Podcast | Healthcare (HC)

Advancing Drug Development with Real World Data

THL’s Healthcare in Action, Episode 7

The future of drug development is being reshaped by real world evidence, and TargetRWE is at the center of that transformation. In this episode, Jon Lange sits down with Ed Seguine, CEO of Target RWE and co-founder of Clinical Ink, to unpack how technology, structured data, and clinician insights are accelerating clinical trials and changing how we understand complex diseases. From unlocking the untapped value of messy EMR data to addressing ethical concerns, Ed shares how real world data can reduce trial costs, shorten timelines, and lead to better outcomes for patients.

Key Takeaways:

  • Real world evidence (RWE) is becoming essential in designing and supporting clinical trials, particularly for rare and complex diseases
  • Real world data can help drive efficiency and can even help address ethical concerns, including how to structure clinical trials to make sure patients are getting the benefit of the best data and insights available.
  • AI will increasingly play a critical role in organizing clinical data, improving clinical decision-making without eliminating the need for clinicians

[00:00:01] Ed Seguine We believe that we can shorten the time for clinical trials because the noise level in the data is going to be reduced. Being able to then say what is the impact on a complex condition and with a variety of confounding factors and how can we get that insight clear so that we can get that drug approved as fast as possible. I think that’s where you’re going to see the shrinkage in the timeline and the cost because you’re applying insights from real-world data all along the way. 

[00:00:32] Josh Nelson That’s Ed Seguine, CEO of Target RWE, and he’s the co-founder of Clinical ink. And I’m Josh Nelson, the head of healthcare at THL Partners. And this is Healthcare in Action. It’s a podcast that explores the latest developments and innovations transforming the U.S. Healthcare sector, from cutting edge technology to thoughtful approaches to patient care. I’m here with my colleague, Jon Lange. Who will lead us in a conversation about data and technology that’s advancing clinical trials. And in particular, the opportunities for real-world evidence to generate insights that accelerate the development of new therapies and cures. Jon, take it away. 

[00:01:15] Jon Lange Thanks, Josh. In recent years, real-world evidence, or RWE, has gone from a buzzword to a strategic imperative in the pharma and biotech industries. Fueled by advances in technology and support from regulators and other key stakeholders, RWE is no longer just a tool for surveillance after drug approval. Instead, it’s becoming a central pillar of how we understand the safety, effectiveness, and value of medicines in the real world. It’s increasingly influencing clinical trial design and supporting drug approvals. In fact, a recent JAMA article written by top FDA officials highlighted that a key priority for the current administration is to advance the drug development process by harnessing big data and in particular real world evidence. And top companies in the industry have been investing heavily in real-world data partnerships to do just that and advance the clinical data landscape beyond traditional clinical trials. In today’s episode, I’ll speak with Ed Seguine, the CEO of Target RWE, one of the leading real-world evidence companies. From his early days as an investor to co-founding Clinical ink, where he pioneered real-time point-of-care data capture for clinical trials, Ed has been a relentless force in advancing how we understand and manage clinical data. His work has touched some of the most complex and high-stakes areas in medicine, from Alzheimer’s research to autoimmune diseases. He’s been a pioneer in rethinking how we use technology to better collect, structure, and apply clinical data. I was so pleased to have the chance to talk with Ed about his journey as the CEO of multiple companies, the changing landscape for real-world evidence, and all the opportunities ahead for technology to advance and accelerate the drug development process. So let’s get into it. Ed, thank you so much for joining me today.

[00:03:06] Ed Seguine  I really appreciate the invitation, Jon. I am looking forward to our conversation. 

[00:03:12] Jon Lange First, for people who aren’t familiar, can you tell us a bit about Target RWE. 

[00:03:17] Ed Seguine Target RWE is focused on collecting real-world data from healthcare settings to better inform and understand: How is medicine practiced? What’s the type of patients that deal with different conditions? And we do that by extracting the details of a physician’s assessment of a patient’s condition from unstructured notes in combination with the structured fields that you typically think of in an EMR of the basics, like lab values, demographics, things like that, and then combine that to provide new ways of addressing novel research questions to say, is this an addressable question and how could that impact the patient over time? 

