Martin Brenner - iBio - 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 Martin Brenner about his time at Pfizer, his move to AstraZeneca, his vision of building a biotech within a large pharmaceutical company, and the importance of balancing structure and agility. If you missed it, be sure to go back and give part two a listen. In part three, we talk about Martin's belief in the importance of mental flexibility and adaptability in biotech, his experience at Stoke Therapeutics, his transition to Recursion the need to challenge long-held beliefs, and a significant turning point for iBio.

 Jon - 00:01:00: And so you've kind of come to this point at Merck. You're saying, I want this nimble, smaller team that can rapidly innovate and iterate. How did you find your first startup to join? And how did you find that opportunity? And what drew you to it?

Martin  - 00:01:13: So I had a lot of conversations with two of my former bosses. One is a really good friend of mine, Gus Gustafson. He just, after many years at J&J, jumped back into a CSO role in a Stealth Biotech right now in the Bay Area. So he's back home. But I talked a lot with him and said, look, how do you choose a good company? What do I need to know? And so it was very interesting because it was always the same answer I got. Whoever I asked, every biotech guy I spoke to said, the team you're getting in bed with, you're going to live and die with this team. Make sure this is a good connection. Step one, right? Number two, they said, you have plans. You need money and time to execute them, right? Make sure you have the runway. And the third was, do your diligence on the technology. But honestly, it's not as important because there's barely a company who actually goes to market with something that they actually initially had in mind. So again, this mental flexibility to say. Right? This was a great idea, but it doesn't work. But we found this on the way. And, you know, it's a much, much better idea. That I looked very carefully. And this is how I got to Stoke Therapeutics. That was a spin-out from Cold Spring Harbor. Adrian Krainer is the godfather of Ligand splicing, right? He had this blue sky proposal. They asked him to write a proposal. And he said, I have this idea how I can make splicing more efficient. And then they didn't fund it. So he was frustrated. And because he had this connection to Spinraza, which was a drug that he invented, his postdoc, Isabel Aznarez, who's an absolutely fascinating and adorable person. She's one of the people I adore the most in our business because she came from South America, made it to her education in Canada, then went to Cold Spring Harbor. And then she just decided because she saw how Spinraza helps children, I want to do that again. And she started Stoke. And so she's absolutely fascinating and one of my heroes in this business. And so I got to work with Isabel. And the idea was great. We operate regular expression. We look at about 200 diseases that we could potentially treat. We had in vitro screening data. And now the point was kind of proof, put together a portfolio that makes sense. It is valuable. That is diverse enough. That's differentiated. And then generate the in vivo proof of concept. And step by step by step, nothing worked. And so you drain your bank account further and further. And what is really kind of a key thing that followed me from my experience at Stoke is resilience. Don't walk away from things, right? You're going to make them work. At one point, something will pop. And the entire team, nobody left Stoke. Although things didn't work the way we initially thought. We all stayed, nobody left. And then we had this really great experiment on SCN1A on Dravet syndrome. And we saw upregulation in an in vivo experiment, which then triggered our series A. So that was absolutely fascinating, but it was tough for a while because, you know, things just don't work out the way you think they would. Right. And so Stoke to this day doesn't have a really broad portfolio, right? They have a few things that are obviously working and they're moving other things into the clinic. But this huge promise of having been able to treat potentially 200 diseases that just kind of didn't materialize. Right. And although I really, really loved working at Stoke, what happened to me personally was kind of as the head of early R&D, you suddenly become a project leader of a single program. Been there and done that for a long time, right? And I had really good experiences dealing with the board, you know, presenting to board members. Also, I had the privilege to work with Art Levin, who's also one of the godfathers of antisense Ligand And so my first approach to write an IND or kind of a plan for an IND, he just laughed at me and said, well, this is like Pfizer would do it. Let me show you what we can cross out here. And literally my plan kind of shrunk to 10% of its original size. It was extremely valuable lessons to kind of have people who really understood biotech, right? And understood, hey, this is important and this is not. And again, there's no knock against, you know, a Pfizer IND. It's just meant to, hey, if we do this, we do it right. And we kind of de-risk everything at the beginning. A biotech doesn't have that luxury, right? You need to get to your next inflection point and you need to get there as fast and as inexpensive as you can in producing quality data. And that's what I learned from Art there. Don't take that, right? And so we built a really cool system where we had this academia network. If you're in rare disease, actually, academicians want to work with you. It's not like in diabetes, cardiovascular or immunoncology, where you basically first have to write a check before somebody picks up the phone. No, these are actually physicians who want you to help their patients. And so this was really nice to be in rare disease. This was a going away from in a grand scheme, metabolic, cardiovascular, now towards rare diseases, neuroscience. That for me was fascinating. One day I worked on a liver program. The next day I could work on a neuroscience program. I love that. And we have this saying at iBio, if you don't know an area, you learn and you get good at it, right? And this is a very different approach than being an expert in a one little disease area. And so that I really, really enjoyed, but ultimately my role really kind of shrunk. And although I really loved the team, it was time for me to kind of look for something that was bigger. And that's when Recursion approached me or Headhunter approached me about the Recursion job.

 Jon - 00:06:33: And coming from Merck to Stoke, and then eventually Recursion you're talking about the cavalry and the resources. Let's say Merck is not doing runway calculations. That is not what they're doing. But now at Stoke, you're talking about these value data inflection points, runway. How was that experience and flip of the mental model of going from the Merck to something where it's just like, oh, this is like game time. This is live or die. How was that experience for you?

 Martin - 00:07:01: It was very interesting because I always had people I could go to and ask. At a Pfizer, at a Merck, at a Eli Lilly, at an AstraZeneca, there's always somebody you can ask, always somebody who has done it before. I was suddenly the guy everybody came to, right?

Jon - 00:07:17: Yeah, yeah, yeah. We need it from you. Like, tell us.

Martin - 00:07:20: Exactly. It was really, really scary. So I had to really think quick, how do I solve this, right? And so luckily, we were funded by Apple Tree, our partners at the time, almost exclusively by Apple Tree. And luckily, Apple Tree had this family of companies. And luckily, some of my former Pfizer colleagues ended up there, Paul DaSilva-Jardine, for example. And so we reached out and it was nice to kind of reconnect. We hadn't connected in a while. And Tim Rolph went there for a while. And it was good to just, hey, you know, yeah, we might not have seen eye to eye when we were at Pfizer all the time. And we might have done things. But it was really, really good advice you got, right? Because they went through this. They had a few years on me at the time. They had been in biotech for a little while. And it was good to exchange on an almost peer level then suddenly to say, okay, how do you tackle this? How can I tackle this? And it was very helpful to have this peer network out of other people that actually went there, right? Other CSOs. And again, this was really, really important that kind of Apple Tree provided that environment, right? So we had these meetings once in a while where all of the Apple Tree companies came together and presented to each other. And it's so helpful to just have somebody, do you have the same problem? Yeah. And how do you solve that? Well, I don't really have a solution for it either. So let's talk, right? And it was really important because, yeah, you're right. Suddenly, you're the driver, right? Again, it's easy to complain about a company. It's easy to complain. Oh, Pfizer does this. And Merck does this. Very easy. But when you're suddenly the head of R&D or CSO or CEO, you can only complain to yourself because you now have the power to change things, right? And if you don't, it's you, right? And so that was another big learning for me to say, wait a second. You can't really blame anybody. You know, you change it. And if you don't change it, you live with it, right? So that was a really hard moment to kind of switch this over. But again, you need people around you who've gone through this, right? I always have, to this day, people I can call. And I'm still, even now, getting situations almost on a daily basis where I say, that's the first. It never happened before. So Google is your friend. Usually. If you have a problem, somebody else had the same problem before. And hopefully it's on Google. If it's not, you need people to call and say, what would you do? And even if it's just bouncing ideas, it's so, so, so important, right? So again, a network of people that you trust, a network of people that you can tap into for just advice and bouncing ideas is extremely important. That helps you getting through these moments where you actually doubt yourself because there are moments where you doubt yourself, but they cannot take over your life because otherwise you're going to be paralyzed. You can't have that as an entrepreneur. You cannot get paralyzed. You need to be clear about what direction and then you go.

