Cover art for The Bioinformatics CRO Podcast episode: Elizabeth Ruzzo - endogenomics for inclusive scientific discovery

The Bioinformatics CRO Podcast

Episode 61 with Elizabeth Ruzzo

Dr. Elizabeth Ruzzo, founder and CEO of Adyn, discusses precision medicine for personalized birth control and her commitment to using pioneering endogenomics to make scientific discovery more inclusive.

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, YouTube, Pandora, and wherever you get your podcasts.

Dr. Elizabeth Ruzzo

Dr. Elizabeth Ruzzo is the founder and CEO of Adyn, a precision medicine startup pioneering personalized birth control.

Transcript of Episode 61: Elizabeth Ruzzo

Disclaimer: Transcripts may contain errors.

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The Bioinformatics CRO Podcast

Episode 60 with Max Marchione

Max Marchione, Co-Founder of Superpower, discusses his experience founding a health tech company, making concierge medicine accessible to all, and the future of healthcare.

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, YouTube, Pandora, and wherever you get your podcasts.

Max Marchione

Max Marchione is the Co-Founder of Superpower, where their mission is to move from reactive to proactive and personalized healthcare.

Transcript of Episode 60: Max Marchione

Disclaimer: Transcripts may contain errors.

[Grant Belgard]: Welcome to the Bioinformatics CRO Podcast. I’m your host, Grant Belgard, and joining me today is Max Marchione, a 24-year-old Australian entrepreneur who’s the co-founder and president of Superpower, a San Francisco-based health tech startup. Superpower is building what it calls the world’s first health super app, aiming to prevent disease and enhance human capabilities through proactive, personalized health care. In essence, the company offers a membership-based digital longevity clinic that helps people live longer, healthier lives. Max, welcome to the show.

[Max Marchione]: Excited to be here. Thanks, Grant.

[Grant Belgard]: So, your path is pretty unconventional, from law school in Australia to dropping out and diving into health tech. Can you walk us through that journey? And what pulled you from law into the world of biotech and startups?

[Max Marchione]: Yeah, good question. It seems unconventional, but around three years ago, I was sitting there thinking to myself, what do I want to spend the next 20, 30, 40, 50 years of my life on? And at the time, I had just left my job at Goldman Sachs, and I was running two small companies. One, my brother’s now the CEO of, he does a way better job than I ever did, and the other, a friend of mine runs. And I was sitting there thinking, what do I want to spend the next 30 to 50 years on? And three things really had to be true. One is, whatever I worked with had to be deeply personally meaningful. It had to be something I was really obsessed over. And health was one of the few things that I would spend my weekends — my weekends obsessing over, my spare time obsessing over.

And that really started after going through a 10-year period of misdiagnosis. It would take me three hours to get to sleep every night. I had chronic headaches, chronic sinusitis. I saw over a dozen doctors, had surgery, was told to medicate for life. No one knew it was wrong with me. No doctor could get to the bottom of it. And when that happens to you, you start taking health into your own hands. That’s what I remember in like 2015, 16. 16, wearing a big fat [aura] ring. And all my friends at high school would bully me because tracking your sleep back then was not something you did. Or a year or two later, a continuous glucose monitor. Same story. People thought I was slightly psychopathic, putting a little microneedle in my arm.

But over that period of trying to solve my own health problems, I became a huge health geek, right? And it was something I was really obsessed of. I’d go to doctors and be like, I swear I’ve read more papers than you on this topic. And I ended up getting to the bottom of what was going on by finding something I call a 10X. A 10X doctor, like a really great doctor. The kind of doctor Jeff Bezos might have. And it made me realize there’s a huge gap between that model of care and what everyone else has. And there’s a gap between the best of healthcare and what most people have. And for as long as that gap exists, someone or some company has to come along and close the gap. So health was deeply personally meaningful.

I mean, thinking about it for a very long time, I didn’t really know what to do in the space, but I was really obsessed with it. The other thing that had to be true is I was thinking about what matters to the world, right? What matters to the world. There are a few problems on this earth that genuinely matter. And health is certainly one of them. And then the final thing is, if I’m going to be doing it for 30, 40, 50 years, the company has to have the potential to be at least a $100 billion company. I’m not saying we’ll get there. There’s a lot of things that can get in the way, but that means that we’re actually working on something that is of sufficient scale to actually matter in the world. So I was debating lots of different ideas. One was building a new city.

One was building a new healthcare system. And I decided building a new healthcare system came first. And the US healthcare system, the system is larger and more broken than anywhere else. So three years ago, I moved to the US. I knew no one here. And I moved to San Francisco. And here I still am today.

[Grant Belgard]: What was the light bulb moment for Superpower? Was there a specific incident or conversation that made you say, I’m going to build a new healthcare system?

[Max Marchione]: Not really. So in August, 2020, 2022, I was really, really obsessed with this idea that AI would perform cognition or computation far more effectively than humans. In other words, every single person on earth would have an AI doctor. And I remember when I said that to people in 2020, 22 August, they laughed at me and they’re like, well, what is this not possible? Come on. What about, what about the art of medicine or the humans of medicine? A couple of months later, November the 30th of November, 2020, 2022 ChatGPT comes out. I’m like, see you guys. They’re like, no, no. What are you talking about? This thing’s like, it can hardly even, even write. This thing can’t do anything. And then around a year later, I’m like, okay, now this thing’s getting really good.

And it was around that time when Superpower started. And, uh, in the period between that I explored lots of different ideas, right? I had a belief around what I thought healthcare would look like, but very few people agreed. The inside had said, no quality is fine. What do you mean? Quality standards are fine. No one cares about prevention. Consumers will never pay for, for anything you want to create. There are all of these reasons to not do the thing. And I came full circle after going through this, this trough of disillusionment to be, to realize, hold on the very idea I started with, which is making the very best medicine that today costs a hundred thousand dollars accessible to everyone, making it cost a hundred dollars is actually the idea, which still really matters.

And AI is the enabling technology. So it was no single moment. It was instead this, this long period of ironically coming full circle to where I started.

[Grant Belgard]: Tell us about your co-founders. How, how did the team come together and what unique strengths do each of you bring?

[Max Marchione]: Yeah, totally. So I met, um, Jacob. In, uh, several years ago now, and we’re introduced under the auspice of “you’re the two most health obsessed people I know.” He’d recently, um, lost several organs in hospital and been through a whole health crisis himself, and we’re both running small venture capital funds at the time. So we, we, we would like share [deal flows], we’d invest together, we’d constantly jam and ideas together. And, uh, we both knew we were starting health companies, but we kept it a little hush, hush from each other. And, and then when we started revealing, we’re like, hold up, we’re building basically the same thing. Rather than competing with each other, what does it look like to actually join forces? So that’s what we did.

And then Kevin, uh, the, the third leg of the stool went through Launch House, which was one of the previous companies, kind of like a Y combinator that Jacob started. And, uh, several hundred or maybe even thousand founders went through, uh, Launch House. And Jacob said Kevin was the best engineer he’d ever met. And they, they became good friends through that period. So the three of us, the three of us joined, joined, um, joined forces. And here we are.

[Grant Belgard]: You spent a year in stealth mode, building Superpower before going public. What were you focused on during that time?

[Max Marchione]: I think that if you’re going to commit 30 plus years of your life to something, it makes sense to be pointed in the right direction. And I think that hype is transitory and momentum, if momentum’s low, it’s inertia and stays low. And if it’s high, it’s also inertia and stay stays on the up. And the implication is I actually think that it pays to think very deeply about what to work on iterate on lots of ideas before actually starting to put brand and marketing capital behind it. Because if you start putting brand and marketing capital behind everything, then when you actually do the actual thing, it’s all gone and you can’t actually use it. So that was a big reason for stealth mode. Um, people disagreed with it at the time. Um, why are you in stealth?

And, uh, I think in retrospect, I would, I would certainly do it again.

[Grant Belgard]: What early challenge, challenges, uh, or pivots did you have as you refined the concept?

[Max Marchione]: Uh, we started far more upmarket. So the, the initial belief was we’re trying to take a hundred thousand dollars concierge medicine and make that accessible to everyone at a far lower cost. Surely it makes sense to start with a hundred thousand dollars a year concierge medicine. And to slowly reduce costs and automate more. And that’s not really how it works because when you start having that much capital to play with, you end up making product decisions and engineering decisions and operations decisions and strategic decisions based on that, right. And you end up running essentially another services led company. And you’re competing with all of the other services led companies that charge very high prices.

And yes, there’s a small market that’s willing to pay and therefore it can be a fast path to revenue. But it means that you’re not, we’re not actually thinking sufficiently about scalability from day one. So the change we made was to actually do the opposite, which was to start at an attractive price point, $42 a month or $499 a year. And have a set of things included at that price point that we think are amazing and then just keep adding features, right? So rather than keeping the features the same and reducing the price instead, start with a very low price and keep adding features. And I think that’s a far more scalable way to build because it forces you to make the scale decisions right from the start.

[Grant Belgard]: Superpower’s mission is to move healthcare from reactive to proactive and practical terms. What does that mean for an individual? Can you give an example of how traditional healthcare might miss something that your approach might catch?

[Max Marchione]: So the cancer someone gets at 40 or 50 starts when they’re 20 or 30, the heart disease at 60 starts when someone’s 20 or 30, the Alzheimer’s at 70 starts at 20 to 30, maybe even earlier. Now the healthcare system today will only react one to two years before it actually happens, not 30 to 40 years before it happens. But I think we can and should intervene early. And we have the tools to understand how things that are presenting themselves very early to someone actually could result in something down the stream. For example, heart disease is a classic one. If all of someone’s parents and grandparents died of a heart attack, you’re probably going to die of a heart attack.

But if you use any sort of risk scoring algorithm, 10 year risk is what is used, it’s going to say your 10, if you’re 30 years old, it’s going to say your 10 year risk of a heart attack is very low. It’s so low. We’re going to do nothing about it now. We’re not going to. So I’m like, wait, hold up. I know that’s my 10 year risk, but my 30 year risk, I’m like going to die of a heart attack for almost certain. So why don’t we take preventative measures today? And we can actually start to test for someone’s lipid burden. We can test apolipoprotein B. We can test lipoprotein little a, which basically shows your genetic risk of heart disease. And we can take steps to reduce atherosclerosis early. We can reduce calcification of plaque in the arteries.

And we should do that early if that’s the most likely thing you’re going to die of. And it’s the number one cause of death in the United States. And that’s one example with heart disease, but that applies to everything. And we can see how being out of range in certain biomarkers correlates and likely has some causal effect to diseases downstream. A very high A1c, a sign for being diabetic or pre-diabetic, will increase your risk of all cause mortality, largely from the biggest killers. Having liver enzymes, which are highly elevated when you’re younger, will increase your risk of all cause mortality. And if we look at the way the system, it doesn’t respond. I remember I was 13 years old. My liver enzymes were just outside the normal range.

