Sydney Perelmutter:

Good day to everyone joining us and welcome to today's Xtalks webinar. Today's talk is entitled Real-World Data By Design - Incorporating Different Data Types Into Clinical Trials. My name is Sydney Perelmutter and I'll be your Xtalks host for today. Today's webinar will run for approximately 60 minutes. This presentation includes a Q&A session with our speakers. This webinar is designed to be interactive and webinars work best when you're involved, so please feel free to submit questions and comments for our speakers throughout the presentation using the questions chat box, and we'll try to attend to your questions during the Q&A session. This chat box is located in the control panel on the right hand side of your screen. If you require any assistance, please contact me at any time by sending a message using this chat panel. At this point, all participants are in listen only mode. Please note that this event will be recorded and made available for streaming on xtalks.com.

At this point, I'd like to thank PicnicHealth who developed the content for this presentation. PicnicHealth is a healthcare technology company that partners directly with patients to build deep real-world data sets. The company leverages state-of-the-art machine learning combined with human curation to port complete medical records into an easy to use online application. The platform gives patients unprecedented access to and control over their medical records and, with their consent, the opportunity to contribute this valuable data to further scientific research. Now I'd like to introduce our speakers for today's event. As the head of Analytics Innovation and Digital Health, Gaelan and his team are responsible for creating and developing innovations across R&D. He co-leads the BMS digital innovation pillar for global drug development, which is enabling a spectrum of digital solutions, including several types of digital trial capabilities. In past roles, Gaelan has led and developed strategic partnerships with large academic medical centers and networks. He has also supported trial design and startup for the BMS oncology and immunology programs.

Next I'd like to introduce Sneha. Sneha is a certified PMP with over 15 years of industry-specific project management, risk planning and mitigation and regulatory experience spanning all phases of the drug development life cycle across a variety of therapeutic areas. As the Global Lead of the RWE Innovation Pillar of Decentralized Studies, her focus is to define the corporate innovation strategy for this pillar through strategic review and landscaping of industry trends and customer needs, engage key stakeholders and guide the ideation process to define and refine the existing innovative and strategic portfolio for decentralized studies. In her nearly decade-long tenure at IQVIA, Sneha has been responsible for providing senior oversight to cross-functional teams across a wide variety of therapeutic areas and study designs with a special focus on rare disease registries and post authorization safety studies.

Andrew Larsen is the VP of Partnerships at PicnicHealth and an industry leader in creating fit-for-purpose real-world data solutions. At PicnicHealth, he works with partners across the life sciences ecosystem to help advance disease understanding and support the development and access to innovative medicine for patients. Prior to joining PicnicHealth, Andrew worked with partners across the life sciences ecosystem to help advance disease understanding to develop portfolio and asset strategies across all stages of development with a specialization in evidence generation needs.

Lastly, Evelyn Pyper is Evidence Strategy Lead at PicnicHealth, a patient-centric real-world data company. Her career in real-world evidence spans the public and private sector as well as regional and global markets. Prior to PicnicHealth, she worked as Associate Director of Market Access at J&J Global Public Health, focused on securing access to HIV treatments in Sub-Saharan Africa, and as RWE Manager of a diverse portfolio of partnerships and research projects at Janssen Canada. Evelyn has a Bachelors of Health Sciences with a minor in Psychology from McMaster University and a Master of Public Health degree from Queen’s University. Now without further ado, I'd like to hand the presentation over to our speakers so you all may begin when ready.

Evelyn Pyper:

Wonderful. Thank you so much, Sydney. Good morning or good afternoon from wherever you're joining us today. We are really pleased to welcome you to this second webinar in the PicnicHealth 2023 webinar series. When the vision for this series was coming to life, it centered around two core ideas. The first was recognition that the conversations around real-world evidence really needed to move past these very didactic sessions on what is real-world data, what are its challenges and opportunities into more kind of topic and context specific conversations. Second, the question of who are the people from across different organizations and perspectives that we'd want to ask to sit down for a coffee and pick their brain on these nuanced topics. With those ideas, the webinar series was born and I'm really excited to have with us here today Sneha, Gaelan, and Andrew to share their unique perspectives on the use of real-world data and innovative approaches in clinical trials.

Evelyn Pyper:

With that, we'll dive right in. As all of you know, the overall theme for this webinar series is “RWE ROI”, which is really about going beyond the return on investment of RWE to really considering what's the risk of inaction, the risk of not doing something, when it comes to use of real-world evidence. On that note, if we reflect back on how clinical trials for years have been done in the past versus how they're starting to evolve to be done today, we can all start to appreciate how much might not have been possible if trials had not evolved to incorporate new designs or new types of data. My question for all of you, and I wouldn't say it's a softball question to start off by any means, is what comes to mind for you when you think about how has the use of real-world data or decentralized approaches opened up new possibilities for clinical trials? I'll start with you, Sneha. What comes to mind when you hear that question?