‍Jon - 00:10:03: Absolutely. And talking about just like the data not coming back how you want it. It's almost like a pit of despair. And as a leader, you need to be able to keep the morale up because the morale is like a big part of it. Like if people don't have the will to carry on, it's game over. And also having that additional kind of community and network of folks who you can lean on is critically important. It truly takes a village in any entrepreneurial endeavor, any project, really. It takes a team and it takes a village to do it, especially in science, where you can just get like, no, every single ass. This is not what we were anticipating. And then finally, you have that breakthrough moment where it was all worth it. It was all worth it. So Stoke, it sounds like you came to a point where you're starting to get a little bit too focused for your liking. So your Recursion the opportunity at Recursion came around. Can you talk a little about what drew you to Recursion And how did that opportunity come about?

 Martin - 00:10:58: Yeah. So again, I had 15 years of big pharma under the belt at this point, right? So I had my first biotech and, you know, drug discovery is a very empirical job, right? So you do things, if they work, you do them again. If they don't work, you'd never do them again, right? This is kind of stupid to do things that way, right? And so for me, what was important was that can we actually combine technology with drug discovery? Can we actually make things more fluent? Can we improve on things where we go down the path and think this is not going to work, right? And can we actually make it work? And Recursion provided all of that, right? Again, I want to make very clear the Recursion I was the CSO for was a very early iteration of Recursion Recursion today is a very different company, right? I think they actually made it with the last two acquisitions and now focusing on data sets, because again, you can't say you're an AI company unless you actually are Enveda right? And you can't say you're an AI company unless you actually provide the algorithms, like an OpenAI, right? So what we can do as a tech bio is we can provide the data sets, highly integrated, very precise, high quality data sets to train the models. So I think Recursion found that early on, and I think they're doing really, really well. The time I was there, they were really kind of defining themselves, right? And it was very, very hard for me, right? Because I was brought in as the industry expert to help them make drugs. But at the same time, I was also asked to forget everything I know and basically do everything fresh, right? And to be honest with you, I struggled a lot with this because Recursion had on the website, I think we are going to make a hundred molecules of drugs or whatever they called it by 2025. And, you know, whenever I mentioned this, I got laughed out of a room because everybody who does drug discovery, even if you're extremely efficient, like with the COVID vaccine, right? For example, you're not going to make 25 of those or a hundred of those in three or four years, right? But that was kind of the storyline of Recursion You're radically changing things. And at one point it got really exciting because I built a system. They had a hundred programs when I joined and 20 biologists. Usually I work with about three biologists on an early program, and then it goes up to almost a hundred people when you're in a pharmaceutical company for a clinical program, right? So I was used to that. So how do you actually run a portfolio of a hundred things with 20 biologists? So I came up with a system that I started actually building at Merck, honed in on at Stoke, and then basically got to Recursion And I think this is still the most efficient system. So we literally built a line of site program plan, right? Every program needed one page. There were different categories on that page, technical probability of success, market size, patient need. I wanted to hear from physicians, yes, but also from patients because sometimes physicians don't actually realize that patients need something else. So all of those things got ranked. And so we could rank the programs. And funnily enough, right, we had a huge agreement across the company about the top 10 programs and the bottom 10. The rest is a gray zone, but this way I could literally stagger the programs in a way and kind of run a portfolio management system that allowed us to actually run a massive, massive portfolio with only 20 biologists. Staggering the programs was absolutely key, right? So if something is my legacy at Recursion that is it. But there were also massive, massive issues in the beginning because it was the first iteration of machine learning and biology coming together. So all biologists did not speak machine learning. And now ML scientists did not speak biology, right? Well, but the algorithm says it's this and said, well, but if your biological data you feed in is bullshit, right? It's not really telling you anything, right? And so this language problem, right, this communication problem is really something that made working really complicated, right? Because ultimately Recursion is a platform company, right? And all the power goes to the platform, right? Whatever we could say to refine, to do better, it just did not resonate, right? So at one point, it was just not bearable anymore, right? And the goals at the time were set so high, it was impossible to kind of reach that. And again, there comes a time as a CSO where you either have to stick to what you say, or you have to say, I can't stand behind this, right? And I think it was good for Recursion good for me at this time that we separated. It was still a very, very important part of my career because I learned to really break down the truths that I thought are truth, that have built over 15 years in the pharmaceutical industry. Very often after a few years, your truth is not true anymore, right? There's a new truth. There's new information. There's new things that have happened. And for me, this was kind of a catalyzing moment to really look very hard at my knowledge base. What do I know and what is the truth, right? And so after Recursion I really started to deconstruct everything I felt drug discovery is about and is a rule there. And it turns out there's only a few rules that are really kind of universal. A lot is just perception, right? And so this was a big step for me. Again, this reinventing myself after Recursion was really also based on this introspection to say, wait a second, you need to look at what you've done over the years and what you have come to believe. And if this is still what needs to be done, right? And this is in stark contrast to what an entrepreneur like Viswa Colluru has built with Enveda, where he basically just does not accept that things don't work. He will find a solution. And that kind of moment, that I learned at Recursion right? Just challenge it and make it work. You will find a solution. And this is something, you know, I think at this point, after that experience, I could call myself, I'm now a biotech guy. Before that, I was still a pharma guy that was dabbling in biotech. After Recursion , I felt I'm a biotech guy. Now I know what this is about, right? And after Viswa had left Recursion too, we were hiking together and he pitched the idea of Enveda to me, which in the beginning was a very small, cute idea. And which has now grown into a massive organization. And, you know, he's literally on the way of building the Amazon for chemistry for the planet, right? And so again, this, it just shows you that you have to challenge those beliefs. If you actually put your mind behind it, you can solve basically almost anything, right? And so having that belief and having that, if you don't know how, you learn how, and you find a way how, I think this kind of encapsulates everything that is biotech and tech bio today. And this is what, Viswa actually taught me, right? This is what I learned in the early days with Viswa And we had a couple of really great stories when we applied at Y Combinator. You know, we sat in a one bedroom apartment that I rented here in San Diego after I joined Pfenex And we did that video and we got accepted. So we went there and of course, none of us had money. Every penny went into Enveda. So we shared a hotel room and we were nervous, right? And we walk in this meeting and within the first 30 seconds, we lost the meeting at Y Combinator. We just lost it.

Jon - 00:17:54:Oh.

Martin - 00:17:55: We went down the rabbit hole and Viswa was frustrated and depressed. And I felt like, oh, damn. And the next morning I go down to have breakfast in the hotel and Viswa sits at the breakfast table on the phone and pulls the same amount of money down just with a phone interview with another incubator. And I said, so dude, don't worry about Y Combinator. You just did that on the phone without people actually knowing you, right? 

Jon - 00:18:21: Yeah.

Martin - 00:18:21: At breakfast, over some eggs. So this was one of the reasons, actually, my only real investment in biotech that I took was literally in Viswa because he wants to be one thing and one thing only, which is basically an entrepreneur. He never wanted to be anything else. He wanted to be a biotech CEO so bad that there was no plan B, right? So my money is safe. I know he's going to do everything to make Enveda work. And I think that's kind of the people I'm looking for. That's the people I want to work with. And you learn to kind of see them. It takes a while to kind of figure out, is that person geared that way? It's very funny because entrepreneurs come in all kinds and types and shapes. But if you find that common denominator, you have a pretty safe investment in these guys because you feel like, wow, they will move the entire earth to be successful in this. So that's how Enveda came to pass. And I obviously didn't join the company as a co-founder at the time. I'm still the first investor in the company, but never joined because my salary would have shortened the runway. So I stayed an advisor and Viswa is really kind of to congratulate on what he's achieved with that. I mean, it's truly unbelievable.