And every doctor said, it’s fine, it’s just outside the normal range, don’t worry about it. And what I found out several years later is the normal range is the 97.5th percentile. So at 13 years old, I was worse than 97.5% of the entire population. And I was told I was fine. Right? And that’s how the healthcare system is set up. Doctors don’t even know these ranges of percentiles. They’re just like, ah, it’s just the range. So unfortunately, it’s not just the doctor’s fault. They’re not even equipped with the information to know how to respond and behave.

[Grant Belgard]: So you’ve previously mentioned wanting to help people not just avoid disease, but actually enhance capabilities, almost like unlocking a superpower. So what does enhancing human capabilities look like in the context of health? You’re talking longevity, cognitive performance, athleticism. Where do you think the high leverage points are?

[Max Marchione]: So if we look at a sci-fi movie or read a sci-fi book, how often do they talk about, we’re preventing cancer and we’re preventing Alzheimer’s? You don’t hear it. Prevention, no one talks about prevention. And the implication is that in a post-deep biotech world, prevention becomes table stakes. And I do believe we’ll get to that world actually faster than many people think. In a world where prevention is table stakes, what does matter? Well, what you hear in these sci-fi novels and see in these films is that enhancing human performance, enhancing human biology starts to matter. Allowing people to lose weight, allowing people to be smarter, allowing people to live longer, allowing people to be more athletic, allowing people to be more focused and get more done in the day.

These are things which health actually facilitates. Today, it facilitates it through simple things. Even lifestyle behaviors can modify and enhance human capability. Soon, we’re going to see health enhance human capability through more complex modifications. One example or harbinger of that is GLP-1s. They’re good for some reasons, they’re bad for others. But what they’re a harbinger of is healthcare being used for human enhancement. A lot of the people using GLP-1s are actually reasonably slim. They just want to lose a few extra pounds and they’ll turn to pharmacology to do that. And I think that we’re going to see more and more, increasingly healthcare used in this way. For things beyond just… Just I want to lose a few pounds.

And that is what I think of as healthcare for human enhancement. So I say, I often describe Superpower as a healthcare system to prevent disease. Hopefully it becomes table stakes. Hopefully there are dozens of companies supporting that and enhance human capability, which I think is the next frontier for humanity and a biological imperative. Like we need to evolve, particularly in the lights of AGI.

[Grant Belgard]: So a hundred million people rescued from reactive care is a bold goal. How do you begin to approach a number that large? Do you focus on certain demographics first and expand from there? Or kind of what’s your thinking about that?

[Max Marchione]: I think that fundamentally we need to provide something which makes sense for the majority of people to own. And one example of a membership that I think it makes sense for the majority of people to own is a membership, an annual health membership that includes 2 blood tests, there’s a really comprehensive, let’s say, 40 to 70 biomarkers. All of your health data are in one place, right? Wearables, data from electronic or medical records, data from surveys, the ability to interface with that health data via an AI, the ability to chat with a medical team, human medical team that has access to all of that data and is supported by AI, the ability to get prescriptions and referrals and diagnoses from that medical team. And all of this for free.

That’s a pretty cool free health membership to own. And that is entirely possible to do for free. And I think the second a membership like that is free, why wouldn’t 100 million people want to own something like that just in America alone? My sense is they would. And that’s what’s required. How do we deliver massive amounts of value for as close to free or free?

[Grant Belgard]: Let’s walk through the user experience. Say I sign up for Superpower today. What happens next? What does the first month look like for a member?

[Max Marchione]: So today we’re doing three things. One is collecting as much data on someone as possible. And that starts with sending nurses to your home and they’ll collect over a hundred plus blood biomarkers that’s around five times more than an annual physical that will include hormones, toxins, inflammation, metabolism, cardiovascular risk, liver health, and a handful of others. Um, and we do that twice a year. We’ll also in this part number one of collecting data integrate with the EMRs and we’ll pull in all of your past medical records and we’ll also integrate wearables and we’ll give you an onboarding survey.

This, all of this data people love because they’re like, oh, well, I’ve never seen these biomarker before. This is really interesting. Oh, sure. I didn’t realize this was wrong. Now I want to start taking action. But the other reason this data is really important is it defines full context, which is essential in a world where AI is delivering care rather than humans. Very hard for a doctor to process 4,000 pages. The medical records, plus all of this data, very easy for an AI to do it. So part number two is how do we connect the dots across all of this data? We’re inspired by concierge medicine here. If you’re Jeff Bezos and you have a concierge doctor, you probably have a team of five and they spend hours and hours and hours going through all of your data and connecting the dots to get to the root of what is going on and tell you exactly what you should do about it.

That’s what we do largely through AI supported by humans in the loop. So part number two is we’ll say, now that we know everything about you. Here’s exactly what you as an individual should do. Not cookie cutter advice, but here’s what you as an individual should do. And then part number three is now that we’ve told you what to do, how do we actually help you do it? Right? The thing I kind of hated about healthcare when I went through my journey is you leave the doctor’s office and you’re always left to your own devices. We say, no, let’s actually help you do it. So anything you need, we’ll try to bring into one place, follow up diagnostics accessible in one place, uh, supplements accessible in one place. Only our favorite ones, all 20% cheaper than Amazon for members, right?

You shouldn’t have to leave the ecosystem. Uh, pharmaceuticals in one place, ones that we think that we think are highly effective, 20% cheaper than Hims. If you need to message your medical team and you have a question, you can pull out your phone and you can send them an SMS. There’s three people on that team. So the idea is how do we make it really easy for someone to actually follow the protocol we set up by bringing as much as possible into one place, making it cheaper than accessing that in a very fragmented fashion by hunting around the healthcare system. So today we do three things. Test your whole body aggregate data. Connect the dots across that data and then make it really easy to take action. And that is a $42 a month, um, $499 a year membership today.

[Grant Belgard]: So how do you, uh, distill all that information, uh, into, into report to explain it to, to the user?

[Max Marchione]: So we have a report schema and template that we have created, and that is just blank. We’ve built an AI model in house that ingest all of the data. We will ingest our clinical canon, which is our, like basically codified doctors, brains have codified the brains of many of the best doctors and it will take the database of what we know about the patient, the database of what we know about medicine, compute between the two and have an output into the report. And it will generate the first version of the report. And then your doctor will get that report and go through it, review it, edit it. A lot of the time, the doctor will be like, this is so much better than anything I could have created, right? That’s like the, that’s the level of which the AI is performing. At, um, at the moment.

[Grant Belgard]: And how do the physicians then interact with what they get out of the, out of the AI? I guess, kind of, I’m wondering, is it, uh, are they largely getting an, an LLM output or is there, you know, some, uh, statistical kind of work feeding into that as well?

[Max Marchione]: So they get a text output in the format of a report and they have the ability to edit the text within the report. And there are several structured data blocks they can bring into the report. For example, they might want to pull in a biomarker, which gets visualized in the report. They might want to pull in a recommendation, a supplement or a pharmaceutical or a followup diagnostic test, which then then becomes, um, able to be purchased at, at the bottom of the report. But they’re fundamentally dealing with this text report, which is represented to the clinician, the same way as it’s represented to the patient. And they can modify that directly.

[Grant Belgard]: So if someone, uh, goes through the service and, uh, there are a lot of things wrong with them, right? Um, uh, maybe there are lots of, uh, recommended actions for them to take. Uh, how do you prioritize that? Um, so they aren’t overwhelmed by a deluge of 50 things to do.

[Max Marchione]: Yeah. Uh, a lot of that is how we build the AI, which is giving examples of what we think good looks like, how we think medicine should be practiced, how we step through a series of actions, uh, one at a time versus immediately. We take in inputs as well. Like when someone joins the survey, we’ll ask them a question like, um, like how — I forgot the exact framing — but it says like, how much do you like to spend? How many supplements do you like to take? Are you open to pharmaceuticals or just supplements or just lifestyle? How intensive is your regime? How much effort do you want to put in? And all of these are also inputs into understanding what to recommend.

And again, this is the beauty of a world of technological computation, which is that it actually has all of this context in mind. Whereas in a five to 10 minute consult with your PCP, like it’s hard for them to know all of those. Uh, data points about you. So we, most of the time, we’re not saying do everything at once. We’ll say looking at the set of all of the monitored issues. So we’ll say here’s 15 monitored issues, looking at all of these monitored issues. We think there are these underlying drivers of all of them. So we’re going to start by targeting these underlying drivers.

Maybe what we’re going to do is we’re going to fix hormonal balance and we’re going to fix metabolism and let’s just start there and we can take some simple, we can follow some simple interventions to do that and then we’ll retest and we’ll see the effect. And then we can do just more things, um, get downstream.

[Grant Belgard]: That’s interesting. So, um, adherence is a big issue in preventative health and you mentioned, uh, ways you try to, to assist with that. So, so how do you tackle the behavior change aspect?

[Max Marchione]: My sense is that before even getting to behavior change, one of the important things is empowering people with information because information does drive action. I, uh, there’s a hundred million Americans who are pre-diabetic and 80% of them did not know it. I found out two years ago, I was pre-diabetic, right? I had no idea. I’m like, I’m slim. I seem healthy. I was pre-diabetic. And the second I found that out, I’m like, shit. Okay. I’m fixing it. Like, how do they, like information alone has sparked the desire for me to take action. And we see that a lot with our members. Typically they’re actually lacking information and when they can see viscerally what’s wrong with them, they want to take action from their part of driving behavior changes, making it as low cost as possible and as frictionless as possible.

The reality is low friction and low cost drives action. So it should be a single button to get whatever you need. If you want to send a text message to your concierge, they can get you whatever you need. You shouldn’t have to think, how do we reduce the friction to the maximum amount possible? Same with costs, right? We care a lot about everything within the ecosystem and cheaper than the care you would get outside of the ecosystem. Because. Reducing the cost of action, um, or of an intervention will also drive up behavior from there. We get into the actual behavior change stuff. Right.

But I think that so many people would jump straight to the actual behavior change stuff before actually being like, hold up, let’s just improve quality and data and information, reduce costs and reduce friction. Like Uber Eats drives behavior change is like reduce friction, kind of reduce costs and improve quality. If I look at the actual behavior change stuff. I think we still have a, a long way to go, but part of it is via the concierge, which can nudge, which can outreach, which can hold you accountable. And the AI is really good in this world because what the AI does is it drafts or, drafts messages and it puts them in a backlog and the clinicians review it and say, do I want this sent to my patient or do I not? That’s something the AI can do. Cause it has, it can do that infinitely.

