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Ep. 20 Martin Brenner on Intersecting AI and Biology for Obesity
54 Plays19 days ago
Martin Brenner is the CEO of iBio, a company advancing antibody therapeutics for obesity using multiple AI engines – but the company started in a very different place. Listen to Martin describe the company’s transformation, how leaders in AI and biology can refocus their narrative on reality, and what kind of team he builds for success.
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Transcript
Introduction to Spark Time and Mighty Spark Communications
00:00:00
Speaker
Hi, everyone, and welcome to Spark Time. I'm Dani Stoltzfus. And I'm Will Riddle. Of Mighty Spark Communications. Our mission is to use scientific innovation to drive transformative change. We believe that compelling storytelling is the most effective tool we have in our arsenal to motivate and inspire audiences to invest themselves in audacious goals. We are scientists by training, storytellers by experience, and entrepreneurs by nature. Let's get started.
00:00:28
Speaker
Today
Creating Obesity Therapeutics: The Complexity of Obesity
00:00:29
Speaker
we spoke with Martin Brenner of iBio, who are creating obesity therapeutics with the target population in mind. Of course, obesity is a crowded space right now, and while Martin highlights that Encretans have been very successful and for good reason, he's also aware that obesity is a highly complex disease, and one solution for this disease will never fully address the need.
00:00:50
Speaker
Yeah, that's totally true and I really liked how he spoke about what the end product should look like. When iBio is building a product from the ground up, they use both essential tools and expertise, but they also add in the precise engineered AI tools that cut their timelines in really impressive ways.
00:01:09
Speaker
And as Martin highlights, it's really an art form to build a strong platform to make a successful drug. So the peek into how he does both was fascinating. Not to spoil it, but a resilient team plays a big part.
00:01:22
Speaker
Yeah, definitely. Well, Martin's rare expertise in the overlapping areas of AI and biology are so essential in this era where it's really still difficult to see at first blush whether a company's an AI leader or simply looks like one. For the amount of time that AI has been in the mainstream drug development, he's had a lot of exposure to
AI in Biology: Identifying Genuine Leaders
00:01:45
Speaker
it. So it was a treat to hear how he thinks about building a company, a team, and a pipeline around the technology.
00:01:51
Speaker
I agree it's often difficult to see from a company's communication materials whether AI is really factoring into a company's efforts from a base level and that it poses a communication challenge to the company's lead in the space. I really enjoyed Martin's perspective on these questions, so let's dive into the episode and let him speak for his work.
00:02:11
Speaker
Today, we're joined by Dr. Martin Brenner. Dr. Brenner has a strong history of success heading drug discovery and development teams at several of the world's leading pharmaceutical companies. Most recently, Dr. Brenner served as a CSO at Phoenix, Inc., which created an advanced pipeline of therapeutic equivalents, vaccines, biologics, and biosimilars. Previously, Dr. Brenner served as a CSO at Recursion Pharmaceuticals, a company focused on accelerating drug discovery for rare diseases and diseases with high unmet medical need.
00:02:40
Speaker
Prior to recursion, he was VP and head of research and early development at Stoke Therapeutics, a company using antisense oligonucleotides to increase gene expression for the treatment of rare diseases. Before that, he was executive director at Merck, where he built a biotech unit from scratch, focusing his team's research on diabetes and NASH.
00:02:59
Speaker
Earlier in his career, Dr. Brenner was a Senior Director and Head of Cardiovascular, Renal, and Metabolism Biosciences at AstraZeneca. In addition, Dr. Brenner was an Associate Research Fellow at Pfizer, where he led the iLIT Biology and In vivo Pharmacology in the CEV MED Target Exploration Unit before assuming the role of Head of the Insulin Resistance Group.
00:03:19
Speaker
Well, Martin, it's fantastic to have you on the podcast. And so how are you doing today? I'm fine. Thank you very much. And thank you for having me. Absolutely. So I want to start with thinking about your career in the cardio metabolic disease space, um followed by the time at the early AI adopter recursion pharmaceuticals. So now, of course, you're at iBio. And would you mind telling us about what iBio drew you in as the ideal confluence of these two fields?