Jon - 00:19:30: That's amazing. And to get back to something you said about like checking your assumptions and ways of doing things really stuck out to me. And it's kind of the Recursion experience being the really formative one, which made you into the true biotech, not the big pharma entrepreneur that you are today. I think that is not overly complex, but it's just hard to do. Because you get comfortable. Well, this has worked. So why won't it stop working? And it should work because it always has. But the simple part of it is just like asking, why do we do this? Why do we do this? And you're just asking why a lot of the time. And people don't like it. That might be the hard part is that like people don't like getting asked. And even myself, when someone's like, why are you doing this? It's like, you kind of get defensive. You're like, well, we've always done it and it has always worked. Right. And so I think the difficulty. It's just kind of like not getting too wedded to these beliefs and being okay. And just like loosely holding them and not getting too strongly attached allows you to kind of like peel back these layers and just like figure out, oh, because there are certain there are plenty of times that exceed or where we look at a process that we made years ago. And then we just peel it back. Oh, that is not relevant anymore. It might have been relevant like six years ago, but let's like rebuild this thing. Not necessarily from scratch, but it might be time to rebuild this thing. So I love that that was what Recursion did to you. And I think it's an invaluable lesson for any entrepreneur that is coming from this experience. I wouldn't call it baggage. It's just a previous experience. It's to check that experience and be honest with it and introspect.

Martin - 00:21:08: You're absolutely right. And I think being able to keep learning and reinventing yourself is really, really important, right? And again, like you said, it's very easy to get comfortable, right? If you're getting comfortable, time to move on, right? So like a rolling stone, you know, gathers no moss. It truly is that case, right? And so again, I'm really curious where my future journey leads me because I want to do that again, right? Because it feels good. It feels good to kind of go in there and basically say, wait a second, we have done this this way. Is there a better way? I'm always fascinated by people finding big problems, right? Don't waste your time with small problems. Try something that is a big problem. Life, the environment, UVC will tell you if it's too big and they will shrink it down for you. No worries. It'll happen. I think what is really important is that you start with what is it that you want to solve, right? Can you solve a problem that nobody could solve, right? And can you solve it with something that people don't have a solution for right now? And I think this is something we're doing a lot at iBio right now, right? So what is the problem in antibody discovery, right? And we're really kind of moving away from, we're not calling ourselves an AI company anymore, right? For a less educated investor, yes, we say that, right? But for every sophisticated investor, I can't really say we're an AI company. We're not, right? We're an antibody discovery company and we solve very specific problems with machine learning, where it's appropriate. We use computational biology, good old fashioned one, if it's appropriate, right? So, but again, it's about what do you solve, right? And so I think this continuous learning and challenging yourself, it has become a little bit of a, it's almost like a drug. You want to have that again, right? Because you feel like you're getting rid of baggage that you've accumulated and then you're building something new. And at one point, you know, this new stuff will become baggage again and you'll peel it away. Like you said, this, I like the peeling away thing. Again, you can't just shed things non-discerningly. You need to really kind of think about this, right? And think if it's useful, if it's valuable. And I think that becomes really your life and challenge and the challenge that you want to put your teeth in next. So personally, I have no idea where I'm going to end up after iBio, but I know it's going to be something that is exciting.

Jon - 00:23:24: Yeah. And you make a great point here. Don't throw away good baggage. Don't do that. Like I'm not saying peel it all the way back, irrespective of what it is. So all listeners. If it's good, keep it. Definitely. There are things like processes and ways of doing it that are rock solid. And we have that too, where it's just like, this is tried and true, continues to work, not a problem. But like, if something is not performing or generating data the way we like it, or it's not working the same magic that it used to, there's perhaps something like exactly what you said about kind of like the engineering mindset is like, all right, let's reopen this thing up again and like figure out how does this work and not just assume, you know, that this is going to be the solution forever. So after Recursion you make your way over to Pfenex How did that opportunity come about? And can you talk a little bit about Pfenex in your time there? 

Martin - 00:24:10: Yeah. So Pfenex was literally a reaction to Recursion right? I wanted a team of really, really experienced people around that I could learn from. We spoke the same language. There was a main driver. I wanted a team that I felt really comfortable with. And it became a magical place. Pfenex was a magical place. My boss at the time, Eef, I'm saying his last name wrong. He's Dutch. Schimmelpennink, I believe it's the right pronunciation. Eef was a guy who could make you better at your job. We literally, the entire C-suite, we made each other better. We were very different people. Shawn Scranton, who's like a brother to me now, always worked in micropharma or microbiotix for people and below, did a lot of clinical development. We literally kind of connected, not only from our experience level, we were just enough overlap to really understand and help each other. I had Lucy on the business side. Pat is a typical business developer from Massachusetts. You want Pat on your side of the table when you negotiate. And then Eef kind of rounded this all up with, he could read people. He could really understand what people needed and give it to them so that they could actually develop. This was a magical team. And this expanded into my team because for the very first time, and I think this is again, one of these moments in your life, in your career, where it's a defining moment. I had a team of an introvert. I had a team of somebody who was basically going with his head through the wall because typical type A personality. And then I had a person who was a former military, very organized. And I thought, how do I get all these people to work together as an orchestra, right?

Jon - 00:25:49: I was going to say very different.

Martin - 00:25:50: Complete opposites, right? But we had an executive coach and she came in and said, no, no, no. Utilize the strengths of these people. So Diane Retallack, she was the VP for the platform. She very introvert person. But when Diane speaks, everybody listens. So Diane maybe needs an hour or a day to kind of come with something. And we sat down as a team and said, Diane, it's perfectly fine. You do you at your strongest. And if you need a little time to kind of tell us something, we're going to wait for that, right? Dillon Phan, who I'm still working with, he's just one of these people who can execute to the team. He was always head through the wall. He went fast, fast, fast to kind of rein him in or sometimes not possible. But then why, right? You could let him run. The problem was not letting him run. The problem was really then kind of saying, okay, we need to communicate this with the rest of the team. So you need to slow down. At one point and bring everybody on board, right? Jimmy was our head of purification and downstream. He was military. When the pandemic hit us, we had a plan defined that people came in extremely structured so that they would never be close next to each other at the bench. There was always six feet distance. We were working in shifts so that everybody was safe. Jimmy was the master of this plan, right? He developed these things to the T. We could not have done this without Jimmy. And then Jeff Allen, who was running our analytical team. Jeff is one of the best team builders I've ever, ever seen. We had these little competitions within Pfenex for Halloween and other things. Jeff's team just cleared every award. He was such a great team builder and his team kind of really lived this life with him and lived this being together, right? And allowing everybody of these people to be themselves at their best, bringing to the table what they can do best, made us more than the sum of individuals. And this was the first time it was kind of a light bulb in my head right now. And I thought, huh, what did we just do? It was literally magic happening in front of me. And so I tend to hire people that think like me, act like me. And, you know, I'm a little bit more type A. I want to go fast, fast, fast. But once you have your team built and the necessary foundation laid out, you need to bring in that diversity. And it has never to a degree worked for me like it has worked at Pfenex But this taught me really kind of you need to bring in these different aspects because it adds value to the discussion, right? It might be difficult to communicate sometimes, but that's why we have personality assessment tools, right? If I talk to my general counsel today, I know he needs more information than just me riffing off of a blue sky project, right? He needs a plan. And this is perfectly fine. So you can adapt to this and still let people be at their best. And I think this is something it took me 20 years in this business to kind of get to this point. And again, it's to me, these are moments that I will relive forever because these are defining moments and where you think now I got it right now. I really understood why this is important.

Jon - 00:28:40: It so interesting. It's like, when I think about my experience playing sports growing up, one of my very formative coaching experiences, Dan Norris, who was the coach at UC Berkeley, he always had a saying of like, know your personnel. And in the sports context, it was like, if someone's right-handed, try to pass to their right hand. Don't pass to their left hand. Like try to put people in the position of strength. And I've always thought about that experience in the same way. Everything you're describing at Pfenex it sounds like similar. It's like, okay, if someone wants to go fast, let them go fast. If someone needs a little bit more time, give them more time. Knowing your personnel and what is the strengths and weaknesses that they have, and how can you put them in a position where. They thrive. I think it's hard because everyone's different and a little bit different in all kinds of ways. And also it makes it very exciting too, because you're now learning how to work. And that's where you get your best work because you're bringing together all of this diversity and ideas. And that's when magic happens. And I'm the same way when you described, when I talked to legal, there was like, okay, we need to level set first. Let's have a level set conversation first before we just like, go, go, go. Because I'm the same way where I'm just like, all right, let's go. Let's go fast and move. And I wouldn't say break things, but I was like, I can bump into things sometimes. And legal doesn't really like bumping into things. But that's amazing. And just quick question, how big was Pfenex at the time when you had this dynamic team of very diverse individuals? 

