[RADIOLAB INTRO]

LATIF NASSER: Hey, I'm Latif Nasser. This is Radiolab. And today I want to share a conversation that I had with a guy named David Fajgenbaum. He's a doctor and a professor at the University of Pennsylvania, and there's a combination of reasons why I think his personal story is so extraordinary and why I wanted to share it with you. Part of it is this staggering series of crises that he faced in his personal life starting when he was in university. Part of it is kind of his personality, like how he—what there was in him that made him stand up to these crises in a really particular way. And part of it is the way that he—he took his response to those crises, and now he's scaling it up using one of the most controversial technologies around: AI.

LATIF: The result of all of this is that he is right now in the middle of doing something wildly ambitious, something I find kind of miraculous, also maybe troubling. Either way, it is definitely going to change the medical system down to the level of the pills that you put in your mouth.

LATIF: That said, I just found my conversation with David so fascinating, and his personal back story in general is kind of so dramatic that I wanted to let it unfold at its own pace without jumping too quickly to the end. So here we go.

DAVID FAJGENBAUM: And then join studio. Latif?

LATIF: Hey?

DAVID FAJGENBAUM: How's it going?

LATIF: Oh my God, it's great. How you doing?

DAVID FAJGENBAUM: Doing good. We've got double mics here. I've got a friend who's here helping with ...

LATIF: Nice!

DAVID FAJGENBAUM: ... helping with the audio. So we're double-miced right now.

LATIF: Great. So are we good to begin? We're good to begin?

DAVID FAJGENBAUM: Sure.

LATIF: Okay, let's begin with the Beast. Tell me who is the Beast, and what is the Beast? And where did the Beast start?

DAVID FAJGENBAUM: [laughs] So yeah, I don't know if I've ever answered a question this way. But so when I was in medical school, I worked out all the time. And part of that was because for the previous 15 years of my life, I was obsessed with wanting to be a college quarterback.

LATIF: And why? Why was it? Why was that the thing?

DAVID FAJGENBAUM: You know, it's—so I grew up in Raleigh, North Carolina, where college sports are really big. And NC State had a football team, and I grew up sort of, like, loving their team. But I think maybe more than anything is once I started playing football and I started, like—I'm biased, but I think football is unique among team sports in that you connect with your teammates in such a way, because literally, like, your health and your life is, like, on the line if someone else doesn't do the thing to protect you or you don't do the thing to protect them.

LATIF: Especially the quarterback. Yeah.

DAVID FAJGENBAUM: As a quarterback, yes. And so I just fell in love with football as a kid. I mean, as a kid I was just in love with it. If you could have seen my walls when I was growing up, literally every corner of every wall in my bedroom was covered with charts measuring, like, how far I could throw a football, how fast I could run, all with the goal of getting better.

LATIF: But you actually get—you get your dream. You get to ...

DAVID FAJGENBAUM: I do. I get my dream. I get this opportunity to go to Georgetown to play football. And it was the dream where it was like, okay, I can go to a great university that has a health science program, so I can keep studying health science. And I can play football. I really loved the coaches there. But then, you know, I got to school, and I was there for only a couple weeks before I got this just horribly devastating call. My dad called and said, "David, your mom has brain cancer. You need to come home right away." So I immediately went back home to Raleigh. I was able to see my mom just before her brain surgery. And then she—we went to Duke for her brain surgery. And ...

LATIF: And what was the—what was the—like, what was the horizon of possibility here? Like, what did you think was going on? What did you think was possible?

DAVID FAJGENBAUM: Yeah, so before the brain surgery, they just said, "It's a brain tumor. It looks like it's brain cancer, but we need to go in there and actually see what it is." So, you know, my family and I, we were just—were so just nervous about everything because, you know, they did warn us before that, you know, the person that comes out of surgery isn't always the person that goes into surgery.

LATIF: Yeah.

DAVID FAJGENBAUM: And I remember going back to see my mom with my dad, my two older sisters. And she had this wrap around her head, she had a bandage around her head from where the tumor had been resected. And she had this bulb that was coming out of the incision site. It drains out fluid from the incision. And we were all so nervous. No one really knew what to say. I said, "Mom, how are you doing?" And she pointed up at her—at her head, and with this bulb with the wrap around it, and she said, "Chiquita Banana lady." [laughs]

LATIF: [laughs]

DAVID FAJGENBAUM: Referring to, like, if you look at the sticker on your bananas, there's, like—you know, there's the Chiquita Banana lady. She has a wrap and she's got all the fruit.

LATIF: Yeah, yeah, yeah, yeah.

DAVID FAJGENBAUM: Her head kind of looked like the Chiquita Banana lady. And, like, it was exactly what we needed. It was, like, exactly what we needed. Like, we all burst out laughing and crying and we're snot crying. And, like, our mom's with us. Like, it just—like, that—yeah. That's just ...

LATIF: She's still herself.

DAVID FAJGENBAUM: She's still herself. That's, like—that's who she was.

LATIF: Yeah. What was the sort of prognosis after that? What was the timeline?

DAVID FAJGENBAUM: Yeah, so the doctors came in and they explained that it was—it was—they explained that it's grade four glioblastoma, which the average survival is around six months, and I think they said the longest someone had survived, I think I remember it was around five years.

LATIF: Mm-hmm.

DAVID FAJGENBAUM: So I spent a few more days at home after surgery and just, like, would not leave her side.

LATIF: And then when—and how long did your mom last?

DAVID FAJGENBAUM: She lived 15 months after diagnosis. She was diagnosed July of 2003. She passed away October 26 of 2004. But while I was home, we had a lot of just really special time together. One of the things we did was actually go through old home videos. Like, you know, we had these, like, Betamax videos. I don't know if you remember these, like, old home videos.

LATIF: Yeah, yeah, yeah. Totally.

DAVID FAJGENBAUM: Went through them and just we did, like, all the things that you would, you know, want to do. All the things that, you know, you'd want to do before, you know, someone like my mom passes. And this was 21 years ago. We did all those things and—and it was—it was really special. And it also, as you can even tell, this is 21 years later, it created such a drive in me, to just say, "Yeah, I want revenge. I want to do whatever I can to take this thing on."

LATIF: Yeah.

DAVID FAJGENBAUM: And I told her. I was, like, "Mom, I'm gonna dedicate my life to trying to help people like you." Like, that's just like full stop. Like, this whole football thing? That was fun these last eight years, but yeah, I'm gonna be a doctor and I'm gonna dedicate my life to just find treatments for this horrible thing that was taking my mom from me.

LATIF: Yeah. Okay, so now cut to—so you finish college, you get into med school.

DAVID FAJGENBAUM: Yeah.

LATIF: The same way that you were calorie counting and—and quizzing yourself on playbooks, like, you're doing the same thing, except now cancer is the—is the other team.

DAVID FAJGENBAUM: That's exactly right. And of course the challenge in med school is it's very much a training period, which is hard for someone like me, right? It's like, I want to take this on. I want to make a difference. But I'm in this period where yeah, I've just gotta train, train, train.

LATIF: So you're—so you're doing that. And then—and then at some point—yeah, okay, and so what happens?

