, how can we solve this? And we realized that initially to solve this, the best wedge into the market was to just create no code interfaces for the biologists to be able to process their data, upload it to our platform, run it through different workflows and then visualize the results. And so that’s where we started. After 3 or 4 months of market research, we raised our Series Seed, and we started working with the IGI and we started working with [AlgoBio] and we even worked a little bit with Bit Bio, which obviously you’re super familiar with accessing these no code interfaces and giving them to biologists and collecting these biologists as users of this platform. And first hundreds, then thousands. And once we created that movement, now we’re moving on more to generalizable bio development framework, and that is a type of marketplace dynamic that we want it to become the AWS plus GitHub for biotech companies. And what that means is anytime you have a marketplace dynamic, you’re going to have a chicken and egg problem. Like why are biologists going to use our platform if there’s no workflows available to them? And why are bioinformaticians or bio developers going to upload workflows to the platform if there’s no biologist to use them? And the real answer is they won’t, neither side will.

[00:04:52] So what we initially did is we uploaded all the workflows. So the no code interface I was telling you about expose them to biologists. Now that we have all these biologists using the platform, now we’re going back to the developers, to the computational biologists, to the bioinformaticians, and telling them, Hey, instead of uploading your workflows to GitHub where no one’s going to be able to replicate your academic code, to put it nicely or to react where you’re going to have to maintain a server for the next five years, just use our open-source SDK to upload it to the large platform. We automatically generate a no code interface that biologists can use. We also automatically generate an SDK and an API that other bio developers can use, and we take care of the cloud infrastructure, the scaling, the data traceability. And so we want to create the self-propagating positive feedback loop marketplace dynamic that becomes the central AWS plus GitHub for any biotech to just plug into from day one and start analyzing their data and getting insights ten times faster by not having to code and not having to build any data infrastructure. So that’s what we’re building at Latch right now and we’re well on the way there. We recently raised our series A, and we’re just building as fast as we can to be able to accelerate all these amazing companies that we’re working with.

Grant Belgard: [00:06:13] Cool. How does Latch fit into the broader ecosystem of companies in this space?

Alfredo Andere: [00:06:20] I see two main levels that we deal with. You have the all the biotech companies or really the biology companies, whether it’s Bioproduction or actual therapeutics or 100 other things. And then you have the software providers. Within the software providers, I really see it as fitting neatly into the DBTL cycle. The design build test learn like what part are you augmenting with your software that is serving then the biologists and the biology companies. I think within that DBTL there are many tools that have been growing a lot in the design and the build, so things like Benchling and other types of software that helps with the life scientists in their work. All these lymph systems that are coming along and different notebooks, they help a lot in the design and the building. Then as this data grows more and more, you have the testing phase you’re going to put into some sequencing or you’re going to create a large amount of data. And the testing phase is really interesting because there’s so large gap there from the testing that was previously biological. The microscope makers, the different wet lab makers that were doing test machinery to now it’s all computational and there’s a huge gap and there’s some companies that came along and are trying to fill that space. But it’s still a very new space, that space being computational.

[00:07:54] So that is where we fit in. Once you get your data back, you upload it to Latch and then you start processing it, visualizing it. We are not yet going into machine learning. We do a little bit of machine learning, which I see as the as the computational part of the learning phase, because that learning phase was previously human. It was you look at the results, you use your brain and you learn what experiment you’re going to do next. That part is also turning more and more computational. It’s also going more and more into machine learning, guiding the next experiment. I don’t think the space is ready for someone to build a prominent software tooling for that space because first we need to get the data right in the testing space. The data that is coming in, we need to get it organized, we need to get neatly processed. But whoever takes this testing phase is going to be very qualified to then build software for the learning phase because machine learning models are very similar to bioinformatics workflows. If you look at them from input output perspective, it’s a lot of similarities. I think whoever can build a strong foundation in the testing phase is going to be uniquely equipped for that learning part.

Grant Belgard: [00:09:01] So what’s your long term vision for Latch?

Alfredo Andere: [00:09:04] That’s a great question from that perspective, because I believe in 5 to 10 years, Latch will completely replace AWS and GitHub. And then a lot of the computational and data analysis will take place on top of Latch. We are going to be the central marketplace if you want to see it that way, where all the computational analysis are going to live, all the machine learning analysis are going to live. And so the second that an experiment finishes, all the data is automatically going to be analyzed. And then that data that is being analyzed and then the results are going to be given to a learning phase, a different machine learning algorithms that are going to be deciding the next experiment and then Latch is automatically going to send a signal to some type of cloud lab or a scientist that is manually doing these experiments to then start the next iteration cycle. And so we believe if we can build good software here, we can completely automate that whole part of experimentation and we can not just automate but also empower the people that are doing the more complex analysis. And so yeah, I mean replacing and GitHub would be the long term vision.

Grant Belgard: [00:10:22] It is ambitious, which is good for a startup. That’s where you want to be, right?

Alfredo Andere: [00:10:26] It’s hard. Yeah.

Grant Belgard: [00:10:27] So I remember the first time I spoke with you guys. It was pretty early on. I think your origin story with Latch is really interesting. Can you tell us about that?

Alfredo Andere: [00:10:39] Totally. Yeah. First time we spoke, if I remember correctly, we were inquiring about the bioinformatics CRO and the great work you’re doing there and trying to learn a bit more about the space and the market and what kind of customers you were serving. And we literally just booked a meeting with you as if we were looking for a service and had talks with you and some of the other people you work with. So the company officially and we had been working together before that Kyle, Kenny and I, but the company officially started in February 2021. And when we initially started, we had to incorporate because we went to some investors in Berkeley and we were telling them like, Hey, this space is really messed up. We are seeing just from a high level overview of Kenny working on the labs and on a couple companies. We’re seeing USB sticks being passed around. We’re seeing people running scripts on their laptop that should be running on the cloud. We’re seeing pipelines written F-sharp. We’re seeing all these kinds of messed up stuff. And then the investors were like, okay, so what are you going to do about it? And we were like, We don’t know. We’re going to go find out.

[00:11:55] And so blessed Silicon Valley, they actually gave us some money. And actually one of them invited us to Taiwan. There was no Covid there at the time. And invited us to Taiwan to just to a hacker house to go there for three months. And what we told ourselves is we’re actually not going to build anything. We’ve made this mistake in the past in other projects we’ve made. And so this time we’re not going to build anything until we know people have a problem and we understand the problem. And we have people that would be willing to pay for a solution to that problem. So for the next three months, we just talked to people and did contracting and we would do everything from booking 100 bioinformatics CRO and clicking book to emailing hundreds of people every day to LinkedIn messaging. Like we would have a rule where Kyle, Kenny and I every day would have to max out our LinkedIn requests, connections, and some people might not know this is possible, but it is. And so every day we would have to max out our connections.

Grant Belgard: [00:12:53] And if you do it too many times, they can ban you without notice.

Alfredo Andere: [00:12:57] Yeah. And we would have all these hacks around that and we got blocked from multiple email servers from companies because we would blast them with emails, not selling them anything, just asking them, Can we please have 20 minutes of your time to talk? And then when we did, we would have multiple calls a day and we would just ask them, What are your pain points like? We’re not going to tell you anything, but like you tell us what are your pain points? And then we would test hypotheses on them like, hey, we’ve been hearing people complaining about this. Is this right? Is this not right? And after three months, we had so much data on what people were struggling with. And even more valuable, we narrowed down our hypothesis of where we had six customers that were willing to pay for something, for a vision of something that sounded similar. And so only at that point, after having been contracting with some people and then having all these evidence and then having these six people that were willing to pay for something, then we went to investors for a seed round and we told them, Hey, there’s this huge problem. We want to hire two of our friends to help us solve this and we’re going to go and build it. And at that point, we raised our initial seed round. We were actually super surprised that we were able to raise a seed round and it got competitive and everything. And at that point, I mean, we had dropped out of school four months ago and at that point then we raised our seed round from Lux Capital and we started building out the platform that today is Slack.

Grant Belgard: [00:14:25] It’s fantastic. So when you were newly at Berkeley, were you planning to start a company or anticipating starting a company, or did this just naturally bubble up from the problems you were seeing?

Alfredo Andere: [00:14:40] I think it’s a combination of the two. I was planning on starting a company at some point, and it’s something I’ve always had in my plans being. I think being from Mexico, a lot of the people I know are business owners, whether medium to large business owners. And so a lot of the role models around me were always just business owners, not startups, more like commodity. Commodity sellers are different types of businesses, but always business owners. So I always had that in my plans, but it didn’t have to be out of college. I was planning on graduating. I was fully planning on graduating. I mean, I had a semester left. I was actually adding a major at the time in math on top of CS and planning on seeing if I could minor in it. And then during the summer of COVID when I went back home, I started talking initially with Kyle actually, because him and I were really interested in neuroscience. And so we started talking not about starting a company, but more so about starting a project like could we build something that people would use? Could we make something useful for people? And talking through different projects, we started working on neuroscience and I think a literally a couple of weeks after that, we all went back to Berkeley and bless COVID, which is something you rarely hear.

[00:16:00] I was supposed to be in Palo Alto at Google. Kenny was supposed to be in Boston at Asimov, and then Kyle was supposed to be back in L.A for the company he was working at. But because of COVID, we were all working remotely from Berkeley. And so as soon as we got back, we went to a party with Kenny and we started telling him about some of the ideas. We had ideas at that point and we were like, Kenny, you should consider working with us. And he decided to join. We started working on projects initially on a cognitive science, a neuroscience project to try and predict people’s emotions from an EEG headset and try and predict their focus and their emotions and just hacking on different machine learning and neuroscience stuff and then looking where to apply it. And so we looked at focus applications, we looked at marketing applications, and we started building some projects there. But really, that is the worst way to ever make a startup, like starting with a technology, well maybe in biotech. But generally when you’re making a startup, starting with a technology and then trying to fit it to a problem is what I didn’t know at the time, a terrible way to do a startup.

Grant Belgard: [00:17:18] Well, I think that’s probably one of the reasons biotech is so hard, right? Is it often is based on technologies. And some have great commercial potential and others don’t. But it’s just such an IP minefield.

Alfredo Andere: [00:17:36] Yeah. 100%. And I think so too, because in normal software and tech land, that is the worst way to do a startup. And the real way you should do it is you find a problem you’re completely passionate about and you want to be solving for the next ten years and you try out a thousand different solutions. If you start out with a solution, you’re going to be left in a very unflexible way. But we did that. We didn’t know. We were just working on a project and and working on different ideas. And at some point while working on the project, we actually started giving it to people. Nobody wanted it and we couldn’t understand why, like, this was such a good idea. Like we could predict people’s emotions with an EEG headset. But we started getting noticed. And at that point we actually had a YC interview. We applied to YC. We were like, Oh, let’s try this. We’re still in school summer and then went into the school semester and a week before YC came into my map and in our map and we started reading about the mistakes not to make. I mean, we started reading a lot about startups, but one of them, the mistakes not to make when making a startup. And for me it was more like a checklist. It was like, Oh yeah, we did this. Oh yeah, we also did this, Oh yeah, also this thing. And at some point, I think it was me, but generally agreed to by the team. We were like, Guys, we need to pivot. Like if we actually want to make anything out of this, this is not the right way to start.

[00:18:59] And so that’s around September, November. But the special thing there and going back to your question of when we started working together, the more special thing there wasn’t the idea, the more special thing was having worked with Kyle and Kenny and all of us working with each other. We knew there was something special there. We knew that the way we got along and the way we shot ideas and the way we discussed and the way we got to the truth and the way we built and the hard work we put in, we knew there was something very special there. And so we knew that we wanted to work together further and build something as big as we could. We decided to go for it and we were like, Hey, let’s trash this idea. Start from zero. The only fundamental being we’re going to work together to solve a huge problem. What do we solve? So let’s spend some time really figuring out what to solve. And so for the next month, we just read through stuff textbooks, articles, papers trying to figure out what to solve. I remember I told you that the company was founded in February. Around January, we had to make the decision by January of whether we were going to continue with school or work on this full time, and we were getting excited about biology. We knew there was so many problems to solve here. We knew we wanted to work together, so we decided to fully drop out one semester left of school, we were like, This is the team that is going to build something really big and we’re going to figure out what that is. And so we just dropped out of school.

Grant Belgard: [00:20:35] How did your parents take that?

Alfredo Andere: [00:20:37] I would say my parents were not very happy. They were actually the most unhappy out of our three parents. I know Kyle’s parents were just like his mom was just super encouraging. Like, Yeah, yeah, this is what you were meant to do. Kenny’s parents were a little put off, but he told them it was a break at first. My parents were completely pissed off, like my mom up until recently, she was still telling me, like asking me when I would go back to school. I still joke that if we IPO one day my mom will be super happy because then I’ll be able to go back to school to finish Berkeley.

Grant Belgard: [00:21:15] Go finish your last semester. Sorry. Y’all like, technically on an extended leave.

Alfredo Andere: [00:21:21] Well, that’s the beauty of Berkeley. I think they give you something like ten years to come back and finish your degree. So, I mean, you never know. I might just take some summer courses for fun because I love school. I love classes and there’s so many classes I would love to take. So it really was dropping out because the team was there and we just were all doing it. So you were not going to get left behind. So yeah, I’m glad we did. But yeah, my parents weren’t too happy.

Grant Belgard: [00:21:48] So one other thing that sticks out about Latch to me is your brilliant social media presence. Some of our listeners may have seen your coordinated pictures. You’re wearing black tops and sunglasses on a white background and things like this. Can you tell us a little bit about your marketing?

Alfredo Andere: [00:22:11] I would say there’s two branches to the marketing, the aesthetic and the different part, which is the pictures, which is a couple quirky things we do here and there. And then there’s the aesthetic parts. And I’ll start with the more quirky parts and I’ll say that actually came off. It comes off naturally from Kyle, Kenny and myself. We like to push the edge of what is allowed, where the line is at and what you can do. But those pictures actually come from a funny story. Back in the day when we were in our Berkeley office, the Berkeley office was probably not much bigger than one of our conference rooms in our current office, literally could barely fit three people. And in there, we had never raised a single dollar of venture capital. We were just three guys with a dream. And we looked at TechCrunch and you see every picture of a new financing. And I was actually making fun because it’s either in a couch or with a nature, a tree background, every single picture. And I’m seeing like, these are the people that are changing the world. These are the people who claim to be doing the most innovative, different work, the ones that are thinking outside of the box. And every picture you see is the same thing with three founders or two founders or a couple founders just on a couch or on a tree. Like, why does no one do anything different?

Grant Belgard: [00:23:40] I bet they use the same small group of photographers and probably a lot of the same backdrops.

Alfredo Andere: [00:23:47] Yeah, I would not be surprised. And so we’re looking at this and we’re like, we’re never going to be like this, we’re – if, and it was a clear if, right. If we ever raise any venture capital and if we ever go on the news and make a news announcement, we’re going to do something very different. And so we started brainstorming like, what would we do? It was either Kenny or Kyle pulled up the Lonely Island pictures. They’re really well known for taking just wild pictures. And they have one where they’re right behind each other. In that one, they’re in black turtlenecks and glasses in one of them, and in the other one they’re in blue turtlenecks and they’re like, We’re going to do something like this. And it was just like a a running joke, right? That if we ever did get funding and did big enough to get an announcement, we would do something like that. And then we got our seed round, which was actually a pretty hefty amount of money and from Lux Capital. So it was newsworthy. And the decision came and it was like, are we going to go back to the norm and do what every founder does, like we made fun of? Or are we going to stick to what we said and take those pictures and publish them? We obviously did the second and we took the pictures and we had a great time in the photo shoot. We actually took the whole founding team at the time, six people. And yeah, I think those pictures are going to go down in definitely the history of Latch and hopefully history in general.

[00:25:15] That’s the pictures and then the aesthetic of the website and the aesthetic of our Twitter page and all our whole platform, I would say that’s in big part too. I think the whole team has a lot of an eye for aesthetics and for making sure things look good from a user perspective. But really two people, Max Mullen, our founding engineer, and then even more Nathan Manske, our founding designer, he was a blessing because he was Max Mullen and Aiden. They were good friends from ours in Berkeley. But Nathan, I interviewed 100 designers and I wouldn’t like any of them. And then Nathan came along. I interviewed him. I loved his designs. He came to SF from Minnesota to interview for three days. We loved his work and we hired him. And he has been one of the biggest blessings to Latch. His ability to design and his ability to just take in your input like, Hey, I want something like this and that and then make it into the science that look beautiful and then code them up himself if need be, either in the front end of the platform or for some landing page. He actually coded our whole landing page himself. He has been a blessing to this company and we love him.

Grant Belgard: [00:26:36] You’ve brought up a couple of employees, and I’d like to talk a bit about that now. Because obviously starting a company like this in college, you’re not coming into it years of management experience. And also, how have you found the experience of, not just starting a company and having the vision and all this, but building out a team? And how many people do you have now?

Alfredo Andere: [00:27:03] I think at this point, it’s 14 people. Three of them are interns. So it’s really 11 people full time, which is wild. If you had told me a year ago that we would be here. But yeah, that’s a great question. And I think it’s one of the biggest problems that you face as a company founder is just building a team and not just leading the team, but putting the team together and finding the right people. Because if you want to build an iconic company, you have to bring in people that are very, very special and that are going to lead the company themselves and lead themselves. And this is just a constant thing that we think about every day. Thankfully, I was blessed by always being interested. And I can say the same thing about Kenny and Kyle. We are have always been interested in finding really special and talented people, especially engineers, and not because I was like, Oh, I want to recruit them in the future. It’s just the type of people I find really interesting are the scientists, the engineers, the people that are that are technical and builders and creating things. And so in Berkeley, I would say we surrounded ourselves a lot with those types of people. Machine learning at Berkeley was a club we were in at Berkeley and it was very selective. And it’s the people there are incredible. And actually I think half of our company now is from that club itself.

[00:28:23] And so we’ve always been very keen about this. And so now when we did start a company, it’s like, Hey, now we have a cost, now we have a mission. And it’s not an ad optimization mission. It’s not another SaaS startup. We’re actually building software really hard engineering problems that then help biologists and scientists tackle some of the world’s biggest challenges. Let’s recruit some more of our friends and let’s recruit some of the people we know. And so it’s something that we think about a lot in the company. We actually have meetings every week where we talk about the people that we’re talking with and the people that we want to hire in the future. And no timeline is long enough for someone that you want to hire. Like you might want to hire someone in a year or two years and they’re currently busy, but you know that they’re amazing and that at some point there will be a great fit for the company and it is part of your job for every engineer, for every person, for myself especially to continue building relationships with those people and following them and setting up the company so that in the future they’ll be uniquely positioned to drive our mission forward. And so it’s something we take very seriously and that we drive forward every day.

Grant Belgard: [00:29:38] Who and what are your near-term plans for Latch in terms of expansion? It sounds like you’re in the Bay Area. Does everyone work on site? Do you have a hybrid team? How does that look and what are your plans going forward?

Alfredo Andere: [00:29:53] So we actually work fully in person, which seems to be different these days with remote COVID return, but we hope to stick to it fully in person for the foreseeable future, at least to 20 people, hopefully to 100 people. We work from San Francisco, from the Bay Area. We’re hoping to double over the next year and we’re about to get a bigger office down in Mission Bay. And yeah, actually it’s funny enough, it’s the first question I ask now in interviews. As soon as I hop into an interview, I ask them about are you willing to move to SF and work in person six days a week? And yeah, it’s six days a week. So we actually work on Saturdays, so it’s Monday to Saturday and then NSF and that disqualifies a lot of people, which is surprising because it’s one of the top things in the job postings. But that’s currently the first question I ask.

Grant Belgard: [00:30:50] I wonder if people are really reading them thoroughly.

Alfredo Andere: [00:30:53] That’s the thing. But that’s the first question I ask and it disqualify some people. But the people that are coming into the office, they’re very special.

Grant Belgard: [00:31:05] What do you think are the influences from when you were growing up and so on that led you to do this, the semester before graduating. I think most college students wouldn’t have the confidence to do that. They would be too scared, frankly.

Alfredo Andere: [00:31:23] Yeah, I think for me definitely a combination of the entrepreneurs that have talked about their story and have inspired me since I’m young and then my parents. And in the sense of the entrepreneurs, I think my first vision of Silicon Valley was actually when I was 13 or 14. I read Steve Jobs biography by Walter Isaacson, and I remember just feeling chills through my body throughout many parts of the book where I was just so surprised and impressed that something a brand I already loved. Like at that point, I was already a huge fan of Apple and then seeing how it had been built by a team and a person and just been built out from scratch in a lifetime, less than a lifetime. And I think that put a lot of things into perspective and inspiration. And from there, reading about Elon Musk and then reading about the Twitter founder, which I mean, it’s a little more problematic. I love biographies and reading about all these people doing it and going through it, It was always a huge inspiration for me and what actually inspired me to then go study at Berkeley instead of in Mexico where I’m from and try and pursue that myself. And so when the opportunity came and it felt like the right opportunity, I knew that my parents, as much as they would love for me to finish school or they really want and have always pushed me to do, is to just follow what makes me happy and follow what I love. And so with that combination, when the right opportunity came and the right team was there which in my mind is the most important thing. I just knew I had to do it and school could wait and school is going to be there. And I think it does take a little bit of being fine with risk, but also some logic around risk and what you’re really missing out of. So yeah, that’s how we had the ability to drop out. And I think I don’t regret it at this point, but initially I definitely had my days where I look back and I was like, Wow, is this the right choice?

Grant Belgard: [00:33:38] What have been your biggest surprises?

Alfredo Andere: [00:33:41] I would say two. One of them is definitely the timelines, stuff takes time. And it’s not just hard work, but hard work over a long period of time. And just understanding how long stuff is going to take and how hard building good software is and how hard getting a single user is, and then how hard getting ten users is and 100 understanding that a lot of the superficiality that you see in TechCrunch is actually just very few data points over a lot of companies. You’re always seeing companies getting funded and so you’re always thinking like, Oh yeah, this goes really fast. But when you actually look at the background of these companies, they’ve been being built for five years, ten years. And it’s only now that they’re reaching the point of really rocket ship trajectory. And so stuff takes time and stuff takes a lot of hard work over that time. And understanding that we’re dealing with ten year timelines and 20 year timelines has been a huge surprise to someone that my biggest timelines had been four years for college. On the other hand, it also surprises me how from the inside, a lot of companies that look very prominent and like everything is going perfectly are actually not that great. Actually, if you can find a team of people that are really willing to work super hard for an extended period of time, let’s say ten years, actually that in itself is super special and super hard to find. It’s very rare. So those are two of many surprises I’ve had while doing this.

Grant Belgard: [00:35:30] What advice would you have for young entrepreneurs possibly starting a company during undergrad or grad school or whatever?

Alfredo Andere: [00:35:39] My first advice would be to not take anyone’s advice. Not anyone’s advice, but be very selective and filter out a lot of the advice you get. I like to say that all advice averages out to zero. And so I think you have to be very selective because when you’re a young startup founder, especially in biotech. Like if I had a dollar for every person who told me that I cannot start a biotech software company without a PhD, I wouldn’t have to make a startup. But it’s very discouraging to talk to people and to get everyone’s advice because most people will tell you can’t do it. And sure, the probabilities are low. But the other advice I would give is don’t do it just for the sake of doing it, because it’s really, really hard and it’s not a good life. I mean, unless this is your calling and you might not know if it is. You should probably not do this. It’s really hard to make a startup. It’s working seven days a week, 16 hours a day, every single day for the next ten years of your life. It’s really painful in many ways. You miss out on a lot unless you are convinced that this is your calling and this is what you want to do, you will not enjoy it. And so that would be my other piece of advice is don’t do it. And I like to give that advice because I think if you’re the person that’s the type of person who’s going to do it anyway, you’re going to do it anyway. But I would recommend not doing it.

Grant Belgard: [00:37:16] If you’re contrarian enough. Yeah, it’s rough. It’s really hard. If there’s one message you would have for our listeners about Latch, what would it be?

Alfredo Andere: [00:37:32] We are not a no code platform. We are a development framework for bio developers to be able to easily yes, create no code interfaces, but also to easily create other interfaces and also to easily deploy to the cloud and to easily be able to track data, traceability and all of these benefits that wrapping your code in our SDK gives you. And so we’ve been dealing a lot with that. We were initially a no code platform because we were dogfooding our own SDK and a lot of people are like, Oh, I can’t do my work in a no code platform. We know, we understand, and now we’re open sourcing our own SDK that we use to create those no code platforms to the rest of the world and hoping to get the developer community in bio to adopt it and accelerate their work and accelerate the work of their company. And so if I can say one message and you’ll hear me saying this a lot over the next coming months is Latch is not a no code platform. It is a bio development framework.