Martin - 00:30:17: So I think my team was about 45 people at the time and Total Pfenex probably about 60, 80, roughly. Again, it also went through a couple of iterations before Eef took the company over. And we were heading on a way to get our first portfolio programs out there closer to the clinic. And then obviously, as a public company, you always do right by your shareholders. When we got the offer from Ligand, we just could not say no to that. It was the right thing for our shareholders to do, although we felt all a little disappointed in our hearts because we had something really good going, right? But again, I always see this as now I have people that for my next company, I can reach out to and I can recruit. So hopefully we can bring the band back together.

Jon - 00:30:57: Yeah, yeah, yeah, yeah, exactly. Just like, let's run it back. Like, why not? And so you bring up Ligand. Can you talk a little bit about, was this your first experience with M&A? 

Martin - 00:31:08: Yes, it really was. I think we blew the Ligand team away with our management presentation because, again, we like to practice and we basically had each other's back and we could read our minds sometimes, right? So just almost kind of telepathically knowing when you hand it over to somebody or have somebody else answer a question, I think that made a huge impact on the Ligand team that kind of did all of the due diligence. But it was stressful for us because, again, in the due diligence, you have to deliver, right? And you suddenly start to think about things like, yeah, they want to see data. It's almost like an audit that you get in a larger company. Pull the data out. You have five minutes. Here's a graph. Pull me the raw data out for that graph in five minutes. And it was a little like this. So it was a stressful time to kind of get everything together, get the presentation done. And then there's in every deal is this cooling off period where you come from intense preparation and you think you're going to get the deal over the finish line. And then suddenly everything falls apart. And you don't hear anything from the other company for weeks. And you think, what happened, right? This is just going from a hot shower to a cold shower. So it was an exciting time. But also, it was pretty stressful. And of course, I was thinking about my team as well, right? So we were very worried about Ligand just coming in and just monetizing what we had and not utilizing the platform of the pipeline anymore. But I think everybody made pretty well out afterwards. Ligand was very generous, to be honest with you. Ligand retained a lot of people. Which was great. And I think there's still a lot of people working at Ligand. And Ligand just spun out the former Pfenex Technology as a new company. So it's now a private company with some significant financial backing. And most of my former colleagues are still there. So again, Ligand has done really, really well, not only with the acquisition of Pfenex and the monetization of the revenue streams that Pfenex had generated over the years, but also kind of getting the technology back out there. And they've been very active. They've spun out OmniApp. They've spun out Prices. It's the new company called. And so they have been very, very active in this space. So kudos to those guys. And I'm hoping everybody is going to go forward with that technology. But having a niche technology is a tough business. It was a tough business at Pfenex And it was a tough business at iBio, which is why we ultimately had to divest the CDMO part at iBio. Because again, there's just not enough business coming in. And together with the downturn in the biotech sector, we just kind of sailed into the perfect storm, if you will.

Jon - 00:33:34: And something that stood out to me, you mentioned that your Pfenex team, there's some folks who are still there, or at least post-Ligand acquisition. During these due diligence and the M&A discussions, we're talking about this dating, right? When you join a company, whether you're joining as an employee or merging, just combining two entities together. Was there just like a cultural resonance that you can just sense and you just knew that this is the right home for it?

Martin - 00:33:59: Not throughout, to be honest with you. Matt Foehr at the time, I believe he was the COO at Ligand. There was an instant connection. Matt is a really good guy and he knows his stuff, right? I think this helped a lot kind of to translate between the two cultures, right? So that made it a lot easier, right? And Matt was also kind of the go-to person for the people who stayed on. Diane stayed on, Jeff stayed on, Jimmy stayed on for a while. There was really kind of a good relationship building by that. I think Ligand operated a little bit differently than Pfenex did, because I think their former CEO came from the banking or real estate sector. So you can always feel like if somebody comes in that is not necessarily in the tech bio, biotech sector, the company runs a little bit different, right? And Ligand's strategy was very different from Pfenex strategy. We were just a very, very good fit at the time to kind of bolt onto what they had. But I think it worked out really, really well. And it's also kind of a testament to the quality of the Pfenex team, right, that they actually made well at Ligand. And then now basically are all, you know, moving on to that company. And it's been our company. So I think it's a really good example that those were really high quality people that we had at Pfenex at the time.

Jon - 00:35:05: Amazing. And so the Pfenex team is now part of Ligand. And we're getting up to present day now. We're at iBio. What drew you to iBio? And how did this opportunity present itself to you when you were figuring out what was next?

Martin - 00:35:19: So I went back to the basics, right? I thought, well, they had an experienced team with Tom Isett there, with Randy Maddux, with longstanding manufacturing expertise. They were just hiring Rob Lutz as a CFO. Lisa Middlebrook was there, who was a very, very experienced chief human resources officer. So the team was really, really solid, right? There was enough money as a runway because we just ventured into that pandemic space and allowed iBio about a year or a year and a half before I joined to kind of raise a lot of money. So I had the runway and I was brought on basically to build a biotech arm, right? You have a manufacturing facility. You only have a few clients that use them at the moment. So build a team that actually feeds programs into that facility. Smart idea. So I like this. What I also liked was that, you know, I did antibodies at Pfenex but with a bacterial expression system, you're very limited. We were more or less limited to nothing that was glycosylated, right? Which had challenges if you were working in immunology or oncology, right? So plants, actually can make very complex molecules. We could basically a few cause late antibodies with the correct plant line. We could produce very complex molecules by specifics if we wanted to. So the plant technology sounded really cool. What was unfortunate was that the technology was relatively old. And after I really dug in, after I had joined, I realized that if I want to bring that technology to the newest level, I need to spend probably $50 million, right? And so there was always this thing. Well, we can't use the current facility. Apart from what it's built for, right? It was built on a hydroponic system. It was not very high density plant growth. The newer models today are aeroponics. They are super high density. You have multispectral cameras that run down on the leaves and actually tell you through an AI algorithm when the plant is ready to harvest, right? There's really cool stuff that you can do. So I truly believe plants are really kind of an important thing. But we had this issue that only a few small companies approached us. They couldn't raise money in the current environment, right? So they paid with equity. You can't live off of equity of a company that can't raise money for a long time, right? So it was really hard for us to kind of get paying clients into the manufacturing space, right? There was one problem. And then the next problem was, of course, that we tried to market this. We tried bio-inks. We tried blood constituents. We tried almost everything. But there was just no market that was opening up, right? And it turned out that our system, the expression system was also not the most efficient one. It's a patented one, but it hadn't been updated in a long time, right? There wasn't a lot of work going in there. And so that was the next step, right? Where do you find the talent these days in a company that's public, right? It was not the sexy AI company that a plant biologist would choose. So how do you make this attractive? And then on top of this, how do you make this attractive to people that are then supposed, because the plant expression part was being done in Bryan, Texas? How do you actually get people to Bryan, Texas? And for manufacturing, this is not a problem. There's a manufacturing hub. But how do you get somebody who just comes out of a postdoc to move to the middle of Texas, right? Specifically in the political environment, it is almost impossible to get female executives to Texas that are not already living there. And so it was really, really hard. We got great college kids. Our intern program was great. We got kind of the seasoned people who like that there's no Texas in Texas. But the middle part of the organization, it was really hard to hire. And so all of these things with specifically the market conditions deteriorating by the day, that actually just led us to one point where we said, we're going to run out of money real fast, right? And that was probably the first conversation sometime 2022, middle of the year. And then we had worked with Rubrik before and we licensed the program from Rubrik. It was a small machine learning based antibody company in the Bay Area. And Rubrik at the time also, because of the environment, they had debt on their balance sheet. They couldn't raise more money. And so we acquired the assets of Rubrik together with a part of the team. So Matt Greving, our head of MVP of machine learning, who's the inventor of the technology and co-founder of Rubrik, joined us in San Diego together with two of his colleagues. And so we could basically rebuild initially the Rubrik system last September, September 2022. And since then have actually kind of improved and broadened the technology stack that we brought on. But that was basically the turning. And then in November last year, we had to basically lay off everybody in Texas because we just could not afford that anymore, right? We were running out of money real fast and we needed to get anything that was expensive. And having 130,000 square feet facility and 70 people is a very, very expensive part of the company. We just had to stop this. I thought back at this moment in November, and this was the biggest layoff I ever did in my entire career. It was 70 people. And that was not fun. I was the only one there together with Lisa Middlebrook. And if you speak with people the entire day that lose their job, this is gut wrenching, right? It's also one of those days you don't want to forget, but you don't want to relive them.

