Spark of Ages
In every episode, we’re going to do a deep dive with our guest about what led them to their own 'eureka' moments, how they went about executing it, and perhaps most importantly, how do they get other people to believe in them so that their idea could also someday become a Spark for the Ages.
Spark of Ages
Cracking the Code of Life/Ashwin Gopinath - Multiomics, Product Led Growth, Jonas Salk ~ Spark of Ages Ep 19
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What if you could harness the power of AI to revolutionize drug safety? Join us as Dr. Ashwin Gopinath, MIT assistant professor and co-founder of Biostateai, shares his incredible journey from electrical engineering in India to the cutting edge of biotechnology. Discover how his startup is utilizing generative AI to transform drug safety and toxicity forecasting, and learn about the personal experiences that fueled his passion for healthcare innovation.
This episode unpacks the complex yet fascinating world of "omics"—genomics, transcriptomics, proteomics, and metabolomics—and how these different types of biological data are critical to understanding biology. We delve into the challenges of mapping these molecules due to biological complexity and individuality, and explore how large language models in AI offer promising solutions. Dr. Gopinath also sheds light on his startup's recent success in reducing data collection costs and the strategic move towards developing a product-led growth model in biotech.
Finally, we discuss the future of drug safety across species and the potential of personalized medicine. Dr. Gopinath provides insights on translating experimental data from animal models to human biology, emphasizing the importance of reducing costs and accelerating data collection. The episode wraps up with a lighthearted game of "Two Truths and a Lie" centered around pharmaceutical mishaps, adding a fun twist to an enlightening conversation. Tune in for a compelling discussion that bridges science, entrepreneurship, and AI, aimed at revolutionizing healthcare.
Ashwin Gopinath LinkedIn: https://www.linkedin.com/in/ashgopi/
Ashwin Gopinath X: https://twitter.com/ashwingop
Biostate.AI: https://www.biostate.ai/#/
Producer: Anand Shah & Sandeep Parikh
Technical Director & Sound Designer: Sandeep Parikh, Omar Najam
Executive Producers: Sandeep Parikh & Anand Shah
Associate Producers: Taryn Talley
Editor: Sean Meagher & Aidan McGarvey
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Website: https://www.position2.com/podcast/
Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/
Sandeep Parikh: https://www.instagram.com/sandeepparikh/
Email us with any feedback for the show: spark@postion2.com
Hello and welcome to the Spark of Ages podcast. I'm your host, rajiv Parikh. I'm the CEO and founder of Position Squared, an AI-focused growth marketing company based in Silicon Valley. So, yes, I'm a Silicon Valley entrepreneur, but I'm also a business news junkie and a history nerd. I'm fascinated by how big, world-changing movements go from the spark of an idea to an innovation that reshapes our lives. In every episode, we're going to do a deep dive with our guests about what led them to their own eureka moments, like in this one mega biotech, and how they are going about executing it and, perhaps most importantly, how they get other people to believe in them so that their idea could also someday be a spark for the ages.
Speaker 1:This is the Spark of Ages podcast. In addition to myself, we have our producer, sundeep, who's occasionally going to chime in to make sure we don't get too in the weeds with science and tech jargon. Today, I'm going to say mitochondria a lot, because I remember that from ninth grade biology class Hope that helps. Mitochondria, nuclei, golgi apparatus, proteomics, Cellular wall, nanomaterials All kinds of cool stuff today. We're really happy today to have Ashwin Gopinath here. He is leading in this intersection between biology and computer science. It's really cool how it's coming together. We're going to talk about how you enable data collection that can save your life.
Speaker 1:So Dr Ashwin Gopinath is an assistant professor of mechanical engineering at MIT, and Dr Gopinath is also the co-founder and CTO of a company called Biostateai, an innovative startup pioneering generative AI in forecasting drug safety and toxicity, with a focus on salvaging failed drugs. He's going to make failures succeed. Biostateai targets a $100 billion annual market and has the potential to revolutionize everyday health monitoring and intervention. Prior to Biostateai, dr Gopinath co-founded Palamedrix, a biotech company combining semiconductor fabrication with AI, which was acquired by SomaLogic in 2022. Ashwin is an associate professor at MIT, where his academic pursuits intersect, and here's a whole bunch of great buzzwords AI, applied physics, biology. He has researched that with the intersection of DNA, nanotechnology, microfabrication, synthetic biology, optical physics and material science. And yes, we will make this approachable for all of you. Sounds like a bunch of gut courses.
Speaker 2:That sounds easy.
Speaker 1:He's like taking foods and dudes at UNH.
Speaker 1:He earned his degree in electrical engineering in India before pursuing a graduate studies in applied physics with a minor in neuroscience really boring neuroscience, you know, really easy at Boston University. At MIT, his groundbreaking research on light transport within disordered media at MIT earned him the prestigious Best PhD Thesis Award. So that's really impressive because, as I've learned, only three or four people read your thesis, including your mom, and so obviously more people read that. In late 2019, dr Gopinath returned to MIT to establish his independent lab dedicated to pioneering advancements in real-time biological monitoring and AI technologies capable of emulating human cognitive processes, that's, brain processes such as self-reflection. Oh gosh. So there you go, ashwin. Welcome to the Spark of Ages. Pleasure to be here.
Speaker 2:Well, great to have you. So there you go, Ashwin. Welcome to the Spark of Ages Pleasure to be here.
Speaker 1:Well, great to have you. So we're going to have so many interesting things to talk about today, especially since you've traversed science computer science, biology, nanomaterials so this is going to be a lot of fun to talk about. Let's start with some basics for the audience. So what's a day in your life like You're a professor, company founder? What's it like?
Speaker 2:The way in which I actually phrase it is. I've been lucky enough to basically working on things that purely interest me, so like on a day-to-day basis. You know I don't have a very large lab. I have a very small lab. That is by choice, because I want to work on things that interest me and, more recently being working on things that are more translational. My day starts quite early because I travel around, but West Coast has my base, but you know, many of my things are happening in Boston as well, so my day starts typically very early. First few hours, like three to four hours, are typically for working on grant stuff, papers, talking with my students, and the rest of the four, six hours is basically on my startup. So that's my typical day. And then at the end of the day it's basically either gym run or you know, and you know you're done. It's a boring life, you know.
Speaker 1:I I would like to claim that I run and it's very full, yeah, so, so I I don't hear kids in there. Huh, it sounds like, uh, you're able to pursue all these things because you don't got not yet not yet. Not he's married, though I am married not yet okay, all right, he's married to someone they worked with at caltech, right?
Speaker 2:yeah, yeah, yeah, but. But. But me and my wife know each other for, like, I think, more than half my life now. So yeah, Amazing.
Speaker 1:Now let's talk about your really interesting startup, Biostateai. Maybe you could just tell us a little about the challenges you're tackling right now in the healthcare industry.