00:03:48
Speaker
Yeah, so
iBio's Transformation and Innovation Focus
00:03:49
Speaker
ah first and foremost, iBio is a real great turnaround story, complete with all the drama ah you would expect from a biotech journey. um So i buy when I talk about iBio, I need to always go back because iBio is a very old company. iBio was started in 2008 with a completely different goal. um Actually, it is a plant-based CDMO at the time making proteins from plants um with the sole purpose to make vaccines for the military. And so fast forward to 2021.
00:04:19
Speaker
ah The company wanted to build a biotech arm that would feed programs into the CDMO. While the biotech arm um basically was built from scratch, and this is what attracted me to it, um the CDMO didn't work out really well. and The reason for that is it's just getting people to jump from a well-established expression system like mammalian cells for antibodies back to something very novel, it was really, really hard. and so Again, this this business unfortunately did not work out. Our main competitor, MediCargo, went bankrupt, although they had a vaccine developed, so it it it was a hard business. But on the other hand, what really attracted me is it was a blank canvas for the biotech side. yeah So I could build the team, I could you know build and and create the facility here, I could be ah build a research strategy, and it started all with um us buying the assets of a small biotech company in the Bay Area called Rubrik Therapeutics.
00:05:14
Speaker
Rubrik was really a pioneer in and in AI-enabled antibody discovery back in the day. They were literally two to three years ahead of their time, which, as you know, can cause issues with funding. yeah And so um I knew the Rubrik technology from my last company, Phoenix. I know the co-founder really well and you know admired his work and always wanted to do something. And when the opportunity arose that you know we could buy the assets of the company,
00:05:40
Speaker
ah bring down the technology to San Diego and also bring the team down, that was particularly important. And so that's where the real iBioTurnaround story really started. And we yeah as you know, as you mentioned before, I was at Recursion as the the first non-co-founding CSO. um So these early times in AI-enabled discovery were really, really hard. so So some things worked really well, other things didn't work. And based on that learning, I really had a good idea of what I wanted to do with
Scalable Data Architecture for Drug Discovery
00:06:10
Speaker
iBio. And the first and foremost ah important thing to me was build ah an architecture that is completely you know built for future and for scaling. um As you know, some of my former companies in in Large Pharma, they have you know millions of datasets, if you will, but they're not really kind of created in a way that you can all combine them. So you normalize them until basically your signal to noise ratio becomes zero. And so
00:06:36
Speaker
it's It's not really useful. So we wanted to build a data architecture that A, you know, collects the data always in the same way, but also enables our data scientists to read out the data and train the new models really, really rapidly. And that was kind of the the foundational part. We wanted to build that architecture from scratch.
00:06:53
Speaker
The second part was really the team um because what we learned in in in early times AI in in drug discovery was that it is really, really important that, you know, the different disciplines communicate. And it was very hard in the beginning for data scientists to understand biology and for biologists to understand, you know, data science. And, you know, I have a and before even my my time in as a veterinary in veterinary medicine and studying that um I have an engineering background, so I always was looking out for things that I could combine and translate. And so bringing people in here that had a biology background but were trained in automation, in engineering, in data science was really, really kind of what what made iBio very different at the early times.
00:07:37
Speaker
Now, more and more talents come along. But and at the time when we started, there there were literally only a handful of people on the planet who actually had this multilingual background. And now it's actually coming nicely along. And that was actually the trick, what what caused you know us to kind of move quickly forward.
00:07:53
Speaker
Yeah, it's really interesting that you say that, Martin, because I come from a highly multidisciplinary background as well. And finding people that can speak more than one language in a research environment is so critical to the speed at which you can advance the science. So yeah I very much understand that that need. And congratulations on successfully executing upon that.
00:08:13
Speaker
And then I guess you know once you had the team, you had the infrastructure, know the first challenge you really faced was thinking about you know finding antibodies with ideal characteristics for any you know particular disease that you might be targeting. And that's especially challenging at the clinical stage because there's a lot more ah criteria that go into what a successful antibody looks like besides just efficacy. So how do you think about arriving at the most de-risk candidate by the time you finally get a program to the clinical stage?
