Innovation & IP Strategy: The Blueprint for Success in Biotech | Eswar Iyer (3/4)

Intro - 00:00:06 Welcome to the Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup from pre-seed to IPO, with your host, Jon Chee.

In our last episode, Eswar Iyer shared how graduate school helped him develop his identity as a builder—from writing image analysis software to machining lab parts—and how a summer internship led him to the Wyss Institute. If you missed it, check out part two.

In part three, Eswar reflects on joining 10x Genomics, where he helped launch a first-of-its-kind spatial biology platform and learned to scale ideas into products. He also shares what it took to build cross-functional teams, navigate tough trade-offs, and start thinking seriously about founding a company of his own.

Jon Chee - 00:01:09 I'm seeing it all tied together. If I were in that person's shoes getting the opportunity, you're so grateful for it that you feel like, "I don't deserve this. I need to work hard to deserve this." You feel that you cannot let this opportunity go to waste. I think it's always the most motivating. I always think about when people get opportunities that they don't think they deserve. For instance, in my first lab, I was a liability. I was probably causing more trouble than I was helping, but when someone is willing to give you that shot, you just think, "I need to do everything I can to pay them back."

Also, something that I noticed you talked about was the multiplexing and that being a focal point of how to really make exponential gains. It comes back to the efficiency gains that you've been making all along the way. It's like the slides—how can we get 100% rather than losing 75%? And all the way to, "I'm going to machine this and actually make a more efficient tool here." I love that. I'm very much an optimizer. It's like, how do we optimize this to the greatest extent, give it to people to use, and then let them run with it? Because that's when the magic really happens. So it's really cool that you found that in George Church's lab. It sounds like a super fun experience, one that you've learned a lot from and took a lot of lessons from. When did you know it was time to move on? I could see someone just continuing to bask in that kind of cool environment.

Eswar Iyer - 00:02:52 That's a great question. It's a great lab, and you would want to almost stay there forever. And some people do, and there's a reason for it because it's not going to get better than that. You don't have to worry about money, and also the support that you get—you could write to another Nobel laureate easily, and all of that really helps. But I was working on a technology that was pretty hard, this in situ sequencing. I was trying to improve it. There's a company that was spun out called ReadCoor. I was working on a technology for improving the sensitivity by a few orders of magnitude because I needed it for what I envisioned doing. I wanted to do massively parallel pooled screens of guide RNA on cells and so on. So I needed to read what was going in and read the changes very efficiently.

That prompted me to work on the problem. And given that there's a company that has spun out, that company ended up hiring some of the people that were getting trained in the lab. It was a really interesting learning experience for me in many ways—sort of managing conflicts, priorities, teams, budget, and expectations while keeping everything cordial. There was a lot going on. And at some point, the funding for that sort of dried up because, I believe as part of the company spinning out, there was a change in what was prioritized versus not. So I suddenly went from a team of eight or so to just being one. At that time, it was a really difficult moment, and I think that defined me a lot. I could have abandoned the project, knowing that there was no way this could be done, or I realized that what I did at the time was going to define who I am. I said, "Okay, I'm going to just honey badger it. It doesn't matter who cares or not. I'm going to put my head down and finish it. It doesn't matter. I believe in this. I don't need validation from Cell Press to tell me that this work is good. I'm going to put it in bioRxiv if necessary. This work is going to be good."

So I put my head down and actually did a lot of the work, and, fortunately, a significant amount of work got done. And George saw that, and he really encouraged and supported me. That's when he said, "Hey, let me look at your data." I really appreciate his encouragement at that very, very critical time. Also, there was an opportunity outside where someone I really liked had offered for me to potentially join their startup in some way, and I asked George, "Hey, what should I do? Things are really hard here, but I'm getting this opportunity."

And George gave me advice. He said, "Look, at your stage, you should be very good at what you do, and you are very good at what you do, so you should continue doing that. There's value to being very good at something. At a later stage, you may be paid to be more of a generalist." To me, the faith of a mentor—I had enough faith in him that I said, "If George Church has said it, that's it." In my mind, I just closed off all of the options, literally burned all the bridges, and I said, "This is what I'm going to do."

