#249 Brice Challamel: How Moderna is Using AI to Disrupt Modern Healthcare

This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking. Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved. Offer only for new US customers with a minimum financial commitment. See if you qualify for half off at http://oracle.com/eyeonai In this episode of Eye on AI, Craig Smith sits down with Brice Challamel, Head of AI Products and Innovation at Moderna, to explore how one of the world's leading biotech companies is embedding artificial intelligence across every layer of its business—from drug discovery to regulatory approval. Brice breaks down how Moderna treats AI not just as a tool, but as a utility—much like electricity or the internet—designed to empower every employee and drive innovation at scale. With over 1,800 GPTs in production and thousands of AI solutions running on internal platforms like Compute and MChat, Moderna is redefining what it means to be an AI-native company. Key topics covered in this episode: How Moderna operationalizes AI at scale GenAI as the new interface for machine learning AI's role in speeding up drug approvals and clinical trials The future of personalized cancer treatment (INT) Moderna's platform mindset: AI + mRNA = next-gen medicine Collaborating with the FDA using AI-powered systems Don't forget to like, comment, and subscribe for more interviews at the intersection of AI and innovation. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI (00:00) Preview (02:49) Brice Challamel's Background and Role at Moderna (05:51) Why AI Is Treated as a Utility at Moderna (09:01) Moderna's AI Infrastructure (11:53) GenAI vs Traditional ML (14:59) Combining mRNA and AI as Dual Platforms (18:15) AI's Impact on Regulatory & Clinical Acceleration (23:46) The Five Core Applications of AI at Moderna (26:33) How Teams Identify AI Use Cases Across the Business (29:01) Collaborating with the FDA Using AI Tools (33:55) How Moderna Is Personalizing Cancer Treatments (36:59) The Role of GenAI in Medical Care (40:10) Producing Personalized mRNA Medicines (42:33) Why Moderna Doesn't Sell AI Tools (45:30) The Future: AI and Democratized Biotech

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Drug design is not a problem for Moderna. If anything, we design way more drugs than we can bring to market at the moment. So we have more than 50 ongoing clinical trials. You can check them out, they're on our website. What is complicated for us is to go through the clinical trials, to do the medical training, the regulatory approvals process. So it's this entire ecosystem for all the right reasons, because we're saving lives, millions of lives, and we need to be very, very thorough on how we do it. But this is probably where AI is going to have the most impact for us now. The AI solutions that we use, both to design the drugs, but also to curate the environment, the regulatory environment, the medical environment, the contractual environment,

• 0:43Speaker 00

  every component of the vast ecosystem of life science and healthcare and pharma, they are products and they have a product lifecycle. They need to be designed, they need to be tested, they need to be adopted. People need to get proficient at them, they need to be improved, curated. And at some point, some cases need to be deprecated and replaced with others.

• 1:05Craig S. Smith?

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• 1:49Craig S. Smith?

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• 2:10Craig S. Smith?

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• 2:49Speaker 00

  Hello Craig, my name is Bryce Chalamet. I am a Head of AI Product Innovation at Moderna, the biotech company. I, prior to Moderna, I worked at Google Cloud for five years as Global Head of Transformation, which really entailed designing with corporate executives the future stage of their evolution with technology. Prior to that, I was the CEO of my own company dedicated to innovation and transformation. I committed four books in French, so you never know how bad they are, but they exist. You would learn them on Amazon. And prior to this again, I was a consultant at the Boston Consulting Group and I worked in marketing in a consumer brand called L'Oréal.

• 3:34Speaker 00

  My background is in business school. And the core of what I do is to combine strategic business needs with technology understanding to drive change. And that change can be within a paradigm, which we've mostly lived in the last 10 years within the paradigm of cloud, SaaS software, online collaboration. Or it can be through a change of paradigm, in which case transformation is the name of the game and no longer innovation. I think of trans-formationation, it's almost three words in one. Trans is from one situation to another through a barrier.

• 4:19Speaker 00

  Think about transparency through class, translation through language, transcendence from this world to the next, right? Form is both your organization, but also your culture. Think about the formal dinner. So this is from one culture and organization to another through a barrier of change. And nation is always process and result. For instance, if you think of evolution, it's a process as demonstrated by Darwin, right? Darwinian evolution. Also, it's an outcome. I can show you this pen and say, hey, look at this. It's a fantastic evolution. It's an outcome. So transformation is the continuous process with continuous outcome

• 5:02Speaker 00

  of evolving from one stage of our existence to the next through a barrier of change. And that's what happens when a full new paradigm is emerging. And I deeply believe that that's what's happening right now with the access revolution to AI.