Grant Belgard: [00:38:41] Nice. Well, thank you so much for joining us today. It was great.

Alfredo Andere: [00:38:46] No, thank you Grant. Really appreciate you having me. I’m a huge fan of your work so thank you for inviting me to your podcast.

The Bioinformatics CRO Podcast

Episode 49 with Joshua Hare

Joshua Hare, Professor of Medicine at the University of Miami and co-founder of Longeveron, discusses the regenerative and reparative potential of MSCs and how cell therapies will revolutionize medicine.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Joshua is professor of Medicine at the University of Miami and co-founder of Longeveron, a biotech company using MSCs to treat chronic diseases. He is also founding director of the Interdisciplinary Stem Cell Institute at the University of Miami’s Miller School of Medicine. 

Transcript of Episode 49: Joshua Hare

Disclaimer: Transcripts may contain errors.

Grace Ratley: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley, and today I’m joined by Dr. Joshua Hare, who is a professor of medicine and director of the Interdisciplinary Stem Cell Institute at the University of Miami, as well as co-founder and chief scientific officer at Longeveron. Welcome, Joshua.

Joshua Hare: [00:00:15] Thank you so much. It’s a pleasure to be with you.

Grace Ratley: [00:00:18] Yeah. So tell me a little bit about Longeveron and the therapies that you’re working on.

Joshua Hare: [00:00:23] Longeveron is a biotech company devoted to solve diseases of aging and aging related conditions. We founded Longeveron in 2014 with a license from the University of Miami for technologies related to using cell based therapy to treat a condition called aging frailty. Since then, Longeveron has branched out to also focus on Alzheimer’s disease, the metabolic syndrome. And as every good rule needs to have an exception. Our exception is a disorder of neonates. So we’re an aging related company with most of the aging related conditions. And in a program we’re very excited about for neonatal congenital heart disease. The technology is based around a cell based therapy that is a culture expanded product that comes from healthy bone marrow. Young, healthy donors give their bone marrow. And we have shown that this product can be used as an allograft and doesn’t require immunosuppression. So that’s a very big feature of convenience because it’s an off the shelf product and therefore gives a great economic scale.

Grace Ratley: [00:01:43] Sure. And the cells that you’re working with are mesenchymal stem cells. Can you tell me a little bit about those and why you chose mesenchymal stem cells as opposed to other stem cell types?

Joshua Hare: [00:01:55] Well, first of all, whether or not they should be called mesenchymal stem cells is highly controversial in the scientific literature. And just in the interest of time, I think we can stay away from that controversy. But because of that, we don’t call them mesenchymal stem cells anymore. There are other names that have been given to them to maintain the MSC abbreviation. So two other names that some people use are mesenchymal stromal cells or even more away from that is medicinal signaling cells. So three versions of what MSC could stand for. Those of us in the field chuckle a little bit because it’s the kind of thing where whatever you call it, we all know what people are talking about, but a huge amount has been made of what to call this cell entity because of the debate really about what a stem cell is. And so some purists feel that if the cell doesn’t engraft and differentiates that it should not be called a stem cell. And I can agree with that. I think it’s really more semantics than anything else, because we’ve been using this product for decades now, literally decades, without the expectation that it would engraft and differentiate. So it’s not like we’re making something up in terms of what the mechanism of action is, but we can agree also to not call it a mesenchymal stem cell. Now as we’re a biotech company and are making a product that we’re trying to get through regulatory agencies, it has to be given an official name anyhow.

[00:03:39] And the name that our product has been given is Lomecel-B. So we refer to as Lomecel-B right now. Now, in terms of why we chose to go this route, I’m a cardiologist. I’m an expert in heart transplantation. I’ve been working on cell based therapy for 20 years now, and I started to work in the early part of last decade on this particular type of product, trying to understand particularly the immunology and then the reparative capability of the product in heart disease. I studied it extensively in animal models, and then very early on in the life cycle of human trials, I was one of the first in the United States to give it to people with heart attack. We started doing that about 2005. The reason I’ve stuck with MSC work is very, very simple and some people misunderstand this. I’ve stuck with it because the data I’ve been getting has been positive. Now, some people debate that, but the fact of the matter is I’ve done a large number of clinical trials in human heart failure, and our results have been interesting and provocative and have warranted further work in my view. Now, we pivoted away from heart failure alone to the whole body when we started to look at aging frailty. Because a very interesting observations we were making in patients with heart failure, their whole body was getting better, not just their heart. So the cells which have four basic actions that we know about, none of which is related to being a stem cell.

[00:05:28] So this is why I think that what you call it is much ado about nothing. But we know and we’ve documented that the cells have an immunomodulatory effect. They have an antifibrotic effect. They have a pro vascular effect, which is multifaceted and they stimulate endogenous repair. They themselves don’t engraft and differentiate, but they can create tissues that have stem cell compartments and have proliferative ability to undergo a reparative process. And we’ve known about that for at least ten years. When we looked at what was happening to people with heart failure, we saw that things like their quality of life and their six minute walk distance was improving. So there were effects that their whole body. We then went on to show that endothelial dysfunction was also improving and endothelial dysfunction is a very important part of heart failure and aging. So with all of these things we were seeing in heart failure patients, we said we got to try this for aging frailty. And there was one more important piece of the puzzle which was in our heart failure population, we looked to see if age was a factor where older people not responding as well as younger people. And in fact, we found that older people and younger people were responding the same. So it became very logical to then say, okay, aging frailty is a major worldwide problem. It’s characterized by a lot of the things that affect heart failure patients. Let’s see if we could try this product in patients with aging frailty.

Grace Ratley: [00:07:09] So tell me a little bit about aging frailty. What are some of the symptoms of that and how do you differentiate it from the normal aging process?

Joshua Hare: [00:07:18] Your question is great and hits the nail on the head. We have this unfortunate view in our minds because it’s just the lens we see through right now that aging is inevitable and that an aging decline in function is inevitable. That’s not true, by the way, but that is our view because that’s our world experience. What we also know is that some people are aging more successfully than others. We all know two 80 year olds or two 75 year olds, one who’s doing great and the other who isn’t. Everybody knows that. But we believe and we accept because the old joke, the only two guarantees in life are death and taxes. So we all know we’re going to die and we all know that we’re going to age and we all know that we are aging right now. Every day is a day we’re a day older. It’s this issue about how what our quality of life is and our functional capacity is at the end of life. And this is where their misconceptions. And so it’s a very important field in geriatric medicine, the study of aging frailty.

[00:08:28] And what’s been shown is that, in fact, some people are aging successfully and others are aging unsuccessfully. And the people who are aging unsuccessfully have a greater vulnerability to diseases. And it’s gone so far that there’s a new hypothesis in medicine right now called the Geroscience hypothesis. The Geroscience hypothesis holds that aging is the number one risk factor for all other diseases. So some are advocating that we should be focusing on treating aging as the pathway to treating heart disease, cancer, Alzheimer’s. So I’ve been familiar with this literature and this science for a long time, and it made a lot of sense to me to apply MSCs or Lomecel-B to people with aging frailty because it’s a major unmet health need, it’s biologically based and therefore it’s amenable to be treated. We also know what causes aging frailty, right? It’s low grade inflammation. It’s endothelial dysfunction, and it’s sarcopenia, which is a loss of skeletal muscle. So these are processes that are biological processes and can be addressed. And it made sense to us. What we had observed in other patient studies was that Lomecel-B might be an effective treatment for aging frailty.

Grace Ratley: [00:10:01] I am curious as to how you hope Lomecel-B will be used since aging affects everyone. What are the indications that it will be used? Like will you give it to only people over the age of 65 who meet a certain criteria? Or do you hope that it will be more of a commercial medicine product?

Joshua Hare: [00:10:20] How it will be used will be determined like every medicine’s use is determined and it’s determined by regulators. And very typically what happens is a manufacturer of a drug negotiates with the FDA or the European Medicines Association or the agency in Japan, whatever country you’re in and you pre negotiate the indications and then you have to do studies to prove that in that setting, the drug works. How I hope it will work is not really up to me. It’s up to a very open dialogue between Longeveron the company I co-founded and regulators. Now the big challenge is that these regulatory authorities have never approved any drug or any treatment for aging frailty. And because of this whole issue of the controversy about whether is it an actual disease or not. So there’s a lot of new ground being broken here. There’s huge interest in anti-aging therapies and geroscience therapies. And this is something that’s really coming to the forefront right now. Science and medicine is really shifting its attention to understanding and acknowledging that aging is a biological process and should be treated and that great benefits will accrue to society and the individuals and families who are affected. So how it actually gets used will depend on the study that we do. And what we’ve completed right now is a phase two B study. It was done in patients between 65 and 80, depending on how the FDA views that study and tells us what to do next, we’ll determine of how it’ll be used. The age range, the indications and so on and so forth.

Grace Ratley: [00:12:24] That’s really exciting. Congratulations on your completion of that phase and I wish you the best going forward. Do you have any idea of when you might expect it to hit market?

Joshua Hare: [00:12:35] Well, we have to at a minimum do one if not two phase three studies. And we have to negotiate with the FDA what the endpoint of those studies will be. We’re not even sure that they’ll say you can go ahead to phase three. Right now, they might say do another phase two. We just don’t know until we talk to them. Our plan is to talk to the FDA with the data that we have now and make a determination. If we go to phase three, we’ll be able to at that point decide with the FDA’s advice what the endpoint should be and how long the trial will be. So the time to market depends on how big that study has to be. If we could get away with a study of just hundreds of patients, it’ll be much quicker than if it’s thousands of patients. The funny thing is I’ve been working in this field for 20 years and we keep saying it’s five years away, it’s five years away, it’s five years away. And every five years comes ticks by and we still say it’s five years away. That’s the problem with any new field. It takes a long time. A brand new idea like this can take anywhere from 30 to 40 years to get proven and accepted by the general community.

Grace Ratley: [00:13:55] So you’re collecting these cells from young, healthy donors from the bone marrow. I know there’s a general shortage of people who donate bone marrow for other uses, like cancer therapies and whatnot. How do you think you’ll overcome that?

Joshua Hare: [00:14:13] One of the great things about the allogeneic technology is that one donor can generate enough material for hundreds, if not thousands of doses. So it’s not one donor, one recipient the way it is for bone marrow transplantation, for lymphoma, leukemia. This is a culture expanded product that we culture expand in a specific lab called GMP lab, a good manufacturing process lab. We can generate thousands and thousands of doses from a relatively few number of donors. Now in the future, one of the main focuses of the company is to scale this up. If this gets approved for aging, frailty or Alzheimer’s disease, I have to tell you a little bit about Alzheimer’s disease. These are major unmet needs, and we’re going to need huge amounts of material and we’re going to have to address a scale up plan. And we are in the midst of doing that right now. I can’t tell you anything about it because it’s all on the drawing board. But if you invite me back in a year or so, hopefully I can tell you a little bit more about the scale up plan.

Grace Ratley: [00:15:27] We’ll certainly have to have you back on. But yeah, do tell me a little bit about its application in Alzheimer’s disease.

Joshua Hare: [00:15:34] Yeah. So if people really understand and study what we’ve done, they’ll see it’s very logical and systematic. I told you how we went from heart failure to frailty. It was based on observations in the heart failure population, the biology and preliminary observation. So it made sense to do it. It wasn’t just throwing spaghetti at the wall. And the same with Alzheimer’s. What we noticed was in an earlier frailty study we did. We did a crude measurement of cognitive function, which is a simple test you can give to a patient called a mini-mental score. And the Mini-mental score improved in the patients who received the predecessor version of what is now Lomecel-B. And we were like, Whoa, that’s surprising. That wasn’t expected. Then when we spoke with experts in the field, they also informed us that indeed there was a hypothesis that made sense, which is that there’s inflammation in the brain in Alzheimer’s patients, and that might go unaddressed by some of the other approaches that were being developed. That’s called the neuroinflammation hypothesis. And with all of the troubles in the Alzheimer’s field, there’s been a huge shift towards looking at neuroinflammation. So knowing how our cells work, remember I told you the four things we know about Lomecel-B, anti-inflammatory anti-fibrotic, pro vascular and stimulation of endogenous repair plus the observation in the earlier study that there was an improvement in mini-mental, it may again make total logical sense to go ahead and try it in a small Alzheimer’s study, which we did. We were very fortunate that the Alzheimer’s Association funded it under a part of the cloud grant. So I want to acknowledge them. And we did a 33 patient study that’s been published in the journal Alzheimer’s and Dementia, which is a journal of the Alzheimer’s Association. And the results were really, really interesting. Of course, they’re preliminary and provocative. It’s just a phase one study. But we did show safety, But from a preliminary standpoint, we saw suggestions of efficacy and they seem to be meaningful. So Longeveron is presently enrolling in a phase two study.

Grace Ratley: [00:18:12] And one of the things that is really amazing about cell based therapies is the safety of it. Can you talk a little bit about that?

Joshua Hare: [00:18:22] So I have a general prediction, and I believe that over the course of this century and it’s always good to look back a hundred years and sometimes it seems like not much changes. But if you look at medicine over the past hundred years, it’s been incredible the changes. We didn’t even have antibiotics 100 years ago and now we’ve got hundreds of different antibiotics and heart failure drugs. I mean, there’s now 8 or 9 different classes of heart failure drugs, cancer chemotherapy and so on and so forth. My general prediction for the 21st century is that cells are going to become much more commonly used as medicines. We’ve already started. That process has already begun with a type of cell therapy called Car-T therapy, which is a very effective way to engineer T cells and use them to kill a variety of cancers and leukemias and lymphomas. And these are approved. I think cells are going to generally be used as medicines for a whole host of indications. And one of the main reasons is safety. I think if you have a situation where you could cure a disease with medicines or you can cure it with cells, the cells are going to win out over the next four, five, six decades.

[00:19:44] Why? Because I think cells are going to be much safer to use. Think about it as a smart bomb or addressing something surgically than with a sledge hammer. So I’ve told you these four things that the MSCs do. The cell is a living thing. It doesn’t just go in there and do that everywhere. It just goes to the area of injury and just does what’s needed to be done at that site. So that gives it a balanced action. And also they have a very long standing action. So, for example, in the Alzheimer’s trial and in the frailty trial, we gave a single dose of the cells to people at the beginning and followed them for nine months in the frailty and 12 months in the Alzheimer’s. And those effects from the single dose were still accumulating 9 or 12 months later. So incredibly safe, incredibly effective, long lasting. I think it’s going to overtake medicines in quite a few areas once we really figure out how to engineer the cells better for each specific thing we want them to do.

Grace Ratley: [00:20:57] Yes, I’m very excited about it as well. I hadn’t learned very much about these therapies before this interview, and at first I was a little skeptical. I was like, That sounds kind of ridiculous, like injecting cells into someone’s blood and just seeing what happens. But the more I learned about it, I’m really excited about it. So let’s get into a little bit about you and your journey to Longeveron and into science. So tell me about when you first became interested in medicine.

Joshua Hare: [00:21:30] I became interested in medicine very, very early on in my life, around the time I was 13 years old, when I was in the ninth grade. I always loved science. As a kid, I loved science, I loved chemistry and electronics and biology. I was fascinated by animals and I put it all together by the ninth grade saying, hey, medicine is the thing for me because it combines science and the human condition, the human experience. And so I decided very early that that’s what I was going to do. And I wanted to be a research doctor, not just a medical doctor. So I pursued that very early. I worked every summer at the National Institutes of Health in Bethesda, Maryland, starting from the summer I graduated high school all the way through college and into medical school. I published my first papers out of those summer experiences. Then I went to Johns Hopkins Medical School, which was fantastic and really research institution where research was highly valued. And very early in medical school, I decided I wanted to do cardiology. So I’ve always been very lucky that I’ve been able to know what I wanted to do early.

Grace Ratley: [00:22:52] That’s really wonderful. I’m glad that you had those experiences at a young age and really guided you to where you are. So then you went into academia?

Joshua Hare: [00:23:03] Yeah, I have not left academia. I think academia is the most exciting thing there is to do. I feel like I started academia on day one of medical school because I went to a research institute and I’m still a professor of medicine at the University of Miami.

Grace Ratley: [00:23:21] I think in a lot of academic settings, it can be difficult to learn about opportunities to take your research to market or to actually take research and apply it into populations. I think a lot of scientists working in academia, they’re doing their basic research and they’re really passionate about it and they’re writing these grants like, yes, this could be used in cancer, but they’re maybe hoping that someone will just take that idea and do it for them. So how did you get into biotech?

Joshua Hare: [00:23:54] Yeah, you’re absolutely correct that there’s a lot of, I would say, cognitive dissonance between basic science and the laboratory and what’s called technology transfer. One of the major things that was done in the United States and this goes all the way back to 1980, was that the Congress of the United States recognized that there was a problem and that intellectual property was being squandered. Because if a scientist made an inventive discovery and published it before they filed a patent, they would obviate the intellectual property. Because once something’s in the public domain, you can’t patent it and then everybody can access it. Now, what everybody recognized at Congress and on the business community that it’s intellectual property or IP that drives commercialization because an investor isn’t going to invest in something that’s in the public domain because then they can’t get a return. The other thing that’s important to know is that all medicines are generated through industry, not through academia. Universities are not for profit entities and they are not set up to become companies and take on the risk that a company takes. So there’s still a lot of tension between this. But in 1980, there was a law passed by Congress called the Bayh-dole Act. And this was very, very important because you always have to remember that in the United States, the NIH funds billions and billions of dollars of taxpayer money every year to research universities.

[00:25:33] Now, the taxpayers of the United States want treatments as a result. So the law was passed that universities can and should patent their discoveries before the results are published, and that they should set up technology transfer offices and go to investigators at the university, particularly the ones who have NIH grants, and make sure or help or assist that patentable material is being patented. Now the law holds or I’m not sure if it’s the law, but it’s what’s done. The university owns the patent, not the investor, not the investigator. And their job is to license it and make sure that that it gets put into the right hands. Now, that’s not as easy as it seems, because you still have to convince investors to invest. And they’ve got a lot of different choices. There’s a lot of stuff coming out. But the long and short of it is the Bayh-dole Act provides for universities patenting inventions and for universities, licensing them to industrial partners and gaining royalties that can be ploughed back into the academic mission. That’s what’s supposed to happen. Now, in my case, I became the inventor. And to an extent the investor, because I helped start the company and I sought the investor. Now that’s also okay. And a university professor can and the laws and the rules do allow so long as the person is disclosing. And if you look at any of my papers, you’ll see I disclose in every one of my papers that I am a founder of Longeveron and own equity in Longeveron.

[00:27:22] What made it all work was that I was the person who made the inventions and then became the person on the other side who became the licensor. I raised the money from investors to start the company as well. So that’s the reason I think why it’s worked well. There’s a lot of start ups that really never get off the ground because they’re undercapitalized in the beginning or the right relationships between the inventors and the company aren’t there. The companies that are really successful, if you look around the world, in the country, there’s some very successful startups. A great example is Moderna. Moderna, the company that made of the COVID vaccines is only ten years old. It’s a startup. It’s a startup that licensed technologies from universities and commercialized them and the licenses and the patents and the IPS to use RNA as a drug. And they figured out it could be used as a vaccine. The companies that made CAR-T therapy were all startups. The ones that are really successful are the ones that engage with the inventors and have the proper relationship with the universities. That’s going to be a win win situation for the company, the university and the inventor.

Grace Ratley: [00:28:51] And I think that is done better in some places than others. So of course, San Francisco and various places in California and Silicon Valley, Boston and Miami. Miami is a definitely a growing hub for biotech. And I think universities in those hubs tend to maybe encourage investigators more or educate them more about these opportunities. How have you felt being in Miami in this biotech community?

Joshua Hare: [00:29:26] Biotech is just a burgeoning field in Miami. It’s nowhere near Boston or the Bay Area. But there’s a lot of smart people here. There is the University of Miami that has a lot of investigators and a lot of NIH funding. So there is a lot of opportunity here. But there’s also not being in Boston or San Francisco, it can pose challenges to a startup. There’s a lot of interest in this city from the mayor’s office and the governor’s office to see South Florida become more of a biotech hub. And I think it will. There are lots of areas that are growing and it’s a huge part of the economy. It’s a huge part of the investment base. Investors or their investment firms are just specialized in biotech and study and understand biotech. I think it’s very exciting. I think Miami’s got a bright future. We’re proud of the fact that I think Longeveron is, if you just look at it on the basis of market cap, let’s say one of the most successful, if not the most successful spin out of the University of Miami. And so having something like Longeveron based in Miami hopefully becomes a catalyst for other companies to also successfully launch and then go all the way to a public offering. So Longeveron went public February of last year.

Grace Ratley: [00:30:56] And this isn’t the first biotech you’ve founded. Correct? You have also founded Visteon and Heart Genomics. Can you tell me a little bit about those companies and your experiences there?

Joshua Hare: [00:31:08] It shows in my view how hard it is to get all of the pieces right. So some people think, Oh, all we need is a patent and if the patent is good, it will just automatically attract money and attract people. Like everything in life, everything requires hard work, connections, experience, nothing that’s worth anything just happens by itself. So Visteon and Heart Genomics were sort of my early attempts to learn how to do this correctly. And in both instances we raised money, licensed the patents, but unfortunately never got to the point where a Longeveron is, where the capitalization at Longeveron and the infrastructure that was developed and the plan was just much more successful. So Visteon and Heart Genomics still exist. I wish them the very, very best, but they haven’t become as successful as Longeveron. They didn’t get to the point of being able to go public. They do have really good technologies, really good technologies, and I think there’ll be opportunities to advance those technologies.

Grace Ratley: [00:32:28] What did you learn from those early experiences? What were the most significant pieces of information that you would give maybe to a new founder who is looking to patent their technology and try and take it to market?

Joshua Hare: [00:32:46] You have to go into this understanding that anything you do, it requires a lot of hard work and that it just doesn’t happen by itself. The other thing that’s really important to understand is it’s just as scientifically rigorous. You have to go through all of the same steps you do just in your academic lab, which is raise money and build a team and build a facility and take the technology forward. So it’s very, very difficult. And you have to also learn and understand how investment economics work. You have to take on some knowledge of business and how things are valued. I always joke that I missed the finance lecture in medical school. Well, of course they don’t have a finance lecture in medical school, but you do have to understand finance. So the Bayh-dole Act got us so far to say, it’s okay to patent, but then the people at university still have some misconceptions. First of all, they still have somewhat of a stigma against it in some quarters. Some research professors feel that, Oh, it’s dirty to go into the commercial space and they don’t want to do it, that there’s something wrong with it.

[00:34:07] And then there’s another side where people feel that they have just incredibly unrealistic expectation of what something should be worth. So you’ve got to be able to understand the economics of that, and you’ve got to seek out the right people to invest. And you’ve got to understand enough about the deal terms that the deal terms are fair and reasonable and will allow the idea to grow. Because at the end of the day, in my view, the key endpoint is the advancement of science. We worked all the way in the university to advance the science and we’re working on the other side in the technological sphere to advance the science because it’s only through correct science and accurate science that we’ll get to effective treatments. The truth always wins out. So if anybody has falsified anything or cut any corners at any point of the way, it’s going to come out. Rigorous responsible science is critical at every step of the way.

Grace Ratley: [00:35:15] That’s some really awesome advice and some great wisdom that you’ve shared with us today. Thank you so much for joining us today. I learned a lot.

Joshua Hare: [00:35:21] Wonderful, wonderful.

Why do COVID Deaths Vary by State?

Cumulative COVID deaths are an endpoint to compare the effectiveness of states’ pandemic-related health policies. As the major risk factor for death from COVID is age, and some states have younger populations than others, it’s important to adjust for age when trying to understand factors that contribute to variable outcomes among states. Here we examine the relationships among age-adjusted COVID deaths and several variables of interest. Note that such comparisons cannot, on their own, be used to identify causal contributors to cumulative age-adjusted COVID deaths. Many features are highly co-linear and may serve as an imperfect proxy for underlying causes. However, these plots can be useful for hypothesis generation, and can support (but not prove) the lack of a strong causal relationship when there appears to be no association at all. We recommend bearing in mind the ecological fallacy.

