Life Sciences Innovation in 2026: Predictions and Goals
As we enter the new year, the mandate is clear: evolve clinical research or get left behind.
Join us to revisit conversations between Medidata CEO Anthony Costello and some of the leading industry minds—including Every Cure’s David Fajgenbaum, Digital Medicine Society’s Jennifer Goldsack, and Click Therapeutics’ David Klein—as they look to the future of life sciences in 2026 and beyond.
From unlocking deeper data insights to scaling radical innovation, our guests issue a direct call to action: harness today’s cutting-edge technology to build a faster, smarter, and healthier world for patients everywhere.
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David Fajgenbaum 0:00 Us humans have developed about 4000 drugs for about 4000 diseases, but there's 14,000 more diseases with no treatment
Anthony Costello: 0:07 In the world of AI, everything's moving much faster now than it ever did before. 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. As we kick off 2026 we're revisiting some of the most forward looking conversations from the past year to ask a simple question, where are life sciences and clinical research headed next? In this special episode, you'll hear predictions and calls to action from leaders we spoke with in 2025 including David Fajgenbaum of Every Cure, Jennifer Goldsack of the Digital Medicine Society, and David Klein of Click Therapeutics, from how AI and technology can accelerate treatment access to the changes needed to make research more inclusive and effective for patients, these insights point to what needs to change in our industry and why it matters now. Welcome to from Dreamers to Disruptors.
Anthony Costello 1:15 I feel like in this talk, we've been circling around calls to action the whole time, right? Because if you're nibbling at innovative, dreamy ideas, but they have to somehow become disruptive, there's many actions that have to take place all throughout that timeline to bring these things across the finish line. So, we've covered a lot of ground here, but if we stick with where we're at right now, we've got these initiatives. It doesn't have to be thought of as women's health, because it can be thought of as brain health, in the case of Alzheimer's, for example. We're moving towards precision medicine. Precision medicine will help us understand the way people react differently to therapeutic options, whether they're men or women, or any other type of demographic group that we want to measure. Digital therapeutics, all the while, can also help enhance the visibility into what's happening, maybe even in the case of some of the things Click has done, accelerate the value of the therapy and so on. So can you sum that all up for us in a call to action that you can give the millions of people that will listen to this podcast?
Jessica Federer 02:25 Just millions?
Anthony Costello 02:28 Well, I was taking a little leap there, but what do you want us to do? And Medidata too, like, what do you want all of us to do to make this stuff disruptive faster, so it lives in the dream state a little less long, and it gets to the change state faster.
Jessica Federer 02:48 All right, I'm going to give you two. First, I want everybody listening to this podcast, and everyone in the pharma industry and pharma-adjacent, CRO-adjacent industries, to enroll in a clinical trial. I cannot tell you how often I hear an executive at a pharma company or a CRO talking about the patient experience, or the clinical trial experience. And then I asked them, “Oh, when was the last study you were in?” And they've never been in a clinical trial. So if you're going to work in this industry, you need to participate in a clinical trial. It's just doing your homework; it's getting the experience of the person that matters most. And stepping back, it is always important to reflect on the fact that we work in an industry that runs on volunteers. We work in a trillion-dollar industry that runs on volunteers. And until you have volunteered, and you've been in that really uncomfortable situation in a hospital gown, going for a scan or waiting for a blood draw, or wondering if your information was uploaded correctly, and if you're going to get a copy of the consent form, and where did it go to, until you do that, and you do that regularly, you're probably not in the best position to create change that's really meaningful for that end user. So first sign up for a study. If you don't know what study you can sign up for, I'm going to recommend, just go to JoinAllOfUs.org, which is the NIH study. It's been running for 10 years. It has close to a million Americans in it. Enroll in that, go give some blood and urine, participate in the clinical trial that the NIH is running with a million Americans. It's just important to be part of it. So that's what every single person can do on their own in this space. And frankly, if you're going to talk about patient experience at any event or conference or podcast, you gotta be in a clinical trial, and you gotta be ready to encourage people to be in clinical trials. So once you're in a trial, tell people about it. Break down the stigma around clinical trial