Transcript of Episode 56: Tony Altar

Disclaimer: Transcripts are automated and may contain some errors.

Grant Belgard: Hello, and welcome to The Bioinformatics CRO Podcast. I’m your host, Grant Belgard, and joining me today for the second time is Tony Altar. Tony is a legend in the field of neuroscience and drug discovery research, and the president and COO of Splice Therapeutics, and a rad skateboarder. Tony, thank you for joining us today.

Tony Altar: Hello, Grant. I’m glad to be here. Well, I really want to thank you for asking me to come back to the Bioinformatics CRO podcast. I’m glad that my neuroscience research and the other experiences in drug discovery have helped some of your listeners navigate their own careers. I think that was the nicest feedback I got. And so I plan to continue that theme today, a little bit more with a personal perspective.

Grant Belgard: Can you discuss some of your personal career history and any advice for people as they progress in their careers for young scientists today?

Tony Altar: Yeah, I’d be happy to. And the reason is, I think, you know, we’re all presented with many career choices during a long, and these are the most consequential decisions that we’ll ever make, not just for ourselves, but our families and for whatever our passion leads us. So I’d like to share some of those experiences. My first career step was in high school, and I was very lucky back in 1969, which was a great year for other reasons. I was 17, and I found myself in a neurobiochemistry lab at the Los Angeles Veterans Administration Hospital. It was a great setting, very cool. There are eight PhDs in this lab. Some were faculty at UCLA, and there were also a really talented team of bench scientists. They were all working on the chemistry of the brain. So as a 17-year-old interested already in psychology and things like the brain, this was kind of a great chance for me.

Tony Altar: The lab itself was pretty unusual. It was on the Veterans Administration Hospital campus, and it was basically one of the first neurobiochemistry labs ever. It had four trailers that were kind of stitched together. One of them had the vivarium where the rats and mice were kept, and those odors permeated the other three trailers, which had – one had benches for surgeries. Another had the equipment, and then the fourth trailer had – So that’s where the bioinformaticians would be, but maybe not in 1969. No, our bioinformatics was basically done on paper. And in that lab, I learned about serotonin, how to do rodent brain surgery, and how to get rats to self-inject heroin, which, surprisingly, many learned to do overnight. It was a valuable experience, but even more valuable, I think, was some of the advice I got from these people in the lab, the daily interactions, person-to-person contact.

Tony Altar: You know, these were focused and passionate scientists doing pioneering neurobiochemistry research. There was a guy, Dr. Lenart Wedderberg, who was from Sweden. He was there, and we marveled at what he had done. He’d done serotonin immunofluorescence, and we marveled at these greenish, yellow, glowing gut pieces from rodents that were pinned up on a wooden board. And that was serotonin, visibly. And ever since then, I’ve thought of serotonin as yellow. I guess that’s why. And our lab chief was Dr. Art Eweiler, and he had a mantra, which I loved, and that was to think good thoughts. Art’s brilliance and his dedication really spilled over to everyone in the lab. The fact that Art thought good thoughts, many probably thought well of us, and he did. This helped define a collaborative lab culture, which helped me to do well there.

Tony Altar: So my first piece of advice from that experience, and really from most of the others, good and bad, is find an inspirational, honest, and successful person to work with, and think good thoughts. Years later, Dr. Eweiler drove up to Santa Barbara to be on my Ph.D. dissertation committee at UCSB. That was a dream come true. It was my first met Art. I didn’t even understand most of what he said, but by now I’d finished my Ph.D. He was there at my final oral exam, but the committee didn’t even let me get past slide 12 of what was a pretty full carousel full of slides. So when I learned that I actually passed the oral exam, but also received the first psychology department honors ever awarded for a Ph.D. at UC Santa Barbara, that was really something.

Grant Belgard: Nice. And yeah, I think your point about finding inspirational, honest, successful leaders as mentors early in your career is really key. I mean, it’s the same advice that I would usually give to undergraduates and master’s students searching for a Ph.D. lab. And likewise with a postdoc lab, right? There’s often a big focus on learning the right techniques and methods and things like this. And that is important, but sometimes the kind of quality of the mentor gets short shrift, in part because it’s a lot harder to assess than just looking at someone’s website, looking at their publications and seeing what they’re working on. You know, you really need to get in and talk to people in their lab, talk to their colleagues, you know, really get to know their reputation. You know, it’s important.

