Curt Medeiros on Revolutionizing Precision Medicine and Scaling Ovation
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What people are looking for is what is that target protein or that biomarker that's going to help people understand what patients will respond to a particular treatment and what patients won't. And that way you can get them on the right treatment at the right time. Welcome to Founded and Funded. I'm Madrona partner, Chris Picardo. And today I have the pleasure of hosting Kurt Medeiros, the CEO of Ovation.

a genomic data company committed to accelerating precision medicine development and enabling life sciences researchers to advance drug discovery and development more efficiently. This is a particularly timely conversation as precision medicine is gaining traction and the need for robust, scalable data solutions is becoming central to the future of biotech. Madrona first invested in Ovation in 2019 and Kurt came on board in 2022. You can hear more about the early days of Ovation with myself and founder Barry Wark at the link in the show notes.

In this episode, Kurt and I will dive into his vision for Ovation and the unique journey that brought him here from leading business units at large organizations to scaling a startup poised to reshape the industry. We'll also discuss how Ovation is generating significant momentum with groundbreaking partnerships, exciting traction, and a robust pipeline for 2025 and beyond. Kurt also shares lessons he's learned along the way, tips for landing high value customers, and his predictions for the future of data and precision medicine. And with that, let's get started.

Thanks for having me, Chris. Yeah, thanks for being on. This is really fun. I think it would be great for everybody if you could just do a little bit of a reintroduction to Ovation. Why was the company created? What's the role in precision medicine and data innovation? And how do you think about Ovation in this emerging data world? Absolutely. So the founders put together Ovation with a simple concept.

There's a ton of clinical data and samples flowing through labs across the entire United States, in fact, the world, that are being used for clinical care, but the data and samples are not really being used for research. And so how can we tap into that? And that was the founding vision for Ovation. As we've evolved, we set up a software-enable platform that works across clinical laboratories

to be able to help them with their workflows, but also be able to understand what are high value patients for research flowing through those systems, identifying them, de-identifying their data and samples, and then being able to bring them to the market as multi-omics data. What we see as a really important role in precision medicine is really enabling large scale multi-omic data sets

Right now, it doesn't really exist in diseases outside of oncology and rare disease. Folks have been building data sets in oncology for almost 20 years, and they've seen the benefit of that. As the landscape gets more competitive with precision medicines and oncology, and there's still a lot of room to grow there,

We see pharma, biotech, other researchers in academia really starting to turn to how can they leverage data and these tools to create precision medicines outside of oncology and rare disease.

That's where we see our critical role. What is the relation of data to precision medicine? And how important is it? I know, broadly speaking, people have heard the term precision medicine. I think it's not always precisely defined, but certainly in your view, Innovations' view of the world, the data side of this and the precision medicine side of this are pretty linked. So I'm curious if you could expand on that a little bit and how you see those two fitting together.

I mean, if you go all the way back to the beginnings of the industry, people were looking for extracting different chemicals from plants. That has evolved to understanding animal models and how they can represent different human systems. But obviously, animals are not really a close link. They provide valuable data and they provide a roadmap to how people react in the clinic. But by no means is it perfect.

We're focused on human genomics and multiomics. It's the closest you can get to understanding what's going on at the biological level across an entire human, across their different organs and systems. And having that data from the genomic side, so whole genome through RNA expression of

of how that is actually expressed in different tissue to the proteins that are produced and ultimately to glycomics and metabolomics really helps paint a broad picture of the cascade that's going on at the biological level.

And that's what's necessary because what people are looking for is what is that target protein or that biomarker that's going to help people understand what patients will respond to a particular treatment and what patients won't. And that way you can get them on the right treatment at the right time.

Yeah, that makes a ton of sense. It's basically like you need this giant cross-sectional database of individual patients and their associated data to really figure out what are the best treatments that we can build going forward.

Absolutely. And it's really there to power models for future discovery. So we're getting close to the point where there will be enough data in the next five or 10 years where you can start to model again, all the way from your germline genomics, all the way through what's happening with the proteins at the cellular level. And that's what's going to enable a much more precise approach to these targets and biomarkers.

much higher success rates in the clinic and ultimately much higher success for patients, which is the ultimate goal. - I know that as we just continue to accelerate in this world of AI models for everything,

A popular one has obviously been broadly applying AI to human health on the sort of protein folding side. That's been really interesting and very compelling, but it's less built out on the rest of the healthcare world. Would you say that's largely due to the data challenge or people not having resources like Ovation before in order to train and iterate on these models? Well, it's both an availability challenge as well as an economic challenge, right? So let's start with the economic challenge.