participation. I was talking with a researcher who I love here, based in New York, Dr. Lisa Mosconi. She wrote The XX Brain, the woman's brain, and she was doing these menopause studies a few years ago and really struggling to get women to enroll in the clinical trial, because it requires an MRI and a PET scan. And so I threw a dinner party, literally a dinner party, and invited so many women, and I said, there's a catch. You're going to have to listen to this researcher, and I'm going to ask you to see if you're eligible to enroll in her study. And everybody wanted to participate. Once they learned about the research, they met the researcher in a non-clinical setting, they learned how they could help contribute to an understanding of the brain. Of course, they want to participate, and then they know their friends are doing it. They don't feel so bad going and getting their MRIs. So make it accessible. So do it and share it. Now, let's get down to our corporate identities and our business and our mission of transforming healthcare through data and technology, which is what you're passionate about and what I'm passionate about, and why, why people are probably listening to this podcast. I think it's time to go ahead and set a goal that digital therapeutics become integrated and become more commonplace within the next five years. I think we can go ahead and just set some clear goals and work backwards towards it. And in every one of our organizations, we're very adept, especially in big pharma, at making the 10-year plan, the 5-year plan, but there's always an end. What is that end? Is it launching the product in 10 years? Okay, so then, four years out, we're going to start our market access preparation and training our KOLs. And, six years out, where are we? So you work backwards. So I think it's time to set a goal and say we know that digital integration to traditional therapies is on its way. It's coming. Let's say, here's the date we want to have that be commonplace. So then that means we've got to work backwards. We need a couple years before that for it to be new before it gets too commonplace. And then what are the barriers? Who are the partners around the table? What are the ecosystem changes? What are the regulatory or reimbursement changes that will take a few years to make? And just make the pragmatic plan of how we're going to do that together and share it, because none of us work in a vacuum and in our industry, it doesn't matter what one company does. We need all of our companies to be doing similar things with similar approach and similar guidelines if we're going to get the scale that we need. So sign up for a trial and put together a plan. Start at the end and then work backwards.
Anthony Costello 07:51 Yeah. I think those are two great calls to action. And I was thinking when you were talking about a five-year plan to digital therapeutics, which I love, of course, but you won't be shocked, but maybe some of our listeners would be shocked to know the number of times that we still run into customers that are doing paper data capture for clinical trials, or paper data capture for eCOA or ePRO. Everybody still does paper for consent forms, right? So the traction that we've gotten in the industry over the last 10–15 years on bringing digital to clinical trials sets us up in a great way to bring digital therapeutics now into the commercial setting and the label, as we've talked about, but there's so much that we could also do earlier in that value chain to just bring digital to the trial so that the trial happens as fast as possible, and you get to an outcome or an answer that the drug or digital therapeutic doesn't work. We need to get to those bad answers sooner too, because the always-limited investment dollars should be put in the best place. And those are the places that we can have the most likely success.
Jessica Federer 09:05 Yeah, and I'm gonna put one more out there. I think it's time that everyone participating in a clinical trial gets a tax credit. So if you participate in a clinical trial, you get a tax credit. And if we want to encourage clinical trial participation, and we want to encourage people to go out of their comfort zone and go out of their way to advance science that will benefit all of us, we need to put the right incentives in place, and right now, actually, participants can be taxed on what they're getting out of a clinical trial, and so they should be getting a credit. And if you get a credit for making a donation to a nonprofit, you should certainly get a tax credit for making a donation of your physical self and your samples and your time and your disease experience. So I think if we make some industry-wide changes to make research more accessible and attractive, then we will see a greater increase in participation that will make our clinical trial enrollment much faster. It'll make our studies more robust and more diverse. So I think if we're going to put a couple other things out into the ether, that would be one that all of the companies that work with Medidata and Medidata themselves can probably get accomplished in the short term, is, let's give everybody who participates in a clinical trial a tax credit.