• 5:22Craig S. Smith?

  Yeah. And when you talk about head of AI product, is that what you said?

• 5:29Speaker 00

  Yes.

• 5:29Craig S. Smith?

  For Moderna. Moderna is, I mean, AI is in the drug discovery process. But does Moderna have AI products that it's selling externally?

• 5:43Speaker 00

  So that's a great question. We don't sell at the moment, it's only AI products. It might happen at some point in the future, the same way that Amazon ended up selling Amazon Web Services, which was not their initial product, which was selling books. But that took decades and a very specific strategy orientation. But at the moment, we don't have at Moderna. We sell drugs, therapies. However, the AI solutions that we use, both to design the drugs, but also to curate the environment, the regulatory environment, the medical environment, the contractual environment,

• 6:27Speaker 00

  the every component of the vast ecosystem of life science and healthcare and pharma.

• 6:34Speaker 00

  They are products and they have a product lifecycle. They need to be designed, they need to be tested, they need to be adopted. People need to get proficient at them, they need to be improved, curated. And at some point, some cases need to be deprecated and replaced with others. And so we really try to have a great product mindset around AI solutions at Moderna. I'll explain why. If you are, as we are, in the perspective that AI is a utility, that everyone should have it as they have electricity, as they have laptops, or as they have the internet. Then they are going to start generating AI products.

• 7:18Speaker 00

  You call them GPTs, you call them agents.

• 7:24Speaker 00

  And let's just focus for two seconds on an obvious one, which is the GPTs, right? Those encapsulations of instructions, knowledge components, and delivery which you share with other people in the company. Let's say you're in the travel and expense team and you have a travel and expense GPT.

• 7:40Speaker 00

  If for any reason you forget to upload the new travel and expense policy, but people refer to the GPT that you produced for their next trip, then something unpleasant is going to happen. So you need to be a product owner and remember that you have a GPT out there, leveraging AI to reply to people's questions written in plain French, English, German, about their next travel and that you need to keep it updated, that maybe the model evolves you to also evolve the instructions, that maybe at some point it does a new solution, you need to move on to a new solution. You see what I'm saying, right? Yeah. One of the most misunderstood challenges of vast adoption of AI at the workplace

• 8:25Speaker 00

  is that it turns every knowledge worker into a digital product owner. And that's why I'm head of AI products and innovation.

• 8:33Craig S. Smith?

  Yeah, and how across the enterprise, how many products, AI products are there? I mean, you mentioned I hadn't thought of that, the GPT for expenses. I mean, for people understanding.

• 8:51Speaker 00

  Well, the expense was just a, an easy example. We have about one thousand eight hundred GPTs in production at Moderna. We probably have as many AI solutions, which are applications calling APIs from different models to a platform, an ML platform, which is called Compute, and our API orchestrator, which is called MChat. And so the combination of all those products is probably around four to five thousand. And the reason why I don't have an exact number for you is that my number one role is to be a platform owner for those products and to make sure that their data is safe and secure, that they are compliant to our own policies

• 9:35Speaker 00

  and to some degree for those who necessitate with pharma regulation. And so I don't mean to deep dive into each and every single AI product. The same way that if you were, say, in charge of the internet, you wouldn't want to deep dive into every time someone searched Google or browsed the net or used a SaaS application. You would want to make sure, though, that your internet platform has maybe a VPN, has a good bandwidth, is always available for people. You see what I'm saying?

• 10:11Craig S. Smith?

  Yeah, yeah.

• 10:14Craig S. Smith?

  You know, AI and Big Pharma sort of came into the public consciousness with the development of the COVID vaccines.

• 10:26Craig S. Smith?

  Are those solutions, were those products that Moderna already had developed?

• 10:32Speaker 00

  Absolutely. We had at the time, Moderna is an AI-native company. There are more ways to design an mRNA that codes a protein.

• 10:44Speaker 00

  One specific protein, then there are atoms in the universe.

• 10:48Craig S. Smith?

  Hmm.

