Amy Abernethy Futurism

 https://thehealthcareblog.com/blog/2022/01/13/futurecasting-with-amy-abernethy-verily-real-world-data-clinical-trials-health-policy-in-2022/

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Unknown Speaker  0:10  

Hey everybody, its Jessica Massa with WTF health, what's the future health, I'm talking to the who's who of health tech and healthcare innovation. And today, you may recognize the lovely face beside me. This is Amy Abernathy. She is the former principal deputy commissioner for the FDA and now is president of verily, clinical studies platforms. Amy, it's so great to have you here. I've been dying to catch up with you. Dress, it's awesome to be here. WTF, our future?

Unknown Speaker  0:40  

Okay, we are going to check in on the sentiment of all things, clinical trials, digital health, specifically as it relates to the regulatory environment. And then we're going to also get an update from you on what's going on with verily and what we can expect for you guys in 2022. So first off, though, I understand, Amy, that you, you testified before Congress a few weeks ago specifically about clinical research and personalized health care. So talk to me about what Congress was after, what were they looking to find out? And what did you tell them? So interesting. So, you know, I was asked to testify to the house and the house is trying to understand what kind of investments should be made into the future to make sure that personalized health care becomes reality that we fulfill the promise to the American people. And also, you know, not only what investments need to be made, but what needs to happen from a policy perspective, and what do we expect that future to look like? And Congress asked about a number of things, especially in the clinical trials and evidence generation front that they wanted me to talk about, they also asked a lot, a lot of regulatory stuff, as you can imagine. So within the context of clinical trials, the questions were around the move towards virtual clinical trials, sometimes called decentralized clinical trials, how do we make sure that all patients have the opportunity to participate and that our clinical trials are fully representative of our diverse population? America? They wanted to know how do we leverage things like sensors and other wearables to collect data with reducing while reducing the burden for people who are participating in research? And then a number of questions were about the future with respect to what's it going to look like, as we evaluate medical products, such as software as a medical device? And how we're going to do that? And what does that mean for evidence generation writ large? All right, Amy, you've got a dish for us. So I mean, like, what did you tell them? And like, I'm curious about what you told them. And then I'm curious about how you felt like they took it like, I mean, what's the sentiment around some of this stuff? Like, I mean, I'm curious, like that Washington insider look like, I mean, when it comes to looking at digital health and regulatory, I mean, is there what's the prevailing sentiment around that, or even decentralized clinical trials? And including all this real world evidence? I mean, what did you tell them? And what do they think? Well, so what's interesting to me, so this is the committee at the house that was responsible for 21st Century Cures. Okay, as a reminder, 21st Century Cures is a piece of legislation that was passed in December of 2016. And really set us on this path of leveraging real world data, being able to solve for problems of diversity and clinical trials through leveraging, for example, electronic health record interoperability, and thinking about decentralized capabilities, and also new clinical trial designs, and the ability to invest in capabilities such as software to collect patient reported information. So that was really a key part of 21st Century Cures. And, you know, the Washington insider kind of view was several whole, from my perspective, one was, it was terrific to see their pride in 21st Century Cures, and the promise show up even in how we've been managing the pandemic. So that was one thing that I thought was interesting. There's a piece of legislation now on the hill called cures 2.0, that now takes this story forward with increased investment in decentralized, or virtual trial capabilities, real world data interoperability, patient report, and there's a lot of interest to see and maintain that momentum. And then there were a lot of questions about how do we learn from the pandemic? What did the pandemic teach us? And you know, this is whether this is about being able to develop vaccines so quickly. We're also what did the pandemic tell us, we're going to have to do differently in the future. So for example, going from very traditional clinical trials, to now pairing traditional clinical trials with real world data. All right, I know that's something that you guys are building over there, verily, is this whole, you know, opportunity to do more of a hybrid model where you have on site you have virtual and then you can can mix and match both of those to different degrees. I'm curious on the the decentralized clinical trial front, you know what you're hearing too about this in the context of your conversation of health equity. So I mean, like, I feel like this has been a conversation. It's like the two worlds colliding, you know, in terms of how do we make clinical trials not only more accessible, but like how do we make sure that they're accessible across all public

