Enabling the next generation of biomarker discovery with Dr. Mo Jain, CEO of Sapient



    Lifesaving drugs only work 50% of the time. The reason? Broad-stroke treatments that gloss over the individual variations in our bodies.

    For Dr. Mo Jain, the thousands of biomarkers in our bloodstreams are the secret to targeted, personalized medicine with maximum impact.

    Enter his new project, Sapient.


    About Mo
    Dr. Jain is a physician-scientist with nearly 20 years of expertise in physiology, biomedicine, engineering, computational biology, and mass spectrometry-based metabolomics.

    He was the director of the Jain Laboratory at the University of California San Diego (UCSD), before forming Sapient in 2021 with the big objective of accelerating drug discovery with mass spectrometry, computational biology and population-scale clinical studies.


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    Access the complete transcript of our chat with Mo below.



    Today we're excited to have Dr. Mo Jain, CEO of Sapient. Dr. Jain, or Mo as he prefers to be called, is a physician scientist with nearly 20 years of experience in physiology, biomedicine, engineering, computational biology, and mass spectrometry- based metabolomics. He started and directed the Jain Laboratory at University of California, San Diego for a number of years, and that's actually where Sapient got started as well.

    You can read the full bio about Mo in the show. Sapient is one of the largest capacity biomarker discovery labs in the world, and well on its way to transform biomedicine forever. Sapient has multidisciplinary team sponsors and therapies with biomarker-guided insights. For all of our science and bio nerds listening in, I'm sure you'll be fascinated by the insights from Mo today.

    Thank you so much for joining us today, Mo. Welcome to the show.
    Thank you so much, Kelly. My pleasure to be here, and thank you for having us. 

    To get started here, you have such an accomplished background as our listeners can see in the show notes, but tell us briefly what interested you to pursue a career in pharmacology and bioactive metabolites in human disease. 

    Absolutely. I think my background is a reflection of more severe ADD than anything else, in that I've been searching for, what do I wanna do when I grow up? And I've had the distinct pleasure of being able to work in several different areas, both as a practicing physician, as a professor and a researcher, and now an industry with Sapient.
    And this career really got started with a fundamental question of wanting to understand why some people stay healthy over the course of their life and why other people developed diseases and being best to help those people.
    And this is why I became a practicing cardiologist in my early life. And as that process evolved it became really clear to me that even for all that we know about human disease, we really understand a very small portion of a very complex system, which is the human body.
    And we're really bad at predicting who's gonna develop what disease over time, and we're even worse at predicting who's gonna actually respond to a given drug. And the numbers are actually astounding.
    As you go through the actual data, whether it be for clinical trials or real world evidence, essentially the best drugs work in about 50% of people.
    Which is again a number that's just absolutely appalling to me.
    The fact that we're not able to diagnose disease decades in advance when we know these processes take years or decades to actually come to fruition is also just really not acceptable as a civilization, as a way of practicing and so it was that desire to want to be able to evolve how we diagnose disease, how we develop drugs, how we distribute, and whom we use particular drugs that gave rise to initially my research.

    And then ultimately Sapient. 

    As someone who operates in-industry, from a quality perspective, of course we're always looking at those kind of percentages and I think the greater public really doesn't understand how much your individual biology plays into all of these things.  
    Doctors prescribe us a drug, we expect it to just work, we don't understand why it doesn't, we get frustrated, we blame the company, whatever. It's crazy to me that people maybe, I don't know, maybe we just don't do a good enough job in high school biology.

    Yeah. But part of it is an education issue. But part of it is also just how we go about deploying drugs.
    The way we think about this is that everyone who has a disease is diagnosed as a universal grouping of individuals that, as you said, will all receive the same therapeutic for the most part, and we know there's huge variations within any disease population, both in how the individual developed that disease and whether or not they're going to respond to a specific therapy.
    Part of that has to do with the fact that, Kelly, you're a little bit different as a human being than I am.