That's when I went down, finished the work, put it out on bioRxiv, and I realized that it was time to start looking and moving. I was thinking of doing my own startup, but fortunately, I was connected to 10x Genomics, and they wanted to go big in spatial omics. I had no idea that the space I was working on was going to suddenly explode and become so big so fast, but George predicted it. He told me, "Look, this is going to be big. Everyone is going to be doing spatial. This is how I felt before next-gen sequencing. This is how it's going to be. You're good at this, so continue." I'm very glad that I listened to my mentor. I literally shut everything else out, and then I said, "I'm gonna do this." And I did it, and then I joined 10x Genomics. So I got a job offer, and again, I was not even taking the opportunity that seriously. And George said, "Oh, 10x, you know, it's a good company." Or he said something positive about that company at the time. So I thought, "Oh, crap. I should take this seriously," because I thought I was going to do a startup at the time. I was doing all the Harvard and MIT courses. So fortunately, I took that opportunity seriously. They mentioned, "Hey, we are planning to go big on spatial." Tarjei Mikkelsen, who was big at Broad, was working there, and that's what drew me to 10x Genomics. I took that opportunity because I thought it seemed like a good one. The pay was perhaps not as great as I might have hoped it would be in industry, but I was not optimizing for pay. Again, I was optimizing to work with the best people, work on great things, and I was hoping that all of these things would figure themselves out. There was advice that I got from both my father and father-in-law as well when I was considering it. So all of that sort of fell into place. That is how I decided to move and go to my next position.

Jon Chee - 00:07:25 Very cool. And the 10x Genomics opportunity, did George say to go take a look there, or was it something that you found on your own?

Eswar Iyer - 00:07:33 I found it on my own. So I was doing all the startup courses, and I saw an ad. I was told, "Aren't you looking for a job?" And I thought, "Shouldn't you be looking?" That's kind of how I felt. Because I'm doing this very risky startup thing, shouldn't I have a backup where I should look? So I said, "Okay, I'll do a search and see." I saw many opportunities, none of which I liked. But I saw one ad from 10x Genomics that seemed to be really well-written. I was quite surprised, and I thought it actually matched my skill set well. It was just well-written. I don't know why. And it compelled me to write an application, and it was probably the fastest application I'd ever written. It probably took 20 minutes to write a cover letter. There was no ChatGPT, none of those. I literally wrote a cover letter, forwarded my resume, and I forgot about it.

And then I got a call in two days or so, and we hit it off. He said, "Why don't you come to California and just look at it?" I said, "Okay, I guess if someone is paying for the ticket, I'll go." But before I went to California, that's when I told George, "Hey, by the way, I'm going for this thing. It's the only job I applied to." And he said, "Oh, 10x, you know, it's a good company." That's when I took it seriously, so I prepared on the flight. I learned about every person. I knew what they were doing, what the technology was, and I made deep preparations. That ended up being a good thing, and during that interview, they said, "By the way, we might go big on spatial." So it ended up being a very good bet.

Jon Chee - 00:08:55 Yeah, absolutely. How was moving to the West? How was moving to California from the Northeast? How was that for you and your wife?

Eswar Iyer - 00:09:02 I was almost done with Boston.

Jon Chee - 00:09:04 Yeah.

Eswar Iyer - 00:09:05 It was amazing, but it's cold. Yeah. For the first three or four years, I was so busy that I didn't realize or notice. Everything was a blur. I was just so focused that nothing mattered. And then the last year, I realized I was really vitamin D deficient. I lost a lot of weight, and there were a lot of things going on. So I like California. It just seemed very sunny and nice and felt like the place, and I had always wanted to be in a place like that. So that was nice. And coming to California was very expensive.

Jon Chee - 00:09:36 That's an understatement. Yeah.

Eswar Iyer - 00:09:38 But there was a lot of nature. It was never too cold to go out, so I liked that. The combination of it being too expensive and nothing being too close—you need to drive to all the places. And I realized that on the roads in California, there are random things that will be on the highway. For example, there will be blocks of metal or a whole desk or things that will be falling on the side. Yeah. And I purchased a car—or leased a car, rather—as soon as I came to California. And within one month, that got totaled because when I was driving, there was a big piece of hunk of metal on the highway, and I couldn't avoid it in time. It went underneath and slashed the underside, and they had to total it.

