This transcript is generated with the help of AI and is lightly edited for clarity.
REID:
How can AI really help medicine?
DAVID FAJGENBAUM:
We want to unlock the full potential of every drug to treat every disease and every patient possible. There’s 18,000 human diseases, and 14,000 of them don’t have a single approved treatment. I spent six months in the intensive care unit, nearly died three times. I started to wake up, and I was like, ‘Oh my gosh, I have to do these two things.’
REID:
Imagine this: You get sick. But your symptoms don’t just stop with a runny nose and cough. Your body begins to fail. Your organs are shutting down, and the best doctors in the world look you dead in the eyes and say, “We don’t know what’s wrong with you.” You’re left weak, scared, and now confused.
ARIA:
This is the reality for people with rare diseases, often referred to as “orphan diseases.” There are over 10,000 rare diseases affecting 400 million people worldwide. Yet 95 percent of these conditions have no FDA-approved treatment. And even if you’re lucky to get diagnosed, maybe even have a disease foundation stewarding research on your behalf, the road is incredibly steep.
REID:
Research for rare diseases is chronically underfunded. Data is scarce and the medical system is often too fragmented to provide clear answers. For patients and their families, the toll is devastating and hope can feel like a waiting game. One of those patients was David Fajgenbaum.
ARIA:
At 25 years old, in the middle of medical school, David was diagnosed with Castleman disease, a rare immune disorder that nearly killed him—five times. But David didn’t just survive. He turned his fight for life into a mission to save others. He co-founded the Castleman Disease Collaborative Network, discovered a life-saving treatment by repurposing an existing drug, and launched Every Cure, a nonprofit using AI to uncover treatments for rare disease.
REID:
Today, David is a physician-scientist, a best-selling memorist, and one of the youngest tenured professors at Penn Medicine. His story is one of resilience, radical innovation, and hope. In this conversation, we talk with David about chasing his own cure, the transformative power of AI in drug discovery, and what it will take to unlock life saving treatments for millions of patients who’ve been left behind.
ARIA:
Here’s our conversation with David Fajgenbaum.
REID:
David, I have been looking forward to this for months, since we first met at Wharton. Welcome to Possible.
DAVID FAJGENBAUM:
Thanks so much for having me. I’ve been looking forward to it so much as well.
REID:
So, before we dive into your incredible story, I know that you guys actually go back a bit—even before you became a doctor. So, how did your paths cross?
ARIA:
So this was almost 20 years ago, I was working in an organization—DoSomething—and we were trying to support amazing young people who were doing incredible things in high schools and college campuses. And enter David.
DAVID FAJGENBAUM:
So yeah, I was, at the time, a senior at Georgetown, and I had started an organization in memory of my mom. She passed away, as you know, from brain cancer when I was a sophomore in college. And before she passed, I promised her I would do two things: One is that I would create an organization for grieving college students. I didn’t know what I was talking about. I was literally, like, having this—I didn’t know what I was saying. I was like, “I’m going to call it AMF.” Those are my mom’s initials. “It’s going to help grieving college students. And Mom, I’m also going to dedicate my life to trying to find treatments for patients like you.” And she loved the ideas. And she had brain cancer, so her speech was limited, but she said, “Unconditional love.”
DAVID FAJGENBAUM:
Those were two of the words that she had left. She said it, and I was like, “Oh my gosh, I have to do these two things. I have to.” And so I went back to Georgetown, started this organization for grieving college students, started helping students at Georgetown. Turns out that grief is a real problem on college campuses. No one talks about it, but a lot of students experience it. And then students from other campuses reached out and said, “Can we start chapters on our college campuses?” So we scaled it, spread it, and then enter DoSomething, which is this amazing organization where they’re helping to take things to the next level. And so we won a Brick Award. We were on the CW together, on television together.
ARIA:
That’s right. Very important!
REID:
That makes it real.
DAVID FAJGENBAUM:
That’s right. Exactly. The DoSomething organization supported our work with AMF to grow. And what’s amazing—Aria, we haven’t chatted about AMF in a while—but I haven’t been involved with AMF for over ten years now, and it still continues to grow strong. I met a student on Penn’s campus who recently said, “I’m part of this group called AMF.” I was like, “Oh, really?” And she was telling me about it. And she had no idea that it stood for Anne Marie Fajgenbaum. Had no idea. Which I love! Like, the idea that you can scale something and that it continues to go many, many years after you.
ARIA:
And so it was actually just a few months ago, we were all at a dinner—Reid and I were at a dinner at Wharton with Adam Grant and Angela Duckworth—and I remember they were like, “You have to meet this amazing doctor. His name is David Fajgenbaum.” I mean, I lost my mind. I was like, “What are you talking about? I’ve known him for 20 years.” I remember running through the campus, and you were like, “Wait, I’m still here.” And I was like, “Oh, I’ll come see you.” And it was just such an incredible—I had followed your career and seen what you’ve done. But again, to hear that Students of AMF is still around—and even though you’re doing something slightly adjacent now, it was that that motivated you to become a doctor. So tell us more. How did this propel your career now into becoming a doctor and, of course, finding a cure?
DAVID FAJGENBAUM:
Yeah, so these grief support group meetings we would have, they’d meet every two weeks, and we’d meet with sometimes as many as, you know, two dozen college students from Georgetown, and then from Penn when I was in school there. And you’d hear these heartbreaking stories of just feeling totally isolated. You’d hear about suffering and loss. And at the time, I was pre-med at Georgetown, and then I ended up coming to Penn for med school. But sitting in these meetings and hearing about all the suffering people were going through, it really did two things for me: One, it just galvanized that second promise I made to my mom, which is I want to help people that are suffering from all these diseases. Because every week I’m hearing from people talk about what these things like ALS and these horrible conditions are doing.
DAVID FAJGENBAUM:
So it really galvanized, “Okay, this is really what I want to do with every moment that I have.” And the second thing it did was it gave me this opportunity to build and grow something from this idea, “Okay, mom, I’m going to help grieving college students,” to, “How can I create a nonprofit that’s helping people all over the United States, even Canada?” And so it was sort of the ultimate training ground in a way of how do you go from an idea to helping a lot of people? And it showed me this really interesting circuit that I keep observing. And that’s that when you’re hopeful for some sort of future—in this case, a future where college students that are grieving have support—you’re hopeful for some sort of future that can lead to action. So you start doing something, and then when it leads to impact, that can make you more hopeful for a future. And it creates this circuit—hope, action, impact, more hope. And I found that that same circuit is what’s fueling me right now.
ARIA:
Amazing.
REID:
By the way, I love the acronym in part because it reminds me of human-centered AI—hope, action, impact.
DAVID FAJGENBAUM:
Oh, I like that.
REID:
I was thinking about it. But anyway, you have this very unique, rare experience of being both a doctor and also a patient. So we’ll get obviously to Every Cure and AI. But what was the landscape for someone with a rare disease diagnosis before? What were the options or lack of options? What did it look like before you got kicked off on your path?
DAVID FAJGENBAUM:
Yeah, it’s a great question. So as a third-year med student, I had this idea—25 years old—about, to your point, of what it’s like to have a disease or to be a patient. And in medical school, when you get asked questions about diseases, they give you multiple answers about what’s known. And what I learned is that we only get asked about things that we know about, but actually, it turns out that the vast majority of diseases, we actually have no FDA-approved drugs, and we don’t know how they work. So in medical school, they don’t ask you like, “What’s the cause of this disease?” And the answer is like, “Oh, we don’t know.” Right? They don’t ask that question. But the reality is, the majority of diseases we actually don’t know.
ARIA:
It’d be a boring test.
DAVID FAJGENBAUM:
I know.
ARIA:
A hundred questions in a row, “Still don’t know, still don’t know, still don’t know.”