And now clinicians just approve or disapprove so much easier than saying to a doctor, here’s your patient panel of a thousand. Think about when to message all of them. Well, it was just a very. Hard thing to do in a, in a human paradigm, whereas the AI can do it in a very personalized way with human in the loop review. So that, that kind of nudging is one example of how we then get to behavior change. I don’t think we’ve solved behavior change yet, but I think there’s this, uh, uh, if we solve behavior change, we’re in a really interesting place.

[Grant Belgard]: So since this podcast is for, you know, uh, comp bio folks, uh, I have to ask, how are you managing and analyzing the sea of data each user provides? Um, do you use machine learning models trained on an internal or external data sets? Both, for example, uh, are you able to predict, uh, uh, people’s individualized risk of a condition through the biomarker patterns? I mean, I, I guess at this point you’re, you’re still quite new, but I would think over time you’ll be gathering longitudinal data.

[Max Marchione]: So I’ll start with what’s the data we gather, how do we process it and then how can we predict risk and what can we actually do? Clinically, there are three main types of data we’re gathering and we’re maybe the, we’re one of the only companies in earth that has all three simultaneously. One is multimodal multi-omic data, right? There are very few companies that will test blood biomarkers, genomics, microbiome toxins, all sorts of imaging tests, and bring that into one place. So we have multimodal multi-omic data. And if we do a good job for our members, they stick around with us and they keep testing through us.

The second thing is we have a longitudinal clinical data, partially because we aggregate medical records, but partially because we actually take care of patients and do it in an ecosystem that, that is data rich and data forward and tech forward. So we don’t just get the survey data at the point in time where they collect or some sort of, where we collect some sort of omic. We also see what’s happening to the patient over time. We see how they evolve and that’s really valuable, right? 23 and me just had survey data point in time at the point of collection. We actually have lots of. Points with a single patient, single, uh, longitudinally. So the second thing is longitudinal clinical data. And the third thing is continuous wearable data by integrating with wearables. We also have that as an input.

And I think wearables are still nascent in terms of being able to predict risk and link what’s happening in wearables to clinical data, to omic data. But I think when we actually build a rich enough data set, we enter a world where the connections are possible. Today, we don’t do any, any clinical risk scoring, like any clinical risk scoring that, that, that is. Is approved that could be used in a hospital to, to modify really complex care. Any risk scoring that we rely on is something that already exists or as guidance and advice to our practitioners, rather than an actual clinical risk score that is meant to modify a treatment plan. My hope is that we get there. I think we’re collecting a really interesting data set.

I don’t think I fully understand the value of, of, of the data set, um, because I’m not as deep into the bioinformatics world as some of your listeners, but I’m optimistic that I will understand the value of that data set. In the, in the next, um, one, one to two years, and that puts us in an interesting place.

[Grant Belgard]: Yeah. I’m wondering if, uh, you’ve thought about, you know, using the data set for, uh, for research purposes, right? Because if you have genomic data along with all the rest of it, uh, there’s, there are really a lot of questions you could ask about, uh, causality and so on that are, that are pretty, um, fundamental questions to require a very large, uh, number of. Of, of patients, um, with, you know, genetic data, then it becomes especially powerful when you, uh, follow large multi modal panels over time.

[Max Marchione]: Uh, yeah, totally. Like Regeneron just bought 23 and me for $256 million. No one saw that coming. Word on the street is that that company would sell for $30 to $60, not $250 million. And the data there was not that great, right? There were 15 million pople with a small number of like, like a little bit of SNP data in the multi-array test that LabCorp was performing, and there was some survey data, but simple questions were asked at a point in time, and that was worth 250 million to someone, wild. So I, I am optimistic that in the long run, we’re able to actually, uh, discover things by having these three types of data and discover things that we don’t actually understand today.

And I think the other thing is just, is the, the richness of the, the longitudinal clinical data. I’ll give one kind of trivial example, but I think it’s kind of interesting because it’s slightly esoteric. When I had pre-diabetes two years ago, I couldn’t get my A1C down, nothing. I tried, I stopped eating sugar. I was exercising a lot. I was slim. I was healthy. Nothing to get my A1C down. Like what the hell is going on? And one doctor said, oh, I’ve heard mega dosing thiamine, vitamin B1, uh, helped. And they gave the me, the mechanistic reasoning that I do not remember. And they said, look, a normal dose was five milligrams, but take 400 milligrams. And I did then my A1C came down, right?

If you look out in the world for clinical studies on thiamine being used to reduce A1C, they don’t really exist. If you look at Superpower, we saw Max bought thiamine and that was new. And after he bought thiamine, we saw that A1C started coming down because we had the, the marketplaces. Like, interconnected data points as well. And that’s a trivial example. I think I’m sure people will nitpick that and tell me why it’s imperfect, but the, the gist of what I’m getting at is that we do enter a world where having this much data does allow us to discover new things.

[Grant Belgard]: So how do you ensure Superpower’s recommendations stay up to date, both as your own data set grows and evolves, but also as new, new research continually comes out, right? It’s a bit difficult to stay on top of everything, right, these days.

[Max Marchione]: Yeah. So we don’t build our own foundation model. Um, we use the existing ones. The magical thing about using these existing ones is that hundreds of billions of dollars are being invested into them. And they’re very good at aggregating research and they’re very good at staying on top of research and the amount of funding going into them and the ability for them to just aggregate talent continues going up. So my sense is that is a reasonably solved problem because foundation models are the magical things they are. The thing which is not necessarily a solved problem is aggregating what I call latent knowledge, which is knowledge that’s in the brains of doctors that is not on the internet. And there’s a lot of this, the thiamine example is like one example of that.

So to do that, what we, what we do is we work with many of the best doctors around the world and they tend to be in their sixties, seventies, eighties. And we say to them, look, you’re making like tens of millions a year in your concierge practice and you should keep doing that, right? You see 500 patients and they pay you a lot. And then you should keep doing that. But if you want, one thing we can do is actually immortalize your brain and we can codify it and make it something that everyone has access to. And many of them are like, holy shit, I’ve always wanted to do this. I, cause they will have like really are proud that they deliver high quality medicine, but they’ve never been able to scale it. They’re like, I’ve always wanted to do this. Let me tell you of everything I know.

And now we start to build out a database of what I call latent knowledge, um, which does not exist on the internet and LLMs do not have access to.

[Grant Belgard]: So here’s, uh, here’s a bit; healthcare is a tough industry for startups. It’s heavily regulated. There’s a need for clinical evidence. Trust is a huge factor. Uh, what have been the biggest challenges you faced, uh, building Superpower in this space?

[Max Marchione]: We tried to do too much too early with not enough capital. And I think that we were naive to how hard things are. It’s so hard to do anything well. So the implication is just do fewer things and do them really well. And we were naive to how costly healthcare is, right? It’s not the same as building software. Healthcare is a complex, operational, legal, clinical problem alongside the usual product engineering design and go to market challenges, right? So it’s basically doubled the complexity. Um, so I think that we just tried to do too much too early. And that was one of the bigger challenges, um, that, that we faced.

[Grant Belgard]: And, uh, you mentioned in an interview that early on a challenge was getting absolute clarity on what to build. And then later it was all about speed. Uh, could you elaborate on that? Uh, what helped you find — what helped you find clarity in your product, uh, and how are you instilling speed and urgency in the team now that you’re scaling?

[Max Marchione]: Yeah. So I think there’s so many people try to speed up before they actually know what to focus on. And it’s kind of like speeding up at running sideways doesn’t actually do anything. So I think that the rate limiting factor in the early days is typically clarity rather than speed or resourcing or who’s on your team or capital. And the implication is that you actually need to move somewhat slow in the early days. With a small group of people with a limited amount of capital, because they’re the set of factors that can increase clarity, right? And when you have clarity, you can start moving quickly. If I look at the way to get clarity, it’s doing those things. It’s moving slowly. It’s thinking deeply. It’s chatting with lots of people. It’s chatting with customers.

It’s testing different things with having hypotheses. It’s seeing how the market responds. It’s thinking deeply. It’s definitely taking action as well. You can’t get the clarity just by thinking, um, often the rate of learning through action is faster than the rate of learning through like twiddling your thumbs and scratching your beard. And those are the things that I think result in clarity. Once you have clarity, once we have clarity, then we can move quickly. Then we can raise more money, hire more people, work faster because we know the direction in which we’re headed.

[Grant Belgard]: And, uh, Superpower’s offering something quite comprehensive for $499 a year, which sounds like a lot of service for the price. So how do you make the unit economics work? Uh, is the idea as you get more data and automation, the cost to serve each customer stays low?

[Max Marchione]: Uh, no, our unit economics are positive today. And we’ve done that through good partnerships, good technology, good AI, good operations, a really, really amazing team. I’m fortunate to work alongside. So unit economics are quite strong today.

[Grant Belgard]: Speaking of your team, uh, you’ve, you’ve onboarded a number of ex-founders, uh, as employees and attracted some big name investors at a young age. Uh, what do you — What do you think convinced them to buy into your vision early on?

[Max Marchione]: I think one is the mission, the importance of what we’re working on. Two is the size of what we’re working on. Right? Like everyone knows that if we succeed, we are one of the more important companies in earth. And that’s very energizing for people. Um, a lot of the people we work with could found the company. Right? So that’s the opportunity cost to them. I think three is the way in which we are thinking about things. I think a lot of the more sophisticated founder types we hire appreciate how we think through strategy, products, go to market, marketing brand. I think four is the existing brand foundations, which are resonant, distinctive, draw people in, give people the sense that we’re going to be a serious brand in the space. So there’s some of the things. Yeah.

So there’s some of the things. And then probably the final one is the company is doing well. I think that when you have momentum, it begets momentum. We have capital, we have, uh, customers and many more coming in. We have a strong foundational team and existing team. Um, and it’s become as a result, more easy to hire or easier over time to hire really great people, right? There’s like a little bit of a J curve initially, and then you come up the J curve and it gets exponentially easier with time.

[Grant Belgard]: So paint us a picture. If Superpower succeeds wildly, how does healthcare 10 years from now look different? Uh, do we all have personalized health dashboards and routine AI health checkups? Uh, what changes for the average person?

[Max Marchione]: I think about this a lot because my belief is that the structure of the healthcare industry is going to fundamentally change. And I think that one of the key changes is that the first place people turn when they have a health question is going to be not to Google, not to ChatGPT, not to their primary care doctor, but to an AI. That will be the first place I believe people will turn. And this AI will know everything about you. Everything. It will know everything about medicine. And it’ll be able to take action, right? It will be able to order, diagnose, prescribe, do whatever you want. And it will be so good that you don’t even question whether the AI is worth trusting.

