The Bioinformatics CRO Podcast

Episode 55 with Mo Jain

Mo Jain, Founder and CEO of Sapient, discusses the importance of small molecule biomarkers and his approach to biomarker discovery research.

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.

Mo Jain

Mo Jain is the Founder and CEO of Sapient, a biomarker discovery CRO using next-generation mass spectrometry technology.

Transcript of Episode 55: Mo Jain

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 Mo Jain of Sapient. Welcome.

Mo Jain: [00:00:07] Thank you so much, Grant. Pleasure to be here today.

Grant Belgard: [00:00:10] So tell us about Sapient.

Mo Jain: [00:00:12] Absolutely, Grant. So Sapient is a discovery CRO organization which is really focused on biomarker discovery. And the way we operate is through leveraging novel technologies, particularly in the mass spectrometry sector in order to enhance human discovery. And we primarily serve as a partner for large pharma, early biotech, and even some foundations to help them in their biomarker discovery efforts as part of their drug discovery work.

Grant Belgard: [00:00:39] Can you tell us about the history of the company?

Mo Jain: [00:00:41] Yeah, absolutely. The concept of Sapient admittedly dates back almost two decades now. I’m trained as a physician, and one of the common questions that one receives when treating patients is, why did I get this disease? How do I know next time if this is going to happen to me? How can I protect my loved ones and family members? What are the diagnostic tests that I can use to know if I’m going to respond to this drug? And one of the really humbling aspects of medicine is, despite the massive amount of knowledge that’s been gained over the last several hundred years, really what we still understand and know represents a very, very small fraction of all the knowledge there is to know. And for most of these very insightful questions, the answer typically is I really don’t know the answer. And at this time, when I was in the midst of training, the human genome was really coming to fruition in the early 2000, when the initial draft of the human genome was reported, and genomics was going to revolutionize the world as we know it. And the basic idea behind this was by understanding the basic blueprints of human life. We could leverage that information to understand how healthy or not healthy you may be over the course of your existence, what diseases you were going to develop or predispose to, what drugs you were going to respond to, and essentially we would be able to transform the way we think about diagnosing and treating human disease.

Mo Jain: [00:02:06] The challenge has to do with the fact that however the amount of information and the type of information that’s encoded in the genome doesn’t actually enable that to happen in most cases. And at the time when I was in the midst of training and I apologize for the long answer, you’ll see where I’m going in a moment. But at the time of this, we were doing a thought exercise, and that is well if we could parallelize sequencing to the nth degree, and if we essentially could line up every single human on the planet, and we knew everything about everyone and we sequenced everyone’s genome, how much of human disease could we explain? And the hope would be 80, 90, 95, 98%. In actuality, when you look at the numbers and there’s many ways you can calculate this as a heritability index, population attributable risk index, etcetera. But the true answer is probably somewhere in the 10 to 15% range. And that’s a theoretical upper limit. If you really look in actuality, the numbers probably in the single digits for how much of human disease we can truly explain through sequence. And perhaps that’s not surprising that we know the way in which you live your life. Your genome is set from the moment to conception and the way you live your life. Everything you eat, drink, smell, smoke, where you live, how you live, we know is massively important in how healthy or not healthy you’re going to be over the course of your existence.

[00:03:25] And none of that information is captured in your underlying genome. And so I became very interested in that 85% of population attributable risk that’s not encoded in genetic sequence, understanding once again, diet, lifestyle, environmental factors, how one organ system may communicate with another organ system, how the microbiome that’s part of our gut and skin and saliva influences human disease. Again, none of that information is encoded in genetic sequence. But it turns out that that’s encoded in small molecule chemistry. So when you ate something for breakfast or lunch or dinner depending upon where in the world you are and what time it is, that gets broken down at your gut into small molecules. Those small molecules enter it into your bloodstream. And because we all eat only healthy things, they do good things in our organ systems and allow us to be healthy over time. And the basic premise is that well if we could capture that information, if we could take human blood and probe the thousands of markers that are floating around in human blood, we can begin to understand how humans interact with their environment both internally as well as externally, and leverage that information now in the way the genome was supposed to in understanding and predicting who’s going to develop what diseases over time, how long someone may live, whether or not I’m going to respond to a particular drug A versus drug B et cetera.

[00:03:25] So that was the basic premise. Now this is not a new idea. Every year you go to the doctor. They draw two tubes of blood, typically about 20ml of blood. And in that, we measure somewhere in the order of 12 things to 20 things depending upon the test you get. Half of those are small molecule biomarkers, creatinine and cholesterol levels, glucose etcetera. The challenge is that there’s tens of thousands of things floating around in your blood, and we’re literally capturing less than 0.1% of them. And so how do we develop technologies that allow us to very rapidly measure these thousands of things in blood, and to do this at scale across tens of thousands of people in a manner that allows us to discover, well, what are the next 12 most important tests? What are the next 12 after that? And how do we leverage this information at scale to to really predict and understand the human condition at its earliest disease points? And so that was the basic premise of Sapient. It was born out of academia, where we spent the last decade prototyping and developing these bioanalytical technologies. And as these were coming to fruition, we spun them out to form Sapient and that’s how we came to be today in.

Grant Belgard: [00:05:47] The work you’ve done at Sapient, have you seen a large number of complex, non-linear, non-additive interactions among factors, or are you finding the major signals are things that can be reduced to more simple and straightforward guidelines looking at LDL and HDL, new markers along those lines?

Mo Jain: [00:06:11] Yeah. The simple answer is both, which I recognize is not all that helpful. But it comes down to what type of predictive analytic you require, what’s the threshold that you require for actual diagnosis. Now often times for virtually all cases, you can reduce down information to a single marker or at least what I would say is a practical number of markers, somewhere below half a dozen that we can measure under the most stringent of laboratory methods clinically in hospitals around the world, and provides us the information we need to know. That works well. You can imagine in the same way, cholesterol is highly predictive of those individuals who are at risk for heart disease. Developing simple tests like that for cancer, Alzheimer’s disease, liver disease, lung disease, GI illnesses, pregnancy related complications, etcetera, etcetera is oftentimes quite functional. And that’s where we spend most of our time at Sapient. At the same time though, as you’re suggesting Grant, much of human disease is non-linear in its etiology. It’s rarely a single case or an additive case of two events that cause disease, but rather it’s a much more complex interaction of many, many different [inciting] etiologies. It may be a genetic predisposition, which increases risk somewhere in the order of several percentile. Added on with an environmental exposure, together with a particular initial acute insult that collectively results in a disease process cascading and starting. And so this is where we’ve become much more interested in taking these very complex data sources, where we’re measuring tens of thousands of things in human blood, and using much more advanced AI based statistic modeling now to be able to much more holistically predict and understand these complex interactions.

Grant Belgard: [00:07:48] How much added power do you see from that?

Mo Jain: [00:07:50] Yeah, quite a bit, which is both an incredible opportunity and is incredibly challenging. As you can imagine, in the same way if you look at a picture, a painting, oftentimes with a very small reductionist view of that painting where you’re looking at only several pixels, you can oftentimes tell something about the painting. This is a blue painting. It’s of the ocean or something to that extent, but with a very, very small snapshot. But as you took a much more global view of the underlying image, that’s where the real granularity begins to emerge. And as we go from simply saying, well, your risk of disease X is Y percentile or it’s increased in this manner to a much, much more holistic view of across these 100 different diseases, this is your sort of combinatorial risk. This is how you want to optimize life and diet. This is how you want to optimize medications specifically for you. This is how we want to develop new drugs. This is where allowing for that complexity is absolutely critical.

Grant Belgard: [00:08:52] Would you say this approach is more powerful for risk of onset of a disease that hasn’t yet occurred or for prognosis?

Mo Jain: [00:09:04] Yeah, it very much depends upon the disease and the biological question. And you can break these down into diagnosis, meaning early diagnosis prior to disease onset or at the earliest stages. Prognosis meaning once disease has become clinically apparent, understanding long term outcomes and then prediction regarding response to therapeutics, which is really the third component of this. And as you can imagine, the added value of more complex modeling versus reductionist testing of single molecules partially depends upon which of those three question baskets you’re in. And then also the specific disease and the complexity and heterogeneity of the underlying disease state. Now the good and bad is we do a relatively poor job of this today. And when you think about a complex disease, whether it be heart disease or diabetes, this probably represents half a dozen or more diseases, all of which have a common end phenotypic variable of metabolic insufficiency or hardening of your coronary arteries that were lumping all together, even though they have very different mechanisms of action that allowed someone to go from a normal stage to an abnormal stage. So even for these very heterogeneous, complex multi-organ systemic diseases, even being able to break it down into those broad categories, what are the four types of subgroups? What are the five types of subgroups can be extremely valuable? But now being able to take that even further and using more complex modeling, these AI based nonlinear approaches where you can be able to say well I’m not interested in the five subgroups, I’m interested in the 100 subgroups and understanding which one of these specific subgroups is going to be optimal for a particular therapeutic. This is where adding complexity and nuance becomes critical.

Grant Belgard: [00:10:43] What is the path to clinic look like for what you do?

Mo Jain: [00:10:45] So this is where it becomes really, really important. And this is absolutely an evolving area that’s changing literally week over week. It used to very much be five years ago that clinical translation had to be dependent upon a single test, a single molecule that was well measured that we could enter into what we call a CALEA Accredited Laboratory. And that was a one test, one diagnosis. That modality and that way of operating has completely changed over the last half decade. And we’ve seen this now. There’s something on the over 100 different tests that are at the FDA that use much more complex ML based algorithms or AI based algorithms for diagnostic purposes. We’ve already seen this in early pathology and histopathology and in radiology, and I wholly expect that the inflection is only starting now. So I suspect that over the near future here, I’m literally talking about the next several years, much more complex, nuanced, blood based testing is going to become the norm. We already see this in a number of conditions, whether it be diagnostic tests, whether it be Cologuard, for instance for colon cancer, whether it be a genetic testing for particular chemotherapeutics in the setting of cancer. We’re already seeing this evolution happening in real time, and I suspect that’s going to not spill over, but extend to virtually every single human disease.

Grant Belgard: [00:12:07] How much of the work you do is brought to you by clients or sponsored by partners versus internal R&D to develop these tests?

Mo Jain: [00:12:17] Yeah, it’s a great question Grant. We’re somewhat multi-personality if you will, let’s say, and that we’re a front facing CRO. So a good portion of what we do over 80% of our time and attention is really based upon servicing large pharma and early biotech and their drug development efforts and simply put there. We’re engaging these sponsors. They’re bringing biological samples to us. We’re analyzing them on our proprietary mass spec technologies, generating that data, doing the statistical analysis, making the discovery, and returning that discovery to them for commercialization as part of their drug development efforts. The other 20%, as you suggested is really based upon our internal R&D efforts. And so at the same time, because we have such ultra high throughput mass spectrometry systems that are capable of generating data faster than any other technology worldwide, we’ve also at the same time been able to go around the world and collect hundreds of thousands of biological specimens internally as part of our R&D efforts, analyze those samples, generate now what is the world’s largest human chemical database. And amalgamate that information in a centralized repository internally here at sapient that we now are subsequently mining for novel diagnostic purposes.

Grant Belgard: [00:13:28] What are the biggest challenges you face doing that?

Mo Jain: [00:13:30] Yeah, it’s a really good question. Up until several years ago, this would have been a simple technology issue. How do we actually generate the data? And I’m very glad to say that the efforts of Sapient have enabled us to generate now and handle data very, very quickly, meaning handling 100,000 to several million biological specimens for mass spectrometry analysis now is no longer a dream effort, but is very practical. It’s a daily ongoing here. So you can imagine that bucket now has been or that can has been kicked down a little bit of the road where it’s no longer a data generation issue. It comes down to a data understanding interpretation issue, whereby how do we take this complex data now and really commercialize it in a way that for the betterment of society. How do we develop the diagnostic tests that are going to be most meaningful. In many ways Grant, it becomes the kid in the candy store problem. If you have massive amounts of data, you can theoretically answer hundreds of questions simultaneously. And so what are the most high yield, high impact questions for different populations that we want to answer first and bring to clinical testing as quickly as possible? And that’s very much a personal sort of question.

[00:14:42] Obviously there’s a business use case behind it. But you can imagine, if you ask a particular foundation that operates in the rare disease space, they may have a particular preference. If you look across prevalence of disease across large populations of adults in the developed world, it’s a very different answer, may be heart disease and cancer, may be basic aging. And if you ask foundations that are operating in low to medium income countries, whereby there’s arguably the greatest need for human health and development, it’s obviously a very different set of questions around early childhood development, pregnancy nutrition and optimisation of in-field testing. So that’s one of the largest challenges that we face on a day to day basis. Now, certainly that’s a good problem to have. It’s very much a “first world problem” as to where you want to go first, and how do you want to operationalize and commercialize a very large data? But it’s a very real problem that I think many organizations that operate in this space are facing every day.

Grant Belgard: [00:15:40] Is there a system you use to make those decisions?

Mo Jain: [00:15:44] Yes, there is. And like the best of systems, you can imagine Grant that it oftentimes goes out of the window within the first three sentences of a discussion. So there’s certainly a lot of business use cases that we think through understanding what’s the addressable markets, what is reimbursed look like, these things that point us in particular directions. But at the same time, we’re fortunate enough just given how we operate, to have a little bit of leeway and the other questions that may be of equal if not greater importance, but may be of slightly less commercial value. And thinking through some programs that we have in understanding maternal nutrition in the developing world, programs that can have massive impact in large numbers of people that can move needles but may not have the same commercial relevance as coming up with a diagnostic test that tells us if we’re going to develop cancer in the next several years. Equally important, just slightly different commercial market.

Grant Belgard: [00:16:37] How do you think about causality or do you think about causality? Are you just really focused on strong associations? What’s the most predictive regardless of causal relationships?

Mo Jain: [00:16:46] It’s a fantastic question, and I’m happy to provide an answer. But ask me tomorrow and I’m sure I’ll give you a different answer. And as you can imagine, this is one of those things that fluctuates quite a bit. In the end, it depends upon what you’re using that information for. So let me give you an example, HDL is an extremely strong predictor, stronger than any other predictor for heart disease over time. But it’s still very questionable whether HDL itself is causal for coronary disease. We call it the good cholesterol, but likely what the evidence really points to is that HDL is reading out some other phenomenon that’s actually the causal agent. Now if I want to understand what my heart disease risk is over time, I just need a valid correlation that we know is specific and is statistically rigorous over time. And so HDL serves that purpose for me. Now, if I want to develop a drug and I want to use as a marker of drug efficacy HDL, well then having a causal association becomes much, much more important. And we spend quite a bit of time thinking through this process, because our goal is not only to come up with diagnostic markers, but to develop new drug targets and to validate those targets to develop new nutraceutical and natural product based therapeutics et cetera, et cetera.

[00:18:01] There’s a lot we can do with this type of data. And part of this has to do with understanding that causal question. And this is where we do quite a bit of multidimensional data integration, particularly with genomics information together with these small molecule biomarkers. In essence, doing a Mendelian randomization type of approach from which we may be able to infer causal relationships. And as you’re well aware having worked for many years in this space, MRI based analyses particularly MR bidirectional is very useful when it works, but in the absence of information, doesn’t necessarily negate causality. And so this is the way we certainly think about it. Again, it all has to do with how you want to use that data and what’s the objective function. In the end for a diagnostic test, it just a matter if it accurately diagnoses and predicts people who are at risk of a disease state.