Unadjusted COVID deaths versus Age-adjusted deaths | y = 0.934 * x + 15.3 | R^2 = 0.912

The chart above shows the relationship between age-adjusted COVID deaths and unadjusted deaths. States with younger populations such as Texas perform worse after age adjustment, while states with older populations such as Florida perform better.


  • Vaccination rate is the single biggest predictor of age-adjusted deaths by state.
  • COVID deaths do not correlate with state stringency after adjusting for age and obesity rate.
  • State stringency correlates with unemployment rate but not with non-COVID excess deaths, depression or suicide vs baseline.
  • Partisanship is a strong predictor of vaccination rate, but not of deaths after accounting for age, vaccination rate and obesity.

This is incomplete and there are many factors as yet unexamined. We will add more visualizations over time.



Vaccination is highly effective in preventing severe outcomes and death from COVID infection. Indeed state vaccination rate has a strong negative correlation with age-adjusted COVID deaths (P<0.01).  In the 65+ age group, there is a similarly high correlation (P<0.01) with age adjusted COVID deaths. Note that the CDC caps vaccination coverage metrics at 95%, due to issues tracking first and second doses. Because vaccination over 65 is the strongest single predictor of age-adjusted deaths, we also present cumulative COVID deaths adjusting for both age and vaccination rate over 65.

Vaccination Rate and Age Adjusted COVID Deaths

Fully Vaccinated versus Age-adjusted deaths | y = -555 * x + 645 | R^2 = 0.4

Vaccination Rate Over 65 Years of Age

Fully vaccinated over 65 versus Age-adjusted deaths | y = -1113 * x + 1282 | R^2 = 0.39

Vaccination Rate 65+ and Age & Obesity Adjusted COVID Deaths

Vaccination over 65 versus Obesity and Age-adjusted deaths | y = -527 * x + 770 | R^2 = 0.144

There is a moderate negative correlation (P<0.01) between vaccination and COVID deaths when adjusting for age and obesity. The reduction in correlation is due to collinearity between state obesity rates and vaccination rates. That is, states with higher adult obesity rates tend to have lower vaccination rates (P<0.01). In a multivariate linear model of age-adjusted deaths, vaccination over 65, and obesity, both showed significant correlation with deaths and had a multiple R² of 0.48. 

Stringency Index

Stringency Index is a useful tool for comparing government response to the pandemic. It is the average of nine policy metrics, including: 

  • School closures
  • Workplace closures
  • Cancellation of public events
  • Restrictions on public gatherings
  • Closures of public transport
  • Stay-at-home requirements
  • Public information campaigns
  • Restrictions on internal movements
  • International travel controls

The higher the stringency index, which has a maximum value of 100, the stricter a government’s response to the pandemic. Stringency index does not factor in compliance with government policy. For more information on Stringency Index and its calculation visit 

The charts above show a moderate negative correlation between the average strictness of pandemic-related policy and age adjusted COVID deaths (P<0.01) and a weak negative correlation between vaccination and age adjusted COVID deaths (Vaccination 65+ adjusted P=0.045). However, when deaths are additionally adjusted for obesity, there is no longer a significant correlation (Obesity adjusted P=0.16, Obesity and Vaccination 65+ adjusted P=0.22). Although some studies have suggested policies are effective in reducing COVID deaths and reducing strain on hospitals (1, 2, 3), effectiveness may decline over time with reduced compliance as more people are experiencing “lockdown fatigue“. Additionally, a recent meta-analysis found effectively no impact of lockdowns on mortality. As we are visualizing cumulative deaths across the pandemic, we will not see short-term impacts.

Mean Stringency Index and Adult Depression & Anxiety 

Mean Stringency Index versus % Change in Anxiety/Depression | y = -0.0058 * x + 0.639 | R^2 = 0.04

Stringency Index and Suicide

Mean Stringency Index versus % Change in Suicide Mortality | y = 0.0017 * x + 0.013 | R^2 = 0.024

All states in the US observed an increase in depression and anxiety symptoms over the course of the pandemic. However, these increases were not significantly correlated with mean stringency index (P=0.16).  Further, mean stringency index was not correlated with the % change in suicide mortality between 2018 and 2021 (P=0.28). 

Stringency Index and Unemployment 

Mean Stringency Index versus Change in Seasonally adjusted % Unemployment | y = 8.66E-4 * x + 0.0185 | R^2 = 0.28

Stringency index was moderately correlated with the % change in unemployment from the start of the pandemic to Sept 2021 (P<0.01) For further reading about the economic impacts of the COVID pandemic and government interventions see the following articles: The COVID-19 crisis: what explains cross-country differences in the pandemic’s short-term economic impact?, Epidemiological and economic impact of COVID-19 in the US, and
Pandemic Impact on Mortality and Economy Varies Across Age Groups and Geographies.


Our data show that adult obesity rate has a moderate positive correlation with age adjusted COVID deaths both before and after adjusting for vaccination status (P<0.01 and P<0.05 respectively). This is consistent with prior studies which identify obesity as a significant risk factor for death from COVID infection (OR = 1.61). However, the slope of these plots is a few fold higher than one would expect given the individual-level risks of obesity alone even after adjusting for age & vaccination, which suggests that state obesity rate may serve as a proxy for other population-level risk factors for COVID death.

Obesity and Age Adjusted COVID Deaths

Adult Obesity Rate versus Age-adjusted deaths | y = 1198 * x -97.8 | R^2 = 0.371

Obesity and Age & Vaccination 65+ Adjusted COVID Deaths

Adult Obesity in 2021 versus Age and Vaccination adjusted COVID deaths | y = 523 * x + 122 | R^2 = 0.117

Poverty & Income Inequality

The Gini Index represents the degree of inequality in the distribution of income in a particular location. It’s value ranges from 0 to 1 with higher values indicating greater income inequality. You can find a more detailed explanation of its calculation here. The charts below show a significant (P<0.01) positive correlation between income inequality and age-adjusted COVID deaths, even after adjusting for obesity and vaccination. 

We also examined the poverty rate, using the supplementary poverty measure (SPM). The SPM takes into account differences in regional cost of living as well as taxes and the value of government assistance programs. Click here for more information about its calculation and comparison to the official poverty measure. Although Gini Index and the SPM are highly correlated with one another, it appears that Gini Index demonstrates a higher correlation with COVID mortality, especially after adjusting for vaccination and obesity. 


We used the percent of the population who voted for Trump in the 2020 presidential election to examine associations with partisanship. Although this will not be directly causal of COVID deaths, there has been a partisan aspect to vaccination in the United States, which does influence risk of death from COVID infection. Our analysis shows a very strong negative correlation between vaccination status and percent Trump vote.  Additionally, although there was a strong correlation between obesity and Trump vote, in a multivariate linear model with obesity, and vaccination over 65, percent Trump vote was not significantly associated with age-adjusted COVID deaths (P=0.76).

% Trump Vote (2020) and Vaccination Rate

% Trump Vote versus Fully Vaccinated | y = -0.756 * x + 1.02 | R^2 = 0.775

% Trump Vote and Vaccination Rate 65+

% Trump Vote versus % Fully Vaccinated Over 65 | y = -0.282 * x + 1.03 | R^2 = 0.442

Most states showed high vaccination rates in people aged 65 and older, and significantly reduced vaccination rates in younger populations. Although states with high Trump vote had significantly lower vaccination rates in the 65+ age group (P<0.01), the difference was exaggerated in younger populations (P<0.01). This is also shown in the chart below (P<0.01). 

Difference Between % Fully Vaccinated Over 65 & Under 65 and Trump Vote

% Trump Vote versus % Vaccinated Over 65 Minus Under 65 | y = 0.519 * x - 0.057 | R^2 = 0.674

Stringency Index and Age Adjusted COVID Deaths (Colored by Governor Partisanship)

Summary Statistics for States with Democrat Governorss

Mean Age-adjusted COVID Deaths/100K = 272±77 (std dev)

Mean Stringency Index = 40.4±6.3 (std dev)

Summary Statistics for States with Republican Governors

Mean Age-adjusted COVID Deaths/100K = 305±79 (std dev)

Mean Stringency Index = 31.4±6.0 (std dev)

Race & Ethnicity

Members of some racial and ethnic minority groups are more likely to experience severe outcomes from COVID-19 infections. The prevalence of underlying comorbidities that increase risk of severe outcomes from COVID infection (i.e. cardiovascular disease, asthma, obesity) vary by race and ethnicity and they may face differences in access to adequate healthcare resources.  As the racial composition of Hawaii is significantly different from the rest of the United States (i.e. an outlier), it was excluded from this analysis. 

Percent Population White versus Age-adjusted deaths | y = -555 * x + 645 | R^2 = 0.4 It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all). White P<0.01 | Black P<0.01 | Asian P=0.22 | Indigenous P=0.66 | Hispanic P=0.30
Age-adjusted deaths versus % Population Black | y = -2.7E-4 * x + | R^2 = 0.214

Race & Ethnicity and COVID Deaths Adjusted for Age, Vaccination Over 65 & Obesity

Percent Population White versus Age-adjusted deaths | y = -171 * x + 426 | R^2 = 0.114 It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all). White P<0.05 | Black P=0.19 | Asian P=0.26 | Indigenous P=0.70 | Hispanic P<0.01
Age-adjusted deaths versus % Population Black | y = 105 * x + 282 | R^2 = 0.036
% Population Asian versus Age-adjusted deaths | y = 316 * x + 282 | R^2 = 0.027
% Population Indigenous versus Age-adjusted deaths | y = 94.3 * x + 291 | R^2 = 0.003
% Population Hispanic versus Age-adjusted deaths | y = 238 * x + 263 | R^2 = 0.212

In a multiple regression model with obesity and vaccination over 65, % Hispanic population was significantly positively correlated with age adjusted deaths (P<0.05).

Mental Health

There are many new stresses associated with the pandemic that could lead to increases in mental illness in the population: worry about health, death of a loved one, chronic symptoms from long COVID, increased unemployment, and social isolation. All states observed an increase in depression and anxiety symptoms over the course of the pandemic and many had increases in suicides. However, based on our data these increases were not correlated with COVID deaths (depression & anxiety P=0.76, suicide P=0.12) or stringency index (depression & anxiety P=0.16, suicide P=0.28).

Age Adjusted COVID Deaths and % Change in Anxiety & Depressions Symptoms

Age-adjusted deaths versus % Change in Anxiety/Depression | y = -7.64E-5 * x + 0.454 | R^2 = 0.001

No Correlation P=0.76

Age Adjusted COVID Deaths and % Change in Suicide Mortality 

Age-adjusted deaths versus % Change in Suicide Mortality | y = -2.7E-4 * x + 0.126 | R^2 = 0.067

No Correlation P=0.12

% Change in Unemployment and % Change in Anxiety & Depressions Symptoms

% Change in Unemployment versus % Change in Anxiety/Depression | y = 0.935 * x + 12.9 | R^2 = 0.915

The chart to the left shows no significant correlation between anxiety and depression symptoms and the difference in seasonally adjusted unemployment from Jan 2020 to Sept 2021 (P=0.053)

Excess Deaths

Excess death is the difference between observed deaths in a specific time period and expected deaths in the same time period. The data below show the excess deaths in each state from Jan 2020 to Dec 2021. Expected deaths are based on historical trends from 6 years prior to the initial outbreak. The data below show a negative correlation between excess deaths and vaccination over 65 (P<0.01), which may be indicative that some deaths coded as non-COVID may in fact be related. For example, several studies have shown that even mild COVID infection significantly increases a person’s risk of cardiovascular disease. One found that the risk of stroke was 52% higher and heart failure was 72% higher in people who had been infected with COVID than those who had not. 

Excess Deaths and Vaccination Rate

Including COVID Deaths y = -0.252 * x + 0.291 R^2 = 0.218 | Excluding COVID Deaths y = -0.10 * x + 0.072 R^2 = 0.073

Including COVID Deaths:  P<0.01 | Excluding COVID Deaths: P=0.057

Excess Deaths and Vaccination Rate 65+

Including COVID Deaths y = -0.627 * x + 0.688 R^2 = 0.376 | Excluding COVID Deaths y = -0.278 * x + 0.255 R^2 = 0.156

Including COVID Deaths:  P<0.01 | Excluding COVID Deaths: P<0.01

Excess Deaths and Mean Stringency Index

Including COVID Deaths y = -0.0016 * x + 0.195 R^2 = 0.071 | Excluding COVID Deaths y = -2.2E-4 * x + 0.019 R^2 = 0.003

Including COVID Deaths:  P=0.061 | Excluding COVID Deaths: P=0.71

Excess Deaths and Obesity

Including COVID Deaths y = -0.296 * x + 0.042 R^2 = 0.066 | Excluding COVID Deaths y = 0.0681 * x - 0.0112 R^2 = 0.007

Including COVID Deaths:  P=0.071 Excluding COVID Deaths: P=0.56

Notes on Methods

  • P-values for plots were calculated using the Fisher R to Z transformation
  • P-values for multivariate models were calculated by multiple regression
  • Significance level was set at 0.05


The Bioinformatics CRO Podcast

Episode 48 with Alex Shalek

Alex Shalek, Associate Professor of Chemistry at MIT, discusses new methods and technologies in systems biology that have enabled advances in the diagnosis and treatment of autoimmune diseases and cancer. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Alex is Associate Professor of Chemistry at MIT, where his multi-disciplinary research aims to create and implement broadly-applicable methods to improve prognostics, diagnostics, and therapeutics for autoimmune diseases and cancer. 

Transcript of Episode 48: Alex Shalek

Disclaimer: Transcripts may contain errors.

Grace Ratley: [00:00:00] Welcome to the Bioinformatics CRO Podcast. My name is Grace Ratley and I’ll be your host for today’s show. And today I’m joined by Alex Shalek, who is a faculty member at MIT, the Ragon Institute and the Broad Institute. Welcome.

Alex Shalek: [00:00:12] Thanks so much for having me, Grace.

Grace Ratley: [00:00:14] Yeah, it’s great to have you on. So your research is incredibly multidisciplinary. It spans fields such as microbiota research, cancer, immunology, nanotechnology, engineering and genomics. Can you tell us a little bit about how you tie these fields together?

Alex Shalek: [00:00:31] Oh, it’s a complicated question. I think that we’ve ended up spanning a number of different disciplines just because it’s necessary for the kinds of questions that we want to ask. If you asked me when I was an 18 year old, if I would be a faculty member studying the things that I’m studying now, I would have thought you were crazy. In college, I focused on chemistry, physics and math and actually originally went to graduate school to do some theory work. And due to a series of different events, ended up doing Nanobiotechnology, which got me into immunology and then into systems biology and then very concerned and confused by sort of heterogeneity among cells, which took me down this rabbit hole. And what it really taught me at the end of the day was that I had to be very careful when I was looking to system to make sure that I understood all of the components to measure as many of the components I could. I didn’t necessarily have the background to assume what was the most important factor. And so I just got very interested in how could we comprehensively profile as many things as was possible, understand how they might be working together in synergy, just so that there weren’t really those blank spaces in the microscope that could be incredibly important that we were just missing. And so you start studying for example, the gut and all of a sudden there’s this incredible amount of diversity among the microbiota that’s in your gut so it’s this big missing piece.

[00:01:52] And so you have to look at it in combination with some of the cells that you want to look at. Then even just thinking about the guy, that’s one part of your body that interacts with many other parts of your body. So you start to think about the assumptions that we make when we focus on any specific thing. It just made me uncomfortable. It was one of those things where as we started trying to be more comprehensive, we naturally had to bring in principles from lots of different places. One of the fantastic things about being in Boston is just the strength of this collaborative ecosystem where you can work with partners in genomics, at the broad immunology at the Ragon, all the different engineering disciplines at MIT, some incredible teams over the hospitals across the river. And we’ve just tried to take advantage of that, working in these collaborative networks to do things that would be hard to do in other contexts. I constantly get reminded of how little I know, but it’s also incredibly exciting because you’re learning new things every day and seeing things in the intersection that you might have missed otherwise.

Grace Ratley: [00:02:48] Going from theory to where you are now is I mean, those are two very opposite things because I mean, theory is pretty hands off and now you’re all applications and hands on. But still trying to understand the bigger picture. It’s really amazing how you’ve brought those two things together.

Alex Shalek: [00:03:03] It’s funny, I’ve ended up in a similar place. When I was going to graduate school, I’d really like the idea of theory because I was told that if I had an internet connection, I could work anywhere in the world as long as I could send along my research and make progress during my PhD. And then in the work that we’ve done, it’s become increasingly global, collaborating with people on six different continents and multiple different countries. And so the travel has become a big part of what we do, as has the international collaboration. But it’s very different than I originally envisioned, sort of this remote science being.

Grace Ratley: [00:03:33] Yeah, certainly. And we have the bioinformatics here. We’re very familiar with remote science. And then back on your research, like this concept of understanding all of the tiny pieces of the system really ties in with your research associated with single cell sequencing. Can you tell us a little bit about the developments you’ve made in single cell sequencing?

Alex Shalek: [00:03:56] Yeah, of course. Happy to. We’ve just been a part of this. I think that there’s been this incredible effort among the community to develop a number of the different technologies that have now become standard and applying genomics to single cells. I happen to be at a very fortunate place and there was a moment of one of those things that serendipity. I wouldn’t say it’s purely fortune because you have to recognize the opportunity. But on the other hand, it was definitely in the right place at the right time. For me, through some of the technologies I developed with others in Hong Kong, Parkes lab and graduate school started doing some immunology and we were doing these genomic assays and they were really cool because now you could look at all of the genes. I didn’t have to pick one or two that I thought were really important. But as you know very well, whenever you’re doing genomics, you do these guilt by association analyses and you use correlations and you infer what networks are. And really what you have to do after that is you have to go back and systematically test your predictions through different perturbations.

[00:04:50] And unfortunately when I started doing these perturbations with others to try and understand whether the predictions we were making were in fact correct, I just see that every single cell was looking at looked a little different. They would express different levels of RNA as I thought were important or the protein abundance would be different or it would be localized differently, but cells just wouldn’t look quite the same. Or we’d be trying to kill all of a cancer and we would find that we could only kill some of the cells. So there was just this heterogeneity that I didn’t understand and where I was coming from and that chemistry, physics and math background. I had this idea that if you put one input like one drug on one system, it should give you one output. And so when you see that you’re getting this variety of different responses, it was really unsettling. And so in some of the work that I did in my postdoc with a number of others around the community and this large cloud project, I got very interested in trying to figure out whether or not there was any information in this variation. We could assume that all the cells are the same or just like scientists, they’re all scientists, but they all are very different. And if you just took the time to get to know each and every one of them, you might begin to understand a little bit more about the subspecializations and the things that go in their training.

Alex Shalek: [00:05:58] And so I did what at the time was a relatively insane postdoc trying to sequence individual cells, trying to just see if what you could measure was actually biological. And then even above and beyond that, if there was any information in it because you could have imagined it was just all noise due to the stochasticity of gene expression. Long story short, we profiled some cells and we started looking at sort of variation. And what we found was that there was actually a lot of structure in gene expression covariation. What that told us was that maybe there was some biological signals that we could pull out of this very complex data. And we went on to show that some of this derives from differences in cell states, and that derives from differences in cell circuits. Sometimes you have differences in subcellular processes. And when I think back on it, it’s not as surprising as you might think because what we’re really saying is that different cell types have different accessible regions of DNA and different proteins. And so they’re going to produce different transcripts and it’s going to look like these major differences. But at the time, the idea of sequencing like 0.1 picograms of RNA just seemed relatively nuts for lack of a better way of saying it. And once it turned out that there was going to be this hidden trove of treasures and looking at single cells in the same way you might think about like looking at people in any country. And then rather than just looking at like the average, they have 1.8 children or they have a median income of 50,000, like looking at each individual one and associated variables, we’re like, wow, there might be something really transformative we could do here.

[00:07:24] And so even though we started off with a really small number, we always were like, Oh, well, we’re probably going to have to hit these huge numbers and really get power. We’ve got trillions of cells in our body, how are we going to start doing the science that we want to really understand what’s happening there, to understand what’s happening in different bodies, to understand what’s happening in different diseases. And so from this initial demonstration that we did with a number of people around the Broad Community, it spurred us into this charge to try and develop technologies that would help us scale, you know, partnering with people originally on the West Coast at Fluidigm, trying to use microfluidics and then trying to develop things that worked on droplets here in Boston with a number of people across different institutions and different backgrounds ranging from computationalists to incredible experimentalists.

Alex Shalek: [00:08:08] It was just this group of people coming together to try and figure out how we could do more. And then from there,I got really interested for some of the stuff we were doing in our work over at the Ragon and how we could make these things more translationally oriented. I really like some of the basic biology, but my home department at MIT, I’m sort of joint appointed between the Institute for Medical Engineering and Science and the Department of Chemistry with affiliation at the Koch. But IMES really is sort of that bridge department between Harvard Medical School and MIT. So there’s really this biomedical engineering approach facing out into the community, trying to think about medical problems. And through my connection with the Ragon, where we were focusing on infectious diseases and kept on thinking these tools are really powerful, but they’re not addressing real world problems. They’re not having a major impact on human health. So one of my good friends and colleagues, Chris Love at the Koch, we tried to simplify some of the technologies to make them easier to use, limit the number of peripherals, make them simplified. And it turned out that was broadly enabling. And so we started teaching others how to do it. And all of a sudden, we were engaged in partnerships all around the world trying to apply these same technologies to address problems that are of global significance, but very often don’t get the same research time either because they’re not problems here in the United States or because practical parts associated with actually studying them is just daunting.

Alex Shalek: [00:09:29] Like the idea of doing a study of Ebola, which we recently did with a couple of people down at Fort Detrick, the idea of doing genomics on a BSL-4 pathogen in vivo infection like it was just really hard to figure out how you would make that all come together. But given this community as challenges arose, we just had the opportunity to pull from all of these incredible scientists that were around us and start to think about collectively how we would start to begin to tackle these things. A lot of that’s been great and it’s been nice to see the entire community come and start doing similar stuff in the context of COVID. If there’s any silver lining to what’s happening right now, it’s that so many people are working on similar problems, bringing together different vantage points and different trainings to really try and make substantial inroads simultaneously and open sharing of data of ideas. And hopefully it’s a model for everything going forward because I feel like it’s amazing what the scientific community can do when they come together as a collective to try and tackle problems.

Grace Ratley: [00:10:24] Yeah, I think the research really addresses this problem of accessibility with things like sequencing and when sequencing was first available, it took years to sequence and it was incredibly expensive and only two labs could do it. And the cost of sequencing came down a lot and more studies and more publications and more research and everyone working on these problems together. And you’ve really through the development of SQL, your single cell sequencing platform, you’ve kind of made that sort of research available to more scientists, which hopefully will enable more reproducibility within science and the consideration of all of the cell differences in systems research.

Alex Shalek: [00:11:09] So I hope it does. But it’s not totally selfless. What we’ve learned from doing all this stuff is that it’s important to develop tools and then to give them to people, make them accessible and and see what sort of the failure modes are, because that actually gives you insights into what the next generation of tools are. So I’ve been a big believer in this idea of you find biological problems, you try and figure out how to address them, use tools that exist. But if there aren’t good tools and you go work on new tools and then you apply the tools and you figure out what they can teach you, but also why they suck, for lack of a better way of saying it. And so I think this has been a constant point in the science. We’ve done in a number of others. And so even thinking back to some of the early three prime barcoding work that was done to make massively parallel sequencing happen in dropsy, Evan Macosko and Steve McCarroll and others did this incredible job of creating resources and putting stuff out and making everything accessible to the community and having help lines. And so in a lot of those things, we were trying to follow sort of best practices. But as we saw and heard that there were needs obviously pushed us to try and figure out how to make it simpler and easier, to make it easier to move to a clinic, to make it easier to move to another country.

Alex Shalek: [00:12:19] I think one of the things that is not lost on me is that there’s been this tremendous advance in the molecular techniques that we’ve been able to use over the last little bit. And if we think about where we were a little bit ago and where we are now, if you think about our ability to edit and manipulate the genome and our ability to record and profile single cells, I mean there’s just been this quantum leap in what we can do both on the measurement and perturbation scales. But on the other hand, it’s also created increasing inequities in the science. And that’s because as you were saying, it used to be two labs that could do these things and now more labs can do stuff. But there really is this incredible concentration of some of these techniques in places like Boston and San Francisco and parts of the UK and others. It doesn’t mean that others can’t do it. It just means that the rate at which science accelerates in some of these regions is different than in going and working with partners in other parts of the world. You began to see how important the problems were. You know, but it’s not really tangible until you go visit. I remember that Bruce Walker, who is the co-director of the Ragon, took me down when I first started at MIT to Durban, South Africa, to Cape Wrath, which is now the African Health Research Institute to see what was happening in research there, to go out into the community, see these places that were hit very hard by the HIV pandemic and by tuberculosis.