DAVID FAJGENBAUM: So I'm on my ObGyn rotation, and just started noticing that I was more tired than I'd ever felt. And I—I sort of always was able to run on low amounts of sleep and lots of caffeine, but I was really, really tired. Like, this fatigue that I had never felt before. And I remember sort of like, trying to just, like, put it out of my mind. Like, whatever this is is gonna go away. And I went into the hospital to take this medical school exam. And I remember during the exam I was, like, dripping sweat head to toe. And then I was like, "You've never felt like this before. Something's going wrong." I also had noticed these bumps appearing on my body. They're called blood moles, and they're normal as you get older, but they are abnormal to appear rapidly. And it's like, as I was studying for this exam a couple of days before I, like, noticed these blood moles on my body. And so I actually handed in my exam, and I just walked down the hall to the emergency department in the same hospital that I was taking the exam in, and I just told them about my symptoms. They did bloodwork. And, you know, I had worked in that ER just a few months before, and doctors are usually really slow to come back and it's, like—you know, things take a while.

LATIF: Right.

DAVID FAJGENBAUM: Unless there's something really wrong, and they come back really quickly. The doctor came back really quickly. And he told me, he said, "David." He said, "Your liver, your kidneys, your bone marrow, your heart and your lungs are shutting down. We have to hospitalize you right away." And I was just, like ...

LATIF: That's, like, the whole—like, what—what else is there in your body?

DAVID FAJGENBAUM: Yeah. There's—like, your brain's left but, like, pretty soon that was gonna, you know, not be as clear.

LATIF: Right.

DAVID FAJGENBAUM: But yeah, it's this concept called "multiple organ system failure," where everything was shutting down. And so they hospitalized me. And I just—just went downhill from there. I started getting really sick really quickly. And I knew things were bad, and the doctors were using the language that I had used when I talked to patients with things that were really bad. And ...

LATIF: Like, what were they saying, exactly?

DAVID FAJGENBAUM: "You know, we're—we've run a lot of tests and, you know, we're—we're, you know what? We're on top of things, but we're not really in a position yet to tell you exactly what we think is happening."

LATIF: Right.

DAVID FAJGENBAUM: And it took a total of about 11 weeks before we finally made the diagnosis. And with that diagnosis came almost immediate use of a type of chemotherapy.

LATIF: Before we get there—before we get there, so what was the diagnosis? What was the ...

DAVID FAJGENBAUM: Oh, yes. Yes. So the diagnosis was what's called idiopathic multicentric Castleman disease. Castleman disease describes a group of these rare diseases where basically your immune system attacks your organs for an unknown cause. We call it idiopathic because we don't know what the cause is.

LATIF: And had you ever heard of it? Had you ever heard of that, like, as a med student?

DAVID FAJGENBAUM: When I heard it—when I heard it the first time, I vaguely remembered, like, I think I heard that once in med school. That's how rare it was. I was, like, a third-year med student and I think I heard it once.

LATIF: Yeah.

DAVID FAJGENBAUM: But definitely—I definitely wasn't familiar with it.

LATIF: Right. And—and was that—you get the test done, finally. Yeah, can you talk about that moment?

DAVID FAJGENBAUM: Sure. Yeah, so we were, like, really happy that it wasn't cancer. We were like, "Yes! Like, this is not cancer!" You know, we thought it was lymphoma this whole time and it's not. And then there was this, you know, really quick realization shortly thereafter that my subtype of Castleman's, idiopathic multicentric Castleman disease, actually has a worse survival rate than lymphoma does, and that actually the thing that we were hoping it was not actually would have been better than the thing that it turned out to be. And I was so sick when the diagnosis came in that the doctor told my family, "We don't know if this medicine is gonna work, but he's so sick that we don't think he's gonna survive much longer. You should go ahead and say goodbye to him and—and prepare him for—you know, for not being here."

LATIF: And you were awake and aware of that happening.

DAVID FAJGENBAUM: I don't know if I was mentally able to—I wasn't totally there. But I do have, you know, some memories, and those memories are the room being really dark, my family hugging me and crying. And they just started telling me all the things that I told my mom, right? Like, you know, what I meant to them. And, you know, we're reminiscing on old memories. And then I remember the priest coming in. I mean, of course I'd never had my last rites read to me before, so it sort of like, confirmed my biggest fears, which is that, like, this is gonna kill me. But just a couple of days before the priest had come in, the doctors had tried this one chemotherapy. It's the only chemotherapy they thought to try. Like, there actually are others they could have tried, but this was the one they tried. And amazingly, it just started to kick in, really within days. And it didn't last long term; I relapsed about a month later. And it was a real rollercoaster, because the euphoria that we all felt when I was feeling better, and the hope that we had, and then just, you know, a few weeks later when it would come back, just the heartbreak. And that cycle happened a total of five times in three-and-a-half years, where ...

LATIF: Oh my God!

DAVID FAJGENBAUM: ... I went from being, you know, totally, you know, critically ill in ICU, to much better, to then back again.

LATIF: And what was the moment—was there a moment where you kind of engaged, where you were, like, “Okay, I'm—I need to—I need to sort of activate."

DAVID FAJGENBAUM: Oh, yeah. Yeah, that moment for me, I remember very vividly. It was May 12 of 2012. It was sort of—I mean, if I think back on, like, my life in these moments, like, the moment when I got the call from my dad that my mom had brain cancer and the moment I was sitting in the hospital room, and my doctor explained to me that the only drug that had ever been studied for my disease wasn't working, and that there was nothing else, like—and I was just searching for something like, "Is there a gene or a protein or a cell or something that we might know about this thing? Like, give me something." I'm begging for, like, some lead. And he just was clear. "There's nothing. Like—like, you are going to die from this disease. The chemotherapy is going to stop working, and there is nothing out there."

DAVID FAJGENBAUM: That was when I—everything changed, everything in me shifted. If I want to survive, like, if I want to spend more time with this girl beside me that I love, Caitlyn, and I want to get married to her one day, I want to spend more time with my family, like, I've gotta activate. And it was right around that time I was learning about how a drug that was being used for Castleman's was also working for other diseases. And I was like, "Wait a minute. There's a Castleman's drug working for other diseases. Is there anything somewhere? Maybe there's another drug for another disease that could work for me." Like, it was just sort of, like, this very simple concept. And it—frankly, it was the only path. It wasn't like I was like, "Oh, it would be great to do this or to do that." It was like this was the only path was to find an existing medicine. And that became just my central focus.

LATIF: So what do you do? How do you even start that?

DAVID FAJGENBAUM: Yeah, so the first thing I did is I went to my mentor, Arthur Rubenstein. He was the dean of the medical school before, and he had just retired. And so I went to him for advice and his support. And he said, "David, I'll support you." And he's been amazing all these years. But I wanted to go to someone who sort of like, could give me advice. I didn't know what I was doing.

LATIF: So you're like, "I just need—I need—I need help. I need a team. I need people."