• 10:49Speaker 00

  The same type of challenge as the Go game. And the only way to do this properly and to design single-stranded mRNA, which is the only one that is going to work in the cellular environment, that codes reliably and efficiently a given protein out of those trillions of trillions of solutions, requires AI to be designed properly. So no AI, no mRNA.

• 11:17Craig S. Smith?

  Yeah. From, can I ask about what, you say you don't do a deep dive in every product, but on, on that, and, and you mentioned, did you mention AlphaGo, but of course, everyone thinks of AlphaGo. Is that reinforcement learning that, uh, that you use to, to, to train the model to find, uh?

• 11:44Speaker 00

  Yes.

• 11:44Craig S. Smith?

  The correct.

• 11:45Speaker 00

  So that's a great question. The models that we're discussing right now, the ones that, uh, contributed to the origination of the mRNA, uh, industry really, they are machine learning models.

• 11:57Craig S. Smith?

  Hmm.

• 11:58Speaker 00

  I say this because very often now, when we talk about AI, we think about generative AI. And generative AI is a fantastic access revolution to a technology revolution that was machine learning.

• 12:11Craig S. Smith?

  Yeah.

• 12:11Speaker 00

  And just now, instead of having to code your requests in Python or in R, you can just type them in English. So I, in some ways, even though it has a lot of other merits, I think of generative AI as I think of the graphic interface for computers. Before that, you had to type commands in Unix.

• 12:29Craig S. Smith?

  Yeah.

• 12:29Speaker 00

  And then you could just click on the mouse and drag and drop objects. And you had Visual Basic and the world of computing transformed itself. That's another moment of transformation because it wasn't a few hundred people in the world. It was millions and millions of people who could use it. So generative AI allows you, in plain language, even speaking or typing or even with an image.

• 12:59Speaker 00

  To trigger under the hood an ML process. So at the time, back in the, in, you know, the 2010s, when Moderna was founded, AI at the time was mostly machine learning. And it was this five-star hotel at the end of a dirt road. It was hard to get to, hard to come back from, and very, very difficult to implement efficiently. There was the handful of, of people and use cases. And certainly, Moderna was, for me, like the invention of the radio after the democratization of electricity, right? Compute power was there for a while, and then Moderna appears. And I was at the time at Google, and I knew that I was seeing the first real use case

• 13:43Speaker 00

  of all that compute power and all those machine learning models and those reinforcement processes that you mentioned. And now the next stage of this is, well, if it is an access revolution, how much access? Should I give access to everyone? And how much power behind that access? And then how do I organize to make sure that they are proficient? And that they really embrace the new way of working that goes with it?

• 14:08Craig S. Smith?

  And so, talking about the drug discovery end of this, are there a family of products, quote unquote, either models or, or systems that you're, you've developed and are developing? That then the, the scientists that are designing new drugs have access?

• 14:36Speaker 00

  Yes.

• 14:36Craig S. Smith?

  And, and are, is there like, I imagine there's a dashboard or something where you, you see all of these available tools?

• 14:47Speaker 00

  Well, then again, we have an ML platform, which I mentioned earlier, called Compute. All of our solutions point to Compute, and Compute can be called from almost all of our solutions. We are a platform company at the core. Which means first and foremost for us, a biological platform company. Because the mRNA chemistry, those strands of RNA encapsulated into nanoplipid bubbles, they are a platform unto themselves. They can help cure cancer, they can help with immune and inherited ailments, they can work for virology.

• 15:30Speaker 00

  There is a huge share of the burden of disease that can be potentially either cured or supported with mRNA technology. And when you learn something from, let's say, a development in oncology, you can most often reapply that learning to virology or to rare disease. And that's the property of a platform, is that you have at every given moment, an overview of everything that's running there that's interconnected, and you have scale effects in the shared learnings and best practices of every component of it. And then, on the, on the side, side by side to that platform, which is the code of life,

• 16:13Speaker 00

  right, mRNA, is another platform, which is the code of software, AI. And those two platforms combined are the beating heart of Moderna. Because all of our AI solutions speak with each other, share common data repositories, our AI engineers collaborate with each other to improve the solutions, and it all serves the biology platform in return. So, one of the most interesting points here is that everyone's focus right now is on drug generation, like novel therapies.