Sneha Kishnani:

Thanks, Evelyn. It's a very interesting question. What comes to my mind is access to additional patient populations. When we think about decentralization of different data collection approaches–patients that may be very ill, patients that may be in rural areas, not necessarily having access to care–allowing and enabling data collection to happen in their workplace or in their home enables additional patients to be part of that research, and we can then strive for representation and diverse representativeness in the clinical trials that we're after.

Evelyn Pyper:

Great. Thanks so much. Gaelan, what about you? What comes to mind when you think about what's possible today?

Gaelan Ritter:

Yeah. I mean, Sneha's right. I think a lot of it is that kind of the flexibility has opened up access, so clinical trials were a very closed world in the past, and you needed to be in a very specific place in your life and in your finances and in your disease state to be able to participate and making things more flexible, adding the opportunities to participate in the kind of ways that work for you as a patient has really changed that dynamic and opened the aperture of who's able to actually participate and how they're able to participate and the amount of effort that it is to be in a clinical trial. I mean, clinical trials have always been and probably will always be more difficult than standard of care to be a participant, but at the same time, that barrier is coming down a bit over time with these new technologies, which is something that makes it more palatable to be a part of a trial. It's really been a nice kind of, it's leading to nice opportunities for patients coming in. Lot of work still to be done, but it's heading in the right direction.

Evelyn Pyper:

Certainly sounds hopeful – feels like the theme there. Andrew, what about you? Anything else to add?

Andrew Larsen:

Yeah. Well, first off, very excited to be on a panel with yourself, Gaelan and Sneha, and looking forward to the conversation today. I think both the points they brought up are absolutely critical. How do we get more patients involved in trials where the actual data is then going to be much more meaningful to really all downstream stakeholders, regulators, payers, providers, and ultimately patients where it's going to be more reflective of the populations that are going to receive the treatment at the end of the day? I think there's another sort of component when you think about the challenges that have existed for trials. They take a lot of resources, a lot of time to pull them off and there's always been this bit of a bottleneck where it sort of comes to inflexibility where every...you know I'm sure if there are ClinOps people on this call, I'm sure they all have their own horror stories with a dozen amendments involved and it's really hard to do, but it's often critical because the understanding of diseases change, the understanding of the population, the treatment, what you're trying to actually answer.

A big part of real-world data is how do you actually create a system with these trials which allows for a lot more ability to evolve what the data can be pulled in to answer new research questions as it changes.  I think there was just a JAMA article or publication last week that basically said one out of five phase III trials in oncology changes their primary endpoint. I'm sure we could spend time discussing that data point the rest of this call, but I think it really does speak to the fact that research questions change–and how you bring in additional data sources, without trying to pivot the Titanic, to actually answer those questions and really optimize your trial from the start, to have that flexibility baked in.

Evelyn Pyper:

Thanks so much. I'm all ready to dive right into things, guys. We're off to a great start. Thinking about all the different ways that real-world data may now be leveraged for clinical trials–from early informing study designs to capturing additional outcomes to even serving as an external control–I'm wondering, Gaelan, where are you seeing the greatest need or opportunity for real-world data today, if you had to prioritize?

Gaelan Ritter:

Yeah. I think in terms of need and opportunity, it really goes back to what Andrew's saying, so I think data acquisition. We've done a lot of work in patient finding and enrollment and trial design. I think that data acquisition is the next kind of frontier for real-world data. The way we collect most of the data and studies today using EDC and capabilities like that is something that's 25 years old at this point, in terms of a technique, and it leaves enormous burden and gaps on patients and sites to be able to participate in that activity.

To Andrew's point, there's no flexibility built into that system. So you need to–I think with real-world data–taking that next step, working on the acquisition side, incorporating more complete medical records into the clinical trial record, images, scans, genomics, wearables and other data sources that we see coming through, that are honestly becoming standard practice in certain diseases, and clinical trials are taking a longer time to catch up unfortunately. Rather than leading new technologies for data acquisition, trials are lagging in a lot of instances, and so I think that's the space where we really need to start accelerating the work in real-world data to be able to bring trials to parity and then kind of push beyond the techniques that you're seeing in standardized practice. It's going to lead to a lot of opportunities to get more information and more learnings out of the studies we run and also make them more efficient for everybody that's involved.

Evelyn Pyper:

Right. Thanks. Yeah, certainly sounds like many of these approaches, they're innovative in the context of clinical trials but not innovative in their own right necessarily. Sneha, from your perspective at IQVIA–which we know is a large organization, with a wide variety of solutions and teams working in this space–does what Gaelan shared align with what you're hearing from customers across the board? And given your title, which is Global Lead, RWE Decentralized Studies, can you speak to some maybe emerging solutions that are increasingly needed in today's trial world?

Sneha Kishnani:

Yeah. I am hearing what Gaelan is saying.  The lack of flexibility is creating burden across the whole system in essence. Not only at your sites with your patients, but with your sponsors. With respect to the emergent solutions, I think participant centricity is critical. Optionality is critical. I just attended the DTRA decentralized clinical trials conference back in mid-April up in Boston, and the consensus was unanimous. There needs to be simplicity in what we are doing and it's really complicated and confusing for all of the stakeholders involved. It doesn't need to be, so I think we need to get back to our roots. We look at what is the core of what we're looking to collect. How do we simplify that for our sites and patients through these methods of different types of data acquisition?