Unknown Speaker  5:00  

So I'd love to hear like what your when your you've got your ear to the ground there. It's like, what are you hearing about about that, in that particular context. So, you know, one of the things that the pandemic brought into focus, we knew this before, just but, you know, sometimes it was either hidden, or we didn't want to pay as much attention, which is that our clinical trials often don't represent all of us. And further, they're often hard to participate in. And so the, you know, the merging of the health equity conversation with the how do we essentially modernize clinical trials conversation really has come to the fore with asking, for example, where do we meet people where they are whenever possible? So leveraging video visits, for example? How do we make use of all available data? So we reduce the burden of data collection itself? And also, how do we think about really solving for operational details and clinical trials, like the delivery of the drug being studied directly to your local doctor, or even to your home? And so there's many things that I think have kind of come into this space within the landscape of decentralized clinical trials, but also made really visible and COVID? Okay, I'm gonna give you Oh, sorry, let's go, No, give us about me. Let's go.

Unknown Speaker  6:20  

There a couple bucks in this. First of all, one of the things that's kind of happened is there's almost been a complete shift to decentralized clinical trials with only thing we want to talk about, but patients, and also for most of the things that we're studying, it's not always safe to do it at home, right? So really, clinical trials needs to marry the solution for the situation. And the first thing that I would say important. The second thing is a look in from my FDA side, which is your what's a clinical trial about a clinical trial is about building and generating a confident data set. So you can have confident answers to the question at hand. And if it's a regulatory question, that's often a high risk question, like, do we approve this drug for prescription for all people? And so making sure that whatever clinical trials solution, we're thinking about, generates high quality data and keep study participants Safe is a very big question from the FDA that at least from an FDA side, FDA wants to see that virtual clinical trials and more easily attainable, clinical trials happen. But people are kept safe, and the data quality exists. And so there's a big kind of like, buts in this story. No, and I think that that's important. And I feel like, it's interesting to hear you talk about it that way. And it's like, I keep hearing and other aspects of health care, particularly after like two years of this pandemic, you know, it's like, there's in person, there's virtual, and there's a time and a place for each one. And so omni channel seems to be the word of the word of the year, in my opinion, in terms of figuring out how do we make sure that things are reaching people on the right channel for the appropriate thing. And I it's interesting to hear that you're also picking that up in this space as well. 100%? And, you know, frankly, that might be one of the WTF things that comes from today is omni channel clinical trials and figuring out how do we make that happen? All right, I want to hear about what verily is doing in that space, but not quite yet. Because I have one more thing I want to get your input on. So you talked about building a confident data set and how the FDA looks at that for regulatory? And like, what are the the topics of conversation that I feel like his has increased in terms of its volume over the last couple years has been the conversation around real world evidence. And so I'm wondering if you could talk to us about that. And so and maybe start out first, like, in terms of real world evidence, and you could define it real quick, I have a real broad Health Innovation audience. And so for those who may not entirely understand what that encompasses, if you could talk a little bit about what it is. And then, I mean, we saw an incredible use case for it with the development and approval of the vaccines. And we're still seeing that. And so I'd love to get a sense of you know, what, what is this as Amy Abernathy defines it. And then you know, how, what is the regulatory sentiment about that? And what do you where do you think real world evidence is going moving ahead into the next year? Such an exciting topic for me? Oh, again, good, I'm glad I like to refer to.

Unknown Speaker  9:07  

So first of all, as I think about defining real world evidence or real world data, these are data sets generated outside of the traditional clinical trial setting, so they can be passively created datasets, such as information that has been captured in electronic health record or claims data, or sensors in a watch. Um, they also can be intentionally in prospectively collected data, such as a prospective registry or pragmatic clinical trial. All of those live in this landscape of real world data and real world evidence, really real world evidence is the clinical assessments and answers generated from the analysis of real world data. And practically speaking, one of the advantages of real world data and world evidence is that it's more generalizable. It's more representative of a larger populations and often has less burden for data collection and getting the answers but one of the challenges is that the datasets themselves have