DAVID FAJGENBAUM:
Well, if you think about the numbers, there’s 18,000 human diseases, and 14,000 of them don’t have a single approved treatment. So the majority would just be, “We don’t know.” And so that was my mindset in 2010 when I first got sick with my rare disease. “Oh, we’ve got treatments for things and we’ve got answers for diseases,” but what I quickly learned—Reid, to your point—is that the reality actually for the vast majority of people with rare diseases is that we don’t know what causes your disease. We don’t know how to treat it. And, in many cases, we don’t even know what your outcomes are going to look like. And that was just frightening and heartbreaking to learn.
ARIA:
Can you go into—just for those people who don’t know, everyone should know, so if you’re watching this, share it with a friend—but can you go through, you were 25, you’re diagnosed. You went from, again, quarterback of the Georgetown football team a few years prior to all of a sudden you’re bedridden with this disease. Go through what your mindset was like during that.
DAVID FAJGENBAUM:
Sure. So, yeah, I was a healthy, 25-year-old, third-year med student. And, by the way, most people may not know that Georgetown has a football team. So it may not be actually that impressive to hear.
ARIA:
It’s still D1! You know!
DAVID FAJGENBAUM:
But yeah, so I went from being totally healthy, former college athlete, to just over the course of, really, it was just a couple of weeks. I went from being totally healthy to noticing that I had lumps and bumps in my neck. I noticed fluid in my ankles and horrible abdominal pain. More tired than I ever felt. I mean, I was a med student, so you’re always tired in med school, and it was like a different type of tired. And then I took a medical school exam, and then I went down to the ER to get blood work don,e and the doctor came back right away and he said, “David, your liver, your kidneys, and your bone marrow are shutting down. We have to hospitalize you right away.” I’m like, “Wait, what do you mean these things are shutting down? I was just helping someone yesterday in the hospital, now I’m going to be admitted?” And so I was transferred to the ICU shortly thereafter. A retinal hemorrhage made me temporarily blind in my left eye. I needed transfusions daily to keep me alive. I was on dialysis. I mean, imagine going from as healthy as you can be to as sick as you can possibly be. And again, with no diagnosis. So, Reid, to your point, all of a sudden I’ve got this—actually, it wasn’t even a rare disease diagnosis, I had no diagnosis—but just there was so much uncertainty and fear. And it also broke down how I thought about the healthcare system. And I had this concept that, like I said, we have an answer for every disease.
DAVID FAJGENBAUM:
“I can fill out a multiple-choice answer and tell you about every disease.” I had this idea that we knew things. I had this idea that you just had to be a great hospital, and you’ll have a solution. “We’ve got drugs. Not only that, if we don’t have a drug yet for your disease, we’re working collaboratively to find a drug.” In my book, I call this the “Santa Claus theory of civilization,” and that was that I had this naive idea that for every problem that we face, especially in medicine and healthcare, there must be a team of elves working together collaboratively to find solutions, and the solution will be delivered to your door right when you need it. And all of a sudden I’m like, “Wait, I’ve got this horrible disease and I’m at this medical institution and there’s no one working on any solutions.” And I’m talking to Santa Claus, who’s like the world’s expert, and he doesn’t know what we’re going to do.
ARIA:
You’re diagnosed eventually with Castleman. And so, during this is also when you co-found the Castleman Disease Collaborative Network with Dr. Frits van Rhee. Can you take us to that moment? You’re still suffering, and yet you’re founding this amazing organization.
DAVID FAJGENBAUM:
Yes, thank you. So, I spent six months in the intensive care unit. Nearly died three times during that period. I had my last rites read to me in November of 2010 at 25 years old. But my doctors started giving me chemotherapy with each of these near-death experiences. And the chemotherapy would sort of get me out, and it would temporarily help me. And I was obviously so thankful, and I would get back to semi-health, and then I’d be back in the ICU. So that happened for six months. And then I got out of the hospital, and I was put on an experimental drug. There happened to be a drug in development for my disease—Castleman—which I was so fortunate about. Because, I mean, so few rare diseases have treatments in development. And we thought this was it. It’s going to put me in remission, I’m going to do great.
DAVID FAJGENBAUM:
But then I relapsed a year later. And that for me was so difficult, emotionally, because this was it. Like this was the thing. And my doctor—who, as I mentioned, was like the Santa Claus of Castleman disease—explained to me that there were no more drugs in development, and there were no more promising leads, and that I was approaching the lifetime limit for the chemotherapy—Adriamycin—I received, and that I was going to die shortly from this disease. And I remember my dad and my sisters, and my girlfriend, Caitlin, were around me. Dr. van Rhee had just walked out of the room, and we were just bawling. I mean, we were heartbroken about this news. But then something sort of switched, and I started thinking about the fact that I just received a bunch of chemotherapy, and those chemotherapies weren’t made for my disease.
DAVID FAJGENBAUM:
And so I say to myself, “Wait a minute. My doctor’s telling me there’s no more treatments for my disease, but I just got seven chemotherapies that weren’t made for my disease, and they temporarily worked in the past, and I hope they’ll work again.” Like, what if there’s an eighth drug out there for another disease, too? And so I talked to Fritz, and he said, “Well, it’s unlikely. There’s probably nothing else out there. There’s no more treatments.” And so for me, that became a moment where I told my family that I would dedicate the rest of my life, however long that was going to be, to trying to find a treatment for this disease. And I knew I couldn’t create a new drug from scratch. We know how expensive and time-consuming it is to create new drugs.
DAVID FAJGENBAUM:
I didn’t have the funding or the time to create a new drug, but I had that realization from those chemotherapies that had saved my life that if these drugs made for lymphoma could treat my Castleman’s, maybe there’s another drug made for another disease. So that became my laser focus. Created the Castleman Disease Collaborative Network with Fritz. And we also—in addition to focusing on repurposed drugs—we also realized that we had to include patients in this entire process. We couldn’t just have doctors and research working on it. Let’s get everyone together, let’s crowdsource. We used a website called Codigital back in the day to crowdsource research ideas from patients, from physicians, from researchers. And then we determined among all the best ideas, what should we move forward.
REID:
So two questions. First, a personal one, and then one on the medical side. The personal one is, if people are encountering literally death’s door, last rites, a lot of people tend to give up. A lot of people tend to go, “Okay, it’s over.” You went, “I’m staying with it.” What would you give people to give them some sense, on the personal side, of this is why to try to hang on, and this is what to do. That’s the personal one.
DAVID FAJGENBAUM:
Absolutely. So I think there were three things that helped me when I was at these lowest moments. The first one is that I spent a lot of time visualizing and imagining the world that I was hoping for. A family with my girlfriend at the time, Caitlin. I saw myself treating patients, doing research to find drugs for patients. That vision for the future was so critical. When you’re going through a tough time and it feels like there’s no hope, you can’t have hope unless you have a vision for what you’re hoping for. The second is that you have to have amazing support by your side. So my dad, my sisters, and my girlfriend literally never left my side. At all times, one of them was holding my hand. And there’s this incredible amount of strength you can get from the people around you.
DAVID FAJGENBAUM:
And the third is that I think you get overwhelmed if you think about, ‘Okay, I’m going to try to somehow survive what will end up being a six month period of suffering and pain.’ Like, I don’t have the strength to make it through that, by any means. But what I could do is I could make it through one more breath at a time. And so I was like, “Okay, like it’s painful to take every breath, but I can do one more, and then I can do one more.” Those things add up and compound. So, I think for anyone facing what feels like just a hopeless time, I think, envisioning what you’re working for, having support, taking things one step at a time. And then if related to health, the thing that gave me a lot of hope was this idea that there were drugs that were made for related diseases that could potentially end up saving my life.
REID:
And so—this is the first time ever I’ve spoken this drug name aloud, so I don’t know if I’m pronouncing right, but—Sirolimus?
DAVID FAJGENBAUM:
That’s right.
REID:
Okay. So, it’s an existing drug, normally used to prevent organ rejection for kidneys. How was it identified as a possibility for Castleman? And then, what did that process look like?