Speaker 2:Yeah, the long term picture of Biostateai is just, you know, both me and my co-founder, dave, the reason why we started this was we wanted to develop a technology that allows us to sort of be able to understand biology, to be able to predict biology, in the spirit of basically, you know, helping human health diagnostics and all of that. But as you, you know, pan back. It became important for us to look at a problem that has economical value now, because otherwise you're just building something you know with a, you know you get you, you make money only when you get to the mountaintop. Until then you need to basically get it funded by somebody else. So we were trying to focus on problems that are now so the unmet area that we looked forward.
Speaker 2:Very few people are working on trying to improve drug safety. Everybody thinks about trying to make drugs more efficacious and make it better. So that is to say, you know, if you have cancer or if you have a particular disease, you want to figure out a molecule or a drug or a therapeutic that helps cure it very nicely. But whenever you actually build such a drug or a therapeutic, there'll be some portion of people, just because of the diversity in the population and diversity in biology. Some of those individuals will have adverse effect. The drug will have an adverse effect, which is what the side effects and things like that. It could be very large. It could be very small. If you take aspirin, typically it's not that bad side effects, but as the conditions become harsher, the more the side effects are going to be.
Speaker 2:So the vision of where we wanted to go was how do you make sure that you can tell very quickly if a drug molecule is going to be safe and who is it going to be safe for? So that and that was also. If you look at the clinical trials and the way in which a drug goes through regulation, this was the area that we saw very few companies working on, especially using AI and big data to kind of truly kind of impact that area. So we decided, okay, fine, since nobody is there and it has real value, not only to make the life of individuals better but also unlock economic value. We decided we will go there first. But the AI and the tools that we are developing is much more general. It can be applicable for everything else, but we are applying it to make better, safer drugs. So, if I'm if I mean if you do- everything right, yeah, so let me.
Speaker 1:We've always heard this, or I've heard this notion of it takes $800 million to make a successful drug. So to do that, you go through many drug candidates or many different molecule types, and in doing so many of them fail Like they may work in an animal, but then when they get to humans they're judged as unsafe. So a lot of those things may be very promising but because of safety is you have to apply them to a pretty broad population. If they're unsafe for a decent enough size of the population, it gets excluded. What you're?
Speaker 1:doing is by understanding the safety profile of the drug and understanding the individual's genetic makeup, the omics and you're going to explain to it what that means later the different proteomics, genetic makeup, who you are as a person. By understanding that as an individual, you'll be able to take this drug candidate that may be unsafe for a bunch of people but safe for you and actually and then apply it to solve your specific problem Precisely precisely.
Speaker 2:I mean to understand that. You know you need to kind of understand how a drug molecule becomes. You know you get FDA approval At first. You start with several hundreds of lead candidates. That's what a pharmaceutical company does that's based upon. You know you look at the biology and you know what targets you're looking at and you have a lot of molecules. They whittle it down to a few, maybe a dozen of them, and then they do preclinical trials where you do these experiments on animals, where I mean you do the models.
Speaker 2:If it has, you know you put a cancer on a rat or mouse, you put it to the. You know you give them the drug, you see if there is an effect and things like that. You go to the FDA with all the data results and they say, okay, fine, now you can start human clinical trials. And then when you start the human clinical trials, the first thing that they do is test it out on random healthy individuals to see how it affects it. The idea is, if you give the drug molecule to a bunch of healthy individuals, it should not have adverse effect.
Speaker 2:The adverse effect can go all the way from I have a slight headache to you know I have rashes to. In the worst case you have death as well. So you give it to a certain cohort of a certain set of healthy individuals and that's phase one trial. And usually about 40% of drugs that start human clinical trials fails there. So it doesn't matter, you don't even get to test it on the individuals with the disease because it fails the safety I mean. Sometimes what happens is you have adverse effect. But if the condition, if there are no other drug molecules or if there is no other option, you kind of get to push it through to phase two and phase three as well. But that's the politics and figuring out exactly what is the, how you choose, what are the conditions and things like that. But the first thing that FDA cares about is safety of a drug. It doesn't matter how good the drug is if it's not safe.
Speaker 1:So now, with what you hope to do with biostate AI is by, through your methodology of testing and bringing down the cost of those tests, you're then able to take what may have been hundreds that have been thrown away and then reuse them. So potentially the cost of the development of drug can be much lower, and maybe what you're also saying is it can be more efficacious or more effective on a person. So you're like recycling stuff that was thrown away for perhaps the wrong reasons.
Speaker 2:Yeah. So there are different business models. One can build around it and we are thinking about a few different ways to do this. One way in which one could use this is look at failed drugs or drugs that were approved for a particular purpose, to be able to reuse it for something else, but, you know, or, if it has failed for a safety reason, to be able to figure out a test to go with it, so that you can say, if this individual's RNA profile looks a certain way, then this drug is going to work for you, or if it looks in a certain way, this is going to be toxic for you, we won't give it to you. So there is precedence like this. There are a lot of different tests that you end up getting. So that allows us to kind of not only take drugs that would have otherwise failed and, you know, make it useful, but also speed up the process Because you know there are different ways in which pharma can use the models that we are building.
Speaker 2:Because one of the things that we are doing is and the reason why we are, you know Rajiv mentioned that we are reducing the cost, and the reason why we are reducing the cost of these omics tests is not necessarily to make it quicker, or for the test to give a very cheap test to people quicker, or for the test to give a very cheap test to people. We are making the cost lower because in order to build the models, we need to get massive amounts of omics data. I need to be able to collect the transcriptome or the RNA profile for a lot of different samples, and if it is very expensive, then I can't collect the data. I can't build the AI. So if I reduce the cost of data collection, it allows me to.
Speaker 1:So hold on, I'm going to pause you just for a second, for my non-PhD mind. Can you explain what omics are?
Speaker 2:I might have an answer for that, ashwin is going to expand on it.
Speaker 1:I'm going to start. I looked it up it's genomics, transcriptomics, proteomics and metabolomics, and there's others. Okay, now you have to explain it, not using the suffix omics.
Speaker 2:Okay, so that's just one of the definition. No, no, no, I can explain it to you.
Speaker 1:So it's very Can't use omics in the definition of omics.
Speaker 2:How do I put this? The best way to think about it is and I won't even try to use omics Think of omics just as a word that represents a certain kind of data. So everybody has heard about genome, like DNA, okay, like everybody understands in some sense that DNA defines characteristics in your body, so, but DNA is not the full story. In biology, you have every cell in your body has a certain DNA in it, which basically is your genome. You call it as your genes and collectively you call it as gene-ome. Okay, it's a collection of genes. Okay, and the DNA typically ends up making RNA.