[00:03:58] Jon Lange Great. For those who aren’t familiar with real-world evidence and the problems it addresses, can you just frame that up? What is the problem we’re trying to solve here, and how does real- world evidence address that problem? 

[00:04:10] Ed Seguine Great question. Typically, people are pretty familiar with the concept of clinical trial data. You have a new drug, you’re going to test it specifically for, is it safe, is it effective? And real world data is complementary to that, both on the beginning before a clinical trial happens, but also afterwards. So think of it as a kind of like the bookends of clinical trial data. And it can help to address understanding the disease in such a way that it can inform the design of the clinical trial in the first place, understanding things like disease prevalence or disparities in different populations, how that might have changed over time, and what may be the typical progression of treatments. But how can you get that data from really the messiness of the real world to understand what’s happening in practice? 

[00:05:02] Jon Lange And maybe to make it even more concrete, can you give an example of maybe a drug or the journey of a clinical trial and how it was informed by real-world evidence? 

[00:05:13] Ed Seguine So there’s plenty of examples because this is a very common routine practice. Oftentimes, you can get put into a situation like a catch-22, if you will, in a clinical trial where in the case of liver, there’s a condition called PBC and that condition is relatively rare and severe. And there are two new drugs that have been recently approved. And they are now asking those companies to go back and run some placebo-controlled clinical trials. I was just at a liver conference in Europe and the hundreds of people that were there said, this is totally unethical. Why would we give a patient who could benefit from this drug a 20 percent chance or 30 percent chance of going on a placebo when it is literally a life-threatening condition? And so that is what we’re also trying to impact is the ability to speed drugs to patients so they get them at the right time and they can be effective, but also not to get that up in the historical processes of how we’ve been doing things. 

[00:06:16] Jon Lange Got it. That’s really interesting, and I think that frames up the role of real-world evidence so well, because on one hand, you have the speed aspect. Where you’ve all this data happening, you know, and you’re able to collect it in the real world in addition to clinical trials. And so that should allow for, you know, more speed to approvals, to approvals of additional indications, but then also this really interesting ethical question as well, which is if you already know or should know from real world data that a drug is effective, that drug is safe, then it really isn’t ethical to be running a trial, giving a bunch of people the placebo. You should be using the best data you have to get the best drug to the right people. 

[00:06:59] Ed Seguine Correct. Correct. 

[00:07:01] Jon Lange So, you know, maybe taking a step back, before you joined Target, you were the co-founder and longtime CEO of Clinical ink. And so can you just tell us a bit about that company, how it came together, a bit of the history, and then how you ended up at Target? 

[00:07:15] Ed Seguine Yes. So, Clinical ink, I was the co-founder with two other extremely talented individuals, and we started that company in 2012, and it was focused on the operational aspects of how to collect clinical trial data. And this was based on an experience at the time. I’d had about 10 years as an investor on the behalf of Eli Lilly’s venture capital team in how to accelerate the operational infrastructure to conduct clinical trials. And I had sold a previous company to Metadata, which was one of the leaders in the space. But what we were really seeing at that time was simply a conversion of paper-based processes to an electronic process. So instead of collecting clinical trial data and write it in on paper, and then writing it on another copy of the paper and sending it in, we had evolved to the point of writing it down on paper and then typing it into a web-based database, a glorified SurveyMonkey, if you will, really. And in combination with my business partner, we said, there’s gotta be a better way to make this electronic from the moment that matters. During that patient visit, let’s get it electronic. If we can do that, we can eliminate a lot of externalities and issues that come up with having to confirm what we recorded on the paper, really what is the data that got entered into the clinical trial. And then it really shifted to very rapid growth. And so for a period of five years, we were like 40 percent compound annual growth rate over, year over year over a year for five years. That’s a lot of change. And in that we shifted and expanded to cover not just data collection at the site by physicians, but also data collection at home by patients. And our focus was really, how can we address the highest complexity therapeutic areas, areas like Alzheimer’s that had been highly resistant to hundreds of clinical trials, but because they’re very complicated to understand what’s going on. And a lot of that complication came from the difficulty of implementing studies in that therapeutic area, focused on lupus and other neuroscience and autoimmune disorders, because our thinking was to just simply ask a couple of questions of patients like, hey, how well did you sleep last night? Or did you get sick after you took the drug? Like that’s simple, but to do the really complex things that have an indication on effectiveness and patient impact that required clinician judgment. That’s what we built the platform to deal with the highest complexity types of instruments. 

