Terry Lo - Vizgen - Part 3

Intro - 00:00:01: 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, we spoke with Terry Lo about his transition from big pharma to diagnostics, the challenges of moving into the diagnostics industry, and the contrasts between drug and diagnostic development processes. If you missed it, be sure to go back and give part two a listen. In part three, we talk about Terry's introduction to Akoya Biosciences, its strategies post-merger, and subsequent focus on spatial genomics, the challenges of scaling up a business, and VisGen's platform, mission, and future developments.

 Jon Chee - 00:00:58: So now you're the president of Akoya. And just to kind of set the table, what is Akoya seeking to solve post-acquisition?

 Terry Lo - 00:01:06: I think, and not to speak for Akoya today, but certainly at that time, we were trying to revolutionize this field of spatial biology. Nobody really knew about it. It's still kind of not a familiar term in the field. And this concept of spatial biology, which is, again, really looking at what's in the tissue, using molecular markers to identify those cells that are in the tissue and figure out how they're interacting to better understand the biology. And we can talk a little bit more about that with PhysGen. But the strategy, again, was, one, is kind of continue to push this into the clinical side with that perconomer platform. So it's very embedded in what we call the translational space, which is kind of like in between research and clinical, but doing clinical research and then trying to take that into the clinical field. But also because there's a platform that can do much higher protein detection that way. So it was more of a research type of platform. So the idea at the time was that you can cover sort of the spectrum of sort of earlier research, the translation, all the way to the clinic once you get diagnostics approved out of it.

Jon Chee - 00:02:15: Excellent. And that sounds like a lot for a company that is now like 50 people strong. So that sounds like a lot of initiatives. And it sounds like it was like multifaceted. There was like, you know, I think we said like translation through the clinic and you're covering the spectrum. At the time, were all of those just like the key initiatives as president?

 Terry Lo - 00:02:32: There's also the whole company building process, right? It's like, how do we establish international subsidiary? How do we get our supply chain going? And how do we manufacture product? So 50 people, I mean, most of those are some R&D and some salespeople and support people, right? But the other infrastructure part didn't come with us, right? Because those were not easily separated out. So for a year, we had this sort of transition services agreement with Perkin Elmer, where effectively we're paying them for our services to help support our business. So now we're separate companies, but they've been supporting this business all along for however many years. So it shouldn't be too difficult. But now that we're two different companies, it's a little bit arm's length. So we had one year in order to kind of extricate ourselves out of there and then build out our own infrastructure to be able to do that. So that was a big piece of it. It's just scaling up the business. I mean, today, the companies, I don't know, like 300 some people, 350 people, but it's certainly grown tremendously, I think, from the early days.

Jon Chee - 00:03:37: Absolutely. And yeah, that is an absolutely big component. It's like not only the science and the products, but just like company building. It is no easy feat. It is a lift. That is for sure.

Terry Lo - 00:03:46: Yeah. I mean, there's so many different functional areas within a company and, you know, you have to cover them all. You can't just leave one out. You need it all. Yeah.

Jon Chee - 00:03:56: Yeah, we're just like, well, we'll just like address that later. Finance, maybe we'll just like address that later. So after Akoya, when did you know it was time to leave Akoya and join Vision?

Terry Lo - 00:04:07: Sure. So in the spatial field, so saying most of the focus has been on protein. And at the time, this has been going on for a number of years. You know, I always thought like spatial meant protein, like it didn't even occur to me that this could be done in genomics or in this case, RNA. And when I started to see some of the papers coming out of Harvard University on something called MurFISH, that was such an eye opener. And I think there started to be a real buzz in the field about like, wow, this is something that can really address the genomics fields, but having spatial with a high number of targets and high resolution. And that's just something that had never been done in the genomics field or in the spatial field. So this new area that we call spatial genomics, which is a, you know, sub component of spatial biology or spatialomics, that was the opportunity that I said, this is going to be massive. Like, if this could actually work for the first time, I actually thought something could work. That would be just amazing. So that's why I had started talking with Viz Jan about coming over. And by the time I came over as CEO, the company had been about one year into it. Like they'd been founded the year before. So I would think I was somewhere between 12 and 14th employee of the company. When I was interviewing earlier in the year, I was like, there was only five employees at the time. So that's how much earlier, like now it's like really early that we're talking about from a company standpoint.

