How Drug Repurposing Unlocks New Uses for Existing Medicines: Every Cure’s David Fajgenbaum
David Fajgenbaum’s relationship to drug repurposing is personal, professional, and one of the most inspiring stories in modern medicine. Diagnosed with Castleman disease and running out of treatment options, David used his research skills to identify an existing drug that saved his life.
Join David and Medidata CEO Anthony Costello to explore the origins of Every Cure—and how David’s experience became the seed for a nonprofit that uncovers life-changing uses for treatments already approved for other conditions. Discover Every Cure’s innovative approaches to data, the scope of unmet needs across healthcare, and how pharmaceutical companies can contribute to a future where we unlock the full potential of treatments for all.
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Anthony Costello 00:00 How are bold ideas born, and which ones survive to eventually shake up the status quo? We'll hear straight from our industry's greatest visionaries who are making waves and learn how they turn their dreams into disruptive reality. This is from Dreamers to Disruptors, a podcast powered by Medidata. Most breakthroughs in medicine start with a lab, a research team and a long timeline. Very few begin with a patient fighting for their own life. But sometimes the catalyst is the person closest to the experience. When David Fajgenbaum was diagnosed with Castleman disease, he was told there were no answers and no path forward. What happened next has become one of the most inspiring stories in modern medicine. David identified the treatment that saved his own life, then turned that experience into a mission to help others searching for hope. Today, David is the co-founder of Every Cure, an organization using data and AI to search across all existing medicines and all known diseases to find life-saving matches that are already available but overlooked. Instead of waiting for new drugs to be invented, Every Cure is working to unlock the potential of the medicines we already have and to build a system that can deliver treatments faster to the patients who need them. In this episode, we'll talk about David's personal journey, the early breakthroughs that shaped Every Cure, and why repurposing generic drugs is both powerful and often ignored. A major part of that challenge is financial. Once a drug becomes generic, the incentives change for companies to invest in finding new uses, even when the scientific potential is clear. We will explore what it will take to scale Every Cure’s work from nine active programs to many more, and how technology can accelerate that progress. Most of all, we'll look ahead to David's vision for the coming years and how all of us can help. Welcome to from Dreamers to Disruptors.
Anthony Costello 01:56 Hi, everyone. Welcome to another exciting edition of from Dreamers to Disruptors. I'm very honored and excited today to have David Fajgenbaum with us. David, thanks so much for joining. I've been looking forward to this for years. I think we've been doing a lot of work. Medidata has been doing a lot of work with you. You and I have known each other for years, but in the meantime, your story has really gained a level of, I would say, sort of national appreciation that it deserves. And I know you're really you've become kind of a celebrity in the medical world for a very good reason. And I know many people in our audience may already know your story, but I'm going to ask you to start off here with the assumption that some people don't, and you can tell the story that never gets old and never gets boring, and frankly, should, should be an inspiration to all of us. I want to go back in time a little bit and talk about kind of where you started, why you started doing this, we'll work our way into kind of what Every Cure is and and what it what its mission is, but your story is besides inspirational is really kind of a solution to one of, I think our industry's biggest. Problems, which is creatively looking for ways to solve unmet medical need and and you created a way to do this that I think is just remarkable. So without further ado, please kind of start us off with the story of David Fajgenbaum and Castleman disease and how you started your organization.
David Fajgenbaum 03:41 Sure. Well, thanks so much for having me. I've been so looking forward to this for so long. It's awesome to connect. Sure. I'll start back in 2010 when I was a third-year med student here at Penn. And I promised my mom a few years before that I would dedicate my life to trying to find drugs in her memory. She had passed away from brain cancer when I was 19, and that experience of watching her battle brain cancer and then pass just drove me with every ounce of energy I had to want to become a doctor, to want to try to find treatments for patients like her, and I was really making progress towards that as a third-year med student. And then out of nowhere, I just started feeling more sick than I ever felt. I was more tired than I ever felt. I had these enlarged lymphoids in my neck. I had horrible abdominal pain. But I also had a med school exam that was a couple weeks away, so I was like, I just got to, sort of, you know, battle through. I just got to make it through my exam, and then I can sort of figure out what's going wrong. I did that, and then I went to the emergency department for my med school exam, and my doctors told me that my liver, my kidneys and my bone marrow were all shutting down, and they had to hospitalize me right away. I was put on dialysis. I needed daily transfusions. I gained over 100 pounds of fluid because of the organ failure, and I literally was actively dying, and we had no diagnosis, Anthony. It was horrible, but about 11 weeks into it, I was finally diagnosed with a disease called idiopathic multicentric