[00:09:45] Jon Lange Got it. Maybe switching gears. One thing that seems to be a theme across your career is that there is so much data in pharma, whether that is clinical trial data or real world evidence, and it is so poorly managed and collected. And in a way, you’ve spent a large portion of your career trying to address this problem in different ways. So can you just frame up the state of where we are in terms of pharma data today, and then what the opportunity is, where you see five or 10 years down the road, how can we manage it better? 

[00:10:24] Ed Seguine I’m very passionate about this. I have, in fact, tried to do true innovations and business models throughout my career. And that’s why I’m so excited actually about Target RWE because I think that the opportunity in the broader environment now presents the ability to have an impact in ways that we couldn’t even like five years ago. And I know it’s so fashionable to talk about AI, and it actually drives me crazy because it’s a shortcut for people who don’t really understand. And then AI will fix something that we don’t really understand. But many people said, hey, EMRs are gonna rule the world back when I started Clinical ink. And I knew that that was not the case because the way that we use data in medical practice is so different than the way we use it in clinical trials and that’s for a good reason. Don’t bemoan the fact that we wish it would be something else or would be better, just accept that it is that way. And the purpose for medical information being collected the way it is in that EMRs is largely because you’re trying to justify a diagnosis for insurance purposes. But the substance of what that clinical judgment is and how is that disease progressing and what are the specific symptoms that lead you to, you know, a particular ICD-10 code that you’re going to bill for, that’s where the meat of the data is. And that’s where clinical link came in, initially, because we said we have to get that specifics just for the purpose of the clinical trial. But in the real world evidence case, historically, the way we’ve accessed that data is through manual abstraction. Literally, people go in and they’ll do a chart review or they’ll code things. But that’s what I see as being valuable is we at Target RWE are starting with the head start of having some established relationships with health systems, some understanding of how those medical records are structured so that we can apply AI and then train it. That’s what we’re trying to accomplish, not just shorten the time frame on a clinical trial, which a company like Clinical ink will certainly do, but the whole bookend of that insight to practice is what we’re trying to solve. And that gets me very excited. 

[00:12:36] Jon Lange And it’s really interesting to hear you talk about the role of AI. And one thing that’s come through in a handful of conversations recently, is this dichotomy between AI addressing administrative problems versus clinical problems. And it is really interesting in this use case that correct me if I’m wrong, but I think what you’re getting at is AI won’t change the decision-making, it won’t replace a clinician. But it is so good at natural language, and so good at organizing data that already exists that for lack of a better word, has been a mess for the past 10, 20, 50 years, that if you’re able to bring AI to bear in that sense, it makes the clinical decisions and the clinical trial decisions so much clearer, so much better, accelerates that process in a way that really advances medicine. Is that fair? 

[00:13:26] Ed Seguine That is definitely fair, in that, like, think of even just the way someone would explain, you know, how their day went. Everyone has a different way of talking that, obviously, is still reflected in how physicians describe a patient. And so, if you just turn AI loose on the unstructured notes of a clinical trial, you are still going to get a mess. 

[00:13:46] Jon Lange And maybe with that in mind, paint a picture for me of what success looks like, 10 or 20 years down the road when we have brought AI to bear, when Target RWE, and other companies have been able to make sense of a lot of the data, organize, manage the data in a way that hasn’t been done before. What are the benefits? What does medicine look like when we had that success? 

