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

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 expertise in physiology, biomedicine, engineering, computational biology, and mass spectrometry-based metabolomics. Sapient is one of the largest capacity biomarker discovery labs in the world and well on its way to transform biomedicine forever. For all of our science and bio nerds, you'll sure be fascinated by the insights from Mo.
 
https://sapient.bio/ 

About Mo Jain - Dr. Jain is a physician-scientist with nearly 20 years of expertise in physiology, biomedicine, engineering, computational biology, and mass spectrometry-based metabolomics. Prior to founding Sapient, he formed and was director of Jain Laboratory at the University of California San Diego (UCSD). There he led a multi-disciplinary research team of chemists, engineers, mathematicians, epidemiologists, and physicians to develop next-generation rapid liquid chromatography-mass spectrometry (rLC-MS) systems to probe the non-genetic landscape of disease across population-scale human studies. His work was supported by the National Institutes of Health Outstanding New Environmental Scientist (ONES) Program grant and over $30M in federal, foundation, and industry funding. Dr. Jain founded Sapient in 2021 as a spinout of Jain Laboratory to expand upon the mission of accelerating human discovery and drug development through the nexus of high throughput analytical mass spectrometry, computational biology, and population-scale clinical studies. As CEO, he develops and directs the organization’s strategy and guides Sapient’s scientific, business, and technical operations. 

Dr. Jain has held faculty positions at UCSD since 2013, most currently as a Professor of Medicine and Pharmacology. He obtained his MD and PhD from Boston University School of Medicine, and subsequently performed clinical residency and fellowship training in Internal Medicine, Cardiology, and Preventative Cardiology at Brigham and Women’s Hospital, Harvard Medical School. His postdoctoral work was performed at the Broad Institute and Massachusetts General Hospital in the HHMI laboratory, developing methods for large scale, mass spectrometry-based metabolomics and integrative computational analysis to define the role of bioactive metabolites in human disease.

https://www.linkedin.com/in/mo-jain-md-phd-373895ba/ 

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Transcript

Kelly Stanton: 0:17

Hello and welcome to From Lab to Launch by Qualio glad you're tuning in today. I'm Kelly, your host, and I'm really excited about today's guest. Before we jump in, just a reminder to please rate the show and share it with any of your science nerd friends. We know you have some. Also check out the show notes if you have a story or a product you'd like to share with. Today we're excited to have Dr. Mo Jain, CEO of Sapient, Dr. Jain or Mo, as he prefers to be called as a physician scientist with nearly 20 years of experience in physiology, biomedicine engineering, computational biology, and mass spectrometry based metabol omics. He started and directed the Jane 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 researchers accelerating drug discovery and transforming 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.

Dr. Mo Jain: 1:32

Thank you so much, Kelly. My pleasure to be here, and thank you for having us.

Kelly Stanton: 1:36

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.

Dr. Mo Jain: 1:48

Yeah, no, absolutely. Um, I, I, I think my, my background is a reflection of, of more severe ADD than anything else in that been searching for, what do I wanna do when I grow up? And I've, uh, had the distinct pleasure of being able to work in, in, in several different areas, both as a, as a practicing physician, uh, as a professor and a researcher, and now an industry, uh, with Sapient. and this career really got started, uh, with a fundamental question of, of of wanting to understand why some people stay healthy over the course of their life and, and why other people developed diseases and, and being best to help those people. And, and this is why I became a practicing cardiologist, uh, in my early life. and as that process evolved, it, it became really clear to me that. Even for all that we know about human disease, we really understand a very small portion of, of, of a very complex system, which is the human body. Uh, 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. uh, a and the numbers are actually astounding. A, as you go through the actual data, whether it be for clinical trials or real world evidence, a and essentially the best drugs work in about 50% of people. Wh which is again, a, a number that's just absolutely appalling to me. Uh, the fact that we're not able to diagnose disease decades in advance, uh, when we know these processes take years or decades to actually come to fruition. I is also just really, uh, not acceptable, uh, uh, as a civilization, as a way of practicing and, and, and, 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 and whom we use particular drugs. Uh, that gave rise to, uh, initially my research. And, and then ultimately sapien. Yeah. As someone