 Jon Chee - 00:05:35: That is a very drastic transition and much more company building taking place. And that probably had a very rapid clip. Can you talk a little bit about that early team, a little bit of Viz Jan's history and the team behind the technology as you joined with a five-person team?

Terry Lo - 00:05:51: Yeah. So as you can imagine, I mean, the core of this really is R&D, right? It's like, brought this technology over from Harvard. Now we need to develop this, turn it into a product. So we call that technology MERFISH. And then eventually we did develop the product. Well, I say eventually, but very rapidly we developed a product, which we call MurScope, to be able to commercially launch it. And the heart of it was really just having this core R&D team to be able to rapidly turn this technology into something that we could sell to customers. And so I joined, I think it's September of 2020. And then by the beginning of 2021, we had like 17 people. I think the next beginning of 2022, we had like 60 people. And then the beginning of this year, we had like 150 people. So it became a very drastic scale up, particularly for our tools company. Some other industries, I think you can build really fast, but this was a pretty rapid build out. And we actually took that technology out of Harvard and then launched it first as commercial pilot within less than two years. And that is incredibly fast from a commercialization standpoint. And so by the third year of the company, we had our first full launch year of the product. And we had an install base after the first year of like 70 instruments. So it was a pretty great journey in terms of scaling and building up really fast.

Jon Chee - 00:07:17: And, with a journey that fast, at least for McSeed, our experience is kind of like that experience is like building a plane as you're trying to fly it. And maybe just to kind of set the table for folks who are maybe not as familiar with the spatialomics field, can we like zoom out for a moment and just like learn from you, what is the current state of like spatial genomics as a market? And why is it that way?

Terry Lo - 00:07:40: Yeah. So I think when VizGen launched Merscope at the end of 2021, beginning of 2022, I would say that was the first spatial genomics commercial platform that we call HyPlex, meaning it can detect a lot of RNA, a lot of targets, and also high resolution, which means you can get a resolution at single cell or subcellular level to be able to detect. Detect. So that's why it was such a huge excitement and demand at the time, because it's never been done before. This is like brand new researchers out there for the first time will be able to generate data that they'd never seen before. That was in 2022. There have been a couple other platforms that have come out at the end of 2022, so about a year later. And I would say the market today is really just these three platforms right now that are out there. And so we're still in the very early stages of that. And I think that's a really good thing. Early innings, right? It's like literally has been less than two years since we launched. So researchers are still kind of starting to build out data and generate data from these types of platforms.

Jon Chee - 00:08:47: Absolutely. And with a market that is very early innings like this, and there's only a handful of platforms, can you talk a little bit about VizGen's platform, your guys' mission and focus? And what is VizGen's approach and strategy in terms of this go-to-market in this more nascent budding industry?

Terry Lo - 00:09:04: Yeah, we have a certain advantage in that our core technology, which came out of Harvard, that really was the established approach in terms of setting the blueprint. Like, how do you do this? And I think there's a learn from that and they're trying to develop their processes and products kind of based on some of that blueprint. What really, I think, distinguishes what we do and what we want to do as we move forward is that the data quality itself is kind of the primary goal here, right? Which is a lot of what we're learning right now from being able to use a platform like this is really fundamental biology. Like, we're learning about what cells are located in the brain, what cells are located in different types of tissue or in cancer, or as we develop, you know, as organisms develop, how are those differentiated? So understanding the detail of that at that molecular level, but also understanding the data quality itself. Making sure that we understand it and get it right. Yeah, yeah. There's a lot of potential to get noise or it's hard to know sometimes what is ground truth because this is a new technology. There's not a comparator, right? It's not like this is something that we can compare against something else that's been very well established. There are other kind of orthogonal platforms that you can kind of compare to. But fundamentally, it's like this is new stuff. Making sure that when researchers are being able to use and generate this kind of data, that they're getting the data right. That's the target, right? That's the goal. And not as trivial as maybe as it sounds, there's a lot that goes into it. So, you know, in a one centimeter square area, you're trying to detect more than a billion RNA molecules and discriminate them within 50 nanometers of each other. So it's a lot of precision, a lot of detail that goes into that. That so it's not easy to do. And so the focus is really to make sure that we're generating the best quality data that's out there and then also making it as accessible in terms of being able to use this for different types of research. So not just one specific application or one specific area or one specific organism, but it's just like sequencing in that sense, right? You want to be able to use it everywhere that you can imagine you've got gene expression happening. So I would say that's really the direction that we're trying to drive the platform.