It could be, as Gaelan mentioned, wearables and other devices, but also with respect to the point that sites are not going away, so how do we simplify things for our sites? Really putting our stakeholder needs first. There's more and more use of technology. What we're seeing at the sites now, what we're hearing from the sites is that, "Hey, you're throwing six different platforms at me. I've got a different login for each one." There's a movement in the industry towards things like single sign-on and leveraging things that other industries are doing so well. It's finally coming into our space. But I think we need to continue to move in that direction and bring that kind of rich customer experience to healthcare.

Evelyn Pyper:

Yeah, absolutely. Similarly, Andrew, as VP of Partnerships at PicnicHealth, I'd say you have your finger on the pulse of pharma customer needs and how they might be evolving. Can you share a little bit more about the work specifically that PicnicHealth does for those that aren't aware, the types of use cases that it might support, and what sort of interest you're hearing from customers related to real-world data use in clinical trials?

Andrew Larsen:

Yeah, absolutely. To start at the highest level, PicnicHealth is a patient-centric real-world data platform where we work with consenting patients to create longitudinal complete real-world data across their journey, and that means operating in a site-agnostic manner. So wherever they've received care in the U.S., we're able to procure the full set of medical information from that facility, including medical records, physician notes, labs, imaging, and create a harmonized data set that's deidentified for researchers and also present that back to the patient for their own benefit and really empowering them to be part of their care journey as well. That same connection that we have with the patient is used to also collect primary data in the form of primary or patient reported outcomes where we can actually create a much more holistic view of the patient experience and what they're going through. I think to Sneha's point, a huge part of this is really you can't add 15 different options for 15 different data modalities, and really how do we streamline this process to collect the information that we need that's critical for the study with the least burden on all the stakeholders in the process?

That's really been something that PicnicHealth has strived to do – starting with the patient, where essentially it's really just the sign-up that's the lift on their part to actually create these data sets for researchers and how that's evolved to actually apply to the trial space, so there's obviously a suite of needs, but maybe [I’ll give] just to do two examples. From day one when a participant enrolls, we're able to collect a deep longitudinal history on that patient, such that you can really have a nuanced view of really what is the stratification between all the different patients in the study and tie that back to what the variations and outcomes that you see are, and this is nice to have for a lot of studies, but really critical when you think about the ultra [rare] or orphan space where there are places where you may not be able to actually find enough patients to run a comparator arm and there's no actual natural history that exists. So actually using that either directly, or as supporting evidence for the comparator of those trials when they actually take an investigational therapy, is critical.

Then on the back end it's all about following patients, whether it be due to the burden of the study, they are a potential risk of lost to follow-up, or additionally, it's something where it's like after the site component has ended, how do you continue to capture what happens in the real world? This is both, I think, incredibly important when you think about both regulatory and especially payer applications. When we think about the wealth of accelerated approvals, we've seen that tied to surrogate endpoints. Really, how do we capture the outcomes that are happening in the real world later on? Then additionally, I think there's an aspect here where when you think about physician assessments that don't occur in the real world. Having a single data set that ties together the physician assessments that you were able to administer as part of this study and actually tying that to the real-world outcomes you can see, to extrapolate what these implications are–in ways that HTA bodies or payers may understand the value that this therapy brings to patients in a way that’s more apples-to-apples that they're used to seeing.

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Evelyn Pyper:

Thanks so much. I'm a big fan of clarifying terminology, especially in this space when we have lots of solutions, lots of solutions providers, and we hear a lot of terms like patient-centric, patient generated, patient mediated. Andrew, can you clarify what these mean and how you would look at the differences between each term?

Andrew Larsen:

Yeah. Good luck getting a clear definition of patient-centric out of anyone, so I'll tackle that last because that's probably the “squishiest”. But starting with patient-mediated. Patient-mediated is really about when you're working with a consenting patient and that ability to unlock the medical information that they can contribute to research. That's sort of the gateway into that category, though I think the actual spectrum of outcomes from a patient-mediated approach really is contingent on what is the infrastructure and technology that you're using to unlock the information once you have that patient opting in. And I think really PicnicHealth has been built around from day one: how do we take this burden off the patient of capturing this longitudinal journey, and both giving that back to the patient but also enabling researchers as well for that information? Then, patient-generated data, probably the most straightforward of all these. Data that you're generating through engaging a patient, whether that be wearables or patient-reported outcomes.

Then lastly, for the patient-centricity, I think this is something where, as I sort of alluded to, I'm not going to create the dictionary definition, but I can at least talk a little bit about how we think about it internally, where we are an organization of patients as well, so we try to abide by a system that is really built [around] putting them first, and that means starting with them consenting into all of our studies and they have that control over their information. It's also something where we try to–for all the information we collect–we present back to them for their own benefit. Then, also the fact that really our ability to engage and work with patients to make sure that we are capturing what's needed to advance their disease condition, it layers in their input as well through that ability to engage them and collect information, whether it has to do with symptoms, outcomes, quality of life. That's a key component of our operating system.

