IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Hello, and welcome. Thank you for joining us for today's webinar, applications of real world evidence and insights in support of decision-making throughout the product life cycle to increase efficiencies and reduce risk. My name is [ Lamise, ] and I am from the Real World Solutions business unit at IQVIA. I will be the webinar host today. We appreciate you spending the next hour with us as our panelists share more around how applying essential real-world evidence approaches, also known as RWE can help you reach your next milestone with speed, agility in order to maximize your asset value. They'll also discuss how real-word evidence can help you demonstrate the value of your assets to investors, with the final objective of bringing your new treatment to patients faster. Moving forward, before we start, I'd like to review a few housekeeping items. At the bottom of your audience console are multiple application widgets you can use. [Operator Instructions] We will answer all your questions via e-mail after today's session. You can extend the slides by clicking on the maximize icon on the top right of the slide area or by dragging the bottom right corner. If you have any technical difficulties, please help -- please use the help widget for online troubleshooting, which covers common technical issues. And at the conclusion of the webinar, please provide your feedback via the green survey widgets. Your feedback is important to us, and it helps us further improve our future webinars. Finally, an on-demand version of the webcast will be sent to you via the next day -- within the next day. And it can be accessed using the same audience link that was sent to you earlier. Let me introduce today's speakers to you. My colleague, Barbara Arone will moderate our panel discussion today. Barbara will be joined by experienced experts representing biopharma companies and the investor community. Each of our panelists will introduce themselves. So Barbara, with this, I would like to hand over to you.

Good morning, good afternoon, everyone. Thank you so much for joining us for this morning's panel session. My name is Barbara Arone. I head up the Medical Affairs offering category within the Real World Organization at IQVIA. I am delighted to be here today with 4 important panelists, who are going to share their perspectives on today's topic. But I'm going to ask everybody first to introduce themselves. We will start with Bruno.

I'm Bruno Osterwalder, I'm a trained Board-certified hematologist and oncologist. After many years in clinical patient management, I joined Hoffmann-La Roche in Basel, stayed for 20 years, plus 7 years in Merck in Darmstadt. Focus was on global drug development, innovative drugs, hematology oncology and immuno-oncology, initially more on late-stage drugs with approvals globally in solid tumors and lymphoma anemia in some cases. And then later on, on early drugs, compounds moving from research into clinical early trials and emphasis on translational medicine, biomarker aspects. Since 6 years, I'm a consultant in this field, mostly dealing with smaller biotechs. And with regard to real-world evidence, data generation, I have to admit, it's a more recent experience I have, which is actually the basis then for my points I can make today.

Thank you, Bruno. Patrick?

Hello, I'm Patrick Jordan. I am CEO of Mycovia Pharmaceuticals and partner at NovaQuest Capital Management. Mycovia is an emerging biopharmaceutical company based in Durham, North Carolina. And we have submitted an NDA for a potential first FDA-approved therapy for women's health concern called recurrent vulvovaginal candidiasis or RVVC, also known as chronic yeast infection, which is defined as 3 or more acute infections per year. It affects 6 million women around country in the U.S., 138 million women globally each year. I've been CEO of Mycovia for the past 4 years, since its inception. And 2018 when NovaQuest Capital Management founded the company through an acquisition of Viamet Pharmaceuticals in Durham. NovaQuest, where I'm, as I mentioned, a partner, is life sciences, private equity firm with $2.75 billion of assets under management. And we provide product financing to emerging biopharma companies for promising late-stage assets, many very exciting assets that are at the forefront of pharmaceutical development. NovaQuest was launched out of Quintiles in 2000. It spun out of Quintiles in 2010. Prior to my being at Mycovia and at NovaQuest, I spent 20 years at Quintiles, now IQVIA, in various roles across operations, analytics and corporate development. Delighted to be here today.

Thank you so much. We'll pass over to Sam.

Very nice. Thank you for the invitation. So my name is Sam Parker. I'm currently working as Chief Patient Access Officer for a company called Innoskel . And Innoskel is focused on pioneering in gene therapy for rare bone disorders. So I've spent all my career or 25 years of my career focused in rare diseases and orphan drug development. And most of that really and, certainly, the last few years in emerging biopharma. So faced with some of the challenges of the small and the way that we have to think around a lot of -- I think around how we can speed up and accelerate our development. And so my focus in -- I've been doing quite a lot of work in real-world evidence. So initially, I started working in registries. I've moved through, and had roles as adviser at the European Commission and on European Reference Networks on how we developed disease registries and really moved away from initial thoughts on collecting data in rare diseases around drug registry. So move to that kind of looking at the overall disease. I've worked a lot on epidemiological studies and natural history and also thinking about how you collect real-world evidence through remote technologies and using videos. So delighted to be here today and talk about this exciting topic.

Excellent. And then our fourth panelist, Chris?