Jon - 00:40:25: Absolutely. Well, first, I'm glad that the pain is still there. And that is not an experience anyone wants to live through. And when you think about the company, sometimes that's the leadership kind of decision you have to make. You kind of are between a rock and a hard place. And ultimately, it just comes on to your responsibility as a leader to make the decision because no one else will. And you're kind of now with the Rubrik technology, you're now positioning yourself away from the previous CDMO strategy. Can you talk a little bit about iBio's technology now? What is the current state of the market? And how does iBio technology approach it differently than the current status quo?

Martin - 00:41:02: So we are the oldest biotech startup in the world, if you will. So we're basically a year old. We started to tell the new iBio story in January this year at J.P. Morgan, right? And we've done for the environment and for being A, in this environment, B, being a very young company, an unknown company, we have made really, really great progress. But it's been challenging, right? So basically, iBio in its current iteration is a developer for antibodies for the next generation, difficult targets, and also difficult modes of action of antibodies. And at the same time, we are improving the developability of antibodies and enhancing the safety. And there's a reason why I've put this all in one sentence, because that's literally my problem statement, right? As you know, the first antibody was approved in 86, 1986. And then there was, like with every technology, a lag phase, right? The first one was approved, and then it didn't take off immediately. And by 2014, when the first IO drugs got approved, we had a very, very strong growing market in antibody discovery, right? So we had 6 to 13 antibodies per year approved between 2014 and 2022. And that led to 162 antibodies approved in the US and the EU by 2022, and a total market size of 200 billion, right? So antibodies are here to stay. They've proven that they're extremely efficient and efficacious. But the market also is maturing. And so what happens with a maturing market is... And you can see this very clearly, 162 approved antibodies target only 91 drug targets. And what is even worse is 40% of all approved antibodies target only 10 drug targets. So mainly PD-1, right? CTLA-4. And so why is that, right? Because again, the low-hanging fruit are somewhat gone, right? So now if you think about the theoretical space of drug targets that antibodies could reach, which is extracellular, so surface proteins, that's surfasome, or secreted proteins, again, there's the speculation because not every protein that is in theory there, kind of not every open reading frame becomes a protein, right? But basically, there's a very nice paper by, let me look at this so that I'm not saying something wrong. I think Bausch-Fluck in PNAS from 2018, they use actually a machine learning tool to predict how many proteins the surfasome has. And they come out, I think the model is pretty decent, pretty good. So it's about 2,900 total targets. Now imagine 91 that we're targeting, 2,900 potential ones that have not been targeted. So you can see something is not adding up. So the low-hanging fruit are more or less gone, right? So the easy targets that we all can track with antibodies are gone. Now, why can't we go after these other targets, right? So first of all, we believe existing technologies just are not able to kind of get after complicated things. One classical problem in antibody discovery is take an ion channel, right? If you put a full-length protein in, you raise antibodies, but probably against regions that are irrelevant, right? And you have probably antibodies that are binding to subdominant epitopes, but your screening technology where you wash away antibodies that don't bind very tightly basically eliminates all of that. You have a haystack and you have needles in there, but by taking the haystack away, you're also taking the needles away. So in the best case scenario, it takes you rounds and rounds and rounds to kind of get to an antibody that does what you want on a complex target. In the worst case scenario, you never find that antibody, right? Then the next problem we're seeing emerging is bispecifics are also here to stay. Antibody drug conjugates, there have been so many deals. Big Pharma is now fully bought into ADCs, right? But the problem is we're also seeing, well, there is significant side effects with these molecules. They're very potent. So how do you actually make them tumor-specific, for example, or disease tissue-specific, right? It's not necessarily only tumor. It could be inflamed tissue in immune diseases. So that's another problem that needs to be solved, right? How do you make them specific for the disease, for the target tissue? And last but not least is the developability and the safety of an antibody from a perspective of how can you predict the safety from an immunogenicity standpoint? How can you actually predict this better? So that's a problem small molecules have 50 years ahead of us, right? We already have done small molecules 50 years before we got to the first antibody. So now how do we catch up with this? And I think this is where we have focused our efforts on really hard. So first of all, how do we get to these novel targets? How do we do these really complicated drug targets? That's where we use epitope steering, right? So engineered epitopes are small representations of regions on large drug target proteins. They're 30 to 50 amino acids. And the way we design them is we use Alpha Fold2 or any other structure prediction model as a foundational layer. But then we apply our own algorithm to basically say, hey, now that we know the structure of the protein or can predict it, take that one region, build a protein exactly with the same surface and build a scaffold underneath. And then we optimize this A for similarity, for structural rigidity. But then we also kind of school of hard knocks, learn to build in water solubility, right? Because if your protein crashes out like a rock, it's not useful in biological experiments, right? And then last but not least, what helps us also raising antibodies against scaffold residues, we actually do a loss function for the residues of the scaffold. So we'd use the amount of residues that are just structural, right? Now, these engineered epitopes, we have generated some against loop displays, junctional epitopes, so where you don't have a continuous amino acid chain, like in a GPCR. We've made this against complex, really secondary structures where folding, becomes really important. And we've done this against membrane proteins as well. And so we have proof, not only theoretical technology able to make these antibodies against these hard targets, we actually made against each one of these complicated epitopes, we made an antibody successfully. Now that's one equation, right? You have the epitope, but the other one is the antibody and the function and the mode of action, right? So we have a T-cell engager platform that requires to activate T-cells. That's very different than blocking PD-1, right? So activating a cell, very different mechanism of action. We were able to do that. Because we select the right epitope, we were able to be very tumor specific by just using a tumor specific protein like EGFRvIII, that's only expressed on tumors, where we actually epitope steer our antibodies against that specific region that's only expressed on the tumor. And then we have some antagonistic molecules against MUC16 and against CCR8, which is a GPCR. And then we have our latest coup that will come out probably tomorrow or the day after. So I can share this with you. But we have a lot of so you can imagine the safety on tissue efficacy is really, really important. So how do you make an antibody only being active in a certain tissue? So we have actually used our engineered epitopes that not only fish out antibodies out of basically the soup and raise antibodies against this, they then can be used as caps on top of an antibody and then linked through a peptide linker so that only in the tumor through a certain enzyme, these caps come off and the antibody gets activated. So it's safe in healthy tissue. It's only active in diseased tissue. Now, other companies are doing this as well. The difference that we apply is we have that built in. So we already know we have a cap. It's basically an integral part of our process that we apply to every drug discovery program. And that'll allow us to make very, very safe capped antibodies where we're basically capping not only the tumor target, we're also capping the T cell engager if we have a bispecific. And that allows us to kind of make a lot safer antibodies than others do. And, you know, we don't have this risk of immunogenicity against a artificial peptide that a computer just kind of tried to assemble because we know it reflects the endogenous surface of a protein. So we're fairly sure this is going to be a pretty big hit. But of course, what we very stealthily became probably the leaders in the world is combining machine learning with mammalian display. This is super important. As you know, if you need a broad library, really broad library, you do phage display because you can cram billions of molecules into phage. But what you don't have with phage is the developability aspect, right? And you do everything sequentially in phage display. Whereas in mammalian display, you can only cram about 1 to 10 million molecules into a library. But at the same time, you know that your antibody expresses because it's in the same cell type that you later use for manufacturing. And second, you can co-evolve at the same time the antibody, the mask, and the linker. So we can do this in one consecutive step through a fax sorting step. And basically, Cherry picked the right antibody with the right mask, the right linker, and the right potency, selectivity, cross-reactivity. And we've shown this now very nicely for our Mach 16 biospecific. And we've also developed our CD3 antibody panel by that. So this is really kind of cool technology because we can use machine learning to take the diversity of a 10 billion library and basically project it into a 10 million library. That's where the machine learning tool really, really comes in handy. We call that stable hue. And then the mammalian display is really something that within weeks, we can now optimize an antibody in less than four weeks. And so I am allowed to say this because Matt Greving , our head of machine learning, said, you can only say this if we've done this actually a couple of times and reliably. I can now say we can reliably optimize an antibody in under four weeks.  