Unknown Speaker  10:00  

not been prescribed with respect how they are going to get collected. So there's no full study protocol for all of the details of the data collection. And as a result, often the data quality is lower or more suspect, oftentimes, there's missing data points either because for example, the electronic health record, it just wasn't something important to capture, such as data death, you're not going to go back and put that in the electronic health record after person passed away. And so the other reasons for skepticism in this space have often been kind of that the outputs therefore may be less credible or reliable, because the data that was generated in this space is less credible and reliable. Why is this become such an exciting space? Several things. So first of all, innovation in the health tech space, in the data space, and also innovation in the scientific methods, space has allowed us now to start to think about new ways to clean up the data, improve the credibility, and now get to more credible answers, leveraging real world data for robot evidence. That's one reason. Second, we mentioned before 21st Century Cures, and 21st Century Cures compelled FDA to start to develop a framework and a way of thinking about how FDA would consider robot evidence within the context of regulatory decisions. And exciting in 2021, is a series of guidances that have come out in the last two months from FDA spelling out in detail, here's how FDA thinks about the datasets, their quality, how to Lincoln generate them, all of these kind of critical questions are spelled out in these guidances, their draft guidances. And so for this entire audience, if you're interested, I would go and you know, submit your comments, FDA can get these finalized. But this actually starts to give us a codebook, so to speak, or a blueprint to how to leverage all the evidence of robot data for all kinds of research questions, including high risk regulatory questions. Last two things I'd say. One is this rural data space, it's really been heating up, especially as new capabilities have been developed on the data side and the health tech side, and people started to see the possible with respect to leveraging wearable data. It's also starting to be heating up because of COVID. So you mentioned for example, how real world data, think about the information from Israel that helped us understand the vaccines and the durability of effectiveness as well as the understanding of do we need a booster right, these critical questions came from large datasets generated outside of the context of traditional clinical trials. And that sort of set of use cases and COVID have allowed us to start to think through. And the last thing I'd say is, they're starting to be an understanding of, Hey, how can I pair traditional clinical trial data, and real world data to start to get an even bigger picture, what I like to call totality of the evidence around how a medical product performs, and that is now really the story that's unfolding in 2021. Going forward is totality evidence, and using that information to understand how a drug or device or medical product is going to perform across the entire lifecycle of that product, not just for one specific decision, like a regulatory decision, I'm glad that you said that. So it's like it because I was gonna ask you that, you know, and maybe go a little bit further on that and just connect the dots all the way through to the real value of it. So it's like, it's not only just having this data for the for the initial approval of it, but the entire lifecycle. So other indications, maybe novel indications for drugs that do get approved, as they are even like refining for side effects, refining, refining for different patient populations. But from you like what, like you said, you're excited about this space of what do you see as the real value of this whole real world data and real world evidence space? Bottom line is Amy.

Unknown Speaker  13:45  

So I'm gonna put the value into three categories. Yes. So first is, as you've highlighted, there's a ton of use cases across the entire clinical development spectrum. So real world data and real world evidence to help with clinical trial planning, whether that's designing the trial or planning how you're going to conduct it, rural data and real world evidence to help with scientific discovery. So for example, pairing biospecimens with longitudinal clinical data can now help us make discoveries about drugs that might work faster. There's also real world data and robot evidence that can be generated to understand how a product performs after it's gone on market. So a lot of times we might think about secondary indications. So you know, now understanding how to use that drug for diseases or specific scenarios beyond which it was originally developed for in real data and evidence can help there. And then finally, one of the things that, you know, there's been a lot of interest in is the use of real data and real world evidence, for example, to replace the need for a control arm in clinical trials. So for example, this sort of synthetic clinical synthetic control arm story. So that's one big category of the house and the robot data space. The second big category is this idea of continuous evaluation of a medical product across the lifecycle.