Tony Altar: Very important. In fact, being shy can be a detriment. I mean, a lot of us have shy components to our personality, but I think you have to be outgoing and really reach out to people. If you don’t put it out there, it’s never going to happen. And so, yeah, being in the right environment, not just the person or the people in the lab, but the institution as well is really important. And that makes me think about my next career move that happened after my high school, and that was going to college. And, you know, we were living in Los Angeles, so I ended up going to UCLA, which is a terrific school. But I think for many freshmen, it’s more of like a full frontal assault. The classes were personal, large, and there was what I thought of as a tsunami of very intense competition. And so I didn’t do well, you know, taking chemistry, physics, and lots of very challenging early classes.

Tony Altar: I didn’t do well. So I never forget the day that through tears, I called my parents from campus and told them I really didn’t think I could continue.

Grant Belgard: Well, I think that’s really important for people to hear, too, because it’s such a common story. You know, when you talk to really successful scientists that, you know, at some point in their education, they had a hiccup, right? I mean, they had a really rough time. And, you know, they persevered through it and, you know, did amazing things on the other side, but it can be really discouraging. And for a lot of people, they’ll go and change major and never look back.

Tony Altar: Yeah. I mean, the hiccups continue. I mean, UCLA wasn’t my only tough place to persevere. I mean, look, in career, in academia, industry, even government, you know, you’re always going to have difficult people, difficult situations. And perseverance is the key. That’s what it’s all about. And if you love what you’re doing, you will be able to persevere. So I did love the lab work. I mean, I had experienced that already in high school. And so I knew that that’s something I probably wanted to do, but I had to take a break. So instead of dropping out of UCLA, I just enrolled in very different classes, smaller classes, too, in philosophy, economics, perception science and psychology. And interestingly, through all of these, you know, I sought out the teachers. I developed relationships with them.

Tony Altar: I went to their office hours, which is a terrific resource that students really should take advantage of. From these relationships and also by volunteering in some psychology labs, I realized that my passion lay in psychobiology. And that was a very new field at the time. To this day, I’m still fascinated by the structure, the function of our brain and how that guides, well, pretty much everything. So I was lucky. I think also by volunteering to work in some of these psychology labs, I really got a feel that, yes, I do like being in the lab and I like doing this kind of work. So three years later, I graduated UCLA with departmental honors in psychobiology. That was the first year that program had ever been offered at UCLA. I ended up teaching my own study sections. I had two different offices on campus. What a turnaround after nearly dropping out of college.

Tony Altar: So I think part of it was luck. The field of neuroscience was growing rapidly at that time. And another career tip then is see where things are heading. You know, and if you love biology as a general field, where is it heading in terms of technologies, therapeutic indications, important new fields? See where things are heading. If that excites you and if you have talents in that area, and I really do want to stress, you really need to feel that you’ve got some particular talents and drive. You know, find one of the first trains that’s leaving the station and hop on board. You know, go for it. So for me, the future of neuroscience, well, continues to be using these technologies that are new. One of them right now is gene profiling, genetic analysis, and using what we learn about the biology of genetics to guide pharmaceutical target selection.

Tony Altar: And also using gene therapies to treat disease. I think that’s going to be particularly, and has already shown particular promise in treating neurological disease. So just to complete the training portion of what I want to talk about, I was accepted to the Ph.D. program at UC Santa Barbara. And before classes started that summer, I got excited and I drove up from Los Angeles to UC Santa Barbara. And on a Saturday, I went to the psychology building. And I was kind of surprised that the building was locked and there wasn’t anybody there. So I had an interview a couple of weeks later with a potential faculty sponsor. And I mentioned that. I said, you know, I came up a few weeks ago. Nobody was here. I wasn’t sure what was going on. He heard that and offered me a job in his lab on the spot. I guess he realized I had this little intense work ethic. And I think that’s important.

Tony Altar: You know, you want to genuinely show your passion for it. Yeah, you got to work hard in science.

Grant Belgard: So on our last podcast, you said you thought young scientists were too reliant on pulling information from the Internet. Four years later, with, you know, everything going on with AI retrieval and so on. What are your thoughts on that?