Jon Chee - 00:10:21 No. That is a brutal way to enter California. Just to be on a California highway and total your car. I'm sorry. That's a big bummer.

Eswar Iyer - 00:10:32 But I was fortunate again that I was fine and everything was okay. So yeah, I like coming to California. I like Boston as well, but the weather got to me after several years. So it was a good move.

Jon Chee - 00:10:44 Good transition, sounds like it. And to talk about 10x Genomics, you said, "We're going big in spatial." So you're on the frontier of a boom. Talk about the early days at 10x, and what was that like for you?

Eswar Iyer - 00:10:58 Yeah. So when I joined the assay development group, the leaders had a priority for going into spatial, and it was something that they had thought about strategically. But they didn't have the experience. So given my deep experience working with the person who was the inventor of laser capture microdissection—the early spatial technology—and working on in situ sequencing, I naturally got pulled into some of these conversations. And they were like, "Okay, how do we go big in spatial? What are the technologies out there? What is the IP landscape like? Which products should we build first and why?"

So it became a great opportunity for me to build something huge. And one of the pivotal moments was—I usually worked over the weekend as well, which all my friends thought was very weird, but I liked my work enough. And my wife was still transitioning; she was in Boston, so I had a little more time. So I used the time to work on things that I thought were important that I couldn't work on in my normal nine-to-five hours—the next generation of things. And I had many ideas that I'd written up on how the next space was going to evolve and so on.

And one of the weekends when I was working, the CTO, Ben Hindson, was also in the lab or sitting at his desk, which was very accessible. Something that I liked at 10x Genomics's culture at the time was that it was very open and free. You could access them, and the CTO was just sitting openly in one of those cubicles. And we started chatting. He told me about some ideas. I kind of told him why there were some limitations there, but that he should think about this. After an hour of conversation, he said, "You know, you have a bunch of ideas. Have you filed any patents on them?" I said, "No. But they're all sitting on a sticky note somewhere," and I had a bunch of, like, 30 sticky notes somewhere. He said, "You know, you should file them." And they were actually considering moving big, and so I ended up thinking a lot and driving that space.

So I filed a lot of IP there to strengthen that IP portfolio. He said, "Imagine you have $50,000,000. What will you do?" And that really made me think differently about that space. Then I realized they were serious, so I had to be extremely diligent. I helped think about what technologies were out there, the companies. We were acquiring the company called Spatial Transcriptomics from Sweden. I was the first one to test the technology. And here's the thing, I got told in a very brief time to sort of test this. We didn't have a lot of time to evaluate. So a lot of my past experience doing these things efficiently came into play. I was able to test these technologies rapidly, do my report, and say, "Okay, this is working." That led to being involved in the product development of that product. It was a super nice experience. My job was to help streamline some of the workflows.

Then I proposed the next generation of the product, which is now called Visium HD, but I had this idea that you should build the next single-cell spatial arrays, but it's very hard to build. So in some ways, I sort of pitched that idea, but I was told that was going to be hard. And I realized I should just make that array somehow. I made a prototype and then passed it around in a meeting. And that really inspired some of the leaders to say, "Okay, let's give some more resources to this guy and see if we can do this." So I was very fortunate to get a very small team. They were all very talented. They had really talented people at 10x Genomics. And I really liked the culture at the time. It took a year to de-risk the key ideas.

In some ways, it was building a startup within a startup. So, what are the primary risks? Who are the key stakeholders? How do I convince them that this is something that is not only scientifically doable, but also doable from a manufacturing and COGS perspective? All of the ways it's actually doable. And it's going to be a product that meets the specifications on the other things. So there was a lot going on; it was pretty complex. Within the year, I was able to convince the leadership that this is something that you could do and address some of their concerns.

So then that became a product development pipeline. It was a super nice experience. That became a product; now it's called Visium HD. Of course, there's a lot more that went into the product besides just me doing this. The team became really big. Lots of talented people joined who helped really steer and shape it, and we had to explore multiple pathways. And one of the pathways was chosen for manufacturing and all of that. So it kind of tells you about how technologies evolve from small scale to medium and all the way, and all the talent that you need. It really is a very humbling process to know. And it also gives you a sense of what it takes to put something out there.