• 16:54Speaker 00

  I'm not sure this is not a little bit misled, because we are already making inroads in drug generation. The way that amaranth therapies are being conceived, if you think of the prior generations of pharma products, which were finding herbal medicines in a corner of Africa or Southeast Asia, looking at the molecule that would actually have an effect, refining it, producing it, or filtering it, putting it in a pill or, you know. And now here we are,

• 17:28Speaker 00

  generating therapies from software, assembling them as nanoparticles in a production site, and pointing them as nanotechnologies to the heart of the cell, to inform the way that the cell generates proteins, either to metabolize components

• 17:50Speaker 00

  or, you know, biological ecosystem, or to alert of threats and trigger the immune system, or to set regulate on cellular production. What an extraordinary, different paradigm of pharmacy, of life science, right? And so, drug design is not a problem for Moderna. If anything, we design way more drugs than we can bring to market at the moment. So, we have more than 50 ongoing clinical trials. You can check them out, they're on our website.

• 18:24Speaker 00

  What is complicated for us is to go through the clinical trials, to do the medical training, the regulatory approvals, you know, process. So it's this entire ecosystem for all the right reasons, because we are saving lives, millions of lives, and we need to be very, very thorough on how we do it. But this is probably where AI is going to have the most impact for us now. Because drug design, we've got this. And we've had this for a while now. And we share a lot of this with the rest of the industry. Our oncology practice, our cancer treatments, is done in partnership with Merck. So we have strong industry partnerships to go together to market.

• 19:08Speaker 00

  But every clinical trial that we can accelerate, every regulatory filing that we can accelerate, every moment of technical development and industrialization that we can accelerate.

• 19:22Speaker 00

  We can translate in lives saved. And this is where we see the most impact of AI now, incremental impact of AI.

• 19:33Speaker 00

  And this is why we want to think of AI as a utility and share it with every component of of the company ecosystem. Because those companies, they don't have a department that is just there because it's pleasant. Everyone matters. And it is a chain. It's as strong as the weakest link.

• 19:52Craig S. Smith?

  Yeah.

• 19:53Speaker 00

  If any of those links is slowing down, the entire chain is slowing down. We can't afford to have twenty percent of our people making progress at the speed of AI and eighty percent waiting. And we can't afford either, by the way, to have eighty percent of our people accelerating at the speed of AI and twenty percent on the sideline.

• 20:14Craig S. Smith?

  Yeah.

• 20:15Speaker 00

  It it needs to be mass adoption. And that's how the entire ecosystem is going to thrive and accelerate the go to market of this huge pipeline that we have and that begs to demonstrate it's a clinical trial, to be approved by regulators and governments, and to go meet the patients who are waiting for them.

• 20:36Craig S. Smith?

  Yeah, I have a question about that. I I had a fascinating conversation with a startup in Australia a few months ago. Trial Key was their name, but their parent company, I'm not going to be able to remember. Trial Key was the name of the product. And they've trained a model to predict the likelihood of a clinical trial moving to the next phase. Because you invest so much in in preparing and putting drugs into clinical trials that if if it fails the trial, you've wasted a lot of money, a lot of time.

• 21:20Craig S. Smith?

  Does Moderna have something like that where you you don't go into a clinical trial blind? You because you have to choose which drugs you're going to put forward that you can gain the the likelihood or the percentage of success that you can expect from a clinical trial so you don't waste time on drugs that aren't going to make it?

• 21:45Speaker 00

  This is a great question. I don't know that we have that kind of of model.

• 21:51Speaker 00

  Sorry, I don't know that we have that kind of model. I will say this, our technology is very reliable in the way that

• 22:03Speaker 00

  a huge majority of clinical trials have had the expected outcome.

• 22:11Speaker 00

  It was, we couldn't know as a company before we truly engaged how efficient the technology would be. mRNA was an unproven technology 20 years ago. We started doing clinical trials, we, the humans. Started doing clinical trials with mRNA technology in 2001, even though the design dates from the 50s. And it wasn't proven on the market when COVID broke. There was no mRNA therapy on the market when COVID broke.

• 22:44Speaker 00

  Today, we have two mRNA vaccines, one for COVID and one for RSV. And we are the only company in the world to have two mRNA products. Pfizer also has a COVID vaccine that is based on mRNA, but their RSV vaccine is a traditional vaccine technology. And so in this, we are very unique and different because Moderna really is an expansion of mRNA, mRNA Moderna, right? This is, we are an mRNA company, is what we do, the platform.

