 Okay, thank you and thanks for the opportunity to share our ongoing study with you guys. This is a great meeting and I've enjoyed it. So I'll try and comment, there's been a bunch of good discussion this morning. There's a few points that I'll try and address directly as we're talking about this. And one of them, you know, one of the studies about the clinical trial design and who's actually in your trial. I think Julie might have mentioned earlier that if you're doing things where it's, you've got a population that has readily access to come back to the clinic, get their, you know, if it's warfarin, get their testing done every two, three days. That may be a whole different population than if you're talking about an underserved population where the parents have to decide whether they're going to spend their $3 to go back and get this some INR test done or whether they're going to use it to buy another box of cereal so their kids can actually eat. And there's, so I think one of the things that was the initial motivation here is the pharmacogenomics might be a little bit different if you're looking in a different study population. But the overarching sort of question about why did we run this study is really trying to figure out how we can get pharmacogenomics implemented in order for it really to be implemented widely. Number one, it has to have clinical value in a practice setting. I don't think nobody will argue with that. But it also needs to be economically valuable in such a setting and such that genetic testing really should only be widely implemented if it can be shown to be of high value medicine. But genetic testing will only be implemented widely if the providers and quite frankly whoever is paying for it is properly incentivized. And so we thought that in really an economic analysis alongside the clinical studies will generate information that will help for this to become more widely adopted. This is involved in a variety of people that we quite frankly weren't used to working with including economists that really had to be involved in some of the implementations and addressing some of the questions we've talked about before. So it's really a group effort of a variety of us from the School of Public Health to the Center for Computational Biology and Bioinformatics, Regan Streep, and the Clinical Pharmacology. So this, the study that we named it after having a couple beers and a day or two to think about it. So it was the Indiana Genomics Implementation Opportunity for the Under-Served. The acronym we use is Ingenious, that's where Mark's comment came from. And this is funded, it's one of the six sites funded by the NHGRI, the Ignite Network. And the goal or what we're doing is testing the effect of prospective, reactive, pharmacogenetics genotyping on healthcare costs and adverse events. And this, you know, as we were thinking about this, we debated, as we talked about, there's a variety of different type of trial designs you could use. I think, you know, one of the comments was, if we could figure out what the trial is we need to do, I don't think there is the trial. I think it's, you first have to decide exactly what question we want to ask and then figure out there will be different trial designs for each type of question. This is one, we've set a line, the end points of the trial are total healthcare costs and adverse events. And it initially was started in the Eskenazi, which is our local county hospital. But since then has expanded out to the IU Health patients, which is a center, or which is a healthcare system of 18 hospitals across Indiana. And we are currently in six of them, but expanded by the week actually to additional ones. The design is that they're randomized to either get one of 27 different medications, which I'll show you in the next slide, and either when they get one of those scripts for the first time, or at least not have gotten in the last 13 months, we get, we randomize them to either genotype guided or just standard of care, which they are not contacted at all. So we're, and then we just follow their healthcare costs through their medical records. And that, so there's pros and cons to all these different parts of the design and I'm happy to discuss those either at the end or in the discussion. These are, as probably most of you can guess, the logical set of drugs on there, mostly ones that have CPIC guidelines and the corresponding genes that are included on there are also ones that most people in the room would suspect are the ones that are relevant from the CPIC guidelines. As we started the trial, we went through each one of these gene drug pairs, created a little flowchart like this, and actually this one now needs to be updated to exclude anybody under 12, I think for coding, but the principle here is we had basically a flowchart that we could use in our adjudication committee when we got the genotype and the drugs to figure out what we were going to do, so it was consistent as the trial was going on. And so we have those, and they are being deposited, if not already, into the Spark Toolbox. For any of the genotyping gurus, if you're interested, there's 51 SNPs and 16 genes. Genotyping assays using the Quant Studio, using the OpenArrays, we have a separate assay for the copy number variations for 2D6. We chose this because it was accurate and flexible. Some of the things that we initially thought we would have on here, some of the gene drug pairs we've actually taken out because things like hepatitis C therapy has completely changed and people aren't using interferonamores, so that becomes irrelevant even during the time of our trial. It is a CLEA-approved and CAP-certified genotyping platform. So you can't read this, I'll show you in the next slide, I'll blow this thing up a little bit, but just to show, so the sort