    Part of it has to do with even if we have the same disease, our diseases are quite different from one another.
    And so being able to understand how we deeply phenotype disease, particularly at its earliest stages,  and then deploy drugs in a way that are targeted to help us target our specific therapies, is really the crux of this problem.

    And this is the goal of personalization of medicine in general. And it's always been a sort of a wonderful idea and a wonderful concept. And in certain therapeutic areas, particularly in the oncology space, this has really been transformative over the last decade, but medicine as a whole has not very much evolved in thousands of years.

    Arguably we still diagnose disease based upon a certain pathology, and it's a one disease, one drug type of relationship, and  that's proven to not be correct. 

    Definitely. And it's fascinating to me too, right? My spouse has a particular fascination with all things microbiome and how that plays into all of this too.

    But, I think as humans we wanna have that, here's the single answer and the whole idea of correlation isn't causation, there's actually a whole lot of factors at play. How do you keep from the gene becoming just the magic bullet answer as well? 

    It's a good question.

    It depends upon what the underlying objectives are, and I'll explain what I mean by that, Kelly.
    Sometimes simple correlation can provide a lot of diagnostic information.
    And let me use an example of the good cholesterol, HDL. There's an abundance of evidence that shows HDL is not actually causal for protection from heart disease, and it's really about other factors, but it's still an exceptional diagnostic for telling us who's at risk for developing a heart disease over time.
    And if you're trying to drug it, it's not a good therapeutic target, as the pharmacology world and as pharmaceuticals will tell you over the last decade.

    But it still provides a tremendous amount of diagnostic information. So it's about really understanding  the objectives. Understanding that we're all, again, while we're all equal, we're not identical. And then trying to understand how our disease processes may be different in a way that allows us to specifically target our disease process.

    Now, I think, again, in the oncology space, biomarkers have proven to be transformative. And the example I always use is when I was in medical school, the way we diagnosed lung cancer or classified lung cancer was based upon its pathology, was either a non-small cell lung cancer or squamous cell.

    There were essentially three buckets of what lung cancer looked like and that was based upon what the diagnosis was on a pathologic examination. When you took a piece of that tumor out, you put it on a slide, you look at it under a microscope, I can classify it as one of these three groups. And then over time we realize that there's specific mutations, genetic mutations that occur in various lung cancers.

    EGFR being the first one that was identified. And then subsequently, now, when we look at lung cancer, the way we classify lung cancer now, it's one of 40 different diseases based upon the specific mutations. Now, based upon those specific mutations, oncologists today will decide what specific therapy to give an individual.

    And so lung cancer went from a disease of three individual components to one. Now that's several dozen different components, and that component classification is exactly what dictates what drug you receive. And this is why the efficacy has gone up quite a bit for treatment of lung cancer. 


    That's an amazing story too, of a positive outcome for sure. 

    The question is, now how do we extend this though, right? Because this worked really well for cancer, how do we think about this for other non- oncologic diseases? 

    And we're of course, always, as a founder, where do you get the most bang from your buck? But certainly being able to see the applications of that technology across other spaces outside of cancer. So you know, here we're talking a bit about your passion for it, but let's talk about Sapient for a minute. So it's a spin out from the lab, there at UCSD, literally from lab to launch which, we love, of course.

    But tell us a little bit about Sapient and how you guys are trying to bring that transformative technology to a different therapeutic. 

    Sure. Happy to. And then happy to walk you through this evolution.  Sapient was founded around this idea that if we can better classify disease using what we call biomarkers in the same way we use genetic biomarkers to classify lung cancer.
    We can better align a specific individual with their specific disease process and ultimately understand the specific therapy that's best suited for them.
    And again, this is not a theoretical idea. Certainly there's a tremendous amount of evidence in the clinical literature that when drugs are developed together with a biomarker, whether it be the oncology space or in other therapeutic areas, the approval rates go through the roof on an order of magnitude.

    And this has certainly been borne out and we were quite interested in the idea. When we observed what happened in the oncology space and how genetics had transformed oncologic understanding and treatment, it was really around not classifying the host, meaning you or I, but rather our disease processes.