Unknown Speaker  15:00  

Take a software as a medical device, an AI enabled device as an example. So so first of all, you know, first there's the initial evaluation of that, that device potentially leading to its clearance. But then you want to understand how does that device perform across time? What if it gets updated. So for example, update cycles of an AI based software system, and being able to get us longitudinal data for real for real world datasets, and real data sets paired with clinical trial datasets, allows you to continuously assess that AI device, AI based device across time and make sure it's performing as expected. So I think that's enough for a second really excited to share, because that's been one of the criticisms over within this health tech space is, you know, in terms of just the technology evolving over time, and so what got approved and where it is now, version 9.0, maybe two totally different things. And what's the third one, me I don't want interrupting? That's right, the third one, the third one built from that story. And that's what we're starting to see now is a shift in evidence generation writ large, we're starting to see this shift, really, from a traditional planful way of doing clinical trials, phase one, phase two, phase three regulatory approval, we don't think so much about clinical trials anymore, to earlier approvals. So for example, approved on phase two study data, all of this, we saw in the context of COVID, emergency use authorizations. And then after the approval, or the authorization, the expectation of continuous evaluation of the product. So for example, the vaccines across time. And so what we're seeing now, with the move towards rural data, mobile evidence, the bringing in of health tech solutions, is this change in the shape of evidence generation, so that there's continuous evidence generation on across time, and lots more focus on the post approval, a post authorization space, and lots more focus on using real world data. They're using decentralized or virtual solutions. They're leveraging digital solutions, like biomarkers they're from or sensors from a watch all kinds of changes in what evidence generation is gonna look like in the future? That's cool. Do you think that that change or that evolution in the way that the FDA is doing this is gonna stick post COVID? Like, is like now that they've kind of adapted their process and are including these different types of data, they're including, you know, going to go into approval faster, but keeping their eye on things? Is that gonna stick? I think it's gonna stick for a lot of reasons. So it's not gonna stick because FDA is starting to get their head around, what does it look like to leverage rule data totality of the evidence, continuous evaluation across time, and actually, the devices Center at FDA CDRH is already asking for this. And I think we're going to also see it with continued focus on accelerated approval pathways and other ways of getting, you know, really novel drugs and other products, for unmet medical needs to people who need them as quickly as possible, then we need to keep cross checking how they perform. So that's gonna stick I think, Okay. Second thing is that the payers, right, the other other end users of all of this information, are also going to need more and more information more and more detail about how do you personalize? How do you figure out which populations might have been missed in the clinical trials? And we now need to understand better? How do we really start to think about pairing and combining treatments, oftentimes, treatments are studied as monotherapy, or one offs. But what happens when you think about a combination of a healthcare delivery system and a particular drug, like a lot of that I think, is going to continue to pull this along. And just the way that you've said, so my expectation is that this pace, this shift to more and more evaluation in the post approval setting is indeed going to be true. And we had already seen it post COVID, I mean, proof COVID. And now post COVID, it's going to speed up. Oh my god, Amy, this is so fascinating that the insights you have thank you so much for sharing all of that with us. And this is the beauty of talking to you, Amy is that not only do you have a lot to say in terms of being like Washington insider, former FDA, but you're also a verily which is like no small thing. I mean, one of the more exciting companies in the like tech and health space, so I've got to know what's going on at verily, so you were brought in, to basically take what they what they really had started with it with its clinical research program and project baseline, which I think most of us are probably pretty familiar with. And like evolve that and grow that as a business unit. So I would love to hear how things are going and I would love to hear what you have on tack or UNTAC or on track for next year.

Unknown Speaker  19:34  

On top and

Unknown Speaker  19:39  

um, you know, I'm gonna connect all the dots just Oh, great. I love when this happens.

Unknown Speaker  19:46  

And you also brought up this kind of critical issue of how do we use software and technology better to conduct clinical trials smarter and be able to reach all populations and then we talked about real data and real world evidence and how's this whole space?