Tony Altar: Well, it’s interesting. You know, in the intervening time, I think more bad information has appeared. There’s a lot of great information, too. But at the same time, more tools have been developed to ferret out the bad information and reveal the fraud that unfortunately has become more apparent. I don’t mean to say it’s necessarily more prevalent, but we’re detecting it, which is probably a good thing. So, you know, we receive a lot of information now from the Internet, even more than we did four years ago. So many scientists now work remotely, not so much the bench scientists, I hope, but many of scientists, scientific work is done remotely. So we get our information not from each other or from maybe as many trusted sources, but now more online, which includes misinformation and also importantly, an overreporting of biased or more extreme outcomes, more representation.

Tony Altar: And I think this is true for all of us, not just for scientists. I mean, we all feel this. We’re bombarded by too much information and it takes on a disproportionate perspective in our lives. And if that happens in our scientific way of thinking, that can be a disaster. So my advice is, you know, get as much of your information firsthand from colleagues and even better from the lab, from simple, well-controlled experiments. You know, there’s been a hangover from the post-COVID period. People learn to be kind of more sort of self-reliant or on their own. And so the reliance on Internet information has even increased more. I think this is contributing to a polarization of our social and also political views. I think that’s kind of an understatement. And that’s been further separating people and diminishing healthy interactions. So even for us in the lab, that’s still a problem.

Tony Altar: And relationships outside the lab are important. Yeah.

Grant Belgard: Oh, and the massive advances in generative AI, I think in a lot of ways could exacerbate that, right? Not only are they trained on only what’s online, which is a small fragment of knowledge, but it can also be used in the commission of scientific fraud, right? Like for years, there have been problems with fake Western blots and so on. And there have been some really concerning examples just showing how easy it is to show a model, you know, some real blots and then to have it make some fake ones that look real. But, you know, previously, this was very easy to discover, right? I mean, you would say, well, this lines up exactly pixel for pixel with this other one, or it’s just reversed or whatever. And now, you know, you can no longer do that. So, of course, I mean, people obviously talk about this in the context of AI image generation for, you know, outside of science.

Grant Belgard: But it could potentially make fraud, you know, easier to commit and harder to detect than it has been in the past, which could, you know, further pollute the literature and make it harder to, you know, basically waste everyone’s time and resources, right? Because you have to go and make sure that you can properly reproduce it and that, you know, kind of makes literature less trustworthy.

Tony Altar: Yeah, you have to be in a lab situation where you can go test something and see whether that was really a true finding or not. Yeah, I think the fact that we’re so visual, I mean, our visual cortex is a huge part of our brain. And so it’s not surprising that Western blots are one of the easiest things to ferret out as being fraudulent. But, you know, it makes you worry about tables and other kind of pieces of information that are just as likely to be detected or fraud, fraudulent, but not as easily detected. So, you know, I think that’s why we have to be very careful about AI, but it’s a great tool. And I think, you know, within months or a year from now, you know, talking about the misinformation by AI is less of an issue. I think it’s going to get better. For example, I recently did a study of a particular topic using chat GPT.

Tony Altar: At the end of it, the information looked kind of questionable to me. And at the end of it, I said, oh, thank you, AI, but tell me now, what are the publications that you used that you described in all this information together? And it gave out a list of references. And when I checked them, three quarters of them were actually all made up. I couldn’t find the menu on PubMed or I mean, so that’s inexcusable. So I coined a term caveat scientia, which means beware of knowledge. We’ve all heard of caveat emptor, which is buyer beware, which is true if you’re paying for AI. But even if you’re getting AI for free, caveat scientia, beware of the knowledge that you’re getting and make sure. So I think a lot of this came out of, you know, this change that we’ve come through, the Internet revolution and COVID and isolation and relying more on Internet.

Tony Altar: So my piece of advice is really reach out to others and form relationships, put down the screen, pick up the telephone, talk to people, share information at real time, one on one. There’s lots of studies that show that frequent interactions with others, even random strangers, can decrease the risk of depression. That’s a simple adjustment we can all put into practice and improve our mental health. You know, we’re now several years after the COVID lockdowns and I see people seemingly now more engaged over the last year or two, you know, getting back to the habit of striking informal conversations, small talk, you know, more frequent conversations to basically say, I think we’ll both be better if we talk to each other. And I think we are, you know, we just need to engage each other and not so much with our screens.