Anthony Costello 10:33 Yeah. And I think in the context of a CNS market in general, that is by all expectations, is going to grow dramatically over the next few years. First of all, why is that? Why is there such huge expected growth in the CNS space? And then, of course, bringing these costs down to make those trials more affordable to run is it becomes paramount.
Brad O'Connor 10:55 I think there's probably a couple of factors in terms of CNS growth, I think the first is, we're just getting much better at keeping people alive, right? So it comes to this health span versus lifespan stuff, right? And so, if once you're not dying of heart disease, the focus on the neurological disorders becomes a lot stronger. But I think probably the compelling reason is the advent of biomarkers, our ability to understand what's happening inside the brain. Before, we were just guessing. So we didn't understand mechanisms of action in Alzheimer's disease a lot before. We go back to Solanezumab is one of the highest profile failures of a Phase III Alzheimer's disease trial, the post hoc analysis. And at the time that that trial was designed and conducted, we didn't have the ability to measure amyloid plaques in the brain. The post hoc analysis showed us that something like 30% of the patients who enrolled in that trial didn't have amyloid. So you're giving them an amyloid-reducing drug to people who don't have amyloid. No wonder the trial is not successful, right? So it's been the advent of these biomarkers that allow us to understand what's happening. That means that the investment becomes less risky. If you're a pharma executive and you've got multiple, you know, programs you've got to decide which you're going to invest in. Every company from Cogstate through to Medidata, through to Eli Lilly, has to have prioritization discussions. Right? This hunger game of ideas, where only the strongest survive. And the question is, if you don't understand what's happening, it's really hard to—running an Alzheimer's disease Phase III trial, you're probably dropping a billion dollars or something. It's a big investment if you don't know what's going on, right? So now that we've got more confidence, so we understand what's happening, we can make more informed decisions, and then as we're making these more informed decisions, and we have a better idea of what's actually happening and the mechanism of action, we're seeing greater success levels. So as the success ramps up, right, then the R&D ramps up. So, you can look at a whole bunch of different industry analysts reports about this stuff, but there's certainly a number who believe that central nervous system diseases will begin to approach oncology in terms of size of R&D spend over the course of the next sort of 10–20, years. And certainly, that's what we're seeing. We're seeing a huge increase in whether it's Alzheimer's disease or rare diseases. So the number of rare disease trials that are going on at the moment with their central nervous system disease focus, the CRISPR stuff around gene editing. I mean it's just amazing.
Anthony Costello 14:02 It's amazing, yeah. And I think Cogstate’s really nicely positioned, sitting at the middle of this explosion. So we like to wrap up these podcasts usually with a call to action for our listeners, who, as you know, are kind of all over the map. We've got pharma, we've got CROs, we've got investors. So if you want to maybe sum this all up, you're a tax guy who went in as a temp employee to a company going public, worth like $20 and going public, you became CEO 20 years later, the dream pivoted, became a disruption, has more potential for disruption in the future as this becomes more and more commercial and maybe even targeted directly to consumers. I mean, there's a lot we packed in there in the last few minutes. Sum it all up in a call to action for our industry, maybe, or however you want to frame it. But what do you want to leave people with in terms of what they should do?
Brad O'Connor 15:06 Yeah, so, and I'll frame it in the context which I get, you know quite often, which is, you've been doing this for 20 years. You still find this exciting. Like,how long are you going to do this for? And my answer to that is that it's never been more exciting. I think this is true of startups, but it's true of different indications and drug classes, the first however many years, is just such a hard slog, right? Everything's really hard, and no one really knows what they're doing. We're at a stage now in central nervous system disease where we've got such an understanding now of what's happening, and the ability now to use technology to make the conduct of trials easier, to be able to take those trials into a broader section of the community, so get greater diversity in terms of patient population, because we can use technology to better train site staff to be able to conduct these trials. We can do it with less error. We can understand the mechanism of action better. We can fast-forward these trials so we can spend less to improve our health outcomes.
Anthony Costello 16:32 It sounds like you're getting close to making a prediction, so maybe we can. Are you ready to do that?
