Precision Medicine in Pharma: Sanofi’s Frank Nestle
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Today we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcasts. Artificial intelligence has the potential to scale drug discovery like never before.

Find out how one pharma company uses AI on today's episode. I'm Frank Nestle from Sanofi, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and business strategy guest editor at MIT Sloan Management Review.

And I'm Shervin Kodobande, Senior Partner with BCG, and I co-lead BCG's AI practice in North America. Together, MIT, SMR, and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate. Today, Shervin and I are talking with Frank Nestle.

global head of research and chief scientific officer at Sanofi. Frank, thanks for talking to us. Welcome. It's really nice to meet you, Dr. Nestle. Hi, Sherman. Nice meeting you too. Frank, maybe start with your current role at Sanofi. What are you doing?

I'm the global head of research and CSO at Sanofi. Sanofi is really a company making medicines available across the world, across 100 countries with approximately 100,000 employees, providing medicines and vaccines literally to millions of people. We're currently going through a very exciting transformation. We've been originally in primary care and now transitioning into specialty care, providing medicines in immunology, oncology, rare disease, including rare hematology disease,

neurology as well as vaccines. And my role as global head of research is to really discover and then translate the next generation of breakthrough medicines to patients who are really waiting. I can tell you that. Tell us a bit about your background, Frank, how you got started in your career and how you ended up here. I'm a clinician and a scientist.

I trained originally in dermatology and clinical immunology, allergology, but I was always driven by the quest to make a difference to patients. And I did that by trying to understand disease mechanisms and then translating those mechanistic insights into therapeutics.

hopefully and ideally precisely tailored to the needs of an individual patient or a patient population. Now it's called precision medicine, but that's what always drove me. And in terms of the science, I tried to manage to get to those disease mechanisms. One is the science of the immune system.

And I'm happy to say with maybe all your listeners now that during the pandemic, we all became immunologists. So there's a lot of know-how out there about what is the difference between an antibody, a T cell and a B cell. I'm not sure there's a lot of know-how. There's a lot of experts. So lots of experts. That makes actually doing the science I do even more fun because you can...

totally dominate any dinner conversation. The other topic I could easily talk about for quite a while is dermatology, which is always a nice topic across dinner rounds.

But it's incredibly fascinating to think about the immune system. It's essentially a collection of mobile cells circulating through our body, and they move back and forth between tissues where cancer happens or the immune disease happens and the blood. And they're really accessible by a simple blood draw, and it's quite exciting to understand how this connected system works.

My strong belief is all biology is connected, is playing out in cancer, for example, to protect us from cancer. And we have huge successes in checkpoint immunotherapy of cancer. But also, if it gets out of control, if it overreacts in terms of autoimmunity, and the fundamental hardwiring for the immune system is really what we apply and exploit in vaccines. Our immune system has evolved to fight pathogens.

Tell us how the new world versus old world of immunology is being transformed by technology, possibly by better analytics, better AI.

Yeah, so there's actually a great story how the great conversions of life sciences with data sciences and engineering has played out to increase our knowledge about the immune system. And it all converges on the topic of single-cell immunology. We can now assess and analyze the immune system of a patient with just a few thousand cells. And we do this by applying single-cell immunology technologies

where we can study 2,000 genes per cell. So think about the magnitude of gene modifications we can study if we take 100,000 cells and across these 100,000 cells, we study 2,000 genes per cell. And this has been possible because of progress in engineering, microfluidics. It has been possible because of AI. For example, at Sanofi, at our lab,

Institute, we are writing code, we're writing AI algorithm to then essentially analyze those data and to rediscover cell fates. And we annotate then if a cell is a B cell or a T cell or some new immune cell we never ever heard about. The exciting fact is also that we can not only get cells from the blood, from circulation, we can go into, for example, the joints of a patient with

arthritis, or even the cerebrospinal fluid of a patient with multiple sclerosis. So all of a sudden we can go into the compartments where disease plays out and then literally put this disease under, you could call it the molecular microscope. And if you think about the fact that the cell is the common denominator of

of a physiological system of the immune system, then it could call it essentially the atoms of the immune system. We can study the immune system literally at atomic level resolution. And the use of AI here is both to understand the mechanism as well as to maybe come up with new drugs.

Exactly. So the first part is, I'm always saying, first you have to explore and create a map, a landscape of physiology and of pathology. And this is what we are currently doing. And then once you have all of that inside, you create the theory behind it, how mechanism plays out, and then you make drugs.

First, we want to understand how disease mechanism plays out. And that's exactly where we use these AI algorithms. Imagine you have 100,000 cells and they have 2,000 genes up or down. And we don't know if it's a T cell, a B cell or some new immune cell. So if you run a learning algorithm on these data sets, that algorithm gets better and better to actually tell us. So you essentially reorganize or rediscover the cellular annotation of the immune system.