    As I mentioned, being able to understand how a tumor may be different from another person's tumor by the specific mutations that are located. And of course this works well for cancer simply because cancer is read out by genetic sequencing where you can tell what the molecular drivers are and the specific mutations.

    And the question  what about those other diseases? Heart disease, lung disease, neurodegenerative disorders, liver and GI illness, all those other diseases for which genetics has not proven to provide the same type of information. How do we begin to classify those diseases and better understand them, meaning everything outside of the world of cancer and even in certain cases in cancer.

    And we became very interested in this technology referred to as mass spectrometry. Now these are pretty big devices and obviously given your chemistry background you're quite familiar with them. But these are really amazing devices, bioanalytical devices that allow us to take complex biospecimens that are composed of thousands of molecules and decompose them and measure the actual abundance of each of these molecules that are present in a biological.

    And the challenge with mass spectrometry was the same one that was posed to sequencing about 20 years ago, and that it's an incredible technology, incredibly robust, very accurate and precise in its measurements. It's just too dang slow to do on a population scale, right?
    And so when we launched our laboratory at the University of California, one of our real objectives was to take a mass spectrometer and simply make it go a hundred to 500 times faster than it ever gone before.

    And that's what the objective was. And we spent many years tinkering and prototyping and developing new hardware systems, developing new software systems.
    And as we were going through that process, we were slowly solving each of these bottlenecks in a way that we were able to continue to accelerate the process as a whole.
    And as we were doing this, there was a number of organizations that started coming to US government organizations, academics, large foundations, the Bill and Melinda Gates Foundation, large biopharma organizations where they started asking, can you help work with us in order to be able to analyze this large population of biological samples that we have from this clinical trial or from this epidemiologic study?

    And we began doing this work and as we started doing this we realized that there was a tremendous amount of information, both diagnostic information, prognostic information, as well as drug response information that's encoded in these small molecule biomarkers that are floating around in our blood that could be detected by mass spectrometry.

    And again, this is not magic. When you think, when you go to the doctor, anyone who's gone to a physician for your annual checkup. They draw those two tubes of blood, the purple top tubes, and we typically measure about 15 things in those blood samples. And there's tens of thousands of things floating around in your blood.

    So why are we only measuring 15 of them? And essentially what we are doing here is using these mass spectrometry systems to measure 15,000 things at once in that biological specimen. And the simple answer is that as we measured more things, we were able to learn more things. We could tell who was gonna develop what diseases over time, how people were gonna respond to particular therapeutics, who was going to have a more indolent response to a disease process versus a more precipitous response to a disease process. 

    And as we began to do more and more of this work, it was clear that there was a larger sort of opportunity here to bring high throughput, mass spectrometry to drug development in a way that would provide services and aid in drug development and discovery across the world for many different organizations, whether they be, again, academic foundations, governments biopharma partners, et cetera.

    That gave rise to Sapient and so Sapient was spun out with that exact idea. 

    Yeah. And my next question, and I'm just sitting here spinning on this right, was to talk about technology and its application evolving over the next several years, but as I'm sitting here thinking as a general public sort of person. I have access to gene testing and those kinds of things, right? Like I have a family history of breast cancer. So I do that testing every couple of years cuz it keeps evolving. But I'm like the general public. Can I just send you a vial of my blood? What sort of plans do we have?

    I know targeting this to drug development is a place to start. But what about the benefits to the greater population as a whole? Do you see it evolving that way? 

    Absolutely, Kelly. Fundamentally what we've built is a tool and this tool allows us to accumulate a massive amount of underlying data that then allows us to develop new diagnostics.

    And so there's many phases to Sapient. The first is, as you suggested, just being able to service those that are developing drugs. And much of our attention in our early years here has been on supporting biopharma organizations with a discovery as a service type of model here whereby we provide services to them to analyze their biological specimens, whether they come from preclinical studies or clinical studies.