Castleman disease, where your immune system attacks and shuts down your vital organs for an unknown cause. And unfortunately, at the time, the only treatments were chemotherapy, and we didn't even really know what to target. But we thought, you know, if you just give a bunch of horrible chemotherapies, then maybe you can destroy the immune system, which at the time was was really trying to kill me, and I was so sick that right around that time, a priest actually read me my last rites, because my doctors were sure that I wasn't going to survive. But thankfully, with the diagnosis, I was able to be started on chemo, and the chemo sort of just worked just in time to save me but then I was back in the hospital a few weeks later, this time, I got seven different chemotherapies that all the highest dose possible that got me in remission, but we knew that this disease was relentless. I was put on an experimental drug. Of course, as you know in the work you do at Medidata, clinical trials are so important for advancing therapies and for giving patients hope, I was so thankful when I learned that there was a trial of a drug called siltuximab for Castlemans, and I got put in that drug, and I hoped so badly that it would keep me in remission. But then I relapsed again about a year later, and back in May of 2012 when I had that big relapse for me, that was such an important moment in my life, because I was now told that we were out of options. The drugs that had been tried, those chemotherapies couldn't keep me in remission. This experimental drug, our new hope didn't work. I relapsed on it, and my doctor told me, we've tried everything. There's nothing more that we can do. And that was a moment that really changed everything, because I promised my family that I would dedicate my life to trying to find something that could maybe help me and other people. Help me and other patients like me. But Anthony, as you mentioned at the very beginning, we wouldn't be able to take the typical approach, right? I didn't have $2 billion in 15 years to make a new drug, right? I had, you know, probably months to live. And I had, you know, no money so, you know. So what do you do to try to save your life when you've limited resources like that and and pretty quickly I realized, I mean, it was like, literally within, within minutes, I realized, wait a minute, I'm getting these chemotherapies right now infused into me, and none of them were made for Castleman disease. And I know that they're not working long term, but they have been able to put me in remission a couple times. So they're doing something, you know, how do we know there's not something else that was made for something else that couldn't also help me. And so it became really clear that my only way to survive would be to find some other drug for some other disease, just like the seven chemotherapies that can maybe put me into a longer term remission, and that became my mission.
Anthony Costello 07:31 It's an incredible story. And so maybe fast forward a little bit into that to the story of, of really founding Every Cure around this idea, right? And we, I guess I should have noticed up front, I'm wearing my special my one of my most favourite Glen de Vries sayings of all time is, do epic shit. And, you know, we, we live by this standard here at Medidata. And there probably isn't a better epic shit story out there than yours, so I wore this shirt today in honor of you, and hopefully I'm not offending too many of the podcast audience. But this is truly an epic story, right? I mean, it's epic enough to save your own life with this kind of creativity. But then to figure out a way to leverage this kind of creativity, this kind of creative solution, and frankly, a bunch of off-label very highly experimental repurposing of drugs, that's an epic endeavour, and it worked for you, but also helped you segue that innovation into a whole organization that does this. And I know we're going to talk a lot about what the organization does and how they do it, but maybe just the origin story of Every Cure you know. How did you get from “I'm going to save myself” to "I can turn this into a thing that you know aspires to save other people as well.”
David Fajgenbaum 09:01 Sure. Well, I'm so happy you're wearing that shirt with Glen saying, as you know, Glen was a dear friend and such an amazing partner on the research that that we've done over these years. So I love it. Yeah. So, so from that moment where I made this promise that I would try to find a drug to where we are today, I ended up storing blood samples on myself every few weeks leading up to what ended up being my last relapse of the disease. And when I survived that relapse thanks to chemotherapy, I did a series of experiments on my blood samples. So I did serum proteomics, I did flow cytometry, I even did something called Immunohistochemistry on my lymph node. And eventually, from those experiments, I pieced together what looked like a really strong signature from the mTOR pathway. mTOR is important for your immune cells to communicate, and I thought that maybe the mTOR pathway was turned into overdrive, based on the research I did in the samples and talked to one of my doctors about trying an mTOR inhibitor. It had been around for decades. It was approved for organ transplantation, but it had never been used for Castlemans, and he agreed to try it. And Anthony it's now been over 11 and a half years that I've been in remission on this drug. I never feel like I can round up. But I don't know when this podcast is going to be aired, but it'll be somewhere around hopefully I'm going to knock on some wood, hopefully around 12 years from the time that I started this medicine, which just feels like a dream. I mean, talking about Dreamers to Disruptors, it totally feels like a dream that I'm here, you know, almost 12 years later.