Hi, Barbara, thanks. Happy to be here. Yes, in my 30th year in the biopharma sector. The first 25 years I spent doing initially consulting. I went to Abbott Labs Pharmaceuticals where I did new product development, brand management, then join-ed Gilead Sciences in the mid-'90s, was there about 8 years, during their growth years and served in roles in medical affairs, commercial development, business development, corporate strategy. Then went to Celgene and was Head of Corporate Strategy there, again, driving strategic planning, M&A, was Chair of the Portfolio Review Committee. From there, I became a public company CEO of a turnaround story in the rare disease genetic technology space, it was called AVI when I joined. I renamed the company Sarepta, moved it to Boston, Cambridge. But across my career have been involved in, as Samantha was saying, registries, thinking about real-world evidence before it was a term and how we augment clinical trial data to really understand how drugs work and how to engage with the regulators. In the last 5 years, I've been an investor. So I started an accelerator seed fund called Xontogeny, where we partner with early-stage companies, founders, first-time CEOs, entrepreneurs. And then I also joined a larger fund called Perceptive Advisors, where I lead their venture funds. So we have 2 venture funds with over $700 million of assets under management. And we invest across the spectrum, not just drug technologies but devices, diagnostics, tools, service providers and we are actively thinking about these real-world evidence issues, and in fact, are close to making an investment, which you might see in a couple of weeks in the wearable sensor CRO state. So really happy to be here and excited about this topic.

Great. Thank you so much. So I am very excited to have such an amazing panel together. People bring really unique perspectives, and I am certain we will have a lively discussion. Our topic for today is the applications of real-world evidence and insights in support of decision making throughout the product life cycle to increase efficiencies and reduce risk. So we're going to talk today about how RWE has woven throughout the product life cycle and how our panelists think about RWE and think about the use of RWE to help reduce risk and increase efficiencies during the development and then the other aspects of the product life cycle. So for our discussion, I'm going to be leading with a number of questions. The speakers are going to comment and expand upon the topics, and then we will do a Q&A for our entire webinar series today at the end of the discussion. So if the audience has any questions during the course of the conversation, you are welcome to put them into the webinar platform using the chat function. Questions will be addressed at the end, and any questions we don't have time for, we will address via e-mail following the meeting.

So let's do a warm-up question. In our discussion today, in the previous discussions, we were focused on RWE to increase efficiency and reduce risk. So Bruno, let's start with you. Are there situations in which the organizations that you have worked with are already using RWE proactively to enhance the clinical development plan?

Thank you for the question. Actually, as I said, I'm among the new ones getting experience in this field. And this is more in my context of my consultancies. You have to remember back when I started in Roche in the '90s, the basis for the guidance, for the clinical strategies, there were 3 sources. First, published data, historical data studies presented, which allowed us to do the study design issues. Second, there were certainly the competitive environment. You were looking what competition is doing, which ones will be your major threat, et cetera. And third, the commercial people. They were telling you the indication has to be big. The indication, the label has to be broad, and that was driving a lot of the clinical strategies. And I have to admit in my many years in Roche, I actually do not really remember we used real-world evidence, at least also then in Merck, in my time in Merck as well. Now meanwhile, things have fundamentally changed. We are now living in a world of small indications, niches, sub, sub-subpopulations, genomically defined and rare diseases. And suddenly, these indications became also commercially attractive. The problem is certainly a few patients, hence, randomized studies as we traditionally did become difficult, if not impossible. And that's where I now see the space. You have single-arm studies. You have limited patients. That's where I believe the real-world evidence comes into the game and can become very important for your planning at the early stage for your internal decision making for also the interaction with the outside analysts, investors. You hope with this kind of data you can make your data more trustworthy. And certainly, then, the regulatory part later, which I personally have no experience, so I would focus a bit on this early stage where I see the role of real-world evidence becoming more and more relevant.

Very interesting. And I do think that is the trend that we're seeing is real-world evidence earlier within the life cycle. Patrick, same question to you. Are there situations that you have seen in which organizations are using RWE proactively for the development plans?

It's a great question, Barbara, because I think we know, and throughout the industry, the patient recruitment has been a long-standing challenge for clinical development. In fact, when I started my career at Quintiles in '97, the statistics were pretty sobering. Fairly commonly known that 80% of studies did not meet their time lines. And the industry would go to great lengths and great expense to address it. And actually, I joined the company through an acquisition. It was a unique capability at the time with the patient recruitment company that use radio and television to inform patients about clinical trials. It was novel during the day, pretty common place now, decades on. But to show the significance of patient recruitment and the importance of solving that problem, it was common that sponsors spend millions of dollars on advertising, airtime. And the economics make a lot of sense for that deployment of capital, $1 million a day or more for every day that could be recovered on getting a drug to market. And so you could quickly see how it would pay for itself. Fast forward 25 years, what percentage of trials failed to meet their time lines? 80%. So it's the same. I don't think that means that we haven't, as an industry, improved. I think we have. The reality is trials are far, far more complex and patients are far, far more nuanced than they were in '97. And we have new tools in the armamentarium. And one thing that I've been very excited about was this foray into anonymized patient-level health care data. It creates an opportunity to be much more fine-tuned in addressing and finding patients to design protocols that are more appropriate for the patient population, choosing investigators to help ensure a better result for patient recruitment. And I think these data sources, and the FDA enumerates them and I think they're probably well known, but you already hear them coming up in our conversations so far, EHR, claims data, disease registries, patient gather data, and I'm particularly interested there. And I know we'll come back to that. I hope we will later on. Wearables, Chris had mentioned, that's a space that his firm is interested in. I think all of these data sources create a lot of opportunity for solving this long-standing clinical development challenge of recruiting patients into trials. Barbie, that's a warm-up question. I feel like I might go for a marathon on this one. It's a good one to start.