DAVID FAJGENBAUM: I need—I need to build a team. Exactly. It was like, I don't know what I'm doing, but I need to build a team. So first I went to Arthur. He came on board. The second task would be to understand what was going on in my blood and in my immune system and see if there was something that was already approved for another disease that could maybe be repurposed to treat me. And that's when I, you know—I guess did the equivalent of, you know, covering my walls in posterboards for throwing a football, and it just became all-encompassing. I've gotta find a drug for this disease. And I remember turning to Gina, my sister, and saying, "Gi, I need you to call UNC and Duke. I need you to get all my medical records, ship them to Philadelphia. I need you to get all the blood samples and lymph node samples of each of the hospitals. They need to be in Philly, because I'm gonna get out of here in a few weeks, and when I get to Philly, like, the clock's ticking. I need to get to work." And her and Caitlyn just got to work, and a few weeks later I was back in Philly. And the blood samples were there, the lymph node samples were there, the medical records were there. And I—just it was all day, every day, to try to find a drug.

LATIF: And I presume at that moment too you're like—and another—because another flare is, like, right around the corner?

DAVID FAJGENBAUM: It's coming. Exactly. Yeah, and Caitlyn and I ...

LATIF: Like a train coming.

DAVID FAJGENBAUM: It's a train and it's hit me—it's hit me five times. There's no chance it's not coming back. This was it's coming, and I don't have another shot. And I had a really big date in front of me: May 24, 2014, was Caitlyn and I's wedding day. We were—we were engaged. And now we're talking January of 2014, so I had, you know, about four months between getting out of the hospital and making it to our wedding day.

LATIF: Like, if you last that long.

DAVID FAJGENBAUM: If I last that long.

LATIF: Yeah.

DAVID FAJGENBAUM: Exactly.

LATIF: Yeah. Okay, so then—what do you—what do you do?

DAVID FAJGENBAUM: So I thaw all those samples, and I start doing something called serum proteomics, where the idea is you measure a thousand different things in your blood, or a thousand analytes or proteins in your blood. And then we did something called pathway analyses, where we try to understand what are the signals in the blood that are coming from these proteins being up or down? I did something called flow cytometry to look to see which of my immune cells were turned off and turned on. And then cytokine panels, where we measure these 13 different proteins and their changes in the blood. And what emerged was that my mTOR was in overdrive. And mTOR is a communication line your immune system uses to turn on, to turn off, to proliferate. And when I saw that result, I immediately remembered that there is a drug called Sirolimus—the other name for it's rapamycin—that is really good at turning mTOR off. It's an mTOR inhibitor. So, like, I saw the result and it's like, mTOR is on. And I was like, "Oh my gosh, isn't there a great mTOR inhibitor?" And it—there is.

LATIF: Wait. Rapamycin? That's the drug?

DAVID FAJGENBAUM: Rapamycin is the drug that saved my life.

LATIF: Oh my God, we did a—we already did a story about rapamycin. I didn't even connect it in my head.

DAVID FAJGENBAUM: And I love that story. Latif, I listened to that story and I loved that story.

LATIF: Oh!

DAVID FAJGENBAUM: And of course, it's, you know, found in the island of Rapa Nui. It was ...

LATIF: Yeah, yeah, yeah.

DAVID FAJGENBAUM: ... hidden in a freezer in Canada.

LATIF: I connected mTOR, but I didn't connect that it was rapamycin. It was literally rapamycin.

DAVID FAJGENBAUM: Yeah.

LATIF: So it's like, you find in the tests of your blood and your lymph node and whatever, like, it's like that is leading you to a problem and then you're like, "Is there a drug that solves this problem?" And you're like, "Oh, there's one drug right there."

DAVID FAJGENBAUM: That's exactly right. And so I told one of my doctors, and I went through all the data and I just said, "Do you think that we should try this? Like, I know it's never been used before for Castleman's but, like, can we try it? Like, should we try it?" And his thought process was like, probably it's about a 10 to 20 percent chance it could work, but it's a zero percent chance if I don't take it. And—and I'm willing to take that risk, and so he said, "Yeah, I'll prescribe it." And ...

LATIF: So you do it and then what happens? You take it? It's a pill?

DAVID FAJGENBAUM: It's a pill. It's three pills. Well, at first it was—I took five pills. And then—and now it's three pills. But within a couple days I started to feel better, and the bloodwork started to get better more rapidly than it would have otherwise. But again, I still wasn't ready to say, like, this drug is working. And so for me, I was like, "I'm not gonna get my hopes up. It's gonna be a test of time. Am I gonna make it to my wedding day? Am I gonna make it a year? Am I gonna make it longer than that?" And yeah, just four days ago marks eleven-and-a-half years that I've been in remission on this drug. I mean, I almost died five times in three and a half years before, and now it's eleven-and-a-half without this disease coming back.

LATIF: Wow!

DAVID FAJGENBAUM: Amazingly, you know, it weakens my immune system in the right way so that I don't attack my own organs.

LATIF: Wow.

DAVID FAJGENBAUM: And I mean, the moment that that drug—the moment that I started thinking that drug was helping me and knowing that it was, you know, always there for something else, and then certainly as the time went on, when I got to marry Caitlyn, and then as the years have gone on, I've just gotten more and more obsessed with this idea because I'm literally breathing and alive because of a drug that wasn't made for my disease, I just feel this tremendous sense of responsibility that, like, hey David, if you're gonna get lucky enough to have one of these medicines help you, you sure as hell better spend the rest of your time trying to find as many more of these medicines to help other people.

LATIF: What happens next is that this story moves from being a personal story about David finding his own medicine for his own disease, and thanks to some supercharged technology, it becomes a story about all medicines, and all diseases, and the entire way we figure out which works for which. That's after the break.

LATIF: This is Radiolab. I'm Latif Nasser, and we are back with a conversation I had with Dr. David Fajgenbaum, who after almost dying five times started obsessively studying his own body, his own disease to try to find a drug—any existing drug out there—that might be able to help him. And he did. And after three years of being repeatedly at death's door, he has now been in remission for 11 years.

LATIF: After he found that cure, he turned his kind of mono-maniacal mind—and not just that, his whole lab at the University of Pennsylvania—toward understanding his disease, Castleman's disease, hoping to do the same for other people who are suffering from it.

DAVID FAJGENBAUM: So that led us to then say, "Okay, we need to do more laboratory work, we need to start uncovering more pathways that might be important, more genes, more proteins that are important. And so we started getting really involved in that sort of laboratory work, and in parallel, the next probably big milestone to go from, like, okay, we helped someone else with my disease, was actually my uncle was diagnosed with angiosarcoma, which is a horrible form of cancer the same week that my brother-in-law was diagnosed with ALS. I went down to Raleigh to be with my brother-in-law, happened to be the same week my uncle got diagnosed with angiosarcoma. So I went with my uncle to his doctor's appointment, and the doctor explained, "You know, there are these two chemotherapies, and they'll—they'll, you know, give you a couple of months to live, but they're gonna stop working."

DAVID FAJGENBAUM: And so I suggested that we start, you know, looking for drugs that could be repurposed. And this doctor explained, like, there just—there isn't anything for angiosarcoma. I'm like, "Yeah, I know. But, like, there wasn't anything else for Castleman's. And like, you know, I'm here. Maybe we can find something else for angiosarcoma." And that's when we came across a study that had been published ...

LATIF: And wait, is that annoying? Like, are you annoying to them when you do that?

DAVID FAJGENBAUM: I'm so annoying to them. They're like—they're looking at me and they're like, "Your uncle has a terminal illness. The last thing he needs is for his nephew to tell him or me that there's a treatment out there that could help him."