David Fajgenbaum 10:26 But you know, Anthony, from the moment that that drug, sirolimus, started helping me, and it became clear that it was, it was working, I haven't been able to get out of my mind how many more drugs are sitting at our local CVS that could help other people. I mean, no one had ever thought to try sirolimus for me, and I'm alive, you know, years later, because of it, how many more drugs are out there that you know, that folks at Medidata have been a part of doing clinical trials of to get an approval for, you know, X, Y or Z, disease. But what if it can help another disease and another patient somewhere? And so that's really been my laser focus. I joined the faculty here at Penn. I set up a center called the Center for Cytokine Storm Treatment & Laboratory, and we started doing this for more and more related conditions. So first, of course, focusing on Castlemans, using sirolimus in more patients, and then discovering Ruxolitinib could work in patients and thalidomide, other Castlemans patients. Then we started to open up the scope and look at what about related diseases? So a rare cancer called angiosarcoma, we were part of identifying and advancing using a PD-1 inhibitor for the first time in angiosarcoma, and then POEMS syndrome and data too, and and sort of the more and more that we looked here within my center at these rare inflammatory diseases and cancers, the more we found drugs that could be useful in new ways, which was just like so exciting and so amazing that we could literally save patients lives with drugs that already existed for other diseases. And so about three years ago, I spent a lot of time with my co-founders Grant Mitchell and Tracey Sikora. Grant had spent many years working utilizing machine learning for drug companies to find new disease populations, new drugs. But we started wondering, well, what if we could actually apply machine learning/artificial intelligence, not just to find one new sub-population for one drug company, but what if we could scale what we were doing in my lab, looking across all drugs and all diseases? And so it really was very much a dream, like, what if we could use AI to quantify the likelihood of every drug to treat every disease, so we could figure out, what are the things at the top the drug disease matches that are the greatest potential opportunities to move forward. So us humans could do epic shit, and, you know, study them in the lab, do clinical trials, and really figure figure out how these drugs could actually help patients. And we decided to launch this nonprofit in September of 2022 and I should mention that, you know, we mentioned Glen de Vries earlier. Glen sadly passed away about one year before that, in November of 21 and you know, as we were launching Every Cure, I remember thinking to myself so often I told Glen about this dream before he had passed. But gosh, how much he would have loved to be a part of this. And you know, taking this, you know, this experience of helping people repurpose drugs and really scaling it. So I'm so glad that we're having a chance to chat about this today.
Anthony Costello 13:08 Yeah, yeah, it's really an amazing story. And, you know, I want to get into some of the aspects of AI and how that can really accelerate what it is that you're doing. But, but a couple things that you and I have talked about before that I'd love for you to just share with the audience. I mean, first of all, you kind of have a you've got a great way of explaining these complex ideas to to regular audiences. And I think one of the things that I've seen you do is talk about the concept of a knowledge graph, like a like a Netflix sort of matching criteria and how that relates, or kind of maps over what you're doing at Every Cure. So can you talk about, talk about that for a second, and then maybe that'll, that'll help us kind of get into where AI can be beneficial here.
David Fajgenbaum 13:54 Sure. And maybe, before I get into the analogy, I'll just share, sort of, one of the reasons that drug repurposing works is because diseases that may appear very different clinically and symptomatically can share the same underlying problems in the body, and therefore the same drug can treat it. And so good example of that is Viagra. You know, we all know about how it was repurposed from heart disease to its very well known use erectile dysfunction. But what most people don't realize that it's also been repurposed for rare paediatric lung disease. So kids were dying because they weren't getting enough blood flow to their lungs. And so in the same way that lack of blood flow was causing erectile dysfunction, lack of blood flow to the lungs was causing these kids to die; they weren't getting enough blood flow. And so you can use the same drug for two completely different conditions because they had the same underlying problem, and that one drug can hit that same problem. And for another reason repurposing works is because sometimes drugs can actually hit multiple things in the same body. And so thalidomide is an example where it's effective for both leprosy and myeloma, for some reasons that are shared, but also for some reasons that are different. That same drug, thalidomide, hitting different targets in the same body, can have effects on two different diseases. So when you think about that as your framework, then you think to yourself, “Wow. So there's sort of like, locks and keys for different things in the body.” You know, this like, you know, blood flow might be a problem in this drug or this disease, and that disease and a drug might affect that. Now imagine that across all drugs and all diseases, you know, there's various things that are drivers, and, you know, various ways that drugs work in the body. So what we do is we utilize knowledge graphs that start out by sort of representing all of those drugs, all those diseases, all those genes, all those proteins and how they're all connected. We know that mTOR, the drug that saved my life in or, sorry, sirolimus, the drug saved my life, inhibits mTOR, and we know that mTOR is important in Castleman disease. Therefore in our knowledge graph, you've got a node with mTOR with an edge to sirolimus and also an edge to Castleman disease. And so that's how these three things are connected. Now imagine doing that across everything, and then Netflix, as you mentioned, utilizes machine learning, artificial intelligence, to try to predict which movie you might like, and they actually uses their ingredients a knowledge graph. It's not a medical knowledge graph, but it's a knowledge graph of actors, directors, producers for all the movies you like. And what Netflix does is it trains machine learning models on shows that you've watched and that you've watched all the way to the end that means that you like the show, right? And trains these models on shows that you've watched, and then ask the model, “Okay, now find other matches for me.” What are the other shows that might have similar actors, similar directors, similar patterns to them? You know? What other movies you know might I like to watch? And as you know, Netflix is really good like the shows you might want like to watch, they're pretty good at predicting what you'd like. So we do something similar, where we train our machine learning models on medical knowledge graphs of drugs that we know work. We know siltuximab treats Castleman disease, we know that GLP-1s treat diabetes. So we train on those known treatments, just like Netflix trains on those known shows that you like to watch. Then we ask the algorithm, “Okay, now that you know what works in medicine, give us a score from zero to one for how likely every other drug is to treat every other disease.” And there's 4000 drugs, there's 18,000 diseases. So we get a score from zero to one for all 75 million possibilities of every drug versus every disease. And that is a great place for us humans to get started. Let's look at the top. Let's look at the point nine nines.