Absolutely. And I'm so glad that you brought up the idea and the patient recruitment piece because I do think that real-world evidence is just like it's permeating our conversations with regulators. It's really permeating how we operationalize studies. And I think that's a really key component. Sam, how about the organizations that you work with? Are you seeing a growth in RWE in the development plans?

I must say I'm really enjoying this panel because I'm hearing all the buzzwords that I talk about on a daily basis. And I enjoy Chris' comments earlier. We've possibly been using real-world evidence for a long time, but we haven't put it into the right framework and haven't called it the same thing. And I definitely think from my personal experience, yes, it's something that we're using more and more because we have the knowledge and the capacity of being able to see more data. I'm not going to say anything new from what the panels have just said, but I'd like to resonate again on some of those points. I'm using real-world evidence for -- to look at epidemiology. So very early on in our potential drug development early on even before the clinical. These are questions that we have to answer, not only for the sake of understanding of sustainable business model for an emerging biotech, but also for being able to understand how you will be able to recruit to clinical trials. So you really need to get a good understanding of that. And that's one of the first things that I'll look out for registries that may exist within patient organizations, government registries and academics. Outcomes, again, real-world evidence is going to help us understand what are the potential outcomes. We'll join that with literature research, and we still do our traditional ways of looking for data, for sure. But where it's going to help us is not only understanding what are the outcomes that are important in these diseases but potential time lines and being able to measure those. Again, we've mentioned earlier, natural history studies. And I kind of sometimes dissociate, in a way, natural history studies to real-world evidence. I think we bucket them sometimes when we can, but there is an absolute fundamental difference. For me, a natural history study is a therapeutic trial without the therapeutic. Because particularly in rare diseases, where we can't do randomized controlled trials and also for gene therapies, we can sometimes and when we can, we should. But most often, particularly pediatric gene therapy trials, it's extremely complex to be able to do that. So we want to be able to use them as a control group. And for that, we have to be able to demonstrate to the regulatory authorities, the quality of the data is the same. The selection criteria is the same. We limit all the buyers. So again, I've repeated a lot of the earlier panelists, but these are all great discussions.

Chris, you get to round us up. What are you seeing with the companies that you're working with? Are you seeing a growth in RWE? I think we are hearing it for operations and for plans as well, but I wondered if you had a perspective.

Yes. So I want to actually pick up on one of the streams that Sam started, which is, again, the old way of describing real-world evidence was registries, right? And when registries were done, there were 4 companies to gather more information about the disease in which they were competing in. Sometimes they were treatment registry. So needed additional data about how their drug was working in a real-world setting. And then we're using that for internal purposes, maybe for publications, Genentech early on had their AMI database that they would publish every year. And so that was really useful and had a lot of utility, but it was relatively expensive for what you got out of, right? But what's happening now is the regulators now are coming to the table and say, "Hey, we are open and flexible about accepting real-world evidence and supplemental data." And Sam picked up on this. There's more and more pressure against placebo-controlled studies. That's coming from the IRBs, it's coming from the patient advocacy groups. It's even coming from the clinicians. Even if the company wants to do it, right, they're getting held up. And so what that means is they have to shift to finding other ways to get prospective untreated cohorts that match what they want to study in a clinical trial. Now you can call that a natural history study, but it's basically a data set that you can have more power in comparing what your treatment arm is going to do if you can't do a placebo-controlled study. The other thing I want to mention is that, yes, we are seeing a lot of our clients embrace this, but I'll tell you where it starts. It starts with capturing some of this data. It might be wearables. It might be patient-reported outcomes through video diaries or other means. But they're collecting this to augment and supplement the traditional clinical trial data that's collected. So when they go to the FDA, they can provide greater context around that data. They can understand how their drug is working better, and they can articulate a better messaging and story on why their drug is having an effect and why it's working. Now what that leads to, which is really where the power of this comes in, if you can move from that supplemental data and start to correlate that with the traditional clinical data, let's say you're looking at lung function. And once every 3 months, somebody's strapped into the spirometer and they're getting some data and you have a lot of variability there and you're only capturing it once a month or once every 3 months, well, imagine if you could get a wearable that captures that data, real time or 1 hour a week, is far more amount of data. And you can correlate that data with traditional clinical study outcomes. That's where you start to replace. You get more power. You show that correlation, and that's where the FDA may start adopting these as new biomarkers, these new COAs, the clinical outcome assessments, right, that are validated. And that's when it really opens it up. And the last point I'll make, which Patrick touched on is the cost of gathering these data are much more efficient. You can get a lot more data supporting these types of real-world evidence measures than traditional clinical trials. And so if there's a way you can shift, get more data, make it meaningful and useful, use it as regulatory outcomes and submissions, then you really start to disrupt the traditional CRO business model. And that's what I get excited about and I think that's where I see the trends going.