Alex Shalek: [00:13:36] And just to see like what sample collection looks like, what were the questions that scientists were trying to address locally. And it’s this incredible facility. It’s beautiful. It could be the same thing that you would have in Boston, and it stands it stands out in a lot of ways. But on the other hand, it just helped me to understand if we wanted to really deliver on the things we were writing in papers, like this is going to be a transformative technology like what it would actually take to make it a transformative technology. Met a bunch of great people there, you know, bonded with them, and started trying to figure out what does it take to really move the needle, to move these technologies there, to make it so that these research questions that are incredibly important, that aren’t getting the same attention as they should have the opportunity to really be pushed. And you might say, well why don’t you just do it all in Boston? A big believer that you have to create local capacity and empowerment and create an entire community and get many people involved, as I said at the beginning, like have some training in various places.

Alex Shalek: [00:14:31] And I’m sort of a jack of all trades, master of none. But the idea that I was going to all of a sudden tell people that were working on HIV research, that were these incredible luminaries that had these years of expertise, exactly what to do is foolish and ridiculous. And so it’s really about going down there, creating partnerships, figuring out what they need, going back and forth and trying to get people up and going. And so from that, that spawned into a number of different partnerships all around the world. But really, I feel like these technologies are as good as they are if we don’t bring them to bear on the problems that are important. We don’t make them accessible to everybody. It’s really not going to put us in the position that we want to be. And I think that another thing that the pandemic has taught us is that these things affect all of us. And it may be something that you previously could have said, oh, this is a remote thing that sits in some area. And maybe you read that one case happened in the United States. And something else I think back to like some of the Ebola outbreaks. But I think what this pandemic has taught us is how connected and interdependent we are and how important it is to not just focus on our local problems, but to also think about the importance of solving global ones simultaneously, whether it’s supply chain issues or vaccine creation issues or surveillance issues.

Alex Shalek: [00:15:37] And so excited by a lot of what these things have done. But it keeps on pushing me to think about how do we reach more people? How do we create more capacity, how do we get more people engaged in this and like I said, it’s not fully selfless. It’s one of those things where as people tell you this is why the technology has a problem. This is like the computational problem that we just need to solve. It’s an incredible substrate for research. I mean, there’s been really cool stuff that we’ve done over the last little bit along with others and trying to figure out how to use sequencing of single cells to figure out host pathogen interactions and co-dependencies. And a lot of that stuff comes out of conversations where people are saying, well, I’d love to know how the virus is hijacking some the cells. And you’re like, Oh, well, that’s a great computational problem. Why don’t we go try and put a little bit of time into it? And so I think what I’m trying to say and what I’m trying to emphasize here is that yeah this stuff is super powerful, but there’s this incredible value in networking and collaborating with the entire community, taking advantage of all of these people that think in very different ways and that can push you to tackle problems and to address things that are bigger and more important than you could have ever imagined.

Grace Ratley: [00:16:39] Yeah, and I’d really like to talk a little bit about your five step approach to studying systems biology like you do. Can you tell us a little bit about that?

Alex Shalek: [00:16:51] I think that my strength as a scientist if I was trying to be critical, is it’s this ability to merge together different fields and see how they’re going to mesh and understand particular problems and understand synergies. And I think in a lot of places, some of the ideas that I have in my head are very hard to bring across to others. And so in some places I’ve tried to distill some of them down. I mean, if I really think like fundamentally about what our lab is interested in and what we’re interested in is homeostasis in tissues. And so coming from chemistry and physics, like the idea of homeostasis or equilibrium is like this very basic concept that you would have been taught in high school and even potentially before that in like seventh grade science, Like you understand what that means. But if I ask you to say, what does homeostasis mean in a tissue? Like what does that mean? Like, what are the cells doing? What are the processes? Who depends upon whom? Like in the same way that you might think about your community, like what does it mean for it to be this state of of dynamic flux, but also stability? It’s this really hard concept. And then you might say, well, what are the things that disturb equilibrium? Well, those are the things that drive disease. And how can environmental factors whether it’s an infection or a high fat diet do that? And then you might ask yourself, well, how do you make a community more resilient? And those are things that we’re interested in, the questions that we want to ask, but the language to describe these sorts of things is very nebulous.

Alex Shalek: [00:18:08] I mean, I think there’s a lot of stuff we can learn from the social sciences and from what people have done in ecology. But there’s this approach to thinking about cellular communities that is hard to bring about. And I think similarly when I think about how we like to approach tackling problems, it’s hard to explain it perfectly. I don’t like to be too reductionist because I recognize sort of the multitudes. But on the other hand, it felt like it was easy to say, well, let me think about this in five pieces, that map very nicely, that chemistry and physics, which is like in chemistry and physics, we have this periodic table. We understand what the elements are, right? And so in biology, we really need to understand what the elements are. And so we always think about what are the identities of the cells in our systems. And we’re obviously not alone in this.

Alex Shalek: [00:18:50] There’s this entire grassroots international movement led by Sarah Teichmann, to do this human cell atlas that involves thousands of individuals across multiple different countries and continents and there’s a number of people that are interested in it. Once you know that there are differences, once you know that there are different elements of the periodic table, you might say, well, what are the characteristics that define those differences? And so we know if we look at the periodic table, it’s the number of protons and neutrons and electrons. But when you think about cells, it’s not quite so easy. And so you might say, well maybe it’s the epigenetic state, which could mean accessible chromatin, it could be marks, it could be methylation, it could be TCR sequence, it could be specific proteins. And so we’ve always thought about this first step of saying like, what are the things and then what are the things that differentiate them? So what are the identities and then what are the characteristics? But a lot of what is critical in biology, or at least in the human biology we like to study, is that these cells don’t work in isolation. We are very different than 10 to the 14 cells just hanging out in a pond. We have many different cell types that have these evolved social contracts where they’re dependent upon one another and we’re different, you and me. But we have roughly similar cells sitting in roughly similar places to a first approximation.

[00:20:03] So you have to start to think about these things that you would think about in the social sciences all the time. Well, how does where a cell sits influence its behaviors? And so we like to think about those environments, those fluxes, like what is the milieu which could in this case be like cytokines and chemokines and metabolites. And then we like to think about like, well, who do cells talk to? So much of what’s important in immunology, for example, is conversations among cells, whether it’s an antigen presenting cell, activating a T-cell or something along those lines. And so you might ask yourself, well what are the interactions that exist? And in physics and chemistry, these are the coupling constants, the way in which different things link to one another. And so it’s very intuitive. And then you might say, well now that you’ve started to think about what are the pieces and what are the things that define them. And what do they sit in and who are they talking to? The question really becomes, well, as you start to scale that out and you start to think about it, what is the integration of the synthesis of that? Like how do you get communities and what are the things that drive it? And then you start to say to yourself, Well, when you look at it like this and you recognize that there are things that are going to drive disease. Can you begin to think about what it is that’s going wrong? Is it like that you have too many of one particular cell type? Is there some sort of failed communication? Is one cell not doing its job and another cell is trying to moonlight and so it’s not doing its job as well.

Alex Shalek: [00:21:20] And so those are the sorts of questions we like to ask. And so five steps, it’s one way of thinking about it. But at least when I’m trying to describe to people like holistically, this is where our head’s at. It works. But then there are so many places where you could be like, well, the technology that you’re talking about, is it really this or this? Because you might say, well where are cells in space as a characteristic? But on the other hand, you could say it also tells you something about who it interacts with. And so it gets fuzzy. But I think having some mushy frameworks or some lampposts are a good place for people to begin to say, Oh, well I kind of understand what you’re talking about and where your engineering towards.

Grace Ratley: [00:21:55] Very interesting to me at least just to see it written out because I think a lot of us think about it, but we don’t necessarily put it on paper. So I really like that.

Alex Shalek: [00:22:05] It’s so hard to put on paper. We spent so much time like sitting there going back and forth, Are these the right words? This is the right way of doing it, because you’d be like, Here are the million and a half ways in which this falls apart. And at a certain point, you just have to be like, Look, this is not perfect, but let’s put it out there. Let’s get feedback. Let’s see how people feel about the ideas and the way in which we’re expressing them. And in the same way that you’re doing science and you’re getting feedback on the science you’re doing, same idea here you put out like this is the framework we’re thinking about and we keep on refining it and trying to get to it. And some times people will be like, Hey, this is what we’re thinking. And then you’re like, Oh, well, that’s a much better way of thinking about it. I don’t know why I did that or I like that concept or that word. Let’s try and fold it in. I think it’s all about the synthesis, but you have to put some ideas out there and be resilient and listen to people and hear what they have to say, even if it’s not necessarily the greatest things about the way in which you’re thinking about something to begin.

Grace Ratley: [00:22:55] Yeah, it’s really great. And you put a lot of thought into how you display your research. I have been a fan of your website since I saw it like a year ago. I think everyone should just go and look at it because you have put a lot of time into thinking about your approach to research the way that you frame the different components. And also I really enjoy the section on mentorship and diversity.

Alex Shalek: [00:23:19] Well, it’s not just me. We’re very lucky. We got connected with Dirk and Sigrid at SciStories and they helped us to do a couple of different things with graphics in the past. And then we were thinking about how to describe our science and put it out there and explain it. I think that one of the most important things about science is accessibility. I feel like if you can’t explain what you’re doing in a way in which somebody understands it, if you don’t understand it that well, and I think many people like to make science really hard so that they seem very smart. But when you deeply understand something, you should be able to explain it in a simple way and you should be able to explain the nuances, but you should be able to give a concept across. And I know that it’s so hard because we’re so trained to be very precise and not to let things be squishy. And I think that in a way that’s been a problem during the pandemic when people have come for information, because even my parents have ask me things and I’m like, well I’m hedging and I’m saying these various things and they just want to know should I be double masking or what should I be doing? Or is it safe? And so it’s pulled me maybe a little bit out of my comfort zone in some places. But I do think that making science accessible and making it so that people can understand what you’re doing, give feedback, give thoughts, get engaged becomes critical to this entire thing. We were talking about before this idea of doing stuff as a community, learning from the collective expertise and knowledge of different people.

Alex Shalek: [00:24:27] So we spent a lot of time on building pieces of the website and actually we’re due for a refresh. I’m about to start doing some new stuff now because the lab has evolved and when we started we really were going to do some model system work and all of a sudden, we’re doing a lot of global work in infectious disease and cancer and various inflammatory conditions. And a lot of the things we’ve done relate to outreach and empowerment and some of the stuff that I’ve done with others through the Human Cell Atlas is really focused on aspects of that helped to co-lead the equity group. And so there are new things we have to put up there. We have to do a better job of explaining who we work with around the world, making space and championing their voices and saying, this is the science that they’re doing and it’s great and showing where they are. So we could do better with that. I think that tried to create a lot of resources, but we could do a much better job in making those accessible, putting educational content out. I think when it comes to some of the pieces around diversity, equity and inclusion, I won’t take credit for any of that. I mean, I obviously have tried to contribute, but a lot of it comes from the team who spent a lot of time engaging with these various things.

Alex Shalek: [00:25:26] And we’ve been very thoughtful about this and we’ve been having a lot of ongoing discussions internally and externally trying to figure out exactly what we value and why and thinking about what we want to be known for and what’s important to us and trying to put some of that out. And we obviously have been inspired by a number of others and we tried to give credit where credit is due and some of the things on our website. But I think that normalizing discussions around some of these points and making it clear like, here’s how we’re thinking about stuff and listening to feedback and being resilient to challenges that may arise. It becomes important because we want a place where everybody thrives and where different ideas can come together and where you can collectively tackle problems from multiple different angles so that you make inroads faster and more effectively. I wish that more people spent more time thinking about some of these things. And I’ve been told I’m a little crazy when it comes to figures like exactly what colors need to be used and how it’s done. And I’m like, Well, what are you trying to show me with this? I do think it’s critical. You have to think about how somebody on the other end is going to react to it. And it’s nice when people say that they’ve enjoyed it. And I also enjoy when people are like, Hey, I wish you had done a better job of this because I’m like, Oh, I wish I’d done a better job of that too.

Grace Ratley: [00:26:28] Yeah. So I’d really like to go into a little bit more depth about your path to science. And I know you mentioned you came from a math and theory background and physics and whatnot, but take me back a little bit further. Like, when did you know that you wanted to pursue science or math? And tell me a little bit about that journey.

Alex Shalek: [00:26:47] If I’m being honest, I never thought I was going to be a scientist. I always enjoyed understanding how the things around me worked. As somebody who was a very curious kid, I liked taking things apart. I liked building things. All the things where you would have said, Oh, that that dude’s going to be a scientist. But on the other hand, I was interested in everything. I loved history, I loved literature, I loved philosophy, I loved basically everything I’d say. I was always involved in doing science, but it was never like, hey, this is my passion. It was just like, Hey, I’m pretty good at this. And I sort of used it to try and think of this idea of how to be a little bit more balanced. And so I’d focus a little less on science so that I could try and work on the things that I wasn’t as good at. I think back to this really funny thing, and I always wonder whether it was the right thing to do or not. But where I changed my advisor in high school from somebody who was a math teacher to somebody who was an English teacher, because I wasn’t doing as well in English as I was in math. And I was like, Well I need to go interact with somebody that’s going to help me and do these various things.

Alex Shalek: [00:27:41] And so when I went to college, I didn’t go with the idea that I was going to do science. I actually went to Columbia because I wanted this core curriculum, this broad liberal arts education, where I would take literature and art and music and be in New York and go to a concert and be at the Met and have this experience plus enjoying New York when I was young and dumb as opposed to old and dumb. But while I was doing it, I was taking all these science classes and I was enjoying it, but I understood it. And like it wasn’t one of those things where I was like really going deep into it. And as I kept on doing stuff, just some stuff started to grab my attention. And so I had done a little bit of work at Columbia when I was in high school. And so when I went in I sort of got to start in some advanced classes and so working my way up through physics. And I was like, Well my father had been a physics major, told me that the science that smart people do. So I was like, Oh, I’m going to go do this.

Alex Shalek: [00:28:31] And so I started doing it and started pushing my way through it, taking graduate classes in it. And it just wasn’t anywhere near as fascinating as some of the things that I was doing over in Art and I knew I wasn’t going to be an artist, but I was really enjoying learning about it. I realized that some of the things that made me dissatisfied with what I was learning in physics is sort of the areas that I was studying. And so I started looking at some of the other stuff that was going on at the time. Brian Greene had written The Elegant Universe, and he was at Columbia. So I went and started looking at like string theory and math, and I was like, Oh, this is great. Like, I can push all the math. I understand how to do it and differential geometry is really cool. And I understand all these things. I can think about these things I can do well in the class, but it was never one of those things that really just gelled where I was like this is what I’m meant to do with my life. And so I had also been doing stuff in chemistry because it was part of the things you needed to do if you’re going to be pre-med.

Alex Shalek: [00:29:18] And a lot of doctors in my family, I just figured that’s what I’ll end up doing. And I started taking these graduate classes in chemistry and it was really strangely enough, these classes on statistical thermodynamics and statistical mechanics that really caught my attention, that really made me interested in science and have in retrospect been sort of what informs everything I’d done since. Because those classes are really focused on how interactions among like individual atoms can work together to drive macroscopic properties that we rely upon. When you think about magnetism or what is temperature or what is pressure, those observables that you can see in, how does like the physics that I’ve learned actually do something that I see in the real world and also, I was like, Aha, I get it. And so that’s what I wanted to study. I wanted to understand how you go from something that is very fundamental to something that really you can understand. And I think in the biology research that I do strangely enough, I’ve ended back in exactly the same spot where instead of thinking about atoms and particles and how they drive some of these physical properties like cells, and how do those cells drive these tissue level properties. Because I can see and understand what happens when somebody gets sick. You understand this basic idea of dysbiosis and you can really get down to this like molecular precision of, Oh, there’s this mutation here. But then like how does that mutation ramify through the activity of specific cells? And which cells and how does that change the community and how does that actually result in what happens? It drives poor health or in some cases maybe more robust health. And so we’ve gone through that. And it was one of those things where the steps in between were very serendipitous and like how it ended up. There was always this recurrent thread of trying to understand how these fundamental pieces like really these building blocks came together to put stuff into the hole. And so I remember my senior year applying to graduate school, applying to finance and consulting. And until the 25th hour, I was pretty sure that I was going to not be doing science. And then with a nudge from my parents and a desire to explore stuff in my 20s as opposed to trying to be a responsible adult or whatever that means.

Alex Shalek: [00:31:24] I ended up going to graduate school and struggled in the beginning. I would say that stuff was hard, particularly going from this theory mindset to trying to figure out how to get stuff done because it’s a big difference between doing things in books versus actually trying to create and learn a lot of stuff along the way. Messed a lot of stuff up, but got to a point where stuff started working well and relied a lot upon the community to get the training I needed and tried to pay it back. And so it’s one of those things where it’s been a very nonlinear narrative that couldn’t have envisioned. But I’ve always sort of been interested in these bigger concepts, and I’ve been less worried about exactly how I do it. I’m sure that there are lots of different jobs that I could do where I could study these basic principles. And that’s really what I’ve been trying to focus on, it is like, what are the big things that will make me happy? What are the big questions I want to address? And those microcosms manifest in multiple different spots and science is a great place to do it. It also lets me do the education, empowerment, community engagement, the kinds of things that I wanted to do in medicine. It’s just a different place. And so I’m incredibly happy with what I do and better than I could have ever imagined. But it’s definitely not I would have imagined at the beginning.

Grace Ratley: [00:32:32] As we wrap up the episode, a question that I usually end with is what sorts of advice would you give to an early career scientist or someone entering just the field of systems, biology or genetics, or one of the many fields that you work in?

Alex Shalek: [00:32:49] Oh, it’s so hard. I mean, I have so many nuggets of advice. Most of them are hard fought wisdom by messing stuff up along the way. I think the first thing that I’d say is follow your data. I mean, a lot of places like I’ve really focused on what I’ve seen and trying to understand what it is like if I think back to all the single cell stuff that I do now, I saw heterogeneity. And rather than just assuming it was a measurement error, I was like, What is going on here? What does this all mean? And it led me down this rabbit hole or thinking adjacently when I got into doing immunology because we were developing these little beds of nanowires that we could use to record from neurons. The idea is that we want to study networks of neurons so we can study how the brain works. But in order to do that, you need a lot of electrical presence. So we wondered if we could make these very, very small little needles using nanofabrication to shove into cells and record electrical activity. And we found out that it actually works. The idea is acupuncture needles for cells, not lances. But when we did, it was like, Hey, what else could we do if we can poke a cell? And so that got me into delivering perturbations, which got me into studying immunology, getting back this idea of testing some of those correlations.

Alex Shalek: [00:33:54] So what I’d say is, first off, follow your data. I think the second thing that I’d say is always think about what you want to be known for and what kinds of things are important to you. I think in many places we focus on tangibles like papers as opposed to training or outcomes. What are the things that you personally want to develop for yourself and what are the things that are going to make you feel as though it was a good use of your time and that you were successful? And I never promised people that are interested in joining the lab that, Oh, you’ll get all these papers that will just bring down and it will be fantastic. I’ll say, really my goal is to figure out what you want to accomplish, figure out how to mentor you towards that and to work with you to get to where you want to be. And so I think that if you can think of like going into science, this is an incredible opportunity to pursue something that you’re passionate about and just enjoy the experience and not get caught up in some of the rivalries and complexities. Competition is good. It drives innovation, all those sorts of things.

Alex Shalek: [00:34:48] But I really like the idea of collectively solving problems. And I recognize that I sell these things from a privileged position that it’s hard for many others to view these things in a similar way. But it still comes back to this idea that you should really focus on making sure that you’re doing things that develop you towards the person that you want to be and solve the kinds of problems that you want. I think also there’s too much emphasis on exactly the right problem or exactly the right thing. Science is one of those lifelong journeys where you keep on learning new skills and bringing them in. And so maybe you learn how to do some fundamental work in one area and to write papers and to do grants. You don’t want to overemphasize any piece. And the other thing I’d say is like, you only have one life to live. So don’t get caught up in these externalities of, oh, this is the system, this is how we have to do it. There’s always this idea that if you decide you don’t want to do graduate school that you’re failing or washing out. I don’t think that’s true. I think it takes a maturity to recognize that it might not be something that you want. And academia is great, but I’ve seen plenty of people go on to do biotech companies and do incredible things.

Alex Shalek: [00:35:47] I’ve seen people decide in the middle that they wanted to go to medical school and do great. I’ve seen people just leave to go do all kinds of stuff. And so I don’t think there’s any one path toward success. I think it’s really about taking the time and space to figure out what do you enjoy, why do you enjoy it and how should you do it. And I’m not saying that you don’t need a little bit of resilience and you don’t have to work through hard things because science sucks. I mean, it’s constant failure. I mean, if I think about it, I was describing this just the other day. It’s like the majority of it is here. It’s like you’re at 0% and everything’s failing and then you get a little blip and it feels this incredibly minor change. But on the other hand, it’s like an infinity percent improvement. You went from nothing to something and then getting up to 100 is like so much easier because it’s a small little jump. And so you have to recognize that everything’s always going to fail and it’s always going to be problematic. But if you love it and you love the discovery science, it’s great. That’s what I’d say. It’s really important to collaborate with people and to network within the community.

Alex Shalek: [00:36:41] Science is not like this, like intellectual pursuit where you’re supposed to be just by yourself working in this little room. And I know it works for some people, but all the really good things that I’ve seen in science or that I’ve been involved in have always involved people coming together, tackling problems collectively, supporting one another, building community. And so making some of that a little bit more clear like that. There are different ways of doing things and that they’re equally valuable and figuring out ways to reward that are critical. There are plenty of people that will tell you how bad this, Co-first author thing is with a specific order as opposed to something that randomly shuffles and highlighting that everybody can do it equivalently. I really liked a paper that came from I think it was Garry Nolan’s lab where the order was settled by a video game contest. So it was whoever won ended up in the first position of the Co-first authors. But it’s just one of those things that’s so hard. I’d say in the same way as many others would be like, think about the community that you want to be in and then think about how to be an active participant in trying to create it. And that involves outreach and engagement. And in a lot of places doing stuff for others and just being a supportive individual. There’s a ton of stuff that I’ve done that has not yielded anything, but there are times I’ve done things that I didn’t think were important at all. But they were meaningful to others, and they’ve come back to be incredibly important and transformative.

[00:37:55] I remember I helped somebody do something in graduate school. I wasn’t sure it was the greatest idea, but they were keen and I was like, I would love to help you because people help me. A few years later, when I was interviewing for a grant, that person had left science but was now working at a specific foundation and was on the other side of the table. And so when they were trying to figure out who they were going to fund, they were like, That guy’s very smart and you should support him. And that’s led to some incredible partnerships with people all around the world and a lot of funding. And so it’s one of those things where there are all these places where we call it karma, call it whatever you want, comeback and you never really know. But always err on the side of caution of being just a good dude. That’s just what I like to think of that. Like fundamentally, you just think about the community that you want to be part of and think about how you can go about making it as such.

Grace Ratley: [00:38:41] Well, thank you so much for joining me today Alex. I had an excellent time talking with you. You dropped some incredible wisdom and yeah, I hope you have a great rest of your day.

The Bioinformatics CRO Podcast

Episode 47 with Jamie Smyth

Jamie Smyth, Associate Professor at Virginia Tech, discusses intercellular communication in the heart and how viral infection of cardiac cells can result in heart disease. 

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Jamie is Associate Professor at Virginia Tech, where his research aims to define cardiomyopathy at a subcellular level, searching for potential targets for therapeutic interventions to help restore normal cardiac function to diseased hearts.

Transcript of Episode 47: Jamie Smyth

Disclaimer: Transcripts may contain errors.

Grace Ratley: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley, and today I’m joined by Jamie Smyth, who is Associate Professor in the Department of Biological Sciences and the Fralin Biomedical Research Institute at Virginia Tech. Welcome, Jamie.