Grant Belgard: [00:18:51] How important is longitudinal data for what you do?

Mo Jain: [00:18:54] Very, very important. And I think one of the lessons that we learned from the genomic revolution, well there’s several things that we learned. One, the genome as I suggested earlier on, really imparts a minority risk of human disease, who our parents are, what occurs at that moment of conception when a human is formed, that really provides only a very small amount of predictive capacity for what’s going to happen to us over the next 100 years of our existence. That’s the first lesson. The other lesson that we’ve learned is that human disease is a very dynamic process. Health is ever fluctuating. On different time scales certainly on a decade long time scale, but even on a day to day basis, in an hour to hour basis, when you really dive into the nuance, if I slept last night versus if I slept one hour last night, I probably have a different health state today. Now, the impact of that may not be relevant over many, many years, but certainly you could argue that I’m healthier because I slept or didn’t sleep, or if I ate correctly versus didn’t eat correctly. And anyone who’s ever gone out and had an interesting night and woke up with a hangover can agree with that. And so being able to understand that dynamic nature is critical. Now your genome for the most part, your somatic genome is fixed from the time of conception and doesn’t change over life. And this is where diving into dynamic market, particularly these small molecule dynamic markers that read out communication channels between our internal organ systems, between the external world and the world, and our internal sort of physiology between things like diet, lifestyle, microbiome, toxicants, etcetera, etcetera. This is where small molecule biomarkers are particularly important. And because of their dynamic nature, they have the ability to change quite quickly, which can read out almost in a real time fashion particular health and disease states.

Grant Belgard: [00:20:41] What challenges have you run into collecting longitudinal data and integrating that with clinical data? And I guess I’m going to ask a multi-pronged question here. Have you done this in health systems outside the US? And do you have and experience comparing and contrasting the data you get from different systems?

Mo Jain: [00:21:04] Yeah, as you can imagine, there’s a couple of different parts to the question, all of which are really important. I’m going to answer the final part first. I firmly believe that humans are all equal, but not identical. And one of the core components is where geographically in the world in which we live. And you know the famous quote that best summarizes this is that it’s not your genetic code, but it’s your zip code that’s a better predictor of disease. And statistically, that’s absolutely true. Based upon your geographic zip code, we typically have a better handle on your underlying long term risk of disease than anything else. And so certainly geography plays a huge role in this. It’s one of the core aspects of our interaction with the world. And you can imagine geography feeds into everything from the degree of sunlight, the type of water, the type of diet, toxicants that are local to that environment, socioeconomic state and access to health care. There are so many aspects that are fed into underlying geography. And so this is where our ability to broadly biological samples as well as individuals from around the world has been critical. And so as you can imagine, there’s value in identification of universal diagnostic tests that work independently of where you are. If you’re in Sub-Saharan Africa, if you’re in sunny San Diego, if you’re in Western Europe, it doesn’t matter. The test reads out what it should read out.

[00:22:19] And there’s also secondarily value in having localized or population specific tests, not something that traditionally in medicine we’ve done. But if it’s the case that there’s particular exposures that are unique to a given environment, those are a key determinant of a given disease in a particular location. To not sample that information and use it is silly to me. And so we oftentimes are looking for both of these. What you can find is there’s certainly universal realities and universal markers that denote health and disease states over time or drug response. But there’s also geographically localized markers that may be unique in specific populations, owing once again to diet or whatnot that may be unique to that environment. And I think both of them have the value in understanding the human condition. There’s certainly some practical issues and considerations. We’ve been very fortunate to have a number of relationships with top academic medical centers around the world. And the simple answer is that there’s more biological specimens and there’s more data available in the world than people are using. So it’s there if you’re willing to work within the constraints of the legal constraints of accessing it and whatnot. The real challenge is one that I suspect you’re alluding to, that everyone who works in the large data analytics space has learned one way or the other over the last decade, and that is garbage in, garbage out. I don’t care how good your metrics are. If the data is fundamentally not clean, and if you’re not conditioning on high quality data then you’re just leading yourself astray.

[00:23:53] Now, that doesn’t mean data has to be pristine. I’m actually quite a bit of a fan of using real world data because you want there to be noise. When you see signal emerging from that noise, you have much more confidence that that signal is real, as opposed to pristine data that may be present only in a phase three clinical trial. And then when you extend those same markers to the real world, you see that they have less of an effect that they should, simply because there’s now other confounding factors. But in the end, as much as I’m a fan of our technologies and the type of data we generate, having clean phenotype data is absolutely essential. And so we spend quite a bit of time internally here at Sapient, thinking through ways in which we can clean human data. We can QC that data, we can sanity check it. And ensure it’s of the highest quality otherwise you’re just leading yourself astray. And certainly there’s very, very large data assets out there and data sets out there that are of less than stellar quality. And oftentimes those don’t result in any real meaningful discovery.

Grant Belgard: [00:24:52] Are there any go to external datasets that you’ll look to for validation of what you’re seeing at Sapient?

Mo Jain: [00:25:01] Yeah, it’s a really good question Grant. And this is one I’ve been in the challenges from an R&D perspective for us personally in that when you think about the molecules that are floating around in your blood right now. There’s tens of thousands of these molecules floating around in you Grant. Somewhere in the order of less than 5% of these have ever been measured, analyzed, structurally elucidated, or understood in any meaningful way, which means more than 95% of what’s in your blood right now is a black box. And so this is where I have challenged sometimes using external sources for validation, because they’re very much couched within that 5%. This is the same as the light pole, if you will effect where everyone is looking under that same light shade or lamppost at the same several dozen to several hundred molecules, whether it be genetic factors or small molecule biomarkers or protein biomarkers when the real signal lies outside of that initial light. And I’m a big fan of jumping into the dark, even if it’s sometimes a little bit challenging. And so what this ultimately ends up meaning is that we end up doing quite a bit of validation ourselves, simply because the current publicly available data assets, or even proprietary private data assets are really not of a nature that allows us to adequately validate or not simply powered for true discovery in this space.

Grant Belgard: [00:26:22] What is the future hold for Sapient?

Mo Jain: [00:26:24] Yeah, it’s a great question. The simple answer is I frankly don’t know. There’s obviously many things that we’re hoping to do. I very much believe in our service orientation and really accelerating the drug development process and pipeline together with our sponsors, whether they be large pharma organizations, small medium biotech foundations or governmental organizations. There’s tremendous value in that work that we see. And if we can help bring drugs to fruition, well, we’ve had a good day. At the same time as I mentioned earlier Grant, we’ve already generated the world’s largest human chemical databases, and they’re growing exponentially month over month. That provides some very, very unique opportunities that we have an obligation to bring to clinical translation, whether it be around new diagnostic tests, whether it be around better development and designing of clinical trials, whether it be an understanding and bringing means forward whereby we can predict who’s going to respond to particular therapeutics, whether it be developing natural pharmaceuticals, natural product pharmaceuticals themselves de novo. There’s a tremendous amount that we can do with these data assets. And that’s where I think Sapient is certainly going to continue to grow into the future. Now I hope as someone who’s aging hourly, today is actually my birthday Grant. So I’m aging more than [overlap]

Grant Belgard: [00:27:43] Happy birthday.

Mo Jain: [00:27:44] Well thank you, sir. Thank you. I just was alerted to that this morning. I forgot so I’m actually aging faster than I care to admit. But you know, I hope within the next several years to decade, diagnostic testing is completely different than where it is today. We’re no longer measuring two dozen molecules in human blood, but we’re measuring 20,000 molecules in human blood. And from that, being able to provide much, much more nuanced diagnostic information, prognostic information and therapeutic information regarding what’s the ideal way that I need to live my life, what’s the ideal diet, lifestyle and drug regimen that maximizes my personal health over time. And I’m hoping we’re moving to that position very quickly.

Grant Belgard: [00:28:27] So changing topics a bit, can you tell us about your own history, what in your background ultimately led to what you’re doing now? What prepared you for this? What maybe didn’t prepare you for this?

Mo Jain: [00:28:40] I wish I could say it was all very well planned out and deliberate, but you and I know that’s absolutely not the case. And so I trained as an MD-PhD. I was dual trained in medicine and science. My PhD degrees in molecular physiology. And I absolutely loved, loved, loved clinical medicine. It was a privilege to take care of patients. I very much enjoyed my patients. Those personal interactions and being able to help people in their own personal journey was something I was very passionate about as a cardiologist. I was also very frustrated by it, simply because much of clinical medicine is really about regressing to a common denominator or a common mean, whereby everyone with a given disease be treated with a given drug, even though we know that’s just not the way human medicine works, but it’s the best we can do. I was very frustrated by not being able to answer simple questions. When someone asks, well, why did I develop a heart attack at age 40? And what’s going to happen to my kids? And how do I test them for this? And there’s a lot of hemming and hawing that happens from the physician, simply because the real answer is, I have no idea. And there’s simply no testing we have for you. That, to me is very unacceptable.

[00:29:45] As I mentioned, I was training at the dawn of the genomic revolution and I was very much excited by this idea that parallelized sequencing and genomics were going to transform the universe in a meaningful way that this was all going to change. And at the same time, I was frustrated when saw that that wasn’t going to actually happen. When we really got down to brass tacks and did the calculations, it didn’t make sense how this was going to work. And so I trained initially in clinical medicine. I spent quite a bit of time in Boston at the Broad Institute, at MIT, at Harvard Medical School, and at Brigham and Women’s. In my scientific pursuits, that’s where I started working in mass spectrometry and large data handling. I spent the better part of the last decade as a professor in the University of California system here in San Diego as a professor, where I was privileged to work with really, really bright students and postdocs and faculty members to develop some of these technologies. And now I’ve lost track. I don’t know if it’s the third or fourth career, but this next phase or next adventure whereby we spun out the technology and now I have the privilege of leading this organization and thinking through how we begin to commercialize these technologies and these types of data. So it’s a very wandering path. This is oftentimes the case. I’m excited by big questions. I’m excited by solutions that bring about real change. And I’m still charting that path, if you will.

Grant Belgard: [00:31:08] And what have been the biggest surprises to you personally on your founder’s journey?

Mo Jain: [00:31:14] Oh, boy. How much time do you have Grant? I think there’s some universal surprises that I think anyone who goes through this process learns. You learn how hard it is. You learn that no matter how great your technology is, no matter how unique your data is, this is a people business in the end. And having the right people around the table is really the key to everything. Virtually all questions can be answered if you have the right people. You learn that it’s absolutely an emotional roller coaster. This is something that a number of founders had warned me about, but never really made sense to me that this is something that you’re going to have days where you’re flying high, and then literally an hour later, you’re on the ground in the fetal position, and that high frequency fluctuation is maddening. This is a hard business, if you will. There’s a lot less risky pursuits in life than being a founder and being an entrepreneur. But in the end, there’s frankly nothing more rewarding in my mind. And oftentimes these two things go together. I’m not necessarily a risk taker by nature, but I feel this is what I was meant to do so it makes sense to me in some crazy way that I can’t quite explain.

Grant Belgard: [00:32:27] If you could go back to early 2021 around the founding of Sapient and give yourself some advice, maybe three bits of advice, what would they be?

Mo Jain: [00:32:37] Yeah. Wow. That’s tough. I hope it’s not don’t do this, but I was incredibly naive when we founded Sapient. And I think that’s a good thing. I think sometimes knowing too much prevents people from taking a leap, and leaps require faith, and they require oftentimes blinders. You can’t see the pool if you’re going to jump into it.

Grant Belgard: [00:32:57] You need a bit of irrational optimism, right?

Mo Jain: [00:32:59] That’s exactly right. Anyone who knows me knows I suffer from massive doses of optimism and so I’m not sure if I knew everything I know today. Certainly we would do things differently and whatnot, but I like the fact that I was naive. I think that was really an important aspect of our development, certainly of my own personal growth, but also for the company. Oftentimes coming into an enterprise with bias means you’re just going to do the same thing that the person before you did by the very nature of bias. And not having that experience forces you to question from a very first principle basis, every problem and come up with oftentimes solutions that may not be traditional in many ways more efficient. I think I would warn myself, if you will, just to answer the question about how difficult this is emotionally and psychologically, not something I appreciated. My job was to take care of people who are dying in the ICU. So I said, well, how hard can this be? And I was incredibly naive and ignorant to just how hard it is. But again, that’s not a bad thing. That’s a good thing. And lots of people had given me the advice of make sure you surround yourself with other founders, other CEOs, people going through this. You’ll need more “emotional support” than you’ve ever needed at any point in your life. And I’ve always liked to believe I was highly resilient and had strong emotional backbone, but that absolutely turned out to be true and in many ways has been the difference maker. And so I’ve certainly sort that out over the last year and have some incredible friends who are in this space who are in very, very different fields, who help me every day. I wish I had done that a little bit earlier. I think that would have saved a little bit of sanity and probably some gray hairs.

Grant Belgard: [00:34:43] I noticed just before recording that you’re a YPO member.

Mo Jain: [00:34:47] Yeah, that’s exactly right. I’m a true convert. And when I first heard about YPO, I said this sounds nuts. I don’t need another networking event. And it was honestly a very dear friend of mine who we were having dinner with in the early days of Sapient, who just was a biotech entrepreneur himself and very successful, who looked over at me and said, hey, I’ve got something you need. And I said, oh man. He explained it to me and it sounds like a mix of a cult and a networking event, neither of which have the time or energy for. And my wife certainly at the time was like, wow, do you really want to get involved in something else? But I would honestly say, and again I know I sound like a true believer, but it’s been one of the most important things I’ve done for myself personally over the 40 years of my existence. And so it’s been incredibly helpful to learn from people who are very talented and successful at different phases of their life in a very open and honest way. And so it’s had massive impact on me, not only professionally, but personally.

Grant Belgard: [00:35:43] Yeah, I just went to a Vistage event this morning, so I know exactly what you’re talking about. Fantastic. Maybe third piece of advice. I think we’re on three.

Mo Jain: [00:35:53] Yeah, a third piece of advice would be to ground yourself. And what I mean by that, it’s really, really important. As life becomes more chaotic and crazy and I never thought it could get any crazier than it was previously, but somehow we’ve been able to pack more in. It’s really important to understand what your North Star is. It’s important to have those people in your life that you’re present for, whether it be family and children, whether it be spouses and friends, parents, whatever the case may be. It’s really, really important selfishly to have those grounding mechanisms. And again, I always understood how important it was, but not something I appreciated how important it is professionally. I’m a better professional individual and I think I’m a better founder and a better CEO because I take the time now for those individuals in my life and I ground myself, and that’s really important for me personally.

Grant Belgard: [00:36:46] I think it’s fantastic advice. Thank you so much for your time. I really enjoyed speaking with you. I think it’s been a fun discussion.

Mo Jain: [00:36:54] Thank you so much, Grant. I really appreciate the invitation and I equally had a lot of fun today. So looking forward to this again at some point in the future.