Jon Chee - 00:11:21: Totally. And I can imagine for anyone who's like thinking about adopting a platform, it's like, of course, it's critical. The data has got to be good. Like you said it so eloquently, it's just like, if I'm a lab considering a platform, it's like, yeah, I need to make sure that this readout, I can rely upon it, which especially for new technology, because right now, if you think about it from other instrument platforms, you're like, yeah, real time PCR, doing it for a long time, very easy to communicate. In this instance, it's like exactly what you said. There's like not so much of like a control group, which you can compare against, you're just like, here it is. And so how do those comms like unfold for you? And how do you approach that?

Terry Lo - 00:11:57: It is challenging because it's not as an easy thing to communicate or even demonstrate in some cases. And it becomes very, very technical. There's a lot of bioinformatics analysis to say, hey, you know, how do we show that something is more accurate or that it has higher sensitivity or this is a real signal and it's not noise, right? That is not an easy thing to necessarily explain. And also to conduct the analysis is fairly complex and sophisticated. So, you know, it's much easier sometimes to say, hey, you know, instead of a thousand genes, I can detect 5,000 genes, right? Everyone gets that, right? That must be good. We're doing more. So that's better than, you know, 5,000 is bigger than 1,000. So it's a much more subtle kind of way to message and also to be able to educate on. So it's a process. And I think it just takes time. And I think it's also as researchers getting more experience with it. Again, it's like the first time that... You know, they're really kind of being able to see and access and look at this kind of data. And so it does come out over time. But of course, we would like to get that sooner rather than later. So, yeah, I mean, I think that's the state. That's it. There's also some level of, okay, in some cases, this is good enough. Like, I don't need the best data. I just need data that's like good enough for what I'm doing. But I think the challenge of that right now is that until we really have like gold standard and very clear, like, what is truth? What is ground truth? Then you can kind of move off of that, right? Because then you can kind of say, hey, you know, it doesn't really matter. I'm a little bit off here or a little bit off there, less sensitive or less specific until you really get the, I would say, the kind of the core foundational science down. Like, you want that to be right and as accurate as possible. And then from there, you can kind of say, all right, for this particular application, it's not so important that I know this level of sensitivity for my detection, because that's really not... Relevant for my application. But you don't really know that until you kind of know the foundational biology piece first.

Jon Chee - 00:14:00: Totally. Yeah, it's kind of like getting that large enough sample size where there's like, okay, this is baseline.

