Episode 41: Craig Serra, Head of Clinical Research Scientific and Technical Engagement, Flatiron Health

Episode 38 May 23, 2024 00:35:24
Episode 41: Craig Serra, Head of Clinical Research Scientific and Technical Engagement, Flatiron Health
Few & Far Between
Episode 41: Craig Serra, Head of Clinical Research Scientific and Technical Engagement, Flatiron Health

May 23 2024 | 00:35:24


Show Notes

"It's amazing how many companies there are and how many opportunities for a kind of a match between a human that needs some help and a company that can help." - Craig Serra, Head of Clinical Research Scientific and Technical Engagement, Flatiron Health

Welcome to the latest episode of Biorasi's Few & Far Between podcast. Join host Chris O'Brien and guest, Craig Serra, as they discuss improving and extending the life of every person with cancer and learning from those experiences.

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Episode Transcript

[00:00:15] Speaker A: Welcome to a new episode of the few and far between podcast. I'm your host, Chris O'Brien. One of the things that makes working in the clinical trial industry so special are the stories of what first attracted some of our guests to healthcare in the first place. Todays guest was very young when he heard the call of clinical research, and he has made it his mission in life to get medicine to the people who need it. Craig Serra is the head of clinical research, scientific and technical engagement at Flatiron Health, a company focused on improving and extending the life of every person with cancer and learning from each of their experiences. In this episode, Craig and I spoke about the importance of forging connections between patient advocacy and clinical research, helping sponsors advance their pipelines in oncology, clinical trials, and sharing his own family's experience with pediatric cancer treatment, drug development, and patient care. It was great having Craig on the podcast to share his insights on everything from advocacy to AI. And I hope you enjoy the episode. Okay, let's start the podcast. Craig, welcome to the podcast. [00:01:22] Speaker B: Thank you very much, Chris. Appreciate you having me. [00:01:23] Speaker A: It's a pleasure to have you here. I've been looking forward to this chat for a while because you have an interest, interesting vantage point on what's happening in clinical research and how emerging technologies are really impacting the way we should think about trials. So, lots of fun stuff to get to, but let's start a little bit with your background. For folks who don't know you, you're a pretty visible fella in the clinical research world. But for folks who don't know you, you want to give us the quick tour of your career? [00:01:48] Speaker B: Sure. So, as I like to say, I grew up at a pharmacy, not literally, but blowing out my 14th birthday candles like it was immediately working as a pharmacy tech. And I just love the sort of intersection of customer service, service and science y stuff. And it was sort of like pre perry Internet way back when. And so it was really cool to just be interested in that and then get that exposure at such a fairly early age. The paycheck was not one of the things that was good, but I ended up, I think, like a lot of people after undergrad in clinical, in a. In a space that I didn't know about. I knew about basic research, but I really didn't even have a thought of, oh, how do you bring it from the lab to a, you know, medicine cabinet? It's been a journey of 21 years, 22 years, something like that, and basically have just spent my time trying to help further portfolio delivery of individual studies of programs of, you know, entire therapeutic areas to sort of enterprise solutions relative to clintech or process, and just trying to really get as much kind of advocacy out there around clinical research as possible. You know, it's a potential care option. It's something that, aside from a pandemic, pretty much no one has really heard of, right, until they're, you know, they talk to a physician about it. And so, yeah, yeah, it's, it's interesting thing about, you know, over two decades worth of work in the space. [00:03:05] Speaker A: So, yeah, I found during the pandemic, people were suddenly very fluent in phase three clinical trial language. We suddenly had a lot of experts around the world and all that kind of stuff. But you've also spent time inside of big Pharma, you've spent time in kind of health tech, you've spent, you've done some venture investing as well. So just give us the quick background on that. I want our listeners to understand that sort of part of your story. [00:03:27] Speaker B: Sure. Yeah. So I started working in large pharma and stayed there for a bit. There were certainly tons of learnings and tons of good things that we did. And I think I just, the proverbial kind of entrepreneurial spirit side of myself was sort of longing for helping grow a company or helping execute on one that was kind of already going. And so I helped with a few other people, progressed some good work, I think, at a niche CRO about twelve years ago. And then I found myself go back to Pharma and I did that. And, you know, when I joined Flatiron, which is the health tech component, back in October of 2022. And I think something that we may hit on later is I greatly enjoyed my time at Novartis. Ended up leaving there specifically because of Flatiron's focus on cancer. I referenced this frequently as I have a cancer surviving daughter. And so I wanted to sort of jump right into helping as many sponsors as possible progress their pipelines relative to cancer