Grant Belgard: [00:37:01] Thanks.

The Bioinformatics CRO Podcast

Episode 54 with Evan Floden

Evan Floden, CEO and Co-founder of Seqera Labs, discusses Nextflow, the push for reproducibility in scientific workflows, and his experience as a scientist with a start-up. 

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.

Evan Floden

Evan Floden is the CEO and co-founder of Seqera Labs, the developer of Nextflow.

Transcript of Episode 54: Evan Floden

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 Evan Floden. Evan, would you like to introduce yourself?

Evan Floden: [00:00:07] Yeah, awesome. Thanks a lot for having me Grant. I’m Evan Floden. I’m the CEO, co-founder of Seqera Labs, previously been building the Nextflow project for the last ten years or so. So I’ve been very interested in following the developments in bioinformatics over that time. It’s great to be on the show.

Grant Belgard: [00:00:23] Thanks for joining us. And I’m sure most of our listeners have heard of Nextflow, but maybe not everyone’s heard of Seqera. Can you tell us about the company and its origins and pulling the strings behind Nextflow?

Evan Floden: [00:00:36] Yeah, absolutely. It’s an exploit was started by myself and co-founder Paolo Di Tommaso. And really the idea around Seqera was really a continuation of the project, but really bringing it to fruition in terms of a commercial sense. So whilst we focused originally on a lot of the work that Nextflow was doing on pipelines, now we’ve expanded out a fair bit from that. So Nextflow we began ten years ago. Seqera has been around for about five years now. We’re really focusing on taking some of the principles that Nextflow has. The idea of empowering scientists with modern software engineering came about from the use of things like containers, the adoption of cloud, really enabling scientists to use those tools and to focus on that. And Seqera is just a continuation of that, but now broader sense. So really making the whole bioinformatics pipelines accessible, but going beyond the pipelines as well.

Grant Belgard: [00:01:25] And what’s your business model?

Evan Floden: [00:01:27] Very much focused on bottom up adoption from the open source. So in terms of Nextflow usage, we’re looking at around 100,000 people in total. So use Nextflow and that gives us obviously a really cool base. In terms of business model, it’s mostly focused on selling to enterprises, to organizations, to folks who are scaling up from single bioinformaticians to running things in production and really providing them the infrastructure, the tools that they need to build the pipelines out. And increasingly so the aspects as well.

Grant Belgard: [00:01:58] Have you seen adoption beyond bioinformatics?

Evan Floden: [00:02:00] Interestingly, in Nextflow, yes. Nextflow doesn’t have anything too specific with regards to bioinformatics in the way that it’s written. However, obviously its application is very much being focused and being used in bioinformatics. So we’ve started to see use cases and things. For example, image analysis, you start to see it. For example, satellite image analysis, also radio astronomy. Anywhere there is scientific workloads that have particularly batch component to them. I think an element of that, the user base has developed a lot of content in Nextflow through things like nf-core, and that obviously lends itself to people picking up Nextflow itself and using it for life sciences. But it’s not to say it’s not being used in other areas and obviously we’re happy to support that and see where the community takes that.

Grant Belgard: [00:02:45] How did Nextflow in Seqera evolve? Can you take us back to the beginning and what your thoughts were then and how that’s played out over time?

Evan Floden: [00:02:53] Absolutely. So Paolo and myself were working in a lab in CRG in Barcelona, and our lab was looking at multiple sequence alignment. Folks in Bioinformatics may be familiar with some software called T-coffee. It’s a very commonly used multiple sequence alignment tool that was developed by our former supervisor, Cedric Notredame. And as part of that, the job in the lab of Paolo was to enable us to run those analysis. And it was, we were particularly interested in high throughput so tens of thousands of sequences and looking at how small variations in those sequences can have an effect on the multiple sequence alignment and the resulting outputs. That was the topic of my PhD and that was what was intended to go and study. Obviously as I got there, I started to spend more and more time on Nextflow and that evolved from there. It was a very small project to begin with. We just published it onto GitHub. It started with I remember, after a year I think we had a list of the ten people who were using it or 30 people who were using it, and it was a very a small start. Over time we were able to just continually evolve and adapt it.

[00:03:57] It’s one of the great things about open source is you’re able to get that feedback and people are able to contribute ideas back, issues back, and it allows us to really evolve from there. It’s been a fantastic journey over that time. We got to probably be about five years into the project and realized that there was first a commercial opportunity, but secondly, it’s something that we both love doing. I was getting towards the end of my PhD and I just really wanted to keep working on the technology. I saw a huge potential. Paolo and myself traveling around Europe and doing training courses and just really saw the opportunity to take that to the next level. And that’s the spark for creating Seqera and seeing all the opportunity that there was from that, I should say. So since starting Seqera, Nextflow has increased its usage at least tenfold on that. So I guess there was a slight risk at that time in doing that, but we were pretty convinced on the project and it’s really been the foundation for everything we’ve built so far.

Grant Belgard: [00:04:50] Yeah, it’s gotten very widespread adoption in biotech for sure. I think it’s one of those situations where people will want to use a tool that is nice and robust that a lot of the potential hires they would be looking at have experience with. And I think Nextflow has gotten to that critical mass where it’s not this really niche thing. It’s certainly for people who have been in biotech for a few years, a lot of bioinformaticians have experience with it.

Evan Floden: [00:05:24] Yeah, I think that’s an interesting point on how does something like Nextflow essentially become a de facto standard. It’s an interesting one in that if you look at there’s been many groups or many times that folks have tried to create standards, whether this is in academia or in industry bodies and the like. And if we look into parallels of the areas, things like the Docker container is almost is the standard for containerization. But that was started by a few folks who had an idea and created a company. And now really revolutionized the world of modern software. I think that Nextflow has similar ideas and that it was we were trying to do something a little bit against the grain, not necessarily sanctioned by anybody. And that almost spurred us on in some sense. But then once you get that critical mass has taken off, I think that there’s touching on the aspect of I agree, it’s fantastic that folks can come in, they’ve already got the skills and Nextflow and then there’s that other whole piece to it, which is what I call the content, but it’s really the pipelines and all of that material which enables folks to take take those off the shelf. There’s now things like nf-core, there’s modules. We’re getting up to over a thousand modules there which you can really mix and match the components of your pipeline and obviously use the framework and the tooling to build that there. And that’s really can save organizations so much time just even getting started with that analysis. For example, add their own module in which is specific maybe for their chemistry on some sequencing, but they can use the rest of the pipeline. Those kind of examples were prevalent and it’s something which I think is possible from having this open science approach to things.

Grant Belgard: [00:07:03] And what’s your vision for the company?

Evan Floden: [00:07:05] [] at the start we’ve really been focused on the workflow execution piece and I think this is going to continue to be our bread and butter. We still see the challenges exist with regards to scaling generally across bioinformatics, but also across life sciences as well. The volume of data is not decreasing. It’s if anything, it’s increasing the use cases for sequencing as well. And imaging analysis is increasing. The multi-modality of the work which is coming in is requiring almost different approaches. So we focused a lot on that high throughput piece. There’s an element where we have been building up a collection of open products, things like Nextflow. We have MultiQC, which is the most widely used analytic and reporting tool. We also have FusionWAVE, which are two infrastructure tools which allow folks to run these pipelines at scale. And that’s a like a core layer of infrastructure within building on top of that secure platform, which is essentially the main product which our customers purchase. And as part of that, that’s the piece that we’re scaling up beyond the pipelines into things like data management, into things like interactive environments and going from there. There’s a lot of platforms which claim to do the same thing. I think we have a slightly different approach to that and that’s I think kind of the one of the key differentiators here as well.

Grant Belgard: [00:08:19] And who are your competitors and how are you different from them?

Evan Floden: [00:08:24] There’s been genomics in the cloud. It has been around for a while and there’s obviously been some big players there for a fair amount of time. There’s obviously a whole bunch of of newer ones as well who have received funding recently. We still see the biggest competitor in at least the majority of deals is folks building it themselves. They are typically building these systems. You often have people who are, say, familiar with a certain way of doing things and they try and basically do the same thing in the cloud or they want to scale up beyond single users. And we see a lot of customers who purchased the platform. They’ve already tried to build their own thing first. So that’s the core competitor that we see in terms of building that out. The other competitors there are when I think about generic genomics in the cloud. They’re really focused primarily on a lot of simplification. And I think that there is certainly a subset of users who do need that simplification. But one of the things that we think about a lot is when we think about our value that we provide, we’re not necessarily just helping people sort of simplify.

[00:09:25] We also are making the science easier and also making it possible to do harder things in some sense. So it’s really about taking modern software engineering, providing those tools to scientists. And it’s a little bit like treating scientists like they are developers and giving them the tools to do harder things than to specifically run things in a more simple way. The other aspect of that is that whilst we have our open source roots, that really means that when customers run an exploit pipeline, they run in their environment, they run in their cloud. If you connect up our platform, you connect it up to your cluster. It could be running in Europe and you could connect it up to your Azure instance, which is running in West Coast. You are moving the workload to where the data is in this case, as opposed to the other way around. So it’s a very much more like an open framework and open platform that allows you to connect that as opposed to more of a walled garden, which you see in the other approaches.

Grant Belgard: [00:10:20] What challenges have you encountered the dramatic growth you’ve had in your user base?

Evan Floden: [00:10:25] I think the challenge is often from an organization side of things is really scaling up, how do you go from a group of people, a small group of people, really building something to be able to replicate that across an org. It’s a lot about investing in folks. Not everyone you hire is going to have a PhD in bioinformatics and being able to translate those skills and to be able to have that customer empathy and that customer understanding and almost like scientific understanding of the problem is a challenge. And I think that that’s kind of applies a lot. You see in some other organizations where bringing folks in maybe without any life sciences background or ability or willingness to learn in that doesn’t necessarily translate so well. So from an organization perspective, it’s a lot about building that context and building that organizational knowledge and memory to be able to do that. On the user base side, I think we haven’t really had too many challenges, I would say on that community growth. We’ve been very fortunate that projects like nf-core really came out of the community. They were organic in the sense there is folks who building their own training courses. There is people who have just built so much content around Nextflow, the plug in systems, the AD pipelines on nf-core. That’s really almost I would say, really happened organically and therefore it hasn’t really involved too much in terms of necessity of resources or work from our side other than really just trying to foster that community and enable those people to solve the problems for themselves.

Grant Belgard: [00:11:55] This is your first company, right? So I guess there have been a lot of new things to learn. What’s surprised you the most?

Evan Floden: [00:12:01] I previously had worked at a startup for 4 or 5 years, which was very interesting. Experience was very small at the time. The company ended up going public, so I spent some time there and doing product development. It was at the bench though, so it was very much a scientific role. I saw that there, that it was very interesting. However, it was just very slow to do things at the bench given to what you could do and my inkling for tech really got the better of me and went into the bioinformatics field. When I think about how that journey has progressed and I think particularly the last three years as you start to hire and work, I was surprised at how important the personal relationships have been. I think as a scientist you often think of the world of business or you think of the world of creating an organization. You think it’s very transactional. And when I think about the folks that we’ve partnered with on the investment side or the people that we’ve hired or the partners that we’ve brought on the customers, those relationships are now, in some cases ten years old. And I’ve just been so surprised at how important and how deep they have been just given my maybe slightly naive view coming from a purely academic perspective so I think it is the key one I always go back to when I think about that.

Grant Belgard: [00:13:17] That’s an interesting observation. I think it aligns with what I’ve maybe seen as a broader perception in academia where actually many things are more transactional in academia than they often are in biotech, although in both contexts those personal relationships are crucial and very, very long lived. Because it’s a very small world. And you often work with the same people for many, many years in many different contexts. I know we very often work with the same people, but at different companies because there’s so much churn, they’ll leave one company and go somewhere else. We work with them there and and then someone else from that new company leaves and goes to another, but it’s actually those personal relationships can play a much larger role than the formal relationships with the companies in some cases.

Evan Floden: [00:14:12] Yeah, absolutely. We’ve had one customer who’s on a company number three, and he’s a buyer number three as well in terms of spreading the word there. So that’s the relationships which you think about. They grow over time. I think the community aspect of Nextflow helps a lot with that. We really think that there’s a lot of value you can add and through that community, through knowledge sharing to solve those problems with those folks and hopefully bringing some software to them which adds that value. And then obviously as part of that, that can help them broaden and strengthen the relationship on the academia side. It’s definitely very important. I think particularly around some of the relationships you form with folks like your supervisors across that time. I think those are very special relationships. They can last a long time. I’m not going to go too controversial and try to think about the order of first author ordering as often happens in some academic papers. Thankfully, I haven’t had too many situations like that, but yeah definitely don’t envy that.

Grant Belgard: [00:15:11] For sure. On a completely different topic, how do you think about on site versus remote versus hybrid at Seqera?

Evan Floden: [00:15:19] Yeah. It’s interesting one for us. We started. We really got our Pre-seed funding in March of 2020. I quit my job at the CRG and I was like, we’re going to do this. In February, I started working at home because we didn’t have an office for about 2 or 3 weeks, and then the rest of the world joined me on that. So that was an interesting transition. It’s like we hired our first people during that. We raised our first money in March of 2020. So that was like being forced into it, particularly in Spain. It was particularly long and strict lockdown. As part of that, it forced us to be essentially distributed team from the beginning. And given the focus of the customer bases, which is primarily in life sciences hubs. So you can think of Boston, Massachusetts area, California, so US East coast and offices, some stuff in Cambridge, UK, that was going to always be the central hub for our customers. And we had to deal with that from the beginning. So that was a reality for us and saying, now we try and build ourselves out from hubs themselves.

[00:16:22] So we believe that it’s great for people to be able to get together, if anything, for the social aspect of it and to get to know each other and to build those relationships more than actual the work of. Because most folks are going to be in Zoom calls for a decent chunk of the day anyway. So that’s our take on it, believe in building those relationships. And it’s not easy, though. And I think it’s particularly not easy if you’re a young company and you start like that in a, I would say, non-intentional way. It was definitely not our intention to do things in a sense. It kind of happened and we’ve tried to do the best that we can in terms of managing that, but it’s something that we would have learned a lot from. And I guess like a lot of the tools and like a lot of folks, that’s become the new norm for many things.

Grant Belgard: [00:17:10] If you hadn’t gone down the Seqera route, what do you think you would be doing now?

Evan Floden: [00:17:15] Interesting. I don’t really think about that stuff too much. I think that I still see myself as a scientist at heart. I really do enjoy the scientific process. I enjoy discovering things and learning in this way. I could definitely see myself tinkering a lot and I would continue to do that whether that’s in more product development roles or scientific method development. So very much like what we were doing and doing a PhD. That’s the thing that I really enjoy doing. I think it’s part of this role though. I’ve been learning a whole lot of new stuff which also excites me as well. Like it’s many different things that I didn’t think that I would be doing. So it’s hard to say. I’m very glad that I’ve kind of gone down this path in terms of what I would be doing. In another sense, struggle a little bit to think about it.

Grant Belgard: [00:18:00] If you could go back in time to give yourself advice in 2018 as you started the company, what would that advice be?

