HIBT Lab! Immunai: Noam Solomon

Hello and welcome to how I built this lab. I'm Guy Raz. So, if we've learned anything from our experience with COVID over the past few years, it's that we still have a lot to learn about how the human body fends off and fights disease. My guest today is on a mission to change that. In 2018, Noam Solomon and his co-founders launched a company called Immuni, and it's working to create what's essentially an atlas of the human immune system. There are thousands of clinical trials that take place each year around the world where people with certain diseases and conditions are given experimental medicine.

Immunei partners with hospitals and universities to gather cellular level data from those clinical trials. The company's massive computing power and artificial intelligence programs then analyze that data, which can then help figure out which experimental treatments work and which ones don't. Noel believes it eventually? Immunize technology can help pharmaceutical companies develop more effective treatments for some of the most serious diseases, including cancers. Immuni has offices in New York, Tel Aviv, and in Europe, and it's raised over$200 million for this work. Between them immunize founders with backgrounds in computer science, advanced mathematics, and biology.

Noam, the CEO, joined me from his office in Tel Aviv. So I knew I was going to be an academic uh even before I was ten years old. And the plan was to uh be a faculty member and uh doing research in I think computer science. Um

I have uh Change of plans. From what I gather, you you ended up in the United States Um around two thousand seventeen you came here to do a post some postdoc work.

in mathematics and computer science at Harvard. And then you went on to MIT to do mo more postdoc work in mathematics. And that's really Kind of where your I guess your career path w was like derailed in a good way. Um because you did not

go in the direction of mathematics and computer science. What tell me what happened? what started the the idea that would eventually become I mean I. So about Five years before then I um

For my second PhD I wanted to have some uh you know, additional income. So I worked in a few startup companies. uh as a data scientist uh developed some algorithms in different disciplines and uh fields it was not in biology.

So e commerce. different types of web applications. And it was mainly to uh supplement the income. uh working as an academic Especially in Israel, it's a challenge.

A few years later for my postdoc, um My co founder and CTO at the time we we met as friends, uh he told me about His grandfather. Uh at the time he had cancel. He was getting a combination of immunotherapies.

And It was working for him. So the cancer was regressing But he couldn't stand the side effects, the adverse event, so he stopped taking the medication. So eventually a couple of years later the grandfather died. At the time I was very, very interested to understand if you could use data science and mathematics to better find the the right therapy for the patient.

So this was the starting point for me. whether you can use some of the the knowledge and and background that I have to uh maybe do something that can Impact. Patients. What was the opportunity you saw that

that you could contribute to In developing. technology that could really transform medicine. Yeah, so It it's a deep question.

Immunotherapy is like the name. are drugs that are targeting our immune system. Our immune system is an incredibly complex system that is essentially governing our health. the way that we respond to uh therapeutics, the way that we cope with uh different bacteria and viruses

Of course, the entire world now understands after uh uh the few years that we had with covet how important The immune system really is But how can you know? If a patient is going to respond to an immunotherapy or not and

maybe one patient is going to respond to a drug and another patient is not going to respond to the drug. Can you personalize the the the treatment. So this was the starting point for us. How can you create the the knowledge base To answer this question. Mm. You've described

immunize work is sort of like creating a a Google maps of the human immune system, right? More or less? Mm-hmm. Yeah. So what does mapping the human immune system mean? Right. So the different cells in our body we have uh uh trillions and trillions of cells, they're different.

uh you have cells within your immune system, within the different organs And What is important is first to understand the makeup of our cells but also the way that they interact with one another. So let's say somebody's being exposed to

A virus or a drug. It's not going to be one cell, it's going to be many cells being uh impacted by this. How do you measure on a system level the the the way that cells interact with one another? So this is the type of challenge that I don't think we were able to cope with

five or ten years ago. But I think the increase of technologies. For example single cell technologies allow us to measure Sells. individually. So we can map every single cell separately. Every cell.

is giving you the ability to look at Twenty thousand different genes. And then you can look at hundreds of different proteins, you can look at many, many different characteristics and variables. So for example we are going to generate

one terabyte of information from every sample that we take from a patient. So it's a massive amount of data. That we generate. And then we're trying to make sense of this data.