So 10x was very, very intentional and focused. Then I got involved in in situ sequencing. They acquired two companies. I got deeply involved in that process as a scientific advisor: ReadCoor and Cartana. Cartana is from Sweden, and ReadCoor was from George Church's lab. Then I led some of the early decoding chemistry. So overall, I have always been working at the interface of complex molecular biology that works with hardware, optics, fluidics, and some computational aspect of it. There's always this element. So, working with all the stakeholders to understand what they need and then building the biochemistry that worked well. It was a super nice experience. I felt like somebody was paying me to do what I really loved. That's how I felt at 10x.

Jon Chee - 00:15:59 That's awesome. I was going to say, now there's this layering in of business. You're talking about M&A. You're talking about COGS, supply chain, the business side. How did you learn that? Were you taking a sneaky MBA somewhere, or was it someone taking you under their wing? Or was it just YouTube? How did you learn the business side of this?

Eswar Iyer - 00:16:20 I think it was just from trying to do this. There was no course or YouTube video that actually answered those questions directly. I mean, I looked up some definitions and things, but there were people there who were just very experienced, who'd built the initial factories and so on. So I would be asked to go and talk to this person and get their opinion. Naturally, a new guy comes in and says, "Let's do something big." It's very hard to believe and do this, right? But I thought maybe there's something to this, and I have to give credit to them for saying, "Okay, let's give him introductions to the manufacturing person," and this and that.

So as I spoke to them, I learned about it, and then I did my own homework. So there was a lot of learning that I had to do. And I understood how they were thinking individually, then I understood what the risks were. And then sometimes, I worked with them on an exercise. Also, some people were very kind and said, "Okay, let's do an exercise on this," and then they reviewed some of the work that I did. So there was a lot of learning on the fly and through osmosis, and some of it was also being taught in the process of, "Hey, I think you should know this. Here's how... have you thought about this? Have you thought about the assays that you will do?" That's kind of how it came about.

Jon Chee - 00:17:23 I haven't worked at 10x, but it kind of resembles a little bit of George Church's lab. Like, there are a bunch of experts, and you're just pulling people together and learning on the fly. You're kind of staffing up a team for any given product. From the ground level up to commercial, you have to bring in a bunch of people to make magic happen. So it sounds like a really cool experience. As you reflect back on the 10x journey, what were some key takeaways for you—learnings that you now have added to your repertoire?

Eswar Iyer - 00:17:57 In some ways, yes, it did feel like George Church's lab in that you pull in resources, but it was more commercial product-focused. The frustration an innovator has is, "I build something, but how do you get it into somebody's hands?" Without that, you're just thinking of ideas, and it's very frustrating. I didn't care whether it was the most cutting-edge or not. I just wanted it to make a difference in someone's life. My definition of success was that I use my best skill to meet the greatest need, which is trying to solve some problems in society that make the most sense. And in that process, whatever I earn, that's my success. That's my way of living. That's my reason for existence. That is what is going to give me happiness. So that frustration was there—of what I'm building not going out.

So at 10x, fortunately, I was able to get over that and understand how we build and go through that process by working with the best people. For example, when I was trying to think about this array that I built, there was someone in the sequencing core called Charles who was very quiet but extremely intelligent. And I said, "Hey, I want to do this," and he helped me actually figure out how to run an initial experiment just out of... he just partnered and collaborated and helped me with this. I don't know if he ever got a lot of credit for that, for example. Small things like that, you know, just running things and so on. Of course, I tried my best to always give credit to everybody I worked with, but still, that small thing was important to me when I was a new person and somebody was helping me out.

So the things that I learned from there were, one, you can come up with an idea, but execution is very important. Two, the people that you work with are extremely important. Sounds obvious, but you become an average of the people that you work with sometimes. Innovation—you have to think about the whole aspect. How do I build this product that will be usable, along with the several physical and technical constraints that you have? Things like keeping the COGS below a certain number for the simple reason that the reagent may be too expensive or may interact with the plastics, while keeping a very high quality that's at least an order of magnitude better than anybody else out there. So that puts you into a very constrained innovation mindset and helps you understand the resources that it takes.

