Thank you, John. We were placing bets on how far I get into my speech before the whole computer just catches fire at this point. So let's see if it'll work for me, and it's working for now. So thank you, John. Thank you to my colleagues. As John mentioned, I'm going to talk a little bit about what we're doing and where we've been collaborating and where we'll go in the future. I want to note that I am also on the social science side of things. I'm actually an implementation scientist by training. My background is in ER and critical care nursing, but I left the bedside before COVID, and then I'd like to say a mediocre white manned my way into my current position because I joined the SCCM virus registry, opened my mouth in a meeting, and ended up leading a team and it snowballed from there. And through that work, I've learned enough data science to embarrass myself at cocktail parties. Thankfully, I have colleagues like Danielle to teach me and the rest of the wonderful colleagues in this panel to teach me more about clinical research. By way of my first disclosure, I do want to mention that I do co-chair one of the subpanels out of the data science campaign and serve on another working group. I also want to briefly mention what the Critical Path Institute is. We are a public-private partnership with the U.S. Food and Drug Administration. CDRC is a subsidiary of that, a sub-portion of that. Our funding does come from the FDA and the Department of Health and Human Services. That does not, of course, in any way mean that anything in this speech should be seen as an endorsement by any part of the government. So why work for the FDA? Short answer is no. But some of my colleagues have already mentioned drug repurposing. As the name suggests, CDRC exists to look at how drugs are used off-label, how they can be used in new ways, and how we can advance the evidence around how those treatments need to be delivered to patients. As previously mentioned, repurposing often happens in diseases that are rapidly emerging, such as COVID-19, those that are extremely rare, those that impact vulnerable populations, or those that are already treated by standardized guidelines. Something has already emerged. So for example, a rare subtype of angiosarcoma, which is another piece that we work on in our team, or something affecting pregnant women where you have a hard time developing and actually standing up a trial or on neonates, or something like congenital cytomegalovirus where there is a standard of care that exists that is not on any label, and so you can't actually withhold that treatment to conduct an RCT. All of those can be addressed through a few different strategies. If you go to some of the conferences that my boss and I, Marco Esquito, go to, we have a lot of really wonderful debates about what do we mean by repurposing, and it's delightful and horrifically boring for everyone who isn't like four of us sitting around that table. But broadly, we can think of conceptually a drug that has already been approved and labeled for something else, and we find a new use case for it, the most recent, widely hypothesized would be like Wigovi, goes from diabetes to weight loss, now we're looking at potentially using it to treat addiction. We might reformulate, we add a new component to the molecule itself that might extend the availability, might improve the availability, or make it last longer in the body. We can combine drugs, we see this a lot in psychiatric treatments and in cancers to either reduce side effects, or to just make it more palatable, we don't have as many pills to make it easier for the patient to comply. There are also shelved assets, and we might rescue some of those because it was developed, we decided it wasn't a good use, or it failed trials in one place, and we found a new use for that molecule later on, and resumed study of that drug. Through all of that, there are a number of issues because the system that we have in the United States is designed to incentivize the development of novel molecules, and so what we end up with is a field where there are thousands of medications that have hundreds of potential off-label uses, and we see them in the clinical space, especially in critical care, but because the system is not set up to incentivize the pharmaceutical company to pay to submit that to the regulators, it becomes a lot harder to actually get that data collected, and the evidence submitted to the FDA for review. So we were formed in 2020 with the FDA to become a group that can look at how we can leverage real-world data to advance drug repurposing research in alignment with the guidelines and evidence that had been previously mentioned by my colleagues, and we focused on a pilot study in COVID-19, looking at can we, through the systematic collection of real-world data from the electronic health record, begin to formulate hypotheses that can be later addressed in clinical trials, and can it be a resource for physicians where no approved products exist for the new use? Sounds great, or it sounded great in 2020. We know COVID is no longer quite as clinically exciting. We know a lot more about the disease than we did now, but there's a lot we can learn methodologically, and so CDRC, across its many functions, many groups that we're looking at, many different conditions, we have an area of focus, and I think I, nope, I don't have a, I thought I had a circle on it. Well, I'm just going way too far, but you can see here in emerging and re-emerging infectious diseases, we have COVID-19, and especially building infrastructure to prepare as we enter this inter-pandemic period to be ready for disease X when it emerges, a major concern for regulators and other government organizations, for example, the CDC. All of this is intended to support a platform that the FDA has hosted for about 10 years now called CureID, which is a web-based and mobile application where physicians can submit, and now patients and other individuals can submit cases where they've used a drug off-label. We all know how much everyone loves manually entering cases, and so the idea was to have a project that would start to build pipelines where data could flow in automatically, make larger pools of data available for exploration and for research. Again, in line with the FDA's real-world evidence guidelines. For those of you that haven't taken the time to read the riveting 75-page real-world evidence program report, I keep a copy on my nightstand, just in case I