When you’re in an airplane today, you don’t, you trust the autopilot. If the pilot said, so I’m going to fly without any autopilot today. You’d be like, oh shit. No, you want that autopilot on, right? And I think we get to the world where, where AI is, is quite similar where it’s like, no, I really want my AI. I don’t want to be going at this alone or just with. And just to the doctor without AI. That’s like a pilot flying a, a 737 with zero autopilot and zero technology. Like, no, get me out of there. So I think we have people going to an algorithm as the first part of care. The algorithm knows everything about them and it tells them exactly what to do. And it is so good that we have to trust it and increasingly blindly follow it.

I actually think in, in a little bit longer from now, these things get so good that we don’t have a choice, but to follow it. Right. Because it knows so much more than us. And the only thing we know is that it knows so much more than us. So I think that’s part of how healthcare will look. I also suspect that, um, I, I, for the past 10 years have been very anti-pharma, anti-pharmacology. I’ve actually changed my mind. I suspect that over the next 10 years, we’re going to see many blockbuster drugs that enhance human capabilities and prevent disease. So I, so I think that pharmacology will play a very large role in defining what the future of healthcare looks like because of the rate of change in biotechnology.

And there are several of these molecules or compounds already, peptides being one emergent category, right? Um, still frontier, still taboo, potentially some problems with them, but also able to drive really powerful outcomes for those who use them. And I think, again, they’re one example of many more, um, versions of, of frontier blockbuster, uh, pharmaceutical interventions that, um, are going to emerge in the coming years. So AI, uh, and, and, uh, biotech, I think will define, uh, what the future of medicine looks like.

[Grant Belgard]: So looking at, uh, adjacent fields, do you think this preventative data-driven model could integrate with drug development or clinical trials? For example, you know, how would a, a pharma company partnership look, uh, with, with Super, uh, with Superpower?

[Max Marchione]: I don’t know. I don’t understand the pharma industry well enough yet. I feel like with many of these things, we have to do the set of things, which is like most sensible and reasonable for our business today, and there are all sorts of emergent properties as a result, and every three months that goes by, I’m like, oh, okay, cool. Here’s another interesting emergent property that I didn’t realize three months ago. If I was to speculate, first, we would never share data with anyone without any of our members consent, assuming that our members consent, similar to how members consented to 23 and Me sharing data with pharma to progress human health, assuming our members consent, 85% of 23 and Me members consented. Let’s say 80% of our members consent.

Um, we could do similar, obviously anonymized, de-identified, not being, not possible to be used in any way that can harm people. Um, I think that pharma companies will like to see the mapping between clinical data and omic data. I don’t know the exact way in which they use that, but I do know something that they do like, again, looking at 23 and Me as a case study. There is a world where some like peptides get legalized in the next two years. And just as GLP-1s. Well, like Ozempic was, and Wegovy, were like evolutions on the GOP ones that have existed for 20 years. There might be evolutions on the other peptides, which had been around for 20 years that are patented by big pharma. And there’s a world where we have a data set that shows which ones are efficacious and what doses, et cetera.

I don’t know, again, speculating the short of it is there’ll be a whole lot of emergent use cases and I’m sure we’ll discover them in months or years from now.

[Grant Belgard]: So finally, uh, what’s next for you? And Superpower in, in the coming year that you’re most excited about, or is there any milestone we should watch for? Um, and, uh, where can listeners go to learn more or sign up if they’re interested?

[Max Marchione]: So we’re removing our wait list, um, which is exciting. There’s 200,000 people on it today, and we’re going to be doing everything a little bit more publicly, building in public, sharing more about what we’re up to, uh, and, and growing, which is exciting. And at the same time, we’re building out a lot of additional product features. There’s a whole long list of them. Today, the, the concierge doesn’t handle full stack primary care. We want to be able to handle way more of the stack. We want to be the first place people turn for healthcare. And I don’t think we’re quite there today. Today, we’re still better at testing rather than the full stack of care.

Um, and to build into full stack care without increasing costs is as much, it’s a clinical and operational problem, but it’s primarily a technological problem in the way we address it. So I’m quite excited for that as well.

[Grant Belgard]: Well, I think we’re, our time’s come to an end, but thank you so much for coming on the show. It’s, it’s, it’s been a really interesting conversation.

[Max Marchione]: Yeah. Thank you, Grant. Enjoyed the conversation.

The Bioinformatics CRO Podcast

Episode 59 with Wolfgang Brysch

Wolfgang Brysch, Co-Founder and CSO of MetrioPharm and iüLabs, discusses longevity, inflammation, and his dual path of research into natural and pharmaceutical remedies.

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, YouTube, Pandora, and wherever you get your podcasts.

Wolfgang Brysch

Wolfgang Brysch is the Co-Founder and CSO of iüLabs, which produces plant-based natural compound supplements, and of MetrioPharm, which focuses on small molecule treatments for infectious and inflammatory disease.

Transcript of Episode 59: Wolfgang Brysch

Disclaimer: Transcripts may contain some errors.

[Grant Belgard]: Welcome to the Bioinformatics CRO podcast. I’m Grant Belgard and joining me is Wolfgang Brysch. Wolfgang, welcome.

[Wolfgang Brysch]: Yeah, thanks Grant. Great to be here.

[Grant Belgard]: Happy to have you. Can you tell us a bit about yourself?

[Wolfgang Brysch]: Yeah, I’m a medical doctor by background, but spent most of my life, professional life in research and later on in biotech, drug development. And for the last about 10 years also got increasingly interested in nutraceuticals, natural compounds, etc. All focusing around the topics of chronic inflammation, inflammaging, aging, etc.

[Grant Belgard]: Fantastic. Can you tell us a bit about how you’re translating that? Your two companies.

[Wolfgang Brysch]: Yeah, my main sort of professional hat on is as a chief scientific officer of a company that I co-founded. It’s called MetrioPharm. And there we develop a small molecule, a ethical drug as an anti-inflammatory. And that sort of led to the whole inflammatory research and immunology research led to also get me interested in the whole topic of aging, chronic inflammatory, degenerative diseases. So that that’s my main hat. And through this research over the years, I, of course, in the scientific literature, etc., I came more and more across also very interesting research and results on natural compounds, the, of course, the effect of lifestyle, nutrition, etc. And that also piqued my interest.

And a couple of years ago, out of some, I’ll talk about this later, a special event triggered the foundation of another company called iüLabs, where we produced or develop and produce nutraceutical supplements with the same sort of general, in the same general area, but of course, not on the drug side, but more on the supplemental side.

[Grant Belgard]: So it’s a really interesting strategy, right? Generally, people walk one path or the other, and you’re going down both at once. Can you discuss the rationale behind that? And also, I’d love to hear your thoughts on chatter around changes in regulatory pathways and how that might impact.

[Wolfgang Brysch]: Yeah. So the original impetus to go this in this parallel path was that during the drug development, what I realized that many of the diseases are the chronic diseases are very complex and multifaceted diseases with a lot of different pathologic drivers. And that very often, single drugs or single drug mechanisms are not enough to cover all the different pathways that are involved. And also, what is coming out in research more and more, and also my understanding and my experience is that metabolism plays a major role. Also, also the normal physiological metabolism in driving diseases in also in the efficacy of pharmaceutical drugs that you’re using.

So it’s basically this multi-pronged approach, especially in chronic diseases and aging, that I think we have to follow up in the future. And that’s basically how I came to this dual track.

[Grant Belgard]: What do you think are some of the most promising strategies to control inflammation and its impact on longevity? And how does MP1032 fit into that?

[Wolfgang Brysch]: Yeah. One of the mechanisms that our lead drug is addressing is oxidative stress and the redox balance. And that is, of course, intimately tied with the cellular energy metabolism. And so also over the years, I more and more came to the conclusion that the energy metabolism, energy production, cellular energy production, is really at the core and often driver of all kinds of diseases that ensue downstream. And so that is really where we can really have an impact in metabolism.

And that goes both for pharmaceutical drugs and for lifestyle changes up to all the way to nutraceuticals, optimizing the energy metabolism that will have really very profound and broad acting positive effects on all kinds of disease states and of aging, which is not in a narrow sense of disease state, but it is driving diseases, degenerative diseases of aging.

[Grant Belgard]: What are other strategies for controlling chronic inflammation?

[Wolfgang Brysch]: Well, the one of the strategies that we are using or that we are addressing is to normalize oxidative stress, which is the response of the cell and of organs to all kinds of stressors, external stressors, be it injury or infections, et cetera. So it converges very much converges on, on these oxidative stress, which is a sort of a master signal, again, to, to drive inflammatory responses through certain gene switches like NF-kappa B and RF2. Those are master switches that oxidative stress or these stress responses of the cell elicit to, to then drive inflammation. So inflammation is always already the result of something upstream and it’s on this upstream path that we can really do a lot to mitigate inflammation, chronic degenerative processes.

[Grant Belgard]: What are the largest drivers of chronic inflammation today?

[Wolfgang Brysch]: I would say it’s through, as I said, different insults to the cell or to, to organs, whether they are chemical stressors, infections, they are autoimmune processes. And all of these trigger genetic switches like NF-kappa B is one of the master switches and that downstream then causes the, the expression of pro-inflammatory cytokines, TNF-alpha, IL-6 are very prominent ones. And those then bring this whole machinery of inflammation or start this whole machinery of inflammation, which if you have a, like an acute injury, like a wound or something like that, then subsides again.

But in chronic inflammatory diseases, also if the energy metabolism behind this, on which the cell operates is defunct to a certain extent, very often these pro-inflammatory signals, chronists get chronic. And then they, they, this, the inflammation doesn’t stop. And that sort of over time, then of course injures all kinds of tissues and organs and the chronic diseases that, that we have or that we see are then usually the weak points, the individual weak points that every one of us has maybe genetically. So in one person, the chronic inflammatory process may result in Alzheimer’s disease, in someone else in joint degeneration or kidney failures, things like that.

So that’s the individual differences that we have, but it’s usually very, very common processes that drive all these different diseases.

[Grant Belgard]: After decades in pharma, you’ve become a champion of plant-driven compounds. How do you see traditional herbal medicine and modern biotech intersecting?

[Wolfgang Brysch]: What is interesting is that for a lot of these traditional plant compounds, we are starting to understand what the real mechanism of action is. Why are they beneficial? And that is, again, paradoxically, a lot of these plant compounds are pro-inflammatory or are very mild toxins. And the interesting thing is that these stimulate the cell or the cellular regenerative responses. A lot of the compounds or some of the compounds that are touted as anti-inflammatory are in fact, mild pro-inflammatory compounds. And they train or train the cell to respond better to these kinds of assaults. For example, they improve the antioxidant, the inert or innate antioxidant capacity of cells. Sometimes I say this is metabolic yoga for the cell. It’s the same.