Evelyn Pyper:

Awesome. Thanks. Maybe that can become the definition? We'll see. We'll try. We'll see what we can do about putting that in the dictionary.

Andrew Larsen:

Best of luck.

Evelyn Pyper:

Yeah, thanks. Whether we're talking about patient mediated data or other approaches, I think it's clear that having these options and solutions come into the trial space, it's kind of like a double-edged sword because you have people trying to make decisions on the best approach, but with lots of choice comes lots of questions around like, ‘when is the right fit for each of these things?’. It can be overwhelming, especially for those that are not sitting on the solution side, but really having to make a call with a suite of options in front of them. If we think about patient-mediated approaches, tokenization approaches, or traditional site-based approaches–starting with Gaelan, from your perspective, when do each of these make the most sense or not make the most sense?

Gaelan Ritter:

Yeah. I mean, you make a good point about it kind of being part of the planning exercise, so it really is looking at—we're kind of building out decision trees of when you use these and you kind of maximize the robustness of the data that you acquire, for the least burden on patients and sites that you can acquire it. So when you look at things like patient-mediated and some of the work with PicnicHealth, it's really been a nice opportunity to get large data sets with significant medical history, significant other physician interactions and patient journey without huge burden on patients and sites, and so using capabilities like that primarily is going to be the future of clinical trial data acquisition. Then you go to: okay, I can't get everything from that, so now I'm going to need some things where it's patient input into–whether it's a wearable or an ePRO, eCOA, whatever else–I'll need patient interaction for the next step of things, which reduces some of my burden on the site, but the patient's going to have to be involved.

And so in large studies with hard outcomes that sort of exist in claims data sets, that's usually still sufficient, but there's a lot of places where I think patient-mediated becomes more relevant when you're considering places like needing coverage across the population. And that coverage is very important to incorporate things that are in the unstructured notes or deep within the clinical text, including things that may have to do with imaging, that may have to do with patient-reported outcomes, or may have to do with those sets of information. And then linking to other things as well that may exist in like cost information, like claims for economic analysis. To say probably the most overused phrase on these calls, it has to be “fit-for-purpose” at the end of the day. So the decision trees you sort of teed up earlier, it's like figuring out for what you need, what is the right suite of solutions.

Then your final step is going to be the more traditional approach, where there's just no other way for me to get this, so the patient's going to have to go to a clinical site, the site's going to have to perform the activity and record the information, and that kind of is the one you want to use as sparingly as possible going forward because it's most labor intensive. I think in the middle of the kind of patient-mediated and the patient-directed and then the traditional site is also going to be where the decentralized trial capabilities come in. That's where you're going to see things like someone going to a local lab for diagnostics, or going to a local imaging center, or going to a general practitioner, rather than going to a clinical site in a major city for their clinical trial. That's where you're going to see some of that middle ground of how we can leverage things that are closer to a patient's home. There's still medical clinics, so there's still burden there. Closer to a patient's home, more convenient to make this more approachable for everyone. It's really just a continuum and you really just try to maximize getting the most robust data you can with the least burden you can possibly do it.

Evelyn Pyper:

I like that. Andrew, would you agree with what Gaelan said? Would you add anything? What are your thoughts?

Andrew Larsen:

Yeah. Absolutely. I think Sneha brought it up even earlier where I don't think the site-based component is going to go away anytime soon. To Gaelan's point, it's very laborious, but it is so critical for a lot of having those controlled settings, being able to administer whatever assessment you want, whatever sort of imaging study, having that controlled environment. It's more about what are the ways that you can create the greatest wealth of data, like balancing that with other modalities in addition to that. I think tokenization is a great way to get a lot of low hanging fruit information, and if that is what you need to answer your research questions, fantastic. It's really about stepping back and being like, what do you need this evidence to show? If there is a tertiary data set that you can link to that has that outcome, just making sure you are taking the consideration: When you think about the waterfall of how many patients will actually be able to match? How many patients will have the temporality coverage that you need? Additionally, if there's a secondary or third data set that you need to link as well, applying that subsequent downstream waterfall from those criteria as well.

Evelyn Pyper:

Right. Thanks so much. Sneha, where do you see the most challenges with traditional site-based approaches? Alternatively, when do other approaches make the most sense?

Sneha Kishnani:

Yeah. It's a good question. I mean, thinking very specifically, we can think about rare disease studies–rare disease, ultra rare disease studies–where it's difficult to bring up a site across a geography, a specific country or region, and each site can only enroll 1-2 patients a year because that's all they have. The industry addresses this by targeting specialty centers where there's a larger population of these types of patients, but it's not a perfect solution. I mean, this is where I think the decentralized approaches can really help allow patients to, like Gaelan was saying, get their labs done through the local lab, imaging done through their own imaging centers near their houses.