Tony Altar: The other area that, you know, you asked me about in terms of information, cautious about, and that is disdain for old information, which I don’t have. But I’ve heard a lot of people say, oh, just read the newest publications. In fact, I know of a leading nursing school, which is a great school, but they admonish their students to avoid publications older than five years. I mean, they actually have this criteria, nothing older than five years. Sure, I can understand they want to apply the most up-to-date practices, but look, even our current practices are based on prior discoveries. So don’t avoid old publications in the life sciences, especially if we consider the increasingly abysmal rates of replication, the increasing number of publications that are being detected, and then greater numbers of papers that are being retracted.

Tony Altar: So I’m not saying the old papers are less fraudulent, but, well, maybe they are. I mean, they’re, I think, easier to replicate, and that’s really an important thing.

Grant Belgard: Well, there are also lots of examples of something being purportedly discovered and published in a splashy journal, and, you know, some of the graybeards in the field will point out some paper from the 70s or 80s that essentially reported the same thing, but it, you know, never really permeated the kind of scientific consciousness and got taken up, right? And that happens pretty regularly.

Tony Altar: That’s a great point. You know, I’ve even seen, and I have to warn scientists, you know, you’re going to get used to this. After a few years, people stop citing your papers, which is the other downside of this belief. So initially, I got pretty annoyed when I noticed people weren’t citing important papers that we published that they were saying the same thing, and they weren’t even acknowledging us. I’ve gotten used to that now. I don’t expect even to see my papers published or cited, even if it’s the same observation or very relevant. It’s just what happens, and it’s a sad state of affairs, and we really have to look back at the original papers. And, in fact, that’s a great source of new ideas and good ideas to do new experiments on. So at your peril, avoid old publications.

Grant Belgard: And what are your thoughts on how the changing landscape of information and information access affects how you source information and separate truth from falsehood?

Tony Altar: It’s an interesting question. I like simple publications that make, you know, points that are easily understood. If it’s really hard to go through a paper and figure out what the heck they’re talking about or listen to a talk, and it’s just hard to understand, I tend to avoid those. If it’s really a clear message and easily grasped, and it’s got methods that another lab can pick up and replicate, and hopefully they did, sure, then most of the studies I tend to gravitate towards. It’s actually an interesting point. You know, the methods in some of these papers are really impressive. I mean, you go through some papers that have all kinds of figures and tables and complex methods, and you go, that’s fascinating. I guess I understand the final take-home message. But who could replicate this? What lab has those same tools and techniques that they could put together to find the same thing?

Tony Altar: Many of these papers are essentially only reproducible in the same lab. That’s not good. So I get concerned when I see very complicated sets of studies like that. They’re impressive, for sure, but it’s a concern that they’re not easy to replicate. I wanted to just talk a little bit on a lighter note about, you know, a few other aspects, you know, throughout my own career. I think things that I think for others are really important. We sometimes, you know, sort of our passion, whatever we’re doing, we tend to ignore our own body. In order to understand my brain, I have had to keep my body in pretty good shape, and hopefully my brain in pretty good shape, too. So I think that’s important. Maybe to start with the body. You know, my 70 years has brought a lot in the way, as we talked about before, skateboarding, a lot of falling off of skateboards.

Tony Altar: In the early days, getting harassed by other skateboarders, did a lot of full-court basketball. Actually, once we played against a couple of the New York Knicks, and we beat them.

Grant Belgard: Well done.

Tony Altar: I think they were just warming up for the new season, so I can’t get too excited, but it was quite a fun game. These things kept me in shape, but for the long term, maybe not so good, because I maybe overdid it. And so that’s just sort of a piece of advice here. You know, I recently had a visit with a physician who had done some left knee arthroscopic surgery on me. I said, I’m waiting in the doctor’s office for him. He comes bounding into the room. He’s about my age, and he kind of looks like his knees are doing pretty good. So I said, tell me, you know, your knees look like they’re okay. How do you keep them healthy? So without any hesitation, he said, oh, you know, my knees are just great. I ski, but I don’t take jumps. I hike, but I avoid drops at more than a few feet. I do lower impact forms of sports, and quite a few of them. And he says, it’s great.

