Noam Solomon - Immunai - Part 2

Intro - 00:00:01: Welcome to the Biotech Startups Podcast by Excedr. Join us as we speak with first-time founders, serial entrepreneurs, and experienced investors about the challenges and triumphs of running a biotech startup from pre-seed to IPO with your host, Jon Chee. In our last episode, we spoke with Noam Solomon about his early years growing up in Israel and how his upbringing shaped his high standards and competitive spirit. We also discussed his unique experience starting university at a young age, his early love for mathematics and pursuit of multiple PhDs in math and computer science, as well as how important it is to have resilience, critical thinking and creativity as an entrepreneur. If you missed it, be sure to go back and give part one a listen. In part two, we continue our conversation with Noam, discussing his decision to pursue postdoctoral studies at Harvard and MIT, highlighting the impact of being surrounded by brilliant minds. We also discuss his transition from academia to entrepreneurship, the pivotal moment that sparked the idea for Immunai, the complexities of AI and ML, and how Immunai is trying to revolutionize drug development and diagnostic testing by leveraging the power of data and machine learning.

Noam - 00:01:32: There were a few places I could go. Harvard and MIT were two of them, but there was also other places. And I was actually interested to go to Vancouver. But my fiance at the time, that is now my wife, said, no, if we are going to leave Israel, let's go to the best place. Let's live in the U.S.. And that's why we went to Harvard and MIT. But I was this close to convincing her to go to Vancouver. And the reason why I'm saying it is because it's very likely that if we did go to or had gone to Vancouver, I wouldn't be here. And it's not because of Vancouver or the university, it's because there is something about the perception that when you go to places like Harvard, MIT, Stanford, then your business card becomes very attractive. And I think I realized, and I can say six years later or seven years later, that people really want to summarize you in a few bullet points. They say, you know, check, check, check, and it's very helpful to say that you've been in some of the, you know, that you have the right pedigree, that you have the right things that put you in the hallmark of something, but. This to this day is an insight that I don't really appreciate about the world of business. That you don't really go into the substance. You are, you know, people are okay just evaluating you on superficial things and you need to be aware of this fact.

Jon - 00:03:03: I couldn't agree more. Like, I think it's almost, you're doing yourself a disservice by just judging it at, you know, service level, particularly when you're like recruiting, because there are plenty of rock stars that might not be at the kind of name brand institution. And it's almost feels lazy to use the checklist, like dig a little bit deeper and see what the story is and what their experience is. And we always talk about it as like, something that, and this is our hiring philosophy, it's to hire for attitude, train for aptitude. Like if you like wherever, whatever school you go to, if you're hungry to learn and you want to just get better, I can't train that. Like I cannot train you to want something, but you know, if you have that hunger and you want to learn, come on, we're going to level you up. Like no problem. So I love that. I love that insight. So your fiance, now wife, and you make the move. What was that like for you, you know, moving over to Boston, Cambridge?

Noam - 00:04:05: Well, yeah. Well, I think that I kind of prepared myself for the fact that it's cold out there. And it is, you know, growing up in Israel, in Tel Aviv, the weather is very warm, probably too warm and humid. But culturally and as a mathematician to live in, you know, in the city where you have so many universities and colleges. Cambridge is a place that really appreciates. Intellect. And academia and music. And I mean, I can talk for, you know, for hours about like the experience of actually going to Harvard and MIT and being in the same department with people that you read their textbooks and they are the names that make the subject. And their doors are open and they're open for you and other people like you to just come in and interact. And you said something about the social experience or the solitude. It is actually remarkable to realize that in some small universities, that also have great people because they are much smaller. You don't get to interact as much or to socialize as much, but when you go to these like very big institutions. The PhD level, you know, the postdocs, the PhD students, all the people that surround you are, you know, rock stars, and they are eager as much as you are to, you know, interact and socialize. And it was an amazing experience. Also, people don't know this, but I think mathematicians are, by their nature and upbringing, very humble. And the reason being that you are here and mathematics is here. No matter what you do, it's going to be infinite. And there are also these like amazingly smart people that are out there. So most mathematicians that I know, including the, the biggest rock stars of the space are humble people. And so it's really fun to work with super smart people that are collaborative and humble. And it was an amazing experience for me to spend time in Cambridge. And while I was there, even my idea was incubated. And we seeded it and we founded the company.

