Transcript of Episode 61: Don Alexander

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Transcript of Episode 60: Max Marchione

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[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.

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.

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.

Transcript of Episode 57: Nick Wisniewski

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Transcript of Episode 56: Tony Altar

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Coming Soon …

Transcript of Episode 55: Mo Jain

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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.