DAVID FAJGENBAUM:
Sure. So Fritz and I started that nonprofit back in 2012, and one of the things that I started doing around that time was storing blood samples on myself in the freezer. It wasn’t my home freezer. It was the freezer in the lab, but started storing my blood every couple of weeks because we needed data. You can’t make discoveries unless you have really good data. And I thought that I would relapse again. We all thought I was going to relapse again. And so what I thought was, ‘Okay, if I can get these blood samples leading up to it, if I can survive that next relapse, then I can go back to the lab and maybe I can discover something.’ And so I had been storing these blood samples, sure enough, my disease came back despite I was receiving weekly chemo to hopefully prevent relapse. It still came back, relapsed, said goodbye to my family, and to Caitlin, and to everyone again.
DAVID FAJGENBAUM:
And then—I’ll never forget—I said goodbye, and I was out of it. And, about a-day-and-a-half later, I started to wake up. And that was because the chemotherapy had worked again—like, just barely it had worked. And I remember starting to wake up and being like, “Oh my gosh, I’m going to get another chance.” And I remember I’m starting starting to wake up, and my sister Gina was on the left side. Caitlin was on the right. And I remember turning to Gina, and I was like, “G, I need you to call…” And she’s like, “What’s happening?” I’m like, “I need you to call UNC and Duke, and I need you to start getting—there’s a lymph node at UNC, I need to get it to Philadelphia.”
DAVID FAJGENBAUM:
And I was like, “Caitlin, I need you need to go down to the records.” I was in the hospital. I’m like, “I need you to go down to the records department and get all that information to Philadelphia.” And they’re like, “What is going on?” I was like, “We’ve got a chance. I got one more chance at this.” And so, anyway, we got all those samples sent to Philadelphia, did a bunch of experiments. First—it’s called serum proteomics—we measured a thousand proteins in my blood. And from that experiment, and also something called flow cytometry, we detected that a communication line in my immune system was turned into overdrive. It’s called mTOR. It’s really important. Your immune cells are all over your body, they have to communicate, so it’s really important for them to be able to send signals back and forth.
DAVID FAJGENBAUM:
And the data suggests that it was turned into overdrive. And so I had had a lymph node cut out, so I confirmed from an experimental lymph node that it was in fact turned into overdrive. And I remember looking at the result and being so excited. And the reason is because this drug Sirolimus—that you mentioned—turns it off. This communication line that now I had like visual evidence that it was turned on, there’s a drug—Sirolimus—that turns it off. Like you said, it’s made for organ transplant rejection. But who cares what it’s made for? It does the thing that appeared to be wrong in my disease. And so I started testing it on myself. It’s been over eleven-and-a-half years that I’ve been in remission on this drug. And you can imagine, Reid, the moment that drug started working for me, all I can think about is, ‘Oh my gosh, how many more drugs are out there that could be useful in new ways.’
DAVID FAJGENBAUM:
And so, to your second question around repurposing, the reason that a drug can work in multiple diseases is that though two diseases may appear very different—like in my case, it’s Castleman versus organ transplant rejection, or a disease called LAM—they look very different symptomatically and clinically, but they share the same underlying problem in the body. So mTOR is hyperactive, you know, in all these conditions—one’s a lung disease, one’s organ rejection, another one’s Castleman—but because they share the same underlying problem, you can treat them with the same drug, and therefore treat multiple conditions.
REID:
And so, because most people have no idea what the process of creating a new drug is, I myself have only really started learning this recently—
DAVID FAJGENBAUM:
With your new company, right?
REID:
Yeah, with Sid [Mukherjee] and Manas and so forth. Describe how critical—and how valuable—and what the process is of repurposing a drug versus creating one.
DAVID FAJGENBAUM:
Sure. So, new drug development, you come up with a new compound, and you immediately have to determine: How toxic is it to be in humans? You start out in various animal models. You’re really concerned about toxicity, that thing hasn’t been used in humans. Once you’re comfortable with toxicity, you have to think about: Is it actually going to work? What’s it going to bind to? How’s it going to work in the body? You have to think about all these different diseases to try it in. And this process, from this drug looks like it might be promising, to it actually getting approval is really at the shortest, is going to be about ten years, and more like maybe 15 or 20 years. And you have to remember that over 90% of these early drugs fail, so they’ll actually never make it all the way.
REID:
And that’s the winning circumstance.
DAVID FAJGENBAUM:
Yes, that’s right. That’s exactly right. So, 90% fail, they take a long time. Sometimes, to your point, they actually make it all the way—costs a lot of money to do all that work. Takes a lot of time when you repurpose a drug. And this is what was so surprising for me when my doctors were telling me that we were out of options and then I was like, “Wait, aren’t there like 4,000 drugs out there? Have we tried 4,000 drugs on Castleman disease?” And they’re like, “No, of course we haven’t.” I’m like, “Well, these seven drugs, they weren’t made for Castleman, is there another one?” And so I think what’s exciting about drug repurposing is that when you can detect that there’s a common underlying process—like mTOR being across two conditions—you can really fast-track getting that drug to patients.
DAVID FAJGENBAUM:
So there’s something called off-label prescribing. What’s amazing, I’ve learned, is that between 20 and 30% of prescriptions every day in the U.S. is off-label. So doctors are already doing it all the time. So drugs are being prescribed for things they’re not approved for, oftentimes based on really solid evidence. But the reason it didn’t ever get approval for it is that the only way the FDA can approve a drug is if someone submits that material to the FDA and says, “You should approve it for this use.” And once a drug becomes generic, the drug company who made it back in the day is no longer interested in that drug. So no one’s going to submit the data to the FDA. So you’ve got all these great drugs that can help more patients, but really no incentive to find a new use for it. So really to summarize it, drug repurposing, we can get a drug like Sirolimus—was given to me within a few days of me making the discovery—off-label. And you can do the kind of work that’s necessary towards the tail end of development in months or years to really feel comfortable that this drug can help people.
ARIA:
I mean, every year I get a cold or the flu, and I sit in my bed and I put the blanket over my head and I yell to my husband, “Chris, don’t even talk to me. I can’t do anything. I’m sick and I’m never going to be better.” So the idea that you had been read your last rites five times and were like, “I got this.” I’m very much in awe. So that is incredible. And then what was that moment that Every Cure came into being, and how is that changing the game at scale?
DAVID FAJGENBAUM:
Absolutely. So after this drug started working for me, as I mentioned, I just started thinking, “Oh my gosh, how many more drugs are there out there?” Then we started using this drug, Sirolimus, in other Castleman’s patients, it was helping them. Unfortunately, it works in about 20% of patients, so it doesn’t work for everyone, but it actually worked in the first four patients—so me, and then the next three. So I thought, “Oh my God, we figured this thing out.” Turns out it, we didn’t figure it out fully, but that was important. And then we found another drug for Castleman, for a patient named Kaila, this young girl in Chicago, who’s dying from her disease. And nothing was working, but we tried another drug and it worked for her. And it’s like, “Oh my gosh, we can actually find other drugs for this disease.”
DAVID FAJGENBAUM:
And the next thing was that we actually found a drug for a patient with a horrible cancer called Angiosarcoma. So now it was a proof point that, wait a minute, we can actually find other drugs for other diseases. And as time was going on and as we got to the early 2020s, my co-founder of Every Cure Grant Mitchell—Grant’s a dear friend. I think you met him at a Do Something event years ago. He has the record for rock skipping. I don’t know if you remember if he shared that story, he is world champion rock skipper. But Grant is also a brilliant AI mind. And he his MD/MBA with me and then went on to work at QuantumBlack, and doing a lot of great work in AI.
DAVID FAJGENBAUM:
So, Grant and I kept talking about, “Okay, David, you’re doing all this cool stuff in the lab.” At this stage we were up to almost ten different drugs that we’d repurposed for different diseases in my lab at Penn. He said, “Why don’t we try to really scale this and automate it with artificial intelligence? What if we can actually replicate what you guys are doing in the lab, but actually do it at scale across all drugs and diseases?” And so we kept talking about it, we were super excited about it. We were making good but slow progress in the few diseases we were working on. And then we spent time with amazing AI experts talking about ways that we could really scale this. And so in September of 2022—so three years ago—we created this nonprofit Every Cure to really scale this. And it is on a mission to save lives with repurposed drugs. And we do that by using AI to find new uses for every medicine we have.