Very interesting. And everybody had a unique perspective to bring to that discussion. I think this is, yes, very interesting. I actually want to circle back to something that Bruno raised earlier. So I know that you talked about in the different companies that you worked with who are looking at new therapeutic areas. So can you talk a little bit more about how teams approach those new TAs and use real-world evidence? And when I say real-world evidence, one of the other things that has come up in so far in people's comments is that real-world evidence maybe used to have a narrow definition. It now has quite a wide definition. It can include everything from traditionally what we think of as claims data or prospectively collected data or chart review data to include wearables and even literature used to pull case studies out of the literature, et cetera. So I think our definition of real-world data has gotten a lot wider as well. So Bruno, do you have any thoughts about real-world data to support that very early decision making?

Yes. Actually, you raised the point to define what we mean with real-world evidence, and that can mean very different things. And registries were mentioned then it came back to me, the first time involved in registries was in allogeneic bone marrow transplantation. And this goes back to the 1980s, Fred Hutchinson Center in Seattle, et cetera, randomized trials with such a procedure was not possible. So that's when I remember we used and created these registries. Now it really depends from all of what I heard as well, what we mean with real world. We certainly looked at data as much as possible from what is published from registries. I was more approaching it from the angle of -- I admit limited to the early stages, where you have, and that's my example. And most recently, you have a new procedure, a new therapeutic modality. You have a single-arm study. You have a limited number of patients. And they look promising, we believe. Well, there is not comparative evidence to support your statement, so your analysts and investors will certainly question what you say. And that's where I feel a constructed, matched control group can become very helpful. And that's my example I'm living through, where you really match external patients treated and with normal standard of care somewhere totally independent. And you match the characteristics of these patients with the characteristics as specifically as possible with your patients. And then what you would like to see is that the real world Kaplan-Meier curve looks like this, and your data which you thought are promising gives you a curve like that. And that should help, first, internally for your decision making. Second, should help to convince investors. And then later on for the regulatory path -- the new authors are more able to speak about that. But we also have to remember early clinical trials are not a breakthrough. They deliver bottom line results, limited data. I don't think that real-world data help. If your new modality doesn't deliver, it does not. On the other hand, if you have outstanding data, as we have seen in genetically-defined subpopulations with targeted therapies, tyrosine kinase inhibitors, et cetera, I'm not sure real-world data are essential. Or so the problem with this new genetically defined subpopulations, we don't really know the prognosis. We really understand the course of the disease, having this specific genetic variation of alteration. And that's where, again, can use real-world data to gain knowledge about a population you didn't know before.

Very interesting. Very interesting. Chris, thinking about therapeutic areas because Bruno touched a little bit on some of the different TAs. So thinking about therapeutic areas, do you see special advantages for real-world in different TAs, areas that you think it is more or less relevant?

Yes. I mean I think it's hard to deny that rare disease is an area that's fertile ground for this. And the reason is, is that there's a lot of competition. Many companies across the industry are pursuing those indications and many of them pursuing the same indicate. Duchenne muscular dystrophy, an area I worked in, there's dozens of companies with products in the clinic. And this creates competition for patients, standardization of what is the standard of care behind it. Do you go into other countries where practices might be different? And so you really have an issue with small sample sizes, and how do you make up for that? Because even the FDA will acknowledge that they can't go to the same old P values and too well-designed large, placebo-controlled studies. And so they've signaled this flexibility. Congress has said FDA needs to use more flexibility around accelerated approval. So what's the solution to that? Well, you need to attach a lot more data to these smaller number of patients. And so if you can start to look completely, right, and really look at every different parameter, that's why you see a lot of rare disease companies will go after and look at 10 or 15 different end points, hoping to find the right signals and that they show concordance. So that's a logical one where we should start as an industry in making sure we move in that direction. I think that where there's a quality of life component and where there are other features that can show an indication of benefit, I think those are -- that's where people are talking about oncology, right? I think you can look at almost any disease area. You might be talking about Crohn's or ulcerative colitis, right? These are a lot of quality life components that you can start capturing and augmenting. And even in -- if you think about rare disease, capturing something on a wearable that looks at gate and how they're moving is far more valuable information to add to just the simple number of steps they take in a given period or a 6-minute walk test or a 10-meter run walk or a 4-stair climb test. So I think that there's a lot of other dimensions that you can bring to the table in different disease areas. And while I mentioned a rare disease neuromuscular, that could also apply to multiple sclerosis, that could apply to Parkinson's disease. It can apply to Alzheimer's in different ways, right? So I think that's where I see -- as you need to look at the entire person's experience with that disease and how can we capture all of that data and give a more holistic picture in that regard.

