 All right. At this point, thank you, Simona and Howard, if you could lead the discussion, that'd be great. Thank you very much. So I think it was a great opening. I think that we saw the critical need. Thank you for the Andersons for sharing that story. As Dan said, this happens way more often than what people think. I think we've seen that there's a tremendous number of projects that are underway. How do we integrate these? How do we put them together? And how do we move forward to get into the clinical arena is obviously the whole point of this conversation. I want to start the discussion with a question to Dan. So, sorry, Dan, I was struck by your sentence that we're finally at a point where genetics actually beat the standard of care for warfarin. This has been going on for a long time. You've been in the trenches on this for a long time. And so I'm just curious, as we're trying to move this into payers and we're moving to reimbursement and preemptive and so forth, what do you think was the major limitation for it taking this long for genetics to finally beat the standard of care? I think I should defer to people in this room who are part of GIFT. I think there are people in this room who are part of GIFT. Julia, were you part of GIFT? I can't. So it has to rely on the population that you recruit and the end points that you choose and the event rates in the populations that you choose and the genotypes. So I think that the more general lesson is that genetics applied population-wide is only going to benefit a small portion of the population, the population with the variance that you're interested in chasing where adjusting dose or doing something based on genetics makes a difference for half or more, it shouldn't have any effect. So you're left with either doing huge trials and hoping that you find that right subset or doing these trials like predict. Predict isn't really a trial, but doing these kinds of programs that Mary is doing, that we're doing, that UPGX is doing, where you do lots and lots of variants across lots and lots of drugs. And there are incredible design problems with those kinds of approaches. So I don't know what the answer is for GIFT. I think it is important. It's always irritated me that people have discarded preemptive pharmacogenetics for Warfarin based on a trial that looked at time in therapeutic range, which is a surrogate at best anyway. I mean, if you're going to do something, you really ought to look at hard end points. The other issue with Warfarin is that it's such an attractive target because it has such a narrow margin and we understand the pharmacogenetics and there are these high impact common alleles. But it's going to go away. So I think we ought to sort of, I present it because I think it's a, there are lessons we've learned. But I don't know why GIFT showed something different from COAGA. Mark, I have to speak for the data. Yeah, I mean, I think you touched on one of the reasons that I think there is exceptionalism related to how we treat this. You gave one example, the other example is clopidogrel when I present to cardiologists and I say, if somebody's on a proton pump inhibitor, do you change the dose? Well, of course, there's no data. There's a tenth of the data for that than there is for the pharmacogenomics. Exactly, a little bit of data that makes no difference. Yeah, exactly. And so, but we're used to adjusting medications because of competing medications. It's something that we've learned. And so we are in some ways holding pharmacogenetics to a standard that is above what traditional standard of care is. Now, that doesn't exclude us or exempt us from doing studies, but I think the second piece is that we probably haven't invested enough time in terms of what is the right study design to actually do it? A randomized control trial of the types that we've been seeing here are considerably problematic for the reasons that you articulated. But the effect size, if done correctly, is enormous. So how do we create a trial methodology that allows us to get to that answer for those individuals that are most affected quickly? If we could come up with an answer to that question over the next day and a half, then this conference will have been worthwhile. I think that is, you've just articulated, I think, one of the biggest challenges there is in the field. And I really would be interested in hearing from other people at this table who've thought about this for a long, long time. Mary and then Mary Lynn. I'm not sure if this is what you stated, but I think it's what I heard. The GIF trial is not the first example of a randomized approach to genotyping that shows an improvement in outcome. The Abacavir HLAB trials did that more than 10 years ago. And in fact, it's very difficult to imagine the ethics of doing a randomized trial to present a serious adverse effect like, as Stevens-Johnson or TEN, knowing that there might be an HLA variant, would we ever agree to randomize patients to not have testing before receiving that drug? So I think the GIF trial is great, but there are other examples that show that pharmacogenetics has worked before that came along. And just to echo what you said, Dan, and you amplified on the, is the Sodelol example, with dosing based on renal function, an outlier or a Sodelol specific thing, I mean, as you know, there's a huge proportion of drugs for which the package insert recommends dose adjustments based on renal function and liver function without a single clinical trial to back up those recommendations. And yet, we feel that's definitely standard of practice. So the package inserts also talk about pharmacogenomics, and some of the package inserts sort of say that. So people pay attention to the dose adjustment in liver failure or renal disease, they're used to that, that's the way they were educated, and it would be medical malpractice, I'll say that, to not adjust the dose. And yet, there's the same sort of level of evidence, in some cases, for the pharmacogenetics, and people say, well, yeah, okay, whatever, I mean, and ignore it. And if we could figure out how to solve that problem, that would also be a major advance over the next day and a half. So along the same vein, I found it pretty frustrating that, especially when we talk about pharmacogenetics, it's often in the context of personalized medicine. You know, pharmacogenetics is a poster child for implementing personalized medicine. And yet, we then have others that say we need to do a randomized trial and get a large population, and as Dan said very well, it only matters for the people, not only the people who have the variant, but the people who also have the drug. So that tail is very small. And so, I think Dan is exactly