It's very humbling to know. I was trying to do that in a small team at George Church's lab, and I always felt like I was on the verge of failure because it was so hard. And now I realized that you needed a team that was 10 times larger and 10 times more resources. You need tens to hundreds of millions for this problem. That's when you build this automation and so on. So you get an understanding of the resources needed, the staffing needed, and the different sets of skill sets that are needed—everything from the product development pipeline to marketing, and when they work together and interact with each other. I was also fortunate to work with some very talented people. Some of them are still there: Josephine Lee, Camilla Børsheim. They were at 10x for some time, and they also taught me some of these things just by sitting down. I would just say, "Tell me how this machine works," and then Camilla once just taught and walked me through it. So I think there's a lot that needs to come together for something to actually be useful outside. I think those are the key lessons that I learned. And you can't just make something average. There's a lot of craftsmanship that goes into becoming good. You have to be fast, efficient, resourceful, and it's not something that can be taught easily. You just have to do it. The people there were so good and exceptional, and you learn by osmosis. You learn, "Okay, this person is so amazing. I want to learn from this person." That's kind of how it happened.

Jon Chee - 00:21:25 I love that. I mean, it sounds like the 10x Genomics experience was like drinking out of a fire hose. It's so multivariate, and you're just soaking it up like a sponge. I'm honestly quite jealous of an experience like that because it's very rare when you're on the frontier of a booming field with a bunch of great people who know what they're doing in their own domains, and you're able to just pick their brain. You can just continue learning and continually leveling up. In a similar fashion, when did you know it was time to leave? Again, it's kind of like George Church's lab. In an environment like that, you can see yourself staying there for an extended period of time. When did you know it was time to start your own startup journey?

Eswar Iyer - 00:22:12 10x had grown quite a bit, and at some point, due to the market and the products, they had to do more sales and perhaps less innovation to keep up with demands. 10x also became public. It's a great experience to see the whole IPO process. Once it became public, my job became easier, but I also became less happy in some way because I felt, again, that I have such a small productive time, perhaps, that I wanted it to be very meaningful. I've gained all these skills; I want to work on some hard problems. Not for anything else. I just felt that my sense of purpose was to be working on hard problems. Otherwise, I'm not using my skills. Why did I spend all this time learning these skills if I can't use them? I didn't want to be too comfortable. I can be comfortable after I retire. That was my mindset.

At some point, it felt like maybe it would be interesting to do something else. Also, the market forces were such that it became very natural to leave 10x under the circumstances. So I was always fascinated with, what is the next big thing? Thinking from first principles, there were a few principles that I was following for what I wanted to do next. It was clear that I wanted to potentially do a startup—either do a startup or work with some really promising startup. Basically, I wanted to work with a great team. So, I wanted to work with really good people. That was principle one. It could be somebody else's startup or something else, but I didn't find what I was looking for, so it became natural. It became gravitating towards doing something of our own, working with the best, and trying to find the people that complement you and are really good at what they do.

It was clear that something in protein engineering and machine learning was coming of age. I'd always seen, even at George's lab, my instinct was that machine learning will eventually come of age, but you have to generate data at a parallel scale. So some of my patents there were on massively parallel pooled perturbation with image-based data systems, things that are similar to what Insitro does now. So I had a feeling that that space would come, and I could see that coming, but I didn't know exactly how. I just knew machine learning and protein engineering. I was very fortunate to know Shankar, my co-founder, who was also at 10x Genomics, whom I had worked with. And I was very fortunate to be introduced to my third co-founder, Venkatesh Mysore, who worked with my wife almost a decade back when she was at MIT, and he was at a very premier place called D. E. Shaw Research. Then he went to Atomwise, and he was instrumental in helping build the world's first 10 billion in silico compound screen, helping to scale that. And he was at Nvidia after that. So we just hit it off, and I iterated on a few things, and it was very clear that there was a very fundamental problem that could be solved with an approach like this.