Anthony Costello 17:19 Yeah. I mean, it's an amazing story. I love the analogy to Netflix, because I think it's something that everybody understands and it's clear that AI could really accelerate this process, right? Because it's not, it's not a rare thing for these drugs to be prescribed off-label, right? I mean, in fact, I think there's a pretty low bar, in fact, to just to prescribe a drug off-label. But when you start looking at the off-label usage, kind of one-off prescriptions, off-label that a doctor may choose to experiment with, how do you make, how do you create the critical mass of data and information that that might actually be studied in a real kind of research context and become a new treatment for something? Like that, seems what that seems to be, kind of what Every Cure needs to do at this very, very large scale. And it's been a challenge. I want you to tell the story of getting funded, you know, early on, because I know that in the early days, even though there's unmet medical need all over, there are these generics that could be repurposed and are probably being prescribed off-label here and there. It still is very difficult for you in the beginning to kind of build the right energy and funding behind this program, to kind of kick it into the gear that you seem to have found today.
David Fajgenbaum 18:43 That's right. And yeah, Anthony, you're exactly right. Between 20 and 30% of prescriptions every day are off-label. So doctors are trying things off-label all the time. Now, a lot of those prescriptions are sort of standard prescriptions, so like steroids for a cough, for example, that's technically off-label because no one ever you know got an upper respiratory tract infection, as on the label for corticosteroids. But some of it is, you know, really novel stuff. But to your point, it happens one-off. You know, maybe you go to Harvard and you get this one prescription, but if you went to any other doctor around the country, they may not even know that that drug could be used off-label for that condition. And so you're exactly right. At Every Cure, our mission is to make sure that every FDA-approved drug is used for every disease and every patient it can possibly treat. And so we use AI to give us a starting place. “Okay, if we're going to look at, if we want to advance everything, for everything possible, we got to start somewhere.” And so we use AI to tell us, you know, what are those drug disease matches that are most worthy of our early attention? We have limited time, we have limited resources, so we got to focus on the very best matches. So AI points us towards those. But then us humans actually spend a lot of time thinking through, you know, “Is this drug for this disease going to be a drug disease match that we can move forward better than another drug disease match?” And of course, there's many that we're comparing and sort of are competing against. And then, to your point around unmet need, the numbers are sort of staggering. As we've sort of zoomed out in medicine, we like to focus on the numerators in terms of, like, “this many drugs got approved this year for this many diseases.” And like, we should cheer about that. Like, I mean, 50 approvals every year is amazing. Like, we should be really excited about it. But if you zoom out and say, well, what's the denominator? What you learn is that us humans have developed about 4,000 drugs for about 4,000 diseases, but there's 14,000 more diseases with no treatment. So the denominator is actually 18,000 diseases, and we've made progress on four th- you know, real deep progress on 4,000 of them. And to your point, some progress with off-label use on some of those other 14,000 but there's still a long way to go. So huge unmet need, patients are suffering while there's drugs that exist that could be repurposed and used off-label, but there's never been an entity that's been responsible for saying, “Hey, what are all the other opportunities to use the drugs that we have?” And so really excited about this. And to your point, despite the unmet need, it still was really, really challenging to fundraise for Every Cure and get it off the ground. I think when we added it up, me and my co-founders met with over 300 different people before the first really significant check came into the door for Every Cure. And I think the probably the lesson for a podcast like this, from Dreamers to Disruptors, is that we had a big dream, and it's a dream that I think most people can relate to. In fact, probably most people on this call are watching this, this, this podcast, or listening to it, have probably received a drug off-label, or they have a loved one who has a horrible disease that might benefit. And even still, it was really challenging to raise the funding to do this, and I think it's in part because in our medical system, everything is always oriented around a specific disease or a specific drug. So drug companies have a portfolio, a pipeline, of drugs that they're working on, and disease organizations and researchers have specific diseases they work on. But there's never been an entity that's just looking for the best matches. You know, we don't own any drugs. We work on any drug and every drug. We don't own any diseases that are like our diseases that we're working on. We're just looking for the best opportunities to help people. And that sort of concept of not being locked in on either a drug or a disease was really hard for people to wrap their heads around. And I get that. It's sort of unusual, and, you know, it hasn't been done, but I think that it's exactly what we need in our system. And I'd love your thoughts on, like, the right analogy for this, because I haven't really gotten a good analogy, but it's sort of like we needed someone looking around the forest for the low-hanging fruit. Like, what, like, you know, not necessarily the low hanging fruit on the Castlemans tree, because we got plenty of people worried about Castlemans, you know. But like, what trees are out there? Maybe for a disease or a drug that's a low hanging fruit that could help a lot of people, but it hasn't had attention, because the drug’s either generic, as you mentioned earlier, maybe the disease is really rare. Another, another analogy, and I would love to hear your notes, another analogy. I thought it was sort of like picking up the scraps that are left over. You know, what are those diseases where we know the drug could work in it, but maybe it wasn't profitable enough for a company to pursue it. I don't know, Anthony, what do you think is the right analogy to describe what we're trying to do here?