of the whole workflow that this goes, and the one, this one is for the IU Health, and this bottom one down here is for the Eskenazi. They're broadly similar, but there are some differences, I don't really have time to go through the details of the differences, but some of you may be able to see this, but I'll just walk through. Basically, the sort of short version of it is when a patient gets written a prescription for one of our, or actually for one of the medications, all the data from CERNR in this case gets transferred to the data warehouse of which every morning we get a report written that says, okay, here's all the patients yesterday that got one of these prescriptions, and it was a prescription for the first time. It then generates a report that has that, the patient's MRN number, name, phone number, any other concurrent medications, which then is downloaded to our ingenious team, takes the report and does a few things to look to see if they already, or is there duplicates in the report, or have they already been contacted, and if not, then they get randomized to either the genotype guided or the standard of care option, of which then we have a series of, we call them ResNet research coordinators that actually start calling the patient to try and recruit them into the study. If we fail to reach them within five days, they're just entered into RedCap because that's our time limit, which we need to have them back to get their genotyping report done. And the subjects that are reached, we discuss with them and go through a online consent, so we've set up now, so everything can be done because we're expanding out in IUH, which is statewide, so we have to do all this stuff remotely because we cannot have a coordinator in every hospital in every clinic across the state. So this is all done remotely, so that's why it's phone calls, we do the online consent, and then once they consent and we get it back, then we arrange with them and we've arranged with the blood draw stations and a variety of the hospitals around the state so they can go to whichever hospital they were either seen in or whichever one is closest to them. Even if they were seen downtown, but they live in Muncie, they can go to Ball University, they don't have to come back to the same clinic that they were done. And then based on that, we have the requisition for the blood draw is just faxed to the central requisition resource so they can, when they get to the draw station, say, I'm Sally Smith, and they pull down the wreck, draw the blood and send it off, and that's it. And then there's a couple of things to make sure that it gets done on time. It then gets sent to the pharmacogenomics lab of which it gets sent in, and then the patients are given a check or a gift card depending on which institution. And then the results then are, once the genotyping results are done, it comes back to our adjudication committee, which consists of a physician and a fellow, which pulls all the medication data and the genotype data together, looks at it. If there is a recommendation for a change in their therapy, then the physician sends an email or a note to the physician and says, you know, this patient's on X drug, and they got this genotype, we'd recommend that you change it. And then it's up to the provider to actually decide what they want to do. So the status of it, we've currently enrolled of roughly 500 subjects in the genotyped arm and about 1,300 in the control arm. Now with the expansion into IU Health, we are enrolling about 20 to 30 patients a week in the genotype guided arm and 50 to 60 in the control arm. And that's when we've expanded to six hospitals. We've still got another 10 or 12 more that we could expand into. That is that. If you're interested in what the drugs are, these are the number of patients that are given each of the different trigger meds. So tramadol is the number one. Prodipump inhibitors was pretty high. Coding was up there. And a couple of the interesting things, oftentimes when we're talking about this, we'll get reactions from people saying, well, nobody uses coding anymore. It's a lousy drug. Nobody uses it. While it's out of ours, it was the third highest drug. Same with amitriptyline. We often get it's like, it's only over in Europe that they use and tricyclic antidepressants. Nobody uses that here. And you can see here, some of it's for sleep and pain and different things. But these are drugs that are commonly used. Obviously it's a prospective clinical trial. We can't look at or see if there's any results yet. One of the things that we have actually looked at is the percent of the patients that have had actionable results that have had messages sent back to the providers. And here, as you can see in the red bar, it was about a quarter of them had actions where we sent some notification back to the physician. And about 4% of the patients, the physician actually wanted a consult. So they wanted to talk to our physician or whoever was on the adjudication committee at that particular time. This was with the first roughly 200 patients, but just talking to the people doing the adjudication. I think this is holding up pretty similar as time goes on and we increase our enrollment. So this has also made possible, it's really catalyzed an additional effort that we've got going, which is our Indiana University Precision Genomics Oncology Clinic, where we've actually started using the same genotyping platform and scenario to actually genotype some of our cancer patients. In this clinic, it was started by Brian Schneider and Milan Radovich, which are patients with refractory cancers or tumors of unknown origin in which they get somatic tumor genomics done, sequencing done by either Nantomics, Foundation Medicine, or Paradigm in as much as possible. We're using Nantomics because that gives