You could easily say, okay, it’s the same as with muscle strength or something like that. Nobody gets more muscle strength by sitting on the sofa. We stress our muscles to a certain extent. Of course, we shouldn’t overstress the muscle because then they get tears or something like that. But that sort of builds muscle strength. And that is exactly the same mechanism that is on the cellular level and the metabolic level that is a mild, well-pointed stress can, over time, train the cell to become more resilient. That’s the interesting thing that comes out of a lot of these natural compounds.

[Grant Belgard]: Are these compounds that one would take for a very prolonged period or for a much shorter period of time then? You take it for a couple of weeks and then stop again later?

[Wolfgang Brysch]: In these sort of stimulatory, small or lower concentrations, there’s good evidence that they are very beneficial if we take them over a long time. So there is no toxicity accumulating. Bear in mind that many of these compounds also are in a healthy diet. So we don’t stop healthy diets, fear of having natural compounds for a prolonged period of time. So I think in the dosing is one important point. It’s even these small amounts that do have these effects. And very importantly also that many of these compounds work synergistically. So a lot of the studies on natural compounds are coming from the pharma side or the pharma thinking are made, are done on with large doses of single substances, which is often not what is really ideal.

It’s the small amounts and the synergistic effects of different of these compounds that address and quote unquote, slightly stress different metabolic pathways that have a hugely synergistic effect. Which on the other hand is something that is very hard to test in a traditional way, like in a controlled, placebo-controlled, double-blind trial, because if you test like three, four or five substances at once, it’s really hard to say which, which part of the effect is due to which, which substance this is mathematically you can, if you extrapolate you, you have like a gazillion different potential combinations. So also the study of these things is probably needs to be a bit different from the way we study single drugs.

[Grant Belgard]: How might that look?

[Wolfgang Brysch]: I think observational studies where we, of course, they can be placebo-controlled. I think that is still a very, very valid approach. And, but then what we can do is we just have to do as long as it’s safe or less trial and error, say, okay, we put together or that’s how we do it. We combine different natural compounds of which there is a certain kind of knowledge of the different pathways that they address. We try to, to combine compounds that, and substances that address different parts or different, for example, different enzyme pathways in the cell that are sort of interlinked.

So we are not just improving one pathway at a time, but different, do small improvements on different interlinked pathways, especially, for example, in the energy metabolism, in the Krebs cycle, in the electron transport chain, you can really nudge these systems to a higher overall performance. And there is an example that I often use is if you compare that with an assembly line, if you have an assembly line, you want to assemble cars and you want to increase production by about 10%, it’s no good to supply like 10 times the amount of tires at the station where you mount the tires, but you have to supply 10% more parts at each step. And that’s where you get the synergism and the overall improvement. And that’s the same for metabolism.

Single to, to address only a single step in metabolism is often not really effective.

[Grant Belgard]: What, if any changes do you think could be made to the current regulatory structure to better accommodate that?

[Wolfgang Brysch]: I think that’s the, if you do, if you want to do trials, if you want to get more information about and more evidence about the effect of these things, I think that what the FDA calls real world evidence is. So if you have clinical endpoints that really show in real life settings, outcomes that are meaningful for, let’s say, quality of life, for general sort of resilience or pain reduction in arthritis or something like that. I think that’s the way to go to look at single parameters or use surrogate markers is often very short-sighted or it just gives you a little, only a little fraction of the whole picture. And in the end, I think in medicine, sometimes we tend to treat symptoms and lab values rather than patients.

And I think it’s really important, is the positive change that you can get, is that meaningful for a patient? Is it, of course, is it safe? That’s very important. Is it long-term safe? And is it meaningful for a patient or is it only meaningful if you do a blood test?

[Grant Belgard]: What natural compounds excite you most in terms of scientific evidence and therapeutic potential?

[Wolfgang Brysch]: There are some classics and it’s like the curcumin is one of those compounds. It’s a very potent anti-inflammatory and antioxidant. Again, it’s actually in the small amounts, it’s a pro-oxidant. It trains the cell to be more resilient. Another substance is resveratrol, which has been touted very much hyped and said it doesn’t do any good at all. But it’s also, we understand now that it’s a sirtuin, it enhances sirtuins and it’s called an HDAC inhibitor. So it modulates the gene expression and that has wide, far-reaching, positive implications looking beyond single effects that you might want to see. Those are, for example, two compounds that I’m very excited about where we’ve seen very good results. There are others, phosphillic acids.

A lot of these sort of broadly used natural compounds are very effective. The only caveat or caveat with a lot of them is that their, what’s called bioavailability, is extremely low. For example, if you look at curcumin, the, if you take that as a powder or something like that, the bioavailability is about 0.1%. So 99.9% of what you ingest just goes straight into the, into the sewage, so to say. And that’s one thing where we also done some work and developed some technology to improve the bioavailability of these natural polyphenols, these plant compounds, which is a major, I think, improvement in the efficacy that, that you can get.

[Grant Belgard]: So you took an unusual path in founding the supplement company, a drug development company. What have you learned about bridging those two worlds?

[Wolfgang Brysch]: I think —

[Grant Belgard]: What advice would you give to biotech entrepreneurs who are considering which route they should go?

[Wolfgang Brysch]: You can go both routes as I did. I think what is really helpful is to have a solid scientific and biochemical background. If you look at these things, if you can, that you can critically read the literature and assess the literature, have a good biochemical and chemical understanding of these compounds. Because in a lot of the, if you look at a lot of the general way that supplements are done. And if you look at the people who are behind supplement companies there, I don’t want to dispute any of that, but sometimes there are like soccer stars or something like that. Definitely they know their game, but do they really understand biochemistry, et cetera, or it’s just a lot of hype. There’s the new big wonder natural compound every year that, that everyone is then hyping.

I think that’s stay away from that. I think we, we need to go back and we can utilize the rigor and, and that we are used to from, from the pharma side. And that’s, I think the big advantage or luck that I had that I came from pharma with all the sort of rigor and scrutiny that, that you’re under and then going venturing into the natural compounds. You can sort of transfer that to, to formulating and to assessing natural compounds and supplementation.

[Grant Belgard]: Shifting gears a little bit, I was wondering what biomarkers you’re tracking your MP10 program.

[Wolfgang Brysch]: One of the, the most consistent biomarker is interleukin-6 IL-6 is a good, very good biomarker of inflammation in a lot of diseases. And that is something that we very consistently see with MP1032 that we have a very good effect in, in, in mitigating that. And I think also what is important with that mechanistic approach is that these pro-inflammatory cytokines on the one hand, they drive chronic inflammation. So that’s not very good, but they also have a physiological function and a lot of pharmaceutical approaches in the past and still today are to completely block with an antibody or so completely block these cytokines. And I think that’s, that’s backfiring because then you get immunosuppression, you get an increased susceptibility to infection, et cetera.

So I think to design drugs and treatment regimens that go the middle ground, that normalize cellular function, I think is much, much more important than completely having very strong inhibitors. And that’s also something that I realized when, from the, from the nutraceutical space, where of course you’re not allowed to do health claims and all you’re allowed to say from the FDA and the European is that it aids normal function. And they, the regulators, I think interpret that, well, this is really not doing anything good, but in the end, if you can get your body, your cellular function back to normal, that’s, I think that’s the ultimate in healing. And that’s also —

[Grant Belgard]: That’s what you want.

[Wolfgang Brysch]: Drugs should do this, should not be completely blockers or attenuators. They should also strive to return or get functions, cell functions back to normal.

[Grant Belgard]: On that note, I understand MP1032 is explored for COVID-19. How does one go about designing a study to balance the anti-inflammatory action without blunting antiviral, and what are some generalizable lessons that came?

[Wolfgang Brysch]: This goes exactly along the same line that I just said, is to normalize cellular function. When you have, when SARS-CoV-2, the, when the virus infects a cell, it reprograms the cell. It changes the cellular environment to facilitate viral replication. And that is, and that sort of then downstream causes these inflammatory responses that it’s not really the virus that, that causes the inflammation. It’s the cell, the response of the infected cells and the immune system that reacts to this. And again, by normalizing, so to say, forcing the cellular metabolism and the redox state back to normal, creates an environment which is physiologic for the cell. But it’s very, not very, how to say, not very positive for viral replication.

So it’s, the mechanism is not really targeting the virus itself. It’s targeting the host, making the host normal and inhibiting or prohibiting the virus to change our metabolism in a way that is advantageous for the virus. And that’s basically how, so it’s not a balance. It’s interconnected by normalizing the cellular function. You also normalize immune function. And so you get a double, a positive double effect from this normalization.

[Grant Belgard]: So we’ve, we’ve talked a bit about inflammation. I have a question that sounds maybe like a stupid question. Some years ago, I was at [a recorded?] conference focused on aging with a lot of the leading researchers in the field. And this question went out, what is aging, right? And there was to say the least substantial disagreement in, in, in the audience. To you, what is aging?

[Wolfgang Brysch]: Of course, I’ll give you a very one-sided answer, but maybe that I think aging is the, put it to extreme, is the, our, the ability of our biological system, our body to maintain adequate energy metabolism. Sounds a little bit strange, but it’s the energy metabolism basically drives all our cellular functions, all our bodily functions. It is very well known now that the, the efficacy of our mitochondria of the sort of cellular energy production systems is declining from like when we’re in the late twenties, it starts to decline at age around 50. Our total capacity is on average only 75% for what it was when we were at our prime at 70, it’s only 50%.

And that really has this long tail of detrimental effects on the immune system, on immune function, on cellular function, on muscle function, on, of course, on cognitive. I forgot to say in the beginning, I spent — also did a PhD in neuroscience. So I’m very interested also in, in neurological function. Cognitive decline is very much also linked to energy metabolism. Of course, the brain is one of the most energy hungry organs in our body. And to give you a pointed answer, I would say energy metabolism is really the, at the core of everything. And if you look a little bit further into the theory of self organizing systems, you need energy to maintain structure. Otherwise that this, the cells, our body would just fall apart and flow apart.

So we need this constant energy to have this self organization. So we stay as a, as an individual. So that we stay literally together. That I would say is if we can keep up or maintain good and functioning energy metabolism, that would be on a broad scale. Population wise, I think that would be the single most effective measure for, to counter aging and diseases of aging.

[Grant Belgard]: Very interesting answer. Yeah. I was wondering if you could walk us through your career. How did you get to where you are now?