Mm. But The reason why I called it Google Maps is because we also want to help people get from where they are today, sometimes it's a it's a state or city of disease and they want to go to a State or city of Kew

How do you find the the way though? And this is well The Mapping of the human immune system. is a means to an end. And the end is to

Fine. novel therapies that can help patients get to where they're trying to get. No, has the the sort of the absence of a a map or maps of the human immunity. essentially held science, scientists back, researchers back, from developing

effective treatments for You know, for a variety of diseases. Yes. I think Today the way that medicine

is being discovered and developed is based on a trial and error of scientists in the lab. Having a map of the human immune system will enable researchers to apply uh data mining tools to come up with novel ways to uh develop And discover therapeutics.

W what's been the biggest challenge in in I mean, why hasn't the human immune system been mapped? What what's what's been the technological uh obstacle that has prevented that from happening until now. I think technology is changing very rapidly. What happened in the in the past five years was In some sense, perfect storm where compute power It's grown dramatically.

We have seen More and more and we keep seeing more and more technologies in biology, in chemistry. That allows us to measure things in you know, unprecedented and resolution granularity.

And we have more sophisticated algorithms. So I think This perfect storm of technology and science allow us to do things we couldn't have even dreamed like fifteen and twenty years ago. Yeah. Right.

My assumption is every single human has a somewhat different immune system. Our immune responses very widely from human to human. And essentially if you mapped the human immune system and everybody had access to this mapping you could develop personalized treatments more quickly.

It is true. Uh and similarly I think that uh you know, the colours of our our eyes are Slightly different, but there are categories. And I think that when you map Thousands of people of patients.

And you will find a lot of similarities. So I think the similarities allow us and make us confident that the that we could measure the human immune system and try to understand how patients and that have uh breast cancer or non small cell lung cancer. Open creatic cancer can be clustered into groups.

And those immune systems profiles will allow us to figure out what will be the right therapy for them. I believe that the world is going into a more personalized way to understand our health. And I really hope that medicine is going to go into preventive care. And not only into medical care. Right.

When you're very tired in the in the past few months. Maybe there is an immune reason for that. So When we understand the immune system better and better and w we map more areas of the immune system, we will be able to cope with more rare autoimmunications, often disease and other

Um You know, cancer indications that Only few patients have. And You know, there are certain indications that are not getting enough funding because not enough patients have them.

the vision that I have and the hope that I have is it maybe in fifteen years We'll be able to use our immune profiling capabilities to help people remain healthy. Mm. So Noam, can you sort of lay out what the challenge is right now? are with developing immunotherapy treatments.

So first of all drug development is becoming increasingly Yeah, inefficient. it is super expensive. So to develop a new immunotherapy it takes more than two point six billion dollars. It takes more than ten years. uh of research and development before market.

And More than ninety percent of drugs fail to get an FDA approval. All right, so it's a massive investment of money. In something that is uncertain, so basically I'm assuming that

when pharmaceutical companies take a swing like that, they have to be pretty sure that there's a good chance it could work before they're gonna put two and a half billion dollars into something. Right. And so essentially what you what you're saying is if we can begin to map the human immune system with increasing granularity, You could start to solve Some of these Development and research. Challenges.

So I will tell you something that um a great scientist told me As we will start, he said. We can kill Every type of cancer. In Mice.

And this Really gave me a pause and I've been thinking about this for For years, understanding how to bridge the gap between the human immune response and the way that drugs are being tested in animal models. is I believe the biggest challenge that the biopharma and biotechnology industry should face and will face in the coming uh decade or two.

And this is our vision. Our vision is to improve the drug development process. through an engineering first mapping of the human immune system and through it the human immune response. We're going to take a quick break, but when we come back, more from Noam Solomon on how his company Immuni is using artificial intelligence in the fight against cancer and other diseases. Stay with us, I'm Guy Raz, and you're listening to how I built this lab. Welcome back to how I built this lab. I'm Guy Raz, and my guest today is Noam Solomon, whose company is combining data science and biology to develop more effective treatments for all sorts of medical conditions.

You know, I wanna I wanna ask you about the the the business side of this for a moment,'cause this is a business that you're building, you've raised almost three hundred million dollars. And that that's not enough, right? I mean you you uh I'm assuming that the research and staffing costs are incredibly high. So I mean, how much money Do you think this will take? So There are a few aspects to the business here.

First is that we are already walking and and making money by working with our partnerships, uh, you know, large bio pharmaceutical companies are paying us good money to help them further develop and improve the development of the uh drug candidates and of the drugs. They're paying for your data and your and your insights. They're paying us for not only for data and insights, but for

generating the data on Dell samples. and gleaning insights that are going to improve the clinical trial design. And the chance for getting the drugs approved in the clinic. So this is already something that We are doing and

The nice thing about it is that our machine intelligence keeps getting better and better and better. The other aspect is the co vision and the mission statement of the company is about building something Unique

And differentiated it is super risky. So Our premise is that we are going to map the union immune system But the implications of this is going to

Make A real dent in the way that we discover and develop therapeutics. And You know, it's been four years. We have had a lot of uh progress and successes.