    Help them make discoveries and return that information to them in a way that allows them to accelerate their drug development. At the same time, through our internal R&D efforts, as you can imagine, we're amassing a tremendous amount of data and we have one of the largest human biological data assets in the world at this point, where we've analyzed hundreds of thousands of samples using these mass spectrometry systems now at Sapient.

    And that's allowed us to develop new diagnostic tests and it's our hope over the next year or two here that will begin commercializing some of these tests and making 'em available to the public. 

    That would be exciting. To pivot a little bit, the Sapient team spans so many disciplines.

    You've got chemists, engineers, epidemiologists, physicians to name a few but as the CEO how are you intentionally shaping the culture and efficiency of such a high performing team? 

    Yeah, it's an interesting question and there's many models by which you can build an organization and build teams.

    And my essential model has always been you go out and find the absolute smartest, most talented people that are really excited to solve really hard problems together as a team. And you put them in a room together, you give them really hard problems and you give 'em a lot of food and you just get outta their way.

    And that's really been the model that's always worked for me, whether it be on the academic side or in building Sapient. And so I have to say we've been incredibly fortunate to find just some world class talents across each of these areas. And I essentially view my role as being the glue or the grease.

    And essentially when I need to bring people together it's my job to be able to bring those folks together and bridge syntax divides and communication and sometimes I have to, Grease wheels  to make things turn a little bit faster. But honestly for the most part  it's a train that I'm just holding onto and it's my God, just to help direct occasionally and make some minor tweaks.

    But we're very fortunate to have just world-class people  that do all the hard lifting. 

    That's incredible. I love that story. Especially about the food. As a manager of people, I find that to be the universal motivator.  
    Food and caffeine are critical components. 

    Yes, definitely caffeine as well.

    To talk a little bit about funding, you mentioned you've raised funding from several sources, including the Bill and Melinda Gates Foundation. What advice do you have for other founders with funding on the mind? 

    Yeah, it's a really interesting question, Kelly. And we took a perhaps non-traditional approach to funding in that myself and my two co-founders initially when we had launched Sapien t because we were a discovery and a service organization, we didn't necessarily need funding upfront.

    We had clients who were coming to us, like you said, the Bill and Melinda Gates Foundation, which is public information. We have many other biopharma organizations that were working with us. And so we had revenue almost from day one. And for that reason, we we weren't in a position that we required funding.

    Now at the same time, we realized quite quickly that the demand out there for our services were far greater than we were gonna be able to provide given our small closet model. And we ultimately ended up finding a funding partner that really worked for us. And the approach we took is that we were going to find a partner more than anything else, and  in funding to me the dollars that come in are only one component of it.

    Essentially you're marrying your early investors and you have to be okay with that, good, bad, or ugly. And we spent a lot of time making sure that we found individuals that were aligned with our larger vision that complemented many of the weaknesses and areas that we did not have strengths in.

    And that were excited to go along that adventure from beginning. And I realize that the title of this is Lab to Launch and it's very different having technology and having a successful organization or company. Those are two very different worlds. It's almost night and day, and those Venn diagrams don't cross very much.

    And optimizing technology, optimizing discovery and optimizing companies and processes two very different areas of optimization. And we are very fortunate that we were able to find an investor team that was excited about going through that process with us and walking us along from our lab as to how we actually build a successful company.

    Yeah. I've spent the last, I don't know, almost 10 years working with startups in the space, and I've seen this go well, I've seen it not. So I love your humility in that, because a lot of times what I see is, the tech side person is the CEO and they just wanna hold onto this and they wanna control all the things.

    But at some point you scale to a place where you have to let go. You have to have a good team and you have to trust them to do their jobs and get out of their way. And so  I love hearing how well that's going for you guys, cuz I've seen it not go well. 

    Yeah, I could not agree more. In the end I'm just one cog at a very large wheel and the other cogs have to be able to function, otherwise the wheel doesn't turn.

    And the investor team are a part of that. Your board is part of that.  Your heads of department are part of that. And everyone who works in our organization is critical. 

