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.
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.
And then ultimately Sapient.
Part of it has to do with even if we have the same disease, our diseases are quite different from one another.
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.
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 depends upon what the underlying objectives are, and I'll explain what I mean by that, Kelly.
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.
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 that's what the objective was. And we spent many years tinkering and prototyping and developing new hardware systems, developing new software systems.
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.
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?
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.
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?
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.
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?
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.
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.
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.
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.
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?
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.
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.
And that's really interesting to think about.
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.