It seems like in some applications of AI that we, Sam and I, you've been talking about, it's an existing business process that gets improved a bit or optimized a bit. And Frank, you're talking about applications that wouldn't even exist without AI, I would assume, right? Because the scale that you're talking about with thousands of cells and thousands of genes...

It appears to me these would have been unsolvable problems without the use of AI. I tend to agree. I mean, always the question is, what do you mean by AI? There's a whole spectrum from...

AGI, you know, artificial general intelligence and we're not there to various applications of machine learning where you when I started off many years ago to use machine learning approaches. These were just simple clustering algorithms or these were.

for example, random forest type machine learning approaches. But now we can obviously do much more in terms of applying AI. The discovery of medicines is then a whole different order of challenge. And this is where essentially you get into this question of molecular design. You know, medicines come in different sizes and shapes. If they're

below 500 Dalton, then they're called small molecules or above they are large molecules. Small molecules are your typical pills you take and large molecules could be biologics, antibodies you inject. And as we know from COVID, you can inject antibodies to protect you from COVID, but you can also take

small molecule pills to basically block, for example, the viral replication of the COVID virus. So how do you get to those molecules? You first have to understand your target. So, for example, if you think about

a target in multiple sclerosis or in systemic lupus, you have a certain target. It's a protein, and you have to understand the structure of that protein. And this is called structural enablement. We can now use fantastic technologies such as cryo-electron microscopy,

or X-ray resolution of those targets and we can discover their structure. But what we can also do is we can run virtually, literally billions of small molecules against those targets and then discover small molecules hitting those targets in a way that it has a functional impact.

And these are typically called then allosteric inhibitors. So an inhibitor which essentially binds to a target, to a protein, and does so in terms of an output which is functional. So it can then, for example, block a certain target, block a certain protein,

And this is then the first step to a medicine because we have all of a sudden a target. We have a disease where it plays a role. We have a tool compound, for example, a small molecule. And then we can take this starting chemistry to ultimately then get to a real medicine. So you're talking about trying all these things. Are you trying them in real time or like in physical world? Are you trying them virtually?

It's a combination of the real and the physical world. I'm always talking about AI as the chemist around the table. For example, if you want to come up with a new structure of a small molecule, a medicine, we used to do crowdsourcing. We have more than 300 chemists in our organization, so you could send that crowdsourcing request out there and you would have 300 chemist brains engaged in finding the perfect molecule.

But if you have AI at play and use generative algorithms, then you can literally go through not millions, but hundreds of millions of potential structures and then optimize them. So it's just all of magnitude work.

larger of what we used to do. For example, we did high throughput screenings just with a few hundred thousand molecules. Now we can do it with hundreds of millions of molecules and we can do this virtually. But then that virtual screen will get you just to a hit. We call it like the first iteration of a potential medicine. And then this has to be optimized. And it's actually this crosstalk between the machine and the human, which is happening all the time.

And the human in this case is a chemist? The human would be a chemist, yeah, exactly. So chemists still have a job here. Absolutely, absolutely. I was going to ask what that crowd of chemists thinks that suddenly 300 chemists are not asked for their input. What do they think about this?

Exactly. So they actually enjoy the challenge. Just simply what we call the ligandable space or the chemical space we're exploring is increasing constantly. And through that increase of that chemical space, we can come up with completely new molecules. There's nothing more interesting for a chemist to be faced with a new molecule they haven't even thought about and then put that into motion. But it's a long road from this original hit point.

to ultimately a clinical candidate takes typically four to five years. And then you have to clinically translate this. This takes another eight years. So it's a long journey from this original hit, but this is where essentially a molecule is born. A medicine is then born later in the clinic when we do proof of principle studies in patients. It seems a little bit difficult, though. Like if you've got suddenly many, many more hits,

Doesn't that create a big impact on your process and your workflow downstream from that?

You might think so, but actually the reverse plays out. So our experience tells us that these models, they predict structures and they help us to reduce that enormous space to a much smaller space. So I'll give you an idea. We typically had to synthesize close to 5,000 molecules with a typical drug discovery paradigm we were applying a few years ago. If you use

the support of a predictive model like an AI model, and there are multiple steps in the value chains to get to a molecule to ultimately apply models, you can reduce this to 500. And this is the big promise now, and it's really important to understand that, is that predictive power of an AI algorithm being fed and trained with

a lot of data sets, refining the number of molecules we need to conceptually come up with and then test

in our assay system. So it actually, what it does, it reduces the investment we need for synthesis, and it's expensive to synthesize compounds, but then also to test these compounds. And ultimately, what this can lead to is that we reduce the timelines. And, you know, if you understand the mathematics or the economics of drug discovery and development, it's all about timelines. If you can shave off one or two years from the

10 to 13 years it takes to get a medicine to a CVS near you, then we can dramatically alter the economics of drug discovery and development. So going from the 5,000, which in the old world, I assume would have had to be tested by trial and error, to the 500, you're limiting the universe that then is going to be developed and tested.

