Episode 121: Using AI to Bridge the Translational Gap in Biotech and Drug Development

On this episode of BIO from the BIO, we're not monkeyin' around. Learn how to connect those monkeys to humans using AI. You're listening to BIO from the BIO, empowering biotech leaders with the tools, assets, and expertise to improve healthcare outcomes. Each episode provides insight and information to expand your network, advance your product line, and help your company thrive. Serving the entire Gulf South that recorded in the heart of New Orleans. Welcome back to BIO from the BIO. From your host, Dr. Elaine Ham, the executive and resident at Tulane School of Medicine, and we are actually BIO on the BIO this week. We are taking advantage of our conference this week and having an opportunity to sit down in person with our incredible guest. And our guest today is Dr. Todd Kilbaugh. He's a professor of anesthesiology, critical care medicine, and pediatrics at the Children's Hospital in Philadelphia. But for BIO on the BIO, he is coming actually as the founder of Farros Biolabs. And I'm excited to learn, well, I already know a lot about Farros Biolabs. But I'm excited to share what you guys are working on with our many, many guests. Welcome to the show, Todd. Thank you so much. It's a pleasure to be here and I'm excited to be on your program. Okay, so what is an anesthesiologist, critical care doctor, doing with a startup called Farros Biolabs? Tell us the inception or the origin story of it. Yeah, the insidious story. Where were you? What were you drinking? My career really started here in Louisiana. So I was a pediatric resident here at LSU Health Sciences for several years. So I started all my training here, fell in love with the city. Yeah. My life changed over time and I ended up in Philadelphia, where I finished my training at the University of Pennsylvania and the Children's Hospital of Philadelphia. And during that period of time, I started working in biomedical engineering and just sort of fell in love with the idea of science and research. Yeah. And one of the things that I really noticed was that one of the major problems in pediatrics, in fact, the number one cause of morbidity and mortality, in pediatrics is traumatic brain injury. Really? No FDA approved drugs for traumatic brain injury, bigger problem than pediatric cancer, bigger problem than fresh water, bigger problem than infectious disease. I had no idea. I mean, we think of TBI. We always think of adults, but yeah, children. Yeah. So in a very rapidly urbanizing world, where the United States TBI has gone down, the rest of the world traumatic brain injury has gone up. So huge problem for our warfighters. As you know, it's a signature injury for most of the last several conflicts that we've had, and also still a serious problem for children. Yeah. So I got really interested in sort of the gap in translation. Why was there zero FDA approved drugs? Yeah. I also fell in love with mitochondria at the time. Sure. That good. That happens. It happens. You know, you're sitting right in a bar. Me and mitochondria in a bar. That's right. There's a joke in there somewhere. Yeah, exactly. Three mitochondria walk into a bar. What happens? Use your mother. Exactly. Oh, good one. Yeah, nice science. I'm very lucky that I started working in an institute with a gentleman named Doug Wallace, who's won the last girl award for mitochondrial biology, really is an incredible mitochondrial biologist and sort of sparked my love over the next sort of 15, 17 years of developing therapeutics, devices, and diagnostics in sort of this translational gap, which I really felt was a problem in making the jump from rodents to humans. Yeah. Animals are not the same. Turns out, I think the phrase is mice lie, monkeys exaggerate, and humans just sometimes fail. So making that translational leap is so hard these days. I mean, throughout the history of the development of drugs and therapeutics, that next step is so hard. Yeah, most things fail, right? So if you look at sort of even funded companies, about 80% of the ideas for AdMET, for toxicology, distribution, metabolism, most of that doesn't translate from rodents into nonhuman primates. And so I really focused on poor sign models for a long period of time because of trauma and that sort of built out to extra corporeal support devices, cardiac arrest, traumatic brain injury, all kinds of things in my lab just kind of grew and grew and grew. And sort of the inception of Ferris was really, how do I kind of have a bigger ripple effect outside of even my own lab? And that's how I started looking for collaborations and those sort of things of how we could actually really change the translational gap, the whole translational idea of taking an idea from conception and maybe you are using rodents or humanized mouse models or even mathematical models in silico to come up with ideas of what might work, what might not work in terms of drug distribution and drug efficacy. Yeah. And how could we then in this new idea of AI, organoid development, reduce and reuse and do efficient nonhuman primate studies? Yeah. How could we really just sort of really set the whole preclinical development pathway on its ear? Yeah. Well, and we're really being pushed by this, especially this administration of moving away from animal models. I mean, I've always wanted to move away from animal models, but we are, I don't know if, I feel like we're not quite there yet and we have, of course, a lot of things in the AI world that are helping us get there. Do you think that, how far away out do you think that we are from getting rid of animal models completely? A long time. Yeah, I do too. That we should or different, but I mean, we want to do it right. Well, I think that's the problem. You know, like AI models in general, they're taking data that they already exist and searching faster and giving you answers faster. But the problem is the data doesn't exist. It doesn't, yeah. So an idea for lab on a chip, organized models, those sort of things, all fantastic. Yeah. All part of an FDA modernization, and this is the goal of what we want. The problem is how do you do that safely? How do you maintain regulatory optimization? Yeah. How do you, in this next phase, really, how do you use animals appropriately, optimize their lives, not only for human health, but I think we also have to realize that most of the drugs that are available for veterinarians, for others, come through what we develop with