I think the other thing that we also need to take into consideration is that the geographical landscape of all of the solutions that we're talking about, sometimes the regulatory landscape of that is the regulators are a little bit slower to adapt to some of these solutions that we're talking through, so that also needs to be considered. And in that decision tree that Gaelan was talking about, consider what is the relative risk if we are to take an innovative approach or an innovative data collection acquisition solution here, and is it something that the regulators could maybe be open to? So having those connections and having those pre-discussions with the regulators is also critical in this case.

Evelyn Pyper:

I'm imagining that…. there's a lot of external factors as you mentioned, so even with a very well-thought-out, comprehensive real-world data strategy from a researcher, from industry researchers, the factor of timing is also always going to come into play. So Company X has an urgent need for a data set, they need evidence in six months from now. Probably not an uncommon request for some of you to get. Sneha, how does the timing of evidence generation planning come into play in the work that you do around decentralized trials and what, if anything, needs to change in order for folks to see greater success with these approaches?

Sneha Kishnani:

We've all been there, right, where like you mentioned, there's an urgent need to fill a data gap and we need the data yesterday. So the pre-planning, the early planning, the sooner you can get the right players in the room to start talking through that evidence generation and evidence even dissemination, the better. Before your Phase III study begins probably is the right time. Even earlier is better. But I think that's really it, I don't know that I need to be verbose about this, but early is good, earlier is better. I think that there are all kinds of issues with the lack of data being available. There's cost concerns to the sponsor. There are concerns around product uptake during launch. It creates all kinds of knock-on effects, so if we can be proactive about those conversations, we can avoid some of that and we can make the whole research gathering process a bit smoother.

Evelyn Pyper:

You made a really good point. It's not just about ‘early’, but it's about getting the right people in the room early, which can also be part of the challenge. So like within a biopharma organization like yourself, Gaelan, I know we're seeing this cross-functional decision-making happening and thinking about how data can be used to serve multiple groups. From your perspective, Gaelan, does having related questions coming from across multiple parts of the organization impact what data sources or approaches you use, and if so, can you share any examples of some common cross-functional real-world data needs?

Gaelan Ritter:

Yeah. Absolutely. It certainly impacts it. I mean, the reality is that high quality, large real-world data sources are still rare in a lot of instances, so there's a huge amount of cross-functional need for these data sources, and some obvious examples are going to be ones that no one's surprised by. Long-term patient safety and efficacy is an easy example. That's an important endpoint for clinical trials. It's an important factor for the kind of progression of trials from Phase I to II to III. It's also hugely important for the patient safety monitoring teams and for the health economics and outcomes research teams and for your field medical colleagues. You see everyone asking the same question, and in the past, one of the things we found with normal traditional clinical trials is that the trials weren't designed to answer the questions of some of the teams that came downstream. It was designed to answer the questions of the specific person designing that specific trial.

To the points that everyone's made, that world is a little bit gone in the sense that everyone now recognizes that there's other users for these data sets inside pharmaceutical companies, and the need is to really expand the capabilities by acquiring the data that you need in the different locations and then leveraging that across everyone involved in the pipeline of that assets journey. So, patient safety is an easy one. Patient journeys. I mean, Andrew mentioned tokenization and the value of linking the claims data sets and other things. When you look at patient journeys for years, those have been used in outcomes research and in those types of capabilities, and we're seeing more and more of patient journeys creeping into the kind of earlier development cycles of assets. You're thinking about: How do patients experience the drug that I'm creating? How's it going to change the treatment pathway? How's it going to impact the way medical practice is handled in some of these settings, especially in rare diseases?

One of the keys is better understanding that patient journey. Understanding the journey that exists today. Understanding the journey that's going to be created from the asset that you're hoping will be successful, and if you're seeing a lot of cross-functional use. It's great you're seeing teams communicate with teams that they never did before because they're realizing that they share common interests. And that also means that then on the real-world data side, you're seeing demand for data sets that can accomplish, that can serve multiple people's goals. And that might mean incorporating data into a clinical trial data set that wouldn't have been there before, or incorporating some of those patient-reported outcomes into a later-phase study that wouldn't have classically done them because it's not the primary efficacy endpoint for the trial. But being able to get those learnings means that everyone can cross-use some of those data sets.

Evelyn Pyper:

Right. Thanks. I mean, curious, do you think that we'll get towards the kind of ‘unicorn’ data sets, where they are serving all stakeholders? Or do you feel like this is aspirational at the moment?

Gaelan Ritter:

We're getting close. I think it's going to be a while until you really get there. I don't think your phase four safety studies are going away tomorrow. I think we're getting closer to being able to answer the questions of multiple teams, but there are still pieces missing at different journeys. And I think a lot of that goes to Sneha's point about, unfortunately, as you're building out the depth and completeness of real-world data sets, you're also realizing where your gaps still are and you're realizing how often you rely on some of those traditional clinical techniques in your trials and in your data acquisition, and so we're getting closer and then also realizing just how big the gap still is. I think there's a lot of room left to make improvement there, but absolutely it's moving in that right direction.