I think you make a really interesting point about -- and I think we will -- I think we can explore that a bit later in our discussion. But just this idea of the volume of data that exists and being able to work with that data to get a really full picture of the patients, a really full picture of their health care experience and their lived experience, we have that possibility now, which I think is so unique. And not tied necessarily to rare disease, but really tied to sort of the volume of data that we're creating on a daily basis as we just walk around the world. Sam, I know you have spent a lot of your career in the rare disease space. And in a discussion that we had earlier, we talked about this concept of intelligent collaboration with advocacy groups across the industry, across health care and pharmaceutical and device development. I'm wondering if you could sort of expand on that idea of intelligent collaboration especially as we sort of talk about real-world data being so important in the rare disease space?

Yes. No, absolutely. And I think it flows very nicely from some of the things that Chris just said as well. And international plus cross-stakeholder collaboration clearly changes the world for people living with rare diseases. I also wanted to mention along that thought, I sit and I'm now a Vice Chair of a group called the International Rare Disease Research Consortium. And that's exactly what we're trying to do. We're trying to work in a space that's pretty competitive as well for companies but can collaborate intelligently throughout the stakeholder groups. I'm going to focus on the patient perspective because that's the question that you've asked me. You've asked me how collaboration with patient groups. But also just want to build on, from that perspective, what Chris said about natural history studies, about novel outcomes and that development and a great example of gate in Duchenne. And the way that we think in that collaborative space, if there are so few patients and there's multiple numbers of natural history studies and multiple numbers of companies developing drugs, that's fantastic. And it's fantastic for the people that are affected by these diseases. But wouldn't it be a nice place if they didn't have to hop in and out of loads of different natural history studies with the expectation of course, that they'd be able to get into a therapeutic trial? because that's what the family is hoped for. And so I think we need to think about how -- sometimes it's complex to share data, but that should be an ultimate goal. But also thinking about how we collect those outcomes and the tools that we use, and that we work with the patient organizations that they've become tools that are accepted by the regulatory authorities and are used throughout the different development programs. And I'll give a very clear example of where there have been studies in pediatric neurodegenerative diseases and everybody has agreed that cognition should be the primary outcome. And everybody has gone off and done a natural history study, which is absolutely fantastic, but they've all used a different outcome tool to measure cognition. And so it's very difficult afterwards to be able to merge that data for the benefit of patients. So I wanted to build on that. And then just get back to your core question was how I work with patient groups throughout, from a very early stage, and they're absolutely integral. They've become more and more important and earlier in the discussions. And so at Innoskel at the moment, we're in preclinical stage. We're planning for clinical studies. I'm on board as Chief Patient Access Officer, as C-suite. So really it's strategic within the company to bring that perspective into organized patient advisory boards. You think about acceptability of your therapeutic. You think about the route of administration. You're thinking about what are the outcomes that you're looking in your animal models and how that would translate into the human. So it's really a very integral part of our development early strategy.

That's great. And I do think, along with the growth of real world -- I'm interested in people's reaction to this. Along with the growth of real world, I think that the idea of patient centricity is changing from just a buzzword. Meaning, we're going to try to make sure that there's something in it for the patients to really a concept of how we approach operations and even how we approach the science of the project. So I think patient centricity is taking on a whole new life of its own, probably driven by the COVID pandemic and the fact that we all had to learn how to do things different ways. But this idea of patient centricity, I think, is really starting to weave through all of our conversations as well.

I was going to add because I think being the chief patient access officer, I think patient has the word -- is the right word in many circumstances. And I think where it's changing as well and you said the patient centricity is becoming central. They're experts. They're not just patients. They are absolutely experts in the disease. And I think that we're having another way of bringing that expertise in, not just ticking a box. A patient has read my protocol and agrees that this is feasible. It's really building on that expertise and a deep understanding of the disease and the way that we should treat it. And I'm absolutely convinced that, that's the way you accelerate your development as well because you're meeting the unmet need of patients in a manner that's feasible and relevant to the regulatory authorities. I'm very sorry to interrupt you, but I thought that patient point was one to...

No, I'm very happy that you interrupted me. I wanted to see. Does anybody else -- and we've sort of gone around the house in a number of different topics. Does anybody want to add anything to this? I'm going to turn a corner in a minute. But does anybody want to add anything to this part of the conversation?

Maybe the only comment that you mentioned of how COVID has impacted things. I think people are thinking if you can do care and assessment through telemedicine, not leaving your home and driving in to a clinic or a hospital setting, where there's a lot of risk for infection and bad things to happen, I think you're seeing this in diagnostics. You're seeing this in the growth of telemedicine. And I think that it's going to move into clinical trials and capturing data. And I think the FDA, their signaling of being flexible during COVID early on is like, hey, we know there's going to be missing data. We know you can't get patients into clinical trial visits. That, to me, was an early signal saying, "Hey, they're going to be open to these new ways of capturing data that aren't putting patients at risk to having to go into hospital settings or clinics to get their assessment done."