Tony Altar: And I’m sitting there with my swollen knee, and I, you know, I said, well, now you tell me. Where were your, where was this advice when I was 15? And there’s something to this. You know, people at young ages, like some of the guys on the skateboard team I’m on, you know, they do amazing stuff. I’m worried what they’re going to be like when they’re in their 40s or 50s. It made me realize, talking to my surgeon, that how children really need to learn about medical liabilities at an early age. Now, you might say they’re not going to listen, but at least they should have the option to listen and make their own decisions. So they should listen about how hearing loss can be avoided by wearing phone earplugs at loud concerts. You know, even more recently, there’s now super loud subwoofers at a lot of concerts.

Tony Altar: So what frequently or what previously had been a protective effect, your low-frequency hearing tends to not deteriorate so much with age. Even that now may be vulnerable because of the sound equipment that’s out there. People might lose high, middle, and low frequencies in the mid-50s and 60s because of this. Kids should learn how to maybe avoid tackle football because it clearly increases the risk of dementia, Parkinson’s disease, and for many, professional NFL players, both. So, you know, my advice, go play tag football. It’s a very fluid, interesting game. People should know how to avoid a painful shoulder injury by not jumping over the finish line and landing on your shoulder during a sack race at the company picnic.

Grant Belgard: That sounds very specific.

Tony Altar: Well, maybe I did. It was, you know, overly competitive, I think. And I didn’t even win the sack race. So in the case of skateboarding, you know, people learn to skateboard on one leg and they pump the asphalt with their other. Well, what you really need to do is learn to do that with either leg on the board and pump the asphalt with the other leg. I didn’t do that. And I think that was perhaps, you know, one of the limitations for me. But you can actually become better at these things by balancing out the wear and tear. And this can help lead a healthier life into the 22nd century. You know, there are quite a few estimates now that children who are born today and have adequate nutrition and health care, half of them can expect to reach 100 well into the 22nd century. So these ideas are more relevant than ever. You really can’t start too early because these things do catch up with you.

Grant Belgard: How about that brain of yours?

Tony Altar: I’m checking it now. Seems pretty good. My synapses seem to be in good shape. And, you know, I do attribute some of that to this aerobic conditioning from the active sports and active life that I’ve led. And I still engage in them. I think it’s very important to stay active, to move in a single word. There was a study from Heriot-Watt University in Edinburgh, Scotland. This is like five years ago, I think. They showed that just a few minutes of aerobic cycling a few times a week makes a demonstrable increase in the body’s response to insulin in non-diabetic people. That’s amazing. I mean, it was a total of, I think, 15 minutes a week, some really minor amount. You know, we know that insulin or insulin-like growth factor, IGF, deficiency in the brain is a feature that actually we found in some schizophrenic patients.

Tony Altar: And it’s also been proposed that there’s an insulin-like deficiency in Alzheimer’s disease by Suzanne Kraft and others. I wouldn’t be surprised if exercise increases the brain’s response to its own intrinsic insulin system, just like exercise does outside the brain, and as exercise does by increasing the synapse-promoting protein, brain-derived neurotrophic factor, or BDNF. One recipe for long-term brain health is to keep physically active and to conduct regular but not too intense aerobic exercise. Good against depression. It’s probably good also for cognitive function. It’s just kind of common knowledge because it also increases the cardiovascular system, and that’s directly associated with brain health.

Grant Belgard: Yeah, I was asking Thomas Hardy some years ago about this, you know, in families with a high genetic load for Alzheimer’s, you know, what can you do? And his answer is cardiovascular fitness, right? Stay in shape. And, you know, all these active things, if you love them, they’re fun too, so it’s kind of a win-win.

Tony Altar: You know, there’s another preventative measure against Alzheimer’s or generally age-related brain loss, which I actually was a bit skeptical of, and that was the importance of cognitive activity itself, that it can help. But, you know, I’ve had some experiences recently that make me actually a greater believer that by engaging in challenging new activities that challenge your brain is actually good for your cognitive future. And that’s from my recent work in gene therapy research and development with our company, Splice Therapeutics, where I’m president and chief operating officer. You know, in a company that’s involving gene therapies for inherited and acquired genetic diseases, there’s really a daily challenge in the lab, and I am in the lab. We’re doing lots of lab work. We’re planning studies. We create molecular designs. We conduct lab research. I run the FACS instrument.