Kelly Stanton: 3:41

who operates an industry, you know, from a quality perspective, of course we're always looking at those kind of percentages and, and I think the greater public really doesn't understand how much your individual biology plays into all of these things. Right. And whether, I mean, we all 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, you know, whatever. Like, it's, it's crazy to me that people. Well, maybe, I don't know, maybe we just don't do a good enough job in high school biology. Yeah. But part

Dr. Mo Jain: 4:13

of it is an education issue. Um, but, but part of it is also just how we go about deploying drugs. So, yeah, the way we think about this is that everyone who has a disease, Is, is diagnosed as a universal grouping of individuals that, as you said, will all receive the same therapeutic for the most part, and Right. 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. You know, part of that has to do with the fact that, you know, Kelly, you're a little bit different as a human being than I am. Part of it has to do that. Even if we have the same disease, our diseases are quite different from one another. Right. And so being able to understand how we deeply phenotype disease, particularly at its earliest stages, a and then deploy drugs in a way that are targeted to help us, uh, sort of target our specific therapies, is really the crux of this problem. And this is the goal of personalization of medicine in general. Uh, and it's always been a sort of a, a wonderful idea and a wonderful. And in certain therapeutic areas, particularly in the oncology space, uh, this has really been transformative over the last decade, but medicine as a whole has not very much evolved in, in thousands of years. Arguably, we, we still diagnose disease based upon a certain pathology, and it's a, it's a one disease, one drug type of relationship, and, uh, that, that's proven to not be.

Kelly Stanton: 5:36

Definitely. Well, and, and it's, it's fascinating to me too, right? I, my, my spouse has a particular fascination with all microbiome and how that plays into all of this too. But, you know, I think as humans we wanna, we wanna have that, here's the single answer and, and the whole idea of. Correlation isn't causation, right. There's actually a whole lot of factors at play. How, how do you keep from, you know, the gene becoming just the magic bullet answer as well. Yeah,

Dr. Mo Jain: 6:05

it's a, it's a good question. I, I mean, it depends upon what the underlying objectives are, and I'll explain what I mean by that, Kelly. So, uh, sometimes simple correlation can provide a lot of diagnostic information. And, and let me use an example of, of the good cholesterol, H D L, uh, there's an abundance of evidence that shows H D L is not actually caus. For protection from heart disease, and it's reading out other factors, but it's still an exceptional diagnostic for telling us who's at risk for developing a, a heart disease over time. And, and so, uh, 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. Um, but it still provides a tremendous amount of, of diagnostic information. So it, it's about really understanding what the objectives. Uh, understanding that we're all, again, while we're all equal, we're not identical. A and then trying to understand how our disease processes may be different in a way that allows us to specifically target, uh, our disease process. Now, I think, again, in the oncology space, uh, biomarkers has. Proven to be transformative. And the example I always use is when I was in medical school, uh, 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 was essentially three buckets of what lung cancer looked like, and, and that was based upon what the diagnosis was on a pathologic examination. When you took a. Piece of that tumor out. You, 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. Uh, E G F R being the first one that was identified. Uh, and then, 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, and this is why the efficacy has gone up quite a bit for for treatment of lung cancer.

Kelly Stanton: 8:17

Wow. That's, that's, uh, that's a, that's an amazing story too. I mean, of, of a positive outcome for sure.

Dr. Mo Jain: 8:23

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

Kelly Stanton: 8:31

Definitely. And, and we're, uh, you know, of course, always as a. A founder, right? 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. Um, 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, you know, there at U C S D, literally from lab to launch, uh, which, you know, we love, of course. Um, but tell us a little bit about Sapient and how you guys are trying to bring that transformative technology to a different therapeutic.