Yes. I'm with Chris and Sam on that. There is -- as challenging as the pandemic has been for all of us globally, there are some consequences that I think can be positive. The push to more democratized patient recruitment and clinical development to enhance, I think, what you were saying Barbara, this patient-centric approach, putting technology to patients, to really take control of how they participate in clinical trials when typical business to a physician's office might have been curtailed because of the pandemic. There's a positive development to come from this. And I think Chris is right, agency, the regulators are showing a favorable disposition to moving maybe more permanently to this approach to bring patients more into the fore of how clinical research is conducted.

Why as well -- one thing that we haven't mentioned about all of this is that we're talking about drug development and regulatory authorities. And all of us know that the hurdle after that is talking with payers and health technology assessment. And these kind of technologies, I think, and from a patient-centric perspective, are absolutely fabulous when we have to think about long-term follow-up, particularly in gene therapies. There's some very long-term follow-up. Even in small molecules, you can sometimes have up to 15 years follow-up. So how can you use these technologies that will be acceptable for regulatory authorities, but also for the payers to be able to follow up on a much longer-term basis? I think that's exciting, too.

Maybe, Barbara, I can come back a bit in perspective, that's the only stage particularly in immuno-oncology. The problem is with -- in oncology, there is still a high medical need, and we are looking for drugs which simply improve survival. Sure, safety, quality of life, et cetera, all is important, but at the end, it's always live longer. And the problem in the early stage with all these numerous biotechs working in the field, and I have seen over the last 10, 15 years, hundreds, probably reviewing their strategies, their data, the challenge is early data, will they make it? They look promising in your first 10 patients, 15 patients. And the CEO is happy. And we know going to Phase I out of 10 Phase I compounds, 1 or 2 still make it. That was true back in the '90s, and it's still true today. So the problem is how can we get more solid evidence to believe that this data we are generating have a chance to potentially be a superior treatment to extend the current available standards of care. And that's where we need more information, in the absence of comparative data, real-world data. Also the question you include in your trials always selected patients. Eligibility criteria that's long known, by the way, the patient population does not reflect what comes after in the real world. And I don't think we have really solved it. At Phase III still is a selective population. And so that's why, at the early stage, and that's really a bit my experience and focus now, I believe, real-world data and basically really means concrete data, you have to select patients outside, which fit the patients you have treated as much as possible. And so I see this is an important aspect, which may help to help us in selecting the treatments, which have a true chance to be better later on. Before you discuss with regulatory, it's nice to discuss with FDA, but your data have to look good. And you have to be convinced in order to invest the money that this is worth to be pursued.

I'm going to take one comment on top of Bruno, which is one of the most important things in my opinion, of being successful with drug development is getting your clinical trial design and enriched patient population correct. That's inclusion, exclusion criteria, what are the right ways to power it, what are the assumptions around that. And what Bruno is describing is the earlier you can understand that, right, the earlier you can capture that, the better you're going to be to design the right clinical trial to show an effect of your drug. And that's why getting involved earlier, which were Bruno's focus, is important. You can't wait for Phase III to start capturing these data. You need to start early, and that might be natural history. It might be incorporating it into your Phase II, but I just wanted to reinforce that concept.

That's great.

You point to the issue of biomarkers. You always want to select the patients, who have a chance to respond to your treatment. If biologically, you don't have the genetic alteration, you don't play in the treatment, it will not work. And the problem is we still fail if you take checkpoint inhibitors. Are we able to select the patients, who really get the benefit of long survival? The blunt answer is still basically no. We are not. We have indications, markers, et cetera. But in fact, we are not able to really -- it's not like an ALK fusion gene. That's with CFI, you can diagnose this with the test and you treat the patient who has it, and he has a high chance to respond. So there are the simple cases where, as I said, real-world evidence is less relevant. But with the checkpoint inhibitors, we have not yet managed, despite huge efforts, to really early on select the patients, who are biologically able to respond to your specific treatment. That's the major problem in my view, still in early drug development.

So Patrick, I actually am going to turn to you because we just set you up so nicely for next topic on my list of things to talk about. I think one of the important constituents that we've sort of been discussing a little bit around the edges in this conversation is the investor as a constituent. And so this idea of identifying early who your patient population is so you can design the right studies, identifying early who your patient population is, is also super important for helping the investor community to understand the potential value of the product. Do you want to expand on that a bit? Because this was one of the things that you and I have chatted about earlier.