But the risk is still here with us. And I think that everybody that joined him and I and our investor that invested a lot of money. They understand that this is still A very Амбіш сантифік прожикт. Yeah, I mean you're and you're not the only company out there that is doing this work. Do y do you see the other companies as competitors or as potential collaborators, as as both maybe?

Uh so first I really like competition. I think there are many competitive companies in our landscape and I think some companies are You know, really making progress and for that I congratulate them and I think it's good for all of us. I think that at the end of the day, companies that are building real technology and real science

will succeed and I think that we are working with uh some of the more ambitious ones. We are very collaborative in our uh premise. We have over thirty partnerships. Some of them are with companies such as ourselves, earlier stage companies that are building new technologies, some of them are more mature than us. But

I believe that every company that is able to you know, bring a scientific vision that is real and b uh make progress Uh towards uh an important problem. We survive. I always say

Lady science has to dance with us. We have to prove our our uh premise we have to show that Mapping the human immune system. Can lead to A real ability

to improve patients outcome. and improve the clinical trial design. So the collaboration is part of I believe. The new uh drug development paradigm shift.

What does it mean for the kind of insights that you're able to provide to pharmaceutical companies and companies that are developing these therapeutics. So take A drug candidate. Some biopharma or biotechnology company invested a lot of money in in this uh drug candidate.

there was an FDA approval to start giving this drug as a clinical trial set up to the patients and you ch you start treating patients that have Serious disease. What we are able to do is measure The human immune response.

Of the patients. and try to see whether this immune response It resembles something which is good. That is effective. We do it.

On a way that allows you to leverage the entire power of the database And only a handful of of patient samples from the clinical trial. This is kind of the secret.

So you have a huge database on the back end and then you can use this huge database. To glean insights. By looking at only a handful of patients in a clinical trial setup.

Help me understand what you're doing. I mean, essentially are you collecting Blood samples from from patients all around the world. I mean how do you actually collect the data? So there are a few ways. Uh we have uh many partnerships with academic centers and biopharma and biotechnology companies, and every one of our partners are Sending samples to us. The samples can be uh blood samples.

They can be uh preserved immune cells from from tissues. From uh solid tissues, from bone marrow. And Our work is to try and create the a human immune profile of the patients from the different sources of

Of tissues. And blood that we are getting. And the more partnerships we have, the more biological samples that we get, and every one of the biological samples that are getting to our lab in New York. Is being profiled and sequenced.

w in our lab, and this data is being uploaded to our database. which is called a Mica, it is the largest of its kind uh database of the immune system with a single cell resolution. So today we have a database that spend over one hundred thousand patients. over five hundred different disindications. Thousands of uh studies.

And we're trying to put as much clinical context and clinical metadata into the patients. And the more data that we have, the better our mapping of the human immune system is. So I I understand that in in recently that you've been able to show sort of definitively that there are certain treatments that have improved patient outcomes in certain Cases. Can you tell me about that?

Yeah. So um A lot of our work with our our partners is to help them with the clinical development. So They have a drug. A draw candidate in clinical trials.

And they do a lot of experiments. So they usually have multiple clinical arms. They're trying different combinations of treatments. And we were able to show quite definitively that uh our platform can identify the preferred combination arm That will demonstrate a superior immune response. So it happened uh already

And it's something it gets both us and our partners very excited about the potential to really leverage this immune profiling capability. Okay. Hel help me understand how You can develop. Treatments.

And the types of treatments you might develop. From what you're building now. Yeah, I'll give you a couple of examples. So first of all when

There already are Immunotherapy that are being in the clinic treating patients. And for cancer indications like melanoma. and non small cell lung cancer, we see how effective they are.

Mm-hmm. But even in the most effective setups. They don't kill one hundred percent of patients. Right.

What we do. And we have been doing for a few years now. is that we will map patients that are being treated with an immunotherapy or combination of immunotherapies and chemotherapies. And we're going to try and find separators. uh between responders and non responders.

And the interesting thing is that when we do our immune profiling of patients pre and post therapies. We see that some very subtle Nuance differences. Between responders and non responders.