Anthony Costello 23:07 I mean, I don't know what the right analogy is. I'll put some thought into that. But I think that there's an obvious sort of economics challenge here that I think, you know, we face all the time. And you know, first of all, we've had lots of people on the podcast who started with a dream, and it took a while to kind of turn the flywheel over on that dream, meeting with 300 funders in order to raise your first, I think it was your first million dollars. Yeah, that's, that's, um, that that's extreme. But you know, it's not rare to meet with 30 or 50 or 100 before somebody takes hold of the dream. In this case, and the particular instance that you're that you're trying to solve for, especially in rare disease, there's just a problematic economics problem in our industry that you know, lots of people have been focused on for a long time. If you, if you only have a small number of people in the universe that are ultimately going to be consumers for the drug, it's hard to justify the heavy burden, R&D burden of bringing that drug to market. And if you're dealing in the world of generics as Every Cure is it's a whole other level of economic challenge. So, you know, I think one of the things that's most exciting to me about what we do and what you do is, you know, we're always looking for more innovative, faster, less expensive, more efficient ways to run the clinical research side of this problem, which is part of the very expensive, you know, one of the very expensive pieces of the billions of dollars and the decade or longer that it normally takes to bring one of these drugs to market, you're obviously using the most modern tools you can get your hands on in every shred of data in order to find the right, you know, the right opportunity to chase down. And then you still have to, you still have to, you still have to chase it down. You still have to prove that it works, right? So I think there's, there's some kind of call to action here. We'll, we'll get to your specific call of action at the very end. But you know what's exciting to me every time I talk to you and kind of relive this journey and this story is somehow in our industry, partially through the onset of newer technologies like AI, and then partially due to what you know, what I and I think you too call, more and more a willingness factor of funders and pharmaceutical companies and technology companies like us to engage with programs like yours to accelerate these things as fast as we possibly can. There's some sort of critical mass here that I feel is is coming together in a way like never before, that might be a springboard to kind of make these things happen much more readily.
David Fajgenbaum 25:54 I think so too. And I think that to your point, I think that that AI is really helping us a lot. Because, you know, when there's 4000 drugs and 18,000 diseases, you can only focus on so many opportunities, right that have fallen through the cracks. You know, how do you how do you pick which ones to focus on? And then, you know, we think about it very often as you know, we're looking for the low-hanging fruit in the forest and also the things that we can move forward most rapidly and most efficiently to help people. And so can we take those opportunities to Medidata and to, let's say, the manufacturer of the drug, or maybe it's to Medidata to work on the trial, and we're going to find, you know, a CRO to work on the lab work, and, like, take those things that they're like, the most high-impact, lowest-cost opportunities that we can do together. So that way we accelerate this, this further. So I agree. It feels like AI can help us to focus in on the lowest-hanging fruit, and then we can come together with great groups like Medidata and others to do the lab work, to do the clinical trials, so that way we feel confident talking about the drug and educating around its use.
Anthony Costello 26:53 Yep, so, so just that's a great origin story for the organization, and if we fast forward to today, like slice-in-time moment right now. How many programs are active? Kind of in development with Every Cure, maybe talk a little bit about, what are your successes? What are the what are some things in flight that you're really hopeful about? Anything you can share there?
David Fajgenbaum 27:17 Yeah, absolutely. So we've got nine active programs right now that range from very early laboratory work all the way through programs that are either ready for dissemination or close so meaning that we're ready to start educating about the potential use for that medicine. We have a very broad portfolio, or at least a range of diseases, so on one end of the spectrum, we are studying a very common condition, breast cancer, and utilizing lidocaine, which is, of course, the numbing medicine that you would get if you went to the dentist for your mouth, using that numbing medicine around tumors before surgery, it's called a peritumoral injection, and trying to understand the role that that can play and potentially lowering the risk of metastasis five years down the line after surgery. And the hypothesis is that during the act of surgery, that some cancer cells will actually migrate out of the area, out of the surgical region, and therefore will become a future metastasis to the liver or another part of the body. Idea being that if you use peritoneal lidocaine injection, you'll actually prevent the migration of cancer cells during the surgical excision process, and therefore reduce the risk of recurrence later on, and also actually some direct cancer-killing properties. And so we're really interested in this particular program. It's one of our nine. We've got some more work to do. There's some more review of clinical data. We're actually also doing laboratory data to better understand how the mechanism might work. So, so that's one of our programs. Of course, that's a very common cancer, and it's actually quite, quite mature and far along the pipeline. And then we have another program that's that's with a very, very rare condition that I thought I'd highlight, that's called Bachmann-Bupp syndrome. Bachmann-Bupp is a rare neurological condition where kids are born with increased levels of ODC1, this enzyme that results in inappropriate musculoskeletal functions. So kids are on feeding tubes. They have difficulty moving, sitting up, walking. But these amazing researchers, Dr. Bachman and Dr. Bupp, have done incredible work where they help to uncover that a drug called DFMO is a great inhibitor of ODC1, and therefore, using this drug that was made for African sleeping sickness could actually help these kids that have increased levels of ODC1, to help them to have their feeding tube taken out, to sit up and to improve. And so that's one where we're it's a much smaller condition. Obviously Bachmann-Bupp has only been described in somewhere around 20 kids in the world. That means there's probably a few 100, maybe 1000 that actually are around the world that are still to be diagnosed. But it sort of helps to highlight the range of conditions, because we don't care how common or how rare, or how expensive or how inexpensive the drug is, we just want to find the best opportunities to help people, and that means that sometimes they're very rare, and sometimes the drug is very cheap. Oftentimes the drug is very cheap, because that might explain why the work's not being done with that drug. But I should also mention sometimes these ideas come straight from our AI platform, and other times they actually come from an expert who's working in the field, who tells us about it, and we love that too. We don't care where it comes from. We just want to help people with the drugs we have. And we think of AI as a great a great focus area, or at least a great way to to focus us in on the on the good opportunities, but we know that there's a lot more opportunities out there. And so if someone's listening and they're aware of a drug that could be useful in a new way, you can go to EveryCure.org/ideas, and you can tell us about it, just like Dr Bachman and Bupp did, and we can move that forward.