00:08:47
Speaker
This is a very, very good question. I'm glad you're asking this question because I think a lot of people specifically, if you're looking around at the platform companies, for them it is really, really hard to understand that you know it's an art form to build a high value pipeline, but it's also an art form you know to actually make a drug. and so That's, I think, what differentiates us
iBio's Strategic Program Initiation
00:09:10
Speaker
most. So we never ever start a program unless we have a so-called line of sight document. Line of sight means we're looking all the way at the end what this product would look like. Is there a patient population? Is there an individual patient that will benefit from it? Is that patient willing to you know use the administration route that we're thinking about?
00:09:29
Speaker
um And then it's a doctor that would prescribe the drug because he sees benefits for his patients from this. There needs to be a payer that pays for all of this. And then ultimately, there needs to be a regulatory path, at least somewhat carved out, right, that you know you can actually approve this drug in a phase three approval trial. And then we work our way back to phase two, phase one, clinical development.
00:09:51
Speaker
which means can you enroll patients or are there only 20 patients worldwide? So in obesity, obviously, that's not the case. But if if you're in rare disease, that that can very, very much kind of derail your clinical development because there's just not a lot of patients you can enroll. And then we work the way back all the way through preclinical development because Imagine there's no animal model for what you need to do, and you have to build this animal model. That can easily take 18 months. So it's not that we don't do things if if we don't have certain aspects of that plan, but we want to know very quickly from the start, how feasible is it to move this program forward rapidly, and is there a true product at the end? And I think this is, we we just give this a little bit more thought than some other companies do, and I think that always, always pays out.
00:10:38
Speaker
Now, you have to put this plan into action and execute in it. That's a whole different story, but this is why we have a really good team and um we have a platform that can actually deliver. So, um for example, if you look at our lead program, iBio600, which is a long-acting anti-myostatin antibody,
00:10:55
Speaker
um This molecule was really designed for an obese population. We looked around and for our competitors, some do intravenous administration. You cannot treat a large population intravenously. right Think about this. yeah There's about a billion, probably more now, obese people on the planet. right If you capture 10% of that population, which is a lot, but you know I am an eternal optimist, um that's 100 million people that you need to treat. They don't have a severe disease. Antimatostatin started as as an anti-cacaxia treatment in in ah patients with cancer. Then it moved to neuromuscular diseases, which are rare, really difficult to treat diseases.
00:11:35
Speaker
But now we're talking about obesity, where I have the convenience of, you know, once weekly subcutaneous injections, small volumes, are small needle sizes, you cannot treat patients like this with, you know, an IV push. It's just not feasible. So we sat down and really designed this, can we actually make a sub cue ah Can we make a long-acting molecule? um Can we also prepare for this huge population by optimizing expression levels, which our mammalian display platform allows us very early in the discovery process? And can we also build in the stability, ah you know the low aggregation potential and also kind of the
00:12:12
Speaker
you know, being squeaky clean, not totally reactive, all of these things we've built into this molecule, just ready to kind of treat a much, much larger population. And I think this is where if you have a legacy anti-myostatin antibody that was designed for a different disease, where patients tolerate a different product profile, um this is where we really started from scratch. And we had the um the chance to really kind of build this out as an obesity drug and not starting with, you know, a neuromuscular de disease drug.
00:12:39
Speaker
So hearing you say all of that, Martin, you must be like, you know, just look like gold to investors because I don't think it's very often that we hear ah people speak in that level of detail of how to plan for the clinic. And we hear a lot of people say, you know, we always start with the end in mind, but you basically just gave us a very clear roadmap of how you guys do it at iBio, and I just want to you know, applaud you on that thinking. And I think if there's anything anyone takes away from this podcast, it's what you just said just now. the Thank you. and And again, I don't want to make make this sound like we we just invented this overnight. So this has been you the last five years in pharma that I spent there, plus the but last 10 years in biotech, um we honed in the system, right? I was at very different biotechs. I was at stock therapeutics, you know, employee number eight.