Tony Altar: You know, my neuroscience background is relevant because we have gene editing projects directed to the CNS. You know, I feel my cognitive functions have really benefited from all of this work. Working in a pioneering company with the inventor of RNA transplicing, Lloyd Mitchell, has been really stimulating for me, and I believe that kind of activity is an ingredient for brain health. So what we do at Splice Therapeutics is we design and make what are called RNA transplicing molecules, or RTMs, which have been shown in lots of different examples to correct mutations that cause disease. It’s pretty exciting because just this year, the first such molecule, an RNA transplicing molecule, entered clinical trials. It corrects most of the mutations in a gene called ABCA4 that cause a form of inherited blindness, or Stargardt’s disease.

Tony Altar: This RNA editor is being developed by a competitor, and we wish the patients in this trial the best of all possible outcomes. We see no reason why the technology won’t work in man, as it has shown to work in animal models, including the ability to rewrite the gene for the tau protein, map tau, in dementia models, and reverse the dementia-like phenotypes in these animals, and correcting mutations in a cancer-related gene, P53, which is where the RTMs have shown to lessen tumor growth in cancer models in live animals.

Grant Belgard: The most studied gene of all time, P53, yeah.

Tony Altar: I mean, it’s a critical master regulator, and in these studies, RNA transplicing molecules that replaced the exons that had the mutations for these particular animals, corrected or lessened the tumor growth dramatically. So it makes sense. You remove the mutation and replace it with the wild-type form, the cause goes away. I mean, that’s profound. So there are thousands of genetic diseases, including those we are working on, that can be treated by RNA transplicing molecules.

Grant Belgard: So in the last podcast we did with you, you talked about what you thought was the future of neuropsychiatry. Looking back a few years later, would you like to revisit that?

Tony Altar: You know, I’ve added gene editing to my research path because this approach corrects the inherited or required mutations that cause many diseases. These have been much easier to discern in neurological diseases because the link is very clear. In psychiatry, it’s not as clear. We’ve been pretty hard-pressed, in fact, to find in GWAS or other studies, you know, clear gene defects that create psychiatric diseases. Now, in diseases like autism, Rett syndrome, you know, all kinds of neurodevelopmental disorders, there are mutations that not only cause, you know, neurological phenotypes, but psychiatric ones as well, certainly behavioral problems. So we’re interested in RNA transplicing because it can be certainly applicable to neurological and cancer and other indications. Potentially down the road, it might even be useful to modify gene expression in psychiatric diseases.

Tony Altar: But we just need to learn a lot more about these causes, and that’s been a much harder thing to do. But it’s an exciting approach, as is gene editing in general, because if you can correct the cause of a disease, it’s really not presumptuous to say that the therapy can actually cure the disease. You know, that’s amazing to me. Cure is a word that we basically never use to describe pharmaceuticals. But for gene therapy, that’s absolutely what can be happening to the degree that the gene therapy is efficient at correcting the mutation. So every day, you know, I hear about a disease, I know a friend, or I hear a story about a patient with an inherited or acquired genetic disease that gene editing and RNA transplicing can treat. So that gets me going every morning.

Tony Altar: I think the rate of industry growth over the next 15 years will be very great for gene therapies, especially with the advent of delivery systems that are vastly improved for different tissues, including the brain. And I think these will be used much more broadly, and along with pharmaceuticals. I think that’s co-administration of a gene therapy and a drug is a very likely scenario for the future, because very few biotherapeutic interventions are completely effective. We always seemingly end up with a partial solution, which is good, but maybe the remainder of that solution can be complemented by a drug. So I think that’s another area of importance, looking at the co-administration of these two modalities. But I think without a doubt, there’s also a vast future for pharmaceuticals to treat brain disorders.

Tony Altar: I’ve recently been advising a company, Negev Labs, for developing derivatives of psychedelic drugs to help people with psychiatric and neurological indications, including pain. These psychedelic-derived products are called neuroplastogens because they produce a long-term change, and generally I think it’s a good change, a sprouting, a growth of neurons in the brain. And these long-term effects mean that potentially you don’t need chronic administration of the drug, maybe intermittent administration, and that the changes are long-lasting. And that’s part of the excitement around this field. It’s also exciting to me personally, because many of the drugs in this class of neuroplastogens elevate BDNF. Brain-derived neurotrophic factor causes serotonin neurons to sprout.