• 23:14Speaker 00

  And we tend now to expect a positive outcome out of our clinical trials. We tend not to think that we're playing dice and that maybe it lands, maybe it doesn't, because it has almost always landed the way that we expected it to. Yeah. So the consideration for us and our need to predict,

• 23:39Speaker 00

  the focus wouldn't be on clinical trials at this point. It would more be on The way the market receives the product, and the way that the supply chain can meet the demand, and the way that the regulatory authorities are going to welcome it and ask for more information. So those are more the ways that we point. I, one thing that is really great in your question is that we go back to the fundamental reasons to be using AI to begin with. I know five of them. And I've never met another. And I've been doing this for more than a decade now. You can use AI for perception, to perceive something. Think about the tumor in the X-ray, right?

• 24:22Speaker 00

  Or the spelling mistake on your word document. How do you identify the signal from the noise? Right, that's a huge pillar of usage of AI, which we use for drug design, which we use for regulatory submissions, which we use for contracting. Like, perception is extraordinary from AI. Then there's categorization. Think about the playlist on Spotify, right, that know all your music and put music styles. So this can help us understand client clusters, understand types of industrial processes, even understand our own files to some degree, and create folders around the files, if you would, right? That wraps them in a more efficient way.

• 25:07Speaker 00

  The third is prediction. And you just put your finger on it. AI is fantastic at observing trends and predicting patterns based on input and output of those trends. And that's why I said that currently our prediction needs as a company as a whole, because everyone in every department has their own smaller granular level prediction needs, right? Someone in legal also might want to anticipate the outcome of a litigation. Someone in HR would like to anticipate arrivals or departures of people in the company. You see what I'm saying? So the prediction, really, if you take one level down from the corporate perspective, is useful to almost everyone in the company. And that's why we want to democratize AI. But as a company, it is not so much clinical trial

• 25:52Speaker 00

  as it is go-to-market that is really our focus right now. Like, how do we bring our pipeline to life? And how do we make it save people's lives in the real world? And then the last two would be recommendation based on this. So that would be the next song on your Spotify playlist. And generation, which was there for a while. Remember, we had auto complete in email and it would propose the next word of your email and it just proposes the entire email, right? So generation has made leaps and bounds, but was there since the very beginning of machine learning. It just has taken a whole new dimension with generative AI. So one way that we work with our people, because then again, we want to think in a

• 26:33Speaker 00

  platform way, in a holistic way, we meet with them in workshops, in large environments, and we ask them to think about their reason to be here, their mission, then to break it down into what they need to perceive, what they need to categorize, what they need to anticipate, what they need to recommend, and what they need to generate, and draw lists of those things. Everything in those lists is potentially an AI use case. And then we start creating connections between them, putting them in groups of things that work together, the cats with the cats, the dogs with the dogs. And then we go into AI

• 27:16Speaker 00

  solution design, most of which they can now orchestrate themselves, because then again, we have a plain language interface to machine learning, which is generative AI. And I think that we have not fully yet completely understood the depth and the impact of that revolution of those five superpowers of AI now granted to every single knowledge worker in a holistic environment like modernness.

• 27:44Craig S. Smith?

  Yeah, I have two sort of directions I want to go. One, before I forget, I want to talk about personalized medicine, but I'll hold that. One of the bottlenecks for new drugs is the regulatory process. So you're confident of the clinical trials, you get through the clinical trials, but then it has to be reviewed and go through an approval process.

• 28:16Craig S. Smith?

  Do you work, for example, in the United States with the FDA on helping them implement AI tools to speed up that process? Or are they doing that on their own? Because it seems from their side, they they should be able to, you know, take your your clinical trial report and put it through a an AI system that's been trained on all the variabilities and can can, you know, speed up that process.

• 28:54Speaker 00

  So that's another great question. We we have a fantastic partnership with the FDA.

• 29:01Speaker 00

  They were an amazing partner to Moderna in the approval process of the very first mRNA product ever to go to market in America, which was the COVID vaccine.

• 29:10Craig S. Smith?

  Yeah.

• 29:11Speaker 00

  In trying times. And it created a very strong bond of knowledge and trust and and two-way respect between them and us on how we're going to go together. A lot of things we were doing at the time were very new because then again, it's a new paradigm for life science.

• 29:28Craig S. Smith?

  No.