Evan Floden: [00:18:09] The best piece of advice that I give myself in some sense really about the bigger picture sometimes because it’s very easy to get drawn into the day to day and the small things. And I think particularly as a company scales, you can often you find yourself thinking of those little things. And it’s really only when you step back and you see the growth or the success or the things really matter. So being able to zoom in and yes, the small things do matter, like getting those things right is important, but also being able to scale out sometimes. And I guess just getting that balance right is difficult. It’s a very intense job. It’s a lot of hours and it’s a lot of time. I think that trying to get that balance right, I wouldn’t even call it balance. There’s harmony in your life. And by having those different perspectives and also different perspectives on the different elements of your life, that’s the advice I would give myself to try and work on. And I think you can tell from my description something I’m still trying to work on now.

Grant Belgard: [00:19:06] Are there any practices you’ve adopted over time? Having a protected half day a week or something to focus on that? Or has it been a moving target?

Evan Floden: [00:19:15] Very, very much. For me, it’s about like routine. It’s the way that I’m able to structure my life. That’s typically starts with beginning in the morning, spending some an hour or so with my son before I have to go to work. And then really trying to fit in all the things that I need to do to to feel good around the work. So for me, that involves a cycle to work. I need to get to work. So I cycle there. It takes 45 minutes or so and then I do the work and then maybe at the end of the evening I’ll be able to cycle back and then try and fit in those times just to try and make it work in a way where I don’t feel like I’m going too much in one direction. So being able to pull those things together the way that it works, I do find this is very difficult with travel though. And obviously it makes it very difficult to fit in the routine in that. So I’m trying to be a little bit more structured about that. And one of the things I’m working on to improve as well, I guess a lot of folks have similar challenges there.

Grant Belgard: [00:20:10] Do you have travel system down now? Checklists and things that feel like you’ve optimized?

Evan Floden: [00:20:17] What I’ve been working on now is more around basically having Monday to Friday where I’m trying not to travel during that period. So I will travel on the weekends to different places and I’ll be in a location for a week, even things like staying in an Airbnb if possible, because then you have got a relatively normal house where you can get into those routines, just trying to do that more. That helps me a bit. I’m still not super. I wouldn’t say I wouldn’t call myself having a system down or having particularly a way of doing things. I like to have a set up, a structure. So where I’ve got my laptop with a keyboard mouse guy, so having that set up and structure just helps me a lot as well.

Grant Belgard: [00:20:51] And what things do you find yourself traveling for Seqera these days?

Evan Floden: [00:20:55] Yeah, we’ve got a lot of events that we’re running. And so given the focus in a lot of North America, we’re spending a fair bit of time there. So we have, for example, the Nextflow summit, which is going to be in Barcelona, but also in Boston this year. So we’ll be spending some time there. We also do secure sessions, which are great events for the community to get together. We’ll often have some talks on technology, things that are coming updated, product updates, roundtables, this kind of thing for 3 or 4 hours in an afternoon. Previously done those in San Francisco and in San Diego, Boston as well, the hubs and continuing to build out that. We’ve been doing a few shows around. We’re going to be at ASG this year and going to be traveling a fair bit around that. And those are the most of the areas. We also have, as I said, a distributed team. So being able to spend time with them is really important as well.

Grant Belgard: [00:21:45] Great. We’ll have to have our operations director come say hi. I won’t be going to ASG, but we will have a booth there.

Evan Floden: [00:21:52] Yeah, folks are absolutely welcome to come and say hi. We’ll get you some next swag and always happy to give folks a demo.

Grant Belgard: [00:21:58] Nice. What message would you have for our listeners about Nextflow and Seqera? As I said, I’m sure most of our listeners have heard of Nextflow and probably many our users, but for those who haven’t used it before, how would you recommend they get started?

Evan Floden: [00:22:15] Yeah, absolutely. If you’re thinking about running pipelines in a way where you want to run them in your own infrastructure, where you don’t want to deal with the complexity of setting that infrastructure up, then Seqera platforms are a great way to start out. We have a community showcase where there is collections of pipelines which are available, where you could log in, select those pipelines and run those and get a feel of how it works. We also continue to add in more options around that, which is enabling on the data management side. So by the time this podcast comes out, we’ll have a data explorer which enables you to really browse and search across different buckets, across different object storage that you may have. And we’re also looking to bring out more functionality and interactive space. So that’s a great place to get started. If you go to or if you go to, you’ll be able to log in there and find that out. It’s absolutely free to go, go work that and give it a go.

Grant Belgard: [00:23:07] Great. And for people who are already casual Nextflow users, how would they best further build their skills?

Evan Floden: [00:23:17] Yeah, I think there’s some interesting courses which have come out recently, which we’ve been developers with the community as well as from folks at Seqera around advanced Nextflow usage. That’s been a really useful set of resources which have been built out. I think being around the nf-core Slack and the Nextflow Slack is always a great place. There’s a lot of people doing very innovative things there, platform and being able to connect it in there. And then of course attending the events is always a great place to see that. We have 50 speakers, I believe across the events of Nextflow summit in Barcelona and Boston this year. It includes sequencing companies. Obviously the large cloud providers are all going to be there presenting the latest things. We have customers and developing kits. We have customers working in population genomics sequencing projects as well as obviously a whole bunch in biopharma. So that range of use cases can give people a really nice understanding of what other folks are doing. And I think that format as well, where you can really interact with people, can go a little bit deeper into the specifics of how they’re solving those problems is a great way to learn.

Grant Belgard: [00:24:23] So in theory, something like Nextflow would be fantastic for scientific reproducibility, right? Which is obviously been a major issue in the life sciences. But what do you think are the major barriers to adoption of Nextflow for those purposes? Because you usually hear about Nextflow in the context of people trying to do analysis on their own data for their own projects and so on. And it still seems pretty uncommon to see papers published where they have single button reproducibility anyway.

Evan Floden: [00:25:00] Yeah. And I would point folks to the Nextflow paper from 2017 that we published, which is really a little bit of inception here, but we published an excellent paper, obviously using Nextflow, which is really describes a lot of that. And from that git repository you can reproduce everything calling Nextflow from notebooks. So the idea is of open science, I think they’re worth exploring because it goes a little bit beyond just what people consider open source. And that open science is really is a key part of that. So if you think about open source, it’s almost like it’s a license. It’s like, okay, you put Nextflow software out there, people can use it. People can do what they want with it, the Apache 2.0, etcetera. Open science goes beyond that, and it goes to that point where, as you say, people are, for the most part still just publishing papers. But we start to see more and more adoption of folks who are not publishing papers, but they want to publish the paper and the analysis or even just the analysis in itself. When you want to run that analysis or even reproduce that result there, if it’s not going to run on your laptop, it’s going to be very difficult to do so you’re 100% right that Nextflow enables that piece. It does it through a couple of ways. One is obviously containerization, so that integration of containers means that the environment that the task runs in is essentially absolutely the same byte for byte. The other piece of it is that you can run those containers then in any infrastructure so you can run them in [] or you can go run them in your cluster or you can run them on your laptop.

[00:26:21] That piece then enables people to reproducibly do that and almost validate the result that then has a little bit of a flywheel effect. Because if I publish my analysis in that way or my tool in that way, you can then take it and then you can put your data into there as well. And that’s the real important piece I think that Nextflow has enabled there. If we think about that going further, one thing that we’ve really stressed is this idea of empowering scientists with modern software engineering so you can reproduce the workflow, but how are you going to reproduce the environment that you use to set that up, or how are you going to reproduce the data set that you use in this sense? And that’s really what we’ve been working with Seqera is the whole thing is defined or can be defined from API. There’s a CLI as well. So you can say import this pipeline or define this computer environment in this way, import export from that. And it’s treating the whole research environment in a reproducible sense, not just the individual component. And this is very much in the vein of infrastructure as code like setups where folks have been using things like Terraform for building those environments and just taking it to the next step specifically for bioinformatics.

Grant Belgard: [00:27:32] What do you think it will take to get that to become standard practice? I mean, there are some individuals and a few groups that routinely will do that. But majority of the time, it seems these are done by custom scripts that are available upon reasonable request and nobody ever gets them.

Evan Floden: [00:27:55] It definitely is changing as depending on where you are. So if you are developing a new tool, it’s kind of by default. It has to be there. If you consider it was going to be in a paper, the reviewers would essentially have to run the tool and try it out. I think the more you go down like two different areas, then you’ll see I agree it gets less and less in terms of that compliance. I think it’s probably very much like carrot and stick in this sense. Carrot in the way that if you consider yourself, like when you write something in Nextflow or you write a pipeline or an analysis in a reproducible way, you’re really just doing it for yourself in three months time. Because if you’re anything like me, in three months after you’ve done an analysis, you come back to it and then you have to rerun it because you’ve got a new sample or you’ve got some new parameter. It’s just absolutely impossible to remember how you did it, what you did it like, exactly that. So that reproducibility piece is almost like for yourself in a very selfish way. That implies the carrot. The stick bit is coming from this publishers. So as our former supervisors, Cedric Notredame, he has one of the journals and as part of that, it’s really about publishing pipelines, publishing things in this way. And it is using standards like nf-core to do that. So you have to publish in a completely reproducible way, you can define exactly what you are publishing, and I can really see us moving towards a situation where the paper is just one artifact of the actual output. However, it’s not the main output. The actual main output is often the case is like the actual analysis in the tool and say this is particularly relevant for tool development, which is obviously very, very widely used in bioinformatics.

Grant Belgard: [00:29:31] Nice. So maybe changing gears a bit. Can you take us back to your childhood? What got you interested in science?

Evan Floden: [00:29:40] Yeah. So I was originally born in New Zealand. I spent probably the first nine years there and then got the opportunity with my family. We lived in Malaysia and Sweden growing up for some years. I think in New Zealand it was a very kind of natural environment in some senses. It’s obviously a lot less people and a lot more nature, got me interested in bio. And I vividly remember thinking about biology in the sense during high school. I got a little bit obsessed with scientific nonfiction and saw myself really wanting to go into biotech. Bioinformatics at that time was much less prevalent. I guess it was very early for bioinformatics. So that’s what led me to study biotech and then to spend time going into molecular biology. I had a really interesting opportunity for a couple of years as an undergrad working in a yeast laboratory, and what we were doing was essentially had a knock out set of yeast. So it’s you can imagine very large agar plates. Each one of those plates has got really a couple thousand samples on it and each sample has got one different gene removed. And you can treat this with different chemicals or you can make these yeast together and you can look at chemical interactions or genetic interactions and understand what’s happening there at a genetic level and how it integrates with those pieces. There was obviously a bit of robotics, obviously a lot of yeast culturing and a touch of bioinformatics as well. And I think that’s one of the things that sparked my interest into bioinformatics later on. Although to be fair, I didn’t do that until I went to Italy to study a master’s there. Bioinformatics wasn’t available in New Zealand at the time, so it was my opportunity to jump into the field.

Grant Belgard: [00:31:20] So you finished your degree in New Zealand in 2010 and what did you do then?

Evan Floden: [00:31:27] I joined the start-up. It was a very interesting startup. It was about five people at the time were developing a medical device, which sounds nice and clean, but the medical device itself was coming from the fourth stomach of sheep, so I’m not sure if the listeners are familiar with haggis is essentially one of the stomachs of the sheep. It’s a very interesting material. We were trying out lots of different materials and the idea was to see if we could create a bio scaffold. So essentially a tissue which could be used for soft tissue repair in surgery, you would remove the different layers on the top, decellularized it, freeze dry it and essentially end up with a shelf stable product which could then be used in different applications. So the first few years there did a lot of product development. We got FDA approval for the basics of the platform and really ended up developing several other products, for example, creating multiple layers of this for breast reconstruction or hernia repair. And this was really just involved in that whole start up phase. It was really exciting. It was really interesting. I saw how I saw the determination which was required to create a startup, but I also saw how interesting it could be to work on many different topics and many different things. And that change I really liked and I just was just enthralled by that. And I got my, I guess if I [] place the seed, let’s say, for what was happening with Seqera later on.

Grant Belgard: [00:32:48] And what brought you to Italy then?

Evan Floden: [00:32:50] I really wanted to get into bioinformatics. I think it was something I’d been pushing for and that’s where I got an opportunity. I got a scholarship to do a master’s there. It was a very interesting time. I got to fully focus on that and I knew some basics of programming, but I really got to fully hunker down and spend a good 18 months or two years just purely focused on that. The bioinformatics program in Bologna is quite widely known. We got to do fantastic things. For example, we would build a Markov models from scratch, from the individual components really got exposed to how machine learning was working in sequence analysis itself. It’s quite a mathematical program, but it really gave me the basis for many of the things that came later on. It’s actually where I met my supervisor, Cedric, and that’s what started the journey into Seqera. I had a little bit of time in Cambridge in between in the UK working at RFM, but that was what got me started in that.

Grant Belgard: [00:33:47] Nice. And then after your stint at Cambridge, you went back to Italy, right?

Evan Floden: [00:33:52] It is Barcelona. Yes. Sorry, it’s Barcelona. That’s where I started my PhD, and that’s where I met Paolo. And the story kicked off.

Grant Belgard: [00:33:59] Nice. And then afterwards you stuck around in Barcelona at the CRG. Were you working at all with the CRG during your PhD?

Evan Floden: [00:34:07] Yeah, so my PhD was at the CRG. It’s a research organization, but I mean technically you’re part of a university as well. Although you spend the whole time in the research organization, it’s more of an affiliation so that they can provide you with an academic degree. Yeah, really interesting place. And there’s a lot of the leading biomedical research center in Southern Europe, fantastic location as well, very international. And it provided a fantastic opportunity to learn there and be surrounded by smart people. And obviously it’s what we’re doing.

Grant Belgard: [00:34:39] And this Seqera Avenue formal relationship with CRG or is it kind of just another institute where there are a lot of Nextflow users?

Evan Floden: [00:34:49] Obviously, CRG being home of Nextflow, let’s say the original home of Nextflow, there’s always a special relationship there. The usage of Nextflow is obviously very wide in the organization there. We consider ourselves like a spinoff of the organization. And so the relationship stays special in that way.

Grant Belgard: [00:35:09] That’s great. And do you have any advice for our listeners who might be scientists who are considering the entrepreneurship journey?

Evan Floden: [00:35:19] Yeah, it’s hard one in the sense that, like, you don’t know until you really jump off the diving board in that sense. I found it personally to be very rewarding and very fulfilling. As I said related to your question before, I can’t really imagine myself having not done this or doing something else. At the same time, I fully admit it’s not for everybody. There’s a lot of sacrifices you make in other aspects which are difficult. It’s a way that you can have a very fulfilling role, very fulfilling job. And for me, being driven by the impact of it, I think it’s just the way that I felt that I would best be able to build something that would scale and that would have the most impact on it. I think that one of the reasons behind Seqera at the beginning is really just to spread that I was one of the first couple of uses of Nextflow. It really changed how I was working and I wanted to put that into as many people as possible. I feel the same way about what we’re building in Seqera. There’s great technology which we just want to put into the hands of scientists to help them work. That entrepreneurial journey is for me, it’s really much it’s just the way to get that done. And it’s that the way that it can manifest, I would say.