animal models. Absolutely. So how we deal with our farmers, how we deal with production animals, how we deal with our companion animals. All of these things come through animal testing, which we want to reduce. And the ability to do that has to start with building the right or unidentified models, building the right in silico sort of ideas, building the right mathematical models that then take over, over time, the use of these animal models. Yeah. So it's great to say it. And it's the wish that we want. Yeah. But it has to be done in a way that preserves the science for everyone. And I think that's really the key. And that's what we want to focus on at Ferros. Yeah. And that, you know, we talk about failure. I mean, efficacy, you could have, I mean, I've cured a lot of mice at MS. Yeah. I've cured in mice of endometriosis. My mice don't even have periods. So where do you think that we fail? This preclinical stage that I operate in, that you guys operate in, we have what? The 10% chance of actually becoming a drug if that. What do you see beyond just efficacy? Like, I have efficacy that causes a failure from now until we get it into a patient. And it can be anything. It doesn't have to be just a science part either. Yeah. There's so much study design is one of them. Let's take traumatic brain injury, for example. It's tough indication. So tough indication. Very difficult to get venture capital interested in funding it. You say it out loud. It's a huge problem, right? It is. You can say the total addressable market is X. Yeah. It's the number one cause of disease and disability in our warfighters. Yeah. Like, you can just say all these things. Markets there. Everybody goes, yes, yes, yes. But try to move an asset forward. Yeah. Not interested. Yeah. It's mostly because you can't do good clinical trials, right? Hard. So where you have the oncology field, you know, your oncologist, you don't walk in. And they say, oh, by the way, you have breast cancer. Yeah. They say you have breast cancer. We've done a biopsy. Yeah. We know the genomics of this. Yeah. We know the trials. We can put you in what you won't be put in. And then we're going to follow biomarkers as we go. That will tell us whether we would modulate you on one oncology pathway versus another, versus another versus another. Endpoints are much more clear. Endpoints are clear. Cancer go away. And biomarkers are clear on when to change and when to adapt. Yeah. And so adaptive trial design is a huge problem. And I think this starts in our preclinical phase when we don't use things like oling proteomics and we don't use, and we're not developing biomarkers as we're developing indication to follow these particular assets forward into clinical trials. Yeah. Those companion diagnostics teach us a lot about whether they who to enroll, when to enroll, the differences between genders, the differences between age, all of these different things that we sort of we guess at in a lot of especially the CNS world. Yeah. Has been a huge problem. And I think that starts at the preclinical phase and again using AI models to understand what the biomarkers are in humans, how that looks in primates, how that looks in poor sign models, how that all sort of works together to actually predict your clinical trial, so that we have a fit instead of a 15 year phase of drug development, we shrink that to five. And we do it really quick, tight, and it's telling us what to do in our clinical trial. The cancer field has that unlock down. How do we take that to everybody else? And they've done it really well. And I think the gene therapy groups and those sort of things for rare disease are starting to figure some of these things out too. And that's going to be key to what we do in our next phase of clinical development. Because oncology has the great luxury of having gobs of data out there and in years of it being really focused on gathering data. CNS probably less, I mean definitely less so, I would say probably more quality of the data, especially as you get into some of the more genetic linked sort of rare disease, CNS types of things, but I mean I can't tell you how many times I've had investors say what biomarkers are you going to be looking at in the clinic. You better know these days, your biomarkers and you better be looking at it in mice and in monkeys and see what they can do and fall it throughout the trials. And so having that knowing your prognostic or your diagnostic that you're going to be utilizing, you need to know that in your preclinical stage definitely. And so I think that's where really the AI piece comes into play in helping us bridge that gap. I think so too. And that mathematical modeling, again, venture capitalists are smart. Yep, very. They understand how to model. They understand how to write Python. They understand how to do financial modeling. They are very, very, very, very, very good combatants. Yeah. And so when you as a scientist sort of go in and you forget that their math is as good as your math. Yeah, it turns out it turns out if not better, being able to break down mathematically why your asset will work. Yeah. And why it will not just work from an efficacy standpoint, but why it will pass a phase one, how it will work in a phase two A to B, and how then the mathematical modeling of how it will work to a phase three. Yeah. And that starts, I think, in your pre-IND. And that's where I think the CRO industry fails us a lot. The big CRO industry is really built for you to go as a small medium-sized company to go back a few times because the studies didn't quite work. You really need to do it again. It turns out every single time even you fail a toxicology study, that's a six-month revamp if you're lucky. If you're lucky. It's probably a million dollars or what people suggest is about a million dollars of burn rate for every one to two months that you failed your first initial toxicology study. And that's just talks. We're not even talking about efficacy. Yeah. So, this big valley of death, there's an engine that's sort of there for you to fail. Yeah. It's made for you to fail because there's about a hundred consulting companies that are living off of you. There's the CROs that are living off of you. And they want you to keep needing them in those sort of things. And I think that's where this AI revolution sort of comes in, where if smart people, including venture capitalists and scientists and entrepreneurs can capture good data quickly, understand that modeling, and do it