Transformation in Trials
A podcast about the transformations in clinical trial. As life science companies are pressured to deliver novel drugs faster, data, processes, applications, roles and change itself is changing. We speak to people in the industry that experience these transformations up close and make sense of how the pressure can become a catalyst for transformation.
Transformation in Trials
Innovative Trial Design and Re-Imagined Endpoints with Richard Nkulikiyinka
Understand the complexities of clinical trial design from a seasoned expert. We speak to our esteemed guest, Richard Nkulikiyinka , a senior leader in clinical development. Richard illuminates the often-misunderstood world of trial design, from setting objectives and identifying target populations to the nitty-gritty like data collection, visit schedules, and maintaining quality. The spotlight shines on patient-centric approaches, as Richard persuasively argues for designing trials that are more patient-friendly without sacrificing quality.
As we go deeper into the conversation, we reveal the intriguing process of creating and accepting endpoints for various diseases. Richard presents a compelling case study of heart failure trials, debunking the notion that traditional endpoints are always the best fit. He illustrates how a novel endpoint for counting all hospitalizations was innovatively developed and accepted by health authorities — a process that holds potential for other diseases reducing exercise capacity.
But it's not all about the technicalities. We also underscore the crucial role of collaboration in developing endpoints. To illustrate this, Richard shares his experience charting the benefits of pre-competitive collaboration between industry partners, academic institutions, and health authorities. We also navigate the challenges of innovating clinical trials, discussing the constraints of mega trials and escalating costs, and the collective effort needed to accelerate the development of new therapies. Get ready to reimagine the future of clinical trials with this enlightening discussion!
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Join the conversation on our LinkedIn page
You're listening to Transformation in Trials. Welcome to Transformation in Trials. This is a podcast exploring all things transformational in clinical trials. Everything is off limits on the show and we will have guests from the whole spectrum of the clinical trials community, and we're your hosts, ivana and Sam.
Speaker 2:Hello everybody and welcome to another episode of the Transformation in Trials podcast.
Speaker 1:Today in the studio, we're joined by Richard and Kuli Kiinkar, who is a senior leader in the clinical development space.
Speaker 2:Hi, richard, great to have you on the show.
Speaker 3:Hi, sam, it's good to be here with you, and Ivana, thanks for being back Great to have you on the show as well and appreciate you taking the time.
Speaker 2:Richard, in the Transformation in Trials podcast, we try and theme the episode around a certain topic based on our audience's, our guests' background and just based on your experience and your interests. Today's episode is going to be based around novel trial design and novel endpoints. So I know you've got some really good perspectives and opinions on this and some really good experience that we're going to learn about as we get into the podcast. But to kick us off into level set, perhaps you could start by explaining to our listeners how a trial is typically designed and maybe explain a little bit more about what are more traditional or less what I would call less novel endpoints.
Speaker 3:to get us started, this is also a really fascinating topic because, as you know, we really depend on good clinical trials in our industry and in clinical medicine in general to generate good evidence that people can rely on and know that the drugs that we're putting on the markets are effective and safe. So, ultimately, anything we do in clinical trials is really to demonstrate exactly that effective evidence and safety of drugs, and in addition to that, we always have to make sure that the evidence we're generating is very robust and reliable, and this is something that particularly the health authorities have the mandate to ensure. So whoever is designing a clinical trial has to really keep those three things front and center of their mind. How do I demonstrate safety efficacy in a robust manner? Now, you can imagine that there are many different ways of getting there, and particularly that it's going to be quite different depending on your setting, but there are a few common themes that one has to think about. First of all is what are the objectives of my trial? What is it that my trial really needs to demonstrate? What does exactly efficacy and safety mean for a particular drug?
Speaker 3:So you can imagine that if you're looking at a cancer drug, for example, you're looking for, maybe, tumor shrinkage or that the tumor disappears. And if you're looking at an antibiotic, you might be looking at how quickly does an infection disappear? And if you're looking at something different, so you will have different ways of looking at it and your objectives will vary depending on that. So objectives is, of course, the first thing you have to think about. Then you have to think about your target population, and we do this in trials by defining the inclusion and exclusion criteria. Then, of course, once you have that, you have to think about how you're going to collect the data that are going to help you demonstrate, or demonstrate that you have met your objective. So that leads to a data collection approach, and there are many different ways of collecting data, obviously. And then, once you have that, you put that into a protocol which has a visit schedule, and by visit we mean that how often are we going to be seeing patients during a trial and what is the modality of seeing the patients in clinical trial? Basically, they have to come to the clinical trial center and BC and have tests, etc. So that's your visit schedule. And then, finally, something that is very important in designing a trial is the quality ensuring the quality, you know, looking at the data as they emerge, and this also requires you basically to think about how you're going to do that. So if you take all of that together, you've got a clinical trial protocol Now no-transcript For each one of those things that I mentioned.