treatment. So, you know, the investment part of things is just from a standpoint of pediatric cancer care and just really trying to say stay active in the space in terms of support, in terms of knowledge, awareness of these companies, because it's amazing how many companies there are and how many opportunities for kind of a match between a human that needs some help and a company that can help. Whether it's a trial or not doesn't matter, but I kind of just do it just to hopefully remain a good steward of kind of paying it forward. [00:04:54] Speaker A: Yeah, I neglected a really important part of your story there, when I talked about big pharma, health tech and investing, and also parent of a, now a cancer survivor. I think this ability to focus on the patient while also understanding technology and understanding what's going on in medicine. Thinking your LinkedIn profile, you say something like, I don't cure cancer, but I know people who do, or something along those lines, which I like a lot. It's that ability to stand in a space where you can translate across those different communities is really cool. Okay, so let's dive into that a little bit. So tell me, maybe if we start on Flatiron, say a little bit more about what brought you there and about the promise and the challenges of using data solutions and the kind of stuff that flatiron builds to support clinical research. [00:05:37] Speaker B: Yeah, it's pretty easy. The main reason is we're just steeped in cancer expertise. And so I'm surrounded by unbelievably smart and very active cancer researchers and specialists in portfolios of companies that have cancer manufacturers. The sort of elegance relative to clinical research is with flatiron standing as a leading, or the leading EHR for community oncology practices across the US. When you marry that up with the ability to have insight into the patients that are being seen, and a number of these practices also have very robust clinical research programs. The elegance of saying, okay, we can surface to a sponsor, you know, potentially eligible patients or participants for patients to be participants in a trial, to meet patients where they're at already, with their trusted physician, who also could be the research oncologist and who often is. There's an elegance to that. And so I wanted to see if I could help build out the business relative to some offerings there and some offerings relative to making things easier for sites to actually execute trials. And then entrepreneurial side, it's helping build out a business that is relatively new compared to the historical flatiron businesses, which is twelve years old at this point. And so there's, you know, there's excitement there, and there's certainly plenty of appetite from the market for us to do this. So it was just a, what I hope to be a win win for myself and Flatiron, certainly a win for me. [00:07:03] Speaker A: That's great. So, okay, then let's decompose it a little bit. Let's start to talk about maybe through the Flatiron lens. There are lots of ways in which you guys can be involved in clinical research, and you've talked about utilizing insights from real world evidence to improve protocols, to improve site selection, drive diversity in trial participation, lots of things. Let's maybe go through them in order. So, starting at the very beginning with protocol design, how do you guys play there? And what do you think the promise is, or the potential is for real world evidence as a driver for better protocols? [00:07:35] Speaker B: Yeah. A friend of mine who's a head of study optimization, the large pharma company, just always has a quote of, like, if you want to solve for patient recruitment, have a well designed protocol, have a well designed study. And we hear this a lot, and so it's intuitive, but it's also. We copy and paste protocols from previous studies. You know, we often have kind of a small group of people that are kind of creating the meat of the protocol, the actual experiment that, you know, have done it before for a lot of years and reach out to Kols and, you know, it's. It's not that it's not data based, but it's. This stuff is evolving, and we know how opinions are. My opinion of something easily could be different, you know, a year from now, and if I carried the same ones I had today, it's outdated. Right. So the. The way we want to play there is to learn from the patients that are being seen at these community centers. And because they represent what's actually happening out there, I don't think any of us would look at a weather forecast of, you know, 27 degrees fahrenheit and say, I don't need a jacket. But we routinely do that with the way we design studies. So we want to be able to just leverage as far upstream as possible, incredibly robust data that can. Even with some, what you might see as a minor tweak to a lab value here, or different ie, criteria, there could be both meaningful for operations in terms of being able to find more participants, but also scientifically, maybe you increase the odds of scientific success. We have an ability, when we do protocol optimization, to see if changing certain values actually results in a real world, a different real world outcome. And so that's incredibly powerful, and we think it, among other things, should be a tool and a toolkit when anyone is designing a clinical trial in the cancer space. [00:09:24] Speaker A: Greg, when do companies come to you to do protocol optimization? When should they. Are they coming to you when they should, or are they coming to you a little bit later in the game? [00:09:30] Speaker B: It's a mixed bag. I've seen two