Jamie Smyth: [00:00:14] Thank you. It’s a pleasure to be here.

Grace Ratley: [00:00:15] It’s a pleasure to have you. So can you tell us a little bit about the research that you’re doing at Virginia Tech?

Jamie Smyth: [00:00:21] Certainly. So. My work is primarily focused on the heart, but really, I consider myself a cell biologist and virologist who’s very interested in how cells talk to each other. So all cells communicate directly and in the heart. That’s actually particularly important because that’s how electrical impulses are orchestrated and propagated throughout the cardiac tissue to during every heartbeat. And unfortunately, we know in pretty much every form of heart disease, it’s alterations in how these impulses are communicated within the tissue that lead to arrhythmias, a sudden cardiac death. So we’re really trying to understand how cells communicate with each other, how they set up these connections appropriately, and how these connections are disrupted during cardiomyopathy, conditions of stress ischemia, but also how viral infection can affect this and how viruses manipulate intercellular communication outside of the heart and in the heart, and how that can lead to sudden cardiac death, too.

Grace Ratley: [00:01:15] Yeah. And so you in particular look at things like gap junctions in cardiomyopathy. Can you tell us a little bit about that research?

Jamie Smyth: [00:01:24] There’s many ways that cells can communicate with each other. And we know that gap junctions are the primary direct means by where this occurs. There are mechanical junctions which allow for mechanical communication between cells, but gap junctions actually provide both a mechanical connection and a metabolic connection. And that’s because they actually join the cytoplasms of adjacent cells directly. So they create a channel, a conduit between the two cells where small molecules can pass. So in the heart, for example, these would be ions for electrical impulses, but also second messengers can go through. So like in other tissues, cells can signal to each other electrically or not to propagate signal transduction and what’s really interesting is that in the immune system also utilizes gap junctions, both the innate and adaptive aspects of the immune system. So innate wise, our cells have an intrinsic, innate response to things like viral infection that activates the interferon response, and this can actually be propagated to uninfected neighbors through gap junctions. And then also it’s been shown that short peptides can go through gap junctions. So there is a size limitation, but also the secondary structure limitation. So long as they would be linear and it’s thought that an infected cell could communicate viral peptides to an uninfected neighbor, that uninfected neighbor could present that peptides to a cytotoxic T lymphocyte, for example. And so these are two ways that gap structures can propagate the immune response. But also surveilling cells can also hook up to other cells via gap junctions. So they’re n all tissues and they’re surprisingly dynamic, which is why in the heart it’s particularly important that we understand their biology, because when the tissue gets stressed, we see a rapid remodeling of these gap junctions and that’s where we can see these electrical disturbances. And so if we understand how they’re regulated, hopefully we can figure out how to get them back where they’re supposed to be without opening up somebody’s chest.

Grace Ratley: [00:03:09] Yeah, that is really interesting. And what sorts of viruses do affect cardiac function?

Jamie Smyth: [00:03:15] Several viruses have been attributed to what we call viral myocarditis, which is basically when the heart becomes infected and or inflamed and the viruses that affect it are broad. But the two main ones that I would say that crop up the most are coxsackievirus and also actually adenovirus is another one. But a lot of viruses, there are cases of them being found in cardiac tissue that aren’t normally attributed to that. It’s typically pretty devastating when a virus gets into the heart and infects the tissue. And the thing to think about there is that a virus has not necessarily evolved to infect the heart. Its goal is not to kill the host essentially that way. So it’s more that when either a person is predisposed to this or for whatever reason, the virus does have a tropism for the tissue that we see this happening. And there’s various stages to the disease. They are acute where we can have an infection process where the virus is causing damage by what it’s doing, and then the immune system can come in. And unfortunately, we can see in a more chronic situation, it’s actually the host immune response that’s doing a lot of damage to the heart. And we can end up with a heart failure situation, which why I find the heart is such a fascinating organ to study because people tend to think about heart disease as an inevitable process of aging. But it’s more to do with the fact that I think the heart is this exquisitely dynamic organ that’s constantly responding and changing. So unlike other muscle in your body, heart muscle is made up of individual muscle cells. And so they’re constantly changing and responding to stress and how they’re communicating with each other and how they’re contracting. So that’s the disease process of myocarditis is the virus could be gone, but the remodeling has started and that’s the problem.

Grace Ratley: [00:04:54] So what are the primary endpoints of your research? Are you looking to prevent cardiomyopathy is from arising when someone has an infection? Or are you looking to just do basic research on how the heart works on a cellular level or develop therapies for cardiomyopathies? What are the primary endpoints?

Jamie Smyth: [00:05:15] The lab really mean, it spans all of that in a way. The primary endpoint is of course yes to develop therapeutics to hopefully correct cytological disturbances in the heart. A large portion of our work is on how the proteins that make up junctions are synthesized. They’re called connexins and how they’re translated as interesting. And that’s as a virologist what led me to that was because viruses play tricks on how proteins are synthesized to and so there’s a good overlap there and how we interrogate that biology in terms of the viral aspect of it and viral myocarditis. There’s a great need for understanding the mechanisms of that disease process. And so how and why certain viruses infect the heart, how direct infection contributes to cardiomyopathy versus that host immune response. But also, as we understand the cell biology of how viruses manipulate intercellular communication. Then there’s the thought of perhaps antivirals could come in there as well. So as I mentioned earlier, these gap junctions that in your heart are communicating electrical signals. They also propagate immune responses and antiviral immune responses. So it makes sense that viruses would target these structures as they’ve evolved. And that’s indeed what we’ve found. And while this in an epithelial situation can be, whatever irritating, give you a cough in the heart, that’s going to be devastating and potentially deadly. Then the other thing to think about is that if the virus is targeting a particular structure in the cell or hijacking it or changing it for its own good. It’s doing so probably in the most efficient way possible. And so we can identify the critical signaling hubs in the cell that are regulating gap junctions by seeing what the virus is going for. And then we go beyond viral infection. So we use the virus to tell us what to look for if we want to therapeutically manipulate gap junctions. So that’s where big picture end goals of the lab, really.

Grace Ratley: [00:07:08] So tell me a little bit about how you got interested in cardiomyopathy and everything.

Jamie Smyth: [00:07:14] Yeah, this is where I like to speak to trainees as well about not necessarily having your life planned out meticulously in front of you and that with science, you don’t know what opportunities or interests will come your way. I did my undergraduate in University College Dublin in microbiology and the way that was structured was that you, I did science to start and then you have four subjects and then you specialize every year. And by third year I’d really got interested in microbiology and virology. And then the only time in my life my name was picked out of a hat was in fourth year of our undergraduate, where we had a chance to do a research project and I actually got to work on HIV, trying to identify different serotypes of HIV in the Irish population. And that was just an incredible experience for me to work in a virology lab, but also with actual human samples et cetera. So then hooked on viruses. I did my PhD then on an RNA virus and Trinity College, Dublin that was using these RNA viruses, Alphaviruses, to actually elicit anti-tumor immune responses. So as opposed to virology per se, it was more like using a virus for what we call oncolytic. So basically trying to use viruses that have been manipulated so they’re not necessarily going to replicate the same way to treat cancer and basically elicit the immune response against cancer.

Jamie Smyth: [00:08:34] And then that actually took me to my postdoctoral training at University of California, San Francisco, still focused on cancer biology and virology. And that actually is where adenovirus came in and I worked with Clodagh O’Shea and Frank McCormick on how adenovirus would manipulate the DNA damage response. And then also they’re very interested in developing adenovirus for Oncolytics. But then is when partially intentionally, partially not intentional by people moving to different cities and my personal life not making me want to move to different cities and other opportunities arising actually switched fields into the cardiovascular space. And what was really attractive to me was the fact that as a virologist and as a cancer biologist, up to that point, I got my head stuck inside the cell. So I was always imagining the signaling pathways going on inside the cell and the infected cell. But I wasn’t thinking about how that’s not biology. So it’s lots of cells talking to each other and some of them are infected and some of them are not infected. And that whole, how cells communicate and how that biology really got interesting to me. That’s when I switched into the cardiovascular space and gap junctions.

[00:09:38] And this is where it was quite a challenging time because I was changing fields. But then in retrospect, it was really good because I came at cardiovascular cell biology from an epithelial cancer background and virology. So I had a different angle than other people. I was very fortunate my advisor, Robin Schulz, we basically were able to ask some questions in different ways, and I think that’s really good. So I always say to people, if you find yourself in a situation where things are changing and not necessarily how you plan prior to that, I’m yet to have that happen to me and not look back a year later and go, Thank God that happened. Because things are better. So then that’s when I worked mainly on gap junction biology, how gap junctions are formed, how the cell puts them, where they’re supposed to go, how that’s changed in stress. But all the time in a background was keeping on this virus work that I was just fascinated by. Some viruses like RNA viruses, their life cycle is quite rapid, like SARS-CoV-2 as well. Once those positive sense RNA viruses, once that RNA is in the cytosol, it’s ready to go.

[00:10:42] But then DNA tumor viruses like adenovirus have a longer life cycle, coronaviruses, too. But adenovirus is definitely was saying there’s no way they’re going to leave gap junctions the way they are. So I’ve been looking at that in the background. And then we hit on the translation work, which basically got me at the point where I was ready to start my own team. When I went to Virginia Tech, that’s when I started reintroducing the virology. So we’re working on how gap junctions are synthesized at the translational level. We have some very fundamental work there where we’re just looking at how ribosomes are certain RNA binding proteins bind to RNA, where Connexin is this wonderful tool for that, then translating that to heart disease, but then also how viruses manipulate that, how viruses mutate directly. And then a great pleasure of it has also been able to fall back on the cancer background where now actually actively collaborate with a bunch of colleagues here at Virginia Tech working on cancer biology and gap junctions where they’re very important. Also a full circle on where I started, where I’ve gone from virus to cancer to heart. And now all of them are a key part of my research, which is really rewarding.

Grace Ratley: [00:11:41] I imagine it is. I feel like one of the issues in academia that’s quite common is people go into their PhD in a particular subject and then they do postdoc in that subject and then after postdoc, they start their own lab in that subject and it’s you just get more and more specialized and it’s very difficult, I think, for people to change subjects. How do you think that we could maybe encourage people to look outside of their particular niche and feel a little less stuck? How could we support people in transitioning into different fields?

Jamie Smyth: [00:12:17] Right. I think part of the reason why academia is so research focused is that we need fresh minds coming in. Because people get affected by dogma. And so you have these people asking questions that you would never ask because you think they would never work. And then you’re like, oh, hang on, actually, maybe go for it. So think outside the box and expose yourself to such things I would say. Whatever training institution you’re at would be to seek out. Sometimes there’s common things like Research in Progress seminar series. I know at UCSF where there was one of those and it would be quite diverse presentations from different departments and institutes. And that’s actually when I started hitting on the translation initiation work in the cardiovascular institute that I was in. I was hitting a wall in terms of getting some ideas of what was going on and then I put myself forward for one of these rips, which were common to UCSF. So all postdocs from all over the place. And then some of the cancer team were looking at similar stuff and they were able to provide reagents and advice. And I met with them and that showed me that actually that’s what I like about my research institute here, is that it’s not an institute. It’s a biomedical research institute. So rub shoulders with neuroscientists and cancer biologists and structural biologists and behavioral scientists. So we’ve had many instances where a conversation over a beer after work has led to a successful grant and publication between a cancer biologist and a neuroscientist. That’s the way to go, I think. Just be open to it and put yourself into those situations, even though they’re scary, especially as a trainee. I think that’s what I would say.

Grace Ratley: [00:13:52] It’s a good advice. I feel like with the pandemic, a lot of people did make transitions into looking into virology and seeing how they can support it from an engineering perspective or from a cancer, I don’t know perspective. And I hope that it leads to more openness within academic science to explore other fields and to build collaborations with people in different disciplines of biological sciences or beyond biological sciences. And viruses are an excellent tool for that because there are some people doing crazy things with viruses, building batteries and using them as vectors for therapeutics and just really amazing.

Jamie Smyth: [00:14:35] Yeah. It’s pretty humbling biology, the virus and so that’s what’s kept me hooked on being a virologist is just how it’s just information. But it can hijack something as exquisite as the human cell. So it’s sometimes very little piece of information that’s, again, the power of using them to understand our cell biology, because they only have a limited amount of material they can bring in to take over everything.

Grace Ratley: [00:14:59] Yeah certainly. So how did you become interested in science? Were you a science kid growing up?

Jamie Smyth: [00:15:06] I was always interested in science, very much so. When I look back, I would never build what was supposed to build with a Lego. I would build some kind of machine or something. And then I was always outside digging up stuff and looking at insects. I lived grew up beside the sea. And that was pretty cool to see the life there. I was also actually quite into art, and as a teenager I found myself not really knowing what I wanted to do because I had so many interests. And I think that was difficult, but at one point my parents brought me to a guidance counselor and they basically said that. They were like, Yes, you have parts, you’re just interested in everything. And so I just made the decision to do science then because I just figured I loved the art and everything, but it wasn’t going to be a career for me. My father was an architect, so that was interesting and I always thought about doing that. But then my sister did science and I just got really, really fascinated by what she was saying, which is coming out from college. And the other good thing again, I think I said this earlier about the way it was structured in University College Dublin was the first year you did biology, chemistry, physics, computer science, or some mix of those broad. And then the second year you chose microbiology specific subset of chemistry, biochemistry, and then the third year microbiology and then fourth year microbiology. And so it was I knew going into it I could feel out in the first year of what I was interested in. And then it was that virology project in fourth year that took me to the next level, but nothing specific except that I just found the world a bit fascinating.

Grace Ratley: [00:16:37] Yeah. Just slightly reminds me of the path of Santiago Cajal, and he was a really interested in art as a kid, and then he started exploring connections between cells. I mean, that’s what he got the Nobel Prize for was synapses and gap junctions and things.

Jamie Smyth: [00:16:54] I think one of the things people don’t realize is that a lot of scientists have a creative side and interest so they’re either very much interested in the arts or have some kind of artistic outlet, be it music, be it art, remarkable number of my colleagues. And that’s because I think science you need to be able to have that creativity to connect things. And it’s not just learning things off by heart and this dull studying thing. It’s everybody’s stepping into this black box and you have to imagine and make a leap, connect these distant things like I said and I think having a bit more of a creative background or a creative thinking enables that process in a way that I don’t think people who are not scientists appreciate or understand sometimes.

Grace Ratley: [00:17:41] Certainly, that kind of brings me into this piece about science education and science outreach. And you do a little bit of that with goodwill. Can you tell us a little bit about that work?

Jamie Smyth: [00:17:54] Sure. That’s one of the things I’ve enjoyed about moving to Roanoke here with the research institutes is the accessibility of the community. So organically, the Research Institute has developed various ways of maintaining community interaction from having open lectures, public lectures, etcetera. But then at the younger level, there’s opportunity to expose children to what’s happening in their own city. That’s pretty exciting and hopefully inspirational for them to stay in STEM. And I spoke at a Cityworks. It was called it was an event about urban development and it was Irish scientists and French scientists doing in Roanoke. Let’s go to the premise so we’re just talking about the perfect setup here for doing this. But from that came a relationship with Goodwill, where they saw that talk and they actually run a science camp in Roanoke every summer and so invited me to go out and give a talk. These are children between the ages of about 8 and 12 years of age, and it allows us to showcase what we’re doing at the Research Institute, talk to them about careers in science, talk to them about our journeys as scientists and what took us here. And then we’ve also had the opportunity since then to develop that and actually bring them to the institute, rather than me just going out and talking to them for an hour and they get a full tour of the research institute, get to put their hands on a few microscopes, see some cells beating down there, and then also be exposed to different – me, but then my colleague Sami and his cancer research, Sarah Parker and her simulation lab about which studies how teams operate in hospitals, the idea being to also show the various career paths in STEM.

[00:19:31] So it’s not just academia and becoming a professor. There’s a bunch of other ways. If you train in that field, you can have a really fascinating career and contribute to society. That’s something that we’re still working on developing in terms of being able to maintain relationships with these children. And then as they get into high school, level up, bring them into the labs. We have relationships with some high schools here in Roanoke. I usually have 1 or 2 high school students in the lab. If we get them at that young age interested. But then you’ve got to maintain that contact. Then hopefully get them back into volunteer a little bit later. And this is hopefully going to contribute to not only building the relationship with the community here in Roanoke, but also diversify the workforce more, which is everybody is pretty keen on and supporting these days so as last.

Grace Ratley: [00:20:14] Yeah, those are experiences that those kids are going to remember for the rest of their lives.

Jamie Smyth: [00:20:19] Yeah, we do. Also, we do run an undergraduate program in the summer too. So we have a really good array of imaging equipment at the FBRI and a lot of local universities around here, sometimes it don’t have that kind of material to train undergraduates on and or prepare them for the graduate level if they’re interested in that. So we do a ten week summer program on molecular visualization, and we have students from all over America come into that, some from Virginia Tech, of course, but also local colleges that again, like I mentioned that wouldn’t have those resources. And it’s been really rewarding to see that those programs really work. And we see all of the students, the fellows move into either medical school, but also a lot of them doing PhDs now as well. And that facilitated that. That’s another further stage up, but also important I think to intervene at the undergraduate stage too.

Grace Ratley: [00:21:12] Yeah. And I imagine it’s very fulfilling given that you were inspired in your undergraduate research experience to pursue the field that you did. So it’s great to be able to reach back and pay it forward. What advice would you have for students more at maybe a graduate level or postdocs, these people who are pursuing a career in research?

Jamie Smyth: [00:21:34] Yeah, I think like I said earlier, be careful about closing doors in terms of being too focused on one particular thing especially as the graduate student postdoc thing. It’s a very difficult time. It’s a time of great uncertainty and it’s a time of intense pressure and burnout and anxiety. And some of that anxiety comes from not knowing what’s next. And so definitely, if you know what you want to do, you can focus in on that and make sure you build the appropriate for that next step. Think about you only have a certain amount of energy and make sure that whatever you’re doing is going to be measurable and develop a product that will contribute to you achieving that next step. You can often get sucked into a lot of different things, some of which are not going to appear on your CV. So mean if you want to do academia, definitely papers and grants. I mean, that’s it. First and foremost, everything else is icing. When I look at the CV, the first thing I look at the papers and grants and make a decision and then we look and see other parts of that CV. For PhD students also, I would just say get your PhD. So it’s remarkable. It’s just what you’re doing going to get your PhD. It’s great to get involved in a lot of different things, but again, make sure you’re on track with your committee, etcetera and getting that PhD because that’s ultimate goal.

Jamie Smyth: [00:22:50] You can change the world after that. And then for postdocs, this is where I’ve seen all of my friends over the years either go into academia or industry or something different. This is where I think there’s a lot of imposter syndrome. There’s a lot of that anxiety still. But I mean, part of the career is this 4 to 5 year installments of not knowing what’s next, like I said. And so for again for postdocs, it’s about not closing doors, applying even if I’m not being asked to, I would apply for funding. And building a network is huge and putting yourself out there and presenting at every opportunity you can. And like I said back at UCSF, if there’s an opportunity to interact with diverse scientific groups, do that early in my postdoc even before then. But certainly when I started the postdoc at UCSF, I had a constant sense that I was never going to become a PI and I didn’t have what it took. And then I did have an epiphany when you just suddenly realize that this is so daunting. Everybody is in the same boat. Nobody understands everything. This is where like working with people to achieve the project is what’s just now everywhere. So you can’t as a scientist basically do everything. You can’t be stuck in the corner doing your own projects and expect to move as quickly as people who have actually reached out and felt that way.

[00:24:13] But also I think just the concept, the imposter syndrome really understand that no, these people don’t know all that stuff you think they know. And that everybody’s struggling, I think is something that was really important to me and helped me have the confidence to keep going. And then the other thing is in terms of early career faculty, one of the biggest challenges that I’ve found was balancing grant writing with paper and manuscript publication. So you get stuck in this cycle of generating data for the grant and preliminary data, and then you’re not necessarily producing papers. And then there comes a point where the reviewers for those grants are going are looking for papers. And so it’s that balance is actually critical for early career assistant professors to make sure that productivity is up while still trying to get funding in. And that’s something that I think time management is important for and what is certainly also lacking in the field. That would change if I could and I think is changing is an increased focus on providing management training for academics. They train people to publish papers and write grants, but not have to deal with six different personalities and keep them productive. And each of those personalities is valid even if they’re very different to you. And so doing some training in leadership and management, I think is something that a lot of people should think about doing as postdocs, regardless of what career they end up in.

Grace Ratley: [00:25:33] That skill is very important, I think in all career fields, not just science. Yeah, and I do wonder why imposter syndrome is so prevalent in science. I feel, I mean yeah, it exists everywhere. But I feel it’s especially pervasive within especially academic science. Do you perhaps have any thoughts on that?

Jamie Smyth: [00:25:56] I don’t know either. I do think that there is a feeling in academic science to portray yourself in a way that can make others feel give other people imposter syndrome. People are so scared of showing weakness or whatever, but I would like to think that that is changing.

Grace Ratley: [00:26:15] Yeah, it’s definitely interesting. I guess the constant need to justify yourself and your science and I every time you start a conversation with someone, they’re like, okay, but why is that important? Why do connections matter?

Jamie Smyth: [00:26:30] Right. I think that’s right. So that actually the culture of training is very critical. You’re constantly being questioned and criticized and it’s all part of the training. And it’s now, as somebody who’s evaluating students, I understand it’s not about necessarily them expecting them to know everything, but it’s about trying to understand how far they’re taking their thinking. The process of getting there I think involves growth. The reason I’ve stated it’s so rewarding when you see a trainee, be they from a technician or high school students who are graduates to a postdoc who’s when you just see growth and you see that development and being part of that and helping them get to the next stage is why I’ve stuck in academia. I think more than anything, it’s an interesting career, but it’s incredibly rewarding looking back. The things, the freedom is pretty great despite all the pressure and competitiveness.

Grace Ratley: [00:27:25] And how do you cope with all of the pressure of getting grants and publishing or perishing and all of that?

Jamie Smyth: [00:27:33] So I guess this would again be going back to what to say to trainees. And it’s probably such a cliche, people hate hearing it, but I don’t take it personally anymore. I’ve learned to understand criticism and identify constructive criticism and not be emotional about stuff. And so it’s really about seeing the end goal. So with grants, yes, there’s a lot of pressure, but it’s part of the system and it’s actually a really good part of the system after a few years of being in it. Now as a PI, I understand the importance of being made to distill your ideas into a way that you can communicate them to your peers and ask them to fund your work. So the process of grant writing I now have tried to transition in my head from this horrible, stressful thing to a way of distilling my work. The other thing is I think it’s important to protect time for yourself. So in terms of time, management is very difficult in research because cells don’t know that it’s Sunday, but you can plan your work to protect time where you really need to step away from it because otherwise it just all blurs into this horrible, stressful mess and nothing gets done. And so I think it’s very important to totally step away for periods of time and then refocus. And that’s where extracurricular activities like running or sports come into it and or art or whatever it is that can stop you from being in it.

Grace Ratley: [00:28:57] Certainly and do you still create art? What sorts of hobbies do you have to help you step away from work?

Jamie Smyth: [00:29:04] I don’t do much art anymore except figures for my reviews, but I run, which I really enjoy. That’s what switches me off. I also enjoy a lot of here. We’re very fortunate to have some pretty beautiful hiking around Roanoke, and the same was back at obviously in California. But the great thing in Roanoke is it’s just outside my door. We enjoy hiking, dog. Did you just hear him bark?

Grace Ratley: [00:29:29] So perfect timing.

Jamie Smyth: [00:29:31] What else? Love food, cooking. When there’s not a pandemic, very much into traveling.

Grace Ratley: [00:29:37] That’s great. Do you have any thoughts on where you hope that your field is going?

Jamie Smyth: [00:29:43] One of the misconceptions I think from non virologists, scientists sometimes is that when a cell is infected, it’s just dying, whereas it’s not. It’s altered and it’s turned into the living state of the virus where it’s being manipulated to make more virus and I always remember somebody who is not a virologist, I was showing some image of a nucleus of an infected cell. And they were like, Well, that’s just a mess. And I was like, No, that is an exquisitely repurposed cell that I really hope non virology people get that into their head, that basically cells aren’t just dying. And it’s amazing cell biology that we can understand how cells work during this process and then just how humbling this is and how difficult it is to sometimes think about connecting these processes together is where I like non-scientists to understand what an amazing, creative and fun career this is. And if people are daunted by science or could never do that, it’s absolutely not true. And it’s something that you just need to be committed to and enjoy. People hear you’re a scientist and they suddenly get all, Oh, you must be so smart, etcetera. And that doesn’t speak to me in any way. So I think it’s more of a passion about questions and being humbled by biology than feeling that way.