right, we need to think a lot about how to better design a trial, so it has to somehow be a genotype-guided trial, not randomized. Mary, I agree, the ethics of doing a randomized trial for some of these drugs, we would never do that. But then the flip, it's not only the design of the trial, but it's the statistics that we do. So we have to get our biostatistics colleagues to think about the right way to analyze those data. I think a lot of the trials that have been done probably were significant if we had analyzed them in the right way, but because we just kind of did a T-test and looked for a difference, and because the tail was small, we didn't see a p-value of .05. But we know that for those people in the tail, it made a difference. And when you hear stories like Angela's, those are the people that we're worried about, the people who have the variant and the drug and the toxic side effect. And so we have to think differently about the trial design and the statistics. And I know we're talking about cost-effectiveness tomorrow, but that's the other piece. It might not be cost-effective to preemptively genotype everyone, but it certainly allows us to implement pharmacogenetics better if we have that information up front. And so somehow those cost calculations need to take into account not only dollars, but also patient experience. You know, the patients that go through things like what Angela go through, you can't even put a dollar amount on what that costs to her and to her family. So somehow those cost-effectiveness, I think I went to a PCORI meeting and that really impacted how I think about patient experience and patient outcomes. It's not just cost, but it's also those other outcomes. And I would argue the whole field of pharmacogenetics has set itself up to reach this unattainable bar because of doing one drug at a time, one gene at a time. If we combine all the ones that now we know or have a critical mass of information and use a composite endpoint for that trial, then we have a much higher chance of reaching success. I think of it analogous to an EKG, right? If we do an EKG as a diagnostic test, no one would ever just report the heart rate information from that because the EKG can give you so much more information about all the different phenotypes that you're looking for. In 2017, we now have that ability with our pharmacogenetic tests where we can assay a number of them for basically the same price and report that information for the composite good. So I'm just curious if people in the room may know. So for all the studies that have been done, is there enough information that we could go back and do a genetics-based reanalysis of the data and look at whether or not there really is a difference in outcomes? I mean, it seems that, you know, Dan showed an example of this in his talk. It seems that if you go in and do it based on the genotype, then you're actually likely to be able to measure something. So do we need to do a whole set of new trials or is there actually data out there that needs to be reanalysed or people that need to be genotyped that we could build upon that would make a huge difference? Julie? Yeah, so I think back to the warfarin story as an example, I think, of the importance of study design. So, I mean, in fact, there was really one randomized controlled trial that was negative and that was COAG. And in fact, if you understand COAG well, it was really designed to be a negative study. And so the reality is that there was a genotype-guided approach, but the control arm was very aggressive, not anything remotely approaching standard of care. INRs were measured in both arms of the trial much more frequently than in standard practice. And yet, if you look in the appendix, the outcomes not statistically different, but the outcomes were much, much lower numerically in the pharmacogenetic arm. And so, I mean, I think gift makes sense. You know, gift did a trial, they looked at outcomes that actually matter. They didn't regimen the INR monitoring so tightly to make it impossible for the genotype-guided arm to show difference. They sort of let usual care flow out. And so, I mean, I think you can design trials to make it not work. And in some ways, that's what happened with COAG. And so the key is, I mean, I agree, we can't just do one drug. Well, you have to be very thoughtful about how we design the trial because we can design it to the null. So I think there certainly were many advocates of COAG that actually thought that this was evidence that we don't need pharmacogenomics. And that was actually grabbed a hold of by a lot of people that felt, well, standard of care is just fine. Not the people in this room. But I think that was a major issue. But back to Dan's point. But it wasn't standard of care. The control arm was not standard of care. But that, but it wasn't obvious. Right, so my point then is, is that if part of the challenge then is how do we get the variants in the enough populations to have enough power to be able to see what it is, and we're only doing known variant genotyping, then we have a little bit of a challenge about how do we find those genes and those variants and have enough evidence. So I'm just curious, again, back to the warfarin, that now that we have all the data in place with all the different variants across the populations, I think it would have been a much easier test. So going forward with other genes and other variants that we're finding, how do we move that forward faster than what we have done so far? But I think there is also a cultural shift that's needed in clinical medicine because I think there is this attitude that doesn't pay attention to the non-middle. And so stories like Angela's are very, very sad, and yet, I think all too often, we encounter attitudes that are, it's only gonna affect a portion of the population. I mean, you'll hear the Clopidogrel story later. It's 30% of the population and cardiologists still are like, death stroke, am I? Like, big deal. I mean, are these important events? So somehow there's just a culture in medicine that seems to not pay attention to the ends. The ends are sometimes quite large. I mean, sometimes the middle is only 40 or 50%. So you've got 50% of the ends. So that to me is one of the greatest barriers for implementation is how to shift that mindset among clinicians to focus on the non-middle. So we're gonna wrap up this discussion. I wanna thank everybody for the opening and there's much to cover in this meeting. Thank you for those that spoke. Thank you again for the Anderson's for sharing this story and for reminding us how important this is and how do we get this out there. Thank you. We're on break. Start up at 11, right? On number 1040, sorry.