Marisa Co 16:38 When I think about clinical research of the future, if you're not the head of global development, I think you're going to have a really hard time not looking around seeing a bunch of data scientists around you, because I do believe that there's a ton of opportunity in the industry to use AI, if for a number of things and really achieve the vision that we had with Mytrus, but in a much larger scale. Meaning today, I see two trends. One is healthcare institutions, large healthcare institutions, saying, “I can't have my PIs.” And I talked to a bunch of them. “I can't have my PIs and my coordinators enter data anymore. They're just highly paid, so I need technologies to do this for me.” So I see a lot of HCPs actually partner up with integrators like yourself, like Flatiron Health, like Paradigm Health, like ConcertAI and through AI, marrying the workflows between clinical research and medical practice, and standardize that technology. Why? Because they're sick and tired of wasting resources in mundane, routine activities, right? So more and more it's going to be the HCPs, through the partners, the ones that are going to say, “Sure, I can do your studies, but here's the platform that I'm willing to use, and not the other way around.” So that's point number one.
Anthony Costello 18:47 Okay. So you see, just to make sure I'm understanding this, so you see a big move in, let's call it healthcare data interoperability with EDCs or clinical trial tech, that has to be where the data have to be aggregated for regulatory delivery.
Marisa Co 19:05 Yes, and there's no other way. Because think about this, we have APIs for everything, but not APIs for, let's say, labs. So I have to have API, the Merck lab API, the first Pfizer lab API, and it's just time to build standardized APIs and interoperability frameworks that would allow the flow of data regardless of what system you're using, right? And so I think the providers are pushing for that, and I think we're going to see that if we really want to reduce cost and accelerate timeline, look at that as a first step. The second one is, think about how much routine processes we have in clinical research. I can think of, I don't know, like 100 right? So I like where agent technologies are taking us, where you have AI playing a role, and you train that person or that system to play a role, whether it's data cleanup, whether it's a protocol review, right? So any process that is incredibly routine today can be done by an agent technology. Think about that when you have multiple agent technologies interacting with each other. There's a lot of processes, maybe all those 90 processes that need to be interconnected will be interconnected without having to touch necessarily human hands, unless there are for kind of checking the accuracy of the data that is being developed. So all of a sudden you take, I don't want to say mundane, but I want to things that take less work, or less brain power, and you leave the computers or AI to do that, so then you can focus your precious people to do higher-level activities, which I think everybody would appreciate. Well, maybe not everybody, but I think most people would appreciate. So I see the pickup of agent technologies and I've seen a few prototypes already. And really exciting stuff. And I would go all the way to saying, at least for some primarily U.S.-driven, because I think it's going to take more than five years to get Europe to drive towards data availability, interoperability, and so on and so forth. But at least in the US, I don't see the need to do, as I said, a safety study in the traditional world. Or sometimes a long-term follow-up. Today, we have all the tools to do that with technology. Think about the cost savings that we could drive as an ecosystem in the industry. It's just mind-boggling. It just takes partners to help sponsors think about how to do it, and sponsors willing to let partners with the technology, with the data, and so on and so forth, do what they’re best at.
Anthony Costello 23:29 Yep, well, and it's interesting because, and maybe we can kind of wrap up on this note, there's a lot of what you just said, from a technology standpoint, is coming. Some of it's already here, the agent technologies, as you know, many companies are developing them. Day to day, there's huge shifts, and how that part of the technology market works. But where it really becomes disruptive is when it gets adopted.
Marisa Co 23:57 Yes.
Anthony Costello 23:58 And how it gets adopted and I'll even say the more adoption that at least our customers have and are willing to share learnings, share de-identified data across across sponsor, like these are the types of data sets that help us teach the AI much faster and on a much broader set of experiences from lots of different therapeutic areas and lots of different diversity backgrounds and so on. So all of these things are in what I would call the adoption part of the curve. Like we can build perfect agents, but if nobody uses them, so it feels to me like there's a trend now of adopting and using these kinds of agents. But if you wanted to make the prediction for, let's just call it the next three years, let's say all the technology is there, but the challenge is in the uptake. The challenge is, how do we price it right and get awareness about it, and get comfort, whether it's validation or some other way of developing the kind of comfort that you need as a large-scale pharma company running hundreds of trials a year; how do we get those companies to the point that they can make the adoption which actually creates the disruptive environment that we're all looking for?