REID:
I frequently wonder about the naming of diseases because we kind of do it on a rough set of symptoms to disease progression. Does the fact that you’ve actually discovered different drugs that work for different people with Castleman’s actually mean there’s multiple Castleman’s diseases—and that actually calling it all Castleman is, in a sense, too macro?
DAVID FAJGENBAUM:
I completely agree with you. Yes. Because if you treat a disease with an IL-6 blocker and it makes this patient better, then you know that that’s like the underlying driver. If you hear about this kid with an mTOR and they get better. So to your point, like the, the molecular mechanisms are different. And so yes, I think that we absolutely need to get to a world where we’re actually subtyping and naming things based on what the actual mechanism is. As opposed to—you’re right—right now we name things based on a collection of symptoms. So, yes, I think we need to go there. What’s challenging is that with this collection of symptoms, we’ve got 18,000 diseases. I think if we were to get even further, it’d be like, I don’t know, hundreds of thousands of diseases. But I think that’s probably the kind of granularity you need to really be able to find more treatment.
REID:
Well, because what should the additional blood test panel be?
DAVID FAJGENBAUM:
Exactly. It should be based on what might be the treatment.
REID:
So, tell us about your—I think your AI program is called the MATRIX? So tell us about it. What knowledge does it ingest? What does it accelerate in terms of the rare drug treatment?
DAVID FAJGENBAUM:
Sure. So, what we really set out to do when we created Every Cure is that we want to unlock the full potential of every drug to treat every disease and every patient possible. That’s our broader vision. And the way we’re doing that is through this AI platform MATRIX where we are quantifying the likelihood of every FDA approved drug—all 4,000—to treat every human disease—all 18,000. So, if you were to create a matrix with drugs and diseases, there would be 75 million possibilities—4,000 drugs times 18,000 diseases. And so we basically want to be able to help to prioritize among the 75 million possibilities, what are the best ones for humans to move forward to help people. Not just what’s the best drug for Castleman, or what’s the best disease for this drug, but really what are the best opportunities to help people?
DAVID FAJGENBAUM:
And so we’ve created this matrix—all drugs and all diseases. And our underlying infrastructure is we utilize primarily biomedical knowledge graphs, which—as you know well Reid—map out the world’s understanding of human biology. So our most recent knowledge graph is tens of millions of biomedical concepts as nodes, tens of millions of edges connecting those biomedical concepts. And basically—and I think I’m actually going to show you guys a demo a little bit later on—basically, you can imagine mapping out all of that onto to one, two dimensional space. Every drug, every disease, every gene, every protein. And all the connections between all of them. This serves as our understanding of human biology. And within this understanding human biology, we then train algorithms on known treatments. So we know that cetuximab treats Castleman disease, and we know that sildenafil can treat pulmonary arterial hypertension. So we train on the known connections, and then we ask the algorithm to make a prediction on every other drug for every other disease, and give us a score from zero to one. So if it looks really good, give it a 0.99. If it looks really bad, give it a 0.00.
ARIA:
We would love to see a high score in action. If you wouldn’t mind pulling out your laptop. Let’s do it.
DAVID FAJGENBAUM:
Absolutely. I’m happy to. So, now that I’ve got my computer, let me show you a little bit, and we’ve got this up here on the screen. So, two things. So first off, we have something called Orchard. We call it Orchard because we’re looking for low-hanging fruit across the forest. And we call these treatments “pairs.” It’s like a drug-disease pair. So, that’s why we’re Orchard. So, in Orchard, our tech team basically generates a score for drug versus every disease, presents it to our medical team, and this is our view. So basically, this is the top-scoring drug for every drug versus every disease is this penicillin for sepsis, which makes total sense. This drug treats sepsis. This is what you’d expect.
DAVID FAJGENBAUM:
As you can see, one of our colleagues called it a known entity. That’s what you want to see at the top of your list. What’s really cool is that we have all these things at the top that you’d expect to see that score 0.99s, but then we have this, what’s called suggestions mode, which is basically for our medical team to review through and see things that are not obvious, but are interesting. That the algorithm’s giving a 0.99, but that it’s not already a treatment for that disease. Then they go through and they say, “I wonder why Lidocaine might be scoring highly for breast cancer?” And then they dig into it. And one of the ways they dig into it is through these biomedical knowledge graphs. And so, what I’ve got here—and I pulled up a few—so this is just a tiny little segment.
DAVID FAJGENBAUM:
I mentioned our actual knowledge graph has tens of millions of nodes and edges, but I pulled up just a little tiny segment actually of breast cancer to Lidocaine. And so I’ll try to zoom in just a little bit, so that you guys can see a little bit better. So this is breast cancer over here on the left. Lidocaine on the right. And all of of the connections that could explain why it might be a potential treatment for breast cancer. And so, Lidocaine is the numbing medicine you get when you go to the dentist. There’s actually really interesting data that it has direct cancer-killing properties. And it happens to be the substance that we inject all of our bodies for every surgery all the time. So it’s a quite inert substance. It has cancer-killing properties. And also there’s even some really interesting data that it could prevent recurrence of breast cancer if injected around a breast cancer tumor before surgery.
DAVID FAJGENBAUM:
So for us, that’s really exciting because this is a cheap old drug that’s available already in the OR. It’s already going to be used in the OR. The change here would just be to inject a bit more of Lidocaine, but around the tumor. So the question is, “Okay, well how might it work?” And the thought here is that through apoptosis, which is cell killing, connections between Lidocaine, the genes and proteins that it interacts with, and then also the role that it might have in breast cancer. And so you can imagine we have knowledge graphs—or this is a sub-graph—for every connection that you could think about that our medical team can then use.
ARIA:
And so the AI is again, running through all of these millions and millions of combinations. The problem is that there are so many people who are in need of you. There’s way more to do than you guys possibly have time. Are you triaging based on the number of people that have a disease just based on the top score? How do you think about what to focus on from a research perspective?
DAVID FAJGENBAUM:
Great question. This is the biggest challenge with the work that we’re doing. And it’s because there are so many options and there’s so much potential. Iit’s not like we have to find the one treatment out of 75million. There’s actually a lot in here. And so, it’s a big challenge. So, the first thing is that the score that we get through the MATRIX, from our biomedical knowledge graph machine learning approach gives us a score from zero to one. So the 0.99 is obviously we’re most excited about, but all the way through, low-scoring. That’s how likely is that drug that it will work in that disease? That’s one input. But then to your point, it’s impact is really important to us. We want to go after the really bad diseases because we’ve got limited time, limited resources.
DAVID FAJGENBAUM:
You want to go after the bad stuff first. So we also generate something called a unmet medical need score. And so that unmet medical need score gives us an idea for just how bad the disease is. Things like ALS and pancreatic cancers get the worst. And then things like toenail fungus get the least bad score. But it’s linear, so we can really focus on the things in the top quartile. So we look for really promising biomedical impact score. We look for a really high unmet need score. And that’s when our med team starts really digging into these things. If you look here at Orchard—so this is actually our live platform—and so you go into triage.
DAVID FAJGENBAUM:
So there’s 204 things that our medical team has looked at and said, “This looks really, really interesting, and I wonder why this thing is scoring highly.” 62 have gone through what’s called a med review, where they’ve dug deeper into like, “This looks promising. Why might it work?” 17 have had deep dives. There’s 11 that have been endorsed by our advisory council. And so, once it gets to the medical team’s review—and we have this amazing team in Boston of MDs, PhDs, MD PhDs who review through these things—once it gets to their review, this is where it looks promising enough, AI is pointing us to towards it. There’s enough unmet medical need that now we need to focus on is it really going to help people and how do we get it to patients?