Grant Belgard: [00:36:26] So if you look forward ten years from now, what would you consider a success for Seqera?

Evan Floden: [00:36:33] We really want to see ourselves as first and foremost, having helped thousand biotech biopharma organizations really reach their own goals. And for that, that’s usually outcome in patients. We want to see biotech continue to grow. We want to see the adoption of those technologies. We want to see things like personalized medicine become available to people. We want to see the promise of genomics technology become a reality in that. That’s the first, I think, that we can play a really important role in making the analysis part of this data analysis, part of this accessible, available, open and build the bioinformatics tool framework that in the world that we want to see in there. From an organization perspective, one of the things I really would love to see is that from Seqera, we almost create our own ecosystem as well. So whether that means of employees who create their own things or really new projects which sprout from the Nextflow ecosystem, really seeing that gives me a lot of satisfaction because it shows that you can start one thing and it can really flower into a whole bunch of other areas. Just myself personally, just really ten years would just love to be obviously healthy, still enjoying the job and really hopefully having made as much impact as possible on those areas.

Grant Belgard: [00:37:49] Well Evan, thank you so much for joining us today. It’s been a nice conversation.

Evan Floden: [00:37:53] Awesome. Thanks a lot, Grant. [See anytime] and folks, if you do want to join us at Nextflow summit, both Barcelona and Boston are still open. We’d love to see you there and thanks so much for the time.

The Bioinformatics CRO Podcast

Episode 53 with Linnea Fletcher

Linnea Fletcher, Biotechnology Department chair at Austin Community College and Director of the InnovATEBIO National Biotechnology Education Center, discusses training technicians to tackle real world challenges in the biotechnology industry. 

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.

Linnea Fletcher

Linnea Fletcher is Biotechnology Department chair at Austin Community College and Director of the InnovATEBIO National Biotechnology Education Center.

Transcript of Episode 53: Linnea Fletcher

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 Linnea Fletcher. Linnea, welcome.

Linnea Fletcher: [00:00:07] Thank you. And thank you for having me on your podcast.

Grant Belgard: [00:00:11] Thanks so much. Can you tell us about what you’re doing at InnovATEBIO and at Austin Community College?

Linnea Fletcher: [00:00:18] Sure. So I’m the executive director for InnovATEBIO, the National Biotechnology Center Grant, and it’s funded by NSF, the National Science Foundation. And my job as being PI on this Grant is to support and coordinate bio technician training across the nation. And there’s over 130 programs. But this also means I have to know about the career path starting from K-12 all the way up through PhD, because most of my students and the students across the country who become technicians continue their education. To keep boots on the ground, so to speak, I still run a program at Austin Community College where I’m educating individuals to be technicians. And just to give you a snapshot of my students, I have a high school program. It’s fairly large, and these students get a certificate in bio manufacturing. Most of them go on to four year schools. Some of them come into my two year program, and my two year program has both two year students who tend to be older adults coming back for another career. And the rest of them, 50% of them already have a four year degree. And I’ve been in the area long enough that local industry tells the four year student to come and get an advanced technical certificate from that community college down the street because I’m the one who does. I emphasize hands on training. Some people have said it’s like graduate school in a regulated environment on steroids because they learn a lot in a space of a year, the four year students.

Grant Belgard: [00:02:07] How common are programs like that elsewhere in the country?

Linnea Fletcher: [00:02:11] Well, actually 130 programs is not a huge number across the nation. And I would say they cluster around the biotechnology industry clusters. For example, here’s a lot more in California and also there’s some in Florida where you are. And I work with them because Florida actually is a great job of training the workforce. And there’s more in North Carolina. And of course, there’s a large number in the Northeast. So it really depends on how much industry is present for these programs because they are workforce programs. So you have to justify your existence by having companies in the area that need your students.

Grant Belgard: [00:02:56] How do you get that feedback from local companies?

Linnea Fletcher: [00:02:59] Well, an advisory board, all these programs have an industry advisory board and I meet with mine once a year to get feedback on what I’m teaching. But then also, like many other programs, community colleges don’t have a lot of full time faculty. So all of my courses pretty much are taught by adjuncts and they’re all from industry. So they tell me what I need to put in my program constantly. And as a result, there are a lot of basic biotech people need to know that doesn’t change, like how to make a solution or the math or the regulatory affairs because we have QA QC and we do run our program like a company, so they have to do all the documentation. But all the emerging technologies, we had to modularize all of our curriculum such that when we needed to change out a module to put in a new module based on an emerging technology like we just added metagenomics and we took out another module that was probably a little out of date to add metagenomics and the ability to do next gen sequencing, which is so important.

Grant Belgard: [00:04:18] Do you find a large degree of variation metro area to metro area in terms of the skill sets that are needed? Or are they mostly shared?

Linnea Fletcher: [00:04:31] Well, it’s kind of interesting. Local area dictates needs, but usually local area mirrors what’s going on across the nation in general. Some of the bigger differences are if like North Carolina, California, where’s the large scale biomanufacturing that is going to be different than the small scale biomanufacturing or mid scale that teach because I’m not doing reactors that are doing like 200l. I’m doing much smaller reactors, so there are variations. I also have a lot of medical diagnostic companies and so I do a lot of PCR and real time qPCR because my students need to be able to do that kind of technology in the companies. We also have a lot of maybe more instrumentation, HPLC, GC, maybe some mass spec. One thing I am looking at incorporating is flow cytometry, which I haven’t done yet, and we do stem cell too. So you’re right. I can tell you all the different areas across the country, like in Wake Forest, also in North Carolina, there’s a lot more regenerative medicine. And then in the state of Washington, they have a lot of immunotherapy.

Grant Belgard: [00:05:57] What changes have you seen over time in the skill sets in short supply?

Linnea Fletcher: [00:06:03] So what I’ve seen, I’ve been in training individuals, educating them for 25 years. And I would say that it has become more complex in terms of what technicians need to know as essentially the industry is converging more with other areas. So as biotech advances into other areas overlapping, such as nanotechnology, electronics especially, you see this in medical devices, in regenerative medicine, and then the need for another one that I’m now having to anticipate is AI, artificial intelligence and robotics. So now my students are actually having to learn how to program machines to deal with cell sorting and identification and running some of the automation in terms of the equipment in laboratories.

Grant Belgard: [00:07:10] What areas would you say have the most unmet demand right now?

Linnea Fletcher: [00:07:15] Oh, unmet. Well, definitely in regenerative medicine and immunotherapy and a new set of skills standards have just been published by the Wake Forest Institute for Regenerative Medicine. It was a grant funded activity in this area of what technicians need to know be able to do their jobs. If you think about it, in many cases they actually have to take care of the cells that are removed from patients and grow them up before they’re genetically modified and put back into the patient. So that is a major area that’s not being covered. Large scale biomanufacturing, we’re not meeting the demands for the technicians in this area. And so that’s something that has to be addressed. And then another area that hasn’t even quietly shown up on our radars is Bioindustrial. And I’m already working with process technology programs. And if you process technology, is the oil and gas and chemical industry because Bioindustrial is large scale chemical engineering and the idea of having those programs actually educate students to cross over from oil and gas into Bioindustrial is something that we’re working on for the future.

Grant Belgard: [00:08:45] That’s interesting. Certainly I think at the level of PhD training, you have some quite a bit of segregation among students who would be doing immunotherapy versus those on bio applications in oil and gas. But I guess at the technician level, you have options in essentially the same program where you’d be sending students to those very diverse industries afterwards.

Linnea Fletcher: [00:09:12] Yeah. And that’s what kind of is interesting is so when someone gets a PhD, they usually it’s in one area of research and it’s very targeted and they have to do that, become experts in a narrow range where my technicians and the technicians that are being educated across the country, they learn a variety of skills from cell culture, manufacturing, bioinformatics, instrumentation, such that they can go into a variety of different jobs within the industry. It’s like very similar to what is now happened in MDs, focus on one area. But physician’s assistants, they actually get education in all different areas and they actually can move from one area to another area that’s just like my technicians. And they do it based on changing interests.

Grant Belgard: [00:10:14] And what changes do you see that might be coming down the pike for the workforce?

Linnea Fletcher: [00:10:19] Well, for one thing is we need to do a better job within the governmental sector to make sure that there is a promotion of educational institutions working together instead of working in silos. For so example is if someone is in K-12 high school programs in biotech, their courses need to transfer to a variety of institutions and get credit, two year and four year like advanced placement and then biotech programs, their courses near to transfer to four year schools. And I know it’s hard because they don’t necessarily have the same courses. But the thing is, if you don’t do that, students don’t want to be in biotech, if not all their coursework transfers. And the other trend that needs to be worked on is if someone in industry learns what’s equivalent to one of my modules, they need to be able to test out of that module or a course in my program and get credit for what they’ve learned on the job and not have to repeat any of that training or education. It’s a waste of funding and money and time. So essentially how anyone learns, whether it be on in industry or in an educational program, they need to be made equivalent and we need to work towards that if we’re going to meet the needs of the workforce. Workforce needs are going to increase, not decrease, as biotech becomes more important across the board in all these different industries. It’s now even applicable for solutions in climate change. It will make manufacturing sustainable.

Grant Belgard: [00:12:22] That’s a great point. I really like how your program has this advisory council and it sounds like you’re very nimble and adaptable to what those needs are. I can say from our perspective in terms of what we see, which we’re looking at a very different workforce. We’re typically looking at PhDs in bioinformatics and related areas. There is often a mismatch between what is needed, like the skill sets needed in the biotech industry from the skill sets that are frequently trained in PhD programs. I mean, they’re broadly congruent for sure, but there are definitely some areas where there is an excess of people with those skills that just aren’t aren’t needed that much in other areas where there’s a glaring deficit. And my sense is in PhD programs, the skill sets are driven by the research programs of the PIs in the department. There’s not this close tie or not as close to tie to industry groups as your program has. I think that’s really interesting. And I wonder have you seen much of a movement to try to take those industry needs more into account in PhD training as well?

Linnea Fletcher: [00:13:44] Well, it’s interesting you say that. I have seen that. Of course, what I’ve always seen is if you look at engineering programs, engineering programs traditionally have close ties with industry and they run their labs more like what’s going to occur in industry. In fact, if you look at engineering programs, you’ll see that a lot of their funding comes from industry and they’re educating people mainly to get into industry and not necessarily into research labs. The one thing I thought was really good, I can understand why PhD programs like in Biology or some of the more pure research labs aren’t tied to industry because you do need pre-research, that research that isn’t always necessarily applied because industry research is applied research. But I felt that after getting my PhD and getting involved with educating people for industry, the QA QC that I learned and now I’m teaching my students. The regulatory affairs information and the need for documentation was actually a great way to run a lab and ensure quality within the laboratory. So I feel that even if you’re doing pure research that isn’t applied, taking some of the quality assurance and quality control principles, total quality management and applying it to some of the PhD programs would not be a bad thing. You’d end up with students that are better able to run their own labs once they finish their PhD and do a postdoc.

Grant Belgard: [00:15:40] That’s a great point. It’s so common for people to have trouble tracing exactly what solution was used in a particular experiment and so on and so forth. These things come up in academic labs all the time where the documentation is not to the standard of industry and it causes problems. Other things that you think doctoral training programs could take from this?

Linnea Fletcher: [00:16:07] Oh, I do think one thing I think is really worthwhile is so I was an unusual PhD student in that I did two different postdocs, two different areas. I always felt that that was beneficial, because then I actually had some education in other areas that could be applicable. And I feel at this point there’s going to be more convergence of science areas. And if you’ve had experience in different areas and you encourage that, you’re going to be better able to take advantage of opportunities from other areas in science. So my first postdoc was in immunology on monoclonal antibodies and my second postdoc was on the 3D structure of messenger RNA. Totally different areas, but it has served me well by doing those totally different areas. I’ll tell you, my second postdoc, I had a luckily the other postdocs were very patient with educating me with the molecular biology techniques that I needed to know because I pretty much was a straight biochemist. And then of course, the immunology I had to learn and the monoclonal antibodies. So each time I’ve put myself in a place that was slightly very uncomfortable, but I gained a lot by doing that.

Grant Belgard: [00:17:42] What do you think are the lessons PhD programs can take from that? Would that come at the point of rotations? Not all schools have rotations before students select their labs. So I did my PhD at Oxford, for example. We didn’t do rotation. I think rotations into a lab and finish your degree in that lab.

Linnea Fletcher: [00:18:01] Yeah. I think actually rotations before you make your final choice should be obligatory.

Grant Belgard: [00:18:08] And do you think doing it over the course of a year is adequate or do you think maybe that it would be beneficial for that to be prolonged?

Linnea Fletcher: [00:18:17] Well, I think you’re only going to get everyone to agree to a year.

Grant Belgard: [00:18:21] Yeah.

Linnea Fletcher: [00:18:22] To be honest.

Grant Belgard: [00:18:23] [] what’s possible?

Linnea Fletcher: [00:18:24] No, I think a year would be enough. Right, exactly. Because I totally understand what my husband, he’s also a PhD in microbiology and he ran a lab. I totally understand why he has to have his group focused on the work that he’s doing. It’s so competitive.

Grant Belgard: [00:18:44] And changing gears a little because I guess this is starting to affect everything. How has ChatGPT and the explosion of other AI tools on the scene in a very powerful way in recent months impacted, how you’re doing things and how do you anticipate that will impact the training you provide going forward?

Linnea Fletcher: [00:19:09] So I actually was part of a project that I had to develop an AI module for biotechnology programs, and the other faculty members at the community college had to do it in their area like computer science. The main thing I saw is that just like any new technology, you need to quickly understand what are the limitations and the advantages, especially the limitations. And I think it’s really interesting. I now have students when they have to write up original work, I have them write up their original work and then I have them put it in ChatGPT to show them what it would look like. And if you ever do this with students, the other thing that’s really interesting if you have them use those, the AI write up an original article, what’s supposed to be original article based on what, they should understand how it gains the information then ask it to do references. All the references will be false. So the main thing is they have to understand the limitations. And now it’s being used in medical diagnostics. Well, it’s only as good as whoever programmed it and what’s available. And that little hint of innovation and original thought that very much is a human trait may not be there. So you have to keep that in mind when you use these. They’re only as good as who programmed it and gave it the information. It’s only as good as the databases it has access to and is researching to be able to do what it’s supposed to do.

Grant Belgard: [00:20:57] And given the rapid increase in what these tools have been able to do over even the last 18 months, what are your thoughts on on where that’s headed? I mean if you look out five years from now. I mean, this will certainly be a lot more powerful than they are now. And no one knows how much more. But how would you expect that to shape the workforce?

Linnea Fletcher: [00:21:19] Well, I don’t think it will decrease the number of jobs or anything like that. What it will do is require that my technicians or the technicians across the country being educated will actually have to know how to use it effectively and be able to troubleshoot it. And then once again, it’s adding another area for technicians, depending what they’re involved in, to have to learn. And that’s the other reason why I think we really have to tie our educational institutions together, because students can only, they’ll gain so many competence passes through high school, so many competencies through two year and then four year and on up. It scaffolds the information. And we all have to do a better job of making sure that we interface better with this instead of having no overt. We don’t need overlap, that’s for sure. We’re going to have to do a better job. And one thing I focus on that I think all educational programs is that my students know exactly what are the outcomes for their education, and they can articulate this in an interview. And when they don’t know something, they’re very honest about it and they know what they don’t know, but how to get the information. You can only teach somebody so much, but they have to know what they don’t know. So and be able to articulate it and not promise anything.

