 Dr. Bonnie LaFleur, PhD, research professor and director of the Health Outcomes and Pharmacoeconomic Research Center, the HOPE Center, and Travis Wheeler, PhD, associate professor at University of Arizona. The topic is, our Ken Coyte College of Pharmacy Collaborations and Opportunities. So welcome, Bonnie and Travis. Thank you very much, Sandesh, for inviting us. Travis and I are excited. I think that we have really been inspired by some of the previous talks and feel that a lot of the things that we're going to be talking about as potential future collaborations might be very synergistic with some of the things that we've been hearing about. So how I'm going to structure my 10 minutes is who I am and sort of how I got to where I am. I want to go over some of my, I guess I'd call them methodologic research areas of interest, and then I really would like to focus a lot more on specific examples where I believe VA data and collaborators might be a good fit to potentially work together with us at the College of Pharmacy. So by training, I'm a statistician. Currently I direct the Health Outcomes and Pharmacoeconomic HOPE Center at the College of Pharmacy at the Tucson campus of the Health Sciences for the University of Arizona. And what the HOPE Center does is wear the research arm of the Health and Pharmaceutical Outcomes Academic Program in the College of Pharmacy. We also provide, we're kind of a, I guess I would call it a cataloging of potential access points to real world data for investigators across the university. And this would, when I say university, I mean I said that as university singular, but really and truly I believe that this is really across universities, because I believe that ASU and the University of Arizona College of Medicine in Phoenix also fit under that umbrella. Also probably of interest and basically segueing into the previous high level user experiences that we heard last hour, we actually are a part of workforce development. And when I say workforce development, I'm talking about master students, PhD students, PharmD students, MDs and fellows, and in many departments, so both Travis and I all let Travis talk about his, but we work with a lot of different colleges, both in the health sciences and on main campus at the University of Arizona. So these students come from a variety of backgrounds, including statistics, epidemiology, health outcomes. A lot of the MD fellows that I work with are making a transition to independence and they may be in their fellowship year, or they could be early investigators. And of late, we've been seeing a lot more MD, PhD students who are getting their PhD training in this broad umbrella of clinical and translational sciences, of which a lot of the kinds of investigative work that we've heard today falls under. So it's a very broad umbrella. Additionally, so that's kind of some of the perhaps the linkages that I feel I wanted to highlight to you guys. Additionally, I'm also the associate director for one of the associate directors for the Center for biomedical informatics and biostatistics at the University of Arizona College of Medicine. And my specific mandate is to enable reproducible research by providing infrastructure to investigators at many different stages of their research career. And the infrastructure is encompassing of data management, research portals, which you guys are all familiar with, because I see the VA systems as one of those research portals, we have built several of them as part of some of our large program project grants and U19s. So we're very familiar with how to work within those and even how to construct those. And then we build a lot of analysis pipelines and maybe tools that investigators can use, both for their individual research, but also to share outwardly for other types of collaborations and the data sharing types of activity that is being required by extramural funders right now. So right now, we work a lot with grants and grants support to provide data hubs and these data sharing activities. So my research interests as a statistician, or really a translational scientist, because I really it's not just statistics, right? I mean, we all have a lot of multidisciplinary hats that we wear over now in this kind of research environment that we live in right now. But I enable clinical decisions. And typically, I do this by way of biomarkers. And when I say biomarkers, I'm using this as a very broad term. So a lot of people associate biomarkers with either some sort of assays, geomic, proteomic, immunologic. But biomarkers are just any type of phenotypic description that could indicate subclinical, preclinical disease states or some sort of a risk or benefit phenotype that would enable us to make individual level decisions. That is my expertise is really in these types of genomic, proteomic and immune biomarkers, but I've actually been doing this for a long time and have worked with many people who are not only looking at these eye throughput types of assays. I currently have several extramirally funded grants that are studying mechanisms in immune and cognitive aging, which I'm going to talk about more specifically on the next slide. And also immunoprevention, and specifically right now in non melanoma skin cancers. In my past, I actually went to industry for 10 years and had quite a few actual assays and biomarkers that were approved by the FDA. Most of the clinicians in the room know all about the anti PD-01 and anti PD-01 therapeutic cancer drugs. And in my role in the pharmaceutical industry, I actually helped enable a lot of the diagnostic approvals that predict who's more likely to respond to those therapies. So now let's talk about the real meat of what I wanted to talk about. And that's the collaborations. First of all, I really believe that we're looking to provide our students and mentees with real world experience. And I feel that via medicine, it would be a very good workforce development aspect of their training. I think by and large, a lot of the students that I work with right now, and even some of the junior faculty that I work with, are not, they really don't know that much about the opportunities that exist in the VA healthcare system. And I believe that collaborating with people would really help them. And it also, it sounds like, might also help some of you. So that's kind of like my main bullet point is that I would like to hear a little bit more about how we can collaborate together, both to provide mentees and students with some exposure, but also help improve the VA patient outcomes as well. Very specifically, and this is really Dr. Migrino, I really would like to talk to you a little bit later. I'm actually, I run the data sciences core and also a research project for the Precision Aging Network, which is a NIA funded grant that's examining a lot of these biomarkers and neuropsychologic surveys to improve normal cognitive aging. So we're mostly focused in on the unimpaired to MCI transition, and specifically the MCI transition that might be modifiable. So perhaps is not part of that unimpaired MCI to AD trajectory, but more just the types of normal mild cognitive impairment that might be due to things that we could potentially mitigate. I really feel that listening to your talk and then also just talking to Sandisha for as long as we have been that the VA data would be a really fantastic addition to evaluating and comparing our longitudinal cohort. So we have or we're building a longitudinal cohort that's actually capturing a lot of these biomarkers that Dr. Migrino was talking about that are expensive and often very difficult to obtain. And I believe that we could think about benchmarking VA trajectories to our trajectory because I think the information is very synergistic and we could actually really help modify cognitive decline in some of our older adults that isn't necessarily due to pathologic types of diseases. And then I have a collaboration with a skin program project grant where we're really looking at immunoprovention therapies. And in order to really get a handle on the risk benefit for patients, especially for prevention, whether it's chemo prevention or immunoprovention in this case, we really have to understand the risks and costs for standard of care. And I think that the VA population would be a very good population to help us understand some of those types of features so that we could build out our risk benefit models and help when we do have new immunophobe or chemo prevention therapies, we can provide our patients with more information so they can make those risk benefit types of calculations for themselves. So lastly, in addition to continuing to collaborate with Sandish, which has been amazing, I really feel like our collaboration with Sandish is like we're in month four of our two year getting into VA data. I think it sounds like there might be some other avenues that we might be able to explore with others in the room. I'm also very excited to maybe talk with some of you more about being prepared for the possibility of doing some crowdsourcing and working with the challenges like the one that was available last year in the VA AI and prediction challenge. Because I think that if we get a group of interested parties together, we can respond to those challenges pretty quickly. And that would actually be a really good way for us to enable some of these contributions or collaborations. So thank you and I'll turn it over to Travis.