Speaker 3:I think it was probably about five key central pillars. You can do it in the way we've done it If you want to call it. The traditional way has always been that we try, as clinical trialists, to think how do we best make this work for the clinical trial center? So we're thinking how do I get a busy NHS hospital that is seeing a lot of patients and a professor of medicine XYZ, who's got a lot of things to do, and the study nurses who have a lot of things to do? How do I make this as simple as or as easy as possible for them? And that's a good place to start.
Speaker 3:So we think about the objectives and we try not to have too many.
Speaker 3:We think our target population in the way that the inclusion and inclusion criteria easy for people to understand, and the good patients in the hospital, but particularly when it comes to the VD schedule, the data collection and the quality.
Speaker 3:This is where we have really very focused on on the trial centers and made try to make it easy for them and perhaps lost the patient a little bit out of sight sometimes, and so we would have like protocols that basically require patients to come, like every month or every couple of weeks, or even sometimes even more often, to a hospital and have the travel and the waiting, et cetera.
Speaker 3:And these are some of the things that I think really can be done better nowadays and in the traditional way they created the heavy burden on the patient, but now we have opportunities to make them easier for patients as well, not compromising on the quality or anything in the aspect of the trial. So, to sum it all up, basically there are a few things that are set that we always have to do, but we have many different opportunities to do those things in a way that is still easy for the clinical trial centers but also very patient-centric and easier for the patient, while ensuring the quality is better. And then, of course, there are lots of other advantages that emerge that we can discuss as we go along.
Speaker 1:A follow-up question. You mentioned that there are these endpoints per therapeutic area. Is it right to assume that there is some agreements among different companies working towards this within the same therapeutic areas? Which endpoints you're aiming for within a specific drug?
Speaker 3:Yeah, great question, and this is actually one of the things that I really find very fascinating and interesting. On the one hand, if you want, particularly when you get to a phase three trial, which is a registrational trial, you want to be using endpoints that you know the scientific community and the medical community will be able to accept. So you don't want to be measuring something that doesn't mean anything to anyone. So you go with an endpoint that you think is reasonable, represent something that is of interest for the patient and for the clinician. So that's a starting point. And then sometimes you find that there are some endpoints that have been used many times before and everyone accepts them and everyone understands them. Then of course, you would, if they make sense for your trial, you would choose to use those.
Speaker 3:And this is most of the time what happens that you find that people in the industry or in the same area of clinical trial use well-established endpoints. A very important aspect is the validation status, because even if something is being used in general, it doesn't necessarily mean that it has to have, on the one hand, validation status and acceptability by the health authorities, and that again usually means that there must have been some level of precedence. But and this is where things get interesting Quite often we use endpoints, although we know they have limitations, but because that's what everyone has been hosting until now, we do not have the courage or want to take the risk of using an endpoint that we believe is more reasonable or is better, because we just want to avoid difficulties with our NDAs et cetera. And this is what I think it gets interesting and we can get into that.
Speaker 1:And maybe a follow-up question to that how does one validate an endpoint?
Speaker 3:That depends a little bit on not a little bit, it depends really exactly on what is that you're trying to measure. So let me take a step back and explain a couple of related topics and then maybe we can go into an example. Because when we think about an endpoint, as I said, it's actually a way of demonstrating an objective. So you start off with an objective, you think what it is that I'm trying to show clinically, then you translate that into an endpoint. But an endpoint itself has actually two very interesting and related aspects. One is what is it that I am measuring? And the second aspect related to that is it how am I analyzing it? So, not necessarily measuring it, but analyzing it. So, and the two things together basically give you an endpoint. So let me give you an example so you could say let's start with something that is very intuitive. If we look at the cancer example, you can say I'm going to look at tumor shrinkage as an endpoint. That's my objective. I want to show this drug leads to cancer regression, ie it gets tumors to regress, to shrink or disappear. That's your objective Now. Your endpoint could be then the response rate, and the response rate again can be defined in different ways. So the response rate could be the proportion of patients who have a certain degree of tumor shrinkage or it could be the proportion of patients who have complete disappearance of the tumor, for example. So that gives you an idea to see that what you're measuring is the same. You're doing a, probably a scan, some kind of imaging, to look at the tumor, and that's the what part of the endpoint. But the analysis part you know exactly how you're analyzing that to get to your answer is also very important. So one example, one other example, that is, you know now, a real world example where I think very interesting developments happened here.
