[Evelyn] And finally from the pharmaceutical perspective we have Najat. We heard from your introduction that there's quite a bit that falls under the umbrella of data science and R&D strategy. I'd love to hear about where your priorities have been in terms of real world data specifically and where you're coming from for this topic.
[Najat] Sure, that's great, Evelyn. Thank you so much again and to PicnicHealth for having me here. As you can see I'm trying to get better lighting, but hopefully you can hear me fine, which is what matters the most.
First of all, I want to say, both panelists, the comments are really spot on. You know, maybe just taking a step back from a pharmaceutical perspective and the perspective really focused on how do we make better medicines for patients, right? I mean that's, at the end of the day, the insights: things we can do, like leveraging verbal data, that we couldn't do before, or things we can do better. All are driven to that common purpose of translating it into insights and then a medicine that's for patients. I would say the way I think about real world data is not even just in clinical development. Starting from the very crux of the question that we're trying to answer, what's driving the disease, right?
I mean I think that's where it's really challenging. Like you know even today how much we understand about disease biology. If you think about so many diseases, Alzheimer's, Parkinson's, they're not named after the driver or the causal effect of whatever is driving the disease, but much more so based on the physician that identified the syndrome. So that in itself tells you that there needs to be a deeper understanding of disease biology. So the way I define real world data is of course claims and EHR, but you also have omics, like transcriptomics, genomics, and images, everything that's not clinical trial data. So if you think of the totality of understanding a person's journey, what drives disease, you need to look at the totality of data. And so we are actually leveraging that from the very first part, which is better understanding the drivers of disease and redefining disease, as a result of it.
So as an example, a lot of the audience probably knows about these longitudinal data sets-for instance, Noga was mentioning too-called UK Biobank or Our Future Health, I mean there's so many. But essentially it's longitudinal. You have all of these different data points collected well, good data quality, and what that has allowed us to do is to be able to stratify people that have severe depression to patients that have severe depression based on their sort of genotypic and phenotypic architecture. So really driving precision medicine more into an area like depression, which let's face it, the medicines that we have today work but we could do better. I think we can all agree that we could do better and that insight is then translating into how we design our programs for depression. I wanted to use depression as an example because we talk a lot about precision medicine and oncology but we need to bring that in Immunology and Neuro and so that's just one example.
Another way that we're using real world data a lot is, let's say you figure out what's the target that's causing that protein and you figure out the molecule you need to actually modulate it. The next thing that comes up is how do you run the right trial and design it the right way. And Noga spoke a little bit about this as well, which is, instead of relying on publications or instead of relying on multi-center sort of analyses of the patient's journey, you could now use real world data across the board. Across all of the patients that actually have a disease, then go back and say, "What were the risk factors that were driving them?", and that actually helps you design the protocol in a much more effective way. So you can do certain things such as inclusion/exclusion criteria, you can simulate it using real world data to say, "Okay, is this actually reflective of the patient journey in the real world?", and "Why is that important?".
It’s actually because it's important when you start recruiting the patients. You can't operationalize something if it's not pragmatic in terms of what's truly happening in the real world. And we've done that, like for one of our vaccine programs. For instance, we used 100 million patient lives-de-identified of course-working with external partners and then really understanding what are the risk factors that they'll get this certain infection. And that actually was then translated into the protocol design itself, working with our clinicians and now that study is running in Phase 3. It helps helped us enrich the trial a lot more with the right patient that would be impacted, which is the patients, but it also helped us ensure that a trial is much more efficient so we can get to the data that we need and then hopefully the therapy that we need, knock on wood, for patients as quick as quickly as possible.
And maybe the last example I'll mention is also just a lot of the viewers here probably saw some of the guidance on external control arms that came out yesterday from the FDA. Super helpful, because the way we think about real world studies broadly-and ECA is a part of it (external control arms)-is you can leverage that to really contextualize what's the right standard of care, how are patients doing in the real world with the physician's choice versus the active arm of a trial.
We're not just thinking about it, we've actually leveraged this for our regulatory submissions, working very closely with the FDA. Early pre-specification conversations are super important, so you're getting feedback on your protocol, on the data elements being super transparent about the pros and cons, all of that. The thing I want to emphasize is no data is perfect and no guidance is perfect. There's always a case-by-case discussion, but this is where early discussions and collaborations and transparency and rigor in what's done becomes supremely important and that's something I want to underscore. Like in my organization, we have the highest highest bar because there's a lot of real world studies, but to do it well takes a totally different level of talent and we'll talk more about that and capabilities later.
And then last thing, but not the least, is also the space around what I would say is clinical trial recruitment. We're using real world data to understand where the eligible patients are and then go to the patients, open our sites where the patients are verses the other way around. And we've seen tremendous impact in terms of both how effectively we can recruit, but then also ensuring that our trials are representative of the patients, that we will treat from a demographic perspective, from a diversity perspective, etc. Using decentralized trials and so forth.
So as I have this monologue for the last couple of minutes-and thank you everyone for bearing with me-the point to underscore every single aspect of how we figure out what was causing the disease, to designing that all-important trial, to executing that all-important trial, to generating that all-important evidence would becomes critical for access that ultimately helps to get our medicines to patients. Every single aspect of it is being disrupted positively using real world data.
[Evelyn] Thanks so much. Yeah, all really salient examples. And I know you mentioned that it felt like a real treat yesterday, that the day right before our webinar, the FDA released the guidance on external control arms. I think it's just reflective of month-by-month, things are developed. Like over the past year, the amount of guidance that's come out, the amount of real action around providing industry guidance, but also guidance to companies like ours, like PicnicHealth, where it's really showing this is not just a nice to have anymore, it really is something that the expectation is high quality real world data as part of an evidence submission.
[Najat] And Evelyn if I can just maybe underscore one thing that you mentioned. You know all the examples I mentioned, it's not something that we just do internally, right. Yes, we build the algorithms, the methodologies internally, but the datasets and the partnerships, we have accelerated a lot because of the work that, you know, companies, young companies like PicnicHealth, and others are doing. It's a lot of hard work. Let's just put it this way. To take a lot of the data that's fragmented, not always very clean, not standardized as guidance is coming and pulling that together, creating the cohorts-Noga was saying curating a lot of the work in the structured and unstructured datasets-that all becomes extremely important. So, it's a partnership across regulators, startup companies, and also pharma and biotech companies. It all has to come together. We couldn't do it without each other. It's exciting to hear the examples, but the foundation of it is the work that's being done across the board. So I just want to acknowledge that.