Grace Ratley: [00:30:58] Thank you so much for coming on the podcast today Jamie. I had a really excellent time talking with you and thank you so much.

Jamie Smyth: [00:31:06] All right. Take care of yourself.

The Bioinformatics CRO Podcast

Episode 46 with Amar Gajjar

Amar Gajjar, world-renowned neuro-oncologist and chair of the Department of Pediatric Medicine at St. Jude Children’s Research Hospital, discusses emerging treatments for pediatric brain tumors such as medulloblastoma.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Amar is Chair of Department of Pediatric Medicine at St. Jude Children’s Research Hospital. He is principle investigator on several clinical trials aiming to treat brain cancers like medulloblastoma and improve the quality of life for patients in recovery. 

Transcript of Episode 46: Amar Gajjar

Disclaimer: Transcripts may contain errors.

Angel Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Angel Belgard, Chief Medical Officer at the Bioinformatics CRO and today I’m joined by Amar Gajjar, chair of the Department of Pediatric Medicine and the Scott and Tracy Hamilton Endowed Chair in Brain Tumor Research at St. Jude Children’s Research Hospital. Welcome Amar.

Amar Gajjar: [00:00:20] Thank you Angel. Good to be here.

Angel Belgard: [00:00:23] It’s good to have you. So working in St. Jude in pediatric brain tumors, it seems that much of your research focuses on pediatric medulloblastoma, which is among the most common malignant pediatric brain tumors. Can you tell us a little bit about this cancer?

Amar Gajjar: [00:00:41] Yeah. So when I started, I joined the faculty that was charged given to me is develop a medulloblastoma program. And that’s what I focused my academic career on for the last almost 25 years. So this is a tumor which occurs in the entire spectrum from infants to the pediatric age group to adolescent, young adults, and also very rarely in older adults. We used to treat this disease as if it was a single disease entity. But what we’ve learned painstakingly through all the work that St. Jude investigators and investigators in other big centers like Toronto and Boston, in the U.K. and in Germany have demonstrated that actually medulloblastoma is not a single disease, but a compendium of four very distinct molecular diseases, which have very distinct cells of origin, distinct molecular changes and very distinct outcome based on a treatment. So we’ve really built on that theme and really refined and continue to refine our risk model because it has allowed us to reduce therapy for patients which have good outcomes, which do not require high doses of radiation therapy, so that we can not only just cure them, but leave them neuro cognitively intact so that they can complete their education, go to college, live independent social and financial lives. So it’s a dual mission, curing children, but also curing them in a way that they can be functional adults.

Angel Belgard: [00:02:25] Certainly both of those are important with the molecular subsets that you see. How closely do those line up with the clinical presentations?

Amar Gajjar: [00:02:35] The clinical presentation and the molecular subset based on the imaging, one subgroup, the sonic hedgehog subgroup, the tumors are hemispheric in the cerebellum. The Wnt subgroup, there’s an 85% are in the fourth ventricle. There’s 15% of them are in the angle or CP angle as we call it. And then we have group three, group four, which don’t have any predilection for site. But we can always say when a child shows up in the ER and has a large hemispheric tumor, it will be hedgehog. If it’s midline tumor or a CP angle tumor in an older female, it’s going to be a Wnt tumor. So you get a little bit of inclination on the clinical presentation. In the infants, majority of them are going to be sonic hedgehog. About 65% of them are going to be sonic hedgehog. About the remaining 25 to 30% will be group three. So you start at least getting an idea of what you’re going to be dealing with before you get the tissue in hand.

Angel Belgard: [00:03:42] And prognostically my guess would be that there some delineation with that, too.

Amar Gajjar: [00:03:47] Yes. So the Wnt tumors generally do very well. The sonic hedgehogs based on their molecular features, there’s a group which does very well and a group which has inferior outcome. So it’s not as simple as just the label. It’s again, you dig deeper into the molecular characteristic of each subgroup and then you have to look clinically whether the tumor has spread, whether it’s metastatic disease, whether they could get all the tumor out at the time of surgery. So then you start drilling down deeper and deeper until you come up with a comprehensive risk stratification.

Angel Belgard: [00:04:20] Tell us a little bit about the risk stratification and how you were able to come up with the adaptive radiation therapy for the different groups.

Amar Gajjar: [00:04:29] Currently we are using the lowest dose of radiation therapy for the Wnt tumors because they do best. We’re using an intermediate dose for the ones which are molecularly and clinically good risk. And then we use the higher dose radiation therapy only for the patients which have got high risk disease or metastatic disease. And that study is the first in North America actually in the world. And we are watching the numbers very carefully. The study is being monitored by an external data safety monitoring board, just so that the reductions in therapy that we’ve made, not just radiation and chemotherapy, are not going to adversely impact the outcome for these children. So we’ve got about 650 patients enrolled on that study, and we’ll accrue for a year or more before we’ll open our next study and fine tune the risk even more.

Angel Belgard: [00:05:20] At this point in the study, are you already seeing based on neurocognitive function following the therapy? Are you already seeing differentiation in that?

Amar Gajjar: [00:05:30] Yes, but there’s a big change. We now treat these patients with proton beam radiation compared to photon, which was the older way of treating children. So I think the proton beam children are getting less radiation to the normal brain. And I think that’s been a big game changer as far as their neurocognitive outcome.

Angel Belgard: [00:05:51] That’s fantastic. You have studied, like you were saying ongoing neuropsychological outcomes for them because this is a cancer that can affect children of all ages. How do you assess for those children that are too young to complete neuropsychological testing?

Amar Gajjar: [00:06:09] Not all of them get the same test. So depending on the age of the child, there are different tools which have been sort of verified as they’ve been developed. So for the younger children, they use completely different tests. For the older children, adolescents, they use completely different tests. And there are some children who just cannot finish the cognitive evaluations because of the time required and they just can’t focus. So we try and sometimes we split the time up to shorter two day tests. But sometimes parents can’t invest the time. They’ve all got to get on to jobs. So we try to do the best we can for the time we have with the children when they visit the campus.

Angel Belgard: [00:06:48] It looks like literacy is a marker that you’ve used quite a bit to incorporate all the different neurocognitive skills. Can you speak a little bit about that?

Amar Gajjar: [00:06:56] Instead of Global IQ, we have now split the domains up into processing speed, which we think is the most important sort of feature of cognitive outcome. And that really is the speed at which a child receives information, processes the information, stores the information, and then has the output. And we find that these children who have had radiation therapy at a younger age, their processing speed is slowed down, which then reflects their work output in school and generally with a little bit of adjustment, a little longer time for their tests and a longer time to complete their homework. The parents invest in their children. They do okay, but it’s easier said than done. So because the school systems have to be accommodating, the teachers also have to understand because the children look good, but they’re just not performing where they should be. And some teachers in the bigger school systems have seen children with brain tumors who have had therapy before. And they understand, but somewhat in the rural systems, the smaller classrooms are not that much experience. Sometimes the teachers do not completely understand what is going on.

Angel Belgard: [00:08:11] I know that there will be great differentiation with this, but do you ever see a neurological catch up like a number of years out at which point you’re not seeing significant deficits anymore?

Amar Gajjar: [00:08:25] No, unfortunately that doesn’t happen. We are working to remediate some of the deficits, and that’s a big area of research which investigators at St. Jude and other places are now leading because we’ve done a lot of work documenting the deficits. But now we’re saying what can we do about it? So I think this is a huge area of research. We are working with the children with exercise intervention, reading intervention, cognitive intervention, and these are areas which are being actively researched.

Angel Belgard: [00:08:54] And also looking into hearing deficits.

Amar Gajjar: [00:08:58] You’re absolutely right. So hearing loss is a big morbidity in these children. So we’ve been very careful. We’ve done studies. We’ve reduced the dose of cisplatin. Again with proton beam, we are seeing a lower incidence because it is preventing radiation to the middle ear and the cochlea, all little, little things which seem trivial, but then they impact the long term outcome of the child. So if you’re cognitively slow and then you have hearing loss, it’s much harder for that child to sit and pay attention and get what the teacher is saying. So at least if you can prevent the hearing loss, that’s a huge victory.

Angel Belgard: [00:09:38] Absolutely. Now, in addition to these neurocognitive outcomes for children who are diagnosed with medulloblastoma, your research also focuses on those children who are unfortunate to have relapsed medulloblastoma?

Amar Gajjar: [00:09:50] Yeah, unfortunately we are curing about 75 to 80% of all comers, with still 20, 25% whose tumors come back. And we are trying to devise newer, smarter therapies and we test them out in the children who have recurrent disease to see whether there’s any efficacy. So this is a constant effort which is going on within our group and other large groups in North America, hoping to then move some of these therapeutic options in the newly diagnosed or the upfront setting.

Angel Belgard: [00:10:19] What molecular subtype observations have you seen between the primary tumors and the relapsed cases?

Amar Gajjar: [00:10:27] Very good question. We just published that this year. So a couple of things. Suppose it’s a sonic hedgehog at the first time, it’s going to remain sonic hedgehog the second time around. It’s, I mean it relapses. It doesn’t switch its lineage. What it does do is pick up additional mutations depending on what test you do sequencing or if you do an expression array. The other thing we’ve learned is that we used to just presume that these tumors were recurrent medullos. There’s a percentage of these tumors which are not medulloblastoma, which are gliomas. You can call them subsequent, secondary radiation induced. And there are some children who are genetically predisposed to develop brain tumors. So I think 10 to 15% of these turn out to be glioma. So I think the field has now learned to biopsy these and make sure that you’re dealing with a medullo rather than just presuming that it’s a medullo.

Angel Belgard: [00:11:21] What would you hope to see in furthering your study and where would you branch out next to?

Amar Gajjar: [00:11:27] Well, we want to cure 100% of these children diagnosed with this tumor. So won’t happen in my lifetime. But I wish my team will continue working and mimic the success in ALL that we’ve seen. When St. Jude opened, 4% of the children were getting cured, and now we’re up to almost 97%. We’ve got a room. But when I joined St. Jude, we were curing about 72% of ALL. And now in my lifetime I’ve seen a 25% jump. We’re already at about 75% so we’ve got a little ways to go.

Angel Belgard: [00:12:02] That’s pretty incredible. What do you think are some of the most outstanding questions related to pediatric medulloblastoma?

Amar Gajjar: [00:12:09] Well, I mean there are lots of questions as to the molecular makeup. Why is this disease so aggressive? Some of the younger age groups where we don’t use radiation therapy, so we really stuck with surgery and chemotherapy. What is the vascular pattern? Why does some tumors tend to metastasize and grow, whereas some tumors like Wnt tumors very rarely metastasized? So there’s a lot of biological questions which we are looking at as we are dissecting these tumors. We know the reason why Wnt tumors now have such a good outcome because they secrete a chemical which breaks down the blood brain barrier. So they see a much higher concentration of chemotherapy. So that’s why it’s very effective. Some of these secrets we are getting to know and then some of these secrets left to be explored.

Angel Belgard: [00:12:57] Among the treatments that are up and coming, it sounds like you’re pretty excited about proton beam.

Amar Gajjar: [00:13:03] The proton beam is a form of radiation therapy. As I said, we hope most of the children in North America will be treated with proton beam. I think their centers are getting more available to the public. They’re clustered in a few areas, but they are expanding. So it’s a huge advantage, proton beam therapy. Also surgical resection, the tools available to the neurosurgeons when I started versus the tools available when they’re operating today, they have a lot more advantage technically. When I started, there was no MRI. Everybody was using CT scan, and now we’re looking at 3 Tesla MRs, which the anatomical description, the anatomical detail is just absolutely amazing. You can’t pinpoint to one thing, but every technological advance, if you can harvest it will impact your treatment outcome.

Angel Belgard: [00:13:56] I’m wondering a little bit about your path into science and into pediatric oncology. At what point did you know that you wanted to become a doctor or a pediatrician or an oncologist?

Amar Gajjar: [00:14:08] I come from a family of doctors, so I’m third generation and my daughter is a fourth generation. So next year our family will finish 100 years in medicine. So the fact that I was going to be a doctor, I decided was when I learned to talk probably. I came to the United States and I met a mentor of mine who told me, Amar you need to do pediatrics because you can focus on one disease and you can cure it. Otherwise, you have adult patients that have got obesity and hypertension and lung and this and diabetes and all these chronic morbidities. And then you have to treat everything. Now, of course when you are 22 years old, such a profound statement like that, you don’t understand it. But I knew this man was an incredible mentor and a very, very smart man. So I said, listen you know what? Listen to what he says. So that’s how I went into pediatrics. And then again focus back, I wanted to do oncology. So as a second year resident, I came to St. Jude, did an elective, and then I said, Listen, this is my home. I came back, did my fellowship, heme onc fellowship, and they did a year of neuro-oncology fellowship and joined the faculty. And it did turn out that I treat all neuro oncology, but I focus on one disease.

Angel Belgard: [00:15:25] Your advice from your mentor really rang true?

Amar Gajjar: [00:15:27] Absolutely.

Angel Belgard: [00:15:29] Now, you did your medical training at Grant Medical College in Mumbai, is that right?

Amar Gajjar: [00:15:34] Yes. Yes.

Angel Belgard: [00:15:36] How different was your experience coming from there and then joining the residency at University of South Florida.

Amar Gajjar: [00:15:43] In India, the numbers are amazing. I mean the population is huge and we look after a lot of patients and we don’t have the kind of infrastructure that is available in the United States. And I can speak not just India, but a lot of the Asian countries because I travel. I used to before COVID. In the US, we are lucky that we have all this technology to help us and have better results because of that.

Angel Belgard: [00:16:10] So certainly working with cancer patients in general can be emotionally taxing as you’re talking to families and breaking pretty difficult news on a daily basis and then more so working with young children. How do you cope with that ongoing?

Amar Gajjar: [00:16:28] Well, it’s difficult. But remember, I’ve been very, very fortunate to have been surrounded by a phenomenal team of people. We have focused and motivated and dedicated to our roles in our team. And I think that makes the burden much lighter, that it’s not just one person who makes a difference. It’s everybody who’s sort of rooting for the success of the team. And I think if I were isolated and working alone in a team which didn’t understand or kind of didn’t share the vision or the mission would never be the same. So I mean, if I had to look back at my career and think about what’s the most important thing, and I would say my team, nurses, nurse practitioners, my regulatory team, my data managers, research nurses, [Kristen], my PR, she holds me accountable.

Angel Belgard: [00:17:20] Among your family members since you come from a pretty extensive medical background in the family, did they do their training also either in the US or in the UK?

Amar Gajjar: [00:17:31] Yeah, all of them. My grandfather started it and he was in the UK. Next year it’ll be 100 years ago that he started his medical school in London.

Angel Belgard: [00:17:40] Wow. Are any others in oncology?

Amar Gajjar: [00:17:45] No. No. They’re all pathologists.

Angel Belgard: [00:17:47] Oh really? All of them?

Amar Gajjar: [00:17:48] I’m that way the black sheep. My original idea was to be a hemato oncologist, to have a leukemia practice and have a lab. But that didn’t pan out though I did practice ALL for almost ten years.

Angel Belgard: [00:18:00] What sorts of advice would you have for people entering the field of medicine today, perhaps advice that you bestowed on your daughter?

Amar Gajjar: [00:18:08] Don’t do it for the money. Make sure that you love what you do. Because if you’re doing it for the money, there are many, many smarter ways to become rich. Medicine is not one of them.

Angel Belgard: [00:18:18] That’s true. It seems like a better way to get into debt, right?

Amar Gajjar: [00:18:22] Yeah.

Angel Belgard: [00:18:23] And for those wanting to move from purely clinical medicine to clinical research, do you have advice for them?

Amar Gajjar: [00:18:30] Well, I think having the right environment, having the right supportive mentors and really putting the time and sweat equity into it is something which you got to think about. It’s not all fun and games and then your family needs to understand what you’re committing to and support it. Many times I feel that people who are lucky enough to have supportive families have a much better chance because it’s long hours. None of this is, I mean if the idea is at 5:00 on Friday, I’m going to switch off and come back on Monday at 8:00. And in between, I mean there’s a certain amount of time, dedication, thinking, reading which one has to put in. And I think having family support for that is very important.

Angel Belgard: [00:19:15] Over the years, you’ve talked a little bit about the changes in neurosurgical approaches and the differences in imaging. What other kinds of changes have you seen in the field of medicine throughout your training?

Amar Gajjar: [00:19:27] As I said, the supportive care has improved tremendously. I mean, I remember the days when we didn’t have the simple thing like antiemetics, nausea, medicines and now the children have got so much better control. Pain medicines, we have appropriately used and dispensed long term pain control. Again if you start quantifying, there’s no one single thing, but there are multiple advances which you package together makes a big difference. Remember when I started, we didn’t have even growth factor support. So G-CSF was something which was new. We really started using it in 1998. It was a new thing and now we give it the duration of time. These children have their counts dropped by 4 to 5 days, which is huge.

Angel Belgard: [00:20:19] Is there anything within the system that kind of shouts out as the next thing that clearly needs to be changed?

Amar Gajjar: [00:20:27] Well, one of the things we’re getting better at because we’re getting so much genomic information on these tumors. We’re looking at germline predisposition syndromes, patients which are going to have because of their genetic makeup toxicity to certain drugs adjustments because of the genotype phenotype correlation. So there’s a lot of that advancement and insight that we have just started understanding and approaching. The first human genome took two years to sequence and now we get the result in two weeks commercially. That time President Clinton and NCI and there was a private person attempting to do the same and Clinton had to go and make peace that they will sequence, put the results of those things back to back so that one person wouldn’t be a clear winner. And now we just order it as a lab test and within two weeks we have the entire human genome sequence. So you can imagine the pace at which, science and medicine has moved along and the information highway that we are on.

Angel Belgard: [00:21:30] And certainly the outpacing potentially of where the forefront of that is and where medical education is along the way.

Amar Gajjar: [00:21:37] Oh, yeah, absolutely. The current generation of medical students, I tell them the residents. I said this will be regular sort of clinic talk for you guys at a very basic level. So if we know that you’re going to have a bad reaction to this medicine, this will be a report that will alert you right away. Right now, we are defining all these things, but a point will come when your system will not allow you to even prescribe this stuff.

Angel Belgard: [00:22:04] Yeah, the concept of precision medicine has really come a long way in just the last ten years even.

Amar Gajjar: [00:22:10] Long ways. Yeah, absolutely.

Angel Belgard: [00:22:12] What are some common misconceptions about medicine or cancer that you see with your patients or with your trainees?

Amar Gajjar: [00:22:21] Misconceptions in pediatric oncology, patients are young and families do a lot of hunting on the Internet. And I in fact encouraged them. I said, do whatever searching you have to do, but then do come back if there’s something that you’re reading. Just because it’s on the Internet doesn’t mean it’s true. It’s not verified. Anybody can put anything on the Internet. And I think sometimes it’s a relief to these families because it opens up an open dialogue. Now, adult oncology may be very different and they may run off. But even then, we have people who go on vitamin D or orange juice. There’s all kinds of opinions on chat groups so it really depends on what these people are reading and who is influencing their thinking. But I find that having an open discourse, opening up that communication channel, you can hold them closer and have an open discussion with them. Give you a common, very common mistake. So they’ve understood that when you do a PET scan, the tumor is taking off FDG glucose. They say the tumor is eating glucose. So then they think if the tumor is growing on glucose, why don’t we eliminate glucose from the diet of the child? And then they go on this kick and they completely starve this poor patient. And I tell them our body is made to make glucose. Our brain works on glucose. You may think you’re not feeding glucose, but the body will make glucose. And so try to common sense way sort of guide them so they don’t go crazy or that ketotic diet. Some of them go on that. These are the kinds of things. They go off on a tangent. But generally if you talk to them and make them understand, most people will listen. There’ll be some people who, you know, then they come in with severe ketones in the urine. They’re losing weight because they’re feeding them this stuff.

Angel Belgard: [00:24:18] Yeah and that’s something you definitely want to convey that not all of these things are harmless, even though they’re nutritional adjustments.

Amar Gajjar: [00:24:27] Yeah. Yeah. I mean absolutely. I mean, we’ve had kids who have super high levels of vitamin D and then they’re getting renal stones. And I mean, there’s all kinds of stuff. Sometimes they’re LFTs are completely out sky high. And I said, What’s going on? And then they’re feeding them some herbal tea. And I mean, they’re doing all kinds of crazy stuff. But fortunately it’s the minority. And generally we catch it. Usually they settle down.

Angel Belgard: [00:24:51] Well, wonderful. Aside from medulloblastoma research, your research also focuses on rarer brain tumors as well. Would you talk a little bit about those?

Amar Gajjar: [00:25:02] Yeah. So again, historically we used morphology and how the tumor looked under the microscope. And commonly all these tumors are called small, round [] cell tumors because they all look very similar. Now we’ve got molecular diagnostics and molecular tools. So even if they look similar under the microscope, they’re molecularly very distinct. So we’ve got new entities like ATRT Atypical Teratoid Rhabdoid Tumor, ETMR and other what we used to call supratentorial PNETs, into very molecularly distinct. So they look the same under the microscope. But when you start doing the genetics and some of the immunohistochemistry tests, that separates out each distinct entity. And that is again helped because ATRTs in younger children, they have germline predispositions and they’re very aggressive. They are in turn made up of three molecular subtypes and we are trying to find specific drugs for each of the subtypes which normally 30 years ago would be all considered medullo. So all that fine tuning is coming at a very rapid pace.

Angel Belgard: [00:26:10] How widely available is that fine tuned diagnostic sequence? Is it only available to large institutions?

Amar Gajjar: [00:26:18] Yeah, but most pathologists who don’t see the volume will send it out for consults. So they know. I mean big centers which see large volume, it makes sense for them to invest because they’ve got enough positive and negative control. They can test their strains. They can test their FISH probes. If you’re a center which sees ten of these patients a year, it doesn’t make sense. Less than one a month. It doesn’t make sense because every time you’re going to be shaky, whether this stain is correct, whether it’s working or not. So you’re almost cost effective to just send it out.

Angel Belgard: [00:26:51] Well, thank you so much for speaking with us.

Amar Gajjar: [00:26:54] Thanks for the opportunity. Good talking to you.

Angel Belgard: [00:26:57] And talking to you as well.

The Bioinformatics CRO Podcast

Episode 45 with Jill Reckless & Jon Heal

Jill Reckless, CEO and co-founder of RxCelerate, and Jon Heal, Head of In Silico Designs at RxCelerate, discuss the virtual biotech industry, outsourcing drug development, and using bioinformatics for target optimization.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple PodcastsAmazon, and Pandora.

Jill is co-founder and CEO of RxCelerate, an outsourced drug development platform. Having completed her PhD at the National Heart and Lung Institute in London, Jill was an academic at the University of Cambridge until December 2011 before founding RxCelerate.

Jon is Head of In Silico design at RxCelerate. After completing a PhD at Imperial College London, he founded a computational biology-based drug development company Prosarix, which was acquired by RxCelerate in 2019. 

Transcript of Episode 45: Jill Reckless & Jon Heal

Disclaimer: Transcripts may contain errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is a pair. We have Jill Reckless, CEO of RxCelerate and John Hill, the head of in silico design at RxCelerate. Welcome to you both.

Jill Reckless: [00:00:13] Hello.

Jon Heal: [00:00:13] Hi.

Grant Belgard: [00:00:14] So can you tell us about RxCelerate? What do you do?

Jill Reckless: [00:00:17] So RxCelerate is an outsourced drug development platform. We’re a service provider that can take something conceptually from an idea and all the way to proof of concept phase two clinical studies. And essentially we can provide the services that cover that pathway and that covers biology, chemistry, in-silico design, bioinformatics and so forth.

Grant Belgard: [00:00:40] Is there anything along that pathway you don’t offer?

Jill Reckless: [00:00:43] We don’t do preclinical toxicology at the moment and actually manufacturing, but pretty much we’re covered. The rest of the elements, we have GxP accreditation. We’re able to facilitate in vitro, in vivo preclinical models and program management systems, biology. We have a phage display platform that we can tap in for antibody discovery. So we’ve got a lot of the elements as well as clinical that we’re exploring further.