Grant Belgard: [00:22:56] Do you have any training for job interviews and.

Linnea Fletcher: [00:23:01] Oh, yes. We do from the very first course on. We have like seven courses. So the very first course is career awareness, because I can’t afford them to get to internship and not know what area of the industry they want to be in. I can’t afford to have them get into an internship and say, Oh no, I didn’t want to do this because it takes a lot of time for companies to do internships, so they have to review everything that’s local. Every semester they change their resume based on what they learn in the program. They have to verbally articulate that to the instructor every semester. And then the the Gate is an interview committee when they are doing their internship. We’ve learned the hard way. Oh, the other thing is the class size for my program is 12, not 24, not 200. So that’s why it’s like graduate school, because every student has to demonstrate in the lab they can do it by themselves and not in a team, even though we have them work in a team so they have no chance of getting someone else to do it for them. And they do a lot of presentations.

Grant Belgard: [00:24:28] That’s great. So there’s a book I always recommend to people entering this space by Toby Freedman called Career Opportunities in Biotechnology and Drug Development.

Linnea Fletcher: [00:24:37] Yes.

Grant Belgard: [00:24:38] Are there any books that you would advise for listeners who might be just getting into this space?

Linnea Fletcher: [00:24:44] Well, if anyone wants to educate people, Lisa Seidman for Madison College has like the Bible in how to educate people for this industry and also for the students. It’s by Tech Manual by Lisa Seidman and then other books by Freedman. I’ve read all his books. He has several books that I think are worthwhile.

Grant Belgard: [00:25:09] It’s fantastic. What advice would you have for the listeners of our podcast? many of them are people who are in bioinformatics or they’re interested in bioinformatics, and some may be considering going to biotech for the first time, having only worked in academia. What advice would you have for them?

Linnea Fletcher: [00:25:31] Well, one thing is everyone should keep up on emerging technologies and there are a variety of sources in addition like your podcast and then other areas. You need to always keep aware of where the industry is going and what are some of, I read science religiously nature. I have to admit, I do a lot of reading and listening to podcasts and then the other thing is you have to really consider the fact that whatever you pick for a career, most likely you won’t stay in. So always be open to opportunity. Most people nowadays don’t stay very long in one job. They’re constantly looking, not necessarily because they’re dissatisfied with the job, but their interests have changed. Or they want to try something new. And I say you should always be willing to do that. So keep an open mind. My husband as a microbiologist, he went from the university to the chemical industry and he ended up in the petroleum industry. That wasn’t planned necessarily at the start.

Grant Belgard: [00:26:46] Right. I think that’s kind of a theme from the people we’ve interviewed on this podcast. Very few people had followed a career trajectory they imagined when they were a student. There were a lot of things that were unplanned. There was a lot of serendipity. A lot of people ended up in roles that in some cases they didn’t know even existed at the time, or in some cases didn’t even exist at the time as new jobs are created and so on. How do you think academia, government and industry could work better together?

Linnea Fletcher: [00:27:24] Well, for one thing is I don’t think there’s enough funding opportunities that foster and promote collaboration among the educational entities between high school two year, two year and four year. And I think there should be more. I think if they’re not willing to do it themselves, it should be possibly forced a little bit more with opportunities that foster collaboration. I do feel we need even more input from industry. In fact, there was a paper out by ACC, the American Association for Community Colleges, that was done by Harvard, and it indicated that we need a lot more input from industry concerning these educational and training programs and for them because we need to know more what they need and be able to anticipate. I know they’re really busy, but if they want the very best employee, we need their help and their voice. I think there should be more apprenticeships and more internships and apprenticeship programs that you can get scholarships and are paid for like some of the what the other countries do because that’s the best way. At least I know for my companies with internships, they hire these people or bring them on in internships with the thought that they’re going to stay in the company. And if we could have more apprenticeships, we’d have more transition into industry and it would be more seamless. So I think funding in apprenticeships needs to increase, not decrease. It’ll be money well spent.

Grant Belgard: [00:29:11] There’s a lot to unpack there and it started out with one of the earlier things you said, I’m involved with a school that has been very active in getting articulation agreements in place with regional colleges and universities for credits to transfer. And in learning about the process, I was surprised at how everything it seems, at least where they are, is essentially bilateral. It’s a bunch of bilateral agreements. It’s not being done at the level of a system, for example.

Linnea Fletcher: [00:29:46] No, it’s not. [overlap] finding individual school. I know. Yeah, it is. You’re exactly right. It’s like I have an articulation with the University of Texas at Austin. It’s only for biochemistry and it’s not for the rest of the system. And it was really hard to get. And I understand their concerns because they’re worried about quality. How are they going to monitor the quality of the students they’re getting from my program? It’s more interesting, K-12 in a state, you can get systematic across the state if they’re called core courses and they automatically transfer to all four year schools. I think more needs to be done to figure out better ways of ensuring quality from one program to another. Now, what some states are doing is it’s called still skill standards and competency. So the students graduate from a program and it’s guaranteed what skills they can do and the state controls that. I think if we had more of this to ensure quality, maybe the four year schools would be more willing to do systemic type articulations. I get why they don’t though. We just have to work on coming up with better systems to ensure quality when a student graduates.

Grant Belgard: [00:31:12] What is your most controversial opinion on this topic? Where you’re certain that you’re correct, but a number of colleagues would disagree with you.

Linnea Fletcher: [00:31:23] You have to. Okay. So industry needs to stop requiring a four year degree in biology to be a technician. That doesn’t insure anything, to be quite honest. That does not ensure quality. It does ensure that they made it through a four year program, but it doesn’t ensure that they’re ready for the job. Otherwise I would not have 50% of my students who have a four year degree are coming to me to get an advanced technical certificate.

Grant Belgard: [00:31:55] Yeah, that was a really interesting stat you gave.

Linnea Fletcher: [00:31:59] Degrees don’t ensure quality or at least maybe they ensure quality in some areas, but they don’t ensure that they’ll be able to be what industry needs. So they need to be more focused on competency based education instead of degrees and certificates at least asked for the competencies that are associated with the degrees and say, Can you guarantee me these students can do this and this? I can guarantee industry this because we just don’t graduate them unless they have lab practicals. They test out in all of this.

Grant Belgard: [00:32:41] That makes a lot of sense. Another thing that maybe sticks out to me is not just the difference between the responsiveness of your program to what industry needs, but maybe wrongly. But at least my perception, is that there’s less of that in four year programs in the biological sciences.

Linnea Fletcher: [00:33:07] But they’re not funded to do that.

Grant Belgard: [00:33:09] Right. It seems like it’s much more about getting people ready for grad school and so on.

Linnea Fletcher: [00:33:15] So a lot of my colleagues are in four year institutions and the universities and a lot of four year, they’re not funded to educate people for technician positions, we are. They don’t even have the equipment that I have for educating students and they certainly aren’t allowed to just have 12 students in a class. And so it’s really hard to do that.

Grant Belgard: [00:33:47] You mentioned that you had a, you know, Illumina sequencer and a nanopore sequencer. And I certainly never saw these until grad school.

Linnea Fletcher: [00:33:57] Yes. The fact that we moved from the Illumina platform to nanopore and now Oxford and the fact that we put a sequencing center, it’s grant funded through the NSF at a high school, and that we actually do the same thing in our program at the two year school. So we have high school students who are doing sequencing and interpreting sequencing data as well if our students at the two year and it’s actually in the very first course of our program and the repeated again. And at this point, we’re getting the students to do sequencing projects for other departments. The biology department at Austin Community College is doing a moth barcoding project, and there’s plans to have our students do the sequencing for that project and then share the data with the biology students. So we’ll have some peer to peer sharing of information. So this way, this models what’s going on in industry.

Grant Belgard: [00:35:07] Yeah, that’s fantastic. I mean, for sure, when they get an industry, if they’re doing anything omics related, NGS is going to be a huge component of that.

Linnea Fletcher: [00:35:16] That’s how we got involved with helping start up companies, is doing industry based projects for them. And then the idea of doing undergraduate research in addition to industry based projects. I think that’s the best way to engage students because if you get them involved in real projects, especially ones that make a difference, that then they can see why they should be learning science in the first place.

Grant Belgard: [00:35:44] Great. Would you have any final words for our listeners? Maybe something that you think is an important message to get across that that hasn’t come up yet?

Linnea Fletcher: [00:35:54] I think a final word for our listeners is I’ll share with your listeners the one thing that got me involved in education when I moved from research to education was the realization that the most important resource that we have in this country in the world is our students, our children. So why aren’t we doing more to engage them in what are real projects? That’s the way it used to be. That’s the way it was when people were on the frontier. That’s what they do in apprenticeships in other countries and we are doing in the US too. But the best way to educate people is not in a textbook, or at least not completely in a textbook, but in a lab and using exactly what they use in industry and in research. That’s when students really appreciate their education and their interest in science is have them do the real thing. And we should be doing more of this, not less.

Grant Belgard: [00:37:04] I couldn’t agree more. Thank you so much for your time. It was really great chatting.

Linnea Fletcher: [00:37:09] Thank you. I enjoyed speaking with you.

The Bioinformatics CRO Podcast

Episode 52 with Yuri Deigin

Yuri Deigin, co-founder of YouthBio Therapeutics, discusses developing rejuvenation gene therapies based on partial reprogramming and his role reinvigorating investigations into the origins of SARS-CoV-2. 

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.

Yuri Deigin photo

Yuri is co-founder of YouthBio Therapeutics, a company developing gene therapies for rejuvenation.

Transcript of Episode 52: Yuri Deigin

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 Yuri Deigin. Yuri, can you introduce yourself please.

Yuri Deigin: [00:00:08] Sure. Thank you for having me on the podcast. I’m Yuri. I’m a drug developer. I’m leading a longevity company called YouthBio. We are developing in vivo partial reprogramming gene therapies to ultimately create rejuvenation therapies, but in the interim, create therapies against existing age related diseases like Alzheimer’s, for example. Happy to chat about any topic today.

Grant Belgard: [00:00:36] Yeah, I’d like to learn more about YouthBio and how related do you see the the underlying causes of various age associated diseases?

Yuri Deigin: [00:00:47] All right.

Grant Belgard: [00:00:47] How much convergence do you think there is?

Yuri Deigin: [00:00:50] Yeah, right into it. I guess YouthBio is really the culmination of most importantly, my understanding of how aging works and that is epigenetically driven and that obviously all age related diseases in my mind are caused by the underlying process of aging. And of course different diseases could be different manifestations of the same process or different results of this process of gradual epigenetic slowing down of the repair systems and all other systems that help maintain homeostasis. And so reprogramming is one way to actually intervene on a level of epigenetics, because while reprogramming just epigenetically brings the gene expression pattern all the way into the embryonic like gene expression pattern. And that results in all of the changes associated with cell morphology and function ultimately as it’s of course driven by gene expression. But it all starts in changing the gene expression pattern. And what was noticed that during the reprogramming process, the cell is also rejuvenated both physiologically on the level of its transcriptome basically on the level of gene expression. In the initial stages of reprogramming, it seems that the pattern of the gene expression of an old cell gets shifted closer to a pattern of gene expression of a younger cell, the cell of the same cell type. And so this is really the foundation of partial reprogramming where we try to use this process of rejuvenation during reprogramming and stop it at that precise moment where the cell still remains of the same cell type that you start with. But it gets rejuvenated to some degree.

[00:02:38] And we don’t want the reprogramming process to proceed further because that could make the cell lose its function and lead to all sorts of problems. So that’s the partial aspect of partial reprogramming. And yeah, basically the underlying assumption, underlying hypothesis that aging is epigenetically driven, really synchronizes well with the approach of reprogramming, which as I mentioned works on the level of epigenetics. And that’s why I think it’s a very powerful paradigm. If we can figure out how to do this safely in all sorts of different cell types, because I think it actually is necessary, like different cell types necessitate different approaches to using reprogramming in those cell types, then I think we can have very powerful instruments to rejuvenate those cell types. And so eventually have a systemic therapy where you can target multiple cell types, multiple organs and eventually obtain this holy grail of systemic rejuvenation and make aging either slow down or even reverse aging to some degree in healthy people. That’s the ultimate goal of longevity research to make healthy people healthier for longer and make aging slow down in healthy people and radically extend lifespan and healthspan using such approaches.

Grant Belgard: [00:03:55] How do you think about aging fundamentally and what is the relationship between that and the epigenomic changes that are associated with aging?

Yuri Deigin: [00:04:05] Yeah, it’s a very interesting question because just recently we had a discussion on what is aging for a while. I think it’s been spearheaded by Vadim Gladyshev. And just recently we had another installment, his talk on the topic. So it’s actually interesting that there isn’t a consensus or at least a complete consensus in the field on what aging is exactly, because different people call different things, aging. And obviously there’s some common things that we observe that we realize is what aging is associated with. We all know aging when we see it. We know an old person from a young person. And even non-experts in gerontology, like people obviously are very good at telling apart an older person from a younger person. So obviously we have that really actually deep down in genome recognizing aging and recognizing old individuals from young individuals, even across species. So this is obviously something very fundamental to life, to what we will at least guess mammals and higher animals recognizing old from young. But on the level of gerontology and biology, I think to me, aging was just the process that causes mortality risk to increase with time. So basically, if your mortality risk does not increase, to me that means the organism is not aging. So if the mortality risk stays constant or actually decreases with age, then that’s an example of either a non aging organism or a non aging stage in the life history of an organism.

[00:05:44] For example, I think between ages like 5 and 8 or 5 and 9 in humans, the mortality risk actually decreases and reaches a minimum between ages like 8 and 10. And then after puberty starts, then that mortality risk starts to increase and it keeps increasing exponentially until we die with potentially a plateau between like age 20 to 30, 35. Some gerontologists actually argue whether there is a plateau or there is a slow down or there isn’t, or just an aberration or statistical aberration. But basically this is just, I think, one key observation or definition even of aging. Basically, aging is the process that increases mortality risk. And in humans, of course, it doubles mortality risk every eight years. So it’s an exponential increase. And in other animals, it’s not necessarily. So in some animals, actually, the mortality risk decreases with age. There’s examples of turtles, for example, who seem to have observed, we observed, decrease in mortality risk and actually increase in their fertility in some species of turtles. But of course, there are other ways to define aging as, for example, accumulation of various damage, intracellular extracellular or other manifestations, other hallmarks that people associate with aging. And they call that the aging process rather than the downstream effect of increased mortality. So as any academic field, you can have a lot of debate about the precise details ultimately coming, circling back to what we were starting to discuss in the beginning of this question. We obviously know aging when we see it and we would know rejuvenation when we see it.