LATIF: Yeah.

DAVID FAJGENBAUM: Like, that's not what he needs right now, is what they're thinking. And in my mind, I'm like, "Are you kidding me? He's still here. He's still breathing. I just walked past the CVS, and last time I checked there's 4,000 drugs in that CVS, and I know those 4,000 drugs haven't been tried for him. So until we've tried all 4,000 drugs, you can't tell me there isn't a drug in there that can help him."

LATIF: Okay.

DAVID FAJGENBAUM: And so we find a study that had been published three years earlier that basically says that four out of five people with my uncle's cancer have very high expression of something called PD-L1.

LATIF: Okay.

DAVID FAJGENBAUM: I'm here saying, "Like, let's test this tumor for PD-L1." And the doctor says, "I'm not gonna test it, because no one with angiosarcoma has ever been given a PD-1 inhibitor. I think there's, like, a less than 10 percent chance that this gene panel that you want to order for Michael is gonna come out with anything helpful."

DAVID FAJGENBAUM: And I hear "less than 10 percent" and I'm, like, "That's great! Less than 10 percent? Like, you mean, like, five percent?” He's like, "Yeah." I'm like, "Amazing! So you're telling me there's a five percent chance that this test is gonna give us something that's gonna keep him alive longer than two months? Amazing!"

LATIF: You're like the—you're like the Dumb and Dumber guy.

DAVID FAJGENBAUM: "So you're saying there's a chance."

LATIF: That's right. Yeah, yeah, yeah. That's the guy. You're that guy.

DAVID FAJGENBAUM: Yes. And so I don't blame them, because when you're a doctor and you do this a hundred times and it works one in a hundred times, that is frustrating. But when you're a patient and it helps you that one in a hundred times, it's everything.

LATIF: Yeah.

DAVID FAJGENBAUM: And so I got another doctor to order the test. So we get the test results back ...

LATIF: You are—you are such an annoying patient relative.

DAVID FAJGENBAUM: I'm so annoying. I am, yeah.

LATIF: Yeah, okay. Keep going.

DAVID FAJGENBAUM: So it comes back. Well, first of all I should say I had him order two tests. The first of the tests, it came back with nothing informative. He was totally right. And that was actually the expensive test. That was like the test that cost $2,000, that came back with nothing useful. So I will totally give it to him. The inexpensive test that I wanted him to order came back 99 percent of his cancer cells were positive for PD-L1 expression—99 percent.

LATIF: Huh. Wow.

DAVID FAJGENBAUM: Which is not a guarantee, but it is a high likelihood that therefore a drug that inhibits this might be useful. And we got Michael on this medicine, and April of this year marked nine years that he's been in remission from his angiosarcoma.

LATIF: Oh, wow!

DAVID FAJGENBAUM: Other patients have been treated with this, other doctors learned about this and started treating their patients. And it turns out about a third of people with this horrible cancer—previously uniformly failed cancer—will respond really well to pembrolizumab, to this medicine. It's now standard of care for—for his form of cancer.

LATIF: It's now standard of care?

DAVID FAJGENBAUM: It's now standard of care without ever doing a clinical trial. And that goes to show you when you have a disease that's this bad and you find a drug that works this well, you can change the paradigm for the disease for relatively—I mean, as close to zero dollars as—as humanly possible.

LATIF: But, like, why is it that everything is so—like, it's like we almost have this idea of, like, lock and key. Like, this medicine does this thing for this drug and da-da-da-da. But then it's like—and then you're like, "No, no, no. But this—this key works in this lock over here." Like, why is it that that is—that—anyway, yeah. Tell me there.

DAVID FAJGENBAUM: So the reason I think that our system is like, this drug works for this disease is because in order to get a drug approved, a drug company has to develop a drug for a specific disease and submit it to the FDA for that disease. The FDA approves it for that disease. And if that drug company mentions a single word about that drug working on another disease, they will get fined billions of dollars for what's called "off-label promotion." So when the FDA approves a drug, what they're really doing is they're approving a drug company to market a compound for a specific disease. And that company cannot market that compound for any other diseases until they come back to the FDA to get that change made. But every time a drug company does that, it costs lots and lots and lots of money. So they don't go after all the opportunities they have. But insurance companies and payers realized well, if this drug that's approved for this one thing could also be useful in this other thing and it would be good for patients, shouldn't we allow doctors to prescribe things off label? And so that's something that happens very commonly. About a quarter of all prescriptions in the US are off label.

LATIF: No!

DAVID FAJGENBAUM: Yeah. So it's somewhere between 20 and 30 percent of all prescriptions written every day in the US are off label.

LATIF: Are not for the reason they're supposed to be?

DAVID FAJGENBAUM: Yeah, exactly. So that includes examples like doxycycline for Lyme disease where, like, every doctor in the world is, like, "Yes, use doxycycline for Lyme disease." But doxycycline's a cheap old generic antibiotic, so whoever made doxycycline a hundred years ago, 30 years ago when people figured out it worked for Lyme disease, they aren't gonna submit for a label change.

DAVID FAJGENBAUM: And that gets into the other factor here, which is that once a drug becomes generic, whoever originally made the drug, they stop making money off of the drug because you have generic competition. You have multiple companies that make the identical drug, and the price plummets per pill. And so no one in our system makes any money off finding a new disease for that drug.

LATIF: But why wouldn't—like, you would think from the drug company's perspective, like, that they would want to go to the FDA, get as many uses approved as possible, because then they could go out and say, "This helps this, this helps that, this helps this, this helps that." Like, they would make their market as big as possible.

DAVID FAJGENBAUM: Except it's more complicated than that, because you can only sell a drug for one price. Regardless of what disease you sell it for, it always has to be the same price. So what that means is that you have to pick the first disease that you get your drug approved in, you have to pick the optimal market for that drug for the optimal price.

DAVID FAJGENBAUM: Because pricing is actually not based on the cost of the medicine; pricing is based on the value for that disease. So the fewer competitors there are for a disease, the more expensive the drug. The rarer the disease, the more expensive the drug. There's all these factors that affect how expensive the drug is gonna be.

LATIF: Ah.

DAVID FAJGENBAUM: And you want to—if you're a drug company, you have to maximize your profits, so you need to come up with the highest price for the highest number of people, but it might be that a low number of people at a higher price is better than a high number of people at a lower price. And so you can imagine it gets really complicated really quickly.

LATIF: Right.

DAVID FAJGENBAUM: And it's all about the first disease you get your approval on, so companies have to be really thoughtful and strategic to maximize their profits about what their first approval is. Once they get that first approval, now they have to remember that they can't change the price for the next disease. And so this is this horrible economic issue which is just so depressing. Because, like, on the other side of these economic issues are people suffering.

LATIF: Okay, I want to—I want to hear more about what you're doing now. You're like, "Amazing! This works for me." How does that then go to, "Oh, no. Wait a second. I'm not just doing this for my family. Like, I can—I can big this up."