REID:
So, it’s an amazing use of AI technology, but one of the things that people frequently overlook in these is that it’s also AI plus human.
DAVID FAJGENBAUM:
Yes.
REID:
Right? So say a little bit about what the medical team does. How it’s a human amplification in terms of a journey of discovery.
DAVID FAJGENBAUM:
Yes. I’m so glad you brought that up, because we have humans in the loop in so many aspects of this. So our tech team—we’ve got a 20 member tech team—that embedded within the tech team are a few MDs, a few PhDs who really come at it from a biomedical science perspective, less from a tech side. So they’re embedded within the 20 member team. And so even when they’re generating predictions or improving our models, they’re thinking about this from a physician’s perspective, or from a researcher’s perspective. And thinking about what should this model look like to make sure we’re really finding treatments that’ll work as opposed to maybe just biomedical concepts. So, that it starts very early on with the tech team having that lens.
DAVID FAJGENBAUM:
It also, to your point, once it gets to the med team, these are medical scientists and physicians who are really just thinking about it from a human perspective. Like, okay, it’s great, we love when the algorithm gives it a 0.99, but now we got to really think about what are we going to do with this thing? Is there a clinical trial needed? Is there laboratory research needed? In some cases, we actually come across drugs that look really promising, and there’s actually already been a lot of work done. It’s just about getting the word out about that particular drug. And so our clinicians and our researchers on the medical team are really thinking about this from the end goal of reaching as many patients as possible with this condition. What’s the kind of impact we would have if we move this forward?
REID:
And, part of this, there’s multiple places where the human is in the loop. Decisioning, context awareness, being able to think about, okay, which of these things is potentially a really interesting genius idea, and which of them is actually just a nutty idea. Because they’re just sorting through those. How much work do you do with this in terms of also can you do FDA filings yet using the fact that you’ve got a deep knowledge base which is suggesting this potential off-label use. What does that journey look like? Because I presume that when you add an FDA approved use that gets magnified through the whole system.
DAVID FAJGENBAUM:
Absolutely. Well, maybe I’ll start back at your comment around sometimes there’s nonsense, because, not our platform, but actually another machine learning model that I’ve seen applied for drug repurposing. We were just looking through the top hits for Castleman’s, for example. And I remember seeing that the fifth highest scoring compound for treating Castleman’s disease was car exhaust fumes. And that I was like, “Okay, that’s interesting. Maybe it’s doing something with cytokines? We probably shouldn’t inhale car exhaust fumes, right?” Like that’s probably not a good way to treat it. And so, to your point, there was something in the data that suggested that maybe IL-6 levels or mTOR goes up or down when you inhale car exhaust fumes, but we don’t recommend that. And so, I think that it’s really important for these humans to review through these things and skip over that.
DAVID FAJGENBAUM:
But then to your question around, okay, you find something that looks really promising, how do you make sure it reaches patients? Yes, working with FDA to get the label change so that drug actually has that condition on the label, that’s amazing. And that’s really, really powerful and really important. And we are working with FDA and we’re eager to continue to work with them to find more of these cases where drugs that look promising can actually get a label change. But even when we do work with them, I still think it’s important to know that we would never go straight from AI prediction to let’s give it to patients, let’s get on the label. It’s AI prediction—alright, let’s confirm it in the lab. Let’s make sure that the biological mechanism makes sense. Let’s give it to patients in a well controlled and appropriate environment to prove that it works. And then let’s talk about what the label changes look like.
ARIA:
We’re talking about this AI plus humans to make this happen. And obviously all of the frontier labs right now are hustling to build data centers. Compute is what everyone is hoarding. You guys must use a lot of compute. So talk about how you are using that AI, and how that’s limiting if you don’t have the funds to make it happen.
DAVID FAJGENBAUM:
Yeah. We’re using compute in a lot of ways, as you can imagine, when you’re trying to generate scores for all 4,000 drugs versus all 18,000 diseases. I’ll share the anecdote that when we built our very first version of this two and a half years ago, it took 100 days to generate a score for every drug recovery disease—the 75 million possibilities. Now it takes 17 hours. There’s been a lot of progress. That’s really is not because of work Every Cure’s done. That’s because just the incredible revolution that’s occurring right now with artificial intelligence. But it’s a tremendous amount of compute. 17 hours for every time we run the 75 million. We run it multiple times a month to then come up with what we call a tier three run, which is the best one, that the med team then sees the results of.
DAVID FAJGENBAUM:
We’re able to assess based on known treatments, off-label treatments, just how accurate a particular set of predictions are. That’s what the med team then reviews. And so there’s a lot of iteration. Which is so cool about having a 17 hour turnaround times that you can actually iterate a bunch of times. So we’re both using compute to generate the 75 million scores. We’re also using a lot of generative AI and additional LLMs to help us with triaging and coming up with packets around why something might be effective or not effective. And agents that ask questions like, “Is this the right use for this drug? What’s the safety profile going to look like?” And so, all in, we’re we’re spending hundreds of thousands, probably low million, a year on compute costs. And in many cases we’re actually saying to ourselves, “Let’s scale back on this. Let’s not actually run that extra analysis here,” so that way we’re not spending millions of dollars on compute.
ARIA:
I don’t want to be presumptuous, but Reid, you know something about this. Anything that can be done?
REID:
Well, I mean, look, this is just stunning. I mean, in terms of the work you’re doing. And it is exactly the kind of thing where people need to see the hope in AI. And that it makes a difference in people’s lives, literally and fundamentally. So, before this podcast airs, we will move a million dollars to your compute budget.
DAVID FAJGENBAUM:
Oh my gosh.
DAVID FAJGENBAUM:
Because Aria, Reid this is incredible.
REID:
Because this is life changing.
DAVID FAJGENBAUM:
Well, that is incredible. Thank you so much. That’s amazing.
REID:
No, I hope everyone knows about the work you’re doing and like getting this accelerated. Because look, part of the thing is—just to be clear—we’ve got thousands of drugs that we know are not toxic.
DAVID FAJGENBAUM:
Yes, exactly.
DAVID FAJGENBAUM:
That’s the whole point to the 15 year thing. If they can cure another hundred lives, let alone thousands? Tens of thousands? Oh my God. Literally it’s what life changing means.
DAVID FAJGENBAUM:
It’s what we’re all here for. It’s amazing. Thank you so much. That’s incredible.
REID:
My honor
DAVID FAJGENBAUM:
That’s incredible. Thank you so much.
ARIA:
I think it also speaks to something else. So often when we’re talking about AI, a lot of people want us to slow down. I understand that point of view so much. The world is changing, is your job going to be the same? What is happening? And for folks like you, you want AI to speed up. Because if you could take that a hundred days to 17 hours to one hour and have it be a tenth of the price or a hundredth of the price, imagine what more we can do. And so again, just thinking about how AI can speed up what people like you are doing, that’s what we’re here for. So, thank you both.
REID:
And that’s why we’re doing this podcast too. It’s literally to see what technology can do to make human life better. And this is—I mean like bullseye is too wide of a term. It’s literally on. What do you think the key thing to do here is to help people understand how AI can really help medicine?
DAVID FAJGENBAUM:
Yeah, I think this is the right question. I mean, I think that if you use what we’ve done as a microcosm, at Every Cure, we are looking across all drugs, all diseases, find the lowest hanging fruit, basically the opportunities for humans to help people with the drugs we have. And we’re looking at 75 million possibilities that our lab—you used to be able to look at dozens a year, and now we can look at 75 million. If you think about that, and then just move that to other aspects of healthcare. And you think about, “Okay, let’s get away from this very siloed, this doctor for this disease, and you’re going to have to wait months and months to find that person, and they might not be the right doctor for you. You can imagine there could be this tremendous amount of efficiencies that are created for matching you with the right doctor at the right place to get the right drug and the right diagnosis.
DAVID FAJGENBAUM:
I’ve been so impressed by the research being done around, I think it’s called ambient listening, where basically tools that can look at patients when they walk in, and they may not just listen, they’re also visual.