[00:07:29] If you are able to take a 40 year old person and rejuvenate him, her to look and act and physiologically be the level of a 20 year old or say any two numbers, obviously everybody would agree that that is true rejuvenation. And you can do this, of course, in other animals as well. Mice, for example, two specialists in mice would obviously see rejuvenation. If you take a two year old mouse and rejuvenate to a little one year old mouse, there won’t be any question that this is actual rejuvenation. So from that standpoint, you can have and obviously there’s other biomarkers of aging. I think right now there’s physiological biomarkers of various functions of the organism like lung function, kidney function, heart function. There’s also epigenetic biomarkers are built on the known data of health status, mortality and all sorts of different ways that you can approximate the aging process and evaluate whether you’re able to observe a slowdown in the aging process or actually a reversal of the aging process. If you see, for example, biological age being reduced after some interventions. Sorry, I think I’m starting to maybe go a little bit off.

Grant Belgard: [00:08:39] No, this is good. It kind of raises a natural question. How do you think about aging and biomarkers of aging in the context of drug development? Because certainly different stages you would need to consider different things. And how does all that tie into the question of what is aging? Is that question essentially more of an academic debate or would it really impact at a fundamental level what your primary endpoints would be in a clinical trial?

Yuri Deigin: [00:09:15] I think we have at this point, enough biomarkers to assess to a pretty good degree any interventions that claim to rejuvenate or slow down aging precisely because we have different biomarkers and we can test function and we can test epigenetic age. So from that standpoint, I don’t think there’s anything limiting drug development or therapy development for rejuvenating therapies, especially because at this point and in this regulatory framework or regulatory landscape, you really need to go after interventions after diseases. Interventions have to go after diseases. So it’s not like you can have clinical trial of a healthy taking some sort of rejuvenation therapy and then measure endpoints. You really need to have some patients that you administer the therapy to, and then you evaluate clinical endpoints and biomarkers that are improved by that therapy from that standpoint.

Grant Belgard: [00:10:16] What indications do you think would be especially promising for that as a gateway to ultimately develop drugs that more generally target aging? How would you think about starting?

Yuri Deigin: [00:10:31] Well, we have examples of senolytics and osteoarthritis of the knee. People thought that would be a good indication to try. And I think there’s different ones that you can go after. And well with the metformin trial is another methodology where you have a whole basket of age related conditions and you have people susceptible older people to those conditions. And you then evaluate whether your therapy, your drug metformin in that case is able to reduce the incidence of a number of age related conditions. That’s one way to actually measure the effectiveness of a potential therapy. And other therapies. I depending on the actual intervention, they could have different optimal targets, optimal diseases, which could be the first ones to go after in a clinical setting. Reprogramming is if we’re talking in the context of partial reprogramming, I think it has a whole host of different areas in which you could demonstrate benefits in that particular disease model or actual disease or predisposition to disease, where you can confidently say that not only is this helping this particular disease, it could also have a systemic effect if you’re able to target multiple tissues. In the context of reprogramming, skin is people think is a low hanging fruit because you can have very quick results, visually observable improvements.

[00:12:02] And that’s why I think a couple of companies are going after skin, are going after dermatology. And also other areas in terms of hematology and immunology, people are also considering. But also I think in the context of the central nervous system, brain diseases, if you’re able to demonstrate using approaches of partial reprogramming, even a slowdown in the onset of various neurodegenerative pathologies like Alzheimer’s, Parkinson’s, etcetera, then it’s a very compelling. Again, very compelling case to be made that this is truly a therapy that’s at least slowing down the onset of aging in this particular tissue, the onset of age related symptoms of this particular disease. And also even if you’re able to reverse the symptoms, that’s a much more stronger case that you’re actually rejuvenating the underlying organ. So I think, yeah, while they’re very neurodegenerative diseases, they’re obviously hard nuts to crack. I think the upside in their cases is really high because there’s huge unmet clinical need and Alzheimer’s, for example, you’re able to demonstrate clinical benefit there that could be very compelling case for people to take notice that this approach is very strong, rejuvenating approach.

Grant Belgard: [00:13:23] So I think neurodegeneration is a really good use case because we do know there is some degree of genetic overlap, several neurodegenerative diseases and also gene expression signatures associated with age and then GTLS for those and so on have been identified in the context of brain age. What concerns me a bit though about the paradigm of slowing aging overall and having a substantial impact on the rate of age associated disease is as if there were a single underlying factor of aging that affected overall physiology in a similar way, something analogous to G and intelligence or whatever, you might expect to see that in genome wide association studies of different age related diseases in different organ systems. And to have some of the usual suspects coming up time and time again and within certain classes of diseases of course. You see some of that neurodegeneration tau, for example, comes up quite often, but you see very, very frequently, which you would expect if you have age matched controls versus people with this disease or that disease. On the other hand, of course, there are many facets of age associated biomarkers that are strongly correlated with one another. So these frailty indices and so on. And of course we do have genetic associations with measures like that. So do you think about it as, okay, we will identify treatments that are effective for maybe somewhat broader classes of diseases and reducing the rate of onset? Or do you think there is a good possibility of slowing everything down together?

Yuri Deigin: [00:15:38] I think theoretically there is a good possibility of slowing things down together. I think aging is centrally regulated, to be honest. I think actually it’s a program and I know I’m a minority opinion on this because there’s so many life stages that are definitely centrally regulated. And we have to have in a very coordinated concert to be developing just at the right time and the right proportions to have an embryo develop properly and also to have puberty onset at the right time and all organs start growing at the right time. So there is obviously a central synchronization mechanism. Now we still haven’t figured it out how exactly it occurs and well, there are some hypotheses that the hypothalamus could be one such controlling master regulator of aging because it is actually controlling a lot of daily monthly processes or maybe even on a longer time scale obviously. During embryogenesis it plays a very important role. That said, I think this is a parallel search for this master synchronization system. And also, of course, I think if we were able to slow down development, we see that that obviously slows down aging or actually puts aging on pause if we’re able to delay the onset of sexual maturity. And actually some people are pursuing that as a rejuvenation strategy. There is a gerontologist in Ukraine, Valerie Golub, who’s studying this in rats and reptilian, trying to see because obviously we’ve seen in many organisms that neoteny greatly extends lifespan and also experimentally, if you’re able to pause development or slow down development, that also extends lifespan. And it seems to put aging on pause.

[00:17:35] But coming back to the partial reprogramming approach, it’s a bit of a different way of going about it. It’s just targeting key organs that are playing the biggest role in our age related pathology of course. Heart disease being one of them and other organ systems and hoping that we can target enough key organs and key systems to that result in a systemic effect where we can slow down aging of the entire organism enough to produce sizable gains in healthspan and lifespan. But also there is a hope and a hypothesis that we’re also exploring that maybe by modulating in certain areas of the brain, rejuvenating them, maybe we can then achieve systemic effect. But obviously that’s just a hypothesis that we need to explore. But to answer your question, I do think that it might be possible to have an intervention going at the entire aging process, or at least the actively driven aspect of the aging process, because obviously after some time there are also stochastic things happening that are not part of the epigenetic landscape being driven, but they’re side effects of genes allowing stochastic changes to produce enough damage that it actually starts to accumulate. But that’s a bit of another tangent that I’m happy to explore.

Grant Belgard: [00:19:15] There are maybe a couple of tangents we’ll go off on in a bit, but I think it’d be really interesting to hear about your journey, How did you end up here? What drove you in the direction of science etc?

Yuri Deigin: [00:19:28] Oh yeah. My journey to longevity started with just a regular drug development and meeting people who are also wearing the same drug development ecosystem. But there were also longevity minded people who just shared with me the idea that aging is the causal factor in age related diseases that I was working on. They were working on Alzheimer’s, for example, and basically saying that we can and should try to intervene in aging and slow aging down and ultimately, hopefully reverse it. And that was a revelation at the time to me because as most people unfortunately on the planet, I never thought that aging is something that needs to be intervened in or should be, or is it possible to be intervened in? I thought aging is inevitable, just everything ages and didn’t even think that there is something that could be done about it until my eyes were opened by my friends at the time. And obviously after they made me aware of this whole industry and research community looking into aging, fundamental mechanisms of aging and trying to intervene in aging, that very quickly I realized that it makes all the sense in the world that, yes obviously aging is the causal factor in all those diseases and there is no good reason why we should settle for the lifespan that we inherited biologically from our ancestors. And we have the intellectual capacity to analyze biological processes and finally have the tools to intervene in biological processes that it only makes sense for us then to turn our intellect and our abilities to intervene in biology to the biggest limiting factor in our lives, which is aging.

[00:21:24] And very quickly, I became a very passionate activist at first about aging research, about the necessity to research into aging and just trying to open other people’s eyes, just like my eyes were opened by very, very simple and logical information and just making people aware that there is a thing like aging research out there that it’s possible to intervene in aging. It’s possible to extend lifespan in model organisms. It has been done many times and it’s only a matter of time until we find effective interventions that can be applied to humans and produce sizable gains in both health and lifespan. And eventually I was able to then not only do the activist part, but do the drug development related or start doing drug development related to longevity. And that came in the context of partial reprogramming, because at the time when I learned about partial reprogramming, I was already of the mind that aging is driven by epigenetics. To effectively intervene in aging, we need to intervene at the level of epigenetics if we’re talking about already formed organisms. Of course, if we’re talking about new organisms, probably we can intervene at the level of the genome.

[00:22:39] But unfortunately we’re already cooked organisms using the cooking instructions stored in our DNA. So if we want to change something in our biology, we have to go after gene expression. And as I mentioned in the beginning, partial reprogramming is one way to intervene at the level of epigenetics, at the level of gene expression. And it’s a very effective mechanism because it uses an underlying process that’s already fortunately encoded in our genome to do this, to restore gene expression of the primordial embryonic stem cells. And it’s a very powerful tool that we can use to to essentially hack for our purposes, to rejuvenate adult organisms, adult cells. And yeah, I started on the path to try to translate this paradigm into therapies very early on back in 2017. The paper came out in December 2016, and then I founded the first company in Ethereum dedicated to translating this paradigm in the summer of 2017. I got really excited about it because it fell on fertile soil in my mind, that partial reprogramming paradigm, because I already thought that epigenetics is the driver of aging. So again, I took a little longer maybe than I should have to answer your question. But yeah, happy to dive into any details of the journey or any particular aspects of it that could be useful to other people.

Grant Belgard: [00:24:10] Yeah. I think it’ll be interesting to hear about the lessons you’ve learned through that. But before we get into that, to go on a bit of a tangent, you’re obviously quite early to this. And of course now partial reprogramming and aging research is much more mainstream than even five, six years ago. But you were also very early on the important aspect of coronavirus Can you tell us about that and how did you get there? I recall reading your very, very long blog post years ago, and I was really impressed, what was the impetus behind that? Because at the time that was very, again not mainstream but it’s one of those things that has become much more mainstream in the years since, in large part because of what you kicked off.

Yuri Deigin: [00:25:09] Yeah. It was just another tangent essentially that I got curious about. The origin of the coronavirus, COVID, I guess not just any coronavirus, but the coronavirus that caused the global pandemic. Basically like everybody else was curious in early 2020 where the virus came from, and initially a lot of people made the obvious connection that Wuhan has the premier lab in Wuhan Institute of Virology that has been studying coronaviruses. But in the very early stages of the pandemic, this making the hypothesis that the lab had something to do with the outbreak was considered a crazy conspiracy theory. And the powerful and very respected scientists were putting their names and their reputation behind this to dispel this conspiracy theory. And initially I just trusted them. I thought hey, if nature medicine says that the virus must be natural and there’s like 5 or 6 virologists and Anthony Fauci saying, that there ought to be listened to, then they know what they’re saying. And but at some point just decided to dive a little deeper. And instead of just outsourcing my thinking to them, decided to try to see for myself from first principles. Does it make sense this conspiracy theory makes sense or not? And so I read the Nature Medicine paper, and I was a little disappointed by the logic because once you strip out all the virology lingo, you’re left with very unconvincing logic that basically it’s the absence of evidence that the authors are trying to sell as evidence of absence, evidence of possibility that this could have come from a lab. And then I started digging deeper and I saw that the research that they were doing in Wuhan was exactly potentially similar to what could have produced this coronavirus.

Grant Belgard: [00:27:25] And of course, this was long before the diffuse proposal had come out and so on.

Yuri Deigin: [00:27:29] Oh yeah, yeah, yeah. Diffuse was a year afterwards and yeah, basically it was just circumstantial evidence of look, this is clearly a huge red flag that the lab that’s been collecting coronaviruses from all over the world and manipulating them in all sorts of ways had the outbreak of this coronavirus with the very unusual feature, a couple of unusual features, one of them being this furin cleavage site. And it was very suspicious to a lot of people. And basically, yeah, I wrote this up in the blog in like early 2020, published it, and then it took on a life of its own and got me deeper into the rabbit hole and into the community of people, like minded people who were also very suspicious that this could have come from a lab and were continuing to investigate all aspects of everything surrounding basically the outbreak, the work in the Wuhan virology and other EcoHealth Alliance as well of course, what their involvement was and how they were using grant funding and their close cooperation with the Wuhan Institute of Virology and other labs to essentially collect and manipulate viruses in the lab to make them more infectious for the purposes of assessing how dangerous this would be if it happened in nature. Basically try to second guess or try to guess what nature could do to make viruses more dangerous for humans. And they did this with SARS like viruses. They did this with MERS like viruses, MERS being the Middle Eastern respiratory virus that is very deadly. It’s like a 37% fatality rate. And actually they did a lot of research on that in Wuhan Institute of virology together with EcoHealth Alliance as well.

[00:29:19] We learned that a year or two well, not to a year and a half after, of course, the initial outbreak because of all the Freedom of Information lawsuits that were filed to NIH and other agencies to actually see what has been going on in this research between EcoHealth, NIH, who funded a lot of these studies, and the Wuhan Institute of Virology, who were doing a lot of these studies, but again diverged. But yeah, basically once I gotten interested in this origin of coronavirus, the community of other people that I got together involved with we, started calling ourselves drastic. It was a Twitter based group of activists trying to investigate the origins of the coronavirus. And we investigated some additional aspects, published a bunch of papers on very suspicious discoveries. And basically, at some point, yes, the diffuse proposal was leaked by drastic. The diffuse proposal being, of course, the joint grant proposal by EcoHealth and the Wuhan virology in which they pitched to DARPA this idea of collecting novel SARS like viruses and also engineering novel furin cleavage sites in sites like viruses, because up until now and actually never since SARS, like viruses, never had a furin cleavage site at that spot. And it seems actually to be evolutionarily discouraged in bats because in bats, these viruses are gastrointestinal viruses and furin cleavage sites actually seem to be detrimental to the viruses being tropic to the GI tract or something. Basically, furin cleavage sites for some reason make viruses more preferential to the respiratory system. And but of course, for humans, that’s the biggest way how viruses get transmitted through the once they’re airborne through the respiratory system.