[00:14:10] Ed Seguine Right. An example would be how the dynamics change as different therapies come on. We’ll just take a hypothetical. Everyone in the world now knows about GLP-1s. Like, okay, it’s the obesity drugs that can lead to all kinds of weight loss. Well, in liver, guess what? That has totally changed the impact on liver disease because it’s reducing fatty liver as well, and so if someone has an insight that says hey I think there’s a way we can get to address this symptom now they’re going to have to figure out like how does this how has that changed now that we’ve got essentially zero GLP-1-naive patients, like, everybody who would need it has been on one, so how do we tease out the effect of our new drug from this one. So I have this insight, how do I understand what’s changing in medical practice to now design my trial that accounts for that? And then ideally, having identified those anonymized patients, one of the great things that we can do is this flip on through some tokenization and see other encounters that that particular patient has had in an anonymized way across health systems, literally then go back and consent them into a trial right away, knowing that they have the relevant characteristics, so we can now test this hypothesis in a particular kind of phenotype or profile of a patient that needs certain conditions to then say ah we’ve now shortened the cycle of getting this additional insight. We believe that we can shorten the time for clinical trials because the noise level in the data is going to be reduced, and then your power on the number of patients you need can be you may need fewer of them to get the same certainty of that result because of how you’ve structured that but then starts to accelerate. So there’s a lot that we believe that have been talked about the same way like the cost and the time of clinical trials, you can only go so far with operational improvements. That would be a clinical link kind of approach, which is still valid. You still need those. But being able to then say what we’re really looking at is what is the impact on a complex, you know, condition and with a variety of confounding factors and how can we get that insight clear so that we can get that drug approved as fast as possible. I think that’s where you’re going to see the shrinkage in the timeline and the cost because you’re applying insights from real world data all along the way.

[00:16:38] Jon Lange It’s really interesting, and that’s a great example and reminds me of a phenomenon that I’ve come across a number of times, particularly as we focus on businesses that help seniors, that it is so common for seniors and really others to be on multiple medications, five medications, 10 medications, 15. And it is rare and maybe it’s, it virtually never happens that you have clinical trials of people with those specific five or 10 or 15 medications. 

[00:17:08] Ed Seguine Exactly, exactly. And so that’s where, because in a clinical trial, you’re trying to eliminate all of those other factors to say, is this safe or is this effective? Well, then all of a sudden the floodgates open and you’re going out to all of those people who have those five or six comorbidities or different confounding factors. Now what’s happening with them? That’s another use case for the real world effect of virtually every drug. You could say that there’s a need to look at that just in like real world people. They were not just, you know, singularly categorized as you may be in a in a clinical trial.

[00:17:46] Jon Lange  Got it. Yeah, I think that’s a really important point, too, that maybe isn’t obvious that, of course, clinical trials are fantastic. We are so lucky to have the scientific method and all the advancements that have come with that. At the same time, clinical trials, by design, are not really real world. 

[00:18:01] Ed Seguine They are not at all. 

[00:18:03] Jon Lange And so being able to have real-world evidence and make sense of that and collect that and manage that really gives you a whole different level of insight, which just isn’t available the way reconstruct clinical trials. I want to switch gears a bit. One thing we think about a lot at THL as investors is how to build and sustain competitive advantages. How do you think about that in pharma technology? How do you think about it in software, but also just more broadly in the pharma landscape? What are the ways that you have focused on to develop a competitive advantage as a business? 

[00:18:43] Ed Seguine Well, part of building a lasting competitive advantage means that you may need to be doing the hard things for a while. And so I’ve never felt like you build lasting competitive advantage because you took the shortcut route. So being able to have enough expertise to know, is this like a feature or is this, like something fundamentally different? I would actually argue there’s a few, at least in software, there’s few defensible really meaningful competitive advantages, I think one of them, at least in the case of Target RWE, is like our relationships with the sites that are giving us this deep depth of data. And I think in this AI-driven world, curated data sets become a competitive advantage because of the insights that they can give and what the intelligence they reflect being curated into them. And so that’s where we are investing a lot of time in how do we develop this deep expertise reflected in the data. 