Explain. Or at least partially explain the reason. So we can find that a specific immune cell type and a specific gene with this in within this immune cell type. It's correlated with non response to a therapy. So this can become

A novel Target. for renewable therapeutics. And This is what we are doing, so

The main point is To try and say when you have one Patient. that is responding to a drug and another does not respond. It doesn't say much, but if you have

one hundred patient that respond and one hundred p patient that don't respond. you can start applying data science and when you have the right data sets and the right technologies to measure the the biology of the patients. You sometimes find very interesting patterns and explanations. Okay.

If in fact the You could map the human immune system with you know, incredible granularity um at scale. And it it sounds like you're on the path to doing that. Help me understand what that means for patients for diseases twenty, thirty, forty years in the future. I mean, how how does this impact

how human disease is treated. I mean realistically Could you imagine a world in f even fifty years from now? where all cancers are treatable diseases w where people don't Don't die from them.

Yeah, so It it's a very interesting question and I'm going to say things not in the most accurate way, just to make sure that uh I can communicate the message. You take uh a patient with uh with a cancer, let's say a solid tumor, and you Take the biopsy.

of the tumour, you dissect the the tumour out and you are going to do it in a dish or maybe even give it to a mouse and you will be able to Q the cancer outside the patient. Uh you're going to cue it by finding uh a a new antibody or a new small molecule But what you really want to know is what will happen within

The patient. And the thing is that Cancel. It's almost has its own intelligence. So there is There are different escape mechanisms and different ways that the cancer can

use in order to survive or to grow. And the big Question can we use This mapping of the human immune system. And use different

animal models and different individual models, different lab models. To try and predict. What would have happened? To the patient. So it's a combination of measuring

Cancel patients. That have Different types of cancels. And apply a lot of Computer aided simulations

In order to predict what would have happened. to uh a patient. Will they treat it with this uh therapy? And this is the challenge. Today

The thing is that The human immune system and The cancel. They have this sort of battle. We are trying to understand how to help.

The human immune system. Win the battle. And it's a long, long, long road ahead. We've gotta take another quick break, but when we come back, more from Noam Solomon, co-founder and CEO of Immuni. I'm Guy Raz, and you're listening to how I built this lab. Welcome back to How I Built This Lab. I'm Guy Raz, and my guest is Noam Solomon, whose company Immuni is using artificial intelligence to map the human immune system.

So with your background in mathematics and computer science, and and and your colleagues' background in biology, you're essentially tackling this. this challenge from multiple perspectives and obvious with with a i an emphasis on AI and on on m just massive computer power. Right, so You know, we have about one hundred and fifty people.

Тата Нью-Йорк, Сан Франціско, Тела В. Zurich in Prague. that are spanning multiple disciplines. immunology Medicine.

uh technologies in single cell Machine learning, computational biology, software engineering, data engineering. And The secret is not to ha have th those efforts done separately.

secret is to try to create multidisciplinary teams that are going to work together. Towards a common goal. And mathematicians. such as myself are going to have to study a lot of immunology and biology In order to

Collaborate with their colleagues. that come from biology and immunology. And similarly immunologists are going to study the background that they need in engineering or machine learning. And I believe that this is the new type of

Science. It is More about connecting the dots between different disciplines and finding the bridges. Which make the challenge much How though?

but so much more rewarding when you can figure out a way to use data science. To answer a question. In a different Uh failed. And as a mathematician and somebody that was never used to answer questions in biology.

the satisfaction to be able to understand something that can Explain why a certain patient may not respond well to a drug. And another patient that is very similar to this person will respond amazingly well to the drug. It's very fulfilling. Hm.

No, I'm you wrote an a an article um in a publication that I I read and I I I really love the piece and the the title was something like When Not Knowing as a Methodology. And you say Yeah.

immuni not knowing is a methodology that essentially there's so much m more that we don't know about the human immune system than we actually do. Um which I love. I love that idea that that Essentially You don't You don't really know, but that's that's sort of

That's sort of the the roadmap that you pursue. You kind of you follow this idea that we don't know and let's figure it out. Right. So first of all it's a personal methodology, um I I like to ask questions. I started a company

I didn't know anything about biology, medicine. Business development. And I had to ask a lot of questions and it became part of our culture as an organization. Because the company has such a bold vision And we had to bring expells

from many different disciplines, it became Part of the way that we interact with one another, that asking questions is not only allowed but is encouraged and In some places. You're expected to know the answer.

I think for us being able to ask the right questions really led us to make a lot of uh progress And became part of our Not only research culture, but also the way that we do business. I'm always open minded to collaborating with companies that are bringing something novel because maybe the the union of the two approaches will be better.