• 29:28Speaker 00

  To design drugs the way we do versus the way they were designed before. And I think there's a lot of, then again, of understanding, of respect that was created in those years. There was very important years for for us and I think for the world, really.

• 29:46Speaker 00

  It accelerated the arrival of a whole new generation of medicine that probably would have taken a decade or more if it hadn't been for the emergency of the COVID pandemic.

• 29:55Craig S. Smith?

  Yeah.

• 29:56Speaker 00

  So we have great dialogue. The FDA has published a draft guidance on AI usage for various use cases in January. The industry has is working on a comment on that draft guidance and it is made for this. They published a draft guidance to give a chance to the industry to react to it.

• 30:17Speaker 00

  The FDA themselves have regularly expressed their desire to accelerate the approval process thanks to AI. And I have a lot of admiration for them to do this. They are not a conservative reactive force at all. They are true partners in saving lives with novel medicines. They want this to happen. And in this, we are not accelerationists who are meeting incomprehensive partners. This is not at all the situation. We are a whole new breed of life science design company,

• 30:59Speaker 00

  meeting people who are fascinated by what can be done and who want to approach it in the best way to be respectful of the mandate that the people have given them to make them safe, which of course is also our top priority, but also

• 31:14Speaker 00

  to market as quick as possible. And I love the dialogue we have with them. They are very open to the ways that AI is going to help accelerate

• 31:25Speaker 00

  all that regulatory process that you're mentioning.

• 31:28Speaker 00

  It's also a great accelerant for the way that we reply to their requests. They make a lot of requests, as they should.

• 31:35Speaker 00

  Mostly responding to one of their requests relies on data aggregation, most of the data we have already, and they're just asking to see it.

• 31:45Speaker 00

  And AI is a great way, then again, to perceive and to categorize. So we can perceive the data that is needed to answer the request and we can categorize it to make it easier to ingest by the FDA when we give it back to them. It would be crazy for us not to use it,

• 32:02Speaker 00

  both as a company in terms of efficiency, but also in terms of the outcome, because the FDA wants all the right data in all the right categories and order given to them in the most consumable possible way. So we have a lot of AI supporting our regulatory teams in the way that they prepare and that they organize the data and then they reply to regulatory requests, right? So then again, both in the approval of a medicine, but also in the constant

• 32:34Speaker 00

  Monitoring of how the medicine is produced, how our systems are operating, how we're improving them. There is a constant, very intense dialogue between regulatory authorities and Moderna. And yes, we absolutely use AI to accelerate it. And that's also the FDA's intent, outspoken intent to have it this way.

• 32:59Speaker 00

  We do research at Moderna, we don't do policy. But I can only observe with you that every administration in this country has made the acceleration of go-to-market for great therapies a priority. Every single one of them.

• 33:15Craig S. Smith?

  There's been a lot of talk because of AI, because of things like AlphaFold,

• 33:23Craig S. Smith?

  of personalized medicine, precision medicine.

• 33:28Speaker 00

  Absolutely.

• 33:29Craig S. Smith?

  Is what's happening at Moderna in that regard? And what has to happen for that to become a reality? Where you you you go see your doctor and they do it? I don't know. I don't know how it works. They do a DNA profile and they identify a therapy.

• 33:47Speaker 00

  So in our case, this is very real because we are right now in phase three clinical trials and personalized cancer medicines. So here is how it works. We do a biopsy of the cancer cells.

• 34:04Speaker 00

  And we look at the proteins in those cells that are unusual, that are the outcome of the cancer itself. These are called neo-antigens. We identify them, make sure that they are specific enough that if we point the immune system to them, it is not going to touch anything else.

• 34:23Speaker 00

  And then we generate an mRNA that codes those proteins and that points the immune system to cancer cells. Why this matters? Is because if, for instance, you had a cancer that you went through surgery, that you went through chemotherapy, and you've already been through a lot, and my mom is a cancer survivor, so this is very, very concrete and real to me, what we're talking about now.

• 34:49Speaker 00

  What you need is remission. What you need is for the cancer not to come back. And the way it comes back is most often through dissemination in your body. Most often metastasis.