DAVID FAJGENBAUM: Yeah. The next big milestone was early in the pandemic, I was actually driving down to Raleigh, North Carolina. Had my wife in the car and I'm listening to the radio about this, you know, pandemic. And I'm sitting there thinking, you know, gosh, this involves the immune becoming activated and causing all these problems. And gosh, it's gonna take us months or years to come up with a new drug. It's like, I really wish there was a lab somewhere out there that was really good with inflammatory stuff and could repurpose drugs and could, like, direct drugs to this thing. And then I was like, oh, maybe we should do that.

DAVID FAJGENBAUM: And so that—so we decided to create a program called the Corona Project, where basically we redirected my, like, 15-member lab to focus specifically on COVID. And early on, as you'll remember, there was a lot of drugs that were repurposed—some worked, some didn't work. But there was a lot of repurposing. This was the first time we did, like, a very concerted effort to be like, what else is out there for this one disease? Very much informed by what we'd done previously.

DAVID FAJGENBAUM: And COVID, of course, there's lots of controversy about what worked and what didn't, but the two drugs that—that unquestionably worked incredibly well were dexamethasone and tocilizumab. They saved millions of lives, and they were, you know, old drugs that have been around for a long time. And so that further got my wheels turning on, like, what if we could create a system to automate what my little lab was doing for one disease, but we did it for all diseases and all drugs simultaneously.

LATIF: Whoa!

DAVID FAJGENBAUM: And thankfully, in parallel to those dreams, the field of machine learning and artificial intelligence has matured so much that we can actually do that.

LATIF: Okay, so tell me about AI. How are you using AI to, like, matchmake here? How did you think, like, okay, this part of it can be done by AI or this part of it, or whatever. That kind of thing?

DAVID FAJGENBAUM: So in my case, you know, you could think about this—we use what are called biomedical knowledge graphs, which are just sort of mapping out, like, every medical concept on a map. So you could imagine, like, if you have this giant wall, and every single gene, every disease, every protein, every pathway was put against the wall.

LATIF: Okay.

DAVID FAJGENBAUM: So if we were to start with that concept and say, "Well, what will we do for me?" Well, you find Castleman's on that wall. It'd only be there in one place. You'd find Castleman's. And what you'd find is you'd find an edge or a line between Castleman's and activated T cells, because I discovered that T cells were activated in my disease. You'd find another line to mTOR activation, because I discovered that mTOR activation was really up in my particular immune cells. And then you'd find a drug from T cell activation and mTOR activation to sirolimus.

LATIF: Right. The drug.

DAVID FAJGENBAUM: Because sirolimus is able to inhibit mTOR activation, and it's able to inhibit these activated T cells. And so now within this giant graph of every disease, every gene, every protein, you would find Castleman's with lines or edges to these two concepts, and then lines or edges to sirolimus. And you would see a connection between them. And so now imagine doing that for every disease, every gene, every protein, basically what the world knows about all of medicine.

LATIF: Well, it's almost like mechanical what you're doing. It's like ...

DAVID FAJGENBAUM: It is.

LATIF: It's like you're trying to make a—like, the mechanical blueprint of what is going on.

DAVID FAJGENBAUM: That's exactly right. This leads to this leads to this, and this reverses this, which reverses this which reverses that.

LATIF: Right.

DAVID FAJGENBAUM: Everything is there. It's the—it's everything. Well, now what we do is we train machine learning algorithms on all of those known treatments. So, like, the sirolimus for Castleman's, sildenafil for pulmonary arterial hypertension. You know, insulin for diabetes. Imagine training this algorithm—because machine learning algorithms are so good at finding patterns. And so we're giving the machine learning algorithm lots of information about known treatments. And we're saying, "This is an example of when a drug works for a disease." And we do it thousands of times with all of the treatments that are out there for all of the diseases that are out there. And then we say, "Okay algorithm, now go and score how close of a pattern the connection is between a known treatment relationship for every other drug versus every disease." So if—if a toenail fungus drug looks like there's no way it could work for pancreatic cancer, you need to give it as close to a zero as possible—0000.001. Right?

LATIF: Right.

DAVID FAJGENBAUM: But if leucovorin looks really promising for a subtype of autism, because the pattern of connections are there and there's a clear intermediary between that subtype of autism and that metabolite, give it a high score, so you get a 0.99. And so now what we do, we do all 4,000 drugs, all 18,000 diseases. So it's about 75 million scores that we generate, that our machine learning algorithms generate. And then that gives us a list in rank order from the things that are 0.99 all the way down to things that are 0.00 of every drug versus every disease. And so we come across matches that are incredible that we never could have imagined that now the algorithm is saying, "You should really look at this."

LATIF: It's like the body is so complicated, these drugs are so versatile. It's like—like, our minds can't even comprehend that. It's like that's why you need to go to AI.

DAVID FAJGENBAUM: Yeah. You would have to, as humans, think about 75 million possibilities. Like, my lab's really good at looking at, like, dozens of possibilities for, like, one disease. Like, my lab can spend, like, a year, and we can get through a few dozen for one disease, right? But, like, we could never think about, like, 75 million possibilities and then compare them. And I'm not saying AI is perfect, but directionally it's really good. The things that are 0.99s are way better than things that are the 0.5s.

LATIF: How likely do you think it is that, like, if there's an ordinary person with an ordinary disease that existing treatments don't work for, that there's something in there for them?

DAVID FAJGENBAUM: Yeah, there's two probabilities here. I think that one is that what is the likelihood that there is a drug out of those 4,000 that could work for that disease, and what's the likelihood that you or anyone else is gonna find it, right?

LATIF: In time. Right.

DAVID FAJGENBAUM: It's just like, A) Does it exist? And B) Can you find it?

LATIF: Yeah.

DAVID FAJGENBAUM: I think the A) Does it exist? This is obviously a really hard thing to guesstimate on but, like, I'm gonna say somewhere in the realm of—for any given disease, somewhere in the realm of maybe 10 to 20 percent, that there is something out there. And then in the realm of are you or is a team gonna find it in time for you? It becomes much lower than 10 to 20 percent likelihood, right? Just because the steps that have to happen.

LATIF: Right.

DAVID FAJGENBAUM: And so for us, you know, we're gonna be the organization that is gonna identify and unlock as many of these drugs as possible, so that way we don't have to be throwing Hail Marys. So that, like, when you get diagnosed, it's "Oh, wow. You have pulmonary hypertension? You should just take this medicine. Oh, wow. You have glioblastoma? You should take this medicine."

LATIF: Yeah.

DAVID FAJGENBAUM: And so we've intentionally taken the approach of let's use AI and data to find the best uses for the best drugs so that we can move them forward so that we aren't doing Hail Marys. But the reality is, Latif, is that, like, people are suffering all the time.

LATIF: Yeah.

DAVID FAJGENBAUM: And we are contacted all the time, and we want to help any way we can. And we're gonna be making our algorithms publicly available in about nine months' time.

LATIF: Interesting!

DAVID FAJGENBAUM: But until then, we want to continue to improve them. We feel this tremendous responsibility that once we share it that, you know, it's out, right? And so—so we're gonna continue to improve over the next nine to twelve months, but then we will share it.

LATIF: And you—are you imagining patients would use that? Or are you imagining doctors would use that? Or both?

DAVID FAJGENBAUM: So the intention will be for doctors and researchers to use it, so that way they can come up with new areas for research, they can think about it for their patients. But the reality, I think, is that patients will also use it.

