Sapient Bioanalytics is Using a Multi-Omics Approach That Looks Beyond the Genome in Drug Discovery

Ten years ago, Mo Jain took a leap that would mark a critical turning point in his scientific career. After his successful work as a cardiologist at Brigham and Women’s Hospital in Boston, he embraced a new chapter as a professor of medicine and pharmacology at UC San Diego. There, he not only taught but also directed the Jain Laboratory, dedicated to developing next-generation mass spectrometry systems. Earning tenure at the university was a significant achievement for him, as it symbolized the stability he had pursued. Yet, despite this success, he felt a deeper calling.

In another bold move in 2021, Mo traded academia for the dynamic world of entrepreneurship, founding Sapient Bioanalytics in San Diego. The company uses next-generation mass spectrometry and AI to do large-scale profiling of dynamic biomarkers—proteins, metabolites and lipids—to help biopharma and biotech companies propel their drug discovery and development programs.

Sapient is making waves in the field, particularly in its quest to unlock new insights into human biology using multi-omics to uncover dynamic biomarkers that accelerate target identification, patient stratification and propel development of new therapies. In this month’s Member Spotlight, Mo shares the inspiration behind his career shift, reflecting on how his parents’ journey as immigrants from India influenced his own path. He also dives into how his company’s multi-omics approaches to uncover novel therapies extend beyond traditional genomics analysis, along with how he balances the challenges of entrepreneurship.

Did you always know that you wanted to be a scientist? How did you get into the field?

I’d say that I’m still searching for what I want to do when I ‘grow up.’ [Laughs] I think that’s the most amazing gift someone can have in their career, where they have that freedom, opportunity and potential to switch what they do—I’ve had or three or four careers in the span of my adult life.

From an early age, I was excited by the concept of the unknown. There were also people in my family, who I was very close to, who got sick, and that influenced my thinking about medicine.

After practicing medicine you switched careers and became a professor at UC San Diego and director of the Jain Laboratory, and were there for over ten years. A position in academia, especially a tenured professor position, is known for job security. Why make the leap to starting your own biotech company?

This is a conversation I have with myself often, and my family and my wife.

My parents went through the traditional immigrant story where education was everything and that was your way to grow socioeconomically. Finances were also a subject of much conversation, as is often the case in immigrant families. You can imagine having job security was everything for me, it was all I wanted.

I had an unusual position at the university—one in which I was very much supported and resourced, essentially given the freedom to work on what I wanted without limitation. Many people would say that’s the ideal position, and ask, ‘Why the heck would you ever leave that?!’

I loved the institution and everything about academic culture, but it became clear after about eight years that I was getting antsy. Every career, profession and environment has its advantages and limitations. There are certain things you can’t do given space constraints, administrative constraints and so on, in academia. I started asking myself, ‘What could you do if those constraints were lifted? How much impact could you have?’ That’s where the experiment of Sapient was born: if we could do the highest level of science and not have some of the traditional constraints, but do this on a commercial level in industry, could we have a greater impact?

My parents could have taken a safe route for their lives, but they left for a country 10,000 miles away and came here not knowing anyone, with a hundred dollars in their pocket. For me, I don’t do their sacrifice justice if I look back 25 years from now and say that I took the safe route.

Can you give us an overview of your multi-omics approach in searching for biomarkers for drug discovery and development?

When you go to the doctor every year for your physical, they draw those two purple tops of blood—20 markers in those two blood samples are measured—but there are over 20,000 molecules floating around in your blood. Our goal was to capture those other 19,980 markers to understand which of them provide maximal information to guide drug targeting, drug development and personalization of therapy.

Drug discovery is a fascinating profession because it’s one of the few fields that has a 90-plus percent failure rate and continues to grow. That rate of failure hasn’t changed substantially in several decades and it’s one of the reasons that it’s so hard to bring medicines to clinic (and costs so much).

It’s interesting to think about why drug development fails so often and there have been a number of complex analyses that approached this topic. In particular, economists have looked at tens of thousands of clinical trials and asked what are the predictors of trials that are going to succeed or fail? What it turns out is that therapeutic area, type of modality (small molecule, large molecule, gene therapy), type of patient, age, demographics and so on have limited value in predicting trial success. There was one factor that mattered in whether a drug succeeded and got to the FDA for approval: if it was developed together with a biomarker.

And when you dive into it, these drugs are not failing because they’re not doing what they’re supposed to do. The problem is the population in which it’s being tested is too heterogeneous and the drug is not targeted in a manner that affects the specific patient’s disease biology.

Why look beyond the genome and the focus on proteomics, lipidomics and metabolomics?

Genomics was supposed to change drug development completely and transform this whole space of personalized medicine. But how much does our risk of developing a disease come from genetics versus other factors? It was expected to be quite high, but after a million genomes have been sequenced via the UK Biobank and other consortiums, the percentage of disease risk that is attributable to genetics is modest at less than 20% Now, there are rare populations at the extremes for which someone has a particular mutation or polygenic risk score for which their risk of disease goes up tremendously. But if you look across an entire population, genetics explain a minority fraction of disease. The idea that genomics alone would be able to be used for personalizing therapy, understanding targets, identifying who’s going to benefit from a particular drug, or just even for diagnostic purposes, doesn’t make sense.