need a little bit of help, but there's also a really wonderful primer. John Concato and Jacqueline Corrigan-Curry published in New England Journal, citation down here at the bottom, where they briefly, in three pages, describe the use cases that my colleagues mentioned earlier to explore clinical practice, develop and refine hypotheses, and provide external controls. And that observational research then becomes part of the infrastructure and the environment of developing and understanding how drugs can be used off-purpose. So, now that I've got the elevator pitch out of the way, what have we been doing with SCCM? Why have we brought this group together? It's been my absolute pleasure to work with this group of individuals over the last couple of years and learn from them, as I've mentioned. And out of these ideas, we partnered and applied for and received an $8.3 million grant from the Department of Health and Human Services, from the Office of the Assistant Secretary of Planning and Evaluation. The funding came directly from the Patient-Centered Outcomes Research Trust Fund. And collaborators included, FDA as the primary applicant, subaward to CDRC, we included collaborators from Emory University, from Johns Hopkins, the Mayo Clinic, Oxford University, and of course, SCCM Discovery. This project was intended to build that pipeline to help build the infrastructure to get data out of the EHR, and we've made some pretty good progress. Together with our team, we leveraged the connections and the network through the virus registry, recruited institutions to implement the tools that Danielle described, and pieces of those tools, and helped them get to OMOP. The thing that we've found and the vision that we have is once we can implement the CDM, it's much easier to utilize it going forward. And the wonderful thing about being in critical care research is the amazing amount of overlap between the data sets we need to study different conditions, so the idea is that we can expand from there. But you see here our structure, with our receiving guidance from the FDA and NCATS at the NIH, we've been able to collaborate with these institutions to implement at more than 15 sites, have successfully implemented significant portions of the tool. We have 25 total sites that have engaged with us. More than 100,000 patient records have been submitted to SCCM's Data Coordinating Center. And just to clarify, that's not 100,000 cross-sectional, 40-variable data sets. That's the ultimate deliverable we get out for the FDA. On these 100,000 patients, we have every blood pressure that is captured. We have every medication that is administered. We have every FIO2, every PAO2, the devices that they are interacting with to receive oxygen support. Danielle mentioned the oxygen devices are frequently not mapped. Well, the data standards didn't exist prior to this project, so that's one of the things that we've contributed to the field. And so it gives us the ability to move forward and study things like ARDS, because we now have a pipeline that includes all of those metrics around the ventilator, all of the settings, all of the various pressures, and we get a much more granular view than any traditional registry would let us have, because we essentially have the raw EHR data on 100,000 patients. We have one publication out, but there are five coming up, some of them. Dr. Evans mentioned, and our colleagues sitting over here on the right side of the room, just behind the projector, will be presenting two abstracts later this week on some of our ongoing studies, which include trial emulation, a major area of consideration, as well as multiple conference presentations. I think I managed to get the logo of every conference we've presented at on this slide, but I'm not sure. And that begs the question of future directions. As we collaborate with SCCM, as we deepen our partnership, there's always the question of funding. And so with a pipeline like we have built, the goal now is to enrich and maintain. This is my implementation. Scientists start to come back to the surface, and the idea is looking at all the different ways we can leverage that. So there's obviously drug development, guideline development, there's sepsis research. Not only is sepsis a major public health concern, it's also a major concern to healthcare systems, because sepsis has so many quality metrics around it that it significantly impacts the reimbursement. So a healthcare system has a significant incentive to invest in infrastructure to let them understand their sepsis patients better. That same pipeline, because of all of the information on viral diseases, can be leveraged for syndromic monitoring, could be of interest to the CDC and other groups. And the pipeline can also let us get a really deep, granular look at various laboratory measurements and their interactions with medications, potentially giving us avenues to leverage that as hypothesis generation for industry stakeholders. And so pulling together a very broad collection of use cases, we're able to start building a strategy for how to keep funding available for these sites, not only to maintain with the sites that have already implemented these tools and are submitting data, but to expand to additional sites. And then downstream, we're able to leverage that to address the equity issues by having a way to intentionally oversample on some of those under-resourced or under-served populations. And there was a question earlier about where do I get started or something like that. Dr. Boyce mentioned the Odyssey community, the other place that people really should get involved is the data science campaign. In our work together, as was mentioned earlier, building data standards for SECM, the Society for Critical Care Medicine is leading the way for how do we actually build a larger ecosystem, a larger pipeline for data across different institutions to study a wide range of critical care illnesses and to look beyond the spectrum of care. There's lots of interesting opportunities with EHR data to look at those outcomes beyond the post-ICU syndrome, the recovery from various complications. And as we build and enrich the data hub at SECM, there'll be more and more opportunities to leverage that, not only for grant applications, but also just to advance the evidence in general. And with that, I think I'll wrap up and kick it back over to questions.

drug repurposing

Critical Path Institute

real-world data

clinical trials

EHR data extraction