Grant Belgard: [00:01:12] Where do your clients tend to be based?

Jill Reckless: [00:01:15] They’re global, US and Europe predominantly, and from small biotech, which could be virtual with 1 or 2 people to Nasdaq companies and large pharma.

Grant Belgard: [00:01:28] Speaking of the virtual biotechs, have you seen repeat business from serial entrepreneurs where they’ll bring you one company, they’ll exit, they’ll start a new one, come back to you?

Jill Reckless: [00:01:41] Absolutely. That model is what has helped the organic growth of RxCelerate. That repeat business where you’ve got those serial entrepreneurs, where you’ve worked very closely scientifically to help deliver and drive their drug development program. They have an acquisition and so forth, and then they’re on to the next one. And similarly, John as part of that in silico, build that communication and working with somebody and those relationships to be able to help advise and de-risk and due diligence as well as along the drug development pathway with in silico design.

Jon Heal: [00:02:15] I think that’s one of the key things that we would say about CRO is that some people think CROs are paid service providers that work independently and simply function to provide services for things that people ask for. But actually we don’t see things like that way. We see things that CRO really is problem solvers and that if you work very closely with somebody to solve problems together. In fact, that’s the way in which you can do drug discovery to get the benefits from that type of relationship. So that’s very much the way we see things. And once you start solving problems with a certain customer and you’re successful, then they’ll come back and you can solve more problems with them. But it’s having that mindset that you’re a team, not this arm’s length service provider that provides that.

Jill Reckless: [00:03:03] I often say to clients, particularly new clients, that we’re working together as part of the same team and we’re better than the sum of the two individual parts because we are asking questions and scratching at the surface of something that they may have worked on for 10, 15 years. But from a drug development perspective, we’re able to ask different questions and collectively we can enhance where we’re going and making sure that we’re considering all those elements. And that’s really important when you’re trying to do something that’s novel and cutting edge and innovative.

Grant Belgard: [00:03:37] Do you have any relationships with investors?

Jill Reckless: [00:03:40] Simple answer is yes, because as we’ve just alluded to, you do have that repeat business. And with investors, they will come back knowing the service and things like that. So when they’re looking at other new companies and so forth, they will come to you or encourage those founders to come to you and talk to you. And similarly, investors work together so you get other investors and so forth. So yes, you do build relationships with that of which we’ve got a number.

Grant Belgard: [00:04:08] What are the most common mistakes that you see biotech entrepreneurs make?

Jon Heal: [00:04:13] I mean, obviously it depends on who that entrepreneur is. One thing we do find quite commonly is that if it’s an academic founder and particularly wedded to one area of biology, that typically they will want to see that area of biology through to the bitter end, primarily because they’ve worked on it for such a long period of time. So when you have academic founders, one of the pitfalls there can be knowing when to stop a project, to realize that it’s more complicated than you thought. So that can be one of the problems. So if a biotech is based around an academic group and that’s their ethos, then that can be a problem.

Jill Reckless: [00:04:48] And the other thing is making sure in those early stages that you’re de-risking that pathway. I mean, academic founders, as you probably can think that they really want and believe in it, that it’s going to make it a success. And there’s lots of parameters around that that you might need to de-risk. And as John has said, it’s important that you get to those points. And if they’re not able to be de-risked, then that could be the end of the program as opposed to making sure that it’s taking those right decisions in terms of investment and the scientific output.

Grant Belgard: [00:05:22] So I would imagine sometimes for scientific founders coming out of academia who may have never done this before, they may have not a good idea of timelines and levels of investment involved and so on. Do you ever have people engage with you very early before they’ve raised any substantial amounts of funding to put their plan together?

Jill Reckless: [00:05:45] Yes. So we have and do help with those plans to be able to get investment on those things. When they’ve got an idea to help, make sure that we are de-risking and covering all those bits, particularly if it’s for example a platform company where they might have multiple targets. They can’t take every target invariably all the way as a proof of concept. It’s selecting out which target would you take in one of six. And we can help by de-risking and trying to select out priority orders and those kind of things which they may not have. I mean, they’ve got the academic excellence, but there’s also that drug development piece and particularly with John and his in silico team, it’s about de-risking the approach and actually doing that due diligence so that you’ve got all that information before you start.

Grant Belgard: [00:06:36] So what does your in silico team do?

Jon Heal: [00:06:38] Yeah. So we cover a whole range of activities. I mean, primarily it’s computational chemistry. The most common thing that we get asked to do is to identify new hits against a target using algorithms, usually against a protein structure. But also there’s a lot of bioinformatics involved, whether it’s EWAS or GWAS approaches or sequence finding or protein engineering in fact. So we cover a whole range of different sort of problem solving techniques all the way from sequence up to structure and everything in between. And then once we found hits for clients, we then develop them into drugs. So we’re using computers pretty much the whole process from beginning to end to candidate stage.

Grant Belgard: [00:07:23] What’s the history of the in silico capabilities at RxCelerate? Did you come in as part of an acquisition?

Jon Heal: [00:07:29] Yeah. So just to give you a feel for a bit of my journey, so I did a degree in PhD in Chemistry at Imperial College London in the 90s, and then I was working as a software engineer and financial trading systems for a while whilst I was getting a business plan together to set up a new bioinformatics company in the early 2000. I got that funded with Angel funding and then once we grew to nine people, we got VC funding and that primarily was to build a CRO outfit that would have bespoke software capabilities to help with mostly peptide and protein design. And we then built out more small molecule design capability on top of that, which basically led to many collaborations with pharma companies and small biotech companies. And then we took an interesting turn in 2014 when our CRO company that was called Prosarix at the time got acquired by Swiss Biotech Company and they were interested in developing our methodologies for synthetic biology. So we were working with them for about five years, mostly developing novel protein engineering approaches with bioinformatics and structural biology. And then ultimately we then did a spin out from that company to reform another company also called Prosarix , which was then acquired by RxCelerate in 2019.

[00:08:51] So one of the things that you find with a lot of people in working in our industry, particularly in informatics, is that you have a vision and a design approach of what you want to do. And often in pharma the scenery can change quite considerably around you in a two decades of activities. But actually everything we’ve tried to do, myself and the team of people that I’ve worked with over those 20 years, it’s been pretty much the same just in different settings. But now obviously in RxCelerate, the interesting thing is that because we’ve got a long history of doing in silico design in all sorts of different settings. We can now bring that to bear for customers that come in and want to work with RxCelerate.

Grant Belgard: [00:09:32] How important is client education when it comes to informatics services? Do you have clients come to you? They know exactly what they want routinely. They’re aware of the caveats and things, or is that a bit of a longer process?

Jon Heal: [00:09:47] So it’s a whole spectrum typically. Some people have a very clear idea of what the biology is, what they’re after and why they want to develop a drug but don’t have much exposure historically to in silico approaches. In that case, you have to educate from the ground up. And sometimes people, they expect that computational approaches can always solve the problem. And in fact, sometimes they can’t. There is sometimes difficult problems that sometimes the computer isn’t the best thing to solve the problem. So we’re very upfront about those situations in terms of the education. Sometimes you will have customers that know extremely well what computational capabilities can do and they know exactly what they want from us. So those cases are easier and there’s everywhere in between. So but I think one of the key things we’re finding right now is that because there’s an emergence of AI technology in our field, that there’s a lot of misunderstanding around what that means for drug discovery for projects. It’s something that’s been talked about an awful lot in the media and it’s something everybody’s hearing about the fact that AI is something that’s beneficial for drug discovery. So customers want to know about that and wants to know if it’s true or not. And if it is true, how can they use it. So we’re we’re developing AI approaches now for chemistry in particular molecular design. But there’s not one single magic bullet here yet in order to design compounds. It’s a very complicated field with lots of pitfalls and potential benefits, but also the potential for people to get slightly misled around how good these approaches can be as well. So we’re very careful to interact with clients and put them in the picture as to how we see things as well.

Grant Belgard: [00:11:26] And how do you think about what area you expand into next? So it sounds like you started around computational chemistry, which obviously is a very, very different field from statistical genetics, where you also currently operate. Are you working to build out just an end to end portfolio, everything that’s useful for drug discovery that’s in silico and opportunistically building that out? Or how do you think about that?

Jon Heal: [00:11:53] Our approach has been very pragmatic. What really works, what techniques actually work and can deliver compounds for people. There’s often the nature of media now is that it’s very easy to give a very simple message about the fact that if you use this computer program, it will give you these results. And obviously the fact that papers only give you positive data, they only give you the success cases. And as we know in drug discovery, it’s kind of littered with examples of failures as well. And I think not everything works. So what you have to do is to understand what makes things successful versus the things that aren’t successful. Our job really is to try and find those approaches that work the best and will work for people and to pursue that. And really, there’s no single technology where that’s true, for that always works and one that always doesn’t work. It’s a learning exercise as to the right approach to apply at the right time. And it’s only by working through many projects that you end up learning about that. So we’re quite interested in a lot of new approaches and what benefits they can bring to projects.

Grant Belgard: [00:12:58] And when you look across the industry, what in silico technique or family of techniques do you think is most underused currently relative to what it can bring?

Jon Heal: [00:13:09] Yeah. So there’s a lot of new opportunities potentially with things like AlphaFold from DeepMind. Here’s a classic example where there is a sudden improvement in structure prediction capability, but yet people will find it difficult to understand exactly what the benefit of that is on drug discovery. So we can see that there are potential benefits to structure prediction from this, but actually then reducing those benefits to practice and demonstrating that that translates through to the better ability to design molecules will take some time. If people thought that structure based prediction had potential difficulties in certain places, that they should revisit that thinking because of these new technologies. But equally, it’s not the case that it will work in every case, and the benefit is not going to be on every target. But there will be pockets of targets now that are suddenly the structures of those have improved dramatically on where we were previously.

Grant Belgard: [00:14:04] And what approach do you think is most oversold?

Jon Heal: [00:14:07] This is tricky. One of the areas that I think that there’s a lot of papers and a lot of talk, especially at conferences around the benefits of approaches within lead optimization. And obviously Lead Optimizer is extremely poor because you go from the time when a compound is a hit, which has had marginal value to something where it’s a lead, as you know, very high value. So the value increase between is significant and a lot of computational approaches are trying to gear towards trying to optimize leads from hits. But this is actually a very challenging thing to do. And it’s not least challenging because it requires the integration of many disciplines together. So there are many things to consider in driving that forward. And I think that sometimes the role of the computer in that process can be, people think it is the Holy Grail to do this, this whole process in the computer. But it will only work if you’ve got all those other teams on board and that you’ve got this kind of holistic vision about how all those people can integrate together and drive it forward. But a lot of the newer techniques for lead optimization, especially in areas to free energy production, there’s been some big strides forward in those areas. But because of the difficulty of integrating some of these predictions into a team and the complication of running some of these algorithms that it’s not really delivering yet.

Grant Belgard: [00:15:29] And a question to Jill, what was your thinking in bringing in what now is your in-silico design division.

Jill Reckless: [00:15:38] In terms of that overall vision of RxCelerate, it’s that end to end high value bespoke adding value in that service and clearly understanding with the in silico. It’s right at the beginning of a process in drug development, but it also can be added in subsequently along that pathway and it’s a key part that you’re always wanting to understand that scientific value and readdress those things in each program that we are involved in is bespoke. So you need to have in house that integration of biology and chemistry in silico design. For example, if you’re looking at a lead optimization for a small molecule to be able to exquisitely, constantly be looking backwards and forwards between data and that in silico design and of course, if you haven’t got those capabilities in house, you haven’t got that control and being able to have those internal discussions. And as John has alluded to, being able to discuss all those elements together adds value not only to the client, but also within the teams of what we’re trying to do. And we’re always looking at the runway for beyond. And again, you’re wanting to be able to look at those things along that pathway as you get new and interesting results. So it was an important part of the journey, although we started with biology and the core biology, it was always going to be that we’d want to add in that key service. And of course it was one that we acquired quite early on in our journey.

Grant Belgard: [00:17:10] Can you tell us about the history of RxCelerate and its founding?

Jill Reckless: [00:17:13] Yes. So I’m the co founder along with David Granger. David Granger and I worked at the University of Cambridge in the Department of Medicine there doing academic work and translational work in cardiovascular and inflammation. We did get inward investment and set up companies of which they were that translational piece where we did get venture capital and we’re able to translate some of our ideas and in particular David’s ideas for development of new drugs. And as part of that, as I was helping him with along that pathway and supporting the delivery of those plans, we had to outsource. And it was clear early on in 2006, sort of area that there were service providers that were able to do what I would call a lot of process work, but not a lot of bespoke work. And it was clear when you’ve got those virtual or semi virtual companies that you really need to get the nitty gritty because you’ve not got your own labs. And hence when there was a particular acquisition of one of the companies, it seemed like that there was a sweet spot to set up a service provider that actually would be that architect of drug discovery and help with that scientific excellence in drug discovery and development and be able to design and deliver those plans. And hence RxCelerate was founded in late 2012.

Grant Belgard: [00:18:41] Have there been other similar companies that have come up since then? Is the industry still dominated by more process focused providers?

Jill Reckless: [00:18:50] I’m not aware of a company that essentially becomes a one stop shop of drug discovery and development exactly like RxCelerate. But there are lots of service providers that are specialists, but they’re much more bespoke in certain areas. And what potentially you’re finding is that with some of those larger service providers, they are acquiring certain features along that pathway. But I haven’t found anybody as yet that is replicate of what we started and our vision of what we’re trying to achieve.

Grant Belgard: [00:19:22] And would you say this is a big growth area?

Jill Reckless: [00:19:25] It is. To service providers, it’s an emerging and an ever increasing and growing sector and the space and need for all of these different service providers from the really large ones to the really small ones and the ones in between. And it’s all about clients and biotech and academic founders finding the right services for what they need depending on where they’re going and so forth and which part they’re at.

Grant Belgard: [00:19:54] Is there a particular client profile that you think is an especially good fit for RxCelerate?

Jill Reckless: [00:19:59] The vision to start with, I always thought that it would be for those small semi-virtual companies because they haven’t got their in-house capabilities and being able to design and deliver and work as part of that team. But over the years, it’s become clear to me that our service can be for any size, including large pharma. If they’ve got an idea, why can’t they outsource that idea to us rather than bringing it in-house and trying to manage that capability. If we could get it to the clinic, they could then internalize it so we could help de-risk those kind of programs. Similarly for a medium sized company, they may want to scale up their core capabilities or they may want to have certain parts and portions of that delivery of drug development and they could outsource that. And obviously, as I’ve said, those small companies with 1 or 2 people or even, a very small team being able to work very closely with drug developers is an important part of that because they may have the scientific foundation, but drug development is that additional piece. And we like to think that we are the architects of drug discovery and development along that helping them. And that we can try and help them design and deliver their programs.

Grant Belgard: [00:21:22] When did you know you wanted to be in this industry?

Jill Reckless: [00:21:26] That’s an interesting thing. My degree and PhD was biochemistry and I wanted to do drug research. So from being a teenager, that was a key part of wanting to make a difference and help people in terms of the journey you take the opportunities and of course things have progressed quite significantly in the biotech field in the last ten years or so. And it’s that opportunity that you take and seek and it’s led along this pathway. It wasn’t one that I thought, Oh, that’s what I’m going to do. But certainly it’s one that I’ve enjoyed every minute. And it’s something that is making a real impact on people’s life. John, what did you want to do?

Jon Heal: [00:22:10] I always thought there was things that’s missing. So you do some research. You’re doing a PhD and so forth, and you see gaps all the time in areas of research in informatics. And then you wonder whether some of those gaps are worthwhile and whether some of them will be useful. So this is the whole thing about which of those areas of research that have not been done will actually be useful in the commercial entity and try and go after that. And that struck me as that there were some things to try in the early 2000 and that’s obviously a long time ago now in terms of computational method development, and there’s been some incredible changes since then. Notably the advent of high performance computing, where two decades ago I wouldn’t have almost believed what level of high performance computing you’d get your hands on in order to solve some of these problems. But I think I was always very excited by the idea of trying to come up with something new and test it out. And the real world, as I say, there’s lots of papers of success stories, but I’m also interested in when things don’t work as well, because that also is the bit that doesn’t get talked about very much. But it’s extremely important in drug discovery, and it’s the place where you really learn a lot. When you have an algorithmic approach, you try something and it doesn’t necessarily always work, particularly if it’s on a really difficult problem. I think that’s where you learn an awful lot. And so I’ve always been interested in that in the failures as well as the successes and then how to try and improve things. So that’s really the thing that’s stimulated my interest is how can you make things better than they currently are. And we always know that they can get better. And the nice thing now is that we’ve got computers that can answer some of the questions much quicker than they used to be able to.

Grant Belgard: [00:23:48] I have a friend who I’m sure will listen to this podcast soon after it’s posted and is currently an academic looking at starting a virtual biotech company to prosecute a target. What would that look like ideally in your mind? Someone with no background in biotech coming into it, how would you advise they go about that?

Jon Heal: [00:24:13] Without knowing the target area and so forth, obviously it can be extremely complicated. I think the difficulty with these things is knowing what you have to work out very quickly is what proof of concept looks like. So there’s one thing about having a target and having an interest in it, but you’ve got to work out very quickly as to what that’s for and what will convince somebody else that there’s something interesting about this that’s worth investigating further. What is that experiment? And that’s one of the things that we do a lot at RxCelerate is what is the killer experiment that tells you’ve got something useful and work back from there really. And obviously, there could be a whole number of different services that are required in order to get to that point or actually it could be something very really quite simple. And it all depends on what the hypothesis is and what the person is after. So one of the things that you can do if you’re running virtually is there are CRO companies that offer assay services for you on certain targets, and there are these large companies that will just provide assays off the shelf for you run as a service and there are places that you can buy small molecules from places around the world that will sell you millions of small molecules for under £50 each. So the opportunity is there for somebody with very little income or funding to actually go off and explore a target of interest. But let’s say it comes back to that crucial experiment is how do you know when you’ve got something of interest? What often happens is that people develop an interest in a target without really thinking necessarily about what that needs to be in order to convince somebody else to put more money into it.

Jill Reckless: [00:25:49] That proof of concept is one of the key inflection points for funding, but also it’s having the design of the right pathway. Although you are wanting to get a development candidate for a specific target, it’s what that indication is. What’s it going to look like to market? There are multiple things that you may have to de-risk. For example, in that scale up and processing, although you make things that research quantities to be able to put in vitro and in vivo, how do you make kilos and how do you make GMP kilos. That in itself could be an exercise and it may be an important part in that selection of that development candidate. So it’s making sure you de-risking all of the key elements along that pathway. So you might be thinking about things that are three years ahead that you have to de-risk in those early parts. And as drug developers here at RxCelerate, we can identify those and help clients consider elements of things that they might not even be on their radar because it’s not something they need to do right now.

Grant Belgard: [00:26:52] What are the major cultural differences you see among major biotech clusters, given that you work with clients worldwide?

Jill Reckless: [00:27:02] Certainly in the US, although it’s an ever increasing thing in the UK, there are a lot more platform companies which I think they have a technology which they want to explore. They have a number of targets identified. It’s always surprising to me that in some cases they have a list of targets and they don’t necessarily go through the exercise of selecting out which target would be the one to choose first for the proof of concept, because essentially that proof of concept is the research engine for all the rest of the targets. And for them to be able to do all of those kind of targets eventually, they often will choose the target that they’ve identified the first. And it could be that target number six of ten is the target to do a proof of concept. So it’s making sure that you interrogate them and have plans and look at that wider piece, what that phase two might look like. It may be easier to do a proof of concept in idea number six than idea number one. And these are important things to consider. And certainly with platform companies, that is something that is not always evident and it’s a US thing that seems to be something that I often have come across compared to the UK, where often there’s one idea going for one indication, which is a simpler model to interrogate.

Grant Belgard: [00:28:30] Do you work much with clients in Asia?

Jill Reckless: [00:28:33] It’s an emerging area that we are working with, but to be honest we focused first and foremost in the UK and around Cambridge cluster and obviously Oxford and London and the wider UK, and then have gone into the US where we have an office in Cambridge, Mass and obviously California. So those were our main areas to start with. But as you might expect, it becomes that people reach out to you globally for us to be looking at. So it’s an area that certainly we want to expand into further.

Grant Belgard: [00:29:06] It certainly makes scheduling easier.

Jill Reckless: [00:29:09] Yeah.

Grant Belgard: [00:29:10] And how about in Continental Europe? Are there any distinctive cultural characteristics within biotech companies in contrast to the US and or the UK? Or is it all pretty scientific, homogeneous?

Jill Reckless: [00:29:22] It’s not become clear to me. I mean, you’ve got obviously with Evolver and things like that where you had a bit more exposure into Europe. Would you say that?

Jon Heal: [00:29:32] I think European scientists generally get on and work well together. I don’t think there’s a large difference in culture between different countries within Europe. There may be a slight difference between some of the funding models in different countries. For example within Europe, the kind of local changes of funding. And obviously the government grant schemes and all those things have a big impact on how companies can get funding and different routes for funding. So they change between the actual countries in Europe. And obviously the UK were not subject to the European grant system that we used to be, for example. So there are differences like that. But in terms of actually how scientists like to operate and work together generally the biotech industry is always been cosmopolitan and I think it always will be in that respect.

Grant Belgard: [00:30:21] What advice would you have for early career scientists who are taking their first steps from academia into this industry?

Jon Heal: [00:30:30] On the in silico side, one thing I would say is don’t be scared to try things that are new. Have a go. There are so many examples where people have tried to do something to change your method and make it different, and it’s worked out well. It’s always worth trying differently. And don’t be afraid just because you can’t see the software exists and therefore somebody must have tried it and it didn’t work. My experience says that’s not true. It’s just nobody’s done it yet in the general case. So always follow your nose and try things out. And also if things don’t work, don’t be scared by that. That’s actually again, that’s part of the learning process when things don’t work. Try and understand why that is and try and develop beyond that. But not everything works perfectly, but it’s not the goal of the computer program to make everything work perfectly. It’s to make it better than random. So we want to get better than just randomizing things, experiments. We want to have a positive enrichment. Don’t count the failures as failures. Count them as data points, and you’ll feel a lot better.

Jill Reckless: [00:31:35] Yeah, and certainly from RxCelerate point of view and my point of view, it follows along the same vein. It’s question things. Science is about questioning things. And going back to first principles, don’t just copy and follow everybody else. Take those opportunities and question things. That’s how innovation and those step changes. And that’s something that I encourage very much in RxCelerate is that cross-departmental discussion and brainstorming. Those are the things where you can really get different expertise to enable that scientific advancement. So I definitely think it’s important not just to think that just because it’s written, that means that it’s the gospel. You can question why or, try to think about all of those kind of things ultimately, not to be scared.

Grant Belgard: [00:32:24] Great. Do you have any final pearls of wisdom for our listeners?

Jon Heal: [00:32:29] I think the other thing is really just trying to enjoy what you do. If you want to work in this sector, if you want to work in bioinformatics or computational chemistry or systems biology, one of the allied sciences, it’s trying to enjoy it. And generally that can occur. If you’re curious as Jill was said about questioning things and being curious about things, if you take it that you’re going to be running other people’s algorithms and not questioning what you’re doing and you’re probably going to enjoy it less than if you try and really understand what these algorithms are doing and see if you can improve upon them.

Jill Reckless: [00:32:59] That’s what will make a difference. Yeah, absolutely.

Grant Belgard: [00:33:02] Well Jill and John, thank you so much for joining us.

Jill Reckless: [00:33:05] Thank you for having us.

Jon Heal: [00:33:07] It’s so nice to talk to you.

The Bioinformatics CRO Podcast

Episode 44 with Adam Siepel

Adam Siepel, Professor of quantitative biology at Cold Spring Harbor Laboratory, discusses the applications of evolutionary genomics in anthropology, infectious disease, and cancer.

On The Bioinformatics CRO Podcast, we sit down with scientists to discuss interesting topics across biomedical research and to explore what made them who they are today.

You can listen on Spotify, Apple Podcasts, Amazon, and Pandora.

Adam is Professor of quantitative biology at Cold Spring Harbor Laboratory, where his lab studies molecular evolution and transcriptional regulation in cancer, infectious disease, anthropology, and agriculture.

Transcript of Episode 44: Adam Siepel

Grace: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley. I’ll be your host for today’s show, and today I’m joined by Adam Siepel. Adam is chair of the Simon Center for Quantitative Biology and Professor of Biology at Cold Spring Harbor Laboratory. Welcome, Adam.