very different approaches. Is very early on which is the preferred approach, or, you know, kind of. I jokingly say, like, you know, before amendment seven, they're like, okay, yeah, yeah, let's just go ahead and pressure test this. It's a de risking mechanism. It's an improvement to the actual protocol. It's a, you know, very modest investment, certainly relative to an entire study, nevermind a program. Yeah, never mind time to market. So I see in people coming to us in those two areas. But again, the preferred is certainly very early on to be able to engage not just with an over the fence deliverable of, here's a, you know, 60 page analytic report, but rather working or collaborating hand in hand with people that are research oncologists. Some of them are still practicing. We have a very robust research sciences group and a quantitative sciences group. And so, yeah, early on is certainly better. [00:10:23] Speaker A: And when folks do come. So if you talk about the early on version, am I coming to you at the hypothesis stage, or do I have a synopsis? Do I have a draft protocol? And I'm asking, I know I'm asking a granular question, but I think some of the folks who are listening may be in this chair where they're trying to figure out, how do I utilize a resource like this? [00:10:41] Speaker B: Excellent question. So we can do so with our data. We can do a ton of very early sort of answering of questions, of research questions. So certainly if there's a protocol concept that's barely even written down, that's ideal. Certainly a protocol synopsis is not too late at all. It's when it's what I would say combined with a little bit further downstream, not even a final protocol, but a very robust draft protocol. Plus the mindset of, ah, the horror. We've already done all this stuff. Do we really need to interrogate what we just spent a couple months doing? That's that. That becomes tough, right? Because then you start to look at, well, quote unquote, data versus, you know, experts within companies. It's, you know, people's jobs to write protocols. So it's not like it's devoid of any inherent friction. Even earlier on, and just to mention this, even earlier on, you know, we have announced this very recently, a very deep relationship and partnership with Paris Life Sciences, where we can actually get exome and other very sciencey genetic things relative to data that pair up with our data to build a really unbelievably first in class, best in industry kind of linked data set to really inform even earlier, not just design of trials, but earlier drug development, even going into basically translational and even into preclinical. And so we just have an ability through both our data and other partnerships, to span kind of the entire RDC, research development, commercial use cases. [00:12:10] Speaker A: I think that might be a great time to segue into, talk a little bit about AI. We saw this huge announcement of a billion dollar startup, just recently funded. The next generation of kind of AI medicine companies is either starting, you know, recently started or coming soon in a bunch of places. And one of the things that we've seen is, at least from my chair and from conversations with some other folks in the space we've had on this podcast, maybe an overemphasis on candidate development and the really early stage stuff in drug development, because, you know, AI is pretty well suited to doing that. But one of the places that remains very, very expensive, painful and difficult, of course, is clinical research. So interested to hear, first of all, if you buy that thesis. So should we be, should we be emphasizing the early stages in the drug development process as perhaps we are too much, too little right now and then where do you think clinical research goes? I'm interested both in the now stuff that you think is practical today, but also kind of where you think the puck is headed on practical AI, I'll call it in the drug development process. [00:13:08] Speaker B: Yeah, it's a huge question and set of questions just in terms of sort of framing it up against a world where we can get. I can order some razors, it'll be on my front porch in 4 hours. Drones, we have all these kind of things, but we don't have flying cars as we thought we would. I still have to fax stuff to my doctor. They fax it to me. So we live in sort of this juxtaposition of like where really are we? Is this 21st century? Is this, you know, 19th century? Am I. What are we doing here? So to me, it really is hard to make these predictions relative to specific things, like very specific things. If I were to wager a guess, because I'm not actually betting on anything where I think it is very safe to play, is what I think a few of us without kind of formalizing is. It's almost like not even to the Pareto principle, but like 60%, let's call it 60%. Can I get a 60% draft data management plan? Yeah, can I get a 60% draft, you know, something operational, right. So it's not dealing with clinical data, it's dealing with operational infrastructure, which is obviously an enormous pain point. And it's not finalizing things, it's just making the lift easier. We have a very, maybe it's a human nature thing, but we have a very strong urge to say unless something can be perfect. We're not going to use it. And it's like, yeah, we can just throw that right out because that just doesn't work as a concept for me. So I think the now would be sort of operational stuff around a trial and getting us to a past the midway mark and just really being able to focus on the things that probably matter more. Right. So the 40% or even the refinement of a