Marisa Co 25:16 Yeah. Well, there's always a Pfizer, right? So for us at Mytrus,
Anthony Costello 25:23 Pfizer was the one.
Marisa Co 25:26 Pfizer was the one. Pfizer was the believer. And I do believe that you will have to go to 50 of them to get that one, but they do exist. So I think it's incumbent upon all of us to continue to identify new potential where we can deploy these capabilities and get over the hump of, “Oh my god. What if he fails?” I don't think it will fail miserably. Mytrus didn't fail miserably, right? So we fail in many ways, but we course-correct it, right? And we open up the possibility, endless possibilities, really, when you think about it. So I wouldn't think that Craig Lipset would say, “Oh, that was a failure.” Why? We learn a lot, and so I think we're in a similar situation from a we shouldn't be afraid of trying. There's no failure, because the technologies are such that we can course-correct very quickly. There's no catastrophic failure. There's no risk of catastrophic failure anymore.
Anthony Costello 26:59 AI is moving so fast now in our industry. I mean, it's moving fast everywhere, but in an industry like ours, it is traditionally extremely slow on the uptake for new methods of doing things. To me, this AI adoption curve just seems like it's off the charts, right? And we're talking about an industry that took 15 years to move from paper to EDC. Now we're all moving onto AI. I mean, two years ago, AI was seen as a buzzword in our industry. There were a lot of companies working on it, I'm not saying that didn't happen, but the uptake, the adoption was relatively low. Now it's almost like you can't have a conversation if you don't have an AI angle on whatever it is that you're talking about, right? So it's very speedy, but the regulation has to keep up, right? And we're in a very heavily regulated industry, too. So, give us a crystal ball prediction over the next few years. Like, what does healthy AI regulation look like in clinical trials? That doesn't just completely throttle the adoption that we know we can all benefit from, if we leave it untethered, or relatively untethered.
Arnaub Chatterjee 28:12 Yeah, I'll share an anecdote on, you mentioned, is the adoption curve real. It was about a year and a half ago. I was meeting with my colleague and mentor, Zak Kohane at Harvard. He runs the Department of Biomedical Informatics, and he's now the chair of the New England Journal AI magazine. He had just come back from spending a couple days in a hole with a very large technology company. He's a pioneer in this space and has seen everything. And we'd had many conversations around whether machine learning and deep learning and NLP, we'd written papers together on patient phenotyping, whether that was really advancing the ball here on what we’re truly calling AI. And he'd gotten out of that meeting, we were sitting in the room, and it was as if he'd seen a ghost. He was just like, “This is happening. The change is real.” It was like the most convincing moment for him that this was a fundamental step change and difference from anything he'd seen the previous 30 years. And that's when I was like, “Oh, well, maybe we should pay attention.” So I think, from that perspective, and then now just hearing it directly from providers, from a lot of the sponsors we work with, I do think there's meaningful activity happening, right? And this goes as early as pre-clinical, so we have to think about the stage gates of development here, but pre-clinical, early-stage drug development where a lot of work has taken place. And everything from the protein-folding work all the way through to better and sharper target identification and isolating a molecule. And, I think there was some skepticism even a year ago where people were saying, “Well, is that really AI?” to “No. Like, we are now, like, deep into, like, finding targets that we think are gonna be meaningful.” Right? So we're going through the motions, function by function again, and saying, like, “Is this going to be a big jump in speeding up time, finding a target, finding patients?” Like, the different hurdles that we go through within each aspect of drug development. And I think each of those has AI guardrails, right? In terms of how we use the data, what is agency for the use of that data, the patient piece of it I think has to come into play in terms of, like, what data is actually usable, and how does the patient know about it? There's a