Terry Lo - 00:14:05: Exactly. You need a baseline. In some sense, that's a lot of what's going on in spatial today, which is probably people are familiar with things called cell atlasing. And that is really trying to create these maps. And, you know, you can kind of think about it like if Christopher Columbus, you know, if he actually had a map of where he was, going, he probably wouldn't end up in America. It's like he wouldn't know that was like not the right route if he wanted to get to China or if he wanted to get to India. Like he didn't have a map. So the same analogy is like if you want to know what are the right targets or first you want to have a map so that you can figure out like what is happening there that's driving disease, what is happening there that I can target that potentially will allow me to stop the progression of that disease. So that level of biology, I think it all starts with the fact that you're not going to be able to do that. It starts with having the right sort of mapping. And that's actually, I think our vision statement is that every disease has a map, every cure has a path. And one of the things that are very active right now is these cell atlasing programs. And one of the best examples is that there's a lot of work being done on brain atlasing, right? Because the brain is just a very highly organized organ. And you have very specific types of cells that are located in different parts of the brain that serve different functions. And so it's a very high level of organization. And so it's a very high level of organization. And so it's a very high level of organization. And so it's a very high level of It actually makes a great map. You can actually identify like there are these different cells and you figure out what those cells are so that you can understand what their function can be. The next level beyond that, though, is really to kind of understand how different cells are interacting with each other. So now I know this cell type is next to another cell type. What kind of influence does that have when they're next to each other? And how do they interact? And how does that change the expression of other genes, which then relays downstream activities? This is kind of the beginning point of. Kind of really just understanding, again, some of the basics of biology and where that helps lead us to is then looking at disease, right? Things like we don't understand how Alzheimer's works, right? So why is it we can't develop drugs that are effective against Alzheimer's? We don't really understand the disease mechanisms there. So this is why there's such an amount of resources now being put into this kind of cell atlasing to kind of understand biology first.

Jon Chee - 00:16:19: Very cool. Kind of goes back to the point we were talking about earlier about just like. Having this increased understanding instead of just like forcing mechanism of just like, let's just do more volume and hope we find a hit. This is like, OK, let's actually figure out and try to be more knowledgeable before we go on this road trip. And then what from there we can probably be more efficient in our approach.

Terry Lo - 00:16:41: Yeah, that's exactly right. I mean, we don't really understand what's happening. And so we're just taking random shots, right? We can take enough random shots. And as we go through that process, we'll find ones that work without really being able to really map it out and say, look, this is what's happening. This is what we should be targeting. This is why this works. We can demonstrate it and we can visualize it. So that's the difference. And hopefully this type of technology can be adopted fast enough so that maybe in our lifetime, we can actually see the downstream benefits of what this data and what the science generates.

Jon Chee - 00:17:18: Very cool. And as we kind of talked about, like there's the aspect of the science of a company, but there's also the company building aspect of running a business. And the past couple of years, we've lived through what feels like a lifetime, 2020 through 2021 was this whole thing, 22 to 23 was this whole thing. So how did you navigate these different periods for VizGen and what's your philosophy? So how did you navigate these different periods for VizGen and what's your philosophy around capitalizing a business and running a business through these cycles?

Terry Lo - 00:17:46: Yeah. So, look, I mean, I'd say this is a never ending learning process. You know, every situation is unique. I think this cycle has been incredibly challenging and more challenging than probably most of us have seen in recent experience. When we were in sort of that 2020, 2021, you know, which is ironically, you know, at this kind of height of COVID, but at the same time, the financial markets, the markets were taking off and all the company valuations were through the roof. And so the concept in those days was really about, hey, money is free, basically, because you can just access money so easily. And you really just want to drive as much growth as fast as possible, right? And then, you know, as we got into 2022 and now 2023, it's completely flipped, right? Where it's really now the whole focus broadly across the financial markets is that, look, we want companies that can generate profit. So don't burn a lot of cash, don't spend a lot of money. It's more important that you could be efficient with what you have rather than just kind of growing at all costs. So that's a transition that almost all companies have had to now experience to go through. And I would say that that 2021 period was kind of a great time to be in it. But unfortunately, it may not happen that frequently. So I think we just have to all work on sort of being able to be very adaptable and just recognize, look, this is the new reality we're in, and we're going to have to work things and have strategies that are a little bit different than before. Like if we thought we were going to spend all that money a couple of years ago, like we're not going to spend that money in this market, in this department. So things definitely become a much tighter and much more focused around efficiency than it is, let's say, just around, hey, let's just drive growth. And that's the metric. Yeah, absolutely.

Jon Chee - 00:19:39: And obviously it's still very early innings for VizGen and we're entering 2024 soon. Kind of doing a little bit of a retrospective and then a prospective. On a retrospective, just like thus far, what were some big challenges and triumphs for you at the helm of VizGen?