draft, I think really that's where the nuance of human thinking comes in for me at least. [00:14:57] Speaker A: Yeah. [00:14:57] Speaker B: Is to really kind of pick apart certain things. And you know, the argument can be made like, oh, we can just copy and paste from whatever. And it's kind of like, well, eh, I don't know, that requires the supposition that like, it's not garbage in and that you're copying. Like we've missed four out of five enrollment targets. Historically in industry we've had a bunch of operational failures and things like that. And so maybe we just give AI and peace a chance. [00:15:25] Speaker A: Basically there's a bumper sticker for you. Give AI and peace a chance. I like it. Yeah. No, and I think that's right. We've seen, seen a bunch of companies lately that are doing things like trying to automate the NDA development process. And they're not trying to get to 100%, they're trying to get to 50 or 60% like you said. And I think a bunch of people are trying to do that to sort of get rid of a bunch of the basic work that needs to happen in developing these complex submission documents. That makes a lot of sense to us. [00:15:52] Speaker B: Yeah. And I think with the question of where the puck is heading, I do think that as, and obviously there's people that are steeped in this domain, but I think as we gain both actual technical confidence, that stuff is right. But then just the general kind of human confidence of a, not only is it right, but b, I'm still needed and my job is not being eliminated. I mean, there's a whole, you know, the whole world of change management idea about, you know, are we just creating things that are going to replace us? Is still, I think, very prevalent. It's often unsaid, but kind of start to talk to people and they end up saying like, wow, is it gonna take my job? And so I think where the puck is headed is a few different places. One is getting confidence and proving out that these operational things can work and then hopefully applying the learnings, the actual machine learning learnings. If we're talking about ML to actual clinical data flows, to actual clinical data synthesis, you start to get into the people way smarter than me that talking about control arms, being able to reduce the burden on how many participants we actually need in a trial, how many need to go through getting placebo, things like that. And I also think from a standpoint of industry, and particularly what I would say is new graduates or people that are in college now and people that are, maybe I would call it ten to 15 years in industry, I think that huge bucket of people are going to get more and more versed in AI to be able to pivot if needed. In terms of career, if you know, data management as a function is upended because of AI, where do data managers go? And so I think yesterday was a good time to basically start to get on the bandwagon of let me upskill myself, let me take a course or two, let me get another degree, who knows? But the point is, from an industry perspective of where someone can play, I think being able to pivot is going to be incredibly important. [00:17:53] Speaker A: Hi, this is Chris O'Brien, host of few and far between. We'll be right back with this episode in a moment. I personally want to thank you for listening to our podcast. Now in our fourth season, it continues to be an amazing opportunity to speak with some of the top thought leaders in the clinical trials industry. If you're enjoying this episode, please leave us a review on Apple Podcasts. It really helps people discover the podcast. And don't forget to subscribe to few and far between so that you never miss an episode. One last request. Know someone with a great story. You'd like to hear me interview? Reach out to us at few and far between. Thank you. And now back to the podcast. One of the lines that I really like, can't remember who said this recently, was that AI is pretty good at generating kind of high school levels work. So it's not going to necessarily generate polished pros at the professional level, but it's going to give you like a decent working draft. And so one way to think about where AI goes is the kinds of roles where that's good enough probably go away pretty quickly, and the kinds of roles where we need deep expertise probably don't, or at least not for a while. I don't think anybody has a very good crystal ball for what five or ten years from now looks like. But let's double click on your point about recent graduates or people who are in school right now. Any thoughts for them beyond what you just shared, which I think makes a lot of sense. Take a course, play with AI, start to incorporate it. But any other suggestions for people who are just getting started? [00:19:14] Speaker B: Yeah. When I worked in the pharmacy, the pharmacists were saying, be a doctor, and the doctors that I would talk to would say, be a pharmacist. And I decided against neither one of them. And so I could jokingly say like, stay away from clinical research, but that would not be an authentic, authentic answer. I use the analogy of what the dynamic has been with statistical programming where SAS is dominated not just as a company, but as the language kind of default. [00:19:37] Speaker A: Yes. [00:19:38] Speaker B: How do you program TL's as an example? SAS, it wasn't, there's no other thought in the last, call it six to eight years, you know, open source code R, Python, et cetera. More and more of that. I actually saw my former employer where you would have people either fresh graduates or pretty early on in career that never even heard of SAS, never exposed to