DAVID FAJGENBAUM:
And they walk in the doctor’s office and start coming up with diagnoses before the patients talk to the doctor. Just talking to the nurse, hearing the tone of their voice, seeing their gait. That’s the kind of thing that I think that I’m so excited about in healthcare. I think that I personally as a patient struggled with getting diagnosis, I struggled with—there weren’t treatments for me. I think about my whole journey and how it’s so many levels: Rapid diagnosis, find a drug that could be repurposed to treat that patient, make sure that that drug’s working, follow biomarkers. I think that as you go through the healthcare system, I think there’s just, just endless possibilities to improve our outcomes.
ARIA:
You guys are also promoting radical transparency. You’ve talked about releasing the data—the scores—for millions and millions of pairs of drugs. And that could help other researchers, other folks who are doing the same thing. It could also help patients who are sort of looking for their cure. Talk about that transparency, and maybe any pitfalls it could have, and what effect it could have on patients.
DAVID FAJGENBAUM:
Sure. So, we only started Every Cure to help people. I mean, we started this just because we just want to help people. There’s drugs out there. There’s people suffering. Let’s help people. And we are really guided by—that’s our first core value is patient impact. And that’s our North Star. It’s why we’re here. It’s what we’re doing. So when patient impact is your North Star and your number one value, you have to optimize for: What are the way best ways to help patients? And so we felt from the beginning that one of the best ways to help patients is to make this information available rather than keeping it within the organization, really to share it publicly. But it’s important, we believe, to do it in the right way, and do it responsibly.
DAVID FAJGENBAUM:
I mean, it’s something, Reid, that you talk about a lot. It’s like, let’s push the frontiers of AI, but let’s be thoughtful about how to do it responsibly. And so, we’re thinking a lot about: How do we release them? We’re planning to do it in early 2026. And so releasing with, for example, disclaimers or training information—like you have to go through a training and understand that this is research data. And actually, even, our team at Every Cure, we don’t go from “that drug scored the highest in our algorithm” to “Let’s give it to patients.” We study it in the lab, we look at it in clinical trials. Like, there’s a process to it. And so I think that sharing while also doing it in the right way, I think is going to be the best way to maximize patient impact.
ARIA:
So there’s so much hope and positivity in the transparency piece. And you touched on the, and there could be misinterpretations. And I also feel like for patients who want to have hope there’s some devastation in, “Oh wait, they said this works for Castleman, but it doesn’t work for me.” Or, “Oh, I can’t use it because they haven’t…” And so, on the flip side, can you talk about challenges, misuses, misinterpretations of the data? You see people who have ailments, they’re going on Reddit, they’re diagnosing themself, and then—not necessarily good things happen. So what’s the flipside?
DAVID FAJGENBAUM:
Yeah, you’re absolutely right. I mean, this is an example of, I think, a perfect example of a double-edged sword. Where, on the one hand, information is critical and doctors need it, researchers need it, patients need information. And so providing information is so important. And then the also important piece is that, to your point, there’s a lot of information that’s already out there. When we provide scores, this isn’t the first time that someone’s going to see that a drug might work for their disease in a research setting. You know, that information’s on PubMed all the time. But at the same time, the challenge would be that patients would maybe travel to another country to get a medicine that scores highly in our algorithm when that drug doesn’t work, because our AI algorithm makes predictions for drugs that are just not going to work.
DAVID FAJGENBAUM:
You know, the way that we think about it, and the way that we’ve shown our platform works is that things that score towards the top are much better than things that score towards the bottom. But just because it’s number one on this disease list doesn’t mean it’s better than number ten.
ARIA:
Right. It’s just a candidate.
DAVID FAJGENBAUM:
There’s directionality—it’s a candidate. Directionality. The higher it is, the better. But like ten might actually be better than one. And, you know what—and this is a really important point: For a lot of diseases, there actually isn’t a repurposed drug that could treat that disease. I mean, I am so optimistic about all the patients we can help. Reid, we can help so many people. But we have to appreciate and understand that—I don’t personally believe that every human disease out there can be treated with every drug we have.
DAVID FAJGENBAUM:
What I do believe is that if there is a drug for that disease, we should find it, but not the vice versa, that there is a cure for every disease. And so, the worry is that when people hear, to your point, Castleman’s and pulm syndrome and angiosarcoma—and data too, and these diseases where we’ve transformed the disease course with existing medicine, it’s like, “Well then maybe it’ll be for my disease.” And I hope so bad, and we are trying so hard. And if it’s there, we’re going to do everything to find it. But there are certainly diseases where there is just not going to be an existing medicine that’s going to treat that disease.
ARIA:
Right. We do need new drugs for those diseases.
DAVID FAJGENBAUM:
We absolutely do.
REID:
Yeah, well, and also, I mean, part of the why the ten and one is, we have a rough—as per Castleman, a rough categorization: Well, which version of Castleman do you have?
DAVID FAJGENBAUM:
Exactly. Maybe one is good for you and ten is good for the other person.
REID:
Exactly. And so, you really have to understand this stuff, and it’s one of the reasons why research and understanding is critical. So, you’ve described giving these off-patent drugs, and we obviously, as you mentioned earlier, have a challenge that there’s no market incentive for this. So, how do you envision closing this gap? How do we get this to become an amplified global phenomena?
DAVID FAJGENBAUM:
Yes. This is such a good question, and it’s something we’ve been thinking about so much lately. We set up building this amazing tech team to make predictions. We built this amazing med team that identifies the best treatments and then does laboratory work and trials to prove they work. But to your point, just proving something works is not enough. We’ve got to get it to all the patients who can benefit. So we’re actually now going to be building out a third team called our impact team, which is, “Okay, all the things that work, let’s actually get them to all the people who can benefit.” One part of it, as you mentioned, could be through an FDA label change. Another part of it is simply awareness raising. It’s talking to doctors, getting updates made to treatment guidelines. It’s letting patients know and patient groups know. And so that third piece of this, I think is so critical. And actually one of our board members, Bob McDonald, when we were talking about adding this third piece on, he said, “Well, David, yes, impact is the “last leg,” but impact needs to be infused throughout the first two. And by having an impact team, embedding them with our tech team and our med team, that will help us to create this circuit that we need.
ARIA:
Is there something that should change on the systems level? Should the U.S. government be the one funding this? Should there be a different profit incentive? Is there something else maybe outside of your purview even that could change, whether it’s in regulation, or government, or I don’t know?
DAVID FAJGENBAUM:
A few things you mentioned, I think are absolutely the kinds of things we should be thinking about. One of them is government funding for our work. We’re fortunate to be a recipient of funding from ARPA-H, this few-year-old government agency. They’ve been amazing to work with over the last couple years. And so I’m excited about continuing to work with ARPA-H. So that’s one part. I think that should be part of it. I hope that’ll continue. Working closely with others outside of ARPA-H, whether it’s FDA, NIH—I think really thinking about this as a closed circuit where we’re all working together, we’re iterating, we’re improving—I think that’s a real opportunity for the future.
DAVID FAJGENBAUM:
And then, to your point, around what sort of policy changes could be made, the problem we’re facing is that, as you mentioned, 80% of drugs are generic, which means that there’s no incentives to find new uses for them. But it’s great that they’re generic because that means that they’re inexpensive, and they’re available around the world. But how do you create a system so that drug companies, or anyone, is incentivized to find new uses for these medicines when, if they do find a new use, there’s ten manufacturers and they’re selling it for a penny a pill? So I think it’s going to require a lot of thought. I don’t know if either of you guys have thoughts on this. I mean, how do you fix this challenge?
REID:
Well, I mean, it’s, I think, a public-good challenge. So government, your very first answer, I think is a good answer. The truth of the matter is that by the time that a drug gets off-patent, the drug company has made a ton of money off it. Which they should. And that’s the right way to recycle and innovation. And as a co-founder of a drug manufacturing company in its very early stages, obviously that economic incentive is very important. So, you do the detailed research work, which really matters. And then how does the information of, ‘Oh, this drug, that’s already FDA approved, has a good percentage off-label use for this,’ how does that enter into the therapeutic knowledge set?