Speaker 3:I spent a number of years working in heart failure trials and, as you might know, there are basically two major subtypes of chronic heart failure. There's the chronic heart failure with preserved ejection fraction and chronic heart failure with reduced ejection fraction and roughly for simplicity let's say that basically of all the patients with heart failure, roughly half have one subtype and the other half have the other subtype. And for a very long time we had good therapies for the subtype with reduced ejection fraction, which is basically the type where the heart is, the heart muscle is just so floppy and the heart chambers have dilated so much that the heart doesn't manage to develop enough contractile force to supply the body with blood supply. And One trial after the other. Well, actually there were some good therapies out, seems to be working and improving outcomes for patients with reduced ejection fraction, and they were preventing recurrent hospitalizations, they were preventing cardiovascular deaths. So they were really doing very good things for patients. But none of those therapies worked for the other type of heart failure with preserved ejection function, which is basically again, you have the same symptoms and you have similar issues with hospitalizations and cardiovascular death, but the problem there, physiologically, is different is that you have a very stiff heart muscle that has problems with filling during the asterisks and therefore, ultimately it leads to poor blood supply for the body. So, no one.
Speaker 3:For a very long time we really tried to understand why are these patients not responding to the therapies? And then new therapies started being developed and they also continued to fail, one trial after the other using the same endpoints and failing to show an effect. Well, until then, some smart people looked at this and said we keep looking at heart failure hospitalization in the same way that we looked at heart failure hospitalization in reduced ejection fraction. But in reality, if you look at the data, patients with preserved ejection fraction have so many more heart failure hospitalizations and they are less susceptible to cardiovascular death that if you continue to use the same analysis, we'll never actually see a real result that is relevant to this population. So they developed a new, still looking at heart failure hospitalization, but instead of using the traditional end point of time to first heart failure hospitalization, which is basically where you are in the analysis, until you get your heart failure hospitalization, and then you as patients, you are done. They said well, we have to count all the hospitalizations because these patients keep coming back to hospital and this is what is driving the poor health outcomes.
Speaker 3:Well, guess what? Suddenly, we saw that even analyzing some of the previous trials, but also looking at a new trial, you suddenly see that there are therapies out there that can really dramatically reduce the burden of hospitalizations in these patients. So that became a new end point. And now it's your question about how you validated. Of course, there was a body of data showing that it's doing something clinically relevant, it's measuring something clinically relevant.
Speaker 3:Then a collaboration between academia and industry basically started collecting those data and discussing with the health authorities, with the FDA and the EMA, and particularly in the EU. There was a mechanism called qualification procedure where you can discuss with the health authorities a new endpoint and went through all of that and this endpoint was then qualified as acceptable for approval in heart failure trials With preserved addiction function. So long story short heart failure hospitalizations something that we've been looking at that is relevant to patients, but we were looking at it through the wrong lens, if you like, and now we've got a new way of looking at it that actually shows something relevant for patients and has been accepted by health authorities. That's the. You can see that it's a long way from the time you think about it to the time you actually get it qualified, but it's worth it. That's pretty cool.
Speaker 2:And is that typically how novel endpoints originate? Then, richard, that kind of process of you're looking at something in a different way, using maybe a traditional endpoint, and you realize that perhaps that isn't the right way of looking at this and that therefore becomes the genesis of a new endpoint. That is the right way of looking at this.
Speaker 3:That's, yeah, I think that's generally how this comes about. However, sam, you know another that can, following the same process you don't necessarily always have to follow the same, you know end up using the same instruments. So I can give you another example, which is very cool, in heart failure, again in the heart failure population, but it's also relevant to things like chronic obstructive pulmonary disease or COPD or for other types of diseases that basically reduce your exercise capacity. We know that, or intuitively, we would say. If you're treating someone with an effective drug and they have a disease that limits their exercise capacity, if the drug is working, then they should feel better and have more energy to exercise. So you wouldn't expect that they maybe walk more often and further, that they go up the stairs more easily without having to stop these kinds of things, but when you try to measure that in a clinical trial, it's very difficult.