The genome is largely static and unchanged from the moment of conception. There are diseases in which the genome does change—cancer being the prime example, as there are mutations that occur. What about other diseases for which the genome is not changing? Heart disease, GI illness, liver disease, immunology and inflammatory diseases, fibrotic diseases, neurodegenerative diseases and renal disease are all examples with limited genetic influence.

How do we begin to interrogate that? That was the basis of going beyond the genome and being able to understand the dynamic markers that can read out a person’s health, future disease risk, what drugs they’re going to respond to: key information that we’re trying to capture as a way of complimenting their underlying genomic risk score.

What insights can you provide on how your work in the field stands out?

Mass spectrometry is our weapon of choice, as it’s an interesting technology that has made several technical gains over the last 15 years. We can assay thousands of molecules in a given sample, whether they be metabolites, lipids or proteins. We can do this in a very robust and comprehensive fashion, and quite quickly.

We have deep expertise in this space. As part of my academic lab, I spent years taking apart mass spectrometry instruments and understanding how to make them faster, while measuring more molecules with greater breadth and depth, which is ultimately required for very high throughput discovery across large human populations. Discovery can’t be done in tens to hundreds of people—it needs to be done in hundreds of thousands of people. We have some of the fastest, highest-capacity mass spectrometry workflows in the world. These allow us to take any biological specimen (such as a tumor or plasma sample, preclinical sample or cell culture sample) and assay thousands of metabolites, lipids, and proteins in those samples very quickly.

Are there specific indications you are focused on?

We are disease agnostic, but because we are oriented as a service organization, our efforts mirror where investment dollars are going in pharma: oncology, inflammation, immune-related diseases, cardiometabolic disease, neurodegenerative diseases and rare diseases—those areas represent about 85% of what we do.

In the oncology, immune and neurodegenerative spaces, we spend quite a bit of time in target identification and validation. Especially now that our ability to drug particular genes and proteins has changed tremendously over the last five years—given the development of targeted protein degraders, molecular glues, ADCs and T-cell engagers, and CRISPR-based therapeutics and ASOs—we can now target many more proteins than we ever could before.

We also work in the Phase 1 through Phase 3 clinical trial space to identify target engagement markers and pharmacodynamic markers, to understand dosing, and for personalization of therapy and companion diagnostics. Essentially identifying those markers in blood that tell us if someone’s going to be a responder or a non-responder to a particular therapeutic. We span the entire spectrum of drug development.

You recently partnered with Rancho Biosciences to accelerate the Sapient Human Biology Database and also contributed to a study that was published in Nature Metabolism journal on assaying metabolic activities in intact human liver tissue. What other partnerships are you involved in?

We also work with nonprofits and there are two partnerships we’re particularly proud of. One is with the Bill & Melinda Gates Foundation—we just completed a large study with them that spanned over 40,000 pregnant women, the largest study of pregnancy in the world. Blood samples were acquired for deep clinical phenotyping in areas of the world where healthcare is not completely accessible, and maternal and childhood mortality are high. We did a large mass spectrometry analysis across these 40,000 samples and have come up with some remarkable discoveries. There will be a series of publications coming out on this in the next year or so and we’re particularly excited about that.

Another is the Bay Area Lyme Foundation. It is a foundation that’s very passionate about their work in persistent Lyme disease. With Lyme disease, there’s a portion of individuals who develop a syndrome akin to long COVID and it can be debilitating, and the mechanisms are poorly understood. There’s no diagnostic test and it’s hard to distinguish from other diseases that have clinical overlap, such as Chronic Fatigue Syndrome, Multiple Sclerosis, or certain autoimmune diseases. We’ve been working to develop a diagnostic test that would identify patients with persistent Lyme disease and we’ve been able to cross-validate the foundation’s discoveries in several different ways. The project is advancing under their direction toward a clinical test.

In addition, we continue to serve a number of the top pharmaceutical and biotechnology organizations around the world in accelerating their drug discovery efforts.

What do you know now about running a startup that you wish you knew before you started?

There’s a tremendous advantage in not knowing anything when you start this process. The value of not knowing what’s ‘correct’ is that it forces you to think and have that discussion with your teammates about what’s the best way to do this. In our case, we’ve been more agnostic because we were academics who formed this organization. We did not know much about running enterprise-grade systems and that forced us to have those conversations. I think we got more right than wrong, because we were forced to go through that process.

One of the key lessons that I wish I had known is less so about tactical—this is how you do this or this is how you do that. It’s understanding that to be successful in this, you have to be comfortable being uncomfortable. Failure is inherent in the company building process, as it is in science, and one must be okay with failing, learning, and evolving as rapidly as possible.

What advice do you have for other would-be entrepreneurs in biotech and new CEOs?

You have to think of a startup as every day is just trying to survive. Can you get through the day? If you can, and you get through the next day and the next, and you do that for 1,000 days, your chances of success have gone up massively. You want a team around you that no matter what the problem is, they’re going to figure out a solution and survive, together.

Make sure you find other people who have gone through this and are actively going through it. No one else will understand what you’re doing and why. It’s hard to go home and complain to your spouse, family or friends—that ‘woe is me, being the CEO is tough today!’ No one wants to hear that! It’s a privileged position to be in.

When I was overwhelmed, a friend advised I join the Young Presidents’ Organization. At first, I thought the last thing I need is another networking event [laughs]. But this was transformative. Having a peer group where you could have those honest conversations that you cannot have with anyone else in your life is critical.