Speaker 2:Rna makes proteins, proteins with small molecules, with what you eat, all the other things that you put in your body. That's so. These are the four classes of molecules that basically builds up. There are other things as well. There are fatty acids and things like that, but these are four. These are four classes of molecules that typically that you think of, as you, you know, basis of life, are like, very detailed, very important for life. So, like anything that's studying with DNA, you end up calling as gene genome.
Speaker 2:Okay, a collection of genes, like. You'd call it a genome. Anything that you do with RNA is called. You call it as transcriptome, because the process by which RNA is made is known as transcribing it. Okay, and then you have proteins and you have a whole bunch of them. That is known as proteome. And if you have small molecules, you call it as metabolome, and if you are working with all of them together, you call it as multiomics or omics. Just, it's just a name to represent these classes of molecules, that collection that it's a collection of molecules that that you, you, you, that that represents, uh, like, gives you a significant idea of your biology, or like your identity. So that's what omics it's just. Basically, it's like, yeah, like my, like, my comics are a collection of my stories.
Speaker 1:That I like to you know, like my comics are a collection of my stories, that I like to you know, like forest in the trees, right, yeah, yeah the ground, the dirt, the molecules, and then you keep moving up until you understand how these systems work, and I think that's what he's describing in the way this the system works, so it's become so complex. I've heard of companies that have tried to describe the whole body in a set of equations, mathematical equations, and I think it ends up being really hard to do in building these types of models because there's so much individuality. It's not like a computer where it's just one and zero. You start with a one and zero and you can build off of it. This is like so many permutations and combinations, and I think that's why you went into LLMs, right, that's why you went into AI.
Speaker 2:Correct, correct. So the reason. So, if you trace back as to why I am working on Biostate, it's sort of you know, I was a happy-go-lucky sort of like semiconductor and like funny, like DNA nanotechnology person back in tech 2016 timeframe and I wasn't really that bothered about healthcare much till my wife got leukemia and then we both started basically looking into different ways in which you could track your healthcare and keep track of it. That took me down the path to the question of hey, we have all of these technologies, why can't we actually understand biology? I mean, and to a certain extent, biology is a very complex system, but when you go right down to it, you don't even know all the components and all the different ways in which these molecules interact with each other. So that increasingly got to a point where in like even my previous company, the reason why we formed the previous company was we wanted to measure all the proteins in your body and because if you measure all the proteins in your body, then you can track it over time and then you can feed that into an AI model, build a model so that you can start predicting what is going to happen. And even biostat is, to a certain extent answering that same problem, that general class of problem itself, but we are taking a slightly different route. So it all comes back to how much more and more data that you can collect, annotating that and then building models to actually understand how these molecules interact with each other.
Speaker 2:So to Rajiv's point. The reason why LLMs are really really good is because the architecture is good enough at this point that we don't need to know in detail what are all the interactions. We can just feed it different examples of a system and it will learn how these molecules are connected to each other purely from data. Then you can actually play with the model itself and understand what is happening and things like that. That is explainability. But that's not in some way necessarily needed. Like if I were to feed something into the system and it says that this person is unhealthy. Do you really want to know why? It's good to know, because that's needed for approval and regulations and all of that. But in some sense there is power in just being able to know if something is actually going to happen.
Speaker 1:Maybe you could talk about an example of that, of how that this I think you call it a longitudinal LLM, how that's comparable to chat GBT, and maybe an example of what someone would see from that. Like you talked about healthy versus unhealthy by building a.
Speaker 2:So what we are doing is at every time point, like in humans or any biological system, we are connected over time, At every second.
Speaker 2:What your body is going to go through is dependent on what was the situation a second before that or a minute before that. Like, for instance, if you constantly hear a colloquial way to think about it. A simple way to think about it is if you keep eating McDonald's every day, for every single meal, eventually you're going to get fat, or like you're going to get unhealthy. That's just a result of over time. It just accumulates over time. Or if you have, you know, habits, you will get progressively better. So that is, you are temporally connected to each other. At every single time point, you can attribute, you can take attributes of a biological system, and that is what we call as biostate. Like simple way to think about it is if I were to take, at every single point, as a kid is growing. You are measuring your height, okay. You're measuring your weight, you're. You know how far you can go, how you know all of these, all of these things that you measure by your you know Apple watch. That is a version of biostat. But what we are looking at is we are, look, we are measuring all the proteins and we are calling all the, all the molecules in your body, and we are calling that as the biostat. So at any given time point you have a certain numbers. You know. You have protein, one so much. You have RNA, one so much. You have like that. There's a large list. That's one biostat and at the next time point you have another biostat and the third time you have another biostat and they're all connected to each other. Okay, they're all connected to each other. We don't know how they are connected to each other and we can't predict what is going to happen next.
Speaker 2:At a very high level. That is very similar to language. You can take one sentence and then you have another sentence that follows it. You have a third sentence that follows it. What ChatGPT can do is if you give a three sentence or four sentence, it learns what you are trying to tell and it predicts what is going to happen next. What you are trying to tell and it predicts what is going to happen next. So, just like how in chat GPT, you are taking it, you know over, you know you're, you're giving it more and more words and it predicts what is going to happen next. Here you're predict, you're what you're giving it biostates over time and it's going to predict what is going to come next.
Speaker 1:Each individual because their state is constantly changing. You're going to be able to sense that change and predict where it goes next based on the volume of data that you have about many individuals or the current individual.
Speaker 2:So we have just to be clear we are not doing these experiments, we are not doing the data on humans, yet we are just doing this on humans. Yet we are just doing this. I mean because because collecting human data and doing it, doing it in a control, it's tough and you know it costs a lot more. So it costs a lot more and there are more regulations involved. So what we are doing is we are doing these experiments in animals. We are collecting over time on rats and mice. We are collecting over time on rats and mice. We are basically giving them certain drug molecules which are again approved and observing how those drug molecules change their biostates over time. And can we predict, you know, can we design models wherein, you know, we can predict how their whole body or how their states are going to change when I perturb it in a very controllable manner. So because that's one of the things that we are trying to do and this also segues into another thing that Biostat is trying to do is we are trying to quantitatively map what is experiments done in one species into another, because one of the other things that we started realizing is we want to build these models.
Speaker 2:Of course, we can't do the large amount of data as needed. We can't do these experiments on humans, but we can actually do in a humane way. We can do experiments in animals and there are approved and like FDA gives and there are ethical guidelines on how to do it. We follow all of that and we collect data over on animals and we are all. We look at all animals as fundamentally, all of us are mammals. You can think of each animal as a different language, okay, and it's just like how you can actually take a French book and translate it into Hindi or Chinese or English. Translate it into Hindi or Chinese or English. Our understanding is, if we do everything right, we can do experiments and like do these things on animals and in an approved way and from that be able to tell how it's going to behave in a very predictable fashion in humans.