Jon - 00:06:19: Very cool. And yeah, I mean, you know, just something that stood out to me that you said about, you know, the rock stars in math being humble. And then it reminds me of your first PhD, honestly, where people have been trying to prove something. For decades, if not hundreds of years. There's something humbling about that. And you know, like, you're getting your butt kicked. Like you said, math is up here and we're here. You're getting your butt kicked day in, day out. It's hard not to be humble. You're just like...

Noam - 00:06:52: It's exactly right. It's exactly right. And the best mathematicians out there... They go to work and they fail every day. Once a year, maybe once every other year, they succeed. But the basic experience is of standing in front of a mountain you have no idea how to mount. And I think it's a humbling experience. And I think that's why people that do theoretical math, computer science, physics, it's not just in nature, but the upbringing is to just appreciate, you know, who you are compared to science. And I really love it.

Jon - 00:07:28: That's amazing. And I love how this is, it was kind of like your experience in this kind of melting pot of intellect, creativity, kind of was the, where the seeds of you and I was like born. And can you talk a little bit about that kind of the incubation? Like what gave you the idea? What were the driving forces that compelled you to start this?

Noam - 00:07:51: Already a few years before, I realized that I needed to do something for myself and I needed to go out there. I walked and I moonlighted in a few startup companies. I gave advice, I consulted, I also worked part-time as a manager. And I saw first, the very, very high level of creativity, capability that people in the space, in startup companies have. So this was something I was not sure, being in academia and surrounded by PhDs. What is the basic level of the high-tech employees? I mean, it's very high. And Then I saw the ability in a year or two to move a very, even move mountains with a team of 25 people. And that's another thing that you don't see in academia as much because it's a very singular or kind of a solitude experience, as I mentioned, and you do a lot of the work yourself or maybe one or two other people. But when 20, not to mention 100 people are working together, you can do so much more. I also felt that I have a lot of ideas and I wanted to do something that I would be the owner of. So, a few years later, when I was at Harvard and MIT, I was kind of thinking and looking at different ideas. The actual idea of Immunai was not mine. So the person that became Igor founder and the first CTO of the company. So we became friends in 2018. In one of my vacations from MIT, I went to Israel. We met, he's also was trained in MIT in his undergrad and grad school. And we saw that we have a lot of common interests and common values that we wanted to do something. And then we said, why don't we explore what we could do together? And there were a lot of ideas, we kind of floated a lot of ideas. And then one of the ideas was around the fact that his grandfather had cancer. And he was getting a combination of immunotherapies. And the thing that really shocked me back in 2018 was that this combination of therapies worked for him. So the cancer was progressing. But he couldn't stand the side effects, the adverse events of the drug. And so he stopped taking the medication. And not immediately then, but a year and a half or two years later, he died. But at the time, I thought that with our background in, you know, math and computer science and machine learning, is there any data that you could use to inform the decision of which patient should get this therapy or another therapy? It was really a precision medicine question. Can you find, you know, in the data with machine learning, the right therapy for the right patient. And it was a very interesting question that I found it very interesting. I find it very aligned with what they know, you know, how to do and something that I would consider quitting academia for. So this became the motivating idea for both of us to start something together. And this is how we found in the-

Jon - 00:11:13: Very cool. I love the mission because like, I think. It's why I get out of bed and, you know, and I'm like fired up to be in the life sciences is because you were trying to really move the needle and like improve health outcomes for, you know, family members, friends, you know, whoever it may be. And so I love that. And it's something that. You mentioned about the marriage of your skill sets of AI, ML, math, computer science. And this is just to set the table. As again, remember, I'm not a math expert or a software expert at all. But AI, ML is everywhere. It's in the news. People are saying it's this. People are saying it's that. I feel super lucky to have an expert who I'm speaking to right now. Could you, and in the most, I'm a fifth grade level here. What is AI and ML? Are they the same thing? Can you just like set the stage here? And what is AI and ML or what is it not?

Noam - 00:12:14: Wow, you're asking a question that we can spend at least an hour on. But you are coming from the space and you understand that one of the main challenges of when you're trying to promote the field is to also give an inspiration. I think the main difference between ML and AI is the vision. AI is trying to create intelligence in machines. So it is trying to get your computer to behave intelligently, machine learning. Is about the ability to train software or machines to be able to compute things, compute functions, predict things. Maybe it will reach the level of intelligence, maybe it will be sub the level of intelligence. But this is, you know, machine learning is the thing that you do. And artificial intelligence is maybe where you're going to get if you succeed. I think today, AI, artificial intelligence, is a term that is being thrown everywhere. And as a mathematician, I'm very worried about the... The hype that is creating. Like, yes, there is AI everywhere, but is there really AI out there?