• 35:01Speaker 00

  It's hard to anticipate, it's hard to

• 35:05Speaker 00

  treat through surgery. What you really want is to use the power of the immune system to be on the lookout for any cancer cell that has escaped the surgery and the chemotherapy. And that is potentially

• 35:24Speaker 00

  an agent that is going to trigger the next tumor. So this is as individualized as it could be because every cancer is different. And even if you had two or three cancers, and really I don't wish, but there would be two or three completely different cancers.

• 35:39Speaker 00

  So the drug, the treatment that we proposed there, the therapy, is not only individual to you as a person, it's individual to your cancer as an event.

• 35:53Speaker 00

  That's a revolutionary way to treat cancer. And our current phase three clinical trials, we have published intermediate results, is very promising in that domain. The way this is going to work, then again, is you are going to do a biopsy of your tumor, we're going to code the mRNA that creates a protein that the immune system will recognize as being specific to that cancer tumor. And we'll train the immune system to go fight it every time that it shows up. With the objective to reduce the chances of reoccurrence of that cancer.

• 36:35Speaker 00

  Now, this is not only an extraordinary outcome of AI and of that new paradigm of life science, which we've been discussing through this podcast.

• 36:45Speaker 00

  It's also something that GNI can help with the patient, with the doctor, with the caretakers. If you have, for instance, a loved one who is going through cancer, who's receiving personalized medicine, maybe you can receive personalized advice on how to help them best. Maybe you can anticipate the type of diet that they will need to have in order to not feel nausea. Out of the combination of the chemotherapy, of the outcome of their surgery, and of now the type of drugs that they have to take in the aftermath of that cancer to stay in good shape.

• 37:27Speaker 00

  Maybe you can receive psychological advice on how to work with the grief and the fear of that person. And maybe you can learn more about our therapy, right, which is very much out there and accessible. And I have this dream that the combination of the profound breakthroughs that we have with machine learning to design this new generation of medicines and the very accessible, democratized, generative AI solutions that almost everyone now has on their mobile phone to understand the situation, to take care of their loved ones, to speak in right language with them, to them, to the doctors, to the nurses, to the company if they have to, because there's a side effect they want to declare to us or things like this, to the entire

• 38:11Speaker 00

  ecosystem around, you know, the life of that person is a fantastic combination for a new era in medicine for the patients.

• 38:20Craig S. Smith?

  Yeah. When you say it's in, did you say clinical trial or it's in the regulatory process?

• 38:28Speaker 00

  It's in clinical trial. We are still in clinical trial on those cancer therapies.

• 38:32Craig S. Smith?

  Right. Is that you're, you're, you're looking for approval of that process because every, every drug that comes out will, will be different depending on the cancer. So you can't wait for approval of every personalized drug. You need approval of the process.

• 38:52Speaker 00

  That's exactly right. We, we are seeking approval of the platform, the specificity of each individual. So this is called INT, Individualized Neoantigen Therapy. So I explained neoantigen is those proteins that we identify on the tumor. Individualized, you know, and therapy are kind of self-explanatory. So the abbreviation in the lingo of biotech is INT for this kind of, of treatments. And you're right, we can't afford to have a specific approval process for each of them. The protein that the amarinet designs is a protein that exists on the surface of your cancer cells.

• 39:35Speaker 00

  It is something that the body is already facing and living with. It is not a pathogen. It is not even a component of a pathogen in itself. It's more the outcome of a chaotic cell proliferation that is generated by the body itself. And so. We don't believe, and I expect that the FDA won't believe either, that every single therapy will need the entire approval process. We will probably keep samples of every treatment that was produced that we can go back to. And there's something anecdotally interesting about it is that we are likely to propose to the order of 200 vials of the medicine,

• 40:20Speaker 00

  out of which a dozen is going to be used by the patient and all the rest for the regulatory purpose of conservation and of traceability of what happened in the event of that drug being produced because the system has not yet faced a truly individualized small-dose therapy going to market. And so 95% of our production, you understand when we do like a billion doses of the COVID vaccine, 200 vials is nothing.

• 40:54Craig S. Smith?

  Yeah.

• 40:54Speaker 00

  When we do 10 vials of the medicine for you to boost your immune system, then 200 vials becomes a way bigger production need than the actual therapy itself.

• 41:06Craig S. Smith?

  Yeah.

• 41:07Speaker 00

  Which is just a, it's not an issue to us. Just one of those moments when I paused and realized what a brave new world this was.

• 41:15Craig S. Smith?