Jon - 00:50:22: Very cool. Very, very cool. That's exciting. And I'm thinking back on the AI/ML kind of wave that we're seeing. And I love your guys' approach to it. And also, I love the, like, we need this reproduced consistently. Let's not just have this be a blip. Let's have this be solid. When I hear the use of Machine Learning in these instances, and then I see all the chats about AI and Machine Learning used in the non-life sciences, I'm like, I guess you could write my email. That's great and all. But this is where it gets really cool, in my opinion. I'm probably biased in that way. But that's awesome. And so more from like a business perspective, what is iBio's kinds of like business initiatives? Are you focusing on strategic partnerships? Are you looking to like develop your pipeline more? What is it that you guys are all kind of rowing towards from a business perspective?

Martin - 00:51:17: We have a layered business model. So first, we are looking for strategic partnerships. We actually have four partnerships, which I only can talk about one because larger partners don't want us to really tell early on that we're working with them. Those are licensing deals, usually validating deals. Again, I want to highlight we're only a year old, right? So you start with validating deals. They're smaller. But once a license is taken, obviously, it's a game changer for us. So if we can solve one or two hard problems for large companies, that will be a big game changer, right? So these strategic partnerships, absolutely, we're pursuing those early stage. We also have a proprietary pipeline that we're either moving forward ourselves or we can partner this with the right partners. So we have a PD-1 agonist, which gets a lot of attention right now because there's some very positive data that Lilly showed in the clinic with her PD-1 agonist. But there's some new things coming out, new knowledge coming out. So how to potentially detune an antibody to kind of make it work even better. So we can still implement this into our molecule. It's because we're still in the optimization phase. So we're definitely getting some interest there. And then last but not least is we're small and we need to be laser focused on immune oncology at this point. But it doesn't mean we haven't collaboration with NIH to try to use our engineered epitopes against Lassa fever, right? If we can actually show through NIH that our designs actually work as a vaccine, you know, we would love to outlicense that platform to a vaccine developer, right? And this is exactly the same playbook as Moderna. You can have one disease area, you get exclusive rights for this, you pay upfront for this, and we have milestones and royalties in what happens after. That's exactly what we're looking for. We're looking for partners that can do immunology because, again, I think there's a huge potential specifically now with masked antibodies in immunology that could be applicable. And then we could envision areas like with really complex structures like ion channels that somebody wants to take an exclusive license for pain and utilize our technology platform for that. We're always looking. We're looking for partners where one plus one of the technologies is more than two. That's our ideal situation. But of course, this is really kind of coming down to the partner. So yeah, it's a layered model. And it really depends on which of these deals are panning out. But I think at the moment, what is most critical for us is to kind of have one validating deal announced, which I think we're getting close to. And then once we've done this, hopefully it'll change the valuation of the company, which at the moment, obviously, we're a micro cap. And to kind of keep developing the platform, you're only about nine months ahead of your competition at any single time. And if you don't innovate, you're going to be gone, right? I mean, there's the next coming out of Stanford or Berkeley with the next idea, and they're going to have something better. And if you don't keep inventing and optimizing and getting better, you're going to fall behind. So that's what we're shooting for. We really need kind of one of these validating deals to kind of pan out and then allow us to kind of further drive the technology.  

Jon - 00:54:09: Very cool. Innovator. As the saying goes, how do you at iBio cultivate this kind of like culture of innovation and continuing to just stay ahead of the competition? So we talk a lot about like org structure, but how do you create that environment where your all stars can thrive?

Martin - 00:54:26: So we're still in the middle of adopting some of the tech companies' behaviors, right? Like small stand-up meetings in the morning, right? We're all sharing information. Believe it or not, a lot of information. So, you know, Matt is an extreme climber. So he does high mountains like Everest and K2 and what have you. So he's an extreme climber. I'm getting into ultra-endurance cycling, which takes time. And that's time we can actually listen to podcasts. We do that a lot. Stanford has a program out about machine learning, it's free. You can actually cram that into your brain in a short amount of time while you're on the bike, while you're going up Mount San Jacinto in San Diego here. So we do this a lot. And then we try to get away from really kind of structured meetings for that. But having more free-flowing, have you heard that? What does that mean for us? And so trying to kind of take that step back and see where is the field going? Why are VC not shelling out money right now, right? Is there a common denominator? And so we're literally trying to kind of embrace the entire space. Science, business, if you will, the VC side and the investor side on the public side, try to understand where this is going. I always laughed about our commercial teams that told us, hey, you shouldn't do this because it's not going to sell. And then it became a blockbuster drug. And one famous word that always comes to my mind is somebody, and I forgot who said that, but basically said commercial teams are really good at predicting the past. So I want to have somebody who can actually think about what's the future going to look like. And I think this is one. Not to have structured meetings, to really kind of have unstructured conversations where we really dig deep. Hey, have you heard about this? How does this apply to us, right? And there's a lot of noise that this generates, but out of the noise, there's certain things that then emerge. And if you grab those things that fit to your technology, fit to your team, that emerge out of this noise, I think this is how we innovate most of the time, right? And again, it's always tied into what problem are you solving? If you don't solve a problem, don't even think about talking about it, right? It's not worth our time. And we're not yet there a hundred percent, but obviously we're not as brutal enough in our time management yet to really kind of focus the conversations there. But again, it's an evolution of a company. We're only one year old and we're bilingual. That's the nice thing. We all speak machine learning and biology, which is really good. So we don't have the language barrier, but at the same time, we still need to hone in these communication skills. And how do we actually keep innovating in ever faster circles? How do we actually get the information, right? How do we know where the field is moving? And going to a conference is great. Reading papers is great, but is there a better way to kind of judge and feel where we're going as an industry and where we're needed to kind of solve the next possible problem after somebody took a big chunk out of it, right? Alpha Fold took a big chunk out of it. Nobody could have done this better, but now we're using it. And so what comes after that? What are you solving knowing this? What can you solve having ChatGPT at your fingertips now, right? And like, like you said, it's not just emails. Are there any emerging features you can use, right? In language models? So I think this is a typical way. And again, speed and being a dynamically changing team is all that you need that if you kind of provide this for your team to be able to kind of pitch crazy ideas, I think you're losing out. And again, there's nothing too crazy for us to talk about, right? I think this is one of the things we're very clear about, you know, whatever great idea or strange idea you're coming up with, let's actually figure out if there's something behind that, right? And I think that openness that everybody can come and, hey, sometimes we're wrong and it's not something we want to keep talking about that should not discourage you.

Jon - 00:58:02: Absolutely. And we have a similar culture here at Excedr too. It's just like, bring all the ideas and look, you can't hit home runs every single time, but you have to step up to the base and you have to actually give it a shot. And I love the pulling inspiration from various, whether it be the, you know, software kind of traditional tech and just like hardcore life sciences as well. And I do the same thing when I listened to the podcast, I try to learn as much as I can about an industry that I'm not a part of and hear it from those insiders about how they do it and figure out a pick and choose various, like that sounds really good. That one doesn't actually sound that good. So I'm just going to leave that there and bring it in, which you're exactly evolving and iterating. And so in my opinion, that's like the most fun that can be had is when you're creating this unique. It's exactly what you're saying. You're just like back to Eli Lilly when you're like, all right, figure out how to do a theatrical play on the fly, just figure it out. And it's funny how it comes full circle like that. And, you know, another aspect that I think it reminded me of a story when I was in the lab, it was kind of always like, you're talking about these two languages being like the folks who are on the ML side versus like the wet lab component. And we always in my lab, but heads, I was on the wet lab side, can't pretend to understand ML. But I was like, our side was always like the computer's lying. We need to do x-ray crystallography and like get it in a lattice if I'm going to actually believe it to be real. And all the comp bio people are like, what are you talking about? We can just run it on the computer. Like, well, you don't need to do this. But I love that there's bilingual where both sides can kind of come together and not necessary buttheads like my experience. So that's really awesome. And I guess, you know, just more broadly speaking, you know, a lot of startups have dreams of IPO-ing and running a public company. Can you talk a little bit about what is the experience of running a public like, you know, everyone's like, yeah, like, I'm shooting for the IPO. And then the kind of that's where the story stops. Can you talk a little bit about what is the experience like running a publicly traded company?