[00:19:47] Jon Lange Maybe switching gears a little bit, going back to something you said earlier, you talked about with Clinical ink, how important it is to have tangible, immediate ROI. How do you articulate that value proposition to customers at Target? 

[00:20:03] Ed Seguine Oftentimes, when we start the conversation, it’s, and these are very hypothesis-generated kind of conversations. They have some inklings of data that’s come from how they’ve assessed their clinical trials. And so the first thing that we were able to do is test those hypotheses with retrospective assessments. If you think this, let’s go look back at the data that we already have. And so typically when we initiate a relationship with a health system, we’re actually grabbing five or six years of history all at the same time, and then getting that history updated on a quarterly basis. So we have the immediate ability to run those trained data curation agents and then test, what do you think, does that really exist already in the past? And then it becomes an iterative approach to then say, if that didn’t meet what your hypothesis was, why do we think that could be the case? Our ability to say, how can we give you value lies in being able to test immediately some of those hypotheses and then explore and act on what the possibilities for discrepancy in that hypothesis from what they theorized could be. 

[00:21:20] Jon Lange And as you look out five or 10 years, what do you see as the key growth levers of the business? 

[00:21:26] Ed Seguine So there’s a number of factors that drive the ability to get these insights. And the most important one is this specific therapeutic understanding. So we are not trying to boil the ocean. We’re not basically saying we’re going to take all claims for anything all over the place. There’s a lot of data sets for that. I would argue those data sets don’t get to the level of depth of the clinician insights that do. And so we’re starting with three right now, three therapeutic areas. We need to do that. Well, we need to run some studies. We need to show them through the whole life cycle. And so we may not expand to other therapeutic areas for quite some time. And then over time, we’ll add a fourth or add a fifth. But one of the benefits of our platform is, once we have a relationship with an institution, we just can say, now, hey, let’s flip on your, you know, pick your other disease area. We were getting this for liver, we’re getting this for derm. Now let’s flip this switch for this. We’ve got the agents that can process this and this understanding, and so we’ll do that sequentially over time, but we need to do first things first, deliver real value in our flagship platform, liver, and in our emerging platforms, derm, GI, and autoimmune, and then we’ll move into other areas. 

[00:22:40] Jon Lange Great. Taking a step back, I am a real optimist about what real world data can do for medicine and ultimately for patient outcomes. What makes you most excited as you look out over the next five, 10, 20 years about Target and about real world evidence? 

[00:22:58] Ed Seguine I think that these are things that have been talked about and hoped for for a long time, but the data hasn’t been accessible. The data hasn’t been widely available. And if you look back, I mean, it was only in a few short years ago in the Obama administration where there was this push to get everybody on electronic medical records. That means there needs to be a timeframe of effective use before you’re starting to even capture that data. So I think we’re at a unique period of time where now when we do a look back period, we can actually gather some relevant information and that with these additional insights will only get better. I don’t believe that all of a sudden EMRs are going to just magically be more effectively documented. There’s no possible way you’re going to ask busy, busy physicians to do greater documentation. And so there will still be an accepted kind of degree of messiness in the justifications of their clinical thinking. That is just part of getting that business done, but if the technology can evolve to essentially access that and strip that out, we can give those insights back. And I get very excited about then being able to apply that to have new insights to address different conditions. One of the things in liver disease is that there’s a symptom called pruritus, which is itchiness, and it’s actually not documented well in the notes because to the physicians it’s like yeah well we’re gonna try and treat your liver disease, but like, it can be extreme. But if we can now start pulling these things out that matter to patients, because we’re solving some of the more basic issues, it gives us the ability to inform future development paths that give treatment options for people who have this, which is crazy. I was like, we’re looking at this and it was really actually hard to find this. And we asked people like, well, we know they have it, but we just can’t do anything about it. So we don’t even document it. It will have this virtuous cycle that helps to gather additional insights. 