Jon - 00:13:26: Yeah.

Noam - 00:13:26: That's a question we can talk about.

Jon - 00:13:29: Yeah. And because like from whether it's me or my colleagues, you know, our one interaction is like or, you know, the most famous interactions like, ChatGPT or now Perplexity. And we're like, hey, I know that's I'm I'm for it. That's this is it. But, you know, everyone now is touting it. And it's hard to really discern what's you know, what's the fluff versus what is the real meat of this. And so I guess could you help us understand, like, are there any like myths or common misconceptions about AI and ML that are worthy of we need to dispel here? And like, what are the real implications of it in the life sciences? 

Noam - 00:14:11: Yeah, but these are two questions that are both important. First I do think of the progress that many companies, including OpenAI, that you mentioned ChatGPT did, in the past five, maybe even 10 years, in both natural language processing text. And computer vision, video, et cetera. It is a real. Very willing for progress towards artificial intelligence. So I would even say that until 2016, 2017, People believe we are much, much further than, you know, behind what is needed to achieve artificial intelligence. I think once we've witnessed the capabilities of recent models, both in text and in computer vision, there is a question like what is going to happen next? And there is also a sub question or another question. Is ChatGPT intelligent? What is intelligent? And this is a question that is a philosophical question. We're talking about philosophy. In some sense. I think ChatGPT is intelligent in the very sense that when you have ChatGPT speak with people or converse with people, If it's a short conversation without very sophisticated or specific questions, you may not pick up that this is a computer. In this sense, it meets what's called the Turing test. You can fool a person to think that this is the real person from 12th Sigma-d. In other senses, it really isn't. There is a lot of responses that are flawed that you can find that the human being would never respond in the way that it does. And this is the main risks in generative AI that actually generates responses. It's very difficult in certain cases because you're not an expert to know if this is real or fluff. But I believe that the progress is being made or has been done is making me and many others optimistic that we're going to see more, but it doesn't mean that there isn't another few leaps that need to happen before we achieve what is known as general artificial intelligence. The question on whether or not we are seeing AI in biology, or in my space, which is drug development. I think we are seeing... AI in Chemistry, Alpha Fold, Delta Fold, one, two, three. This is definitely something that many people, including myself, would attribute to be one of the biggest breakthroughs in chemistry and even in drug discovery in the past, probably forever, since forever. But I would argue that biology is more complex than chemistry. And that in biology we haven't seen real demonstration of artificial intelligence yet. And this is why people need to be patient and they need to understand better the expectations we could reasonably expect from AI in biology and what is a hype and what is possibly true in a few years' time.

Jon - 00:17:23: I love that. And, you know, you're absolutely right. Like when it was an eye-opening experience for most people interacting with ChatGPT. So everyone said, the future is now. Like we have solved it. Everything is solved. But I love the nuance that you're providing here is it's like, there are still levels to this. Like, and I also agree that biological systems are more complex and there's still work to be done and patience that is needed to work through these technical kind of like problem sets. So, you know, as you and your co-founder are thinking about your technology and recruiting the first team, can you talk about the early days of Immuni and how you guys started to develop the V1, the MVP of your guys' technology platform? And how did it all come together?