Speaker 3:So enter accelerometry. Have you heard about accelerometry? Presumably you have, because this is like a new kid on the block in clinical trials. So it seems everyone has the phone these days and I actually remember this is not even long ago when pedometers came about and became popular and you could basically get your own one and put it on your belt and measure your steps, your step count, every day. This is not even like probably maximum 12 years ago. So, and now it's integrated in every single mobile phone. No one even thinks about going out and buying an extra pedometer and to have sensors that are measuring a lot of things about our activity, our daily activity, the levels, the intensity, et cetera. So, again, following the same thinking process was like well, we've got all sorts of really pretty limited tests that we use in clinical trials to try and measure if people are improving in their exercise Capacity, why don't we use wearables?
Speaker 2:Yeah.
Speaker 3:You give them a sensor. They've got it with them every day in their actual home setting, not in an artificial setting, when they come to see their doctor, in the hospital, where they're not familiar, when they're a bit nervous because people get nervous going into hospitals, et cetera. Now it gives them a sensor and captures the data in the everyday routine and sees if you can see a difference between before they started the treatment and after they started the treatment. So, and the whole science of trying to derive endpoints from that is basically accelerometry and there's a lot of people working on this out there, been one of them and hopefully will continue to be, and I really think it has a lot of potential because then it can suddenly give you novel endpoints that we've never used before.
Speaker 3:They would have to go through the same lengthy process of showing that they're measuring something that is really clinically relevant and having them validated and accepted by the scientific community, the medical community, the health authorities. But it would be worse why? Because it could dramatically make things easier for patients, dramatically increase the amount of data that we're able to collect on pupils' exercise capacity. It could increase the quality of the data because if you imagine that you're looking at now long periods, every day, several days at a time, several weeks back to back, you're looking at something that is much closer to the real patient experience and it could dramatically potential to reduce the number of patients that we need to actually demonstrate an effect. So it could have a very good effect on reducing the trial size and duration, so everyone would benefit. So I think that's really, really exciting, but it's also very, very complex, I tell you.
Speaker 2:Yeah, I bet I would imagine, rich, that you need everybody to be thinking about all of these things quite holistically, because some of what you're talking about obviously is going to have an impact on things. If you look at something one dimensionally, like Timeline, for example, and progressing a pipeline asset from one stage to the other in a very one-dimensional way, you start to kind of build in some of these novel end point approaches that could take some time to investigate. Then that could potentially limit the progression from a timeline standpoint.
Speaker 2:Again, if you're looking at it very one-dimensionally, so, are those kind of things that you hear about and are considerations, or does everybody look at things very holistically?
Speaker 3:I guess you wouldn't be asking the question if you didn't suspect that it's in all the case.
Speaker 2:A loaded question.
Speaker 3:Yeah, yeah, no, that's a very astute observation. So I think your audience will probably know exactly how, or more or less how, we progress drugs from phase one to phase two to phase three clinical trials in the pharmaceutical industry, and that's a great model, but it has its limitations, particularly when you take it really company by company, because overall, as a concept, it makes sense. But when you think that every company with a small or big, has basically to take one drug at a time through these stages, you start realizing how difficult it is to make commitments for things you're going to do in phase two when you don't know if phase one is going to work. And the same is to start committing on things that you're going to do in phase three when you don't know if phase two is going to work, because commitment means money at risk. So if you're going to use a wearable, for example and then here I'm talking about very real experience you think about it, maybe in phase one. You think about, or before you start, phase one even, and you think, oh yes, let's be smart, and you figure out what wearable you could use and then, if you want to ensure that you will have the supply, you will have the infrastructure to support it. Of course you have to go into contracting agreements with the manufacturer at that point in time.
Speaker 3:And then you're running a phase one and hoping that it will be positive and you start to think about the phase two. You would have to again be planning years ahead and doing the same commitments. And then what happens if the phase one doesn't deliver the results you were hoping for? And this happens all the time. So that already basically throws a wrench in the works. And then quite often you find that you don't have that continuity, that continuity just because of that. So what is the solution? Perhaps that's the way where you were headed, sam. I'm trying to read your talk of phase right now.