[00:25:06] Jon Lange Ed, I really appreciate you taking the time. Thanks so much for joining me. 

[00:25:09] Ed Seguine I’ve really enjoyed it. Hopefully people get something out of this as well. 

[00:25:14] Jon Lange As we wrap this episode, I’m joined by my colleague, Ben Stern, vice president at THL and a leader in our pharma services and technology efforts. Ben, over the past multiple decades, pharma companies have been increasingly outsourcing services that they used to do in-house, and in addition, they’ve also been adopting technologies from outsourced firms as well. So, can you talk about the arc of that penetration curve? And what technologies are really pushing the boundaries and adding new functionality to the pharma value chain today. 

[00:25:50] Ben Stern So, as you mentioned, pharma companies over the past decade or more have gone from building all these capabilities in-house to partnering with technology and service providers who are more specialized and have deep therapeutic area expertise. And thus able to deliver on drug discovery, development, and commercialization in a faster and better way. And so on the services side, we’ve seen specialty CROs or clinical resource organizations and commercialized services vendors who are partnering with pharma to help really plug in the key parts of the pharma value chain. On the software side, I think one of the really interesting trends that we’ve seen is the adoption and clinical trials of eCOA, where other ways to collect endpoints and data from the patient and from healthcare providers that are more decentralized. 

[00:26:46] Jon Lange From the patient perspective, Ben, what should patients, consumers, be most excited about that real-world data can unlock? Where will they see the impact in their daily lives? 

[00:26:58] Ben Stern Yeah, there are a couple of areas that are really exciting for patients. One, as Ed mentioned, is in actual clinical trials. For many clinical trials where there’s an approved disease. There are real ethical concerns with having a placebo arm and having patients who have a disease that there is a treatment for go untreated. Being able to use real-world data to simulate a control arm for these trials will help more patients in the trial themselves and also bring more drugs to market. Patient recruitment for clinical trials is incredibly difficult. And being able to tell patients that they won’t receive a placebo arm is a real benefit and will help reduce the cost of care. The other is in diagnosis. As we’ve talked about a lot of times in our investment efforts, big step in our healthcare system is being able to really identify diseases early. Using real-world evidence to help take data from labs, take data from doctor visits, and other interactions with the healthcare system to be able to identify and really get a treatment to the patient as early as possible is really exciting possibility for patients. 

[00:28:02] Jon Lange Great. And, you know, one thing that really excites me is that Ed brings so much experience in innovation and entrepreneurship to his new role at Target RWE. And I think it was clear from our discussion that there is just so much additional opportunity for innovation in real-world evidence. What are you most excited about in the next five to 10 years in terms of the evolution of real-world evidence and how it’s going to have an act on the U.S. healthcare system. 

[00:28:31] Ben Stern Yes. One of the ways I’m really excited about the innovation in real-world evidence is in advancing the ability to get drugs approved and in market. The FDA recently came out with a statement talking about how they really want to support this effort. Using real-world data to be able to move drugs through this discovery process and development process in a way that is faster and lower cost can really create a step change in the amount of innovation available for patients. And so as we think about a drug development process that takes multiple billions of dollars in 10 or more years to get a drug from discovery into the market. Being able to compress that will create incredible bounds for our healthcare system. 

[00:29:16] Jon Lange Ben, thanks so much for joining me today. Really appreciate your thoughts, as always, and look forward to continuing the conversation with you and with Ed very soon. 

[00:29:25] Ben Stern Thanks, Jon. It’s been great to join. 

[00:29:28] Josh Nelson Thank you for listening to Healthcare in Action, brought to you by THL. To help Healthcare in Action, reach more listeners like you. Either share this episode with a colleague, subscribe to the show, or rate and review us on Apple Podcasts. And for more background on THL’s Healthcare Vertical, visit thl.com/verticals/healthcare. 

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