consortium that I was part of called RAISE which is a responsible AI consortium. And it brought together the Microsofts, the academics, the industry participants, to, like, try to create a framework for responsible use of AI. And we were just scratching the surface, I think. But like, as we're getting closer and closer to now leveraging data in very creative ways and saying we're going to dump this into a GPT and find out what happens, I think we have to start asking ourselves, one, the biggest thing for me on on AI guardrails is around model calibration. And I think over and over again, around the outputs that are getting used, and whether those are point-in-time outputs based on a data set that will clearly be biased, or do we continue to calibrate that model so that we adjust to ways providers treat patients, or ways that patient populations changes over time? That is probably the most important thing I think about because I think that we have to keep understanding that the dynamics of the disease or the patient population will change, right? And they are not constant or at a point in time. And I think that's a lot of what happens is, we're even seeing now that certain algorithms that even the FDA approved on ECGs have to be recalibrated, right? Because the data was saying something else. A separate study was conducted to test the fidelity of that algorithm, and found that there were challenges. So I think we have to revisit a lot of these. And I think we have to weigh progress against, like, what do we need to revisit? And I think with progress, we're saying, “Okay, it's great. CDRH and others are approving AI-based algorithms at a pretty solid clip, right? And that shows that the government and others are taking AI-based advancement seriously.” And then in parallel, I think there are outlets or consortiums or even sometimes the private sector that has to be responsible for pressure-testing the validity of those models, right? And saying, “Do these make sense? Is it actually addressing a problem? How does it fit within the data sets that I have?” Right? So I think that's probably the biggest thing is around, what are we building, what's truly predictive, and how do we know? And then I think it always comes back to me for the data, right? And are we testing on representative patient populations, and are we truly going back to some of our old historical biases and picking the same data sets from hospitals in the East Coast, or are we actually finding community patients in different care settings and ambulatory clinics and, like, saying “This is, like, what is representative of that patient population.” And that's always hard.
Anthony Costello 33:03 Yeah, no, I mean, it's a great way to tidy up this whole story you've been telling, right? Because if you're building inherently biased AI models based on a non-representative data set, and that you need to continue, as you put it, refueling those algorithms or making the data lake underneath those algorithms bigger and better and more diverse, it implies this long-term relationship and ecosystem that we talked about earlier, right? You have to have the broader network, the long-term consent, the follow-up, the real-world evidence, the pre-clinical, the post-clinical data; if you want to hone in on an algorithm that's going to get smarter and smarter about the way that AI is contributing.
Anthony Costello 33:46 We're coming to the end of our time here, and I think one thing that is really important to me on this podcast is that we end with a call to action. So, maybe think about for the millions and millions of people listening to this podcast right now, think about the fact that we've got pharma sponsors, we've got the biotech community, investors, CROs, sites, maybe even patients as our audience. So if you had to sum it up for all of us in the clinical research industry, what do you want us to work on? What should our focus be for the next few years? Something big, something BHAG-ish, that we should all go chase after as an industry, if we can declutter the clutter of all the different things that we're trying to do on a daily basis, like, what's the first time?
Arnaub Chatterjee 33:46 That’s the first time I’ve heard of BHAG-ish. It sounds like a Scottish food?
Anthony Costello 34:50 It's the big, hairy, audacious goal.
Arnaub Chatterjee 34:54 Yeah, put it together though.
Anthony Costello 34:56 How big and hairy can you go here, Arnaub, for our industry?