Anthony Costello 30:46 Yeah, I think that's one of the things that's most exciting to me is if you, circling back to our conversation a few minutes ago about off-label use, at some point the off-label use hits a critical mass where this is a thing, right? Like this is actually, this should be an on label thing, yeah, and if the AI can help find those cases or take a hypothesis, there's enough off-label prescribing that it looks like maybe there's a worthwhile signal that AI can tell you, “Yes, there is.” And then Every Cure can jump in and take it from there, right? But you mentioned nine programs, which is phenomenal, but I'm curious to know from you a little bit kind of, what's the long pole in the tent, like, how can nine become 900? Is it just money? Is it still an AI limitation? Is it the sort of scope of what you can reasonably take on as an organization, the size of your organization today? I mean, how what would the future look like if you wanted to turn nine into, you know, 10x9?
David Fajgenbaum 31:51 Love it. I love that question. So we've raised money over the last couple years to be able to advance somewhere between 15 and 25 programs for diseases that those drugs were not intended for. And in doing so, it's always hard to predict, because we don't know how common or rare the disease will be, but you know, somewhere in certainly, the hundreds of thousands of lives, hopefully in the millions of lives saved and improved because these medicines and that's really exciting to think about. You know, somewhere around 20 diseases treated, lots of lives saved and impacted, and that's over the next four to five years. But to your point, there actually are hundreds, maybe even low thousands, of opportunities out there. So we think about denominators. Got to be excited about the 20, but then you got to think about, okay, there's hundreds, or maybe even low thousands, repurposing ideas that are waiting to be advanced, or that maybe some patients getting it somewhere, but not everyone's getting it everywhere, and so, so part of that is certainly funding. You know, 10x thing, the fundraising that we do is part of it, and I think part of it also is identifying those right partners who we can scale with, I mean, in the same way that that large biotech and pharma companies are spending hundreds of millions, billions of dollars a year, and are able to really, you know, move things forward rapidly. Can we, can we take some of the principles about moving things forward rapidly, but not have to take all the costs associated with it? Can we establish strategic partnerships? WIth you know, companies like Medidata and with other groups, with CROs, you know, with laboratories, to where we can get to a place where we can really speed up the pace and the progress? So I think it's one fun, one part funding, one part strategic partnerships, and then I think it's, it's another part just continuing to build out the sort of world-class, talented team that we've got, because, you know, you're only as good as the team that you have. We've got an amazing team, but we got to keep growing.
Anthony Costello 33:47 Yeah. Can you talk a little bit about the pharma, the pharma, kind of partnership side of this? Is this something that they're excited about and investing in, something that, you know, they like to see happening, but they don't really have a role to play in it? I mean, how has that been for you?
David Fajgenbaum 34:05 I think it's somewhere between the two. So there's a lot of enthusiasm from our colleagues in pharma and biotech, and mainly it's because they've had to be a part of making tough decisions over the years to deprioritize certain diseases, like they know the drug can work in that other in one disease, but they have to focus on another disease. So they know the huge opportunity we have. I mean, everyone who's worked in pharma or biotech is so excited because they know about all these opportunities. At the same time, we haven't yet figured out how to work together in the right way, especially if the drug’s already generic. You know, how can a drug company, you know, you name the drug company that has a drug that's generic, how can they justify spending their dollars on a drug that they're no longer making profit off of? I think that there's ways to do it. Because I think you can think about corporate social responsibility. You can think about positive or at least sort of positive emotional feel around specific companies. I think that there's PR I think there's sort of quantitative ways to say that it can be good. I just don't think we've worked out exactly how to do it. And I think that there's legal things, considerations to keep in mind. But I would love to brainstorm with you, Anthony and others that are in the industry. How do we create a model where a drug company that knows their drug can be useful in more ways, but they can't justify spending money on that new way, and they can't justify doing the work; can we figure out a process where they tell us at Every Cure, “Hey, we really think propranolol could be useful in this other disease. We could just never study it in that way. Can they tell us about it? Maybe we can take it up.” Maybe they can even support the work through their philanthropic arm, but yeah, some sort of way to make it more of a of a collective effort, where it's not like, you know, pharma is doing something, and Every Cure is sort of trying to pick up the pieces and take it forward. But can pharma move forward their pipeline and also know that, “Hey, Every Cure can also move forward some things in our pipeline, even if it's not gonna be profitable for us?”