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Evelyn Pyper:

Yeah. I guess the more we learn, the more we know what we don't know and still need to fill. As we are moving towards more of this kind of personalized healthcare vision, precision medicine, we know that the reality is that decisions–whether it's regulatory, payer, HTA decisions–might not always be made with the same types of coded data that we've seen in the past, so across all types of studies, clinical studies, post-launch studies, there's a growing need we know to go deeper with the data. Andrew, I'm wondering if you can share some examples of how these emerging–and maybe not so novel anymore, but in some ways novel–patient reported, caregiver reported, or clinician documented data are being used in clinical research today?

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Andrew Larsen:

Yeah. Absolutely. Maybe starting with the clinician-documented. Coded information is usually the backbone of a lot of go-to sources, but I think there's a growing appreciation for the sort of wealth of information that is buried in the physician notes underneath the just general coded information, where it's really all about understanding what is either under coded, non-coded or inaccurately coded. And so that can be things about just understanding what the patient population is, subtypes, disease severity. It can be about understanding outcomes for that, so either physician assessments, major sort of signals of activity, disease activity, symptomology, as well as treatment use and effectiveness including mentions for treatment switch or discontinuation. Really I think this is a backbone of a lot of the different studies that PicnicHealth engages in, and I think it's….

There are a lot of places that we expected the value to emerge, but there's even some of the sort of less ‘cookie-cutter’ value adds have even been about understanding the sort of inefficiency of the coded information. So we published in lupus nephritis about the ability to actually more accurately diagnose around 100% more patients by not just relying on ICD-10 codes alone. But going by additional information across the medical record, including narrative text, as well as the shortcomings of relying on those patient populations that are coded today in hemophilia B, where at least 40% of them had an inaccurate hemophilia A diagnosis, a third of which didn't even have any mention of hemophilia B. I think it's actually truly understanding that the patient and their outcomes really relies on going deeper within that information, and then the ability where you're actually talking about a patient-centric platform is like, how can you bring the input of them and their care team into this equation? Which is nothing new for the field, as this has been growing steadily over the past decade where the majority of regulatory submissions now include some component of patient-reported outcomes data, whether it's about symptomology, functionality, quality of life, and those are increasingly making their ways into the actual labels of products.

The way that sort of we think about another unique application of it as it relates to trials is you'll have these assessments done that are patient-reported as part of the study, like for actually understanding bleeds at a very frequent rate is key for a lot of hemophilia studies. And what we've tried to recreate in our real-world data studies is biweekly bleed patient-reported outcome assessments to create the ability to track these outcomes that are never really assessed in the real world. Like you can go in the documented physician notes, but they'll never be as comparable to what you have going on in the trial, so I think that's another major advantage as you sort of look to more holistically understand how trial populations compare to the real world.

Then additionally I would say for the caretaker, this has been a growing focus of PicnicHealth over the last year. A lot of the major areas of therapeutic development are beginning to appreciate that the impact that the therapy brings is not just on the patient, it is on the entire care network of that patient. We are doing caretaker-reported outcomes in Alzheimer's, Huntington’s, a lot of pediatric conditions where it's about: What is the burden that this is bringing on as a societal for the entire care network? How do we actually characterize that? Because this is part of the holistic value that it's actually bringing to society, of here are all the other people afflicted that are absent in almost any data set you look at, that actually, there's a huge way for these advancements to bring relief to the sort of families in addition to the direct patient afflicted by these conditions.

Evelyn Pyper:

Thanks. We're getting a lot of great questions in. We want to make sure we have time for the audience Q&A, but I do want to wrap up with a prospective looking question. We've talked today about how changes and how drug therapies are developed and studied, continues to evolve, and to wrap up this main portion of our Q&A, can each of you share one example of something that you think will look very different in, let's say, five years when it comes to the use of real-world data or alternative approaches in clinical trials? Like what's your five year outlook? We'll start with Sneha.

Sneha Kishnani:

Five year outlook, I'm going to maybe answer this question with a little bit longer time horizon, but I think patients are becoming more knowledgeable and more empowered about their data and I think patients are really the linchpin here in terms of getting access to data. If we can continue to educate them on the value proposition of the data that we are using of theirs and the methods that we're using, and then return those results back to them in a way, I think that five years from now we're really going to see an unlocking of all of the types of data, and to Gaelan's point, “closing the canyon” of the gaps that we're seeing. Maybe in five years. Maybe the time scale is a little bit longer on that, but I am generally hopeful that knowledge is power and it is going to lead us to an overall healthcare system situation of patients being able to be managing their illnesses better, one, but then also leading to just a healthier population. Because I think if, I'll just tell you if it were me, if I am getting information back and insights back about my health and my health status, if I can make changes to improve my health and my life, I'm going to be making those changes, so I think that there's something there.

Evelyn Pyper:

I love that. Andrew, what does your crystal ball say for five years from now?