Martin - 01:00:06: Yeah. So first of all, it's a lot more expensive than running a private biotech, right? Because you need a back office, right? We're listed at New York Stock Exchange. So you need lawyers that help you understand how that all works, right? You need a general counsel, basically full-time, right? Because there's always something. It's not just the filings that you have to do. It's the audits. Your board meetings become a lot more complicated. There's several committee meetings that you have to push out. It can become, if you're a small company, very, very labor-intensive, right? So you're spending a lot of time on that. So I always say, be careful what you wish for. Of course, right? At one point, you should go public. Of course, at one point, you should basically build something or kind of use that money, the IPO money, to kind of build the next generation of your company. But there's a right time and there's a wrong time. And I don't mean IPO window, right? Because the market's ready should not be a reason to IPO. That's a very, very bad situation, right? You can probably, as a private company, always raise money. And people come to me and say, well, you as a public company, you can always raise money. No, it's not true, right? In this environment, none of us can raise money right now. It's hard on the private side and on the public side, right? And specifically with interest rates so high, putting money into a high-risk biotech endeavor, right? It's at the moment, just not an attractive proposition for many investors. And we see this with the funds having very tight purse strings. We see this with the level of interest from investors we get. And so it's not always a good idea. What is really important, and I think this is really something that applies to private and public companies, right? Manage your funds well, be scrappy, don't waste money, be very, very clear about, be capital efficient, right? I think every really substantive company we have in the US, in Europe, is living by the principle, be capital efficient, right? And so I think this is something that is harder to do as a public company than as a private company. But if there is a trigger moment, right? Say you want to move three, four, five, programs in the clinic all at the same time, there is probably a good time to kind of go public, right? Then you have to think about, well, these programs, I might be able to license one after phase two, which generates probably a lot of revenue. You're going to be fine, right? But there's also other companies that went early to IPO to kind of get just to an IND. And then there was this long, long lag phase until they actually generated phase two data or were able to license their programs and basically ran out of money on the way. So I think this is really, thinking about what do you want to create as your company? What is the long-term growth? Because I think a startup is meant you're going in survival mode, right? You need to show it's working. But once you've done this, you need to actually evolve. And this is, I think, what a lot of people really struggle. This is why I'm so really fascinated by what Viswa is doing with Nveda Biosciences. Viswa really kind of looks at his team and says, okay, so I need to do the next step. The company needs to grow to whatever. And... What is the team that actually can get me there? And this can be sometimes very hard decisions, right? Because you have people who fought with you all the way to this point, but you know they're not going to be able to make that jump or don't want to make that jump into a public company, right? So how do you rebuild? And again, it's this constant peeling away of things that you don't need anymore, but retaining, like you said earlier, retaining the good baggage, right? But peeling away the things that doesn't get you to the next level and adding that expertise. And I think this is something you can never, ever stop thinking about this, right? If you don't have a continuous development plan and succession plan and think about who is going to step into that role and what are the roles that you need, what are the skills you need to have in the company, I don't think you're going to make it. And so this is something specifically hard for somebody who's just started the company, suddenly taking that step outside of that. I saw this in large companies, how succession management is done, right? I can adapt that a little bit to my current situation. But if you never been exposed to this, if you've always been in a company that maximally had 30 people, maybe 100 people, you need somebody to advise you on this. And again, if I look at biotechs and I see former pharma executives in biotechs, I can tell you roughly after 10 minutes if they're going to make it or not. I have been in this boat, right? I've been the pharma guy and I can usually tell you relatively quickly if they're going to be a good fit. But you need that support at one point. You need to build the team for the next growth phase of the company. And what I always tell people, do not build a company to flip it, to sell it. That always, always goes wrong. Build a company to solve a problem, to create value. If you do that, success will come, right? But if you're basically saying, I have this and I want to flip it in two or three years, I don't know personally of a single thing that actually works that way. It's just the wrong approach, right? You are having the wrong approach of building a company.

Jon - 01:04:52: Totally. And I think sometimes it could... At least what is spoken about in the broader internet and just like kind of the startup culture is this kind of like step up, step up, step up, almost like IPO flip it mentality. But whenever I talk to early stage founders, it's like, that's almost where your journey kind of really starts. That's not where you flip it. That's where it's like things are getting very serious at that point. And once you get there, there's like a heavy lift and the heavy lift is going to require exactly what you said. Expertise, like a back office, investor relations. These are very different functions than what an early stage company... That might be series C, A, through all the letters is a very different motion and a level of kind of like different operations, really. And how have you learned the skill set to interact with these public market analysts? And for a lot, it's Wall Street. How have you learned this skill? You've been running science and now you're put into the deep end of interfacing with public equity analysts. Can you talk a little about that experience as a CEO?

Martin - 01:05:56: So yeah, it still is. I'm still learning, right? So again, I took over the reins in January. I was interim CEO for about five months until my partner in crime, Felipe Duran, and I convinced the board to remove the interim titles. So we must have done something not too bad to deserve that trust. But again, it comes really down to the team you gather around you, right? So yes, my bias is still on the science side, right? Which I'm lucky to have Felipe around who comes from the finance side, who really kind of then can go with credibility in there. So what is really important is that you listen very, very carefully to your CFO. It's like learning a new language right there. It's the language behind finance, right? It's not the math. It's not adding numbers. It's the language behind that. That is really important. And so it's really important that you align with your CFO, with your general counsel. It's like a pitch, right? If you give a pitch for the first time, it's still a little wobbly and, you know, not everything is really smooth. And you kind of hone this over time. And you're better in honing these things and improving them if you're practicing with your team, because your team can say, that message was not clear. What did you mean with that? And so we're actually practicing a lot, presenting and pitching. And what helped me a lot, because there is an AI hype ongoing, I always start my presentations and say, we are not an AI hype company. We apply machine learning to solve very specific problems. That usually gets you the credibility that you need. You're off to a good start, right? So again, be truthful of what you can do and what you cannot do. I think this is applicable to the most sophisticated analyst out there and also to your family office that is investing maybe half a million, maybe a million dollars in the company, right? And this kind of integrity is really important, right? I know we are evolving companies. Every startup is. So we need to think big and we need to be clear we're not there yet, but we want to get there. But it's also very important that you stay truthful to what you can do today and what you will be able to do tomorrow, right? And so that sometimes is a gray zone. And the more you stay on the side of... Integrity and saying, hey, let's be very clear of what we can do and what we cannot do today, the better these things usually go. And it helps to translate science to family members. Sometimes they are not in the scientific space. But again, my best advisor is my wife. If I cannot explain to my wife in 30 seconds what I do, I'm not doing a good job. And then I sit down and sometimes I just bounce ideas and say, even if she says nothing, she just looks at me and say, oh, and then I think about something, how I can say it and say, thank you. And she said, well, I didn't do it.

Jon - 01:08:31: Yeah, totally. And I think that's incredibly invaluable advice. It is okay to not be able to do everything. And it's always not puppies and rainbows. And Wall Street is the same way. They understand that. When you operate with integrity and that's how you communicate, it's not the public market. We're always speaking to large institutions. It's kind of this weird dance where you're trying to promise a wonderful future, but you need to still be grounded in reality. And it's that kind of threading the needle that you need to do, but always operate from integrity and like air on the side. It's like, Hey, like we're still figuring this out. And we hope that there's going to be a phenomenal outcome out of this, but it could be non-zero that it doesn't pan out exactly what you said about the, it is a language. There's so many acronyms and they're all interchangeable, which is really annoying. And so anytime I like talk to friends who are coming from science and want to get more into like the business and finance side, it's like, why, like, why are they using all of this language that could be said so much more simply, but as it goes with kind of learning another language, this is how it is. And so, you know, looking forward now, like one year, two years, what's in store for you and iBio, what is on the horizon?