Adam: [00:00:15] Thank you. It’s great to be here.

Grace: [00:00:17] Yeah, it’s great to have you. So can you tell us a little bit about the research that you’re doing?

Adam: [00:00:21] Yeah. So we do work in a variety of areas in genomics. My laboratory is completely a dry lab. We do only computational work, but we collaborate closely with a number of experimentalists and really try to stay as close as possible to data generation and biological questions. But we do have strong backgrounds in probabilistic modeling, algorithms, machine learning and related areas, and we try to bring those skills to bear on our work. We are interested broadly in questions involving evolutionary genomics, in particular evolution of gene expression. We are interested in demographic reconstruction of human populations involving both humans themselves and human interactions with archaic hominins such as Neanderthals and Denisovans.

[00:01:11] And we’re also interested in natural selection making inferences about the strength of natural selection, which parts of the genome are affected by natural selection, and natural selection on different time scales: ancient natural selection affecting primates and more recent natural selection affecting human populations, for example. And then, I should say also, we’re interested in applications not only in human population genetics, but also in cancer and agriculture and other areas. We’ve also done some recent work on COVID modeling, for example.

Grace: [00:01:43] Yeah. I saw a really interesting paper from your lab on COVID, looking at the influence of daylight savings time on immune response.

Adam: [00:01:53] That’s right. Yeah, it sounds like sort of a crazy connection. But one of my colleagues here, a senior scientist, Rob Martienssen, HHMI investigator, had a hypothesis that there could be an interaction between daylight savings time and seasonal patterns of COVID infection relating to the fact that at certain times of the year, people are most likely to be interacting with other people during their daily commute, exactly at the time of a nadir in immunity, which occurs around sunrise. And he had the observation that daylight savings time changing the clocks prolongs the period of time when the daily commute coincides with sunrise. And so we did some modeling to show that indeed, there seems to be a signal in the public data indicating that there is an effect and that infections could be reduced by eliminating daylight savings time. So that’s one of many reasons to eliminate daylight savings time.

Grace: [00:02:53] Yeah. I’m all for that research as long as I don’t have to wake up an hour earlier than normal.

Adam: [00:02:58] That’s right.

Grace: [00:02:59] Yeah. So can you tell me a little bit about how you get those data sets to predict how human genomes have evolved over time?

Adam: [00:03:09] Well, all of our research is based on data that has been collected by other groups. Much of it collected by Svante Pääbo group at the Max Planck Institute in Leipzig, Germany. And they have over many years now develop techniques for extracting DNA from fossilized bones. The techniques are quite sophisticated because if you’re not careful, it’s very easy to contaminate the ancient hominin DNA with modern human DNA. And so they’ve developed clean rooms and special DNA extraction techniques and special purification techniques. And then post-processing bioinformatics techniques to ensure that the DNA they’re sequencing really represents the ancient remains and not the modern humans who are handling the fossils. But we can’t claim responsibility or credit for any of those works. We’re consumers of the data that they have produced and made publicly available.

Grace: [00:04:10] Right. Of course, I didn’t know if it was some sort of reverse modeling like taking current human DNA to predict what the DNA looks like previously.

Adam: [00:04:19] Well, there’s some of that because what you get is kind of a noisy readout of the the DNA for the ancient remains. And then we have higher quality representations of modern human DNA. And then we try to model the processes that could have given rise to both of those samples. And that does involve sophisticated statistical methods for reconstructing ancestral DNA, as well as explaining the observed samples.

Grace: [00:04:48] Yeah. And so can you tell us a little bit about what you found in that research?

Adam: [00:04:53] Sure. It’s now fairly well known and well accepted based on findings that were developed over the last decade that there has been a genetic interaction between modern humans and Neanderthals. In particular human populations outside of Africa, including Europeans and East Asians show a signal of Neanderthal DNA at something like three percent of their genomes that traces back to Neanderthals. And the best explanation we have for that signal is that there was some sort of interbreeding between modern humans and Neanderthals, probably outside of Africa, after humans had migrated from Africa, something like seventy or eighty thousand years ago. And that signal persisted as these out-of-Africa populations spread across the globe.

[00:05:55] We came in already knowing these findings and already familiar with these findings. And we tried to develop a model that would jointly explain a number of ancient samples and a number of modern samples from around the world. And our goal was to see whether we could both explain this known pattern of Neanderthal-human interaction, but also possibly detect other signals of interest. And what we found interestingly, and this was published in 2016 in a paper in Nature that I jointly co-led with Sergi Castellano, who was then at the Max Planck Institute in Leipzig and has now moved to London. What we found was that there was a signal, surprisingly in the opposite direction of modern human DNA in Neanderthals. And this was something that hadn’t been reported previously. And the signal was quite subtle. And it was quite difficult to convince the community that it even existed.

[00:06:49] But we were able to convince reviewers of our paper, and it has since been supported by a variety of other analyses. And interestingly, this signal is not specific to out-of-Africa populations. It’s shared by Africans as well, and it appears to be much older. We’ve since in more recent work, dated it to somewhere around two hundred fifty thousand years ago. And so that suggests that there was an earlier integration event that left a signature in the opposite direction from modern humans to Neanderthals, and it affected all human populations. So it probably occurred in the ancestor to all modern humans. Furthermore, that’s interesting because it must have predated the migration of humans out of Africa. So it seems like there was a group of early modern humans that migrated out of Africa interacted with Neanderthals, leaving this signature in Neanderthal DNA that we’ve detected.

[00:07:52] And then that group of modern humans either just went extinct or ended up being absorbed back into human populations in Africa before a second migration out of Africa seventy or eighty thousand years ago. So anyway, it suggests not only another interaction between modern humans and Neanderthals, but one that’s much earlier, and it paints a picture of multiple migrations of modern humans out of Africa. And only the more recent cases led to the current populations that we know of today outside of Africa.

Grace: [00:08:28] Yeah, that’s so fascinating. I feel like bioinformatics is already such an interdisciplinary subject. I mean, taking together biology and computer science, and then you take it a whole step further and add anthropology in there. Do you work closely with people in anthropology or studying human history?

Adam: [00:08:48] Yeah, I have not worked directly with anthropologists myself. Although we did have an anthropologist collaborator on the paper in Nature, although we worked more closely with Sergi than with me. But there is a lot of interest across the field at this intersection between genetics and anthropology and Svante Pääbo and David Reich and others have been quite proactive about interacting across fields. I attended a meeting here at Cold Spring Harbor a few years ago that was organized by co-organized by David Reich. That was a group of geneticists and a group of anthropologists together discussing these issues, and it was fascinating. But it’s not something that’s really in the center of my own research.

Grace: [00:09:29] You seems to have a very broad reach with your research. It’s great. Yeah. So you started out doing computational modeling and phylogenetic modeling in HIV. Can you tell us a little bit about that work?

Adam: [00:09:41] Yeah. So this was my first job actually straight out of college. I hadn’t gone to graduate school yet. Through a friend of mine who had been an undergraduate with at Cornell, found out about this opportunity to work at Los Alamos, doing HIV sequence analysis. And it was interesting to me for a number of reasons. I had done an undergraduate degree in agricultural and biological engineering. And so I had been interested for a long time in sort of the intersection between mathematical modeling and questions in biology. But I had never dealt with DNA sequence data before, and I had never dealt with phylogenetics or evolutionary reconstruction. And when I was exposed to those fields, I just found them fascinating. I mean, they resonated with me in a whole variety of different ways. I’ve always been interested in reconstructing the past.

[00:10:28] I’m interested in random processes. I’m interested in computer algorithms. I’m interested in evolution. And so all of these interests sort of came together in this fascinating area of using phylogenetics. And then that work also had an epidemiological component. We were building phylogenetic trees to describe HIV sequences, but then we were making use of them to understand the spread of HIV across the globe because we were seeing different strains emerge in different regions of the world. And then we began to see interactions between these strains and the production of recombinant strains of HIV. So my first scientific paper was actually on an algorithm that I developed, a very simple algorithm, to detect recombinant strains of HIV, which at the time was a kind of a new idea and something that was of great interest in the field.

Adam: [00:11:23] My first experience was very exciting publishing a scientific paper. I think I was 23 years old and was able to publish a paper that established researchers in the HIV field were excited about, and I got to present at meetings and so on. And after that I was hooked. I was hooked on science, I was hooked on computational biology and I was hooked on evolutionary genomics.

Grace: [00:11:45] Do you still keep up with emerging HIV research and things like that?

Adam: [00:11:49] I haven’t participated in HIV research since that time. I moved on to other questions. Although I have to say I got interested again recently in this question of recombination and viruses with the emergence of COVID-19. And reread some of those old papers and including my own old work on detecting recombination in viruses because there was some discussion about the role that recombination might have played in the emergence of SARS‑CoV‑2 in human populations. But that’s my only experience in that area in the last 25 years or so.

Grace: [00:12:30] Yeah. So I guess speaking of the pandemic, from an evolutionary standpoint, do you think we need to worry about new variants and things like that?

Adam: [00:12:41] The basic evolutionary fact is the probability of emergence of a new variant should be proportional to the number of viral replication events, which is going to be proportional to the overall number of cases. And so we need to get the number of cases down. And the best way to do that is through vaccinations. It’s been extremely discouraging to me to have these effective vaccines, more effective than anyone could have hoped, and see people reluctant to use them. So I think we just have to keep hammering on the vaccination efforts. They need to be available across the entire world, not just in rich, first-world countries. We need to push really hard on getting access to them, convincing and incentivizing people to use them.

[00:13:36] Ultimately, I think new variants will emerge. We will develop over time an increasing sort of baseline resistance for most people in the world who will eventually be exposed. And I think the pandemic will eventually reduce itself to a baseline level. But I think the virus will be endemic and we’ll have to adjust to it being part of life. I’m optimistic that with increased baseline resistance, increased vaccination, increased ability to provide new vaccines quickly and efficiently, that we won’t be brought back to our knees by emerging variants. But it’s difficult to say for sure what could happen as new variants emerge.

Grace: [00:14:29] I always like to hear the different perspectives of people in different fields of science on the pandemic. I think the evolutionary take on it is very interesting. You also did a study in bats, on the evolution of bat immunity and things like that.

Adam: [00:14:46] That’s right. Yeah, we’ve gotten very interested in comparative genomics of bats, in part because of their connection with SARS-CoV-2. But for other reasons as well. In fact, our initial work on bats has been funded by our cancer center here at Cold Spring Harbor. Because bats are remarkably resistant to cancer and we’ve been trying through DNA sequencing and comparative analysis to shed light on the genetic underpinnings of both bat specific immune responses, bat specific cancer resistance and longevity of bats. Bats are extremely long-lived mammals for their body size. If you plot body size versus lifespan in mammals, you see a general proportionality. But bats are an outlier. They live much longer than other mammals of similar size, such as mice. Bats can live 35-40 years or more.

[00:15:42] We’ve been doing DNA sequencing and analysis. We have an initial preprint out on our findings. We’ve found some interesting things in both immunity and cancer, in particular a massive contraction of the IFN1 locus. And a strong enrichment among apparently positively selected genes for tumor suppressors and DNA repair genes. And we’re in the process of working closely with experimentalists to begin to test the actual molecular basis of some of these differences between bats and other mammals. And we’re also in the process of applying for grants from the NIH on this topic.

Grace: [00:16:22] That’s so cool. Because I guess I wanted to ask a little bit about the importance of evolutionary biology in the study of cancer, because I wasn’t necessarily sure how those two topics connected. So that’s a really interesting take on comparative genomics and looking at how that immune system has influenced their susceptibility to different cancers. And bats have a reduced immune response. They don’t have a very active immune system, is that correct?

Adam: [00:16:51] Yeah. They seem to be able to tolerate viral infections without having a very powerful immune response. And it’s interesting because when you look at what makes humans sick when they become infected by SARS-CoV-2 or other viruses, it’s often an overly powerful immune response that makes them very sick. And so in some cases, it seems that viruses are killing us, not because our immune response is inadequate, but because it’s too powerful. And one hope is that we can learn something from bats in the way that they’re able to keep from getting sick from these viruses and yet not have an overly powerful immune response that ends up harming them more than the virus itself. Yeah. So that’s one of our interests in this area.

[00:17:44] Of course, it’s also interesting to just understand the dynamics of zoonotic transmission and the way in which bats are harboring viruses and then transmitting them to people. The fact that bats seem to be able to tolerate such high viral loads does seem to be essential to their role as a reservoir for viruses that get transmitted to humans. And so understanding their viral tolerance is also important and interesting in that regard.

Grace: [00:18:16] Yeah. So I mean, evolutionary biology is kind of a pretty popular science topic. So what do you think are some misconceptions that people in the general public have about evolutionary biology?

Adam: [00:18:30] One misconception is understanding the diversity of selective forces that have influenced humans. People tend to think in conventional terms about the strongest humans being the ones that propagate, you know, the ones that are least likely to be killed by predators and that sort of thing. And undoubtedly avoiding predators was a source of selective pressure on humans. But there are many others that I think tend to be underappreciated. One of them is infectious disease. I mean, humans have been enormously shaped by infectious disease. And one of the strongest selection pressures on us is the resistance to infectious disease. The pandemic is helping make this issue more clear. But I think in general, we tend to have forgotten a lot about infectious diseases because they play much less of a role overall in modern life than they have in the past.

[00:19:28] Another really important selective pressure is sexual selection. The choices people make about who they mate with for various reasons. And then there are very strong selective pressures that influence reproduction in a way that humans have no choice over. So, for example, sperm competition individual sperm cells competing with one another to fertilize an egg. So there are many, many levels at which selection acts. And I think when people just think about a caveman dodging a mountain lion or a bear, they’re only getting at a very small sliver of the diversity of selective forces that have influenced human evolution.

Grace: [00:20:14] Yeah, that’s true. There are some really interesting selection events. So after you worked in Los Alamos, where did you head after that?

Adam: [00:20:22] Well, I was working in Los Alamos in the mid 90s. And I had an engineering background and I had a lot of interest in developing computer software. And at the time, I felt that my interests lay more in the software development area than in the scientific research area. And it coincided with a time where there was a lot of opportunity for software development in bioinformatics. A lot of companies were creating bioinformatics groups. A lot of people were developing and either selling or making publicly available bioinformatics software. And so I took a job at a group in Santa Fe, New Mexico, called NCGR, National Center for Genome Resources that was doing a lot of software development. I went there and I worked for about 5 years as a software developer and learned a lot about software development and then kind of came to the conclusion that I wanted to get closer to the science.

[00:21:20] And after many years of putting off going back to graduate school, I decided I really needed to bite the bullet and get my PhD. I was sort of a reluctant academic, I have to say. At the time, I was of the mindset that I could teach myself anything I needed to know. But I finally decided that there was value in getting my PhD and diving back into scientific research. So I left software development, became a full time PhD student and went to Santa Cruz, California, to join David Haussler Laboratory. And from that point on, I have plunged myself into the world of comparative genomics, population genetics, evolutionary modeling and so on.

Grace: [00:22:01] Yeah. And despite that reluctance to going into academic science, you stuck with it after your PhD because you went and became a professor at Cornell and now Cold Spring Harbor. Can you talk a little bit about that decision? How did you change your mind?

Adam: [00:22:16] I actually had not planned to go into academia. I wasn’t sure what I was going to do. But it was an exciting time, the early 2000s for academic computational biology. There were a lot of opportunities emerging, a lot of new departments, new research centers. And in my third year as a PhD student, I had been working with Rasmus Nielsen, who’s now at UC Berkeley, on a book chapter project. He was editing a book and I was writing a chapter with my advisor. And he sent me a job ad at Cornell and I read this job ad and it just sounded like it was written for me. I mean, they were looking for someone who had exactly the sort of expertise I had. And, you know, I had been an undergraduate at Cornell, so I had a lot of affection for the place.

[00:23:07] Coincidentally, I also was considering moving closer to family. My family’s from upstate New York and Cornell is in upstate New York, and I had two small children and we were getting tired of putting them on planes every time we wanted to see family. So I said, what the heck? I’ll apply to this job. I applied and I got the job. So I said, well, you know, I never really planned to be an academic, but this sounds like fun. It sounds like a great opportunity. I love what I’m doing. This is an opportunity to keep doing what I’m doing. And I took the job and never looked back. I’ve really enjoyed academic work since then and have been able to make it work, been able to keep the lab funded and keep publishing papers and keep recruiting students.

[00:23:49] And I think I’ll just keep doing that as long as I can. But it was a different time. I mean, I mentor a lot of my own graduate students and postdocs in their job searches. And I think the job market is much more competitive now than it was then. There was a lot of opportunity in computational biology in the early 2000s, and I benefited from being in the right place at the right time. Sometimes I see the job searches we carry out now, and I wonder if I would have even gotten an interview for some of these jobs.

Grace: [00:24:18] Yeah. Academic science is a very competitive space these days. But there is such a strong need for bioinformaticians and computational biologists. So I mean, there’s a lot of job security in that, but maybe academia is a lot harder.

Adam: [00:24:34] Yeah. I think there are more industry opportunities now than there were at that time. And, you know, the combination of the competitive academic job market and the opportunities in industry means that a lot of young trainees are going into industry, which I think is great. I have a number of recent postdocs from my lab who’ve taken industry jobs and are very happy in them. But, you know, the pendulum tends to swing from one side to another on these things. And I wouldn’t be surprised if in a few years the supply and demand dynamics have changed and things open up in academia again.

Grace: [00:25:08] Certainly. And how have you seen bioinformatics and computational biology as a field evolve in the last few years?

Adam: [00:25:18] One change is just, as I mentioned, a swing toward more activity in research and industry. Another change that I’ve seen in my time in computational biology is just a general shift toward embracing the biology side of the field. They need to ask good biological questions, they need to engage with the data and people not being satisfied with just taking whatever the latest algorithmic or machine learning advances and applying it to a biological data set. I think when I started in the field in the early 2000s, there was a lot of that. There were a lot of people doing computational biology who weren’t that interested in biology and didn’t know that much biology. They were just taking off-the-shelf computational methods and applying them to biological questions in a not very imaginative way.

[00:26:10] And I think over time, people have really realized that in order to do computational biology well, you have to engage with the biology. It’s not enough to just have a computational hammer and look for nails. You have to really think imaginatively about biological questions and how computational methods can be used to address them. And about the interaction between computational methods and experimental methods. About how experimental methods can lead to hypotheses that can be tested computationally and vice versa. Computational methods can generate hypotheses that can be tested experimentally. That feedback between computation and experiment, I think is extremely important and has become more pervasive in the field.

[00:26:54] I think the field is also just bigger and more competitive. Early on, there were really just a handful of people who had this joint background in computer science and biology. And if you were one of those people, then you could sort of write your own job description. It was relatively easy to find a job in the field. Now there are many, many people who have those backgrounds. There are people emerging from PhD programs in bioinformatics and computational biology. There’s a lot more awareness of these questions in biostatistics departments or biophysics departments. It’s just a much more established and competitive academic field.

Grace: [00:27:39] Do you think you would have chosen the same path if you had graduated in bioinformatics today?

Adam: [00:27:46] I really don’t know. I mean, I think I was attracted to the field being so new. And maybe I would feel today that it was too established and I would look for something newer and more niche. But it’s hard for me to say. I also think it’s possible that if I were finishing my PhD now that I would end up in an industry job rather than in an academic job just because of the dynamics of the field at the moment. But it’s always hard to ask these counterfactual questions.

Grace: [00:28:19] True, true. So given the hyper competitive job market for positions and bioinformatics, can you maybe give advice to people who want to enter that field? Like what sorts of skills are most important today?

Adam: [00:28:37] Yeah. I guess, I think it’s true that a graduate from a bioinformatics program who’s interested in this field needs to be fluent in data science and machine learning, basic statistics. But I think that those things are necessary but not sufficient for success in the field. And I think what really will push a person over the edge is also really thinking like a scientist, not just like an engineer. So developing a good taste in problems, developing a nose for questions that can be effectively addressed using computational methods, developing a fluency in the biological technologies and biological questions of interest, the ability to interact closely with experimentalists. I think these are the things that push a person over the edge from being just a data scientist to being a computational biologist who can lead the way in the scientific side of the field.

Grace: [00:29:41] It’s very good advice. So tell me a little bit about you. Like, who is Adam the non-scientists? What do you do outside of research?

Adam: [00:29:49] Well, I have two kids. My daughter just started at the University of Rochester. So I’m adjusting to going from two kids at home to one kid. I live on Long Island in Huntington, New York and live in an old Victorian house and spend a lot of my time fixing that up. And I like to do a lot of cycling and some hiking and spend as much time as I can outdoors. That’s probably a pretty good summary.

Grace: [00:30:16] Yeah. I actually saw in your Twitter that you were planning on heading to Iceland. Did you make it out there?

Adam: Yeah, we did.

Grace: Nice. Yeah, I was just there a couple of weeks ago.

Adam: [00:30:27] Ok. Yeah, we really enjoyed it. We had a fantastic trip. It’s a beautiful place and it felt like the right sort of first trip out of the country after COVID. Relatively safe and controlled.

Grace: [00:30:39] Yeah, that’s excellent. Yeah. Actually, Iceland is a really, probably a very interesting area to study because it’s so isolated and they have a huge dataset. Haven’t they sequenced everybody in Iceland?

Adam: [00:30:52] Yeah. The studies by deCODE have been extremely influential in a variety of different ways, both for association studies and also for studies of rates and patterns of human mutation, which they’re able to trace in great detail, taking advantage of their genealogical databases and pedigrees. So, yeah, it’s been very important in human population genetics. It’s also interesting to look at Iceland from an ecological perspective. I think the largest land mammal was the Arctic Fox in Iceland when Scandinavians arrived and began bringing agricultural animals. So there’s a very short history of large land mammals there. And then there have been interesting events like the introduction of the Icelandic horses and then subsequent genetic isolation, of those horses. And it’s interesting to see the way they have been shaped by the Icelandic landscape and climate, as well as by human selection. But yeah, it’s a fascinating place for questions in evolutionary biology. Certainly.

Grace: [00:31:59] Certainly. And yeah, Iceland horses are really interesting. They had such strict laws that if an Icelandic horse was taken out of Iceland that it couldn’t be brought back into the country. It was just really interesting. And with humans, they have an app. It’s like a dating app where you can check and see if it’s okay to date somebody based on your familial relationship to them.

Adam: [00:32:24] Ah, to see whether you might be related, yeah.

Grace: [00:32:26] Yeah. You put their name in and I think the generally accepted okay line is like fourth cousin or something like that.

Adam: [00:32:33] I see. Well, amazing.

Grace: [00:32:35] Yeah, it’s a really interesting country. Yeah. So as we wrap up the episode, do you have any other any final thoughts on the future of bioinformatics?

Adam: [00:32:46] Well, I guess the future of bioinformatics, I think it’s an open question whether bioinformatics will remain a distinct field. I think that to some degree, the tools of bioinformatics are being absorbed by broader biological sciences. They’re just becoming part of the toolkit of doing biology. And I think in the future, biologists will need to be much more fluent in computational methods and the use of machine learning and the use of powerful computers. And we may not think of it as a distinct field. It may just become part of being trained to do biology. And I think that’s okay. I think often new fields emerge at the interfaces of other fields, and they may or may not remain distinct. They may be absorbed over time, and I think that’s okay. I’m personally very excited to see quantitative methods and computational methods become so central in biology.

[00:33:50] You know, our center at Cold Spring Harbor, it’s called the Science Center for Quantitative Biology. It has begun as kind of a distinct group of investigators doing developing quantitative methods. But increasingly we’re being absorbed by the broader scientific community at Cold Spring Harbor. And the talks when we gather at our annual symposium or some other event to talk about our research. The talks from the quantitative biologists are beginning to involve more experimental biology and more collaboration with experimentalists. And then conversely, the talks by the experimentalists are beginning to incorporate more data analysis and quantitative methods. And I think the logical conclusion of this process is that we probably won’t be a distinct group anymore. We’ll all just be biologists using whatever tools and techniques are available, a combination of experimental and computational tools and techniques. So I guess that’s what I think about the future of the field. It’s dying, and that’s okay.

Grace: [00:34:56] It’s dying, and that’s a good thing. Fantastic. Well, thank you so much for joining me today, Adam. I had a great time listening to your thoughts on evolutionary genetics.

Adam: Yeah, thanks, Grace.