  Yeah. The other thing that's happening, I talk periodically to Insilico. It's a startup, that's a drug discovery startup. These tools, and that's why I was asking at the beginning whether you're making these tools available outside Moderna. But there are so many of these companies now with, you know, started by biologists or...

• 41:48Craig S. Smith?

  Or cancer specialists or specialists in any particular disease. And they have these tools to to discover and put through clinical trials. Is there a democratization taking place of the pharmaceutical industry where or or do companies come up with compounds through their discovery? And then sell them to somebody like Moderna to take them to market?

• 42:26Speaker 00

  Moderna is its own research company. In this, we are very different from. I think this is the defining difference between biotech and pharma and big pharma. Biotech companies are research companies, most of them. And then have to ask themselves how they will go to market. Because of the very unusual circumstances of the COVID pandemic, Moderna made the decision to go to market by ourselves. Beyond Tech didn't have that luxury, was too small probably at the time and went to market through Pfizer. There's a momentous decision. But we do our own research on our own products.

• 43:05Speaker 00

  And we have the ambition to do our own go to market, even though then again, we have great partnerships like the partnership with Merck for the the cancer treatments. So that being said, the business model of Moderna is to design, manufacture and provide therapies, not AI products or platforms. Those are means to an end.

• 43:31Craig S. Smith?

  Yeah.

• 43:31Speaker 00

  That being said, we have a lot of partnerships, a lot of partnerships with academic centers and research centers, with industry groups, thinking about, you know, the future of life science. With nonprofit companies who are.

• 43:52Speaker 00

  Exploring use cases for amniotic therapies and ask us to produce trends for them that they want to bring to the type of environments in which they do their own research and we can produce for them. So there are a lot of direct and indirect ways into which we share a lot of our knowledge. We are not a secluded fortress of AI knowledge, far from it. We are ourselves the emergence of, I would say, the community around Harvard

• 44:27Speaker 00

  Medical School, around flagship, which is our venture capital and finance birth ecosystem that produces dozens of biotech startups and as modern as one of their crown jewels. And so the main reason why we are very cautious here is we want to make sure that the people who wield these very powerful tools understand what they are doing. And for those, then again, machine learning, you know, five-star hotels, complex products, it does require to have a

• 45:08Speaker 00

  scientific background to understand them, to leverage them properly. This is not as easy as you would think to just, all right, let's just gather 10 people, you know, in Africa and give them the model and let them do their own amniotic therapy. Like, this is not at all. It's a very, very complex, dense, multifaceted ecosystem of best practices, from research to technical development, to clinical trials, to the way we produce, to the way we contain, we ship, we administer. Everything is so specific and novel that it takes thousands of very, very knowledgeable people to make the most of them. And we want to make sure

• 45:49Speaker 00

  that this is always the case. But then again, we publish a lot of research papers, we have a lot of patents, which is a way to share our research with the world. And we have a lot of research partnerships. So not a business model, but certainly a field in which we are interested in what anyone else is doing and hopefully inspiring for others to follow suit.

• 46:11Craig S. Smith?

  No. Do you, then along my earlier question, so

• 46:19Craig S. Smith?

  Outside of Moderna, outside of your partnerships, you can see all of these biotech and, you know, drug discovery companies sprouting up. With these tools, do you think that the drug industry will change? That that there'll be these small specialized companies that have these powerful AI tools that'll be focusing on one particular problem? It just appears to me like that's what's happening.

• 46:55Speaker 00

  That's, you're absolutely right. It is already happening. I think we are in the middle of a a massive wave

• 47:05Speaker 00

  of radical disruption of life science. And I hope to increment on your point that we inspire them to think in terms of platform and not just in terms of single breakthrough. Because AI has the ability to generate

• 47:24Speaker 00

  platforms of new biology concepts that can work in a lot of different directions for a lot of different needs and therapies. And so if anything, I hope that what will stay to anyone listening to this podcast is that the combination of the platform of life science, DNA and mRNA, and the platform of data science and AI science through the products and solutions we discussed, both for drug design, for production, for the broader ecosystem of how they are received by the patients. They are ushering an entirely new era of life science, which I think will be more democratized, more accessible, and hopefully will save

• 48:08Speaker 00

  millions of lives that we already have with the COVID vaccine and we are only getting started.

• 48:12Craig S. Smith?

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• 48:56Craig S. Smith?

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• 49:40Craig S. Smith?

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