Arnaub Chatterjee 35:00 Yeah, look, I think that we still play it safe. Right? I'm gonna say big, hairy, audacious things, just to try to put some pressure on folks. I think a lot of sponsors still play it safe. I think there's a lot of conservatism in how we do things; there's still, “Let me revert back to that same data set that I've worked with for 30 years.” There is still a lot of, well, “If I do this, I really got to get the buy-in from somebody else, and that puts my political capital at risk.” There's a lot of that, and I get it. But I think that we do patients a disservice if we don't actually find meaningful opportunities to reduce that life cycle, right? And I think there are certain sponsors and certain people I've worked with really closely who are saying it's a chance for us to get past things like we work in silos and we don't get to talk to certain regions, and it's a big effort for us, because organizationally, we're not set up for success, right? I think, if you're in a senior position of authority here, to, like, try and take a proactive stance on leveraging different ways of finding technology that can actually shorten this life cycle that we find an end-to-end way of looking at it? Because most people don't think in 10-year life cycles, right? They're saying, “I just got to get this drug to market,” or “I got to get past this safety hurdle,” or whatever it is, right? Like, the incrementalism is not going to get us there, right? And this is more of a behavior change, rather than, like, “five years from now, and what are we going to look like?” But I will say the one thing COVID taught me was that if there's a concentrated effort to go fix a problem, and it's an all-hands-on-deck effort we get medicines out to the market fast, right? And we dedicated money and people, and there was obviously a mission-critical urgency behind it, but there was an all-hands-on-deck effort to figure that out, right? And I think that's probably a level of intensity as well as focus, and just not day-to-day incrementalism that will advance more of these initiatives to take place, right? So it's less of a “five years from now, what are we going to look like? And how Pollyannaish am I about AI and data and technology coming together?” And it's more about the current day is still, for me, riddled with, “Let's just pilot our way to success.” And I think those are important proof points. But I think when you've established enough proof points, it's time for us to take a bigger swing.
Anthony Costello 37:22 Yep, yeah. I mean, in some ways, what you're describing isn't about the technology at all, right?
Arnaub Chatterjee 37:26 Right.
Anthony Costello 37:27 Like it's about the climate, it's about the willingness, the risk-averseness to be able to get out of our own way to try some of these things. So if we have the evidence that these things help, how do we make the behavioral change as fast as possible? Great. I think that's a great call to action.
Anthony Costello 37:42 So I normally ask about a five year prediction. Well, let's cut that back. Let's make a two and a half year prediction from David Klein and with a little call to action sprinkled in there. So what do you want our listeners, the pharma industry, the CRO industry, payers, providers, you name it like. What do you want the world to do as a call to action for digital therapeutics? And where do you predict this goes in the next two and a half years?
David Benshoof Klein 38:09 Yes, so two and a half years again, long time. Look and again, this is more dreaming, but I think I'm right on this. And I'll say, if you look at what's gonna happen in two and a half years, I mean, just imagine in two and a half years. And hold me to this bookmark, this, right? Language barriers are going to be gone. I mean, you know, that's where we're headed, right? Like you're not going to know, you're not going to need to know. You know Vietnamese to operate in Vietnam, you're going to have a Star Trek thing or something like that, where you speak Vietnamese, right? That's where we're headed here. It's going to happen before two and a half years, I would argue. But two and a half years come back to this podcast, and you'll see language barriers gone, and I can go on and on and on about the changes in the world that I think will happen and and the pharma industry is not going to be immune from that right drug. People are going to want drugs that get better, that aren't the same in treatment that was approved 20 years ago, that they're getting now, and people are gonna have expectations that those treatments and experiences get better and better, just as everything else in their life is right. And you know, so if you ask me for a call to action, I mean, I'd say, again, we're happy to look at, you know, people's portfolios, or certain drugs or drugs in development and and analyse, and say, Hey, here's what we think you can do. Here's again, Click. Even goes and speaks to KOLs and payers and so on. And we'll say, we think that there's a great play here for the patients, and there's a there's a really meaningful commercial opportunity. Or we'll come back and we'll say, you know, look, we did a ton of free work for you, but, and, you know, but, you know, we don't have the greatest news, right? We, we don't think that this is an ideal candidate for this, for these six reasons, right? You know, more often than not, we're kind of getting these really good analysis is back where, hey, it does really make sense to do something, but, you know, we're really transparent and direct. And if we don't see that, and Click’s model generally is we're, we don't operate as a vendor, really. So it's not like, it's like, you know, if the pharma company is not benefiting, we're not either. And you know, so we're very, you know, if we don't want to do something, we'll be really candid on why, and all these kind of things. But, you know, I'd say you can reach out to Medidata. You can reach out to BD at Click Therapeutics, calm, that's boy David at Click and we'd be happy to, you know, do an analysis for anything that data people want. And I would encourage people to, you know, embrace this space before your competitors do and and it's really exciting to see all the digital people at pharma that are embracing it, and that, you know, all the all the sudden, have, like, a really meaningful way to engage in the core, core of the conversation. And we'd love to help people, you know, get into that conversation.