it. Right. And so I think that happens a lot with tech and I think that is a similar analogy. There is basically just really try and get well educated and fluent in the things that way, more likely than not, are going to be around for quite a. Quite a while. [00:20:15] Speaker A: Yeah. Less about any specific technology, I think you're saying, and more about what are the fundamental concepts that you need to understand and be fluent and fluid with. Is that where you're going? [00:20:23] Speaker B: Yeah, I mean, I think the AI obviously is a big bucket and so just getting more versed there. Even if someone is saying, well, I want to be a lawyer, like, well, I would probably still want to get some level of knowledge relative to something that's going to be around. Because at minimum it's most likely going to help you either do your job or it's going to be something that you just understand and know how to approach or know how to kind of work with. You know, you could see someone's career being entirely competitive against this thing that's, that's out there. And I don't know many people that want to spend a career kind of pushing against something that trying to outrun technology. Yeah. And it doesn't even have to be tech. It's just the reality of the existence of the world. Like, you can't, there's no rational or reasonable way to really do away with audio, but holding up an iPhone, like, there's no rational way to say, like, well, I'm just gonna not live in a world that, that exists. And it's more like just being able to know how to navigate it and know how to kind of work with it. And I think that's the way it's gonna be with, with AI. And so I even taking a few courses over the next year to really get an even deeper understanding of various, not just applications of AI, but various types of AI and deeper than things like machine learning and NLP and NLG. [00:21:39] Speaker A: Well, one of the things that I worry about is the entry level jobs in a bunch of these professions. You mentioned the law. I think it's also true we saw JP Morgan come out the other day and say they've now got a tool that can take a PowerPoint presentation and turn it into a draft s one for submission. That's not a final document. It's still going to need a bunch of eyes on it and a bunch of edits. I don't know if it gets to the 60% that you and I talked about or what the right target is, but that used to represent a lot of hours and a lot of late nights from junior analysts. So the positive is of those late nights. Go away. The negative is there probably aren't as many of those jobs and the same for entry level lawyers and lots of other professions as we go forward here. [00:22:16] Speaker B: Yeah. And one last comment on it is there is a funny dynamic that I see with tech replacing error prone humans, which is we give humans the benefit of the doubt. You know, we're not too harsh on humans that make innocent mistakes. Or, you know, if you write a, write a CSR and there's a bunch of different errors, you figure out, you correct them. We hold AI and just tech in general to a way higher standard where we actually demand perfection. It's very interesting where we'll say, like, well, human got it 95% right, this gets it 99.5. But if we take away those numbers, you'll hear people objecting to the tech because it's not perfect and it's a very strange dynamic. And in another life, we'll probably get a PhD in understanding human therapy. When it comes to that, I will. [00:23:01] Speaker A: Enjoy reading your book when you get that PhD. [00:23:04] Speaker B: Another life. Yes. [00:23:05] Speaker A: All right, switching gears, let's talk about patient advocacy. Tell us a little bit about how, I mean, you have a direct and personal involvement with this because of your daughter. So tell us a little bit about that story, if you would. [00:23:15] Speaker B: Yeah, this is one that's sort of like in my bones, basically. I remember being very young and during eighties AIDS epidemic, thinking like, how when I grow up, can I help with something like this? And I've always been someone that has seen things like disease or sickness or illness, and just saying, like, well, why can't we try different things? And it's loosely aligned to basically clinical research. So I think that's how I even landed here. But I'm very, very attuned to the fact that clinical research and clinical trials are this foreign thing to basically most people. And so I try and be as helpful as possible in connecting people with providers of care that happen to also participate in clinical research. I myself was one of the 40, I think, 6000 and change participants in the Pfizer two, three pivotal trial during the pandemic. Got placebo, unfortunately. But the idea is to just be very involved, whether it's personally involved in that last example or involved in the sense of someone is looking for a trial for Kras g twelve d positive mutation. Let me see if I can help and kind of marshal the resources that I have in terms of just spending 20 years in industry to help that person. And so I just. I don't know, I just have a gravitation towards that. [00:24:29] Speaker A: I think that's lovely. It's one of the things that makes this industry special is there are a lot of mission driven folks that have stories about what first attracted them to healthcare, whether they're working in trials or drug discovery or kind of, you know, wherever I look, I'm impressed by that talk, if you would a little. Craig, imagine for a second you're talking to a parent of a child who's been recently diagnosed with cancer, with a tricky cancer or a rare disease or something