Tony Altar: We first reported that at Regeneron, and by stimulating serotonin receptors, neuroplastogens can increase BDNF and increase synaptic spines and their contacts with neurons. I mean, that’s profound. I think that’s what this class of drugs is doing. We first not only learned about the ability of BDNF to cause serotonin neurons to sprout, but I propose that there’s a feed-forward cycle that as you do that, you cause BDNF to be increased by serotonin receptor activation. That causes serotonin neurons to sprout, which can further stimulate those same receptors. You can get into a positive feed-forward loop, and that might be a strategy to treat depression, as I first proposed in 1999 when I proposed the BDNF theory of depression. So a better way to augment serotonin signaling than by SSRIs, like Prozac, may be achieved by directly stimulating particular serotonin receptors in the brain.

Tony Altar: So that can mimic serotonin, but in a more selective and more controlling way. And so I think that’s where the psychedelic drugs have come into play, because they may be acting in just this way, the direct acting as opposed to the indirect acting serotonin reuptake blockers. So I think there’s promise here for diseases like OCD, obsessive-compulsive disorder, and other indications like Tourette’s, major depression, anxiety, and PTSD. And actually, there’s quite a bit of literature preclinically and now clinically to support that idea. So this gets to be an important point, because the clinical tests are really where the rubber meets the road for any therapeutic, and that’s true here too.

Tony Altar: So there are studies that show that combining psilocybin, which is a serotonin receptor agonist, or methylenedioxymethamphetamine, which is a very potent serotonin-releasing drug, that when combining either of those with psychotherapy, patients can get a better outcome for their PTSD in the case of MDMA than psychotherapy alone. So it’s highly significant, but I think a relatively temporary setback to the field when the FDA, just in the recent months, rejected Lycos Therapeutics combined MDMA and therapy treatment for post-traumatic stress disorder. This rejection was based on quality lapses and biases in the Lycos clinical trials, which included a treatment unblinding of the psychotherapists who were actually working with the subjects. And also because some of the subjects in these trials themselves had prior experiences taking MDMA.

Tony Altar: So this was a real disappointment that the FDA rejected the application and asked Lycos to go back and do more work.

Grant Belgard: I think the FDA would have been okay with the patients being unblinded, because after all…

Tony Altar: Right, I mean, it’s pretty obvious if you got the placebo, right?

Grant Belgard: Yeah, nothing’s happening.

Tony Altar: Yeah, that reminded me, when I was at UCLA, I enrolled for it. There was a clinical trial or some kind of study on the effects of marijuana and driving. And so I enrolled as a student. Maybe this is why I had trouble the first year. And I went and sat in this room. And when it was over, I said, what happened? And they go, oh, you were in the control group. That was a bit of a disappointment, I guess. So the FDA, you know, they were… I think they would be okay with the patients being unblinded, but not of the therapists who were working with the patients. They couldn’t be unblinded. So these flaws are ones that Lycos and others can overcome. I think many new compounds and more clinical trials are on their way. And, you know, I think there’s going to be a lot of things happening in this field. There’s already a lot of anecdotal information, which I look at.

Tony Altar: I think it’s a positive thing to look at early experiences as a predictor. Because as we know in psychology, past behavior is the best predictor of future behavior. And as long as there aren’t confounds, I think these drugs and the new NCEs that are coming along are going to have a lot of emphasis. I think neuroplastogens are the newest contributor to the growing emphasis on receptor agonists for psychiatry. You know, receptor blockers or antagonists are the kinds of drugs that have dominated treatments for depression and psychoses for 50 years. I mean, it’s always a serotonin reuptake blocker or a dopamine receptor antagonist. But what’s happening now, I see, is agonist drugs, which have a more complex pharmacological action, are really becoming of greater interest.