us both whole genome sequence of both the tumor and the germline. And so we also collect blood on most of them and get germline pharmacogenomics done by our pharmacogenomics lab. That's the same one we use in our ingenious trial. And so as part of that, in addition to actually doing our, running our chip on it, we're actually working to get it so we can extract the data from the whole sequencing data so we don't have to actually run the chip again. This is some of the earlier discussions where we're actually working, doing some of the informatics to try and pull out the pharmacogenetics data directly out of that, which could, I think, then be transferred into programs like FarmCat. And one of the ones we've done is, and published some of this on, is called Ciparipi, which our informatics guys figured out a way to actually get pretty good 2D6 data out of whole genome sequencing. And we're now validating that in this clinical situation because this is purely clinical whole genome sequencing data coming, and we've got our germline genotyping done with our chip and our 2D6 copy number assay, so we can actually use data from 2CLEA environments to actually validate that, and then we're expanding that from just 2D6 onto many of the other Cip genes. So then as we got, as I started doing this, it was like, okay, so I was going to show to our tumor board the genotyping reports, right? So rather than just give them a text file that says they're 2C19 star 2, and star 6, and 7, and whatever they were, of which I was pretty certain the physicians or the providers would not really know what to do with. So I started making a slide like this that would say, okay, how can I summarize the genotype data so the physicians or the providers can actually look at it? And it sort of grew into from what was originally just going to be the genotype data, but then as we were talking about earlier, you can't really just put the genotype data in because you have all the drug interaction stuff on there too, right? So you can, in the same way you can knock out 2D6 with a gene, you can knock it out and get a phenocopy with different drugs, and so then I started looking at all their concurrent medications and considering that, and so on at the top, oops, you can see each of these are the genes, and this slide gets shown right after the history of the patient and the tumor board. So we've got some of the main metabolizing enzymes up here. If they're wild type and they have no drug interactions, they get the green smiley face. If they have in this case a, like an intermediate metabolizer, they have a yellow down arrow with a little DNA double helix on it. If they have an up arrow, it means it's induced or it's an ultrapy metabolizer. In this case, it's got a pill on it, so that means it's a drug interaction that's on there. So we started thinking, well, I'm really to include all this stuff, as was mentioned several times, you really also need to include liver function and renal function. So we actually, I pulled that out of the medical records, and then you'll be happy to see I have QTC on there for ones that have EKGs or that have a whole bunch of QT prolonging drugs on there. So we've got a couple additional snips that relate to the toxicities. And then it also became evident as we were talking that it was really underappreciated the drug interactions of proton pump inhibitors and a variety of acid suppressing agents that can interact with drugs like the tyrosine kinase inhibitors. And so we also then added the stomach pH, so if they're on any sort of acid suppressing inhibitors, like PPIs or H2 blockers, we put that on. So this would be an indication of being on a proton pump inhibitor. So this, as we were talking, it's trying to expect that the genotype itself without considering all these other things is going to be completely trump, all these other things is probably unreasonable and it really needs to be considered in the context along with all these other factors too. So now for every one of the patients, I actually go and create this slide manually. I need to figure out a better way to do it because it takes a lot of time to do it manually. But this has really been catalyzed by the fact that we had the infrastructure set up to start doing the cliogenotyping, which we then expanded into a cancer clinic. And this is purely for clinical purposes that we're using it there. And Teji has given my one minute sign and this is the last slide really. I mean, there's too many people to put on here, but this has really been initially catalyzed by the Ignite Network, and that's what's funded the study. It then expanded into the IU Health Precision Genomics Clinic and a variety of different funding agencies or funding sources that we've had. So with that, I'll be happy to answer any questions. Great, thank you, Todd. Again, we have time for maybe one or two very quick questions, and Manoli, if you want to head up. Julie. So, Todd, on your slide with the actionable results, which I think was about 30%, so is that actionable results relative to the trigger drug or, OK, and you guys just published, Peter, is that number similar? Did you guys have a similar number for, so for even just for the trigger drug, about 30% would have an actionable result? That's right, of drugs there are actually taking, you mean? Right. That's right. So it was like 25%, and that was for the one. Sometimes they also start on multiple drugs at once, so sometimes that can be that. And we're also, as these reports come back, if it's somebody who's already in the database, and they're already on the trial in the genotype arm, we will then obviously send a notification later on. But that was for the initial incident trigger met. Great, thank you. So next is Manoli Pereira from Northwestern University Account Discover and Translation.