Terry Lo - 00:19:54: Yeah, I mean, certainly the financial markets themselves create a different dynamic in terms of, you know, when you're a private company and you're not a profitable company, you're always in some mode of fundraising, right? Because you don't make money yet, right? So you need money and you end up having a budget that will last you for a certain amount of period of time and then you got to raise money again. And it's kind of like you're still, you know, living with your parents and you're kind of relying on an allowance. You don't have your own job. You're not like self-sufficient yet. Yeah, so I think that's one of the things that even though we're a very young company, this is what you would expect with a company this young. You know, just finished our fourth year, right? You're not going to be profitable. You're not at a point in maturity in the company where you're actually generating positive cash within the company. So just kind of trying to navigate and manage through that, I think certainly is a challenge. I mean, certainly for VizGen, but also, like I said, most companies that are in the private and startup world, are going through that. We also grew incredibly, as I said, rapidly, almost unprecedented if you look at life science tools, like how fast did this company scale up? You know, if you look at our revenue trajectory for this year and last year, you know, really you don't find other life science tool companies that have generated this much revenue in such a short period of time, right? Just this type of adoption, market adoption so fast. So, you know, we've been working on continuing to build out, scaling up the business, the organization. Like this facility actually right behind me, this is not our headquarters. This is actually in Waltham. So our headquarters in Cambridge. But we got this facility built out and moved into this summer just for the operations side. From going to no operations to having its own building, you know, that's kind of how we've been transitioning so quickly. So before that, we were literally packed into one laboratory between R&D and manufacturing and QC. And now we actually have some elbow room for people to kind of build things and do things and put instruments in there. So the growth has been a lot to manage. And so we're certainly as any young company or as any young person, you're going to go through those growing pains. So there's no shortage of things to work on and improve upon. And that's what we've really have been doing in 2023.

Jon Chee - 00:22:31: Very cool. And looking forward, like one or two years out, what's in store for you and VisGen?

Terry Lo - 00:22:37: Yeah. So one is I'm super excited now that I feel like we've kind of turned the corner on some of the infrastructure building and getting us in the right place and trying to get also the product itself. Like there's always a maturation of the product, right? It came out very early. You know, we continue to kind of mature and make sure that we can make it consistently, that it can work consistently. And I think we've now at a stage where we're really going to be pushing for new developments in our product pipeline. And that's really where I think it's going to be exciting to see in 2024 as these capabilities, you know, when we first came out, like, wow, that was incredible and exciting. And it's the first time people could do it. But two years later, three years later, okay, like what's next, right? So people are hungry to kind of continue to move on and move quickly, particularly in a field like life science research. Because what really... What researchers are all about is that they're trying to publish. And to publish, you need to have something new. You need to do something that other people haven't. And having the best tools, having new capabilities where people couldn't do something before, that gives them an advantage to be able to do the science, right? To kind of get those insights. Yeah. So we have a big focus on pushing out our product pipeline and new developments. I really can't talk too much about sort of what we're doing because we haven't publicly announced that next year, but we're pretty excited about that.

Jon Chee - 00:23:58: Yeah. Sounds like there's some cool things in store until it's officially announced. We'll wait till then. But that sounds very exciting. And I agree. Like, I think you really hit the nail on the head. It's just like, in order to publish, it's got to be novel. And part of that is making sure you have the right tools. Exactly what you said. Like, if you don't have the right tools, it just becomes just like shooting in the dark. You know, and we did, we shot in the dark for a very long time. But now, at least now, it's like we're actually illuminating it and being a little bit more precise in our approach.

Terry Lo - 00:24:26: Right. You know, the short of it is now we can actually visualize things at a molecular level, right? So instead of just looking at a microscope and saying, hey, I can see the tissue, like you're literally like able to look down and look at the genes, the RNA that's being expressed, the different proteins that are on all the individual cells, you have that level of molecular detail that you can now map out in your tissue. And then you can basically do all of the analysis you could ever dream of to kind of figure out like, how does that all work together? And how does that fit together? So it is definitely a different type of stage, I think that we're in and from scientific discovery standpoint, and I think it's just incredibly exciting to be part of that right now.