00:13:29
Speaker
It requires a different portfolio and and pipeline planning. And then recursion with 100 programs almost executed in parallel, that required very, very careful kind of assessment of what is actually important that you need to move quickly now and what can actually wait a little bit. So again, it it it was built and honed over the years, but it's not like we're the only ones doing it. I think we just more consistently kind of evolved the system ah to fit our needs.
00:14:00
Speaker
Absolutely. Well, I want to come to, because it's 2025, we're obligated to talk about AI. And speaking of the platform, as we were just getting there, we know that iBio has built a very advanced or a couple of very advanced AI engines that drive parts of the discovery engine. So When we come to AI, i mean we know that it's a buzzword with multiple connotations. On one hand, of course, it means a discovery approach that can be almost endlessly scalable, which is you know very enticing to the investment community. But on the other hand, it also suggests that there's a black box that's making discoveries that will end up as therapeutics, which
00:14:41
Speaker
maybe on the regulatory side, that turns into more of a headache. But Martin, from a communications angle, what do you think the most important lesson you can share about unpacking your platform with disparate audiences like these, especially as it relates to AI? Yeah, this, again, AI got a little bit of a bad rep, right? So I think we have really good examples, um local and short term examples where it really um did something we couldn't do before. Yeah. right The whole protein structure prediction engines that we have, they're very, very useful. And they have enabled us to do things we couldn't do before. So for example, our latest program, active in E, that was a protein that could not be made recombinantly outside of the body, right? So how do you raise an antibody against something you cannot make and inject in a mouse? So
The Long-term Potential of AI in Drug Discovery
00:15:25
Speaker
um we isolated ah regions on that protein, on predicted um structures that had a scaffold underneath that would um
00:15:33
Speaker
stabilize those regions nearly identical to the original protein, right? And that allows us to screen antibodies and we found a bunch. And one was even the sub-nanomolar antibody that usually happens after immunization or optimization. But so that's where we literally can enable drug discovery where things were not possible before.
00:15:51
Speaker
We've also used our optimization engine to more or less go from an idea of an antimyostatin molecule to a development candidate in seven months. To my knowledge, this is the fastest, at least in the companies I worked, that I have ever seen. um We literally, seven months after starting with without any infrastructure, started a non-human primate study. um So that's really fast. So we have real application that I can show to you that it's actually doing something. Now, on the macro level, that's a whole different story, right? I always tell people, you know, making medicines, this is long-term effort. We will not see for a decade or two if if AI really kind of makes makes a real difference in being faster, being more efficient, um making drugs that have um
00:16:35
Speaker
less attrition in clinical development. And this is, I think, where where we got a little bit into a bad reputation because some companies, like you mentioned, used it as a buzzword, right? It's it almost, you know, in some ah people, you know, cause this this vision of we have a Skynet like, a Terminator like Skynet in the back, an entity with a consciousness and that's developing drugs. And that's not the case, right? Making a medicine is literally 10,000 steps. yeah And if we can solve three of the steps of the 10,000 that we couldn't solve before with Gen AI, this is huge, right? But will this ultimately translate into the entire pharma market that, you know, we make drugs faster and better? That's obviously the goal. But, you know, this will take years and decades to really figure that out and what it really means. Again, I want to be very, very careful because um i it was just slightly before my time before I started when the molecular ah biology revolution came through pharma. We literally made drugs from plant extracts before, right? And then medicinal chemists synthesized it or tried to, and and then that's how we made drugs. and then
00:17:44
Speaker
suddenly the molecular biology revolution came along and we could express genes and we could you know go on a molecular level hitting a receptor, hitting an ion channel. And you know I remember that you know some old-school pharmacologists you know called it voodoo and and didn't believe in it. And now we cannot even think about you know not having it. And I think what we're going to see over the next years is AI is going to become a commodity to a large degree. right If you don't use it, you're behind. right You wouldn't pack out your typewriter and send them a letter.