where the path is not well, you just go on standard of care and cross your fingers. What would you tell them to do? [00:24:59] Speaker B: I think actually, I kind of separated into two things. So if it's an adult, I think the. The care options typically are actually plentiful. It's a matter of kind of surfacing the knowledge that they even exist. And so I would, you know, my mom is a three time cancer survivor. She goes to a health system in New Jersey, Morristown Memorial Hospital. They have a nice research program, but really she's going there, right, as a non research participant and as a cancer patient. And that would be something that you look at and say like, hey, do I need to go to some, you know, travel some huge distance to get to absurdly, you know, best of the best of the best. And it's like, actually, standard of care is very well known, and whether you go there or somewhere else, like, it's gonna be delivered pretty much the same. And so it's a matter of what's convenient for the patient. Do they like their physician? Is it someone that they trust? And it's kind of a different look then, at least from my perspective, in pediatrics. And this is an area that, I guess the best way I can put it. Maybe we'll talk about clinical development in peds in a bit. But the amount of people that are willing to help a family and a child going through cancer treatment is unlimited. It is remarkable that and just sharing Grace's story is when she was diagnosed, you know, it was, you know, texts and calls kind of. Kind of late at night, and it was responses within minutes, and we ended up at Memorial Sloan Kettering in New York. But we were on the phone Zoom call with the head of surgery at St. Jude the next day, you know, on a simple email request. And he's just saying, send me the images. Let's get on a zoom. Let's do this. You don't even have to come to St. Jude. I'll help you any way I can. Like, it was incredible to see the mobilization of expertise. And so I think in the peds world, it's actually way more in terms of healthcare delivery and healthcare, you know, non trial dependence. It's really unlimited. There's just so much good out there. Just incredible. On the trial side, it's kind of flipped, which is. Pediatric drug development is an incredibly tough area. Oddly enough, over the past couple weeks, we had great news from day one pharmaceuticals. We had great news. I'm trying to think of the other, other great news, but it was last week, but we're seeing some traction. But, you know, pediatric drug development is a tough world. Plenty of guidance on it. It's very convoluted. I will say, yeah, it is an area that I've had personal experience in terms of serving on a board of a Peds cancer foundation for a lot of years, where I remember a father of a recently, his recently deceased son said he wanted to get his son on a trial. And, like, the research director, someone at the site, or even the sponsor was saying, well, they're, you know, creatinine clearance is too low. And the father's saying, my son's gonna be. Be dead in a month. [00:27:47] Speaker A: What does it matter? [00:27:48] Speaker B: And it mattered for the experiment, but that is in. That's at both support, but also at odds with the reality of, like, yeah, what is the problem with, like, really? We're gonna try and design, like, the most pristine experiment, and so it's a really, really tough world. Certainly don't claim any expertise in peds drug development. I just know that it's a really tough area. But on the good side of things, in terms of if a kid ever gets sick, I mean, you're talking intraday responses from some incredible heroes that are real heroes. [00:28:18] Speaker A: Well, that in and of itself is a valuable point. Right. It's reach out and activate your network, whatever your network looks like, because if you can get to these folks, they will tend to listen and engage with you. And to your point, even more on pizza. I also find that in rare disease, that's common as well. Pediatric and non people have that report a similar kind of experience. The trial stuff is what it is. We need more trials and more investment in that space. Yeah. Okay. Well, so I think that's really helpful. What do you think industry is getting right, getting wrong right now I'm gonna give you a chance to get up onto your soapbox here and tell us it could be positive, it can be opportunities for improvement. Thinking specifically about pediatric. [00:28:52] Speaker B: Yeah, a lot of macro things at play, right. There's headwinds, there's some tailwinds. I narrow it down to maybe what companies are doing. I'm not running these large companies. Certainly there's tons of financial pressures. Almost all of them, with the exception of just a couple, are publicly traded. So you have this responsibility to shareholders. But I'm seeing just basically too much cutting. It is cutting, I think, at least from an outside perspective, that is happening because of not a drug failure, not failure in the clinic, but rather just mismanagement from half a decade ago. And that's really tough to see. We want to see growth, right. We want to see additional, you know, additions to pipeline. We want to see companies hiring to advance that pipeline. And so I think one of the big kind of things that I see is just a number of companies that are cutting and just making, just making it harder, just making it harder for within that company, for the morale of that company, within industry to celebrate more and more wins. I think on the flip side, and this is a, to me, a very interesting scenario, is regulators are, which we always like to blame. This