Unknown Speaker  20:00  

Changing, including the leveraging of all data and totality the evidence. And so those things are what's informing verlies. And the clinical studies teams in particular, you know, kind of move and shift into 2022. So our clinical studies platforms team, really as focused as our 2022 goal on it continuing to advance exactly that story we've been talking about, over the last 20 minutes, we are going to do a number of things intended to really help to not just make sure that the tools and solutions are in place for that shift towards continuous evidence generation across the lifecycle of the medical product, but to make sure that we reach all patients, and we do so by reducing burden. So what does that mean? Yeah, we're building software within the context of the clinical research workflow, really focusing on reducing the burden of participation and making sure that anybody who is involved in research really understands the why and what's going to happen, and how to be a participant in a way that is fit into the day to day activities of daily life. So that means we're building solutions like electronic consenting, we're building solutions, like patient reported outcomes, solutions, software for the clinical research site to reduce the burden of being a study coordinator. So that's big one, one big one. The second thing we're focused on is building longitudinal data sets. You mentioned our baseline health study that really sort of is our you know, kind of it's I call it the crown jewels. It's like the starting point of learning how to do this critical longitudinal work. And we're going to be working on how do we build studies that combine robust data from passive sources, like the electronic health record and claims with traditionally collected clinical trials data, so we can create longitudinal data sets that really are representative of that total totality of the evidence story. So that's the second big thing we're focused on. So longitudinal data and 22 is a big thing for us. Sure. Third thing, and 22 is going to be digital biomarkers and study watch, which is our way of collecting sensor data directly from people who are wearing a sensor on their wrist, but then turning that into a digital biomarker about movement, or digital biomarker about physical functioning, for example. And that's a big focus for us and 22. And then also how do we interact with people and participant recruitment, participant interaction giving and returning results to patients who are participating in research and learning how to do that? Well, so that as we go into the future, the research ecosystem is equipped with solutions and tools that reduce the burden of participation, that build longitudinal datasets and plan for all these study design of the future that we've been talking about today. That's awesome. So tell me this, like, as you guys are using this, like, who is who are you guys working with? Like, I would love to hear like maybe like, if you can give me like a favorite example, either from this past year, or like something that you've got on on on tap for the year ahead? You know, like, who are your customers? Like, who are you guys working with on this stuff? So, yeah, we're working with a variety of customers. Some of them are large pharma. Some of them are smart, small farm, our device companies, we have a number of partners across the ecosystem. So for example, the basement house, it was together with Duke and Stanford and we have other companies that we partner with. One example that I think really sort of tells us also about where the future is going is something called the hero together registry, which is a Pfizer evaluation of the Pfizer vaccine across time, really run by Duke in concert with verily, and what's interesting to me about this story is these are people who are participating in a very large registry, where when they have gotten the vaccine, they've kind of basically signed on are being followed across time, gives the chance to prospectively enroll people and then understand how the vaccine is performing. And also think about issues such as adverse effects, and be able to monitor for that across time, as really been working hard on leveraging tools that allow us to meet all people. So whether that's working together with large networks of clinical research sites, and essentially a variety of sites that now can leverage software to enroll people, people quickly, or making sure for example, that Spanish language translation is available in easy for anybody with any in any different language to participate enough to understand adverse effects by population. That's awesome. That's really cool. Thank you for the example. All right, last thing for you. I just want to get a sense from you from the business standpoint, right? I mean, as President of this, of this division within early, you know, I'm curious about what's ahead for you there. I mean, we saw the acquisition of signal path at the end of the summer. So I mean, like you got your eyes on anything else. I mean, what's ahead in terms of scaling out the business? Watch this space just?

Unknown Speaker  24:53  

Well, in 2022, you're going to see us double down on a couple of things that that I just talked about, making sure that we're building out the solid

Unknown Speaker  25:00  

that's in the research work workflow, building and testing, longitudinal data sets and building partnerships to make sure we're getting that work done well, with a number of partners across a set space, I'm very excited about what that's going to look like, you're going to see us really work on making sure that we've got the details in place to do this longitudinal work for the future. And then the question becomes, what else are we going to need to do to make these things happen? And so as the person responsible for the business, you're going to see me thinking broadly about what does that look like? It's going to be fun. That's a little hot tip. I have tea I can hear the tea leaves being read I can tarot cards are shuffling right now.

Unknown Speaker  25:39  

I love the annual one, some of those big stories land, you're gonna have to come back and talk to us and just give us an update on where everything is at. And it's a really, I can't tell you how much I appreciate the entire first half of this conversation, which was about what was what what's going on in Washington in terms of the FDA and regulatory around some of these new technologies that are being put into play. I mean, it's nice to hear, you know, such a, a broad high level perspective of what's going on. And it truly helps to have an expert like you connect the dots for us. So thank you so much for that. I really appreciate it. Thank you. It's so much fun. All right. Well, we will talk to you soon. And until then take care of yourself. Ladies and gentlemen, AB Amy Abernathy. She is the president of verily clinical studies platforms and also the former principal deputy commissioner at the FDA. Thank you again for joining us. We'll talk to you soon. See you soon. All right. I'm Jessica masa for more interviews with the who's who have health tech, check out my YouTube channel over there at youtube.com/wtf health. We'll talk to you guys later. Bye

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