Speaker 1:That's another part of what biostatistic is.
Speaker 1:That's actually a key aspect, because most people would say that oh great, great job on animals, but not everything from animals translates to humans another part of what biostatistic.
Speaker 1:That's actually a key aspect, because most people would say that oh great, great job on animals, but not everything from animals translates to humans. It's a discussion I have with my friends who do everything based on animal studies and read all these wonderful things on the internet about ways to fast or do all these interesting things like not what works there works everywhere, but what you're saying is, by doing the experiments on an animal, you're then going to create a translational bridge between that animal to humans and do a much better job of predicting without having to do all the same exact studies on humans. Are you comparing it to data? Because if you're not doing the actual tests on humans, it's just because you have the volume of data that, as it translates to, say, other animals, or because you see, you know how, like. I'm wondering how are you able to make that assumption off of of rats better than say you know what folks already do? Okay?
Speaker 2:so it's a great question. Uh, so the insight that we ended up having is so the reason why you can't compare what happens in one species to another species is because, like, even though we are all mammals, we all have you know, uh, like, when I say we, I mean like all, like all mammals, like, whether it is rats, or you like mice, or chimpanzees, or dogs or whatever it is, and humans, they all have you know. Or slots, okay, they all have you know. Or slots, okay, they all have same organ systems and they have you know. Very, they have comparable biological systems.
Speaker 2:The fine details are very different. For instance, mice and rats don't have what you would think of as skin, the way you can't take what you see as their skin as comparable to what are human skins. So if you were to basically give a particular drug to a mice or a rat, you will not. And if that and and if it, one of the side effects that it would have on humans is like skin rashes. You would never see that in a mice, okay. However, if you were to look at the epithelial cells, like their inner, if you look at their digestive tracts, the cells on their digestive tracts, inner digestive tracts is very similar to the human skin. You would see rashes there. You won't see the rashes on what you would think of as like on the rats fur and things like that, but you can capture all of these things in the.
Speaker 2:You know, when you do your the omics analysis, if you were to actually measure all the molecules of the in the rat, you'll be able to capture these things. So now the question is how do you translate it? Okay, how do you translate? Which molecule changes translates to human skin changes? Which set of molecules changes in a rat would map to like something on an eye or respiratory things? So that mapping function is what we are figuring out, and once you start figuring that out, then you can start looking at when you do an experiment on a rat. You will immediately be able to say if you were to do this in a human or in another species. This is how it is going to look.
Speaker 1:That's really, that's really wild, but you are still looking at human trials, I mean ultimately right. You still have to look at how does the drug, or whatever, affect humans. That's where we are going. It's just that you're becoming better predictors of it because you have you have so much more data precisely, precisely.
Speaker 2:I mean, that's the, that's the direction, that's the world that you're building, and it, and underneath all of this, is being able to collect the uh omics data. So, typically, if you were to take a drop of blood or drop of urine or drop of any kind of sample and if you want to do like what can be thought of, as you know, we look at RNA. The reason why we look at RNA is just because RNA is what changes over time, like DNA doesn't change over time your genome. You're born with your genome. Unless you went to Chernobyl or you got like irradiated very badly, your genome is not going to change, but your RNA and proteins change over time. And RNA is the most economical ones and the more mature technology that we can look at.
Speaker 2:Even though it is mature, it costs about depending upon who you actually work with. It costs about $400 to $800 to do one analysis for one drop of sample. So if you want to collect, let's say, hundreds of thousands or millions of samples, that's a very large number. So we need to progressively reduce the cost of that analysis itself, which is the first thing that we did, because if you want to collect, if you want to do you want to build a massive like an all-encompassing AI to understand biology, you need to first figure out the data collection engine. So we are collecting the data. We have already reduced the cost down by about an order of magnitude. What that means is what costs about $400 to do right now. We can do it in our hands. Get it down to about $50. We have already done that and it's basically going to progressively reduce from here on.
Speaker 2:We know the way to do it and that allows us to already over just the last year Wow, that's huge. Over the last year, we have already collected 2% of all rat data that people have ever collected. So in just one year of our existence, we collected 2% of all the rat data that is there using these tricks, to 2% of all the rat data that is there using these tricks. And you know, as we proceed, everything that we are building can eventually be done on humans as well. But we just don't want to go there because if we go there, it opens up a can of worms as far as regulations are concerned. We need to kind of follow all these things, and getting access to samples are harder, all of that. So before we get there. We want to get as much as possible like build up all the AI, build up all the frameworks so that, when the human data comes in, we can immediately make it value.
Speaker 1:So now, when you translate this, okay, so then you have the sample collecting that you've already reduced by an order of magnitude. You're looking at probably another order of magnitude that you want to get it down to and then you have the translational capabilities.
Speaker 1:So that helps us understand. You know where you're. Some of what your company is going right what, what do you see as the big goal for where you really want to go with this? Like, as you, as you put this together and you start to hit greater scale, like, where do you want to go and what stage? You know you've done a startup before. Yeah, what stage are you at?
Speaker 2:where we are at right now is we have already shown that in rats and mice. We have demonstrated that if I were to give it a drug molecule like, let's say, tetracycline it's it's basically a particular approved molecule In certain cases you would give it to, it's an antibiotic. You can uh, if you give it a certain dosage, a high dosage and not like a moderate to high dosage uh, we end up seeing that if you give it to a hundred so-called comparable rats, you know 40 of them have uh about an adverse effect. Okay, they are uncomfortable, they are visually uncomfortable, they are in a little bit pain and you know they eventually become healthy or like, or or if some of them have basically passed away as well. But 60% of them have no effect, they are totally fine. So that's very similar to the phase one trial like in the sense that if you were to give a particular drug molecule to a set of individuals who are basically supposed to be all identical and healthy, some of them will have an adverse effect. So we did that experiment and then we trained our model and it turns out that before, if I looked at their RNA profile before I gave the drug molecule, I can predict which one of them with eight times more probability, like eight times which one is going to have an adverse effect and which one is not going to have an adverse effect. So that's basically saying that in an animal we can do what we want to do this whole, you know, individualization of toxicity okay, that can be done, that's already done. We have a paper that is done on that. The second one we have done is we have already shown that I can take an experiment, do it on a rat, do the same experiment on a mouse, do the same experiment on a rabbit, and I can predict. I can look at the data just from a rat and predict with greater than 98% accuracy what the results is going to look like if I did the experiment on a mouse or what the experiment is going to look like if I do it on a rabbit. So at a high level, both individualization of tox prediction as well as what we call as cross-species transfer learning, we have done both of that From a proof of principle point of view.
Speaker 2:We can extend this to humans as well and we can extend it to other animals and humans and things like that. Of course, more data is needed. You know more work needs to be done and all of that. So proof of principle has been done and obviously we have reduced the cost of the data collection and things like that.