Speaker 2:You're framing my question in a more positive light, which is the best way to approach it rather than find next in pessimistic way.
Speaker 3:So the solution, I think, is collaboration.
Speaker 3:You cannot do this, no one will be able to do this in the under round.
Speaker 3:So pre-competitive collaboration between industry partners, between academic institutions and also including health authorities in the conversation, is the way to go.
Speaker 3:And this is something I've been quite passionate about and really been involved in on a number of fronts in my role in the industry, in my roles in the industry. So to start, one example what we're talking about here with the heart failure trials, so there is a consortium called the Heart Failure Collaboratory, which is a consortium of many leading academic institutions, mainly in the US, but we have also thought leaders from around the world participating in that, and a number of industry partners, big and large, big and small I mean sorry as well as the FDA that has been working on these type of topics for the last six years or so, and one of the focus areas is really accelerometry, where we've already put out there some recommendations about how to standardize the thinking, how to standardize the data collection, but now we're still basically in the process of developing then real accelerometry readouts that would translate into something that is close to an end point, a clinical end point that would be acceptable.
Speaker 1:Does this also require some additional competencies with the health authorities when they have to evaluate a new end point, if they are suddenly to consider a digital end point rather than merely a clinical one?
Speaker 3:Yes, absolutely, ivana. I think the earlier you start the better, and that's why I think it's so important having these large collaborative groups that also engage with health authorities independently of any one particular compound or program, and the same thing is going on in Europe. By the way, there was, like, some IMI initiatives that have really been looking into this, and Ivana Moeirov is one that is called for the mobilized E. It's an IMI initiative that is also looking at developing accelerometry based end points, and there's a number of other initiatives, and it definitely makes sense. You can imagine that it's much easier for a sponsor if they turn up with an idea that they're being kind of discussed in the context of the consortium. Then the discussion with the health authority can focus on the specifics of the program rather than really starting from scratch and trying to explain why you think this fancy, novel end point is something they should trust.
Speaker 1:I'm also curious how did you get into this whole space of developing end points and thinking about how to think broader than just the clinical part of the end points? Tell us about your background.
Speaker 3:Yeah, I don't know if I know myself really, I don't know if I know how I got into that. Sometimes you just look back and say, wow, how did I get here? It was a mixture of the daily challenges on the job and just the recognition of potential opportunity. I was in the industry for 15 years in the pharmaceutical industry for 15 years. I started off in the oncology area. I was in drug safety in oncology for phase two and phase three trials and then we were doing very nice, very traditional end point trials In oncology.
Speaker 3:These things might be also slightly different than therapeutic areas, because you have a very clear and single enemy it's the tumor. You have to understand the tumor and you have to get rid of it. But that's where I learned the ropes about how you think about the clinical trials, how do you design the clinical trials, and it was great. But then later on I moved into clinical development and different therapeutic areas that did work in dermatology and anti-infectives and there, for example, what you're trying to do is you're trying to. Your objectives are looking at clinical manifestations of a disease and how quickly and how well you get rid of them. So it's about speed. It's about how quickly does an infection resolve? It's about the completeness. So you have a lot more elements that go in time elements and dimension elements, etc. A lot of things that you have to measure more directly and more granularity that go into definition of an endpoint. And that's where I already started thinking, wow, this is actually quite interesting because you could be looking at the same thing, but the way you measure it can definitely make a very big difference as to whether you're able to show a clinically meaningful result for patients or not. So then I took that perhaps along with me then when I went to cardiovascular drug development.
Speaker 3:So in heart failure and chronic kidney disease, and here then everything really comes together. We have the challenge there that the trials are getting bigger and bigger and bigger because we're doing outcome-driven trials in phase three for approval and the better the existing standard of care gets, the more difficulty becomes to show an improvement on top of that in a reasonably sized trial. So just to make sure that none of our audience think, maybe think there is no medical need to then introduce new therapies. This is a very, very important point to make. People continue to have a lot of poor outcomes despite having a good standard of care, but nonetheless, if the number of sorry, if the standard of care is still helping to stop some of these events happening.
Speaker 3:It means that you have to observe longer and many more patients to be able to show that the additional therapy is also adding additional clinical benefit.