Dr. Mo Jain: 9:04

Sure. Happy to. And then happy to walk you through sort of this evolution. And so, uh, Sapia 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, uh, 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, 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, uh, the approval rates go through the roof, uh, on an order of magnitude almost. And so, uh, this has certainly been borne out and, 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. A as I mentioned, being able to understand how, uh, a tumor may be different from another person's tumor by, 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. A and the question is, well, what about those other diseases? Heart disease, lung disease, uh, neurodegenerative disorders, liver and GI illness, all those other diseases for which, uh, genetics has not proven to provide the same type of. 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. Uh, and, and we became very interested in this technology referred to as mass spectrometry. Now, now these are pretty big devices and obviously given your chemistry background, you, you're quite familiar with them, But these are really amazing devices, bio analytical devices that allow us to take complex biospecimens that are composed of thousands of, of molecules and, and decompose them and measure the actual, uh, uh, abundance of each of these molecules that are present in a biological. Uh, and, and the challenge with mass spectrometry, uh, was the same one that was posed to sequencing about 20 years ago, and that it's an incredible technology, incredibly robust, uh, very accurate and precise in its measurements. It's just too dang slow to do on a population scale, right? And so when we, 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, and that's what the objective was. And, uh, we, we spent many years tinkering and prototyping and, and, uh, developing new hardware systems, developing new software systems. Um, 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. Uh, and as we were doing this, there was a number of organizations that started coming to US government organizations, academics, uh, large foundations, the the GI Bill and Melinda Gates Foundation, um, uh, 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, uh, that we have from this clinical trial or from this epidemiologic study? And we began doing this work and, and as. We started doing this, uh, we realized that there was tremendous, uh, amount of information, both diagnostic information, prognostic information, as well as, uh, 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, uh, sort of magic when, when you think, when you go to the doctor, anyone who's gone. 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, in those blood samples. And there's tens of thousands of things floating around in your blood. Mm-hmm. So why are we only measuring 15 of them? And, and, 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, 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. Um, 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, would provide services, uh, and aid in, in drug development and discovery, uh, across the world, uh, for, for many different organizations, whether they be, again, academic foundations, governments, um, biopharma partners, et cetera. And so that's, Oh, go ahead. No, that's, that gave rise to Sapient and so, uh, yeah. Okay. Sapient was, was spun out with that exact idea.

Kelly Stanton: 13:58

Yeah. And my ne well my next question, and I'm just sitting here spinning on this right, was, you know, to talk about technology and its application evolving over the next several years, but, uh, as I'm sitting here thinking like, uh, as a general. Public sort of person. I mean, I, 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 a general public. Can I just send you a vial of my blood and we can you know, what, what sort of plans do we have? I mean, I know. Targeting this to drug development is a place to start. But what, what about the benefits to the greater population as a whole? Do you see it evolving that way?

Dr. Mo Jain: 14:38

A absolutely, Kelly, uh, fundamentally what we've built as a tool, uh, and this tool allows us to accumulate a massive amount of underlying data that then allows us to develop new diagnostics. And, and so there's many phases to sapien. The the first is, as you suggested, just being able to service those that are developing drugs. And, 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, uh, whereby we provide services to them to, to analyze their biological specimens, whether they come from preclinical studies or clinical studies. Uh, 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. R and d efforts. As you can imagine, we're amassing a tremendous amount of, of, 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 Sapien. Uh, and that's provi allowed us to, to develop new diagnostic tests and, and it's our hope over the next year or two here that will begin, uh, sort of, uh, commercializing some of these tests and making'em available to the public. That

Kelly Stanton: 15:45

would be exciting. to, uh, to pivot a little bit, um, SAP p s team spans so many disciplines. You've got chemists, engineers, epidemiologists, physicians to name a few, um, but as the c e O, how are you intentionally shaping the culture and efficiency of such a high performing