Speaker 2:And where we want to go is our first big milestone would be we want to take a drug that has failed you know, clinical trials because of safety, be able to basically demonstrate that using we can do the experiments on a set of animals and be able to say, if I were to do this experiment on a human, or if this would be the profile of a human for which this is going to be toxic, based purely on the animal results. So does that make sense? And researcher or some pharma basically wants to come and say hey, we want to collect massive amounts of data, we want to use your low cost data collection technique, we are willing to basically work with them on that, and we have built a bunch of AI tools to kind of automate the analysis and everything. All of that we are just basically starting to launch and give it out to the community no-transcript.
Speaker 1:I'm building these models. I have a sampling technique. You have this data, this amazing set of algorithms that you've built. Who are you selling it to and how do you get paid?
Speaker 2:Do they?
Speaker 1:find you or do you find them?
Speaker 2:So we are going through that process. So we are going through that process in the sense that the first set of products that we are releasing is essentially because we have reduced the data collection cost and massively increased the amount of data that we have collected. We need to basically build these kind of AI tools to analyze it and make internal tools to analyze it, and all of that, all of that is being productized and make internal tools to analyze it, and all of that, all of that is being productized and given out to the world. So so, in the sense that if, like I think in the next couple of weeks might be like, yeah, like we are, we are doing a more, more public launch for this, wherein if a, let's say, a grad student wants to actually do an rna profile of a particular sample, we can measure all the rna in, then there are technical reasons why current technologies can't do all of that, like can't measure all the RNAs. It can measure only a small portion of the RNAs. So we have developed these tools that allows us to actually measure all the RNA. They can just contact us and basically we'll do that experiment for them and that is cheaper than anybody else, anybody else, that is, whether it is being done in the Anybody else, whether it is being done in the US typically, whether it is being done in China, wherever it is, we have the cheapest technology or the most economical technology to basically do it.
Speaker 2:Not only that, we are also giving people free access to what we call as Omics Copilot. Essentially, you can conversationally basically analyze all the data. If they have RNA data, like like rna data or dna data or whatever other omics data is there, they can just load it and just basically talk through it and like contextualize it, like to figure out where, what molecule, how it relates to other molecules in the literature. All of that you know, you know. You know, think of it as chat, gpt, but tailored for very specific scientists that work in this field. So both of that is being offered and that starts the revenue generation for the company.
Speaker 2:And the reason why we decided to do this was because, you know, internally we wanted to basically build this model that allows us to say, rescue drug, to understand biology, all of that, but we didn't want to constantly be taking money. We wanted to actually be able to understand biology, all of that, but we didn't want to constantly be taking money. We wanted to actually be able to. Also, we realized that we needed to, like, generate revenue. Ultimately, we are still a company. Okay, since we reduce the data collection cost, you know we owed it to the community to basically give it out. If we hold it to ourselves, you know we'll collect the data and we'll build these things, but we, if we offer it the community basically, can basically get more data as well.
Speaker 1:So in a way you're offering. When we say go to market, how do you market? So marketing sales, customer success, that's what go to market means, right, but what you're doing is actually a form of what they call plg. Right, it's basically product-led growth. You're taking portions of your product, portions, portions of your capability, you're offering it for free to the scientific community. They then get value out of it, they get to start using and getting into it and then from there they'll eventually, when they get to a higher level, subscribe with you to perform analyses or data collection at even a higher level. So that's how you whet their appetite. You don't need necessarily salespeople to go cowl and pound and chase and drive them through. You put out this to the research community, who you know so well anyways.
Speaker 2:Correct. I mean we're not giving it all. Not everything is being done for free, but it is still cheaper than everybody else's. So I just wanted to add that caveat. But the spirit is to enable what we have developed to give it to the larger community, so they can also generate data, and you know we all will in some sense.
Speaker 1:Will you end up developing your own drugs or will they be buying tools and subscriptions from you?
Speaker 2:It depends. It depends upon how it goes right, like, so we look at it, and that is something that we will decide over time. Because we look at it, as there are so many drugs that are failing for safety reasons anyway, it makes like, so we can start and and and and so it might. If we have this kind of a turnkey technique wherein, you know, you put a failed drug in, you follow a system and out pops a method to basically figure out who it is going to be safe for, then I can actually I don't need to have developed my own drugs. I mean, I can basically take the ones that are failing and put them through the you know ringer in some sense, and basically out pops something. And this is something that every drug development company has to deal with. So, like, eventually, if we are wildly successful over the next decade, decade and a half, maybe we will get there, but we don't have to go there. If we don't have to go there, Right.
Speaker 1:So I mean that's cool. You have a whole body to go focus on and, as you know, the difference between something that's in research or something that you get to toy with, versus the companies. You have to drill in on one place, focus in on it. So does that mean you've already raised capital? Are you in the process of raising a lot more capital? Is this the stage you're at? Are you already starting to get revenue and subscriptions from companies that have wanted to license some of that or use some of your models?
Speaker 2:So we have raised a relatively small round of about 4 million, like earlier, like later last year. So we have, like folks like Dario Amodi, who was an angel in us from Anthropic and we are from, you know, matterventure partners and Catapult have put in some amount of money and Caltech also has put in some money. So we've not raised a very large amount. We have raised only our 4 million to basically get all the processes going and to demonstrate these kind of key capabilities. So we have done that and we are in the process of basically giving our services to academia and few other folks. So we are in conversations and to get a few different customers to come on board, we will raise larger rounds in the coming months.
Speaker 1:That's awesome. So you've already raised a modest amount of capital. It's a good size seed and now you're building it up and you're already offering it in the products You're already off and running. So that's really awesome. But if you could hit the big dream with what you're doing, if there's a disease you could solve, what disease would you solve with your technology?
Speaker 2:I mean, see, for me, here is the thing right, like so, I have a I have, you know, a vested interest to basically solve leukemia. I'm interested because I know that biology a lot more. But what we want to get to is, like, the place that I want to get to is I want to basically ensure that we make you know the concept of side effects itself obsolete, like in the sense that if you want to, if you want to take a drug, you want to be able to know very quickly is this going to be safe? What are the exact side effects that you're going to end up having? You know how bad is it going to be, what are the dosages wherein the side effects are going to kick in?
Speaker 2:All of that, all of those things are right now not talked about in, or like you don't try to solve it because you don't have mechanisms and understanding to solve it.
Speaker 2:But it need not be the case, because any drug, there are individuals for whom there are no side effects and it does the job perfectly. So now, why is it that some people have side effects, some people don't have side effects? And if you could understand that and a priori, be able to say you're going to have the side effects, so probably don't take side effects. And if you could understand that and a priori be able to say you're going to have the side effects, so probably don't take this medication, take another medication that is for the same condition, because there are more than one drugs for every single condition. So now the point is how do you select it? And we are not even going to start talking about the dosage problem, because that's another thing altogether, because when you decide, when you want what dosage somebody should have, it is done in a ridiculous fashion right now. So again, those are another areas that we can actually everything that we are building can be applied.