in a the right way, then everybody's talking on the same plane. Yeah. And I think, quite honestly, a lot of those consulting companies and a lot of those others start to drift away. And I think the trick in this is having AI co-scientists and AI co-regulatory. You need the really smart scientists and you need the really smart regulatory people and you need the really smart business people that work with your agentic AI that just gives them data that they can interpret faster, quicker, cleaner, and better. And that sort of merger of those things hasn't quite happened yet, but it will. And the companies that do that, whether you're a venture capital company or you're a scientist or you are a CEO, it won't matter. It matters if you do it right and do it fast. Yeah. And that piece around, so as we think about, it's just nice to have that in your back pocket when you have an investor that asked not just the biomarker question, if you've gone through something like a digital twin process where you can try to predict side effects and say, look, we're about to make that big leap that all preclinical companies have to make going from a monkey into a human. But we have this extra set of data that we've done. And it's early days, but we think that the side effects that we're going to see is this. If you're lucky, you can maybe throw in a competitor's drug as well and do the same kind of comparison. And it just adds to that confidence of the story as we sort of move into a new type of science where we're able to take those AI tools, pair that with the work of a CRO and utilize those monkeys appropriately and be able to help patients faster and hopefully prevent some of the side effects and things that kill drugs in the long run. For us, that's where I'd like to see Ferrosco, the idea of using. Yeah, tell us where you guys are headed. Well, I'd like to see AI, organoids, and non-human primates, sort of in that sort of closed loop validation. Over time, reduce, reuse, and primates disappear. Absolutely. As part of our ecosystem for pre-clinical development. That will happen if you develop the right organoid models, the right in silico models that are based off of, for example, a liver. We want to liver on a chip. That sounds fantastic. Sounds great, right? Amazing. And the FDA says you can just use that and take that directly into a clinical trial. I'm not sure as a patient, I want that. I'm not sure a venture capital group is really ready to bet their house on a liver on a chip. And certainly not Farmer. At the end of the day, Farmer is still going to want all of that. Of course. And so what we want to do is create the correlation and the mathematical modeling between the best organoids and non-human primates that tell us that this is safe. And then use the least amount of non-human primates to then do the best studies that predict in humans not just the toxicology, but also the efficacy. And I think what will end up happening over time is we'll use less and less time human primates for toxicology and we'll use more non-human primates for proprietary disease like Alzheimer's. More than niche type of things that are terribly complicated. Terribly complicated. And so your interest is obviously in women's health. And we've spent no money and no time dealing with how that even works at all. In male mice and males. No model. Yeah. Outside of rare disease for pediatrics. What have we done in kids? Almost nothing. Kids are scary for drug development. Of course. For our women, because they can get pregnant and they might hurt the baby, there's so many. All of those things. And I think that's we haven't even started to build the data sets for the generative AI to go out and co-allate and feed through and do that. And so until that's actually built, then we have, we won't really have anything. But it feels like we're on the cusp of that. And I really am thrilled of groups like Pharaohs of being able to take all of these things. These resources that are out there. And not just say slap AI on your, your pitch deck and say, yeah, we got AI. But really thoughtfully using that in parallel to the resources that are also cutting edge like organoids. And being able to piece these things together to a quilt that actually can be useful for moving things that really big step that most companies are at right now. So that's exciting stuff Todd. Yeah. I think I'm very lucky that I've been a physician and a scientist for a very long period of time. It's a really exciting period, I think, over the next couple of decades. We've watched immunotherapies just kind of explode on biology. And for me, I've watched the, especially around the CNS space. Just kind of eek along and eek along and eek along. And I think with these types of changes or understanding and developing organoid blood brain barriers and AI to predict that and using non-human primates effectively and thoughtfully and those sort of things for a very complicated, deeply ocean of the brain. We're going to see that inflection point over the next decade to two decades, especially with biomarker development and all these things. The ability to do these types of real adaptive trial design, quickly take drugs. You know, Alzheimer's drugs right now. The ones that are getting funded, they changed outcomes by two to three percent and they're billion dollar drugs. And can make it worse if you are apoi for genotype and you have that indication. It can be hard. And that's how starved we are. We'll take that. We'll take that. How desperate we are for to solve the brain. Yeah, I'll take a DNA med because I know. 100%. Yeah. It's been a really exciting time and I'm happy to be a part of it. Well, I'm thrilled for your brain. Someone that can help so that, you know, again, knit that quilt together of these wonderful resources that are out there and use them in a really thoughtful way to make those big leaps. And it is a really exciting time and an exciting time to have a group of like pharaohs to help lead that charge. So thanks for being on the show Todd. Thanks for having me. Appreciate it. Please, we'll have more information about Todd, about TBI, about pharaohs bios in our show notes. Check them out there and be sure to like and subscribe and learn more about bio from the bio. Thanks, Todd. Thank you. Thanks for joining us for bio from the bio and we hope you'll join us again. To never miss the latest updates in biotech, hit the subscribe button now. We'll catch you on our next episode of bio from the bio. [Music]