guidance said this and it says, I can't do this. Regulators, I think, are becoming kind of exponentially more and more receptive to unique and innovative ways of running trials, of recruiting for trials, just more openness relative to some of the 21st century stuff that we want to do. I can't even say believe I'm saying 21st century. It's 24 years in, but basically it's a really positive thing that I've always wanted to tether to. Right. If FDA says something's okay, like, great, give me, like, a permission slip, basically. And so it's really cool to see a lot of collaboration with regulators and more and more kind of go forth and multiply kind of mentality. Whereas even probably 15 years ago, I can think of conversations where you just hear the typical, like, well, like, this regulator won't let us do this thing, and that's it. [00:30:51] Speaker A: What are you gonna do? Yeah, right, right. So I 100% agree with that. We see that as well. And I feel like that's not at all consistent with how the popular sense of the FDA works. I mean, I hear people complain about how expensive it is to develop drugs. Of course, none of us are thrilled with that, but there's a tendency to kind of blame regulators, blame the FDA. And you're right, what I see our clients experiencing very often is some degree of openness from FDA to novel solutions, you know, within guidelines, as it should be. We're talking about human life, but more openness to that. What do you think is driving that? What do you think has changed? And maybe why is that not visible more broadly? [00:31:27] Speaker B: Great question. I think with any sort of central, central authority figure, if they have an opinion on something, if they have an ability to influence and say, do things a certain way, I think the natural response from a lot of people in industry is a, they need to have knowledge of that regulator saying that thing. With that knowledge. They then think of a pipeline. Like, there's a huge amount of people that probably are trained on GCP once and never again. [00:31:56] Speaker A: Yeah. [00:31:57] Speaker B: When you talk about knowing what an agency is saying, you then have to make the leap to say, like, all right, well, we're a global company. We have to operate across so many different geographies and so many different regulators. And I know of one or two that would take the opposite stance. And it's very. Some of it's very, very nuanced, it's very technical. It can be related to conduct of a trial or data flow in a trial. But I think what ends up happening is people say, well, FDA saying to do something, or EMA saying to do it some way. And, like, there's consistency across most regulators, but these other regulators don't even have an opinion. I want to put my drug in the market there, so I'm operationally not going to take a chance. And I think when you pair that up with actual large, small molecules, large molecules, biologics, vaccines, different modalities of delivery, particularly in the cancer world. It just adds up. It costs money. This stuff is not easy. This is not low hanging fruit. This is re engineering your own cells. This is targeting two or maybe three proteins expressed on a cell. It's hard work, and hard work costs money, and there's certainly no shortage of companies trying to solve for that. And like I said, it just adds up to me. I'd rather see sort of an outsized return relative to advances in treatment. I tend to be someone that you get what you pay for. And so it takes a billion to get a drug on the market, but 2 billion would have been able to get you a drug on the market and ten more indications. I'm for the latter. The goal is obviously a billion for the ten indications in this example. But the point is, I like outsized value relative to whatever the investment is. Maybe just seeing cancer so much and how unbelievably debilitating it is. Not just patient, but everyone involved, that I just want more and more options out there. [00:33:47] Speaker A: Well, look, that's a, that's a fantastic thought and close to a closing one. Craig, tell us, tell us, where can people find you? If people are interested in any of the things that we've talked about, if they want to explore partner with Flatiron, or if they have other stuff that they want to come to you for, where should they look for you online? [00:34:01] Speaker B: Yeah, you can just send me a note to my LinkedIn. Craig Sarahmail, Craig Saraflatiron. Simple stuff. Feel free to reach out. Certainly from a flatiron perspective, we stay very active at conferences throughout the year. We certainly can reach out anytime for that. And yeah, very easy. [00:34:19] Speaker A: Yeah, I would call you an easy guy to find. You're a highly visible fella in this industry. All right, Craig Serra, thank you so much for joining us on few and far between. Exciting to see what comes next for you and for Flatiron. [00:34:29] Speaker B: Thank you so much. [00:34:29] Speaker A: Chris, thank you for listening to the latest episode of few and far between. Our podcast is now available on Apple Podcasts and other major streaming services. Please take a moment and leave us a user review and rating today. It really helps people discover the podcast and we read all the comments. Those comments help us to make few and far between better and better. Also be sure to subscribe to few and far between so that you don't miss a single episode. Got an idea for a future episode? Email us at few and farbetween iorossi.com or contact us on our [email protected]. dot. I'm your host, Chris Obrien. See you next time.

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