To sequence a whole genome of an individual not so long ago was tens of thousands of dollars. We're now entering the area where it's hundreds, and soon it'll be only a couple hundred. And that's really exciting because a big part of the challenge was it was so expensive to create this type of data in the first place.

Now that's being solved. The second part, as people start to allocate budgets to doing that as the prices come down, are really the availability of high quality samples.

that can be de-identified and linked to clinical records. Because the genomics and the multiomics are really important, but they have to be correlated with highly, highly curated clinical data on the individuals. So that way you understand not just what diseases they have and what medications they might be taking, but also what's their journey. Are they getting worse? Are they getting better? Are they having side effects?

It's important to understand all this clinical context to really understand what are the right targets and biomarkers to go after. Yeah, so it's basically the single point type data is fascinating, but without the deeply annotated clinical record, the data on all of the sort of

related conditions around that individual patient and their biomarkers, it's hard to piece together the insight that you need. Absolutely. And scale is another challenge. There's some really good data out there. The UK Biobank has done a great job putting together a tremendous asset, but it's a really good start. Most of the clients that we're talking to are talking about millions and millions of patients' worth of data.

And so we're looking to build upon the success of places like the UK Biobank. And one of the things that's unique about our model is it's not something that we've collected over 20 years, like other people that have assembled data. We've put together our biobank of over 1.6 million samples on over 600,000 patients, about a third of which is tissue, which is really hard to get.

But also importantly, it's very representative right now of the United States and hopefully over time the rest of the world where we have a very diverse patient population. And one of the challenges with the existing data sets is that it's greater than 80%, sometimes greater than 90% Caucasians of European descent.

It's hard to find diverse answers when you don't have a diverse population. And so that's part of what we're building. I'm curious, just as you give people the context on why everything you've done at Ovation is so unique, why has this been hard? I mean, there is data out there, right? Like we know that people, like you said, UK Biobank is out there. I'm sure that people have heard about 23andMe and some of those approaches, but clearly it's harder than that. And so I'd love to give you a little bit of a window to say, this is a really hard problem that we've solved.

It is a hard problem. And it goes back to what we were talking about earlier in the economic equation versus the scale equation. So when you're talking about consumer or clinical tests, they have to be affordable, whether someone's paying out of their pocket or whether it's going through their health insurance. And what ends up happening is the amount of information that actually gets sequenced

is a small fraction of someone's actual genome, a very small fraction. It wasn't until we start to see the whole genome come down into the hundreds of dollars where this is starting to become scalable. What we've been able to do on our side is build a platform that not only creates the scale that we talked about, but we continue to add the potential in the next year is hundreds of thousands of samples per month.

And so what that allows us to do is find through our software and our data the most interesting patients to study. And that way we can focus on sequencing those first. When you go into some of the other data sets, part of the challenge is they're big at the top level.

But then when you get into individual diseases and then you want to segment that population into mild, moderate, severe, let's just take the most basic one. And then you add a segmentation on top of that around what drugs are they on. You start to get to really small numbers. And so having not only an existing biobank that has been scaled, but the ability to continue to add new patients, add scale.

add diversity, and then capture patients as new drugs are launched in the future is really important to power this type of research.

Yeah, that's, I think, a really important point is that the ovation approach there is to continue to build out both the depth and the detail in real time so that you have cohorts that are representative of what's happening right now with patients and a diverse set of patients. I think one question that people ask when they hear all this talk about patients is the privacy aspect of it and how you think about that. And I think the corollary to that is when you talk about

larger pharma companies, say, using this data for modeling or approaching, what are they trying to do with it? Is it building amazing new therapies? Is it building great diagnostics? How do those pieces fit together? Yeah. So let me first address the privacy question because that's obviously of critical importance.

We work with common technology in the industry to tokenize, which means we remove all of the patient-specific information. And the software basically translates that into almost like a complicated serial number. And the way the software does it, there's no way to go backwards. So once that patient information is removed, it's completely de-identified.