Martin - 01:09:46: So, I mean, again, the environment is not very conducive right now, right? So we're in survival mode, right? We have a couple of big milestones coming up with the sale of the facility in Texas, hopefully an announceable one or two collaborations coming at the horizon. If we can get there, I think we're going to be home free. But again, at the moment, it's really hard to see what is the future. Now, what we believe we can build, that's a different story, because I think we have technology now lined up and so deeply integrated that we can really kind of solve all of the current, if not all, many of the current issues in antibody discovery, all the way from going after the hard targets, being more efficient in optimizing antibodies, making them safer with conditionally activated antibodies, all the way to, yeah, we can judge pretty early on if a molecule is developable and expresses well, right? So I think we are solving a lot of these problems. What I could see is that we're bolting on technology at the beginning. Right. So how do you create more diversity in your antibody repertoire? Right. Is it a mouse model? Is it something else? But again, I see that as an area where we definitely can grow. And ultimately, it all comes down to, can we generate data sets so valuable and feed them? We call it the data flywheel, right? You generate the data sets, you feed it back in. And the more data sets you feed in, the better you get at predicting. Our dream is that at one point, you tell us the target and you tell us the mechanism of action and we tell you the antibody sequence. Now, this is science fiction at the moment, right? And it doesn't have to be a single molecule. But imagine, based on the target, we can actually express a thousand antibodies, which is nothing, and test these. We can easily test them in current biological systems within a day and select from those thousands the clinical candidate, right? So being faster, and COVID has showed us this, right? If we can make a vaccine in a year and something, we should be able to kind of cut a long, long development time, discovery time, of antibody discovery as well, right? And this is kind of the goal. How do we get? Novel ideas fast to the clinic to prove they're working or not, right? The question still remains, what are the right targets, right? Are they clinically relevant? We see in IO specifically molecules that everybody feels and mechanisms that should work, not working. So again, we're lagging information. We don't have the data sets and we're hoping we can contribute with our data set, at least to the early part of the problem. The later part of the problem, how do you select your patient? Which patient will respond to which drug? There's other companies who are dealing with this right now. And I think we see promising starts. Ron Alpha, one of the formal Recursion guys, just started the company looking into oncology. And so again, I think we see these companies popping up that hopefully will address these problems. And then ultimately we'll see probably a compression. Machine learning, it already has become a commodity, right? So I don't know, John, if you remember the old days when type setting program where you had to basically, if you wanted something in boldface, you had to write forward slash boldface. And then the word, so nowadays you switch on your computer, you open your word processor and what you see is what you get, right? The same thing is true, likely with algorithms, right? They're going to be commoditized, right? So I think the real value of future companies lies in the data sets they can generate to train new models. I think this is what will us get there faster. The models will become better and better. This is natural, right? We've seen this with structure prediction in the space every year. At one point, you know, we just declared it as solved. Right? Alpha Fold, and other systems, they've solved the protein structure prediction problem, right? And hopefully we'll see this on multiple levels, right? What I'm really curious is about linking targets, individuals, kind of, if you will, precision medicine in oncology with antibody discovery. I think this is a really important thing. If you think about big picture, what our society, what humanity needs, we're fighting against infectious diseases, we're fighting against neurodegenerative diseases, and we're fighting against oncology. That's the three things that hold us back to age more healthily, which we need because, you know, although sometimes a 40-year retirement sounds enticing. Think I would be bored out of my mind after a year or two. So we need to stay healthy longer. And I think these are the areas we just need to solve. 

Jon - 01:14:01: Awesome. I agree. I would get bored out of my mind. I'm the same way where I'm just like, I need to continuously go. Left to my own devices, I would just cause havoc. But Martin, thank you so much for your time. There's two traditional closing questions I always like to round out our conversations with. And the first one being, would you like to give any shout outs? And I know you already have. This is not to pick favorites here, but any shout outs to anyone who has supported you throughout your career?

Martin - 01:14:24: First of all, the person I need to name first, obviously, is my wife, Manuela. Without her, I wouldn't be here. She really got me where I am today. She's my confidant, my best friend, the love of my life. And I'm so happy to have her with me and be also my biggest critic when I need it. And of course, my parents, my dad and my mother were a huge help for me, specifically getting over my teenage years. But even during my career, I met really fabulous and great people at every single company, right? Thinking back at Lilly, one of the greatest scientists Lilly ever had, and a good friend of mine, Dod Michael. Funny story is we were on the phone while I was still working in Germany for a long time, and he's from Alabama. And when I moved to the US, people looked at me and said, where the hell did you get a Southern drawl from? So from my phone conversations with Dod, I picked up a little of a drawl. Dod's one of the smartest people on the planet. And he's a good friend. He facilitated so much me getting used to living in the US and being outside of my home country. And then of course, my boss and friend, Gus Gustafson, I learned from him how to put together a portfolio. You can put 10 targets in front of Gus, and Gus will say this one and this one, and you can be 95% sure these are the two targets you want to go after. So he has this really good way of putting portfolios together of drug pipelines. Isabel Aznarez at Stoke, she made a huge impact on not just my professional life, but also my personal life. Then obviously my entire team at, Pfenex and now iBio. And a good friend of mine, Saswata Talukdar, he's an unbelievable scientist. He crammed all of the diabetes clinical trials that have been conducted in the last 25 years into his brain in one night. So I wish I had that memory. So I was always surrounded by really, really smart people, by compassionate people. And I think this is what helps me do my job. This is what I rely on. And I wouldn't want to have it any other way. I'm not a fan of egos being in the way of things. And I like working in a team of, smart and dedicated people. And that's the people I'm surrounding myself.

Jon - 01:16:28: Amazing. Yeah. And I always think about like these moments throughout my career and just like personal life where there's been these huge inflection points that have just like changed the trajectory. And likewise, my wife was so supportive during the early days where Excedr, you know, I was taking a zero dollar salary and she allowed me to split the rent in our apartment so I could like not burn through all of my savings too quickly. So things like that, just like eternally grateful. And my last closing question, if you can give any advice to your 21 year old self, what would it be?

Martin - 01:17:02: Get into biotech faster. Make it even more aggressive, right? Go faster. Again, for everybody who starts a company, try to solve a big problem. Your companies are going to be narrowed down anyway at one point, right? Think big, allow yourself to dream and don't let anybody talk you out of it, right? I think this is really, really important. Jon, we met at Nucleot at the summit, which is a student-led organization to help new entrepreneurs. If that had been a resource for me when I was younger, I'm not sure if I had ended up in pharma, I would have probably gone into biotech right away. And so these are tools and these are developments. We can use them today. And if that had been available when I was there starting my career, that would have been really good. Somebody who had pushed me more into the entrepreneurship sooner, that would have been a really good choice. But again, you live through your environment. You are who you are. I'm very comfortable in my skin as I am today with warts and everything. And I think sometimes it needs that before you can make these decisions. But again, open eyes and looking what you're really good at and what you love doing. That's really kind of the advice I would give myself.

Jon - 01:18:11: Awesome, and I don't know if there's a better place to capstone this conversation. Martin, thank you so much. You've been really generous with your time. And I am abundantly sure that everyone listening is going to find so many valuable insights throughout your journey. So thank you for sharing. And maybe next time we do another podcast where we can deep dive into a topic and double click. But again, thank you so much, Martin. It's been really fun.

Martin - 01:18:35: Thank you so much, Jon. It was a pleasure being here and good seeing you again. I've become a big fan of your podcast real fast.

Jon - 01:18:41: Yeah, thank you. Thank you. I'm flattered sometimes. And it's like, even though doing a podcast, I am kind of naturally shy. So it's a learned language. It's a second language for me. But that makes me feel a lot more confident in doing podcasts. So thank you. I appreciate it. Well, Martin, thanks again. And I'll talk to you soon.

Martin - 01:18:59: Thank you so much, Jon.

Outro - 01:19:02: That's all for this episode of the Biotech Startups Podcast. We hope you enjoyed our three-part series with Martin Brenner. Be sure to tune into our next series, where we chat with Mike Stadnisky, the Managing Director of Thielsen Capital. Thielsen Capital is a seed stage financing syndicate network, which brings together life science investors, operators, and innovators to drive exceptional outcomes for founding teams, investors, and science. Before Thielsen, Mike was the CEO at Phitonex, which was acquired by Thermo Fisher, VP and GM of Informatics at BD Life Sciences, and the CEO of FlowJo before its acquisition by BD. Mike's wide-ranging experience in both life sciences and venture capital offers many insights that founders can 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 Excedr or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.