Yes, I do. Thanks to Chris, Bruno and Sam for teeing it up so well. And it gives me a chance to, I guess, maybe wear 2 hats at the same time, Mycovia, this biopharma company and NovaQuest, the private equity group that's financing the study, what we have found is that real-world evidence is vital when you're determining the value of this asset and the opportunity to investors. And it's especially so when a drug is really innovative, and Bruno got it this early on, where using real-world evidence where there's -- maybe there is no comparator to date, and use that to help inform your trial decisions. Sam and Chris also touched on the vital use of RWE in indications of rare disease. I completely agree. That could be a very strong use case of RWE. But there's something interesting that's emerging here that rare might also be described as the rarely reported. I'm thinking of Mycovia, women with chronic yeast infections thought to affect 6% to 9% of women, but it's probably under-represented like a lot of women's health concerns are. And so if you're looking at ways to quantify the market and investors thinking about how big is this market, I don't think we can just rely on traditional sources of data that may not be reported accurately. Like the rare disease, which we're seeing so much more about, we should also see utility for quantifying a market size for the rarely reported disease. Sam made a great point that patients are experts, patients are the ones living with this condition. And to use more modern forms of real-world evidence to tap into that expertise, I think, is very important for the investors' proposition. And I guess thinking about it, now it's kind of tactically on an investor's validation of a market size. I mean typically, you'll see management companies pitch the market forecast, and they may have their own assumptions. But I tell you, it's a great way to use RWE as a way to validate, corroborate, challenge a management's view of a market. You may even find that there's more opportunity than even the management team had believed. Maybe I'll end it here. If you're talking about investors, you're also thinking about economics. And Sam made a really nice point about the economic value of RWE and positioning this asset to payers. Back in the olden days, it used to be that your real value inflection was let's get just a positive Phase III. Good things will come once you have a positive Phase III. And then it began, well, we get that thing approved. Now it is how do you determine improve to your payers that there is a positive economic results to this therapeutic? Ultimately, that's going to fuel the reimbursement decisions globally. We know that the way that we are consuming health care spending as a percentage of our GDP is not a sustainable proposition. And I think the opportunity is enormous for RWE to help inform the economic value that an emerging company is bringing forward with an asset that's demonstrated safety, demonstrated efficacy and now is demonstrating economic potential. Investors are thinking about that all the time.

That's great. That's great perspective. Bruno, I know we have talked about your experience with the investor community, and that was sort of next on my list, next on my hit list. So I thought maybe I would just open up to the panel to see reactions to Patrick's comments. I think you made some really interesting points there. And I was sort of thinking about this as presenting that value to the investor, but not really thinking about the investor interests are very similar to the payer interest. And so they are sort of sandwiching this conversation with all the regulator stuff in the middle. And so which is kind of a different way to think about that value proposition you have to do early is the same value proposition you're going to have to repeat at the end in a slightly different way. So I just want to open up to the panel because everybody has opinions on things.

So as an investor at the seed and seriously saying, currently, I mean, I can tell you that investors look at untapped value and how they can create value opportunities that others may not see as readily or that they're catching the wave of a trend, right, that's moving in the right direction and they look at everything. They look at how do regulators feel about this? What are the precedents are there, right? If there's a few companies that are good examples of using real-world evidence successfully, they're going to start to look at that and say, why don't we jump on this because this is going to grow. And so I think investors are going to want to, in all the right ways, exploit these opportunities that Patrick is describing. If there's an underserved patient population or their epidemiology can show that there's more patients that could be treated, these are things investors love to hear. And they're going to be listening very intently on those pitches because that means that, hey, not everybody is aware of this. Not everybody -- this isn't obvious to everybody. And we are kind of getting a peek into something that's going to create a lot of value.

I wanted to jump in on those comments as well because I just think it's so true in rare diseases. And when we're trying to think about who is the population that we potentially treat from our therapeutic and the point of Patrick about the underestimating the market. And we do know, I mean, having -- in 1/2 of one of my career was working in education, so educating academia and inborn errors of metabolism. And one of the lessons learned there was while a disease -- the first thing you do if you've got a problem around diagnostics is you look for the treatable diseases. So that's just human nature. You want to be able to look for the treatable. Something that you need to react quickly with and you can treat. And so I believe that there's many of those diseases that are untreated, where either they're undiagnosed or late diagnosis or they're given the diagnosis, and there's no treatment. So therefore, there's very little sort of follow-up and in the literature in cohorts of data. I do believe that there's a lot of data missing on numbers. Where there's some challenges, and I don't know if this is coming up later on real-world evidence in rare diseases, is actually how you collect that data. So if you're going to go back to claims data and you're going to be using current nomenclature or ICD-10 in different health care systems, it really is not -- it's difficult and it's not always fit for purpose. And so it's always very challenging to be able to get those true numbers. But definitely, what we see in the literature is oftentimes an underestimation of true prevalence.

Yes, that's a test we do in oncology specifically. I remember when the first ALK fusion gene inhibitor was developed. I remember the commercial leader in Merck said, never, never he would do that. It's only 5% of the lung cancers, not worth it. Meanwhile, the more you look for, the more you find that increasing the number of patients with this novel disease marker number one. And with ALSK inhibitors, it's a wonderful story. Meanwhile, you have 5 or 6 on the market plus more in development in a disease setting 5, 6, 10 years ago, commercial people would have declined to accept. So that's an interesting phenomenon and it shows. You have to -- you can start early in a program with a selective population and more likely this population will grow the more you will then search for them.