Jon Chee - 00:25:00 Very cool. Also, a small world. Shout out to your wife for making the connection. That's cool. So talk a little about the mission and the focus. It sounds like you now have assembled a stellar co-founder crew. Tell us about the driving force, mission, and focus for Aikium.

Eswar Iyer - 00:25:19 The reason for Aikium to exist is to work on something fundamental that can make a significant change, yet make it feasible business-wise. That is basically how we defined it at the outset. Don't work on something superficial; work on something fundamentally different. Because if you gain so much knowledge in each of your respective fields, you should be pushing something deeper. Yes, it's harder, but isn't that what you really want to do? And isn't that what you should be doing? So that was one thing, but it has to have an exceptional business case. How do you merge these? So, understanding what is a problem—a problem that's big enough, that's not superficial.

That's how the mission evolved. Then we said, "Okay, what's the problem?" And we soon realized that in drug discovery, a lot of the technologies today are only able to access half the proteome. The other half is not as easily accessible because they are floppy. And that's when we realized, "Oh my god, that's a big opportunity." And if you could create a universal programmable binder to these, you could make therapies. You could make a hundred therapies. For example, you do not think much about designing a primer. You know the rules of a primer binding, but you do not know the rules of protein binding as well. So we thought it's a perfect problem for machine learning and data generation. That's what got us together.

So the mission is to build molecules for targets that are not accessible with current technologies, like antibodies or small molecules. Do that in an AI-first, data-driven manner, and do that by understanding that AI is only as good as the data and generating the kind of data that will help you make better predictions. As a result, you make better models and better molecules. So that is sort of the inspiration. We decided to take a bet on the data. That's what is going to drive the space—data that will help improve in this large business case of making better molecules because that's both purposeful and, financially, it will be rewarded. So that's sort of how it came together.

Jon Chee - 00:27:11 Very cool. And as you were scoping out the market landscape, what is the current state of the market? You talked a little bit about how a lot of it is undruggable. How does your technology and approach truly disrupt the status quo?

Eswar Iyer - 00:27:26 Yeah, that's a great question. Today, there are many targets for which you know the biology, but you just cannot make a binder that can modulate it. See, in drug discovery, this is the fundamental thing: you need to make a binder to a given molecule, a protein in the body. It can be a small molecule or a large molecule or a peptide, but it needs to bind and modulate that in some manner that changes the function. On top of that, it needs to follow certain properties. It needs to be non-toxic to the body. It should not accumulate in weird places. It should also not aggregate with each other. It should be stable. It should be soluble. It should be monomeric, with low immunogenicity. So there are certain properties there.

So the state of the art today is that there are technologies that can bind half the proteome. If your protein has a good structure, then you can bind it. Actually, let me take a step back and say, here's the gap in the field right now. Recently, the protein engineering space has gotten a lot of attention because of the Nobel Prize recently, which is amazing. But what is fascinating is there are about 200,000,000 structures in nature, and all the algorithms that are trained today are trained on a few hundred thousand crystal structures. It's a huge breakthrough, but here's the limitation with crystal structures: proteins are extremely dynamic. They're conformationally flexible. But crystal structures are snapshots of a protein in time, so they don't represent their dynamic states, number one. Number two, the flexible regions of the proteins are not captured in the crystal structure. You cannot take a photo of them because you cannot crystallize them. So about half the proteome is sometimes missing in these crystal structures. So that's a huge gap. It's almost like if you had an Oscar-winning movie that is complex, and you chose 0.1% of its frames randomly, like snapshots, and then tried to feed it to a very early version of ChatGPT and ask it to tell me the story. That's where biology is.

Outro - 00:29:15 Thanks for listening to this episode of the Biotech Startups Podcast with Eswar Iyer. In part four, you'll hear how Eswar and his co-founders brought Aikium to life during a market downturn, without a lab, and with a bold plan to rethink therapeutic design using AI and high-throughput data. He also reflects on what it means to be a mission-driven founder, how his team navigates risk and resource constraints, and why staying focused and hungry matters more than ever.

If you're enjoying the series, be sure to subscribe, leave a review, and share it with a friend. Thanks for listening. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for the Biotech Startups Podcast wherever you get your podcasts, and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website, www.excedr.com. On behalf of the team here at Excedr, thanks for listening.

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