Anthony Costello 36:00 Yeah. Yeah. I mean, I think we're probably starting to to work our way into a call to action here, at least I can feel one taking taking shape, right? There's, this is a this is a classic “takes a village” kind of scenario that you're working in. And I think a bit definitely, a big part of what we're trying to highlight on the podcast, kind of episode after episode is, where do we all need to work together in a different kind of model, and find a way that that collaboration can be sort of one plus one equals three in moving some of these initiatives forward, especially initiatives like yours, that you know, where there's just like such a positive upside for humanity to repurpose these as you describe them, you know, essentially failed, or even if they were successful, successful for one particular indication, but potentially successful for so many more. So I don't know. David, I mean, maybe, maybe you can, you can work this into a nice, tidy call to action for us here, and we'll we'll do our part at Medidata and maybe help you get the message out. But if you wanted to have, you know, a mandate for our industry, we'll give you the power for the next few minutes. You know, what do you want to see our industry do over the next five years to really help accelerate this type of research, this kind of secondary research, I'll call it to so that groups like yours can do the work that you do to kind of fast-track this into a real therapy for someone.
David Fajgenbaum 37:35 Well, I love that you're asking me to share this, because, like you said, it does take a village, and at the end of the day, the drugs that we're advancing are for diseases that, you know, all of us are afflicted by, and someone that we care about or we love will one day, you know, develop many of these diseases that we're working on. So it's something that sort of, that's why we call ourselves. Every Cure, every one of us is going to be together, you know, hopefully to unlock every cure out there. So in terms of call to action. I mean, I think that when I think about our organization, I think about it as having three critical functions. So the first is to discover the very best repurposing ideas. The second is to evaluate and prove the efficacy and safety of that repurposing idea. And the third is to make sure the drug reaches every patient who could possibly benefit. So as I think about the end history and how we can work with folks, I'd maybe break into those three categories. So the first is, Can pharmaceutical companies, biotechs and generic drug manufacturers share with Every Cure the diseases that they've considered for their drugs but they weren't ever able to pursue? You know, it may be that our AI platform is ranking these things highly, but maybe it's not. It would be great to know what are those diseases you thought about pursuing, but you never actually were able to put the money behind it because it wasn't profitable or didn't make sense for the company. Maybe it was, maybe it was a disease area that you guys just don't work in, and that it seems like a great idea, but it's neurology, so we don't work in neurology as a company. So so one is to share those ideas with us that would be, you know, tremendously helpful. Another is to share data with us that can help us to find more ideas. And so it might be clinical trial data, it might be off-label use data, EHR data, claims data, sharing data with us that can maybe help us to find matches that maybe you haven't necessarily identified yet, but your data can help us to discover more matches. I think those are probably the two big calls to action in that discover bucket, in the evaluation bucket, where we could really use help is from companies that do laboratory studies, that do clinical trials, that we can establish strategic, low-cost partnerships, where we are sort of, you know, work. We're, you know, locked in hand-in-hand, you know, shoulder to shoulder, figuring out, how do we move forward lab studies and clinical trials as effectively and efficiently as possible? Because unlike a drug company, when you spend money on a drug, on a clinical trial, there will be a return. If that trial is positive, there is no return for us. We don't own the drug, so we do the trial. We help a lot of patients, but there is no flywheel funding. So that means that every dollar less that we spend on one program means $1 more we can spend on on the next program. So I think for two in the evaluation bucket, it's really establishing tight partnerships with companies that can work with us, ideally at cost, or maybe even sub cost, so that we can do this as many times as possible, and of course, also funding in that category too. You know, if there's funding, then we can, you know, obviously spend more with each of our partners, but funding and or services to do the evaluation in the lab and in clinical trials to prove these things work. And then in the third category of impact, this is around awareness raising and advocacy, so in education. So this is the kind of thing that actually anyone, whether you're working within the industry or not. You know, you can help to spread the word. You know, at some point, if the data line up the way we think they, will around lidocaine for breast cancer, at some point, we'll be educating about the trials and the studies that have been done to date, so doctors and patients can think about and discuss if they think it's the right thing for them. You know, raising awareness about that, getting the word out about those sorts of programs, is something that all of us can do. And of course, there's folks who were, you know, really good in pharma marketing and you might be, you know, particularly well qualified to support us in that way. So I think that's how I would think about it Anthony, you know, help us to discover more ideas, help us to evaluate things more effectively and efficiently, and help us to get the word out about opportunities even better.
Anthony Costello 41:21 Yeah, yeah. Those are, those are great call outs. And you know, just for the record, if anyone's listening to this and they are excited to jump in and help, what's the best way for them to reach out to you?
David Fajgenbaum 41:34 Sure. So reach out to info@EveryCure.org or david@EveryCure.org get straight to me, but letting us know about your interest in in collaborating, your interest in getting involved in any one of those three areas. You can also follow us and sort of keep track of our progress. Join our email newsletter every you can, you know, follow us on social media, but yeah, please do reach out. If you're thinking about, “Gosh, I would love to help this nonprofit help more people,” please do reach out.