LATIF: I'm trying to imagine—like, so that is really interesting that you're making that public, and I think it's also—like, it's—there's something beautiful ...

LATIF: So the conversation went on for a while after this, but I was—I was honestly surprised and a little taken aback that this algorithm that David had made, that he was gonna take it public. And it took me a while to kind of process that and figure out, like, what I thought about that or what I even wanted to ask him about that. That part of the conversation, which actually felt a little bit trickier to me, is coming up right after the break. Stick around.

LATIF: Okay, so I did that interview with David Fajgenbaum. And it's funny, he genuinely surprised me when he said he was taking this thing public. Like, I had not heard or seen that before. It has since been reported that he's doing that. But when we did the interview, like, I didn't know he was gonna say that. And so when he said it I was, like, kind of shocked that he would do that. It, like, reslotted the story in my brain from being like a slam dunk, like a no-brainer best possible use case for AI to something that was, like, wait, wait, wait. What—what do I think about this? Should ordinary people be able to look up what drugs AI thinks will help them? Is that—is that helpful? Is that reckless?

LATIF: So I really didn't know what to think. I called up a bunch of my doctor friends. Some thought it was, like, so exciting, especially for people with rare diseases, you know, where there's not a lot of research money. They were like, "Yeah, this tool is gonna be so useful for so many people." But then there were other doctor friends of mine who said, "No, no, no. This is gonna make my job harder and it's gonna hurt people." Which you will hear about in a minute. Anyway, so I called David back, and I had way more questions, and I was just like, "Okay, just tell me the specifics. What are you putting out there exactly, and why are you so sure this is a good idea?"

DAVID FAJGENBAUM: Sure.

LATIF: I don't even know what to call it. Like, okay, your AI matchmaking tool, what do you call it? How do you use it?

DAVID FAJGENBAUM: Yeah, so we call it the MATRIX. So it's an acronym. Everything has to have an acronym in my life. It's—so it's ML/AI-Aided Therapeutic Repurposing In eXtended uses—MATRIX.

LATIF: Okay, so ML/AI-Aided—machine learning aided, therapeutic ...

DAVID FAJGENBAUM: Therapeutic.

LATIF: T for therapeutic.

DAVID FAJGENBAUM: Repurposing. R.

LATIF: Yeah, okay.

DAVID FAJGENBAUM: I, "In." And then it gets a little sloppy then.

LATIF: [laughs]

DAVID FAJGENBAUM: Extended. We're using the 'X' in "extended."

LATIF: Okay. Okay, eXtended.

DAVID FAJGENBAUM: For MATRIX—uses.

LATIF: Okay, then uses.

DAVID FAJGENBAUM: No, "uses" doesn't get a letter. It's not MATRIXU. It's just—it's just MATRIX.

LATIF: Okay, because you're a fan of the movie or something?

DAVID FAJGENBAUM: It actually is a matrix, in that it's 4,000 drugs, it's 18,000 diseases. So it's actually—we're—we're building, actually, a matrix of drugs versus diseases. And a fan of the movie.

LATIF: Okay, so how does it work? Like, say I'm just somebody, I'm a random patient with a random disease and I want to use it. What do I do?

DAVID FAJGENBAUM: Yeah, so we're still working on some of these things. We're actually, like, literally, like, talking about prototypes and—and processes. But I can tell that there will be the ability to type in the name of your disease, or maybe the drug that you care about. Probably more likely the disease that you care about, because most people care about diseases as opposed to drugs. But then actually it will look at a rank order list for that disease. So say, like, "Oh well, I care about ALS." These are the 4,000 drugs ranked in order for ALS according to this AI platform. That, I'm almost certain, will be available in a format like that. But the bells and whistles we sort of—we still have to work out.

LATIF: Is it like a ChatGPT-style thing? Like, I'm imagining you put your disease name into the thing, and then it'll, like, spit back out at you a bunch of names of drugs and it'll give you the percentage.

DAVID FAJGENBAUM: Yeah.

LATIF: But then you can also be, like, "Hey, by the way, I'm also a smoker and I have diabetes," or whatever other conditions you have. And then it'll—like, how much information would you put in, like, as a user?

DAVID FAJGENBAUM: I mean, what you're describing would be sort of like a holistic patient support treatment tool. And we're really not building that. You know, I hope someone does. Like, someone should build that, but we're not building a tool that is—yeah, is gonna be that sort of treatment copilot. Though I would love for someone else to do it.

LATIF: Okay, so this is very much just insert name of disease, and what's gonna come out is a list of names of drugs that may or may not work, and here's the odds that they will.

DAVID FAJGENBAUM: Yeah, and these are—and the same list that we're getting on our medical team and our—and our research and development team. We're giving you the same results and the same scores that we're getting, because we feel this obligation or this responsibility that if we're gonna put our eyes on them, the world should be able to put their eyes on them. You know, here are the tools that we use. Like, our medical team uses these same machine-learning algorithms. You can use them, too. But it's important to remind them that when our medical team uses those machine-learning algorithms and they come up with something—lidocaine for breast cancer, we still then go on to do a bunch of laboratory work of lidocaine and breast cancer. And then we think about doing the right clinical trial of lidocaine and breast cancer. So it's not like we use the algorithm to immediately move forward into action. We use it to then plan out what to do next.

LATIF: Okay, so why did you make the decision to make it public?

DAVID FAJGENBAUM: So our feeling is that we as a nonprofit at Every Cure, we're only gonna be able to go through, like, dozens—I mean, if we can get into the hundreds I would be over the moon about it but, like, there are still thousands of diseases that, like, could potentially benefit from our scores that we'll just never be able to get to, unless ...

LATIF: It's, like, because the list that MATRIX is spitting out is just so big. Is that it?

DAVID FAJGENBAUM: It's so big and it's so powerful. The thing is, like, when we look at the top, we are blown away by the number of promising drugs. We're like, "Wow, it actually—" Latif, some of the cases, there's actually been clinical trials that have shown the drug works, but someone stopped after the small trial because there was no way to commercialize it. So one part is let's make it available to the world so that other people can—can, you know, pursue these things that we're not able to go after. And the other is sort of probably a little bit inspired by maybe we shouldn't be so paternalistic in medicine, and maybe we should, like, you know, allow this information to be out there. Of course, when I say that, I do, like, cringe just a little bit because, like, I don't want us to create problems by putting this out there. But it feels like the responsible thing is to share the scores, but to appropriately caveat them and disclaim them to say, like, these are for research purposes. At Every Cure, our nonprofit, we don't take a score and then put that drug into a person. We take a score, we evaluate it by MDs, PhDs and MDPhDs, we spend months on it, and then we do laboratory studies, we do clinical trials, we work with experts to get in the guidelines. That's our process. So we want other people to take a similar process.

LATIF: Have you thought about what could go wrong by making this public?

DAVID FAJGENBAUM: Yeah, a couple of things to me that come to mind. I mean, number one is patient harm, a patient taking a medicine that causes harm to them, that had not undergone the studies necessary to evaluate it in that disease. Now the good news is that every drug we score is already FDA-approved for something. So we're not—there's no drugs on there that, like, someone's like, "Oh my gosh, this never got a regulatory approval." They all have been approved.