Speaker 1:That's super cool. I love that notion that. It's an incredible vision to have saying, wow, imagine if I have a situation and then you can actually predict whether I'll get a side effect or not and be able to dose me more effectively and just start from a much better place in terms of that level of experimentation that happens when you engage with your physician right and engage with your body. So that's a super amazing way to go with this. Like overall, in terms of just AI and what's happening with AI, there's so much going on right and there's so much fear in the market. There's some fear in the market. There's super excitement in the market. I'm more on the excitement side. I understand some of the fears. Where do you see this AI capability going? Is this something that you saw a few years ago that you should jump into, or are you really accelerating it today because of just the explosion of investment or the explosion of technology?
Speaker 2:So, like I said, my interest in AI sort of began quite early on. So I put myself as sort of like a scientific hipster in some sense. Everybody was going towards a certain direction and I wanted to work in a different area because it was so exciting Like everybody was going in that direction. So in some sense, you know, at least in a very big way in 2015, 2014, 2015, like I said, when my wife was sort of diagnosed with leukemia, at that point onwards it became very clear to me that the only way in which biology can make sense of all of these data is by having some kind of machine learning or some kind of an AI black box to kind of make sense of the data. So my interest sort of like really peaked at that point onwards and I was taking a very different approach for it, wherein, from a pragmatic point of view, I wanted to collect large amounts of data and just sort of focus on developing technologies to kind of collect data and then, you know, use AI stuff like existing algorithms to kind of collect data and then use AI stuff like existing algorithms to kind of make sense of it. It's just that. So it was sort of like a smooth progression in some sense, and here we are. So I wouldn't try to claim that everything that all the AI that we are developing and the algorithms that we are developing is like something fundamentally new. We are taking concepts that are there in other areas and applying it to data that we are collecting here. There are some interesting creative ways to kind of creative ideas that we are doing in ai as well, but you know it is very specific to re.
Speaker 2:As far as, more generally, you know all the fears and things around ai. You know I have constantly, I mean for I'm not that old, but I'm not, neither am I that young I've kept hearing at every time, whenever some new technology happens, it's sort of like end of the world and for some people, for the others, it's utopia. I think it eventually will be somewhere in the middle. So I think it's neither going to be a Terminator jumping around, jumping from the corner, neither is it going to be like utopia for everybody, like some concept of AGI might happen for everybody, like some concept of AGI might happen. Some concept of these things will happen, but it'll be an interesting one.
Speaker 1:So sorry, sunil. You know the writer in you won't be able to. According to Ashwin, it's not going to be this end-all, be-all Terminator sequence. Even though your imagination may run wild, well, that's not going to stop us from, you know, writing the movie where that happens.
Speaker 2:That's over from one of Ashwin's experiments. Okay.
Speaker 1:Ashwin tell us, you seem to be involved in so many different scientific areas and engineering areas. Were you always into technology and science? What sparked that?
Speaker 2:passion in you. So I was always so I put myself as the closest thing that I'm trying to basically compare myself to is that little dog in up wherein it's sort of like squirrel and basically, you know it keeps getting distracted Like I. Am that equivalent in science, in the sense that even though I have traversed different areas, I'm not working on all of them simultaneously. In some sense there is a smooth progression from one to the other, wherein you know I work on a particular problem, you hit a wall and then you, in order to solve that, you might have to find the solution in another discipline. So you go and you know, work on the other discipline and you know, even though you change, you work with another discipline. It's not as if you forget the previous ones.
Speaker 2:So, through my career progression, I started in neurobiology. I shifted to applied physics. So, through my career progression, I started in neurobiology, I shifted to applied physics, then I shifted to DNA, then I shifted to like using DNA to kind of build work with semiconductors. Then I went to Google for a while working on like some AI projects and then I came back to MIT to work on certain other things. So you work on a problem, you hit a wall and then the solution and the inspiration might be in a completely different discipline and you just go there and do it. I mean, I've been fortunate enough to have folks who have funded my curiosity, so it has allowed me to kind of jump around, and so that's the way in which I put it. It's just sort of like I have shifted fields, not because I've been interested in that necessarily, it was like out of necessity.
Speaker 1:I'm wondering. I guess kind of going deeper a little bit is like what is the truth? What is the deeper truth that you seek through experimentation? You know you're a researcher, experimentalist at heart, but what are the deeper questions?
Speaker 2:you're trying to answer for yourself. So for me, here's the thing right, I want to have predictive capabilities, like, for instance, if I'm setting up an experiment and if I can predict what the outcome is going to be and if it is perfectly lines up, then in some sense I have understood the system. So, like I think that is the essence of science in some sense. So, and I want to get that in biology. Like I started out in neurobiology to a certain extent, when I started way back in the day, it started out with this desire to basically have predictive capabilities. Like I want to be able to kind of do X, y, z and be able to take neuronal progenitor cells and be able to convert it to a. You know that's. It's like it was a very specific problem that I wanted to actually solve.
Speaker 2:And if I can't, if I, if I can predictively do that, then I have understood the system. That's the heuristics and in biology there is almost there are very few areas in biology and medicine where that can be done. I mean so in some sense. I won't say biology and medicine are like, definitely like healthcare and medicine is not a science yet because you can't, you won't have predictive capabilities. But you know, if I and you know many of the people in the community not just me like do their things right, probably we can get there in the next decade, decade and a half.
Speaker 1:If I can get there, I've done some things right wow, that's awesome is there is there someone in your life or a historical event that got you into this, into this game either science or even as an entrepreneur.
Speaker 2:This would not be like an inspiration, but one of the people that I am really sort of driving is some sense that you know someone like John Salk, like from the guy who basically invented the polio vaccines. Like in some sense, there is like something very beautiful in amount of the work that he ended up doing and also something that is even more beautiful like the fact that he never patented it and he gave it out to the world. In some sense and even though you know I can't do that, so the entrepreneur side of me can't do that Like I can't take that, you know that much.
Speaker 1:Your investors may not be happy.
Speaker 2:But it's an awesome thing to say that you know that much of your investors.
Speaker 1:your investors may not be happy, but it's an awesome thing to say that right Like what he I mean.
Speaker 2:My desire is that some of the work that I do at least one thing or two things that I do in my life has that level of impact on a bunch of people.