Is it possible that in that process of excluding the other 4,500 that you might throw away some good candidates? I'm curious how the learning happens here. Because in most other AI systems I'm close to, there's a truth data where the algorithm learns from. Yeah, what's the truth? Right. What's the truth? If you're throwing away structures that the engine plus the chemist might think is not going to even work.

Are you possibly throwing away some potentially unborn good drug candidates that never saw the light of day? Yeah, that's a good point. And it also depends on the different steps of the value chain. When we make a medicine,

We are testing different attributes. For example, just one attribute might be very potent binding. The next attribute might be highly specific binding, not hitting other targets. The next attribute might be it's safe. It doesn't, for example, lead to

cardiac malfunctions or other side effects in the liver. The next attribute might be a molecule is well absorbed by the gut. So the next attribute is distribution in the body into the organ system you need. So all of these different attributes are optimized by a dedicated models. And these models are trained

I'll give you a specific example. We use what's called caco cell lines to mimic what gut absorption looks like. And we mimic that by understanding how well this cell line is absorbing or taking up a molecule. So we're running thousands of these caco cell lines all the time. And we are then studying the truth is how well the molecule is absorbed.

in that caco cell line and that gives us then a high ranking of a molecule. And the system then learns from this iterative process what a good caco cell line uptake molecule is. And that ultimately then gives us a better and better model. So this model would be then one of the many different models we would use to predict those 500 we would ultimately test. Does it make sense? Yes, yes it does because what you're saying is

The molecule you would select has to have different attributes. And these attributes are real world tested on other molecules that might look similar to these molecules. So some kind of a clustering or molecules that look like this or have these kinds of chains or whatever, or these kinds of ligands work better. And so that's how you're infusing that learning into the algorithm, if I understand. Yeah, it's very, very good. I think what's important there is I think we were guilty of slipping down into this

binary classification thing where it's either a good drug or bad drug where you've got hundreds of different attributes you're looking at and you can play with each one of those and you can test on each one of those so that it's not just that crude, you know, up, down, yes or no, that we were kind of crudely thinking before.

We call it an optimization process. It's a high-dimensional optimization process where often if you then remove a certain molecular piece of the molecule, you might improve the uptake, but you might decrease the potency. So it's actually a give and take process.

And this is what chemists are very good about. And this is where they enjoy, because it's such a high-dimensional space, and we have so many data available to have that partner, which is AI, to tell them what the AI thinks. This has been fascinating, Frank. So we're going to transition to a segment, which is a series of rapid-fire questions. So I'm just going to ask you five questions.

You probably don't know them yet. And so just give us the first answer that comes to your mind. Are you ready for this? Sure, sure. Okay, so tell us your proudest AI moment. The proudest AI moment was when we, for the first time, could annotate single-cell immunology cell fades with our in-house developed AI algorithm at Sanofi. That's fantastic. When was that?

That was 2018. What worries you about AI? What worries me about AI is that people don't understand what AI is. They always think about artificial general intelligence as like machines replacing humans. That's not at all what I'm seeing. If you see where self-driving cars are at the moment, there's a lot to be solved until you get even close to it.

But what AI really is, is for very specific questions with a good data set, high computational power and a good algorithm to solve problems a human brain couldn't do. And these little small contributions of AI is making all the difference, certainly in terms of what I'm seeing in the R&D value chain. Your favorite activity that involves no technology? Cycling. The first career you wanted in childhood?

I always wanted to go down the route of being a writer and director, actually theater director.

And then I was reading Albert Camus, La Peste, and I was studying philosophy and literature. And there's a person, Dr. Rieu, who is fighting the pest. It's very timely with the pandemic. And he's fighting this absurdity, the absurd existence by doing good, by helping. And I'm not as philosophical. But when I entered medicine, it was just a...

a way of doing good and doing something useful. And now we do it at scale by hopefully finding the next medicine transforming patients' lives. It's a great antithesis to Albert Camus. Yeah, exactly. And your greatest wish for AI in the future?

Make it more explainable, both at the level of getting it out of the black box situation, make it explainable in terms of understanding what it does, but also explain it to people so that there's not this misunderstanding of AI. A lot of people just project their fears or their misunderstanding on that unfortunate small acronym. And once you use it in certain contexts, it's actually transformative.

Thank you, Frank. This has been incredibly insightful and I'm sure very valuable for all of our listeners. Thank you so much for this. Thank you for having me. Yeah, great to talk with you. Next time, Shervin and I talk with Stéphane Lennezel, Beauty Tech Program Director at L'Oréal. I'm always up for a good episode about cosmetics. Please join us.

Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights,

and gain access to valuable resources about AI implementation from MIT SMR and BCG, you can access it by visiting mitsmr.com forward slash AI for Leaders. We'll put that link in the show notes, and we hope to see you there.