Andrew Larsen:

Yeah. Also love that. Clearly biased as our entire platform is built around empowering patients with more of their medical knowledge, so very excited to see that growing as a focus about how we make patients more front-and-center of their own care journey. Not to sort of echo what Sneha and Gaelan have already brought up, but I think integrated evidence planning, while not new right now, is really growing. And I think hopefully less and less there are going to be people showing up at mine or Sneha's doorstep asking for a data set that doesn't exist and needing it yesterday, as there's sort of this more forward thinking about cross-functionally of what we need. I think that's going to play out in more initiatives to future-proof the study by having part of the consent forms ease ability to collect longer term real-world data associated with these participants.

I think sort of the aspects of the planning around it may shift as well. If you think of just more of the broader landscape across drug development, there's obviously some legislation changes that are going to shift a little bit about the focus and urgency that's around the timeline that you need evidence–to not only get approval, but once that approval is there, how do you maximize what you can communicate to all of the stakeholders involved in the care journey? What is the evidence you can show up to payers with day one vs. one year vs. two years–that we used to have a lot more flexibility for that timeline for. Which shows the tangible benefits of this therapy in ways that they can understand. And also bring that same pace to patients and their providers to make sure that they can make the right decision of whether this therapy is a choice worth considering. And so I think there's going to be a lot of evidence planning that is really about how do we ensure that all the stakeholders in the continuum here are going to have the evidence that they need much faster than we've historically considered what is permissible?

Evelyn Pyper:

Yeah. I like that too. Gaelan, final word, what does that five-year future look like from your eyes?

Gaelan Ritter:

I love both of the ones that came before. I think those are exactly on point. I guess for me, the other thing that I might add to it would be I think our kind of patient recruitment tactics and patient enrollment tactics are going to look totally different in five years. I think the advent of using patient journeys and claims data and new world EHR sources to find patients in their medical records and be able to identify best-fit patients for trials is emerging so quickly that I think in five years it's going to become the dominant way that patients are identified for clinical trials.

I think the days of, well, when I was a clinical research coordinator at Georgetown, the days of sitting there on Friday afternoons going through all the charts of the patients coming in, going through many charts of patients coming in next week as if I could get through all of them to find patients for trials is going to be over. And it's going to be kind of targeted lists using real-world data and other data sources and algorithm development to be able to create target lists for sites to be able to really understand what the flow-through of patients is, who's best fit for the trials, and really being able to triage that out is going to drastically change the landscape of how sponsors function and also how sites function in that kind of enrollment space.

Evelyn Pyper:

Thank you. Yeah, certainly the possibilities have me excited. I think it has people in the chat excited. We have some great questions coming from the audience, so I will pass things back to Sydney to facilitate this next, final part of the session.

Sydney Perelmutter:

Thank you all for that very insightful presentation and I would like to continue to ask our audience to continue sending in their questions for the Q&A portion of the webinar. But I've already received many questions, so I'll start with our first one. Our first question is, what are the current challenges in reconciling different RWD collected using a variety of recording techniques and technologies within RWE studies?

Gaelan Ritter:

Who wants to go first? I can jump in with some of the challenges that we see, I guess.

I think one of the things we see is that disparate data acquisition sources can have different….it's not that the data's conflicting, it's that the data's collected with different intent, so if you had a patient-reported symptom versus a clinician reported symptom, those are going to look different. Even though they might be getting to the same underlying part of the disease, they're going to be represented differently and we see that across different types of real-world data. So you'll see a very different kind of catalog of information from a patient-entered source versus a site-entered source. We see the same thing when you start looking at the kind of linkages between datasets that have different intended end uses. So a claims data set is going to represent a disease state very differently than an EHR dataset because they're intended for different purposes.

We actually see a lot of data that needs more, rather than cleaning, it needs concordance. And so we see that a lot more often now as you're starting to, like Andrew mentioned, starting to tokenize and link different data sets that had different reasons for existing. You're starting to kind of see the emergence of a lot of need for harmonization across those, and that takes a little bit more sophisticated effort than just a mapping table in controlled terminologies. So I think that's something that's emerging more for us as we start to get into these more complex, compiled real-world data sets.

Andrew Larsen:

Yeah. I think that's all right. And what Gaelan teed up earlier where it's like data acquisition is one of the challenges. I think the first step for PicnicHealth at least, a lot of because we collect medical records across any care facility, rural PCP versus like high-end academic institution, and part of what we have really put a lot of thought and energy to is like, how do we actually harmonize these data sets, these different disparate data sources into a single usable data set? I think as we've overcome that challenge, the exact next one is sort of concordance and it's like how do you reconcile all of these different pieces of information with different intent? I think our sort of fallback is there is no silver bullet. It's very much like, what is the question you're asking and what are the pieces of information that should be indexed on to best actually reach a defendable conclusion? I'll stop. I'll end it there and I'm sure there's probably a lot left unsaid there, but happy to discuss further if interested.

Sydney Perelmutter:

Thank you for those answers. Another audience member would like to know, what are your thoughts and what are you seeing with sponsors using registry or open access patient journey data as quote unquote "placebo group" for open-label studies?