00:18:15
Speaker
you would send an email, which is you know allows you a lot more flexibility or faster. And I think this is going to be adopted by by many, many people. I just want to make sure that people don't feel like making an AI drug. I want to be clear how complex and how hard it is to make a medicine and how many many steps we enable with AI and how many steps we still do the traditional way. There will be changes over time.
00:18:39
Speaker
they will things will work um and AI will will solve them, and other things AI will not solve them. I think we we just need to be be aware of this. And I think a friend of mine and and a very successful CEO of a startup company, Enveda Biosciences, his name is Vishwa Koluro, he said it best on on when when I was on an an AI panel with him, he said, in 2025,
00:19:02
Speaker
both sides will be disappointed. The the ones that believe in AI and the ones that don't believe in AI. I think that's a very solid statement for this. Something we can count on, yeah. yeah I think that's a ah great analogy comparing it to the advent of molecular biology. it's It enables us to do many more things, but in the end, it's a tool. and And we'll see how it pans out over the next time couple decades.
00:19:25
Speaker
So Martin, I want to go back and touch on obesity again, because you talked a little bit about your lead candidate. I
Unique Solutions for Obesity Therapeutics
00:19:33
Speaker
buy a 600, I think it is. So we all know that there's a large population of obese people on the planet, like you said, something like a billion people and There's numerous ways to improve upon the first generation therapeutics and we know that there's a lot of people that are going after those second generation drugs for this indication. So, when you think about communicating your solution ah ah in the noise of the competition, what are the three most essential tools you use to make sure that it's very clear how iBio is differentiated?
00:20:05
Speaker
but ah um Only three? Why are you limited to three? you can handle no more I think you're right. so i think I actually can break it down to two. so One is obviously having a well sorted strategy. And I will get to that in a second, because I think a lot of um platform companies um don't Don't appreciate how complex and complicated it is to kind of build a very well crafted strategy. And the second part is execution execute that strategy. It's really execution. I think if you can do both, you're going to separate from from the masses. But I agree with you. It's a very, very crowded field. And we had this and you know pleasant situation for the last 10 months that we were just starting. And if you just start, you don't have data to read out. So we literally raised money last year
00:20:57
Speaker
on the promise and on on a dream that we would make these molecules. And luckily now we're in the situation where we can say we've executed on that dream and the strategy is panning out. So those are the two things um that are really, really important. And I want to come back to to the strategy. and So, as you can imagine, the question was, incretance or not? Yes. So, we immediately said, I'm not going to compete with Eli Lilly or Novo Nordisk on incretance. I don't want to be company number 103 to make another incretance. That's kind of silly for a small company like us. So, we said, okay, it's either on top of incretance, or we treat after the cessation of incretance, or we provide an alternative to incretance. This very clear structure, but we're not touching the incretance.
00:21:42
Speaker
Second was really kind of, um and I know a lot of people say this, but highly validated targets. To me, a highly validated target is one that is either genetically validated and myostatin and also active in ER, you know, very nicely genetically validated, but also pharmacological validation. And this is the beauty if you have an ultra-fast platform.
00:22:03
Speaker
um You know, there's some large companies that are really kind of excellent antibody companies that have ah thankfully published a lot about their antibodies in the past. And we can utilize that knowledge and then really kind of um use it as pharmacologic validation. Our Activin-A molecule or the bispecific myostatin Activin-A is such an example because there's companies like Regeneron that have very nicely validated the pharmacology and the the efficacy of Activin-A.
00:22:31
Speaker
um So that is basically the foundational layer. Now, um we try to build most, if not all our targets around that, but we always have, you know, we're scientists and we love these druggable novel things that are crazy. So we always kind of sprinkle in, you know, six to 10% of novel targets that might not fall in that category. But in principle, um the majority, the vast majority of our pipeline is ah for for validated targets.