Bruno, the best example in biotech is Vertex. They were ready to dismiss cystic fibrosis as too small, and now they're one of the highest-valued biotechs in the industry essentially on that franchise.

Yes. Most commercial people, I remember, were wrong in the past. But that's a clinician taking that.

This has been a fantastic conversation, and I have loved everybody's unique perspective. We're going to close out this panel with -- by going around the room, and letting everybody get in last comments, things they wanted to bring up any sort of final -- any final food for thought for our audience, and we will start with Bruno.

Yes, it was a wonderful discussion, and I really appreciate it. I only want to make one statement. It was thanks to the panel discussion that I got more awareness to the topic of RWE. And so my conclusion from today's discussion is I will bring this into my consultancy with biotechs more often than I did in the past. So that's my simple conclusion.

Fantastic. All right, mission accomplished, Bruno. We'll move over to Patrick.

Yes. I echo a lot of Bruno's comments. This is as informative to me as I hope it is to the rest of the panel and today's attendees. It's a great topic. The things that I'm particularly excited about is this convergence of RWE is really the other side of the coin to the traditional randomized clinical trials that creates a more fulsome body of evidence for drug development decisions. I like the way that the industry is heading maybe at the prompting of a very difficult global pandemic to democratize clinical trials, to make it more patient-centric. And there's a ripple effect that's very positive from that of, creating more access. We all know that clinical trials have not been broadly available. But there is an opportunity here and real-world evidence can be part of this move to make trial participation, indeed access to health care as a whole, more readily available. And the last thing that I think is exciting is the potential of technology and the evolution of wearables, sensors at home capabilities, patient-reported outcomes that puts the power into patients' hands that is technologically enabled. That marries up very nicely with our rapidly increasing understanding of the biology of disease and of wellness. I appreciate the opportunity to join this panel, Barbara.

Thank you so much. Sam, final thoughts?

Yes. Sorry, thanks to the IQVIA team. No, I mean I just -- I think what's really exciting here is the way things have changed and the quantity of data that's potentially -- we didn't use the word big data, I think, but it's the one that comes up often. And so that excites me. I think what we need is the signs of quality and being able to ensure that the data that we're looking at, we know what to do with it and we understand it, and we can understand the quality of that data. So I think there's still work and a gap to do there. It also excites me to see that there's really a willingness of the regulatory authorities to be able to think how that they can use that and integrate it into the clinical development plan. And the work that I do in IRDiRC is really a clear sign, and that's something that we're thinking about now and how to set up on a global level. And I'm also very keen to see how we move forward with these remote technologies and the long-term follow-up. Again, I think we've seen a lot of change, rapid change in the last months and years. And that's going to be something that's particularly important for some of these diseases that really have a heavy burden on families that are affected globally and before may not have had access to trials that they can now get access to because of all these things that are happening simultaneously. So a great conversation to have, and thank you for the invitation.

Excellent. And Chris, we'll have you close us out.

Sure. I'll just share the sentiment of the panel. It's a really great panel. I got -- I learned a lot and it really talks about the diversity of how people are thinking about real-world evidence. I wanted to use a phrase that's often used when talking about AI, machine learning quantum. In the biotech area, there's a saying that it's not that computers will replace chemists, but chemists with computers will replace chemists without computers. And I think that's also true with -- it's not that a real-world evidence is going to displace the way we do things, but drug developers, who use real-world evidence will displace drug developers who don't use real-world evidence. And I think that I implore all of those biotech and pharma companies out there, be innovators, okay. FDA has signaled they're open and flexible. Investors are interested in tapping into the potential here. It might be an imperative for you to be competitive because if you're not doing it, your competitors might be doing it and it may allow them to have more label claims, more data to differentiate their product. So I wouldn't wait for the FDA to say you have to do this. You need to be the innovator, start forging this new ground and have that open dialogue with regulators of how this can be used to gain accelerated approval, FDA approval. How do you get this into your label? How do we displace some of the old antiquated end points that we've been using with some of these new, let's say, wearable remote-based capture data end points? So I think the innovators in real-world evidence are really going to be the winners, in my opinion, over the next 10 to 15 years.

Fantastic. Thank you so much to our amazing panel. This was a fascinating conversation. I loved the difference of perspectives. I really appreciate everybody taking the time to meet with us today. [Operator Instructions] Thank you very much to our panel. I really appreciate everyone's time and insights and thoughts.

Thank you to all our panelists for a great discussion. This concludes our webcast today. If you would like to continue the conversation and see how IQVIA can work collaboratively with you to apply real-world evidence approaches in clinical development, regulatory approval and commercialization of your asset, then please send out the post-conference survey or, alternatively, send us an e-mail to [email protected]. We'd be delighted to explore opportunities together with you and find the right approaches for you to confidently reach your next milestone with speed and agility and maximize your asset value. On behalf of today's presenters and the entire IQVIA Real World Solutions team, I'd like to thank you for joining us. We hope you enjoyed the session, and we look forward to welcoming you at a future IQVIA webinar. Thank you, and enjoy the rest of your day.