    On a bit of a more personal note, if you could go back to the start of your career, what would you tell yourself based on what you know now?

    Oh my goodness. I think one of the key lessons I've learned in life is that you don't have to know what you want to do next. And in life, in my career, at least personally, has been really quite an evolution. And when I was practicing medicine, I was convinced that's the only thing I ever wanted to do.

    And when I was a professor and running a research lab and teaching, I was convinced that's the only thing I ever wanted to do. And I really enjoyed it. And now here on the commercialization side, running Sapient, I'm convinced this is the only thing I ever wanted to. I think I've learned enough that I really have no idea what's gonna happen next.

    And that's okay. That's part of the evolution of learning. And  I think folks who are bright, who are talented, who are hardworking, there's lots of opportunities. And there's lots of things you can do. And not to be scared at the idea of transition. 

    Nice. I like that.

    That's the one thing I would say that I've learned over and over again that it's gonna be okay and it's okay to try new things and it's okay to do new things and you should never let fear be the driving emotion behind any idea or any ounce of effort. 

    I like that. Very true. 

    There was another guest earlier, and so this is a standard one I'm using all the time now. I love it. But if I walked into Barnes and Noble , where would I find you? What section would you be in? 

    Oh boy. Bookstores are my weakness and I just love the smell of the bookstore when you walk in.

    And I dunno why. I dunno if it's the paper or the oil or the ink or what it is, but something about it is magical to me. And I suffer, as I think I mentioned early on, from ADD, intellectual ADD. And I tend to wander and I wander extensively.

    And everything from just thinking about what's on my bookshelf right now or on my side stand that I'm reading everything from. Deep science in a book about the genome to a book on regulatory affairs and drug development to a book on management consulting and how you organize teams.

    And so I tend to be pretty diverse in where I am. What I will admit though and something, again, another lesson that I've learned is that, oftentimes we're quite disparaging of social media and learning from alternative sources and there's nothing like holding a book in my hand, so I'm certainly a firm believer in that.

    At the same time, there's a tremendous amount of information to be learned from alternative sources, whether it be podcasts such as yours, whether it be Twitter and other areas. And I'm continually amazed just how much one can learn when you're exposed to such diverse ideas and opinions and thought leaders on these different types of social media platform.

    And so, I'm actually gonna have to admit I'm a huge fan. I'll openly admit I've probably learned more in the last year on Twitter than just about anywhere else. And there's an incredible amount of information around gap accounting and how you think about P&L that come from really insightful people.

    And I find a lot of that information's just on Twitter and so the simple matter is I'll be wandering around the bookstore, but I'll probably have my phone out at the same time. 

    Yeah, same. I found that LinkedIn has become a bit of a rabbit hole for me, which is not something I ever expected.

    Generally, I go to those sorts of things to escape my job. I wanna get outta my head for a little while, but man, same kind of thing as Twitter, and I don't spend as much time there, but LinkedIn too, I have found myself scrolling and then following threads and reading people's thoughts and opinions on things and yeah, for all the drama around it, it certainly is also an interesting way to see a bigger perspective on the world than just your own local area or local people that you interact.

    That's exactly right. I couldn't agree more. And you can really connect with thought leaders and people that... the problem with books obviously is that it takes years to go from inception and writing of a book to publication and social media has taken that and accelerated sort of distribution of information and knowledge and opinions, for better or worse is arguable to all this instantaneous time point.

    And that's really interesting to think about. 

    Definitely, definitely. Where can folks go to connect with you and follow along with Sapient? 

    Our website's probably the best place to go. And we have a marketing team here that does a great job of putting together whitepapers and blogs and publications and other types of information on our website.

    So www.sapient.bio. And there's a lot of information on there about what we do, and we're always looking to connect with people who are interested in these types of tools and technologies, whether they come from biopharma foundations, disease organizations, government, or academics, or even consumers that are interested in particular types of testing.

    Awesome. Thank you so much for your time today, Mo. It's been a fun conversation. 

    Thank you so much, Kelly. Appreciate it.