00:22:57
Speaker
And then last but not least, the third part of the strategy. And this, again, you you can, this is just good, good practice, because this goes through every industry that you can imagine from Coca-Cola and Pepsi, all the way to pharma. um We try to tie um the strategy into our skills of the team and also into the technology platform we have. And that allows us basically to be the only company to execute that, um strategy, at least executed fast and efficiently. And so we're heavily utilizing our platform where we have these advantages. So if we need to go fast, we have proven we can go faster than the rest of the industry. If we need to develop drugs against novel targets that are validated, but you know are challenging to target, we can actually provide solutions for that. And so by tying this in, it's we created something that basically iBio is very unique to execute on.
00:23:52
Speaker
um And obviously, it's in an area that, you know, like you said, is becoming more and more moving into, you know, the focus because we know that incretins are a very powerful class of drugs. But at the same time, we also know they're probably not going to be the only drugs that are needed to kind of, you know, fight the obesity pandemic. Yeah, absolutely agree.
00:24:13
Speaker
Well, Martin, we're coming to the end of the discussion. But before we get there, you know we'd love to hear what you're excited about in in the coming year, whether that relates to iBio or anything in the biopharma space. what What are you looking forward to this year?
00:24:29
Speaker
Oh, I think um this year i I can categorize it in three ways. So first of all, we're we're obviously going to move our lead program, iBio600, closer to the clinic. ah For me, it was always a magical moment when all of the work you put into making a drug for the first time actually is ah being injected or dosed to a human being. this this is such um This is a really, really a big milestone for all of us. um I think I would put this highest, this might not happen this year, this might be happening early next year, um but we're pushing really, really hard kind of to um get this into into people as fast as we can.
00:25:07
Speaker
The second is ah that I think what we're seeing, and which ties nicely into the strategy we developed for obesity, is we're seeing the big ones, illa lili regeneron, focus now on quality of weight loss, not necessarily only lose weight as fast as possible. You can imagine we have to stop animal studies if the animals lose way too fast because it's considered dangerous. In humans, we don't seem to think that. um I think it's very short-sighted. And believe me, as as somebody who's who's you know going towards you know ah the mid-50s, muscle mass is a real thing. yeah And disputable, we lose muscle mass. And I do not want to end up in a situation where your weight cycle on and off incretins a couple of times. I don't want to see what that would look for your for your optimal muscle mass. So I think we're we're starting to recognize there needs to be more than that. There's a tremendous benefit of the incretin drugs.
00:26:02
Speaker
please always keep that in mind that they're really, really fantastic drugs. But I think a single solution for a complex disease like obesity is it's it's a dream. And then um the last thing, what I really hope is I think from what you heard how I'm describing AI in drug discovery, i'm i'm I hope that this year, and i'm I'm looking forward to this year, that we separate basically the non-hype companies. And I know there's a a couple just to name a few, Invader Biosciences, Leish Therapeutics, and us and and many others so that do quality work on the AI side. And there's others that just put a data scientist in the basement and call themselves AI company. i think kind of see more separation I think investors and the public will get a lot more savvy about what is real and what what is not real. I really look forward to this because we worked really hard to be bucketed on the one side, not on the other side.
00:26:55
Speaker
Yeah, that makes a lot of sense. And I think everyone on the the outside as well would really love some guidance on who's who's who's got the the goods to show that they can do it and who doesn't. This is one question we get asked all the time is, well, how do I know if this AI is any good? So any clarity we get on that I think is going to be wonderful. And I'm also rooting for you to be on on the correct side of that divide. so yeah um I guess, Martin, I'd love to finish up with a question that we ask all of our guests, and that is, if you give a single piece of advice to the founding team of a biotech company with an early stage company in particular, what would it be given all of your years of experience in that space?
Resilience and Team Commitment in Biotech Ventures
00:27:36
Speaker
So there's many, many things that you know I think you need to consider as ah as you know as a-founder and you know starting starting your own company. um But I think the one thing, apart from the little luck you need and the right idea you need, I think it comes literally down to resilience.
00:27:56
Speaker
And I can explain that a little bit, right? So I told you about iBio is ah is a crazy turnaround story. So last March, before we were able to raise money, we were literally two days out from being able to make payroll, but not a single of our team members left. They all believed in our technology. They believed in us.