Anthony Costello 42:01 Yeah we're gonna, we're gonna do our part at Medidata, as we have been in our partnership with you for the last several years. But I think we can, we'll find a way to kick that up a notch and be a bigger part of the solution. So, David, you know I want, I'm gonna, I'm gonna push one more time to give you a crystal ball, because that was such a great call to action. You've really become famous these days. I'm not underselling that. I don't think you've got. I'm not overselling it. You've been doing TED Talks, you're you're in Time magazine, you're on the top of everybody's list as one of the greatest innovators in our industry. So you know, with that, with that newfound awareness and power that you have now, I'd like you to use your crystal ball for a second and just fast forward, you come back to from Dreamers to Disruptors, two years, three years down the road, let's say, not too much farther than that, though, because I think in the world of AI, everything's moving much faster now than it ever did before. So I won't say five years. I'll say less than five years. You come back, what's the dream that you still have today, that you really would like to have seen move forward in a meaningful way? These partnerships, I know might be part of. It. But what's, what's the real dream today that you want us to all make into a disruption in the next two or three years again?
David Fajgenbaum 43:27 Sure. So in that timeframe, we've got 10 to 15 diseases that were actively treating patients with drugs that weren't intended for their disease that we at Every Cure have shepherded all the way through. We're actively treating, you know, 10 to 15 diseases and that we are able to hear stories about kids like I mentioned with Bachman-Bupp syndrome and conditions like it, where they're getting their feeding tube taken out, they're sitting up, they're playing with their siblings. Maybe they're maybe they're even, you know, able to talk to their parents in ways that they never would have been able to before because of these, you know, horrible neurological sorts of conditions that we've got patients that had various forms of cancer where there's a drug that either prevented a recurrence or maybe was able to get them into remission, that's all we can think about is, you know, is these, you know, treating these diseases, helping those patients. And I really hope that when, yeah, when I come back, if I come back, if you'll invite me back, when I come back in a few years, I'm able to talk about that. And I think that, you know, you mentioned partnership, and I think it's actually important for me just take a minute to circle back to talk a little bit, if it's okay, about our partnership, with Medidata over these years, and what it's meant to me, and going back 10 years ago, maybe a little bit longer than that, maybe 12 years ago, when I first met Glen, of course, the co-founder of Medidata, and I mentioned a dear friend. And when I first met Glen and started telling him about this, this mission that we were on for Castleman disease, he immediately just said, you know, “Well, how can we help a meditator? What can we do to help you guys?” And we came up with a bunch of collaborative projects that we did over the years. Everything from one of our first ones was analysing proteomics data. So we had measured hundreds of proteins in the blood of a bunch of Castlemans patients, but it's really hard to analyze all this. This is brand-new technology. Glen put together a Medidata team of data scientists that dedicated tons and tons of time, all pro bono, all free, just to find potential repurpose treatments in the data from that work, we found out that a drug called Ruxolitinib looked promising for Castlemans. We treated a young girl named Kyla in Chicago with this drug, Ruxolitinib, within weeks of the discovery that we made with ready with Medidata treated Kyla. Kyla had been in the hospital for a year. She responded right away. She's now a sophomore in college at Marquette, studying nursing. We now have a clinical trial open Anthony of that drug in other Castlemans patients that we've done with her insight that only happened, and there's only patients benefiting because of this partnership with Medidata, we've done other things with Medidata, like update our website and come up with new ways to enroll patients into our registry, to get blood samples from patients. I mean, Medidata has been just this, this, I'll use the term relentless, relentless force for good. You guys, you know, are always asking how you can help. And it's been such a dream. I mean, Castleman disease, the field, is different because of the partnership that we've had with Medidata. And I love the idea, as you said, Anthony, for us to think about ways that we can take this forward into Every Cure, and think about ways that we can do trials together, that we can leverage the amazing data that you guys have. But yeah, I just had to take a moment to share a little bit more about Glen. And then, of course, before, before I wrap up, I just have to, you know, say, what, what an incredible disruptor he was. And of course, he passed away in 2021, but he lives on. We think about him all the time, and we keep trying to do epic shit, shit like you like he wants us to.
Anthony Costello 46:56 Yep, we really do. And thanks, thanks for highlighting some of that history. It's, you know, we're honored to be working in this industry. And I can speak for every meditation here at the company when I say that, I think what drives us every day is certainly the mission to create new therapeutic options for especially for unmet medical need and and David, the work that you're doing with every cure is clearly right at the heart of that so thanks so much for being on the podcast today. Thanks for your partnership with Medidata all these years, we will double down and figure out together how to drive the industry further and faster towards your mission. You truly are someone with a dream, someone with a serious health issue that was that kind of made your dream a necessity to turn into a reality, and and you did it, and that this is a big disruption, and we just really are honored to have you on the podcast today. So thanks again.
David Fajgenbaum 47:57 Well, it's an honor to be here, and this is so so awesome and special for me. Anthony, looking forward to continue the conversation.
Anthony Costello 48:05 Thanks, David. Talk to you soon. Thank you for tuning in to today's conversation. If you've been enjoying this podcast, please subscribe to our YouTube channel and follow us on Spotify, Apple, or wherever you get your podcasts. If you have questions for me or thoughts about the episode, drop them in the comments. I do read them. Thanks again for listening to from Dreamers to Disruptors, and we'll see you again next time.