DAVID FAJGENBAUM:
Yeah, great question. Well, in the current system—and this is another, I guess, area where the system could be broken or fixed, right now it’s very much: A paper gets published in an academic journal. And then hopefully doctors read that journal, and then maybe they talk about it at a conference, and then it spreads slowly. I mean, there are two websites that doctors go to a lot for this information. One of them is a website called UpToDate, where individual experts update their particular page for their disease. And then the other one is Wikipedia. Google of course is the third one. So, doctors are going to other sources beyond, you know, just the published literature. But there isn’t—you would imagine that there was this one database where it’s like, “I have this disease for this patient, what are my treatment options? What sort of research-grade treatment?” But there isn’t a really clear database for that. And I’m hopeful that we will help to push something forward with that impact team.
REID:
Yeah, no, that would be great. And it’s probably also, interestingly, also, I mean, one of the things that people in the tech community have started talking about is: You previously had search engine optimization, a la Google. Now you’re going to have agent optimization.
DAVID FAJGENBAUM:
Interesting.
DAVID FAJGENBAUM:
And one of the things you certainly know is going to happen is anyone who gets confronted with one of these diseases where their doctor says, “There is really nothing for you,” every single one of those people is going to go back and go, “Hey, what about this?”
DAVID FAJGENBAUM:
Yeah, exactly.
REID:
Wrapping that back into it.
DAVID FAJGENBAUM:
And even outside of what we’re doing at Every Cure, people are already doing that in ChatGPT right now. I mean, it’s already happening.
REID:
Exactly. Actually, I’d be curious about, I today say—if you’ve encountered a serious medical thing, including getting something from a doctor—you should go to a frontier model—ChatGPT, Claude, Gemini, Copilot—and you should ask a second opinion. Right? Because it’s just worth getting the second opinion. Because if the second opinion contradicts the first opinion in any serious way, then go get a third. It isn’t “trust the AI,” per se, at the end of the day. But to crosscheck. Am I giving dumb advice? Am I giving smart advice? Am I giving questionable advice?
DAVID FAJGENBAUM:
Your advice is exactly what I say, but it’s usually about a second human. Like, you know, a second doctor. I mean, I say almost the exact same thing: “Okay, you’ve got your opinion, go see another doctor.” And if it’s the same, then you feel pretty confident. But if it’s different, then you’ve got to find a third doctor. Literally what you said. But the only change to replace is that maybe it’s not a second doctor. Maybe it’s actually now an LLM. Maybe what I would say is that: Still consider having another human afterwards.
REID:
Of course: If you have access to one.
DAVID FAJGENBAUM:
That’s a great point. It’s also democratizing.
REID:
By the way, the agents are there right now. I mean, you walk out of doctor #1’s office and you go, “So…”
DAVID FAJGENBAUM:
That’s a great point. I like it a lot.
ARIA:
Are there any global implications, like perhaps dissemination of the possibility of cures is much more difficult globally? So how do you think about the global context versus the U.S.?
DAVID FAJGENBAUM:
Well, I’ll use a really specific example. So, we right now have nine active drug repurposing programs, 11 that have passed through our scientific advisory council. Among those nine, there’s one that is a drug called DFMO for a rare neurological condition called Bachmann-Bupp syndrome. So, only about 20 kids have ever been described with this horrible neurological condition. And there’s really, really interesting data that was actually generated by Dr. Bachmann and Dr. Bupp—who the disease was named after—that a drug that was made for sleeping sickness, called DFMO, could potentially be effective for these kids who are born hypotonic. They basically are on feeding tubes. They’re bedridden. But if you give them DMFO early on in their lives—and the earlier you give it, the better—these kids can start to sit up. And they can have a feeding tube taken out, and they can walk.
DAVID FAJGENBAUM:
I mean, this is a drug for sleeping sickness that’s really only used in one part of the world. But actually you could use it for these kids with Bachmann-Bupp all over the world. And the way that it works is really, really fascinating. It binds to the exact enzyme where there’s too much in this particular condition. And so, this is sort of a reverse of what you’re describing. It’s a drug that’s actually widely available in many parts of the world, but now, you know, could be useful in different places. And so I think that we’re going to find more and more of these. When we got started—I think that my ingoing hypothesis for, “Well, where are you going to find drugs and diseases to repurpose?” ingoing, I would’ve said mostly autoimmunity and cancer.
DAVID FAJGENBAUM:
And in part because there’s so much data on both of those categories of conditions, and there’s a lot of drugs that already exist, so I thought there’d be a lot of matching there. I would’ve probably said that rare monogenic neurological conditions are probably going to be the hardest place to go. I mean, genetic conditions where every single cell in the entire body has the same genetic mutation, these are enzyme deficiencies, these kids are bedridden. I would’ve said: Probably not there. Well, one of our first nine is one of these horrible, rare conditions—and so like, I don’t know where we’re going to go.
REID:
This discussion gives me an idea, and I’m not sure if it exists or not, but it strikes me that either it should exist or it does exist, which is the FDA should have, as it were, a new label process that should obviously be expedited because you already know that you’re dealing with something that has gone through the intense toxicity checks that exist. And, you know, there’s some threshold of: “It’s X percent effective—like 20% in Castleman” or something. But some kind of thing—and with some little detail on it. Does that exist, and should it?
DAVID FAJGENBAUM:
So there’s two processes that exist, neither of them are perfect fits for what you described. So one of them is called the 505(b)(2) pathway, where, basically, if there’s a drug that’s approved for one thing and you want to change the dose or the formula of it, you can rely on all that prior data. So you already know that sildenafil, for example, was approved for erectile dysfunction. Researchers thought that it could work for a rare pediatric condition where kids weren’t getting enough blood flow to their lungs. And so they said, but let’s use a lower dose. So it’s the same drug that could rely on all the previous work on sildenafil, lower dose—it could get approval for pulmonary cell hypertension. So that’s 505(b)(2) pathway, if you change the dose or the formulation. If you want to use the exact same dose or the exact same formulation, there’s theoretically a path forward for that.
DAVID FAJGENBAUM:
But realistically, if it’s the same dose or the same formula, and you’ve created this new drug—let’s say it’s called like “Castlelimus”, which is like sirolimus for Castleman disease—but it’s the same dose, same formulas before. And now your doctor writes a prescription for “Castlelimus,” the pharmacist will say, “Well, sirolimus is the same drug. And it costs less because it’s the generic medicine.” And so, the old drug will probably get utilized. So all that to say, you can go through these paths, but because the economics don’t add up, no one goes through that step of changing the label for the same drug, or for the same dose or formula, because no one’s ever going to prescribe that same-dose-same-formula version.
DAVID FAJGENBAUM:
And, up until now, the FDA has interpreted the law as, only the person who made the drug originally—the original innovator—is allowed to submit to them to ask for them to add a new disease. Because the idea, at least in our current system, is that the drug company who made it the first time really owns that drug. Even when it goes generic. Like, they’re the “innovator.” But what we’re working with FDA on, and what we’re excited about is the potential, well, what if Every Cure could come in and we could change the label? Because we don’t care if it’s profitable. We’re a nonprofit. We want to go after nonprofitable stuff. We’re studying a disease that 20 kids have been found to have. So we want to be able to go after them. And I’m hopeful that we’ll come up with a path with the FDA.
ARIA:
That makes lot of sense. So, one of the things you’ve been talking about is: You have this amazing technology, you find the drug that matches the disease. But then it doesn’t matter if no one knows about it. So can you talk about, you have a recent TED Talk, I think your book is being made into a movie—talk about those promotional activities, because again, getting the word out is so critical.