[00:31:22] So for a virus to jump from bats to humans, the key skill for it to pick up is to actually become tropic due to our respiratory tissues and cell types and inventing or getting a furin cleavage site actually is a catalyst for a virus to do that. It basically turns the virus from a gastrointestinal virus into a respiratory virus, and that makes it very high threat for transmission. It makes it so much more transmissible for humans, especially. And in the context of research on coronaviruses, it’s been very well known that the furin cleavage sites expand tropism of such viruses and make them actually respiratory viruses. And it’s been experimented with other virus types, coronavirus types to engineer novel furin cleavage sites and observe how that changes the tropism or the preferences of the virus to different cell types and different systems, including the respiratory system. And so in that diffuse proposal, one of the things they described to put in their experimental roadmap was to try introducing novel furin cleavage sites, basically to assess the risk of how likely it is in nature for a furin cleavage site to arise and to also model what would happen if a particular virus got a furin cleavage site. How likely would it then be to jump to humans? And of course the SARS-CoV-2 virus has this receptor binding domain that’s very highly preferential to human ACE2 receptor, which is very, very interesting characteristic. It scores like number one on the list of all different animal ACE2 receptors to be preferential to the human ACE2 receptor, which is very odd for a bat virus. But maybe there’s a species of bats who has a very similar or an ACE2 receptor that’s also very high binding affinity to that particular conformation of the spike protein on the SARS2 virus. And of course, not only does the spike protein binds well to our ACE2 receptor, the furin cleavage site makes it so much more transmissible and so much more preferential to our respiratory system. And so once the DEFUSE proposal was publicized in the summer of 2021, to a lot of people, this was very convincing additional nugget of evidence.

Grant Belgard: [00:33:49] That was what pushed my posterior probability on this way over 50%. I recall reading through it and saying some words that would not be appropriate for a family podcast. It was just shocking. And I was certain at that point that these surely global regulations on gain of function research would be coming down swiftly. And the amazing thing is, even though later that summer, we got to the point where most Americans believe, WIV was the origin of the virus. And of course former CDC director during the outbreak said something along those lines. Obviously a lot of people within the US intelligence community were concerned that was the case and several people in Congress. So one would think something would have been done about it by now. What’s shocking is there’s probably more manipulation of coronaviruses happening now than before. It’s extraordinarily dangerous. I mean, even if you don’t buy that it came out of WIV like surely even without that, just knowing how common it is for viruses to escape from laboratories and so on, the cost benefit of this kind of work is so overwhelmingly on the cost side and not the benefit side.

Yuri Deigin: [00:35:18] Yeah, for sure. And yes, the SARS-CoV-2 escaped definitely once from the Taiwan BSL-3 laboratory and there were actually acknowledgments in internal emails that before that escaped within a Chinese laboratory. And the first SARS virus that was much less transmissible escaped four or six times from different labs. So coronaviruses obviously can escape from even high level of security biosafety labs. So and yes obviously, I’m sure there’s countless times where people were infected handling coronavirus samples these days because so many labs now work with the coronavirus. And obviously it’s so hard to track now where the infection you got as a researcher is from a lab or just somewhere outside. I mean, in Taiwan, they could track it down because essentially it was very low circulating SARS-CoV-2, if not zero at that point. And they were able to genomic tracing to confirm that this particular sample is the one that infected the researcher. But yeah, also all sorts of potential gain of function research that people have been doing or are doing now with the COVID virus, SARS-CoV-2, one of the earliest ones was these Italian researchers in Siena. I think in the beginning of 2021, they passaged SARS-CoV-2 in the presence of neutralizing antibodies from people who had COVID or were immunized to actually see how it could evolve to escape those immunity escape, that antibody immunity.

[00:37:01] And they succeeded. They created a whole new strain that was very good at infecting people who already had immunity to the original SARS-CoV-2 virus. If that escaped, we could have had a whole new episode of the pandemic with a whole new strain. And what happened with Omicron to me is highly reminiscent or highly similar to what could have happened if a gain of function research could have produced a strain, then just got out and infected people and started circulating. Because Omicron is just so different from its last known ancestor, it has like 30 spike mutations basically like that. It developed seemingly without any intermediates. And like to this day, it’s still a mystery. How did Omicron arise and where was it circulating between like June or November of 2020 when its last ancestor was seen in November or December 2021, when Omicron emerged. Where was it all this time? And the idea that it wasn’t just someone immunocompromised patient in which it could have developed those 50 mutations, including 30 spike mutations.

[00:38:17] To me, it’s a very implausible hypothesis because we have been observing quite a number of immunocompromised patients and in none of them did we observe such a huge number of mutations arise. There’s usually a couple of dominant ones arising and then maybe a few others. But I think the most we’ve seen is like ten mutations in the spike of a person who had like 18 months infection present for a duration of 18 months, they couldn’t clear. And so but now we in Omicron, seemingly between a year since seeing its last ancestor developing 50 mutations with 30 spike mutations, it’s really, really crazy. So yeah, it could have been a lab leak as well. And who knows what other gain of function research is going on because obviously it’s not a regulated space. And even if you regulate it in one country, who knows what happens in countries that are not signed on to be parts of some regulation. So it’s a huge, huge existential level problem for us, for humanity, that unregulated gain of function research of pathogens going on in all sorts of labs all over the world could lead to pandemics, maybe even worse than this one, I hope not.

Grant Belgard: [00:39:32] It’s just wild to me like when I was reading the DEFUSE proposal, one of my immediate thoughts was this is kind of the Manhattan Project moment for the life sciences, except far more people have been killed by this than have been killed by nuclear weapons. So I was expecting surely there would be.

Yuri Deigin: [00:39:51] DEFUSE proposal was actually much broader than that. They proposed actually creating vaccines for bats with which they would go out into the field and immunize bats preemptively. And who knows what wrong could have gotten from that approach. And you know what? Maybe new combinations of viruses, bats could have developed due to that thankfully DEFUSE proposal was not funded. And that particular aspect of it of trying to actually go out and change ecosystems with preemptive vaccines in wild animal populations. This was criticized by the reviewers, but of course the molecular biology experiments, even if they weren’t funded by DARPA, they could have been funded by many other agencies, including Chinese funding agencies or even like you don’t need a lot of funding to do genetic manipulation of viruses if you already have the viruses and you collected some and to insert 12 nucleotides into a coronavirus genome doesn’t really take a lot of money or a lot of time for your post-docs or PhD students.

Grant Belgard: [00:41:00] Why do you think there hasn’t been a reckoning on this? Most people believe this is what happened. Plenty of people in power have said this as well, and yet there hasn’t been an aggressive effort to shut this down.

Yuri Deigin: [00:41:16] Oh, like to shut down gain of function? I mean, yeah, it’s very hard, I think, to come up with novel global regulation of an entire field of virology. If we’re talking about pathogen research and classifying what exactly is gain of function for a pathogen and what isn’t and in what context it should be permitted or should it be like completely banned? And what about places that don’t sign on to this convention? Virology is just going to migrate or virology labs will just migrate to those geographies that don’t have that regulation. Of course, if you ban it in the United States and Europe, that’s the major sources of funding. So I think that could lower risks of various pathogen gain of function research ongoing and greatly decreased in number.

Grant Belgard: [00:42:08] But I guess the challenge is, it’s much more inexpensive and widely accessible to do this kind of work than it is to enrich uranium and build nuclear weapons.

Yuri Deigin: [00:42:19] Oh, yeah. These days, absolutely. Biology, I think from an existential risk perspective, is much higher on the list than nuclear war or nuclear terrorism, because bioterrorism by comparison is much cheaper unfortunately, Hopefully, we won’t have bioterrorists listening in and like, Oh, okay, I was enriching uranium in my backyard. I think I’ll go build a virus lab now. But I think the process is actually ongoing. It’s slow and we see some of it starting to happen with now the Republicans having these hearings and maybe they will result like there’s two two questions. One is about the origins, which is of course important. But it’s not like even if we don’t find out the exact origin.

Grant Belgard: [00:43:11] Even if we’re not certain, certainly I think it’s undeniable that it’s a very plausible source. And even in light of it being a plausible source, it very clearly is something we need to button up and make sure that that thing does not happen. Or even if you say it didn’t happen the first time, that it doesn’t happen in the future, right?

Yuri Deigin: [00:43:34] I mean, yeah, not like just the SARS leaks I mentioned happening in the past couple of years. And just before the Wuhan outbreak, there was a brucellosis outbreak in China, in Kunming, in Yunnan, in the lab, and like 100 people got infected by it’s much less transmissible, thankfully, bacterial infection, but still, obviously it’s a lab borne infection. So these things happen and these things are dangerous. It’s just a matter of the right pathogen escaping that could be so much transmissible that it could cause a pandemic versus something that isn’t. But yeah, absolutely should be taken seriously. And I think also the problem is that politicians are blissfully ignorant on where biology stands right now. And when they find out their hair stand up how easy it is to manipulate biology and how cheap it is these days. And basically what Wild West, to quote Jeremy Farrar exists in all over the world in labs. Basically what I mean, nobody is regulating what you can do in the lab. If you want to supercharge a virus, the virus police won’t come and say, Why did you do this? Didn’t you realize that this is dangerous? So, yeah, hopefully soon enough, people in power will come to understanding of what’s going on and what’s possible and what’s dangerous.

Grant Belgard: [00:44:58] If someone is just hellbent on doing it, it would be difficult to stop them. But certainly the vast majority of the work ongoing where something like this could accidentally get out and cause lots of problems. It’s funded by funding agencies. People are doing it to publish work that’s peer reviewed. If there were a common consensus even if scientists just got their act together and did this even without any kind of government intervention, that would dramatically reduce the likelihood. But that doesn’t seem to be happening.

Yuri Deigin: [00:45:38] Yeah, exactly. And yeah, I think the incentives are misaligned here because there in order to publish something we have in virology. You have to create interesting research. And right now it’s very hard to do interesting stuff without genetically manipulating viruses or modeling some different changes that could make viruses more pathogenic or more transmissible. I think so. But at the same time, from the benefit of society or humanity as a whole, this type of research supercharging viruses or creating more transmissible viruses or more dangerous viruses, I think has a very, very little benefit, if any. But at the same time, the risks are huge and the risks are obviously shared by the entire world. And so if we’re worried about just one bioterrorist, it’s one thing. But if we’re worried about thousands of virologists doing this thing and making some mistake or not even a mistake or just supercharged virus could escape, even if they follow procedures, maybe some equipment malfunction or something happens that people don’t anticipate, that could lead to a pathogen to escape then the probability of this if a thousand people are doing it versus just one crazy terrorist is doing it, I think are much higher. That’s a different conversation.

Grant Belgard: [00:47:06] So I guess coming back now after, I think it was a pretty interesting digression in your own career, kind of a side project. So if you could go back several years before you started the founder journey, what advice would you have for yourself?

Yuri Deigin: [00:47:25] Oh, that’s a tough question. Invest in Bitcoin maybe. That’d be one advice. I mean, like if we’re talking something useful for people these days who are entering longevity, like in terms of career advice, I think now is a much better time because there’s all sorts of fellowships and programs like LBF Longevity Biotech Fellowship available for people who are just getting interested in longevity and want to quickly orient themselves. I think the number of resources available is really large compared to Decade ago or more when I was entering the field. So just the advice would be to study existing description of the landscape of the field and various areas of research and just, not be afraid to reach out to people and ask for advice if you’re interested in career switching. There’s all sorts of companies open to people coming from different fields. And obviously, if you want to do this, if you’re interested in longevity, one advice I would give is not to defer, not to procrastinate, but to get into it as quickly as possible. Because I think the more you get drawn into a career, the harder it is to to switch over. And longevity, I think right now is at a good point where you can enter into a lot of new companies or even labs on the academic side and the tidal wave that’s coming, it’s going to lift your career and your progress in this field as well. So maybe just don’t delay.

Grant Belgard: [00:49:06] Yeah, it’s interesting because you had a somewhat circuitous path where you were in tech for several years before getting into pharma.

Yuri Deigin: [00:49:17] Oh, yeah. Before drug development, I was in tech. My first career was in tech and I did computer science degree in mathematics and yeah, I worked for IBM. I did a startup actually in mobile applications back in like 2002 before it was cool, but I pivoted then into drug development. I wanted to do drug development for a while. And actually the time has come back, like in 2008 for me to do this. So I decided to do an MBA to help me get into the pharmaceutical business development, and that was the key pivot. But then to get into longevity drug development, that took me also some time before an opportunity came, which I think was a perfect opportunity with partial reprogramming because it was just the right approach for my current and then understanding of aging as an epigenetic process.

Grant Belgard: [00:50:11] Well, I guess in terms of getting it early, you’re two for three. So longevity field, the COVID origins, but not Bitcoin.

Yuri Deigin: [00:50:21] Oh yeah, yeah. There’s a lot of other investments that if you had hindsight, it would be nice to know about back and a decade ago.

Grant Belgard: [00:50:31] Cool. Is there any advice you would give to people who are launching their first company network?

Yuri Deigin: [00:50:40] I think yeah, networking is really, but obviously it’s pretty obvious advice and don’t get discouraged because it’s very easy. As an entrepreneur, there’s always a roller coaster. There’s always going to be down times where you think everything’s going badly, but you just have to keep going and keep the faith that eventually there’s going to be another positive development that will, and like longevity in terms of being an entrepreneur is also very important. You have to keep doing what you’re doing and if you believe in it. Of course, if something changes and you realize that the business model you had was wrong or the therapeutic approach you were exploring doesn’t really work out, then of course you have to change. But if all the fundamentals remain the same and it’s just the environment is not really good, like right now, economy is not great. So a lot of pundits are saying there’s going to be like an extinction of company of startups coming. But do you have to close yourself to the noise and just keep doing your job and just try to achieve your milestones that you put forth for yourself and for your company. It’s probably the same advice everybody gives.

Grant Belgard: [00:52:06] No. I mean, it’s good to know what people emphasize because there aren’t enough hours in a day to do everything one is advised. And so even hearing the same thing time and time again, you get extra votes for the importance of that.

Yuri Deigin: [00:52:26] Yeah. I think everybody says resilience is a key attribute for entrepreneurs and that’s true.

Grant Belgard: [00:52:33] Get punched in the face.

Yuri Deigin: [00:52:35] Yeah, exactly. But it’s true. You have to keep going in the face of adversity. And of course, also the more you do it, thicker your skin becomes and the better you become at managing it. So also, yeah, I think for younger entrepreneurs it could be a little tougher, but it’s just something like with anything else with the time and practice, you get better at it.

Grant Belgard: [00:53:03] You develop thick skin.

Yuri Deigin: [00:53:06] Yeah. Stick to it. Don’t get discouraged. And also, yeah, I think it’s also important to have people who can help you both emotionally and with advice and like, support groups are important. And that’s why I think also within the fellowships like the ODLB Fellowship or the Longevity Biotech Fellowship, these mastermind groups as you know, they could be very helpful because there you can connect with people going through similar things, similar entrepreneurs or scientists, researchers or postdocs going through similar adversity and connecting with them and getting support, both emotional support or just some advice or some other help. I think it’s really important to have that kind of support structure, support network for everyone, entrepreneurs and academics alike. So yeah, get that support structure in place, network and try to form this group of people who can support you through the tougher roller coaster rides.

Grant Belgard: [00:54:04] Solid advice. Thank you so much for joining us, Yuri. It’s been a pleasure.

Yuri Deigin: [00:54:10] Thank you. For me as well. Hopefully it was useful for the listeners. So thank you.

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.

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

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.

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.

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.