[Wolfgang Brysch]: Yeah. I started, studied medicine in, in Germany, in Göttingen and in Cambridge, UK. And then also started while I was still doing medicine, some research in neuro and neuroscience in the Max Planck Institute in Germany. And really was interested in, in, in basic research also. And decided then after my med school, I wanted to go into research at least for some time. Then did one, a year of anesthesiology before that. And in Göttingen and the anesthesiologists were also the, the physicians that were manning the, all the intensive, the acute care, the, the ambulances, et cetera. And I said, before I go into basic research, at least I want to learn some emergency medicine.

So I’m not standing with someone passed out on the street and be just a stupid researcher who doesn’t know how to resuscitate or anything. So I did a year of that. Then went into basic research, into molecular neuroscience. It was just an emerging field at that time where gene function, gene expression in the brain was studied. Did that for five years, was a head of a small research lab there as a postdoc. And out of that co-founded with some colleagues, my first biotech company that was in the very, very early days of antisense technology far before it was even considered that it could be clinically applicable, or there was a dream at that time.

Also, then we spun out another company that, that produced or was in, in cancer, early cancer therapeutics with antisense oligonucleotides, albeit the technology at that time wasn’t par enough yet to really have stable molecules. And then did a stint from 2000 on for a couple of years in, in a company also that I co-founded that, that did special data management systems, electronic data management systems for drug development. We have identified that as a need. So that later on and then started MetrioPharm because I came across some, some old mentions in the literature that a chemical that I had used in my research and my neuroscience research as a lab chemical had promised potentially as a drug, which is one of the variants of that is MP1032.

So that, that, that piqued my interest and, and Metriopharm grew out of that. And I already mentioned how that then spun into also the interest in natural compounds.

[Grant Belgard]: Interesting. So that is a highly varied career path. It is interesting how you were able to bring together those different strands at different points in your career. What advice would you have for, for our listeners? And what are some things that maybe you wish you had known earlier in your career?

[Wolfgang Brysch]: What I wish I had, I wish and wish not, I had known earlier in my career, how drawn out and what kind of long, long game drug development is. I think, had I known before I probably wouldn’t have started. So it’s good. But that is be prepared for the long run. If you start something like that, don’t give up too early. It always takes much longer than you think. In the end, it’s worth it. If nobody does it, you wouldn’t get new drugs. And on the personal side, as I said before, this realization, how natural compounds, how energy metabolism is really, can really positively impact your life. And it’s not just chronic things of aging.

What I really notice and feel if by, of course, lifestyle adjustments and some nutraceuticals, how this improved energy metabolism is really improving, noticeably improving day-to-day life. Especially if you’re still in a job, if you’re really in a demanding job that I am in and probably most of your listeners are in. I think that is something that really has a, can have a massive positive impact on day-to-day life.

[Grant Belgard]: And what supplements do you personally take?

[Wolfgang Brysch]: I take a combination that of course we’ve developed also, that’s the reflecting of, but it’s some anti-inflammatories are resveratrol, resetan is part of that. Alpha lipoic acid is a very potent substance that you can really notice very short term. Some amino acids and your sort of your basic range of vitamins, B vitamins, but not mega doses just to keep the, and of course that that’s a supplement side, of course, paired with very importantly, with a sensible diet. I’m not a sort of a nerd that sort of not very extreme, but a sensible sort of Mediterranean type diet. Enough sleep, if that’s possible, not always. Things like that, that basically all of us know, few of us get really around to do to the extent that we should do.

[Grant Belgard]: I’m always well-intentioned about that, but I flew out to NIH yesterday for a day trip and my return flight was delayed by a few hours. So I was shorted on sleep on both ends.

[Wolfgang Brysch]: That’s how life goes. Yeah.

[Grant Belgard]: Yeah. Thank you so much for coming on the podcast. It was really nice talking with you.

[Wolfgang Brysch]: Well, thanks a lot. It was very nice talking to you. And I think it’s also for me, it was a pleasure to be in a podcast that has, whose audience is something like my background. It’s not just quote unquote, the general public, but so we share the same interests and challenges.

[Grant Belgard]: Yes. Thank you.

[Wolfgang Brysch]: Okay. Thank you.

The Bioinformatics CRO Podcast

Episode 58 with Scott Fahrenkrug

Scott Fahrenkrug, founder of Forjazul, discusses his path toward seaweed research, the importance of genetic knowledge for agriculture, and how Kappaphycus alvarezii can help move us into the future. 

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, YouTube, Pandora, and wherever you get your podcasts.

Scott Fahrenkrug

Scott Fahrenkrug is the founder of Forjazul, which is dedicated to bringing molecular genetics tools to seaweed agriculture.

Transcript of Episode 58: Scott Fahrenkrug

Disclaimer: Transcripts may contain some errors.

[Grant Belgard]: Welcome to The Bioinformatics CRO Podcast. I’m your host Grant Belgard and joining me today is Scott Fahrenkrug. Scott, welcome.

[Scott Fahrenkrug]: Thank you, Grant.

[Grant Belgard]: Can you tell us a bit about yourself?

[Scott Fahrenkrug]: Yeah, I was retired. I had a career doing research in biology, spanning from algae to zebrafish to humans to livestock, generally focused on genetics and how we can use genetics to be more productive and to understand biology better. So, I retired to Brazil after some success in both academia. I was a tenured professor at the University of Minnesota. I left that to start some biotech companies. And Recombinetics is maybe the best known, and Acceligen, these are companies that we’re focused on using gene editing to develop animals with superior traits. So, climate resistance, animal welfare traits, like hornless cattle, you don’t have to brutalize. And so, anyhow, I retired to Brazil after I sold my stake in my companies and got pretty bored pretty quick.

[Grant Belgard]: As often happens with scientists.

[Scott Fahrenkrug]: Yeah, you know, I just, and I had an epiphany, frankly. It was during COVID, and I managed to get out on a boat in Rio de Janeiro and ran across a seaweed farm. Which I didn’t even know existed, that you could have a farm creating seaweed. And that has led me on a very exciting journey around the world to the major seaweed producers in the world, which is in the Coral Triangle, Philippines, Malaysia, Indonesia, but now quite a bit in India as well. Well, what’s interesting about this industry here in Brazil is it’s nascent. It’s brand new. And brand new, except for the fact that there was importation of a tropical seaweed species from the Philippines 30 years ago.

The intervening time has been spent trying to prove that this wasn’t going to be an invasive species, to prove that you could actually run a farm. And Brazil’s come a long way towards that. As it turns out, there’s a match between what seaweed can bring and what industry needs. So, the number one product for seaweed in Brazil is biostimulants for crops. So, this is a giant market. Essentially, simply by extracting the juice of the seaweed and spraying that on crops results in better resilience, better survival under harsh conditions. It induces a stress response. It’s been characterized in a couple of recipient species that have gotten the biostimulant. And they express genes that reflect immune response to the environment. And therefore, they’re protected.

So, that’s an exciting product here in Brazil in particular. You know, Brazil’s the number one producer of soybeans now and sugarcane and cocoa. So, it really is, there’s an opportunity here to make use of this species that, frankly, comes from Asia, but has brought with it opportunity. And would the unit economics of that work out using the species as is, or would modification be required? So, that really is the ultimate focus of my work is to, first, to make this a species. Kappaphycus alvarezii is the name. Bring this species to our current genetic understanding. Okay? So, all the major crops in the world have genetics programs for genetic improvement. And to understand how those various species respond to production. Okay? And so, that was really my first focus.

But my career has really also aimed at trying to accelerate the genetic progress that we can make. And so, indeed, I have some targets for this species. Some of them are more global impact. Some are more focused on specialty products. But they all rely on the same thing, which is selection and direction. And so, selecting for those versions of seaweed that produce more, faster, better, but then also using our comparative biology and our understanding about how genetic systems work to identify targets for improved production. So, really, it’s been a journey because there really weren’t any genetic resources available for Kappaphycus alvarezii. There was an unpublished article that did some genome sequencing. None of the annotation was shared with the public.

And so, I made it a mission to solve that and have, to a great degree, built a bioinformatics system, the Kappaphycus alvarezii Genome Explorer, which has got all the kinds of bells and whistles I always wished for from any of those public informatics sources. And has really revealed itself now to be a great exploratory tool. So, we’re looking now at specific targets that we think would change the production efficiency of seaweed. So, the Green Revolution, most people don’t. Maybe we’re too old because I don’t know if people even know what the Green Revolution is or was. It happened in the 70s. And the naysayers and the pessimists thought the end of the world was upon us, that we had too many people and we were going to all starve. Okay. It was really doom and gloom in the 70s.

As it turns out, there were good genetic scientists, including Norman Borlaug, who got a Nobel Prize, who focused on trying to develop strains that perform better in production systems around the world. And so, lo and behold, they actually came upon a mutation in phytohormone pathway that resulted in producing wheat strains that were shorter, thicker, and produced more grain. And that, along with the development of better fertilizers and pesticides at the time, really led to a dramatic increase in productivity around the world. And really, it changed agriculture forever. We think those same targets, that same biology, also exists in seaweed.

And so, we’re using now our genomic information and comparative biology to develop strains, either select for them or use gene editing tools that rely on this genomic information to develop strains that will grow faster. So, we have a goal. We want to increase the productivity of Kappaphycus alvarezii five-fold in five years. Even if that’s just on our farms, that’s the objective. Okay. We know the world of seaweed production is hungry for other things, too, not just productivity. And by the way, the biggest market for this seaweed, Kappaphycus alvarezii, has historically been carrageenan, which is a hydrocolloid product, and it’s a thickener. You’ll find it in ice cream and toothpaste and various other things.

And actually, carrageenan is great for sort of milk desserts, puddings, and things like that. We like it. And that’s about a billion-dollar-a-year industry in Asia to isolate that. So, there’s real business there. But because we live in a changing world, the productivity of these crops has taken a real dive. We think, although the data is not there yet to make this conclusion, we think the hypothesis is those productivity is going down because this is a crop that has been clonally propagated for almost 50 years. So, there’s no genetic cleanup, right? And so, there seems to be a loss of resilience. There are some diseases that attack the crop. Ice-ice disease. You know, there are people that are working on this disease to understand that disease. And eventually, they will.

But then what do you do with that information, right? And so, our perspective would be, well, we look at how we can grant resilience back to the plant. So, either by finding natural alleles that can improve performance or novel ones, changes we can make to make the species resilient or resistant to that agent. So, what else? The species also has, I see it as a chassis. So, we’ve characterized the metabolic pathways in the species for phytohormones, as I was mentioning, but actually other pathways that are quite interesting to us because this genome of the seaweed is about 370 megabases.