Dr. Mo Jain: 16:01

team? Yeah, it, it's a, it's an interesting question and, um, there's many models by which you can build an organization and, and build teams. And, uh, my, uh, essential model has always been you go out and find the absolute smartest, most talented people that are really excited to solve really, 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. You know, the, the model that's always worked for me, whether it be on the academic side or, or, or in building sapien. And so I have to say we've been incredibly fortunate to find just some world, world class talents, uh, across each of these areas. Um, and, and I essentially view my role as, as, as being the glue or the grease. And essentially, uh, when I need to bring people together, it's, it's my job to be able to bring those folks together and, and bridge syntax divides and, and communication and, and, and sometimes I, I have to, Grease wheels to, to make things turn a little bit faster. But honestly, the, the most part is it's, it's a train that I'm just holding onto and uh, it's my God, just to help direct, uh, occasionally and, and make some minor tweaks. But we're very fortunate to have just world-class people that, that do all the hard lifting.

Kelly Stanton: 17:14

That's incredible. I love that story. Especially about the food, uh, as a manager of people, I find that to be the universal, uh, motivator.

Dr. Mo Jain: 17:23

food, food and caffeine are, are critical

Kelly Stanton: 17:25

components. Yes, definitely caffeine as well. Absolutely. Um, to talk a little bit about funding, you know, you mentioned, um, you've raised funding from several sources, including the Bill and Melinda Gates Foundation. Um, what advice do you have for other founders, uh, with funding, uh, on the.

Dr. Mo Jain: 17:41

Yeah, it's a really interesting question, Kelly. And, and, and we took a perhaps non-traditional approach to funding in that, um, myself and, and my two co-founders initially, uh, when we had launched Sapien, uh, because we were a discovery and a service organization, we dec didn't necessarily need funding upfront. Uh, we, we had clients who were coming to us, like you said, the Bill and Melinda Gates Foundation, which is public information. We have many. By pharma organizations that were working with us. Uh, and so we had revenue, uh, almost from day one. And for that reason, we des we weren't in a position that we required funding. Now at the same time, we, uh, realized quite quickly that the demand out there for our services were, were far greater than we were gonna be able to provide given our, our small closet model. And, um, we, we ultimately ended up, uh, finding a, a funding partner that really worked for us. And, uh, the approach we took is that, um, We were going to find a partner more than anything else, and in, in, in funding to me, uh, the, the dollars that come in are, are only one component of it. It, it's, uh, essentially you're marrying, you're early investors and, and you have to be okay with that, good, bad, or ugly. Um, and, and we spent a lot of time making sure that we found individuals, uh, that were aligned with our larger vision, uh, that complimented many of the weaknesses and, and areas that we did not have strengths in. And that we're excited to go along that adventure from beginning. And I realize that the title of this is, is Lab to Launch and uh, it, it's very different having technology and having a successful organization or company. Those are two very different worlds. They are They are, they really, it's almost night and day and, um, those Venn diagrams don't cross very much. And, and optimizing technology, optimizing discovery and optimizing companies and, and processes are, are two very different areas of optimization. And uh, uh, we are very fortunate that we were able to find an investor team that was excited about going through that process with us, uh, and walking us along from, from our lab as to how we actually build a successful.

Kelly Stanton: 19:46

Nice. Nice. Yeah. I've, um, 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, um, so I, it's, it is, it's, and I, I love your, um, I love your humility in that because a lot of times what I see is, You know, the tech side person is the c e o 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. I love, I love hearing how well that's going for you guys, cuz I've seen it not go well,

Dr. Mo Jain: 20:25

Yeah, I could not agree more. I mean, uh, in the end I'm just one cog at a very large wheel and, um, the other cogs have to be able to function, otherwise the wheel doesn't turn. And, and, and the investor team are a part of that. Uh, your board is part of that. Your, your heads of department are part of that. And, and everyone who works in our organization is a critical.

Kelly Stanton: 20:43

Definitely, definitely. Uh, well, 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?