Gaelan Ritter:

I'll start this one too, I guess. One of the things…so we are seeing more and more of that. You're seeing a lot of [this] especially in rare diseases. If it's hard enough to find patients to participate in the trial and treatment arms of some of these diseases, being able to find patients to participate in control arms is next to impossible. And especially we're seeing more and more that obviously clinical trials are becoming a therapeutic alternative, especially in rare disease and in late stage disease, and because of that, in those assets you literally don't have many patients that aren't part of clinical trials. We're seeing a lot more use of those data sets both in the classic kind of external control space, but then also seeing them being used as–whether it's digital twins or other types of capabilities that are emerging–to be able to provide insight about the patients on the treatment arm of the trial without having to classically enroll like you would for other trials.

You're seeing that emerge in a lot of ways, whether that's in hybrid control models, synthetic models, and like the digital twin model. It's actually been very beneficial though, to be totally honest, because getting drugs to patients that are in rare diseases is critically important, and the timing is so frustrating because delays in those–there are no other alternatives for those patients–so delays in those asset journeys literally impacts people's lives, and so being able to kind of build bodies of evidence through new statistical techniques has been a real benefit to a lot of those patient groups.

Sneha Kishnani:

Yeah. I mean, I can add to this one. We do see this all the time with respect to disease registries as well as natural history of disease studies that are also being run in addition to any kind of broader registry. I think that there is an acceptability around it and it's happening more and more.

Andrew Larsen:

Yeah. I would just say I think at least for the last three years, every year there's been at least one drug approved with this sort of approach, so I think while regulatory guidance still always indexes on preferential sort of the ability to have a classically randomized controlled trial with a placebo or standard of care arm, I think this is something where when you actually…you have to consider on a case-by-case basis is like, is this truly a landscape where the sort of unmet need and the actual feasibility considerations for a trial justify this? I think we've consistently seen an openness to that, which is great for patients. I think then there's getting that initial access to patients, whether it's an accelerated approval potentially, and then there's the follow-on confirmatory studies, which can take a very long time, right? But I think usually there's that consideration where it's like, let's do the right thing now based on this body of evidence that we feel comfortable doing it, and then confirmatory studies as appropriate for any outstanding questions.

Sydney Perelmutter:

Thank you. Again, we have a question for Gaelan specifically and then we'll do one more question. That question for Gaelan is, can you talk about innovative ways that can help to identify the best participant for a research study?

Gaelan Ritter:

Sure. Absolutely. We're doing a lot more with using access to EHR records and to claims records to be able to map where patients are. Some of that is kind of just looking at population dynamics and heat mapping and then taking the next level and deploying algorithms that actually search medical records for patients that meet not only the I/E criteria of the study but look like patient groups that we've seen in previous trials that would benefit from those studies. So it's really kind of about leveraging that EHR data access and the real-world data sources that we have and being able to create algorithms that look for those best fits. It's a lot of the work that the clinical research coordinators and investigators do manually today, which takes an enormous amount of their time, and so being able to automate some of that and present them with those best fits has been a real advantage and a real opportunity for all of us.

Sydney Perelmutter:

All right. We have time for one more question. We've talked comparisons and differences across patient mediated vs. tokenized vs. traditional site-based approaches, so can you all share some examples of if or how these approaches can be used together?

Evelyn Pyper:

I guess maybe a question to that question that I would put out there is, if the spectrum between site-based and decentralized has infinite points in between it, what does that kind of ideal hybrid model look like to folks on the call? It feels like it could be so many things. The way… patient mediated data is not the antithesis of collecting site-based data, so what does that ideal hybrid look like?

Sneha Kishnani:

I think there is no ideal hybrid. Right? I think that's the beauty of ‘hybrid’ is it can be what it needs to be for whatever research question we're trying to answer. It's having the awareness around, what are the different data types and data sources out there. And having the deep insight and the deep knowledge into: does that data source have the information that is going to help us to answer the question that we want, and where do we need to get the data from? So in essence, looking at a protocol, looking at the study objectives, the endpoints you're after and understanding who is the right source of all of this data? Does it need to come from a site? Can it come from the EMR? You know, et cetera. So really understanding deeply where that data lives and who can best provide it in an accurate and high quality way.

Andrew Larsen:

Yeah. I echo that same thought. I mean, it has to be fit-for-purpose. What are the questions? What are the data sources that support it? And really think critically about the feasibility of what you're considering, right? Just because a data source has ‘data element X’, are you actually going to get the coverage you need or the temporality you need for the patient populations that match? I think being diligent pays off and makes this all a much easier process downstream.

Sydney Perelmutter:

All right. Well, thank you very much for those answers. This concludes the Q&A portion of this webinar, and if we couldn't attend to your questions, the team at PicnicHealth may follow up with you, or if you have further questions, you can direct them to the email address on your screen. Thank you everyone for participating in today's webinar. You'll be receiving a follow-up email from Xtalks with access to the recorded archive for this event and the survey window will be popping up on your screen and your participation is appreciated as it will help us to improve our webinars. Now, please join us in thanking our speakers, Gaelan Ritter, Sneha Kishnani, Andrew Larsen and Evelyn Pyper. We hope you found this webinar informative. Have a great day everyone.