Tony Altar: So my own experiences there has been with, one of them has been with Abilify, which my neuroscience team at Otsuka discovered. It became the first approved antipsychotic agonist. It’s a partial agonist, and it’s really the first-in-class partial dopamine and serotonin receptor agonist. This dual agonism at both dopamine D2 and serotonin 5HT1A receptors was appreciated by our colleague, which I had great pleasure to work with, Dr. Arvid Carlson, who discovered dopamine. And he coined Abilify as a dopamine serotonin stabilizer that allowed Otsuka to broaden Abilify sales by tenfold, because now the drug could be used by people with depression and bipolar disease. And Abilify became the best-selling drug worldwide in 2014.

Tony Altar: So not too surprising, the first-in-class agonists, where previously every other drug was an antagonist to treat schizophrenia, and the therapeutic world rewarded the company for this pioneering work. So I think the advent of new agonists at serotonin receptors, at muscarinic receptors, cannabinoid receptors, and others are going to find uses for patients, especially for those who don’t respond to existing drugs. These provide more options, and that’s a good thing for practitioners. Absolutely. There’s so much variability in patient response, and then, you know, something will work for a while, and then it’ll stop working. So the more options with varied mechanisms that can be available, the better. And this gives us a whole new way of looking at the old receptors. It used to be blockers now. Well, what if they’re agonists? What is that going to do?

Tony Altar: I mean, who would have thought that a dopamine agonist would be useful for schizophrenia? Most common thinking was it would make the disease worse. But because Abilify is a partial agonist, it just normalizes the tone, which is, and doesn’t go too high or too low. So partial agonism is an aspect of agonism that has to be considered for this whole class of drugs. For antagonists, you don’t really think about that. You’re just blocking the response, and that’s pretty much it.

Grant Belgard: And is there anything you’d like to leave our listeners with?

Tony Altar: Yeah, there’s a couple of things. One is this, what I think is a really dangerous mantra. People say, oh, it’s good to fail. You should fail a lot. No, no, I don’t think that’s very good advice at all. It actually sucks to fail, and it really isn’t to be encouraged. You know, in research, in the lab, we learn and understand best when our experiments succeed. When they fail, we have very little clue about what went wrong. Oh, was it the buffer? Was it the wrong design? Who knows what? When the experiments succeed, we know that pretty much everything we did was done right. That’s a great feeling in the lab. We get to think the best of good thoughts, learning something new about biology. So try not to fail. It’s not good to fail. It’s obviously a normal component of what we do, but plan for success.

Tony Altar: And in fact, I learned that mantra from Len Schleifer, CEO of Regeneron, back in 1991, when Regeneron was a pretty small company. And that spirit of planning for success and the successful studies since then have helped make what was a 100-person company the big success Regeneron is today. You really want to plan for doing well and move steadily, but gradually as you learn more about biology with the right outlook and passion and reasonable goals to solve unmeted needs in medicine, a company or an academic lab that you are part of really will succeed and humanity will be better for it. So there’s only one other piece of advice I can give, but really compared to anything I’ve said before, nothing is as important as the following. And that is, if you ever find yourself at a scientific meeting, don’t ever, ever go to panel sessions, never agree to participate in a panel session.

Tony Altar: This can be highly dangerous to you. You will have to start as a panelist up there on the stage with three other people sitting in a row with some moderator who probably doesn’t know much about what you’re talking about. You’ll have to start each sentence with, I echo what Jill just said, and I can add, and then you have to think about something new to say. Otherwise, what are you going to do? Are you going to disagree with one of the panelists? The panel will gang up on you and you will never be invited back. I think better, just spend your time at a scientific session, even if it’s neurological control of ruminant bowel function. I mean, you’re going to at least, there’ll be data there, there’ll be statistics, and there’ll be open discussion. Or if that fails, go outside the conference hall, go to the street, find a random person and tell them about your research.

Grant Belgard: Tony, with that last nugget, thank you, and please don’t expect to ever come back to The Bioinformatics CRO Podcast.

Tony Altar: I won’t expect to, but you never know. It might happen.

Grant Belgard: Thanks. It was a pleasure.

Tony Altar: All right. Thank you, Grant.

Transcript of Episode 55: Mo Jain

Disclaimer: Transcripts may contain some errors.

Grant Belgard: [00:00:00] Welcome to The Bioinformatics CRO Podcast. I’m Grant Belgard and joining me today is Mo Jain of Sapient. Welcome.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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