Speaker 3:And that has basically led to a massive blow up in the clinical trials in cardiovascular and you're always hearing about these mega trials and in 10,000 patients 15, I mean there are now some trials starting out that are going into the 20,000 patients, and you can imagine how difficult, how extremely expensive, how cumbersome those trials are for everyone involved.
Speaker 3:And that really got me thinking, because my team and myself were always confronted with these questions how do we develop the next drug without really breaking the bank for the company? Because if you have a great idea and you know it can help many patients, but it's going to be so expensive that no one can do it, you really have to be creative and figure out how to do it. And this is where then we start realizing that actually you know some of these novel endpoints that can help you maintain the quality, maintain the scientific rigor, but make things much more efficient could help. And that's where I really got going then, and that's where a lot of people in the industry also got going. So that's a real need to make sure that we don't start innovation, and that's why we're working on these novel endpoints In general. I think that's probably the motivation for most people.
Speaker 1:And that's awesome and an awesome background. I am curious, though is it safe to assume, then, that there might be drugs out there that are actually better than the standard of care? But because we've measured the wrong things, we measured some endpoints that do not show their promise. They may not have been approved, but they're just lying there.
Speaker 3:Yes, I would be willing to bet that that is the case, and this is something that I think we need to think about really collectively as an industry, as a scientific community. Many drugs end up not really having a chance to show what they can, because developing them would be too complex or too expensive or would not lead to a result developed in the current paradigm. That would not lead to a result that would, for example, convince the payers, and we really have to continue thinking about that. One of the areas that I have a lot of passion for as well, and where I see that we are again approaching the limits of the innovation model, is chronic kidney disease.
Speaker 3:For a very long time, we had very little innovation in chronic kidney disease, and this is a massive problem. There's, I think, about an estimated 850 million people living with chronic kidney disease, just imagine, and there will be one billion at some point. It's almost 10% of the world population, and that's terrible because, ultimately, if you have it long enough, it basically kills off your kidneys and puts you on dialysis, which is a terrible, terrible thing to happen. So there is a massive, massive need to act, and act quickly, and basically stop or reverse the progression of the kidney disease. And yet the way we're doing trials again makes them bigger and bigger and bigger. And so although you're still able to show you can stop some people getting on dialysis, you might need like 10,000 patients and follow them for like five years and that translates into a very difficult trial that most companies will not be able to do and let alone some public institutions etc will not be able to fund. So if you have a drug that you really have reason to believe that it could help, it would be such a shame if we can't get into the patients just because of that.
Speaker 3:And this is another area where still measuring the same things we've been measuring all the time but putting them into different endpoints might help. And there's been a development of the so-called EGFR slope, so the Estimation Luglomerular Flutration Rate slope. So it's telling you essentially how quickly your filtering function of the kidney is declining over time and it's already been shown to actually be like a good way of improving things. Now there are other ideas about how we might look at like hierarchical endpoints that again taking the same things have been measured, but looking at them in a much more efficient way that you can see a treatment, difference between the treatment and the comparison in a much clearer and much earlier manner, and if those get accepted, then I think there might still be a good opportunity for additional therapies to reach patients. So you're absolutely right. I think this is a big problem and we have to continue working on making sure that good therapies do not get stopped simply because of the strategic reason, meaning that we don't find a reasonable way of developing them.
Speaker 1:Yeah, richard, I wish this conversation could continue forever, because there are so many interesting aspects we could dive in to. From here we are going to start rounding off this episode, and we always ask our guests the same question towards the end, and that is if we gave you a magic wand, and with that magic wand you could make one wish that would change something in our industry. What would you wish for?
Speaker 3:Oh God, so I don't get like three wishes, so you'll have to tell me what I have to do to come back to another episode and then another episode. So I keep getting a wish and I can take a different wand every time.
Speaker 1:Well, we can make that happen.
Speaker 1:Sounds like you want to change lots of things, then we were, of course, joking in this part of the episode, but while we were joking, we also realized that we had more things to talk about with Richard and we invited him to come back and do an additional episode so we could explore more of those things. So listen in on the next episode, where we are going to talk to Richard one more time and explore further topics Well before we get you back on the show for more wishes, Richard, where can our listeners reach out to you if they have follow up questions?
Speaker 3:Oh great, well, they can find me on LinkedIn for sure, and I would be more than happy to engage in conversations around innovation, clinical trials.
Speaker 1:And I would also like to thank any other player and get the episodes hot off the editor.
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