DAVID FAJGENBAUM:
Yeah, well you’re absolutely right. And being on a podcast like this is amazing. Thank you, seriously, for this incredible opportunity. Getting the word out about what we’re doing with Every Cure is just incredible. So yes, I gave a TED Talk this spring that just came out on YouTube, and so we’re excited for the reach that’ll come from that. In that TED Talk, we share that anyone listening can really help in any one of three ways. One is that a lot of us have received drugs off-label, you know, drugs that weren’t intended for our disease—and maybe it’s helped, or it hasn’t helped. Let us know about it at everycure.org/ideas. You can actually contribute, and we’ll actually look to see how is that thing scoring or not scoring.
DAVID FAJGENBAUM:
Now there’s donating towards our work. These trials are expensive. The work’s expensive. The compute’s expensive. Although you’re going to help us out with the compute, which is amazing. So, supporting us at everycure.org/donate. And the third, to your point, is around raising awareness. Like, getting the word out about DFMO to help us identify kids with Bachmann-Bupp syndrome, like, a tweet, a social media post could actually help us find a parent of a kid with Bachmann-Bupp, who could get this medicine, and they could take their feeding tube out. These are the kinds of things where that’s not traditional, right? The traditional way is to get an FDA label change, and we want to go through that process. But if we already have compelling evidence, the drug is available, it might be like a tweet or a social media post away.
REID:
So I think it’s helpful to know some of these repurposing stories are. So share with us a couple of the ones that have been magical in your journey.
DAVID FAJGENBAUM:
Sure. So, the first one that comes to mind was, I told you about when it took a hundred days for us to generate all of our scores. So this is two-and-a-half years ago. We ran it for the first time. And, not surprisingly, maybe to you, one of the first diseases I looked at is Castleman’s. Obviously, it’s the disease I study, I wanted to see what’s scored highly. And the number one drug and the number three drug were both TNF inhibitors, which had never been used before in Castleman, but they’re used for related immune disorders. And, at the time, there was a patient who was very, very ill in Vancouver, and his doctor had been calling me every couple days, “Can we try this? Can we try that?”
DAVID FAJGENBAUM:
Basically we tried basically all the drugs we’d ever used in other patients. We tried, they weren’t working for this patient. So I eventually recommended a TNF inhibitor to Luke, this patient’s doctor, and said, “Really early days. You’re telling me that you’re about to transfer him to hospice.” He said, “Yes, I’ve actually already written the order for a transfer to hospice.” He’s got a young daughter, he’s got a wife. They want to try something here. So he prescribed the TNF inhibitor. And, amazingly, this patient’s been in remission for over two-and-a-half years now. We actually published an article on this in the New England Journal of Medicine earlier this year as this example of utilizing artificial intelligence to identify a drug that could help a patient. We also had some really interesting orthogonal laboratory data that helped us to feel more confident.
DAVID FAJGENBAUM:
Because, to your point, you can’t just trust AI alone, but when AI has a more orthogonal data point that really points to it, that’s when you can help people. So that was, for us, so special. And I was actually just emailing with that patient recently and hearing about how well he is doing. It’s everything. So that’s one example that sticks with me. And then I’ll give you two other quick ones, because they’re top of mind. One of them is this young girl named Kaila, whom I mentioned briefly earlier, who was dying in the ICU. She received this repurposed drug. And then just recently, she was texting me. She just finished up summer working as this nursing assistant. Because she’s in the middle of training to become a nurse, which is just so cool. Because of her experience, she wants to now take care of patients.
DAVID FAJGENBAUM:
And the third one, I mentioned the condition Angiosarcoma. It’s a horrible form of cancer, which is a really uniformly fatal form of cancer. And we utilized a drug called pembrolizumab for a patient named Michael, for his angiosarcoma. At the time, no one lived longer than a year with Angiosarcoma, but interestingly—and this gets to the power of artificial intelligence—three years before we gave him this drug, there was a paper published pointing to potentially a drug in that class, maybe being helpful, but it was a laboratory-based study, had never been given to humans. These are the kinds of things that sort of fall through the cracks when—we can’t keep track of billions of papers. You just can’t.
DAVID FAJGENBAUM:
But artificial intelligence can help us to make these connections. When I showed you those points in my knowledge graph, those sorts of things, substantiate knowledge graph edges. And so, Michael got that drug, pembrolizumab. Last year he walked his son down the aisle—eight years after he was supposed to die from his disease. Last weekend, in Nashville, he walked his daughter down the aisle nine years after he was supposed to die from his disease. And so these are the kinds of things you’ll find as you’re hearing me talk about these success stories is that—I love it when we save someone’s life, extend life, that’s amazing. But what I really love, what we all love, is to hear what they’re doing. And this extra time, we call it “overtime.” Time they didn’t think they would have. They’re walking their kids down the aisle, they’re going to school—this for me is everything.
REID:
Alright, so rapid-fire are the questions we ask all of our guests just to have a fun index. The first one is, is there a movie, song, or book that fills you with optimism for the future?
DAVID FAJGENBAUM:
I’m a huge fan of Adam Grant. You mentioned Adam recently. I think Adam’s most recent book, Hidden Potential, provides me a lot of optimism. And it’s all about the untapped potential that’s already around us. And when I saw the title I was like, “Hidden Potential? This is about drug repurposing.” It’s not about drug repurposing, but it is about the concept that there’s so much around us that can help us. That we actually, in many cases already have the tools. We just have to find them. And that concept, that sometimes we describe as horizontal thinking, which isn’t like, let’s dig deeply into finding a solution in front of us, let’s look horizontally, I think is, to me, makes me hopeful that maybe there’s a solution that’s already out there for all the horrible things that we’re facing in our world.
ARIA:
Question number two: What is a question that you wish people asked you more often?
DAVID FAJGENBAUM:
You know what, it’s a question that both of you guys have already asked me today. And it’s: “How can we help?” And I’m very fortunate there are amazing people like both of you that say, “How can we help?” And it’s the thing where we actually need all of us to come together to help all of us.
REID:
And I think we’ve touched on some of them, which you can either highlight or do anew, but: Where do you see progress or momentum outside of your field that inspires you?
DAVID FAJGENBAUM:
I mean, outside of our specific field of drug development, I mentioned that I’m really excited about some of this sort of ambient work that can be done in healthcare that can help to diagnose patients. I mean, I just remember the struggles that I went through and I’ve seen for so many people to not get a diagnosis. I love the idea of putting artificial intelligence to work to find diagnoses and make diagnoses even when we aren’t looking for them.
ARIA:
Can you leave us with a final thought on what you think is possible to achieve in the next 15 years if everything breaks humanity’s way? And what’s the first step to get there?
DAVID FAJGENBAUM:
I think 15 years from now we’re being diagnosed with conditions before they’re even showing any sort of signs, even based on data points that maybe we can’t conceptually even pick up as humans. So diagnoses are coming in rapidly. Drugs are being delivered to your doorstep. Santa Claus theory of civilization is happening; you’re getting the drug right when you need it for the condition that you have. And that we’ve massively reduced human suffering. And I think about all those kids I sat with at AMF support group meetings. I think about the suffering I saw my mom go through. And all the patients that we try to take care of. The idea of a world where we’re able to relieve that suffering with the drugs that we have would just be amazing. So, Reid, I’m looking at you to make sure that everything breaks the way we need to with artificial intelligence.
REID:
We’re trying, we’re trying,
DAVID FAJGENBAUM:
And we can get to that world.
REID:
Awesome.
ARIA:
David, having you on the podcast makes me so, so happy. Thank you so much for being here today.
DAVID FAJGENBAUM:
Thanks so much for having me. This is awesome. Thank you guys.
REID:
Possible is produced by Wonder Media Network. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Katie Sanders, Edie Allard, Thanasi Dilos, Sara Schleede, Vanessa Handy, Alyia Yates, Paloma Moreno Jimenez, and Melia Agudelo. Jenny Kaplan is our executive producer and editor.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Parth Patil, and Ben Relles. And a big thanks to Payton Morrissey and Brent Shaw.