Then as we add in the clinical information, similarly with the matching token, right? So we're not exchanging any information on the patient at all. With that matching token, we then have to construct what the data set will look like and get that certified that it will not be able to be re-identified in the future. And there's complicated statistics and what data is included and excluded and at what level that go into that.

But we are 100% following the same type of processes that I did in my prior life to make sure we ensure that privacy of the folks that are contributing their data. Absolutely. In terms of what happens on the pharmaceutical company side,

At the end of the day, they're looking to make better medications for as broad a population as possible. But in the precision medicine world, the way that they're doing that is by identifying biomarkers that help them understand what treatment is going to be right for them at the specific moment in their care journey.

And then understanding how do they build a portfolio of treatments that help those patients both across diseases as well as along that care journey.

The pharmaceutical companies and biotech companies take the privacy piece just as seriously as we do and the rest of the healthcare industry does. They understand that having the trust of the patient and having the trust of all of the different stakeholders is of paramount importance. And so we work tirelessly with them to ensure that we're all using and transmitting the data in the right way.

Because that end objective of getting to those precision medicines, you know, we're talking about, you know, I was thinking about Anchorman and Ron Burgundy. And I forget what he was talking about, but he said something like, this works 100% of the time, 50% of the time. And when you think about broad-based medications, they work 100% of the time, 30% of the time.

And so when you're talking about precision medicines and some of what we've seen on oncology, I mean, we've seen complete patient populations get very close to 100% endpoints in recent trials. And on average, you're talking about 70 plus response rates as long as they have the correct biomarker identified. That's tremendous. It's not just tremendous for patient health, which is obviously the first priority. The second is by making it more affordable.

Because you're not spending money on treatments that aren't going to work. You don't have to go through two, three, four treatments that aren't going to work before you find the right one. And that's the ultimate objective is first on patient health. And then how can we actually bend the cost curve in the health care system at the same time? It's amazing to look at some of the trials of drugs that have failed simply because they gave them to the wrong set of patients.

And yet they would have worked incredibly well if they gave them to the right set of patients. And I think that the sort of data approach that Ovation is taking is say, hey, go find those right set of patients. There's both better therapies that are already out there for you. If we can just figure out what subgroup are you in, who's the right set of people to give this to, and there's going to be new medicines that will be created using that data that are going to be even better. 100%. And so when you look at the first wave of precision medicines that came on,

It often was through the route that you said. Either the trial failed and then they did a reanalysis of subpopulations and then they went and found the biomarkers and then they redid the trial or they had enough data to submit the data from the original trial, but with that subpopulation. Or sometimes it was even after they were launched on the market, right? The early medicines were launched without biomarkers and then biomarkers came later as they learned from the market and what their success rates were.

We're enabling people to do that from the beginning on purpose. And that's the key thing is why go through that trial and error? And the whole system is learned, right? They've learned these lessons tremendously with oncology and rare disease. And that's why this is a great time for ovation, because as we're getting this data out to the market, people have already figured out how to do this. And now they're just as they're

changing priorities and moving towards other disease areas. They're ripe for this type of data to follow the same playbook they did in those other diseases. Yeah, it's incredibly valuable. And I think that it's endlessly needed by the pharma companies. And that actually brings me to the next great point, which is that on the ovation side, you've really been on a roll for the last year, really landing some of these partnerships with larger companies. And I'm

And I'd love to have you talk about that a little bit and why now is the time and why it's been such an exciting time for Ovation. I will answer that. But before we go on, I wanted to go back to the research point. So our objective, we've talked a lot about Pharma and Biotech, and they're obviously the most active and they spend the most money on R&D.

But our objective is not just simply to serve those customers. It's really to make sure that, and we're exploring academic partnerships, partnerships with health systems that do research. We want to enable research with this type of data broadly and eventually getting it in the hands of the payers. Being able to have a set of truth in the data on what's happening from the research all the way through the market

again, is going to help not only get better medicines and have a better effect for patients' health, but ultimately we want to enable folks to make better decisions and not just clinically, but on coverage. So this also starts to bend the cost curve. So our vision isn't just pharma and biotech. That's where we need to start. But we want to enable research and decision-making in healthcare broadly with this type of data because that's the type of change that's needed.