Noam - 00:18:18: Yeah, definitely. So maybe going back to the fundamental, the early question of whether we would be able to make predictions for which patients should get the therapy and which patients shouldn't, or what is the right therapy for the right patients. So I think very early on. We made an observation that is not, you know, we are not the first in the world to make this observation, but for us, it was a very, you know, important one. Dead. There are fundamental connections or relationships between Cancer? And autoimmunity. And what we've done was to take biomarkers that were used in the clinic for patients with cancer, with oncology indications, and they're being treated with immunotherapies. And then leverage data that existed for patients that have autoimmunity indications, and that they had certain mutations in their DNA that is correlated with these diseases. And using those mutations of these genes in the way that we study the probability or predict what is the right therapy for the right patient, we're able to improve our predictive power. And the conclusion of this was... Because of the immune system. The immune system, we can talk about the immune system. I think it's important. It's our, you know, all of us have an immune system. And it is the difference between how we're going to cope with bacteria, with viral infections, and whether we're going to develop at some point in our future cancer, whether we are going to age more rapidly or less. And we saw it in COVID, right? I mean, all of us, most of us were exposed to the virus. Some of us were completely asymptomatic others died. And the reason being the immune system. So the immune system of billions of people around the world is different from one another. And the heterogeneity of the immune system is the reason why we are going to have different diseases. We're going to cope with disease differently. We're going to respond differently to drugs. And we don't know how to leverage this complexity of the immune system into drug development. Or even into diagnostic tests. And our kind of premise from the beginning was, which is, you know, even I would say stupid, ambitious dream was to fully map the human immune system. We are going to map the human immune system, you know, complex as it may be. And I remember one of my first conversations with investors. So I met with a very strong investor in the Silicon Valley, and he had a PhD from one of the top universities. And he said, that sounds great, but the immune system is infinitely complex. I still remember the statement, because I've thought about this for so long. I'm a mathematician. Mathematicians love to count things. I actually have been counting things for many years. So I was not intimidated by the statement of the immune system being infinitely complex, but it's not infinitely complex. It is incredibly complex. You have in your body, you have more than a trillion immune cells in your body, and me also. And those trillion immune cells are interacting with one another, and they're coming to your organs and they are interacting with your organs. And so if you count trillions of cells that are interacting with one another, et cetera, you get a very, very large number of possible interactions, but it's not infinite, it's finite. Until a few years ago, people said, this number is so high or so loud, we are going to treat it like it is infinite. And we said, no, it is finite. We are going to map it. We are going to map the human immune system. And that was our starting point. There was, I think in 2017, 2018, there was a perfect storm. Well, on one hand, you had new technologies in biology, called single cell technology. Single cell RNA sequencing, for example, allows you to take a tissue, a biological tissue. And map cells in this tissue. On a single cell resolution. Until then, you couldn't do it. So that you are able to take a tissue or blood and sequence different cells and from each cell to get the mRNA molecule and all the gene expression levels that are in the cell. And you have many, many cells. So you can map in one, in one sequencing can map. Hundreds of thousands of cells. And this creates something that nobody measured before, but it's very, very high dimensional, very high throughput. So, on top of these technologies, he also had massive improvement in compute power and new AI models. So it was kind of a perfect store where the logical technologies met compute power and met AI. And that's how we found Immunite. It was on there, you know, through this perfect store. We said, we are going to do this, this and that together.

Jon - 00:23:35: Very cool. And by the way, something that really stood out to me is like the investor telling you something is infinite. And you as a mathematician, you're like, that's not infinite. That's like, there's a real mathematical definition of what infinite is. And that's not that. So I love that. It's just like, it goes all the way back to your earliest days. It's just like, no, this is solvable. It's hard. It's like, no one's saying it's easy. And easy problems aren't really worth solving. I mean, there are problems that are worth solving, but the big, hairy, audacious ones are the most worthy, in my opinion, that are really going to move the needle. And I also like, I love, as you're describing the timing of things that a lot of business is like timing. It's like, you need these kinds of foundational elements, ingredients to really hook up something, you know, that's going to make a dent in the universe. So it sounds like it was like, it was like the perfect storm.

Outro - 00:24:37: That's all for this episode of The Biotech Startups Podcast. We hope you enjoyed our discussion with Noam Solomon. Tune in for part three of our conversation to learn more about his journey. If you enjoyed this episode, please subscribe, leave us a review and share it with your friends. Thanks for listening. And we look forward to having you join us again on The Biotech Startups Podcast for part three of Noam Story. The Biotech Startups Podcast is produced by Excedr. Don't want to miss an episode? Search for The Biotech Startups Podcast wherever you get your podcasts and click subscribe. Excedr provides research labs with equipment leases on founder-friendly terms to support paths to exceptional outcomes. To learn more, visit our website, www.excedr.com. On behalf of the team here at Excedr, thanks for listening. The Biotech Startups Podcast provides general insights into the life science sector through the experiences of its guests. The use of information on this podcast or materials linked from the podcast is at the user's own risk. The views expressed by the participants are their own and are not the views of Excedr or sponsors. No reference to any product, service or company in the podcast is an endorsement by Excedr or its guests.