So, it’s not as small and facile as a bacteria, but it’s a heck of a better size to work with than my prior career that was looking at humans and zebrafish, you know, things on the order of 3 times 10 to the 9th base pairs. So, this is much more amenable. It’s a red algae. It’s one of the earliest kingdoms or species, right? Red algae is ancient. There was some sort of symbiotic relationship established between an algae and a green algae. And so, this species photosynthesizes and that’s, I guess, part of the, really the value of the species is it can take carbon out of the air using sunlight with no fertilizer, no pesticides. It turns that into biomass and that biomass has value. So, as carrageenan, as biostimulant, and some other specialty chemicals.

So, there are some quite valuable pigments in the species. There are mycosporins, which can be antibacterial, have various biological activities, including acting as a really good UV protectant. So, we’re looking at this species as a potential factory for those kinds of specialty products, which is really a change in the approach to seaweed farming because it’s been seen and has had success as a commodity. And the farmers don’t get paid much, and it is really instrumental to the livelihoods of hundreds of thousands of people, but they hardly get paid anything because it’s a commodity. It’s just a food thickener, right? That’s that market.

But imagine if, actually, you could, on the same size farm, be producing a specialty chemical, a compound with biological activity, a drug or fertilizer, and that starts to get much more interesting because we can tailor the species to our objectives. One of the wonderful things about the fact that the species is not propagated sexually, it does it itself in the ocean, and indeed, we’re sequencing some of those wild populations to understand the genetics that are there. But for production purposes, people go out to the ocean, they take a sample, and they bring it back to the lab, and they grow it up, and they sell that to farms. And now, going forward, every 30 days, there’s a harvest, and they leave behind a little piece, and so it’s vegetative propagation clones for 50 years, okay?

So, I pointed out the bad side of that before, which is they’re not as resilient against external stress and get infections. But the good side is that indeed in Brazil, all this time that was spent on characterizing the safety of culturing the seaweed has shown that this, at least what’s in Brazil, is not reproductive. So, the risk when you develop new strains, so think about new strains with specialty products, then the risk of loss, the risk of release is dramatically reduced because it doesn’t sexually reproduce. And so, it’s environment where it can potentially spread to is local. So, that, to me, is also interesting. And indeed, you know, using the very same technologies, we can ensure that it will never be reproductive, simply by looking at genes that are involved in reproduction.

So, that has massive implications, and it sounds like there are many, many moving parts.

[Grant Belgard]: Where do you see Forjazul playing a role within that, and, you know, what does that roadmap look like?

[Scott Fahrenkrug]: Well, so, I think, as I’ve come to understand that we’ve got to play in two spaces. The one space is to understand and anticipate that it’s a commodity crop, and that if we, A, need to be not just in Brazil, we need to be in Asia, we need to be in the Coral Triangle. We need to be providing solutions from that industry that already exists, okay? But indeed, as a startup company, you know, we also have to have bread and butter. We have to have some things we can bite off. And so, that’s why there’s an emphasis on specialty products. And indeed, for us, I think in Brazil, it’s very much focused on biostimulants.

Understanding what biostimulants are there, understanding how to make better biostimulant, how to improve the stability of the biostimulant, and really to go participate in that market, which is something I’ve never done. But the results are pretty compelling, I would say, from researchers around the world, particularly in India, about the efficacy of this extract. Really, you just throw it in a blender and push it across a filter, and then you spray that liquid on the crops, okay? So, really, that’s a pretty, that’s straight from the ocean. So, you talk about farm-to-table, right? So, this is ocean-to-farm, and directly. So, pretty fascinating space to be in. So, both those, we have to, and indeed, we have recently secured some funding in association with a biotech company here in Brazil.

They’ve decided to sponsor a, I should say, an oil company from Malaysia that has oil deposits in Brazil, is supporting a project. Focused on increasing and optimizing carbon fixation by seaweed. And it’s part of, and it’s part of, it’s part of, it’s actually law here in Brazil that people that are extracting energy, whether it be oil or hydroelectric, they have to dedicate money to helping the environment, dealing with those issues that result from extraction. Okay? And so, indeed, they like the idea of developing strains of seaweed that fix more carbon faster. And so, that’s an exciting area, and I think those are big objectives, right? Because it’s not just about developing the strain that grows faster. It’s, then you have to think about how do you get that around the world?

How do you do that? I guess that’s a whole other issue, because it was actually not great that somebody illegally brought the seaweed into Brazil 30 years ago. That is a problem. And it’s a problem going forward, too, because the seaweed industry will grow. But the idea that you would take seaweed from one location to another risks contamination, right, risks disease transmission. So, this is the other side of the genetics, is that now the tools are so efficient for us to make genetic improvements that we don’t need to move a strain around the world. We’re targeting strains around the world. So, we deliver that product. So, we have a Brazilian product and a Brazilian project, okay? The objectives and the needs of producers in the Philippines is different.

We don’t, in Brazil, have ice-ice disease. Whatever it is, it’s not really clear yet. It seems to be, someone told me it’s a Vibrio. I don’t know yet. That’ll be coming out sometime in the next year. But they have that problem. We don’t. So, people in Brazil won’t want that product. They’ll want more and better biostimulant so they can use it on their massive production. It is something rather exciting about Brazil. You know, it’s when they decide to do something, they do it in a big way. And so, my first exposure to this was coming to the understanding that the number one beef producer in the world is Brazil. But 50 years ago, they had no industry in beef production. They decided to solve that, and they did. So, that’s kind of impressive. They did the same for soybeans.

I want to see them do the same for seaweed. There’s 8,000 kilometers of coastline in Brazil. And, you know, that’s a real opportunity for productivity.

[Grant Belgard]: So, what challenges have you run into running such an international company, right? I mean, I think you’re set up as a Delaware C Corp and have, obviously, primary operations in Brazil. It sounds like you’re doing, or at least have a number of collaborators in the Philippines, etc.

[Scott Fahrenkrug]: Look, I think we don’t have a lot of money, but we have a lot of knowledge and passion. And what I’ve found is I can decide to go and create a collaboration and find receptive people because everyone likes good science. And if you’re passionate about the same thing as somebody else, that builds a relationship. So, that’s number one. That’s a scientific thing. But I’ve had the good fortune to receive respect from folks who I’ve reached out to and created that relationship. The biggest challenge, of course, is it’s expensive. You know, the meetings are okay. It’s like the morning for me and the evening for them or the other way around. And that’s fine. But actually getting together and sitting down face to face, it’s expensive. It’s an expensive flight to the Philippines.

And so, that’s a challenge. But that is the, those relationships and those efforts in the Philippines and Indonesia are, and Malaysia also now, anticipate success. But those are the long haul, because I’m not there. I’m focused on creating the opportunity right now, which is to really change the philosophy of how to develop this crop, how to produce this crop, and what to do with it. So, that is more proximal and more interesting. So, we’ve got our eyes on an antiviral protein that’s produced by the species, which we have a great interest in developing along with another feature of the species, which it produces cellulose in large quantities. And that cellulose can be turned into fibers and fabrics and masks.

So, the whole thing was born out of a period when the world was under this COVID cloud. And we all came to understand the difference between an N50 and something that’s not an N50 mask. Okay? So, we’ve discovered a protein that inhibits HIV, influenza, and COVID transmission. How? Because it binds to the sugar moieties on the surface of those viruses. So, I’m very interested in us making masks out of seaweed that have this protein, effectively increasing the, or decreasing the permeability of the mask.

[Grant Belgard]: So, Scott, I know you’ve always been a very early adopter of AI. Can you tell us about how you’ve used that in your company?

[Scott Fahrenkrug]: Yeah, everything. It’s kind of changed everything. Right? So, we’re already, one of the early objectives we had was with the Genome Explorer was to permit us to look at gene expression data and make sense of it. Which is super important for somebody who worked on vertebrates his whole life. Right? Suddenly, I have to make sense of gene expression data in an algae. It’s not really a plant, but it’s closer to that universe. Okay? And so, we have, early on, have implemented the use of ChatGPT to help analyze the data and write the paper. Right? So, you have to analyze the data, make sense of it, and publish. And I have to say that I’m much more efficient in writing now than I ever was on the basis of using artificial intelligence.

I think the future use is going to be even more interesting when the models are smart enough to answer complicated questions about, tell me what product to develop today. I think it’s eminently possible that, with an artificial intelligence understanding the corpus of the species and the economic markets and projecting economic markets, I think this will end up being a driving force for our creativity, if nothing else. And if the economics makes sense, the products.

[Grant Belgard]: Well, it certainly seems to improve capital efficiency dramatically, right? If you’re not having to pay people to do all these things, not to mention, I mean, you can investigate a greater number of ideas in less time, right? So, there’s a cost element, I think, of time.

[Scott Fahrenkrug]: By its very nature, the AI is interactive. That was designed to be that way. And it’s amazing because when I was a professor at the University of Minnesota, I was participating in a group that was developing these large language models, and particularly trying to bring it to the biotechnology corpus. And, you know, they were, the group I remember, they were interested in cancer, breast cancer. I was interested in milk production, same organ, okay, different species, different objectives. But the corpus is the same, right? The terminology around that biology is the same. So, that was, I have to say, 1998, 99. So, people, it’s kind of impressive. It’s impressive, these large language models. I kept wondering, when were they going to bust through?

And, wow, it’s revolutionary right now.

[Grant Belgard]: Yeah. So, speaking of your time at the University of Minnesota, I was wondering if we could kind of go to the beginning. You know, what sparked your interest in genetics originally, and how did your early career shape your path?

[Scott Fahrenkrug]: I liked genetics from the first Punnett Square I did in high school. I guess it’s that I’ve always appreciated the information content. So, in a sense, I’m a biologist, but really, computers have always been part of what I do also. And, really, it’s the same thing. It’s information content. You can realize, you can create biology. I understood that even as a high school student. As soon as I saw that you could follow genetics and a trait, it was clear to me that’s the next programmable opportunity. And so, it was computers that led the way first, but I think there’s going to be this sort of biological revolution that is yet to come.

Now that we can read it all, and we can change a single letter in the genome, provided there’s research funding for the world, there’s all kinds of promising opportunities. And we’ll get out of our hole again and again using biology, just like we did during the Green Revolution. And, like, it’s such a huge feat for humanity, the Green Revolution, because it also was addressing humanitarian needs, right? Like, there’s really people hungry in the world. There’s really people suffering. And that’s a satisfying thing. That’s why Norman Borlaug is my hero, right? Because his solution was do good science. It will help other people, and that’s indeed what happened. So, you know, Norman Borlaug is from the University of Minnesota, I’ll just say. I never met him, but.