Yeah, no, I think that's a great clarification that this data is exceptionally valuable and useful across the entire healthcare and care paradigm. And that from whether it's from the payers or the doctors or the academic researchers who are working on the science, this is the type of thing they need to accelerate their work. Yeah.

So on the traction front, I mean, there's been so much going on. We created our first pilot data set at the very beginning of 2024 and launched it. And one of the things that we learned, as you could imagine, if you were selling just genomics and multiomics data, or you were making available just the clinical data, either one of those are completely crazy scientific sales.

You have to be in every single detail, not only of what can you do with it, but what is it? Where did it come from? How's the data model set up? So on and so forth. And so big part of our learning is we needed to show people that we could do this. For a long time, we were a startup with a presentation and a big biobank.

And although people got very excited when you're asking people to invest in creating these data sets, because even though the cost of sequencing is coming down, it's still not an inexpensive endeavor. Sure. Yeah. So we created the pilot data set and then we're like, how can we get this in the hands of folks so they can see the quality of what we've been able to produce? And so we had to go where the researchers were.

And we did that in a couple of different ways. We partnered with DNA Nexus, really the leader in managing and analyzing this type of data across the globe.

and worked with them both to get to different conferences where researchers in the space, we did irritable bowel disease first, so IBD. Where are those people going to learn about what's the latest and greatest going on in the space? And so we put together posters and abstracts and we get accepted at a couple of the top conferences and we're able to present. We actually had small little luncheons as well for people to come and ask questions and give us feedback.

And being able to connect where those technical researchers, those technical buyers were, where they normally would show up to learn about new things was really important. And DNA Nexus was a huge part of that. And then we did pilots. So we worked with DNA Nexus to get the data in the hands of actual customers and get their feedback. And that was absolutely tremendous set of learnings for us.

And that's what's really created the momentum, showing that we can do it, letting them touch the data, and then being able to say, well, yeah, we've done this in IBD. Now we can start to do this in other areas. We just signed our first contract right after Thanksgiving in GLP-1 treated patients. So both in the diabetes space as well as obesity, as you know, a lot of these patients have multiple comorbidities. So this is a really interesting group to study.

And they're also having challenges getting reimbursement for these drugs. So there's a big push. If you could find a response marker, who's actually going to do well and who's not, that's not only going to help, again, the patients, but it's going to help people understand how do we get this to the right people and not spend money when it's not going to work, right? Really, really important. Also got our first contract in the IBD space at the same time. And we've built now a pipeline across

primarily in IBD, but also in metabolic and cardiovascular. I don't think anyone would accuse those of being precision medicines. And so the opportunity now is we can do a lot, lot better, especially as we're learning how different racial and ethnic groups respond to medications differently.

male, female. And so the opportunity to study those from the genetic level all the way forward is right in front of us. And that's what's helping us build our pipeline. It's really exciting. That is super exciting. And it's allowing, you know, with obviously Ovation and then your partners to deeply understand what's going on

in all of these areas of health. Obviously, there's the extremely big current ones like GLP-1, but also to your point, the pervasive ones like IBD and cardiometabolic and places where people have been chipping away at that for a long time and now have an amazing resource that can help them accelerate and move a lot faster. The other big opportunity that we have in front of us is our collaboration with Illumina, which we're really excited about. We signed that collaboration back in October.

I've been working tirelessly with them to bring this out to the market. And really what excited them about Ovation was not only do we have a large biobank of samples that are already banked and collected, so we have a lot of inventory where we can work with them and pharma partners to sequence. And we're talking about potentially hundreds of thousands of patients' worth of data through this collaboration, which is really exciting.