LATIF: You're not giving cyanide to people.

DAVID FAJGENBAUM: Yes, exactly. Yeah, cyanide is not on our list. [laughs]

LATIF: Right.

DAVID FAJGENBAUM: Actually, I will tell you there is a drug repurposing platform that I will not name on this podcast, where literally one of the top five predicted drugs for Castleman disease—a predicted cure for Castleman's disease—is car fume exhaust as a treatment.

LATIF: No! What?

DAVID FAJGENBAUM: This was a top prediction.

LATIF: What?

DAVID FAJGENBAUM: I guess inhaling car fumes, like, fumes ...

LATIF: And this was just, like, an AI hallucination kind of thing?

DAVID FAJGENBAUM: It was, like, AI was like—AI made the connection. It'd be, like, "You know what? You just ..." and I was, like, "Oh my gosh. That's the problem. I just haven't been inhaling enough car fumes. Like, that's—that's why my Castleman's is out of control."

LATIF: Right. Yeah.

DAVID FAJGENBAUM: "I just needed to inhale car fumes."

LATIF: Every morning after breakfast.

DAVID FAJGENBAUM: So I say that not to just say that, "Oh my gosh, this one platform had this bad prediction," but it's to say that AI is gonna make silly predictions that make no sense.

LATIF: Not silly. Like, harmful.

DAVID FAJGENBAUM: Harmful, yes. Harmful, yes.

LATIF: Right.

DAVID FAJGENBAUM: That's why humans have to be a part of this, and humans who can critically evaluate this and say, like, "This is not good." So I think the most important thing is gonna be, I think, how we communicate around these scores when they are made publicly available, that these scores are intended for our research team to find things for us to research more. These were not ever intended to be, you know, scores to say, "This thing that's number one is what I should get because that's gonna save me." And so I think it's gonna be—I think context is going to be really important.

LATIF: But, like, what's the thing you say? Like, how do you expectation set in a really clear way to make it super distinct, like, this is a machine that generates research ideas, versus something that tells you what drug to take now?

DAVID FAJGENBAUM: I see what you're saying. Your point is that you can say it all you want, but for that not—that, like, just may just not stick, right?

LATIF: It may not stick, and also people are desperate. Like, a lot of these people—I mean you—you know. You know better than I do.

DAVID FAJGENBAUM: Yeah. Yeah. They will do anything. It's a great point. I think that the way to make it stick—I think it's trying to explain that we don't use these predictions to decide how to treat someone. This is not—and maybe even to use the terminology, this is not a solution engine, this is an idea generator. And we at Every Cure do a lot of further work. And so we hope you, if you're gonna use these scores, will also do further work. And so if you're a patient, that might mean working with a research lab to do the work to figure this out. What we recommend is talking to the disease organization that you're a part of, whether it's the ALS Association, or you name it. It's talking to a lab to see if there's further work you can support. But none of those options are, "Go take this medicine."

LATIF: I'm trying to imagine—like, so that is—there's something beautiful and hopeful and democratic about that. I can also see, though, there's a big danger here. Like, so I talked to a friend of mine about what you're doing. And she was like, "I've already seen this play out. Like I—" she's like, "I know exactly what's gonna happen here. I saw this play out with ivermectin during COVID. So all my patients were coming in, asking me for ivermectin even though, like, I knew that wasn't gonna help." She was like, "Last week someone asked me for ivermectin for cancer. It's an anti-parasitic drug. It's not gonna help you for your cancer. So I said, 'No. Like, no. I'm not gonna sit here and prescribe you a thing that there's no evidence for.'"

LATIF: But then what this is doing is it's like—it's, like, driving a wedge between the patient and the doctor, because now once the doctor says no to the patient, then the patient now doesn't trust the doctor. Now the patient is either gonna probably, you know, go doctor shopping until they find a doctor who will prescribe it to them, or they'll wind up at a black market or do medical tourism or some other, you know, non-ideal situation. But anyway, like—like, her point was that the doctor-patient relationship is already in a bad place. Like, it's already in a really bad place, and now if patients come in with these drug recommendations, like, who knows? Maybe they'll be great. But also maybe they won't. And then she has to be the doorstop. Like—like, she has to be the one who crushes the hopes and dreams and has to hold the line. And that ...

DAVID FAJGENBAUM: I agree. And I totally empathize, and can really, like, see the concerns. I think that where I go when I think about COVID is not so much ivermectin, but I go to dexamethasone. So dexamethasone saved millions of lives during the pandemic. It was the only drug, Latif, that was recommended against when the pandemic started. It was, "Whatever you do, don't give people a corticosteroid. Corticosteroids weaken your immune system. Don't take dexamethasone." Literally, there was no recommendation for what to take, there was only a recommendation for what not to take.

DAVID FAJGENBAUM: Well, some amazing pioneering doctor in the UK still decided to do the trial of dexamethasone. And it worked; it reduced mortality by 35 percent. But the prevailing medical system believed that it would actually be harmful. So we didn't know what to do, just don't do dexamethasone. It turns out dexamethasone actually reduces risk of death by 35 percent. So I'm so glad that someone asked the question, "Are we sure dexamethasone shouldn't be used?" So I love that. I'm glad that dex saved, like, millions of lives. And I'm glad that—like, that there was sort of one doctor who was willing to go against, you know, everyone else.

LATIF: Yeah.

DAVID FAJGENBAUM: And so I think that the whole point of this is to find dexamethasones, not ivermectins. If there's a drug that someone thinks might work for a disease and it's snake oil and it's not working, we want to study it. We want to prove that it doesn't work. If there's a drug that looks kind of promising but no one's studying it, we want to study it and prove that it does work. We just want to prove that they work or they don't work.

LATIF: So do you feel like—like, what you're doing, do you feel like, "Oh, this existing medical system needs to be respected. It needs to be shored up," and it's like you're trying to, like, fix a part of it that's wrong? Or do you feel like it's, like, "No, no, no, no, no. The existing systems are failing us in these key ways. We can't be bogged down by them. Like, I'm—I'm building a whole new thing, here."

DAVID FAJGENBAUM: Yeah, I think where my mind is is that I think I'm still so appreciative for what doctors do for patients, and that doctors bring just this laser focus on helping the person in front of them. I'm so grateful for that. And I'm so grateful for all of the things that our biomedical system has figured out are true. Like, this drug works for this disease. Like, I'm so grateful for that. So I don't want to break down any of that. Like, I want everything about our doctors caring about patients and the relationship, and I want everything about all the known knowns, like, where we know this drug works for this disease. I think what I really, really want to bring forward is uncovering the unknowns, so that way those doctors can use—that unknown can become a known, so it can be easy for them to use it.