Anthony Costello 41:01 Yeah, great. I think that's a great place to end. So you're offering to profile the opportunity, do some of the research. We can jointly accelerate the clinical trial. We can usher through the regulatory process, get to a full approved SE and then help, importantly, as you're saying, Help the pharma industry find better ways to be this experience that I think they want their patients to have that builds the kind of brand loyalty and differentiation that you saw during the covid area with the vaccines. Yeah, so I agree.
Anthony Costello 41:34 What does 4,5,6, years out from today look like in Tracy's world, where we really change the industry?
Tracy Mayer 41:41 Yeah, I feel like we've lost line of sight on the goal of really being patient centric in the way that we, you know, get people enrolled in trials, the way that we tackle the problems that we're trying to tackle. So for me, you know, the call to action, or the five year goal, is getting back to true patient centric clinical trials, where the patient's at the heart of it are focusing on, you know, getting a patient enrolled so we can get, you know, the data asset that we need to be able to provide, the outcome, the approval or the decision that we need to make to bring the trials, the trial to market and everything else. I mean, AI, it might help us get there faster. Connected devices certainly will help us get there faster. But at the end of the day, this, to me, it's like we need to kind of compartmentalize that aspect of it a little bit and start making decisions based on that as the goal. I think we've lost sight of the goal a little bit in doing all of these other things. AI in particular, is, is it's a big aspirational thing. It's really exciting people, generally speaking, people like to talk about what's possible there. But you know, our industry isn't, doesn't exist to create AI, or to do AI, our industry exists to bring medicines to patients faster. So let's get back to that thread and figure out how to use these other things that we're talking about to actually make that happen.
Anthony Costello 43:01 We want your crystal ball view of where you think the industry is going. But I also I want you, if you can, if you're willing, I want you to give a directive to the industry. I think you've earned it. I want you to give a directive. What do we all have to do for you and for you know cancer sufferers all over who may be reliant on a clinical trial or just reliant on standard of care and a healthcare system that can understand them and empathetically help them on what's obviously a very complex journey. What's your directive? And then I'm gonna go make everybody do what you say.
Jennifer Goldsack 43:48 I love it. So you're going to be the Enforcer. I'm here for this. So I think, Anthony, it's quite simple. I think that I implore, frankly, our colleagues in clinical trials to recognize that the risk of not embracing innovation, the risk of not embracing access to flows of data from both inside and outside of the clinic and the powerful new set of tools that we have in the toolkit in the digital era, the risk of not embracing those things far outweighs the risk of trying something New. I would ask executives to think about how you message that and how you incentivize that. And I would ask for anyone with decision making or Budget Responsibility to have the courage to innovate on behalf of the patients that our entire industry exists to serve. And the second piece, maybe more crystal ball than directive, is we talk to enough people here at DiMe to know that this is happening. It people may not be as vocal, but there are leading organizations that are making these investments and are making these decisions. They will pull away and leave the rest of the market in the dust, perhaps not as successful commercial entities, if we don't act now. So it's not just to change the risk calculus, but to do it quickly.
Anthony Costello 45:13 Yep, yeah, we see it too. We see it too. Yeah, there's maybe we didn't give enough air time in this podcast to those organizations that really are forward-leaning and really trying some new things. And you can already see the pull away.
Anthony Costello 45:27 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 45:44 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 47:37 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 49:13 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 53:01 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 53:11 Sure. So reach out to info@EveryCure.org or david@EveryCure.org get straight to me, but letting us know about your interest 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: 52:11 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.