But the other part is when we started to show the numbers, because of the mechanism and the platform we have, because we can match the data and understand what diseases folks have before we ever biobank the sample,

We have a lot of very high value patients. So if you go out in the normal population, and customers have told us about other data sets, and I won't mention who they are, sometimes only 20 to 30% of the patients are actually interesting because there's a lot of people that got sequenced that don't have serious diseases, might be healthy and

Yes. Well, right now, yes, at least me. But I'm sure there's something in store for me in the future. And so being able to get folks that have serious diseases, that have multiple comorbidities and not have to spend the time or the money on the healthy 25-year-old that hasn't had a chance to get any diseases yet is really, really important how you put this data together. So that was another thing that was really exciting to them is really high quality,

high disease burden set of population. Third is really the diversity of the population. So we have over 190,000 patients and underrepresented minorities. So that's huge in terms of the diversity of the population and then the diversity of the results. The last part is the ability to continue. A lot of the work that has been done is with biobanks that have been collected over 15, 20 years. You go through and sequence it, you create the data, and then you're done.

either because they don't have any more samples or the number of samples flowing in is small on a monthly basis. So I mentioned we had over 600,000 patients and 1.6 million samples. That means we have multiple samples on average per patient, and that will continue to grow as we collect more. And so to look longitudinally at patients, what's changing, especially when you get into the proteomic side of things,

is really interesting because you would expect to see different data and different results over time as their disease progresses or gets better with treatment. And so those are the things that we're excited about Illumina and we're very excited to be and honored to be in a partnership with them and look forward to get a couple of pharma partners on board to get going.

Yeah, well, huge congratulations. It's such a big achievement and such a big partnership that was the result of years of work and a reflection of how unique the platform and the data asset is. Absolutely, Chris. And I think the good news is we're just getting warmed up. The team at Ovation has done a tremendous job building the network, building the platform, bringing the partners in that are contributing data and samples. And I'm really excited about some of the academic and

health system partnerships because not only will it enable a return of data to them for their own research and put into clinical practice, but also drastically expanding our access to tissue, which is absolutely critical in understanding what's going on in the organs at the disease level. Yeah. I think the

both the vision, the value already, the acceleration into 2025. And it's a good time to be at Ovation. And it's pretty impressive how this is all continues to come together and accelerate. You know, I think one thing we want to spend a little bit of time on is you have an interesting journey to Ovation. You used to run a really large business unit at Optum and are a deep expert in the space. I think it would be

great just to share both how you thought about that transition from a really large, massive company, but running a really big business unit to now Ovation, and also how you think about your leadership philosophy and company building, certainly as you're building all this momentum. Yeah, absolutely. So to all my former Optum colleagues, it was a big business for everyone outside of Optum, but within Optum,

It was not by far the biggest business, but really exciting and innovative team and business that I had the pleasure of leading for many years.

Yeah, I really enjoyed my time both at Merck, another large company for a big portion of my early career, and then at Optum for the decade before I joined Ovation. You know, some of the benefits is you get to see so many different things. A lot of what I'm applying here came from my experience at Merck, you know, working and watching and learning from some of the top researchers in the world.

and understanding how they think about identifying targets and what does a good clinical candidate look like and how do we put together the infrastructure to go after biomarkers and bring them into the clinic with the drug candidates. Absolutely tremendous learning experience. And similarly at Optum, I got to see every single aspect of the healthcare system.

And, you know, my team's role is how do we bring analytics to solve those problems? And that's, you know, data, software and people, right? Moving to Ovation and sort of a startup, I was really looking forward to it. I'd been for a long time in large companies. Optum is very much like a large company that is an affiliation of small companies in certain ways. So it's not quite the same as a Merck.

But still your, your flexibility, your ability to pivot, your ability to gain investment and go try new things is always limited. When you're talking about a large company with quarterly earnings and lots of sign offs and decision makers to get things done. I love the, the hustle, the ability to move quickly, to try things, not all of them going to work. Right. And so you try things, you learn from it, you, you pivot or

or you augment what you tried and try something new. That part of it's really exciting, the pace and the flexibility. Growing up in a couple of those large companies, you are surrounded by people that have had a set of broad experiences and broad relationships and a lot of the same sort of development path in their careers.

In the startup, you're a much smaller team. And so by definition, you have people with very different experiences. That is both a positive and a challenge because sometimes you take for granted, well, you know,

person X should understand this or person Y should understand this. But also on the other end, sometimes you don't ask the question you should ask because you don't know that they have that experience. You know, a big part of what I try to do with the team is make sure that we bring the best minds together on any particular problem, but also create a culture of we're all going to make mistakes and we're all going to fail at certain things. It is

absolutely the right thing to do to ask for help. I ask for help from the team at all times. There's a lot of things that we talked about today that there are much better experts on the team than I.