DAVID FAJGENBAUM: I don't want to create some crazy new system where patients are picking drugs off the AI. Like, I want to use AI so we can find out what we can elevate to the level that a doctor feels comfortable. Like, "Wow, that steroid actually could be useful for this thing. Huh! Never would have thought about it. But you know what? They did a clinical trial and it worked. So, like, I'm gonna do that." So in my opinion it's not about breaking down the system, it's about enabling the system to do exactly what it's trying to do, but that we're caught up in these assumptions that I think we have around what we know and what we don't know. And I think we're really—we're certain. We're very good at what we know. Like, I totally—I believe everything we know in the system is rock solid. I think we're just not as good at understanding what we know to be not the case, versus what we just don't know at all. Like, the amount of our—and actually there's a term for it that computer scientists use, and that's the "ignorome," which is basically the things we don't know about medicine. Like, the ignorome, I think, is a lot bigger than most of us in medicine want to appreciate. And I think if we can be a part of uncovering the ignorome and making it less ignoromous, or whatever it is, then I think that that's where we can serve medicine, doctors, patients, and not to try to, you know, break it down. It's really about, you know, lifting things up.

LATIF: Yeah. I mean—like, I mean, you've already been doing miraculous work. At the same time, I can't help thinking about this other Radiolab story that we did—I don't know, it was, like, a year or two ago—about this ICU doctor named Blair Bigham, who was kind of like you, was like the annoying patient relative. And the story is kind of about him watching his dad basically contract cancer and die. And his takeaway from that experience was, like, we don't need more hope. We don't need more, like, Hail Marys at the end. We need more dignity. Like, we just—we glamorize doing everything we can, you know, because we—because we want to, like, keep the people we love alive. But actually the reality is, like, those last ditch efforts, like, tend to make things worse for the patient, for the family, for the hospital. And now it's funny because I'm talking to you and I feel like you have the exact opposite, 180 degrees different takeaway: fight, fight, fight. Never say die. Like, try every drug in every pharmacy. So, like—like, I just don't even—like, how do you square those two things?

DAVID FAJGENBAUM: So I actually think that during this discussion, I think you've actually sort of opened my eyes a little bit, just because you've sort of, like, highlighted to me—I just told you the three most special months of my life were the last three months of my mom's life. And we weren't fighting for a treatment. Yet I just try to extend other people's lives with the drugs we already have on my own. And so I think everything's context dependent. And, I think that what he said is conceptually correct, but I think that when you feel it, when you experience it, especially when you've experienced, like, the positive side, when—when you do make it, it creates a new sort of value you put on, if you—if you do get extra time. But of course this—at the end of the day, this is, like, philosophical around, like, individual versus collective societal. Like, you know, there are people that will say, like, "I want to die at 70 years old because I don't want society to have to take on my burden. Like, even if I'm not sick, like, I should just die at 70 years old so society doesn't have to pay for my costs."

LATIF: Yeah.

DAVID FAJGENBAUM: And then there's other people who are like, "We're only on this Earth once. Like—like, I'm gonna, like, squeeze out all the time I can get. You know, I'm gonna live as long as I can get." I don't think that I'm sort of on either camp. I think you can, you know, be reasonable and decide. I mean, I should also share about a patient recently that—that I had helped to discover a repurposed drug for. And it got him out of ICU, and I remember sitting with my team and jumping up and down. And, like, when we got the news that he was responding to this medicine ...

LATIF: What was the sickness he had?

DAVID FAJGENBAUM: He has Castleman's, the same exact subtype that I have.

LATIF: Wow.

DAVID FAJGENBAUM: It was just—I was so excited. I remember I cried tears of joy, I was so happy that we found this drug for Paul. And the next couple of weeks went by and, you know, he kept getting better. And he got out of the hospital, and I got in touch with him and—and he explained to me, "David, this drug got me out of the hospital. It turned everything around. It saved my life, but I feel horrible on it. It makes me nauseous. I'm vomiting all the time. Like, it's controlling my disease, but I don't like the way that I'm living." And he was a 70-year-old gentleman, and he decided to go off that medicine for the exact reason you mentioned, Latif, is, "I want to spend the time that I've got with my—with my—my kids and with my wife."

LATIF: Yeah.

DAVID FAJGENBAUM: And I was like, "Paul, but we already found something for you and it got you out. Like, we've shown that we can do it. Like—like, let's try another one." And—and he said—he said, "No, David. I don't want to." And then the two of us just cried, you know, tears of sadness. You know, we cried tears of joy before that, cried tears of sadness. But it was—but it was okay. Like, this was his decision. Like, he knew that if—if he put me on the case, he knew that I was gonna be all in, and he knew that I'd been able to do it once before. Like, but he told me he didn't want to. I was very sad, but I felt that it was absolutely the right thing. And I—I one hundred percent respected it. I understood that, like, for him at this moment in his life, that was the right decision. And he passed away a few days later.

LATIF: So you're saying, like, it just basically—it needs to be personal. It needs to be case by case.

DAVID FAJGENBAUM: Yeah. Maybe that's the big takeaway is that it seems like everyone wants to tell us what everyone else's decision should be. So, like, "It's best for society for you to do this." Or, "It's selfish of you for you to do this." But I think that maybe that's the real fundamentalness that this comes down to, it's gotta be that patient's decision.

LATIF: This episode was reported by me, Latif Nasser. Produced by Maria Paz Gutiérrez. Edited by Pat Walters, and fact-checked by Natalie Middleton. Special thanks to all of the folks at TED, including Cloe Shasha Brooks and Helena Bowen, who introduced me to David and his work. I was right there in the wings when he did his talk on the TED stage. That talk should be on their website, the TED website, very soon. And in the meantime, for more on David's story, you can check out his book, Chasing my Cure, or for more on the work that him and his team are doing, you can go to their website, EveryCure.org. Special thanks to Payton and the rest of the staff at EveryCure.

LATIF: If you have not had enough Radiolab, we referenced two prior episodes today, and I think they're both totally worth a listen. The first was the one I mentioned about that ICU doctor Blair Bigham who basically writes a book about how we should make our peace with death and we should die with dignity, and then while his book is on the bestseller list, his dad gets pancreatic cancer and all of a sudden everything he wants to do completely contradicts everything he wrote in his book. That one is called "Death Interrupted". Second Radiolab episode that we mentioned was kind of the backstory of the drug that saved David's life, rapamycin. It is an entirely improbably backstory. It is shockingly dramatic. It's about one immigrant scientist who basically single-handedly saves this potential drug from the trash can by smuggling it across a border. That episode is called "The Dirty Drug and the Ice Cream Tub."

LATIF: That is all for us today. Thank you so much for listening. Until next time, I wish you good health.

[LISTENER: Hi. I'm Connor and I'm from Minneapolis, Minnesota, and here are the staff credits. Radiolab was created by Jad Abumrad, and is edited by Soren Wheeler. Lulu Miller and Latif Nasser are our co-hosts. Dylan Keefe is our director of sound design. Our staff includes: Simon Adler, Jeremy Bloom, Becca Bressler, W. Harry Fortuna, David Gebel, Maria Paz Gutiérrez, Sindhu Gnanasambandan, Matt Kielty, Annie McEwen, Alex Neason, Sarah Qari, Sarah Sandbach, Anisa Vietze, Arianne Wack, Pat Walters, Molly Webster and Jessica Yung. With help from Rebecca Rand. Our fact-checkers are Diane Kelly, Emily Krieger, Anna Pujol-Mazini and Natalie Middleton.]

[LISTENER: Hi, this is Jenny from Brooks, Maine. Leadership support for Radiolab's science programming is provided by the Simons Foundation and the John Templeton Foundation. Foundational support for Radiolab was provided by the Alfred P. Sloan Foundation.]

 

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