00:28:14
Speaker
And many of them that I brought in believed that I would do the right thing. And so I think building credibility and having a team that is very resilient that can actually withstand all of these pressures is ultimately what what makes companies successful. It's not the first rodeo I had where resilience played a role. I had another company before where you know, we needed to complete one experiment, one animal experiment to trigger a series A. And again, it got pretty tight. But, you know, it was over the holiday period, we all stayed around and we got the study done. It really comes down to do you have the right people around you? Do you have a resilient team that is
00:28:51
Speaker
Is really resisting all of these pressures and and this is my I think my proudest achievement that nobody has left I bio when the times were really the darkest um and people have joined this company because they know I put them first and I put their careers first.
00:29:07
Speaker
And that is what allowed us to actually get through and and be where we are today. and And I would recommend this to every founder, you know, try to bring people around that are passionate about what you do, but they're not kind of, you know, getting away the second it gets hard because it will get hard, period. If you cannot deal with being sucker punched every day, you're not in the right business of biotech. And yeah go into banking or other areas that are less stressful, but science will knock you down and it will come at the most inopportune moment. And you need a team around yourself that can bring you back up. Yes. Well, I love that advice, Martin. and Will and I both had the privilege of working in a team exactly like that when we were in public biotech. So we both experienced resilience and the the sheer joy that comes with knowing everyone's in it together. And, you know, that's I really love that advice. And I think, um you know, people like to think
00:30:02
Speaker
past the human aspect, but it really is you know the humans that make it all happen. So yeah, thank you for sharing. Yeah. Well, this has been such a fantastic conversation. Martin, thanks again for joining us. Thank you so much again for having me. Really a pleasure meeting both of you and and being here on your podcast.
00:30:23
Speaker
Thank you, Martin. Well, Will, you and I know how crazy a ride public biotech can be, and it was really amazing to hear Martin's and iBio's story. To think that the company has gone through such major changes from a CDMO creating military vaccines to crafting next-gen obesity therapeutics. It really speaks to Martin and the team's capability that they've had such success.
00:30:46
Speaker
It sounds from Martin's description that by the time he arrived, they'd started with a nearly blank canvas. Yeah, they did. and and And with that blank canvas, they've been able to build foundational data sets that fuel their discovery and development arms.
00:31:02
Speaker
Of course, we know that data sets don't always pair one-to-one across studies or companies, but really enabling AI to glean those special insights requires huge data sets that, of course, don't normalize out like Martin was talking about. So, they built an essential tool, and I also like how Martin speaks to the use of that tool.
00:31:22
Speaker
Yeah, right. Because we're here from all angles around us that AI is is a tool, but Martin sums it up really nicely. It's a tool that solves for a few crucial steps in the process. Drug development, as he said, remains an art form, but the tools we have to create are advancing, akin to moving from handwritten letters to sending email. The concepts are the same, but we move much faster than before. And this is a great rule of thumb to separate how leaders think about AI versus the hype.
00:31:52
Speaker
Yeah, that's a really good point. Another critical area they're focusing on is the quality of weight loss. So obesity, of course, is an especially crowded area right now, given the success of incretance. But Martin mentioned that in older populations, especially weight cycling with incretance, could be especially harmful to muscle mass. And I agree with him that a single solution for something as complex as obesity isn't realistic. And it underscores the need for more nuanced treatment approaches.
00:32:23
Speaker
I'm sure we'll see multiple waves of obesity drugs and mechanisms of action in the next five, 10 years. Exactly. I especially enjoyed Martin's advice at the end of the discussion.
Belief and Resilience: Keys to iBio's Success
00:32:34
Speaker
I by success is a product of the team's resilience. The team's commitment because of the belief in their mission and in Martin is so critical when there's so much pressure. It's really amazing to hear that's one of his proudest achievements.
00:32:49
Speaker
So join us next time as we continue this journey to power scientific innovation with storytelling to drive transformative change and solve our most demanding challenges.