And that's how I go to who I go to when I need to understand something or there's a critical decision. And so making sure we get, you know, get the right folks in the room to make the critical decisions. And this isn't an army of one in any particular area. This is a team. We succeed or fail together. And so asking for help and asking for people to rally is absolutely the culture that I think we have. And we're continuing to try to foster.

Yeah. And I think, you know, to your point too, it's a much smaller team, but having those diverse perspectives that are different, right. And sometimes maybe unpredictably different going after problems that haven't been solved. And I think that brings me to sort of my last question, which is, you know, what is most exciting for you about Ovation and the potential in the coming years? You know, I, I see us as sort of, uh,

you know first and foremost the world's leading multi-omics data provider with the ability to have very very dense data for each and every patient so the way people are doing these things in general today they might have very small data sets with each of the critical components together

But if they have anything scaled, usually they have one component with one population, a second component with a second population, a third component with a third population. And then they're trying to use analytics and AI to sort it all out. And it's not to say that that's not a good approach, given the history of how this field has evolved, because it's really the only thing you could do.

We have an opportunity to put together all of those different pieces of the multiomics puzzle for the same patient with rich clinical data in a way that can really speed everything up, speed up the model development, speed up candidates into the clinic and ultimately to the market. That's what gets me super, super jazzed.

I also think that we'll have an opportunity as we continue to grow the data set and add more data to become true experts in this and start to transition to building some of those models or providing some of the analytics as well. So our clients can, instead of focusing on how do we find the targets and biomarkers, they can start to focus more on model building and application after that.

How do they actually speed things into the clinic? How do they speed things to the market? Which is where their true expertise lies. Not to say that they're not experts in finding the targets in biomarkers, because they are. But if we can be an essential resource in helping provide those answers, they can then apply their expertise downstream, which we will never have.

So that's super, super exciting to me. What you can do, to your point, with both the data and the modeling and the analytics on top of it is pretty incredible. And I think that brings me to the last question, which is,

you really are an expert in precision medicine. And so broadly, on top of ovation or outside of ovation, what most excites you or what's most going to surprise everybody to the positive in the field of precision medicine over the next five years? I think there's multiple aspects to that answer. So the first is the benefit to the patient.

When I think about 10 years, 20 years from now, what does that look like? It's actually having multiple biomarkers. So you can actually discern even more granularly what is the best fit. Because as competition increases in individual diseases or individual disease states, so say mild to moderate IBD,

People are going to come in, they're going to copy those individual biomarkers. So it's going to start to expand where you start to look at a host of different biomarkers. And I don't know if it's three or seven or 10, ultimately where the science goes, but you're going to be able to discern the patient population even more and more finely.

And what that's also going to enable is a much more structured way of how do you select treatment for the individual, but then how do you actually select treatment across the care journey? So when the first medication, which is a great fit and is working fantastically, starts to work less effectively in year two, already have the data and information on that patient as to what are the signs to look for.

And what's their next treatment? And being able to have it not just at the population level, but across the care journey. I think that's going to be really, really important. And the second part of it is, you know, what we mentioned earlier. So as the population gets continually refined and more personalized, right?

And competition increases because there's more data available. You can go through the development and commercialization process faster. The cost of developing these drugs is going to come down, but competition in the market is going to go up. And so ultimately, it's about getting better care for the patients, but also being able to do it at a much more affordable price. Piling on more expensive drugs is not a long-term outcome.

But enabling this type of innovation in development and commercialization and then enabling the coverage, a clinical selection with this type of data across the entire industry is really going to enable people to do this at a more affordable price. And that's part of the ultimate goal. It's not just better patient care. That's number one.

But it's also how can we build this in a way that's economically sustainable and competitive? So that way we can help drive the cost of health care down for the individual at the same time. I think that's such a compelling vision. And to think if you can really leverage all this data, the ultimate outcome is...

better care for more patients much more affordably such a good vision to have and to build towards and so Kurt I really appreciate you diving into all of this with us on Founded and Funded it's super fun to talk about all things ovation and precision medicine and all of the incredible acceleration at ovation and we really appreciate you having this conversation thanks for having me if you ever have a empty spot in your podcast schedule I can talk for three more hours so let me know

Thanks again, Chris.