So, look, I think there’s a difference between this sort of idea that we want to use science to save the world, but we also want to use science to make money. And money from, you know, our investors. And so far, you know, we haven’t taken the show on the road, so to speak. We’ve been in stealth mode using my retirement money. And some investors from my other companies have come on board. We’re at the point now that my focus has been on trying to make the opportunity heavy, to put as much into the opportunity as possible. And I think we’ve done that now. It’s just so obvious. It’s so obvious when we actually present the results, present the opportunity, and people can taste it. So now is the time for us to break out.

And so now one of the challenges, again, it’s about funding, right, and finding investment. And on the one hand, I told you that the 99% of the seaweed industry is in Asia, okay? So how do I participate in that economy when I’m so far away? But the fact that the industry is so small in Brazil with such a high potential makes it more interesting, okay? And as I say, there’s no place to go but up as it stands now for that industry. And it seems to me like the timing is now. People are willing to pay for carbon credits, so maybe the timing is now. So we just also want them to pay for other forms of carbon, including therapeutics.

[Grant]: So are you targeting Brazilian investors or primarily U.S.-based investors or really everyone who?

[Scott Fahrenkrug]: Well, so our focus was first to build a research capacity in Brazil, right? And so on my limited resources, and I know this is indeed the same reason we ended up engaging with BioInfo CRO, is that for a startup company, really shoveling the ground startup, paying for salaries and health insurance and taking on the responsibility of working with people who have families and having that responsibility to take care of them. And, you know, that’s not the right environment for a brand new company. That’s not the way to do it. So I understood that and have instead been focused on building relationships with Brazilian companies that already exist. And I sought out a company called BioBureau. They’ve been in business for about 10 years.

They reproducibly win top or third most successful biotech company in Brazil for various competitions. And so I’ve built a relationship with that company where I’m paying them to perform services, right? Paying them, it’s their business. That’s how they get paid is by doing the research projects that we envision or other companies envision. So that’s important. And that infrastructure, you know, it was actually quite recently we signed a contract that, again, identified the investment by a Malaysian oil company in growing seaweed. So their employees get paid and we get the results, right? The IP is ours. And so now I have that contract. I feel like I can go raise money. Okay. That’s pretty juicy. Now I can talk to investors and say this is, it’s real, right?

There really is money here for this and there’s real opportunity. And so back on the road again. And so that’ll be the next challenge is I have to look and see how many frequent flyer miles I have for the states. And indeed, it is a major objective for us now to, it’s time for us to, we’ve got a mailbox in the states, but we need to create the laboratory now. Right. So because now we have a machine, now there’s pull, right? Now there’s products that we need to be developing. And so now I can justify to investors that it’s time for us to build a lab again. And, you know, my labs have been quite successful over the years.

[Grant Belgard]: Yeah. So speaking of which, I mean, it’s not your first rodeo, right? Can you tell us about Recombinetics?

[Scott Fahrenkrug]: Actually, you know, there were four companies we ended up creating because the markets were so different for the different approaches. So there’s an agricultural part, which is the company Acceligen, which is, you know, we focused on animal welfare traits. We focused on heat resistance, heat resilience in cattle. Actually, it’s pretty tough for a cow in Brazil, really hot and really rough. And so that’s the main reason that the beef industry here is based on a species from India called Nalori, which is okay. It’s not Angus. But so that was one of our objectives was develop Angus that could survive and excel in Brazil. And so discovered a mutation from actually the my CSO for Acceligen discovered the mutation that made some breeds of cattle more resilient against heat. Okay.

So, again, it was about animal welfare, animal comfort, but also productivity, which are intimately linked together. Better animal welfare is better, right? Better animal welfare is better productivity. And so that’s something that people in the agricultural industry understand. So there were other parts to the company. So our first successes were focused on developing pigs that had human diseases, genetic diseases. So we were early gene editors. We were able to replicate specific disease alleles in people, in pigs, and demonstrate the corresponding physiology, the corresponding illnesses. So pigs are a much better model for human disease than mice. We developed, and by the way, along the way, we discovered some things and people just didn’t know.

Like there’s a dilated cardiomyopathy that it turns out it involves a crystallization of a protein. It’s similar to Alzheimer’s, right? So similar to that protein misfolding granules. Nobody expected that would be the reason for a dilated cardiomyopathy, okay? And so that’s fun when the path you take leads to new discoveries. So those companies, you know, I was, you know, when I started, I was a faculty member at the University of Minnesota, tenured. And, you know, once I realized what we could do with genomes, I decided I wanted to do it, not talk about it. And so I left the university. I think overall we ended up raising about $60 million for that company before I handed it off. So not bad. Not bad for my first try is the way I’m looking at it.

And so the second try is probably harder, but I knew how to get here. And so now I think we’re on the right path. It’s all about the contract, right? So, and I think you guys know, you helped me so much in the beginning, putting together the genetics program and the infrastructure that I needed. And it’s really tailored, right? So this is, I think altogether we accomplished something pretty amazing. It doesn’t just involve Kappaphycus alvarezii. You know, we’re simultaneously analyzing about 15 other seaweed species and are taking the large view on that because evolution has lots to tell us.

[Grant Belgard]: What are the biggest differences you found working across all these different industries, but also fields, right? What are some of the common themes? What are some of the big, maybe unexpected differences?

[Scott Fahrenkrug]: Well, I have to say that there are unique challenges for each of them, but more, I think more interesting is it’s all just the same. Okay. It’s just the same thing in another species. I am not a speciest. I’ve never been a speciest, you know, zebrafish, algae, people, cattle. Well, it doesn’t matter, right? And so there is the, you need to have a reproductive strategy, okay? This is important I found for my pigs, for example, right? So we cloned and we set up breeding programs. You’ve got to have that infrastructure to do that. It’s the same thing with seaweed. It’s the same thing with everything. If you want to do it with yeast, it’s the same thing. You’ve got to have that production capacity, that reproduction capacity, okay?

And that reproduction capacity gives you access to the genome, of course. So what reproductive strategy you use influences the tools that you can bring to bear. I used to say, you know, in the end, you can file hundreds of patents, but it’s all about the animal. Right? It really is all about the animal. How big a herd do you have? Right? So for livestock, penetrating that genetics industry was very difficult. Right? So there’s a few global companies that own the genetics of cattle and pigs, and these are big guys, right? And so, but we were able to make huge progress and compete because our technology was better and faster. Okay. Now the same thing is going on with seaweed, right? So it’s why I have a focus on trying to develop high value products.

Indeed, we want to increase biomass production fivefold in five years. That’s a global objective. Okay. Okay. But, you know, I think there’s, you might be familiar with the sort of value pyramid where at the top, it’s biomedical, and at the bottom, it’s commodity stuff, right? And so we’re driving towards the top, right? Because we’re a startup company. We need to drive towards the top because that means fewer farms to develop and create the value. Right? And so I’d rather, and indeed, we’ve encountered some compounds that the world apparently wants that are worth a million dollars a gram. So can we produce that in seaweed? I don’t know. Is it better to do in seaweed or is it better to do it in yeast? We have photosynthesis on our side, and it’s a simpler genome.

So that’s where we’re headed. That’s the future, I think, for us. And the nice thing about it is that that is amenable to spinouts, right, that are focused on a specific product, right? You’re not putting all your eggs in one basket. You’re equipping a company to produce something, and hopefully that brings money to your shareholders, right? But, you know, I remember encountering early on in my life, I think I was interviewing for my faculty position at the University of Minnesota, and the department chair asked me, So do you work with cattle or pigs? You can’t do both. What? Right? That’s, you know, maybe there’s some truth in it in the, you know, I couldn’t really understand either industry, but I was already an outsider.

And to me, it’s information, it’s genetics, and I didn’t see a line. Like I said, I got my PhD working on zebrafish, right, on embryogenesis, and so it seemed absurd. But so that’s, you know, I think a challenge from an investor perspective, though, right, that they’re going to say you have too many ideas, you’re not focused on any one thing. You know, I think that’s a legitimate criticism in an era where you don’t have artificial intelligence and single nucleotide modification capability, right? So the fact that we can create things so fast now, the key is to spin them out fast. And so we’re figuring that out. We’re figuring that out.

[Grant Belgard]: I think the world is changing faster than ever before right now.

[Scott Fahrenkrug]: Yeah. Change is happening faster than change has ever happened. Yes. In fact, it just changed again.

[Grant Belgard]: So knowing what you know today, what advice would you give your younger self?

[Scott Fahrenkrug]: Well, so one of the lessons I learned was from the very beginning of the companies I started, people would ask me what my exit plan was. I’m like, exit plan? This is my life. Exit plan? Seriously? It was naive. Naive. Because as an entrepreneur, without being greedy, of course, you need to be planning for your future. And, you know, you got to have a diverse portfolio, right? You need bread and butter. And the rest is gambling. And so I was bold when I left my tenured faculty position at the University of Minnesota. I’ve wondered how wise that was.

But seeing what’s going on now with research support, you know, I think it was my nature to be more focused on creating and creating opportunity and creating money. I’m more a doer than a talker, although this has gone on a long time.

[Grant Belgard]: Well, we could go for a lot longer if we had more time blocked off, but maybe we can have a second session later. Thank you so much for joining. It’s been really fun.

[Scott Fahrenkrug]: Thanks, Grant. Thanks for the opportunity. And I look forward to working with you guys again.

[Grant Belgard]: Same on this end.

[Scott Fahrenkrug]: Cheers.

The Bioinformatics CRO Podcast

Episode 57 with Nick Wisniewski

Nick Wisniewski, Vice President of Bioinformatics and Data Sciences at Stemson Therapeutics, discusses his journey into cell therapy research, the rise of powerful artificial intelligence tools for biomedical research, and the future of AI 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 Podcasts, Amazon, YouTube, Pandora, and wherever you get your podcasts.

Nick Wisniewski

Nicholas Wisniewski is Vice President of Bioinformatics and Data Sciences at Stemson Therapeutics, and an expert on AI in drug development and regenerative medicine.

Transcript of Episode 57: Nick Wisniewski

Disclaimer: Transcripts may contain some errors.

Coming Soon …

The Bioinformatics CRO Podcast

Episode 56 with Tony Altar

Tony Altar, President and COO of Splice Therapeutics, returns to The Bioinformatics CRO Podcast to discuss the present and future of neuropharmacology, skateboarding, and how to make the most of your health and career.

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.

Tony Altar and family

C. Anthony Altar, PhD is the President and COO of Splice Therapeutics, which designs and delivers RNA trans-splicing molecules to correct disease-causing mutations.

Transcript of Episode 56: Tony Altar

Disclaimer: Transcripts may contain some errors.

Coming Soon …

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 tower.nf or if you go to seqera.io, 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.