Jon Chee - 00:25:06: Likewise, whenever I learn about this and hear about this, it feels like the science fiction future is now because like, sometimes I'm just like, I do remember just being in the lab and you're like, yeah, like, that's not really high fidelity, but I get the general high level gist of it. But the precision and the granularity, which is like not there. And to like, think about we're living in a time where you can get into that level of precision at that level of like, granularity, it just like, blows my mind. And as kind of a traditional closing question, and Terry, thank you for your time. We always like to round it out here with just two questions. And the first question would be, would you like to give any shout outs to anyone who supported you throughout your career?

Terry Lo - 00:25:46: Yeah, I think the one person I'd probably highlight the most is my dad. Well, unfortunately, he's also the one who wanted me to be a doctor. But I think one of the things that I've learned is that I've been a doctor for a long time. And I've got over that he's been really, really supportive. And he's always been such a wise person in terms of being able to provide advice and things to really think about. And when I talked about career planning, he was very much instrumental as part of that. But I think you can see it's like a lot of stuff kind of has played out in a lot of ways that I had planned for it. It was really kind of a testament to a lot of what he had educated me on and what his feedback was on how to think about things. So I would say a lot of credit, certainly throughout the years to my dad.

Jon Chee - 00:26:29: Awesome. And likewise, for me, I think the familial support is just so critical, especially in the early days, what can feel like you're just like aimlessly trying to figure it out. Having that guidance is so imperative. And so I feel the same way.

Terry Lo - 00:26:42: By the way, I can't leave out my mom because if she listens to this, then I'm done.

Jon Chee - 00:26:46: Yeah. Sorry, mom.

Terry Lo - 00:26:48: So good job, mom.

Jon Chee - 00:26:50: Yeah, yeah, yeah, yeah, totally. And then the second question, if you can give any advice, to your 21 year old self, what would it be?

Terry Lo - 00:26:57: I think I would just tell them, look, stay the course. Things are going to work out. It hasn't ended too badly. So at least not yet. Just keep on going and just be confident in what you're doing and what you're about. I would say the one thing is just be confident. It does work out, right? It does. You get to the place where you think you should be and things kind of work themselves out. And so just be confident in terms of how you're going about things.

Jon Chee - 00:27:21: Absolutely. And I think thinking about what I would need to adhere when I was 21, it would be the same thing. Because sometimes, again, it's kind of similar to your father giving you the feedback. It's just like, it can be so hard. You second guess yourself all the time. You're just like, is this the right thing to be doing right now? And just knowing that it will work itself out is sometimes all you need to hear to keep on trucking.

Terry Lo - 00:27:45: Yeah. I mean, that's life, right? We're going through the journey of life and just go out and enjoy it, really.

Jon Chee - 00:27:52: Absolutely. Well, Terry, thank you so much for your time. This has been a blast. I learned a lot. And I'm also very, very excited by it. It feels like we're moving out of the stone age and we're in this sci-fi future now. And it makes me very excited for everything that researchers are going to be able to do with new technology like yours. So thanks again for the time and thanks for coming on the podcast.

 Terry Lo - 00:28:11: It was great to be here, Jon. I really enjoyed it. Thank you so much.

Jon Chee - 00:28:14: Yeah. Thanks, Terry. Take care.

Outro - 00:28:17: That's all for this episode of The Biotech Startups Podcast. We hope you enjoyed our three-part series with Terry Lowe. Be sure to tune into our next series where we chat with Brian DeCaro, President, CEO and Director at Sherlock Biosciences. Sherlock is enabling the democratization and decentralization of testing to personalize healthcare and make an impact on global health. Its proprietary platforms for DNA and RNA detection, powered by CRISPR and synthetic biology, will make rapid, accurate and affordable diagnostic tests accessible to people at the point of need. Brian has a proven track record of funding and scaling businesses from venture-backed startups to profitable Fortune 50 public companies. His 20-plus years of experience, including his years of working in research and commercial fields, offer insights for founders to benefit from. 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. The Biotech Startups podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Exceda or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.