Speaker 1:Well, look, you know, hopefully your name ends up in the pantheons of John Salk and Marie Curie, but right now we're going to have to put you through the paces and make you the lab rat in my game, the SARTank. So welcome to this ruthless portion of the podcast where we're going to pitch you in a battle of wits against my brother. So tonight's battle of wits, ladies and gentlemen, features our guest, Dr Ashwin Gopinath, the brilliant mad scientist behind Biostateai, going head to head with my brother, Rajiv Parikh, who is just mad that he lost so many of these games.
Speaker 2:I won one.
Speaker 1:He's on a hot streak, so he's won one in a row.
Speaker 2:I'm taking on the other scientists.
Speaker 1:That's his biggest streak, so let's see if we can extend that to two. All right, we're about to play three rounds of two truths and a lie, focusing on the fascinating world of failed drugs, something you may know a little bit about. I'm going to share three tales of pharmaceutical mishaps and your challenges to pinpoint the one that's too outlandish to be true. So let's find out who's got the scientific savvy and the business intuition to spot the pharma fiction. Sure, all righty. So I'm going to list three statements On the count of three. I'm going to count off three, two, one. You're both going to raise your fingers up one, two or three at the same time, so that you can't cheat off of each other. All right, here we go.
Speaker 1:Statement number one a drug intended to treat baldness inadvertently causes users' eyelashes to grow excessively long. Statement number two a weight loss drug was pulled from the market after it was discovered to cause vivid, recurring nightmares. An anti-aging cream containing snail slime was discontinued after it gave some users an allergic reaction, resulting in a quote weeping rash which resembled snail trails. Which statement is the lie? One, two or three? Wait a minute. You have drug baldness that caused eyelashes Weightless. You have baldness that caused eyelashes to grow. Okay, weight loss drug that caused eyelashes to grow. Okay, weight loss drug that caused vivid, recurring nightmares.
Speaker 2:Number three An anti-aging cream. I was trying to figure out what it was supposed to do.
Speaker 1:Created a rash Are you?
Speaker 2:guys ready.
Speaker 1:Lock in your answers. Mentally, here we go Three, two, one, one. I love this because we can see what kind of internet delay we have. All right, you both have said that you think the lie is treating baldness causing your eyelashes to grow. That does sound crazy. Now here's the deal. Certain medications like bimatoprostlatease I guess maybe is the non-generic which is used to treat glaucoma have been known to cause excessive eye flesh growth as a side effect, and this was discovered during clinical trials in the early 2000s.
Speaker 1:So we're both wrong we're both wrong. And later approved by the FDA for cosmetic use in 2008. So I'm so sorry, you're both wrong. Round two An appetite suppressant made from a seaweed extract was withdrawn after it caused users' sweat to smell faintly of the ocean. Okay, number two. A drug intended to treat allergies accidentally triggered temporary synesthesia in some users, which means it caused them to associate sounds with colors. Number three, which means it caused them to associate sounds with colors. Number three an experimental cold remedy containing bee venom was abandoned due to the risk of severe allergic reactions in some patients. Which statement is the lie?
Speaker 2:Are you ready to answer?
Speaker 1:You're going to say it.
Speaker 2:Are you ready? You guys locked it in. I think, so here we go.
Speaker 1:Three, two, one Once Alrighty.
Speaker 2:You guys.
Speaker 1:both again are going with one, which is the seaweed. I'm going to read the thing here Seaweed is rich in iodine and consuming large amounts can lead to increased iodine excretion through sweat and urine, potentially causing a slight change in body odor.
Speaker 2:Oh really.
Speaker 1:And this was observed in the 20th century, when seaweed was used as treatment for thyroid disorders. So I'm so sorry you guys got that one wrong again, do you want?
Speaker 2:to go for it. What the hell? Blind on, blind on, blind on To 50-50?.
Speaker 1:Now we got to do a 50-50?. All right, two or three, here we go Three, two, one, two, synesthesia or bee venom.
Speaker 2:Okay, you both said two and we're both wrong again.
Speaker 1:If this was worth anything, you would both get a point. Yes, correct, synesthesia. Synesthesia, it is indeed a neurological condition where senses are blended seeing sounds as colors, for example but it is not a known side effect for any allergy medication. But yes, the bean of venom was also. It was true, it's used for conditions like arthritis and multiple sclerosis, but carries this risk for allergic reactions. Amazing, all right, very cool Round three still tied at nothing apiece, here we go.
Speaker 1:This one's. I put a little twist on this round. This is about how drugs are repurposed. You always get those interesting, just like we talked about the eyelash thing that became cosmetic use. So which of these repurposings was not true? Two of these are true. Which of them was not true? Number one a drug originally developed to treat tapeworms in animals was later approved for use in humans as a treatment for alcoholism. Statement to a chemical compound initially investigated as rocket fuel component was later to be found effective in treating erectile dysfunction. Statement number three a drug designed to suppress lactation in new mothers was later repurposed as a treatment for Parkinson's disease. All right, which statement is the lie? Three, two, one Let me see your answers. All right, you're both saying one.
Speaker 2:I was going to say two, you guys picked one every single time, so interesting. At some point one had to be the right answer.
Speaker 1:If only I was that. Don't metagame me. Don't metagame me. All right, here we go.
Speaker 2:Because I swore that I have heard the last one, and you are right about that.
Speaker 1:I'm going to go ahead and say that Dysoliferum, initially developed as an anti-parasitic for animals, was discovered to cause severe nausea and vomiting when combined with alcohol, making a deterrent for alcoholism, and this is approved for use in the 1950s. So I'm so sorry you were wrong once again. You know what? It is not number one I thought that it's number two. Two is the line. Yeah, and I thought nitric oxide.
Speaker 1:Nitric oxide was the one for heart disease. Yes, that was supposed to be the one from blood pressure and heart disease, but I thought, who knows, maybe they took the nitric oxide from from rocket fuel or some bullet. You're not, you're not. You're not off on your logic here. Nitric oxide, a component of some rocket fuels, plays a role in vasodilation, also dilation, which is important for erection. But there's no direct link, and maybe byotanaai can solve this. There's no direct link between rocket fuel components and ED treatments.
Speaker 1:I had the first part right, I was going to go that way, and I was like you know what Exactly?
Speaker 2:All right, we got to go with the tiebreaker, we got to go. One of us has to win.
Speaker 1:Get it Get it, so you must choose something different. Okay, these are kind of historical. I reached back for these, okay. Statement number one In the early 20th century, a chemist working for a dye company accidentally discovered the first synthetic anti-malarial drug while trying to develop new colors for fabrics. Statement two In ancient Greece, around 400 BC, hippocrates recommended the use of moldy bread to treat infected wounds, unknowingly utilizing penicillin, which wasn't technically discovered until 1928 in Alexander Fleming's library. Statement three In the 18th century, british sailors discovered that limes could prevent scurvy, a debilitating disease caused by vitamin C deficiency, after noticing the citrus fruits were part of the diet of sailors from other countries who did not suffer from the condition, and this is the reason that the Brits are often called limeys, or were once called limeys.