Maybe one plus one will be three. Um and Yeah, sometimes you just Don't know the the answer. But

Yes, the right question, it will come out. I I read a a a piece by your co founder, Luis, and he talked about when Google developed an AI program that could beat humans in the game Go. And uh the computer beat humans by using this counterintuitive move called Move 37. I don't know the game well, but I read the article and and I guess the premise of the piece is that the move was so counterintuitive, so out of the box, that a human would never come up with that move on on its own. Yeah I mean When we're talking about the AI that you're you're you're using

Could it eventualize sort of a move thirty seven like Approach that. That helps us. Cure cancer. I mean, i i is that what we're talking about? Yes, and I would say that what we are

Doing this mapping of the UN immune system. is much more higher dimensional than than the problem that You just mentioned. I I'll give an example. So We are measuring

From one patient. We're taking Let's say we're drawing the blood. So we We'll measure ten thousand cells.

For each of the cell that we are going to map. We're going to measure. Ten or twenty thousand different genes. And proteins and other measurements. Now we are doing this for let's say one thousand patients.

We are putting this information into a machine. This is not the equivalent of goal. No human being will have the intuition. To analyze All the relevant. Data.

You have to use a machine. to analyze the data. And I think this is where The Um movement or the the unique opportunity comes

There are certain types of problems. where there is no human expertise to guide your ways. And until now we've been only using you know, a very, very small slivel of what is available. And so what To what Louis said.

Uh the uh many counterintuitive things. That humans would never come up. when they de le develop or discover therapeutics and I think a a data driven way to mine a large data set.

Is the way to go. you know, there is this analogy um between what you're doing and and the work of developing uh uh autonomous vehicle technology And with A V is the data that they collect, right? is used to to make improvements.

to vehicles because it it's it's collected from vehicle to vehicle and then the vehicles are automatically improved and and the systems that govern them become improved with the increasing data. How's what you're doing similar to that? Is it the same principle, essentially? So it is similar but also more complicated, and maybe the similarity What you just said.

We are going to train our models. And more and more data coming from clinical trials. um measuring the accuracy of our algorithms when we are wrong, we're going to recalibrate the models and do it again and again and again and and improve the accuracy. But the place where this is really different

is that for autonomous vehicles you have experts. And every driver that can drive a car, you can ask whether an autonomous vehicle drives as good as a driver. Like me and you. for drug development Even the best scientists in the world

they don't know, as as we mentioned earlier, only ten percent of drugs are going to succeed and The question is how can you build a machine that is not going to be as good as a scientist, it's going to be much, much, much better. No, I'm you you mentioned this idea of risk and uncertainty with what you are working on. And with the the business that you've

Built. But it seems like Where you are already Four plus years in. Uh

Is promising. And and that it it seems to be on the right path. Why do you think that that there is still so much risk in involved with what You're trying to do. First of all, I think it's a good mindset.

I think that uh when you understand that the risk is high, you are more likely to take Steps. That are going to be non uh conformist and nonconformal and you're going to think about the problem

What I'm trying to do with immunity and what I'm trying to Two with our board of directors with our future investors, with our Uh team.

I want everybody to know that we are trying to do something. Extremely difficult. Very unlikely to succeed. Not because internally I don't think we're going to make it. I think every year, every quarter, every month we're making a lot of progress and I make I'm becoming increasingly more confident that this problem is solvable and we're going to make it

But the mindset of thinking big and taking risks is the mindset that I want to have. when we are, you know, waking up in the morning when we don't go to sleep late in the evening. And and at least it's my mindset and and I I think it's very productive. That's Noam Solomon, co-founder and CEO of Immunite. No, um thanks so much for being on the show.

Thanks, guy. Thanks for having me. Hey, thanks so much for listening to How I Built This Lab. please do follow us on your podcast app so you always have the latest episode downloaded. If you want to follow us on Twitter, our account is at how I built this, and mine is at guyRaz. And on Instagram, I'm at guy.Roz. If you want to contact the team, our email address is hibt at id.wondery.com.

This episode was produced by Sam Paulson and edited by John Isabella. Our music was composed by Ramteen Arablui. Our audio engineer was Neil Rouch. Our production team at How I Built This includes Alex Chung, Casey Herman, Chris Massini, Elaine Coates, JC Howard, Liz Metzger, Carla Esteves, and Carrie Thompson. Our intern is Susanna Brown.

Eva Grant is our supervising editor, Beth Donovan is our executive producer. I'm Guy Raz, and you've been listening. How I built this.

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