Dr. Mo Jain: 20:52

Oh my goodness. That's a, that's a book I'm not sure I read Um, I, I think, uh, one of the key lessons I've learned in life is that, um, You don't have to sort of know what you want to do next. Uh, and in life, in my career, at least personally, has been really quite an evolution. And, 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, and running a research lab and teaching, I always convinced that's the only thing I ever wanted to do. And I really enjoyed it. And, and now here, uh, on the, uh, commercialization side, running sapien, I'm convinced this is the only thing I ever wanted to. I, I, I think I've, I've learned enough that I really have no idea what's gonna happen next. And, and that's okay. That's part of the evolution of learning. And, um, I, you know, I think folks who are, who are bright, who are talented, who are hardworking, there's lots of opportunities. Uh, and there's lots of things you can do. And, and, and not to be scared at the idea of, of transition. Um, nice. I like that. That's the one thing I would say that I've, I've learned over and over again that it's gonna be okay and, and it's okay to try new things and it's okay to do new things and, uh, you should never let fear be the driving, uh, emotion behind any idea or any ounce of effort.

Kelly Stanton: 22:07

I like that. Very true. So, uh, I heard this fun question. Um, there was another guest earlier, and so this is a standard one I'm using all the time now. I love it. Uh, but if I walked into Barnes and Noble where would I find you? What section would you

Dr. Mo Jain: 22:21

be in? Oh boy. I mean, bookstores are my weakness and so, uh, same, same. I just love the smell of the bookstore when you walk in. I have to, isn't that magical about it? 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. Um, Uh, you know, and I, I suffer as I think I mentioned early on, uh, from an horrible a d d, uh, in intellectual a d d. And so, uh, I tend to wander and I wander extensively. And, uh, everything from, you know, just thinking about what's on my bookshelf right now or on my, my side stand that I'm reading everything from. Uh, deep science in a book about sort of the genome, uh, to, to a book on regulatory affairs and, and, and drug development, uh, to a book on, on, on management consulting and, and how you organize teams. And so I, I tend to be pretty, um, again, a d d and diverse in, in where I am. Uh, what I will admit though, uh, and something, again, another lesson that I've learned is that, you know, oftentimes we're, we're quite disparaging of, of, of social media and, and, and learning from alternative sources and, and. There's nothing like holding a book in my hand, so I, I'm certainly a, a firm believer in that. At the same time, there's a tremendous amount of information to be learned, um, from, uh, alternative sources, whether it be podcasts such as yours, um, whether it be Twitter and, and other areas. And, and I'm, I'm continually amazed just how much one can learn when you're exposed to such diverse ideas and opinions, uh, and, and thought leaders on, on these different types of social media platform. And so I, I'm actually gonna have to admit a, a huge fan. I, I, I'll, I'll openly admit I've probably learned more in the last year on Twitter than just about anywhere else. And, and there's an incredible amount of information around gap accounting and, and how you think about sort of, uh, uh, p and l that come from really insightful people. Uh, and I find a lot of that information's just on Twitter and, and, uh, and, and so, So the simple matter is I'll, I'll be wandering around the bookstore, but I'll probably have my phone out at the same time.

Kelly Stanton: 24:24

Yeah, same. Same. I, uh, I, I, 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. Right, right. 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, and reading people's thoughts and opinions on things and, and yeah, that for all the, uh, for all the drama around it, it certainly is also, uh, an interesting way to get, to see a bigger perspective on the world than just your own local area or local people that you interact.

Dr. Mo Jain: 25:05

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

Kelly Stanton: 25:33

Definitely. definitely. Well, um, where can folks go to connect with you and follow along, uh, with Sapiens?

Dr. Mo Jain: 25:40

Absolutely Kelly. So, uh, our, our website's probably, um, the best place to go. And, and we have a, a marketing team here that does a great job of putting together, uh, white pages and blogs and publications and other types of information, uh, on our website. So www.sapien.bio, so that's s sap i e n t dot b i. Um, and there's a lot of information, uh, 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, uh, where they come from, biopharma foundations, disease organizations, um, government or academics or, or even consumers that are interested in particular types of testing.

Kelly Stanton: 26:20

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

Dr. Mo Jain: 26:25

Thank you so much, Kelly. Appreciate it.