Speaker 1:So which statement is the lie On the count of three? One, two, three. Okay, you both chose something different. All right, ashwin thinks it wasn't the limeys and then Rajiv thinks it wasn't Hippocrates accidentally using moldy bread. Well, in mid-18th century, british naval surgeon James Lind conducted a clinical trial that demonstrated the effectiveness of citrus fruits in preventing scurvy. This led to the adoption of limes and lemons as standard provisions on British ships, earning the British sailors the nickname the Limeys. So, yes, that was the truth. So, rajiv, I can't believe you did it. You've won a second one in a row.
Speaker 2:Oh, my gosh and I beat a real scientist.
Speaker 1:Ashwin, I'm never going to hear the end of this dude, I swear to God, I could have sworn that.
Speaker 2:I have read that you can actually put moldy bread. They used to put moldy bread to basically inoculate on wounds. Maybe I was wrong.
Speaker 1:Yes, Ashwin, of course you heard that.
Speaker 2:You probably heard that from our parents, who say all sorts of crazy stuff about what you can do to help your cuts.
Speaker 1:Yeah, but all right. Well, you did indeed survive at least the spark tank. It's challenging to even walk out of here alive, so well done, ushwin, but uh, unfortunately now I'm going to hear the insufferable no end to achieve bragging about his two in a row.
Speaker 2:Who's the who's the? Who's the elder one? Can't you tell gosh?
Speaker 1:that's an insult in it in and of itself. I see he's 12 years older than me, ushwin. Yeah, maybe it's bad just because he uses all that botox I take a mixture of moldy bread every morning yeah, and snail slime.
Speaker 2:You use the snail slime. Snail slime is actually a beauty product, uh, you know. So I think they do. They do use uh snail slime for yes I think you are correct.
Speaker 1:It just doesn't cause that weird thing that we're talking about, but yes, there's like a look. I think that's the beauty of these try to put a little bit of truth.
Speaker 2:Yeah, yeah, to kind of throw you, that's awesome, that's awesome.
Speaker 1:Thank you for that was awesome, that you played the game with us and, uh, you know, of course, we we had a lot of fun chatting with you today and learning so much uh from you today. So, uh, we also and this is what you'll have to verify we also think that we also heard that you're a failed artist somehow self-proclaimed.
Speaker 2:We're not saying that I got, I got more failed.
Speaker 1:Who, for one of his top papers, had a single cell DNA? Create starry night.
Speaker 2:Yeah, it's not single cell. It's basically by putting single molecules to basically create. You paint the starry night with single molecules. You put individual molecules to be. Every pixel in the starry night was put where it was put, one at a time. So essentially you can make I gotcha.
Speaker 1:Okay, it's like people who put those photo collages. They're like tiny, tiny photos of everything. But then now you pull back and it turns out it's a picture of my mom, exactly, or?
Speaker 2:something like that Exactly, but it was basically to show that we could control. We could put a molecule where you want in a controllable fashion. If I want to put a molecule in a place, I can do it, and I can do this on a massive scale.
Speaker 1:How do you show it?
Speaker 2:So visually trying to show it it's a great effect.
Speaker 1:Yeah, amazing, super cool. Right F visually trying to show it. So it's a great effect. Yeah, amazing, super cool, right failed artist no more, because he can do it with dna. Now is there one? This is like. This is the lightning round. So you get an answer in 10 seconds or less. Okay, you're ready, we're gonna give you a lightning round. What's the biggest surprise you've had being in boston for the over the course of your career? What's one big surprise?
Speaker 2:It's. It's a very unhappy place. People are unhappy.
Speaker 1:Oh wait, we just want to hold on.
Speaker 2:Boston just won the championship. How can you be unhappy? That's why he's in Palo Alto, yeah exactly.
Speaker 1:But you know what MIT I heard is the most all my friends who went to. Mit. Don't don't talk about it with joy. They love it, but they don't talk about it with joy.
Speaker 2:It's awful, I'm glad I didn't get in. Okay, next question.
Speaker 1:If money was no object, what would you do for a job? Two words. Basically paint 10 seconds right Paint, yeah Paint, All right With with.
Speaker 2:DNA molecules or just without DNA. Without DNA, like the traditional way. Okay, here's another one.
Speaker 1:You have all these students in class and you have this chance to impart so much wisdom. Is there a life motto you leave them with after every class that you like to share with everybody?
Speaker 2:Try to fail.
Speaker 1:Nice Love that Try to fail. Love that. It's like what we talk about our podcast Be Ever Curious. Well, ashwin, thank you for having so much, for playing with us today, teaching us today, taking a really complex subject and making it accessible, and I think that's really hard to do. We came in with the notion of enabling you to humanize technical innovation for our audience in terms of how data collection can save lives, and you helped us accomplish our mission. So thank you so much.
Speaker 2:Thank you for having me.
Speaker 1:Who knew that biological and computer and nanoscience could be so interesting? Yeah, well, I think all those fun buzzwords, as you called them, do just equate to life. This is what we're trying to solve, is constantly trying to solve is not just the meaning of life, but how to extend life and how to make life more enjoyable to live. And it's exciting to hear from the front lines of like. You know, when he, when he said like, was it Jonas Salk? You know, you're like, yeah, you know, potentially some of these guys that we end up interviewing here, like could be that absolutely could be that person. And when you talk about his dream and his mission, what he wants to do with this thing, I mean it would. It would completely change the world.
Speaker 1:I'm blown away by how he's motivated by his wife, his wife's journey, his wife's disease, how he wants to get rid of side effects for everyone. He's driven up the wall by the fact that all these interactions are not predictable. Where I had the frustration in starting my own medical device company about how complicated medical science was, he's actually digging in finding a way. It's like the scientist with ADHD who just jumps around and every time he hits a wall, finds another way of solving it, with a different science. It's just. It's the most fun about talking to innovators. I love that. It's like using ADHD as the superpower that basically avoids disappointment and wanting to quit when you fail. No try to fail, he says. I'm really inspired by that conversation. Really cool and I truly hope he sees it, he will.
Speaker 1:He'll get there one way or the other. So thanks for listening. If you enjoyed this pod, please take a moment to rate it and comment. You can find us on Apple, spotify, youtube and everywhere podcasts can be found. This show is produced by myself, samir Parikh and Anand Shah, production assistance by Taryn Talley and edited by Sean Maher and Aiden McGarvey. I'm your host, rajiv Parikh, from Position Squared, an AI-enabled growth marketing company based in Silicon Valley. Come visit us at position2.com. This has been an effing funny program.
Speaker 1:We'll catch you next time and remember folks, be ever curious, try to fail. Fail a lot, you.