 Good morning. It's a pleasure to be here today. I actually thought it was a pleasure to be here until Ken spoke and said he wasn't born in 1976. Jesus Christ. I mean, I was in a rock band. So I feel pretty bummed out, frankly. So it's just a downer from that point onwards. That's all I can say. I'm a physician epidemiologist and I work in the crap world, as Howard said, of translation. So like Helen, I'm definitely not deep in genomics. But I do know a lot about how we can twist and turn our database to be useful in the genomics space. And that's what I was, I think, asked to come here and speak about today. I'm not even going to trust this thing. So let me go to page down, I think. So what I'd like to do is just two things in the 10 minutes that I'm here for. Firstly, give you some sense on how you can use payer type data to either confirm some hunches that many of you may have by things like phenocopying, and I'll show you one quick example, or investigating potential clinical utility for various tests. And then secondly, I would like to pose a point around how the data or the system in which drugs are paid for in this country is really an interesting and scalable solution to promulgate the use of these tests where people might believe that the evidence is there. So this is a busy slide. I'm sorry, it's the only busy slide that I have. But for those of you who are curious, what is payer data and what are we talking about? I don't actually use that term, it was given to me for today's presentation. But in the United States, typically these are the buckets of data that are available within different payer environments. And I work for MedCo, we cover 65 million Americans for outpatient drugs, so we have all of their outpatient drug information. We carry, like Helen was describing, what we call an eligibility file, which is an anonymized but linkable individual number for each person, including their aliases as they switch jobs and things. That gives us age, gender, household relationships, comorbidities in some cases, and it's updated every month, so we know if someone becomes ineligible for some reason. So therefore we wouldn't find any follow-up information. We have all their insurance information, all of their claims data, meaning every outpatient prescription drug that they've received for many, many years. So we would know things like compliance, switching, non-persistence dosing duration. Also information about the prescriber, specialty years since training, which may have some interest to you if you're trying to figure out who is it that's ordering these genomic tests or who isn't. Medical claims, inpatient or outpatient with ICD-9 codes, so we know as long as it's coded what the reason for hospitalization or some event would be, and the presence or absence of a test as long as it's coded, but not usually the value. Now, different than others at MedCo, we also have some genomic information, and I'll give you a sense for that in a moment. So a structured kind of retrospective study in our world, retrospective, meaning not going and approaching patients or doing an IRB type informed consent. With our anonymized data, you could ask yourself a question for drug X. Out of 10,000 people taking drug X, who got a PGX test and who didn't? If you think one of them is ready for prime time or you think it's being used out there in the real world, and then you could ask yourself, well, on the basis of looking at the physician behavior, did they do something about it? And those who did or didn't use the test? Did they select a different drug at a different rate? Did they dose differently? Did they, perhaps like in the case of hepatitis C, pay more attention to the duration of therapy? Did the patient's behavior change? We have a study today underway where we're actually providing the data back to the patient to see if they're more compliant once they know they are genetically at higher risk for an adverse problem, actually a clinical problem. Is the use of a test associated possibly with an increase or decrease of emergency room visits and why, there might be some hypothesis you'd have about reducing some side effect or bleeding or whatever it might be. Hospitalizations, maybe the use of other tests or changes in therapy and costs. So those are the kinds of things you can set up. Could be one example, which you may be aware of. About three or four years ago, we had been reading the literature about clopidogrel, and at that time there had been some pharmacokinetic studies that had suggested that 30% of people are either intermediate or poor metabolizers with 2C19. They were not able to activate clopidogrel. That was all very interesting, but there were really no large scale studies looking at what does all that mean. We have about a million clopidogrel users at MedCo. So for us, if 300,000 of them are intermediate or poor metabolizers and not getting the benefit of the drug, we need to know about that because there might be something we would recommend to them, maybe one of the newer therapies that have hit the market, et cetera. So we went into our claims data with some researchers from Indiana University in this case, looked at new starts of people clopidogrel who had had a recent ACS event, compared people who are on clopidogrel by itself for a year versus those who are taking a concomitant, very potent 2C19 inhibitor for that same time window, and just looked to see the MACE events, the cardiovascular endpoints, and found about a 50% increased relative risk amongst those who were taking a potent 2C19 inhibitor. So obviously not a randomized trial, but was 17,000 patients, and we did adjust for everything on earth. And subsequent to our presentation, American Heart Association and publication, there's been about a dozen studies that are similar with different kinds of payer databases like these that were in JAMA from the VA, looking at the same question and coming out with somewhat similar odds ratios. Another question that I heard yesterday, which was around laboratory reporting back to physicians and what do they do with the reports, we did do a study with the Mayo Clinic reference laboratory, and one of the questions we asked ourselves was, well, if 500 or 1,000 patients, the physicians get back a report on warfarin genotyping, which says something like, patients got a very high sensitivity, would they in fact reduce the dose? Or are they just gonna stick it in the chart or look at it and go, I don't know what that means or not do anything? So here we actually linked up data on close to I think six or 700 patients who were genotyped with warfarin with their subsequent prescription claims files. And what we found was really nicely a kind of a dose dependent change in the weekly warfarin, just the way the genotypes would have suggested. So they obviously got the report. We know that within three weeks of these prescriptions. And if you look at the very bottom, you see the people who had very high sensitivity to warfarin had about a 17 milligram per week drop in the use of their warfarin. And at the very top, the people who needed a higher dose of warfarin, in fact did get a higher dose of warfarin. So physicians who got the report, which was generated by the Mayo reference laboratory, which provided this extra layer of interpretation, physicians actually did change behavior based on the report in a way that you would have expected. So I'd say some of the buckets of things then that we can look at are does genomic test information result in differences in compliance, like fear factor? Can you scare people based on their use or their genomic information to stay on their therapy? Because in our world, we find with most chronic meds, about 50% of people drop off in the first year. And that's despite trying to explain to them how important it is to stay on their therapy. One little twist in the theme maybe that genomic information would be that extra piece of information that scares them enough to stay on the therapy. So we're actually looking at that today. I shared with you the example on physician behavior change and also examples on major clinical events and resource utilization. Lots of limitations with payer data and we can go on to that conversation for three hours. But here's just some of them in full disclosure. I think the biggest one just in terms of from an analytic perspective, in the US anyway, the medical claims part, the hospitalizations and all of that, those get looked at and kind of fooled around with and re-adjudicated for upwards of five months so they're not fresh the way drug data are which are instantaneous every day. Switching gears quickly, yep, two minutes. One of the big problems with promulgating testing beyond figuring out about the evidence is physician awareness of the field. We did do a 10,000 physician survey with the American Medical Association a couple years ago and these data are in press currently. Good news on the far left is that 98% of American physicians do believe the genes do relate to drugs. So that's a good thing because if they didn't believe that we'd really be in trouble. But if you look at the arrow and the bar next to the arrow only about 10% of physicians feeling no enough to even order a test. 90% don't feel comfortable, don't remember having any training or education in genetics. So that's a real problem. And so the one solution I would throw out there that is scalable relates to the only part of the US healthcare system that's 100% wired today, which is pharmacy. Since 1990, all 60,000 outpatient pharmacies in America have been electronically wired. The same data elements being entered no matter where you go. So for the 90% of Americans who have carrying insurance card in their wallet if you go to Hawaii one day in New York the next we're gonna know it. And if there's a drug-drug interaction it pops up right on the screen with decision support rules right there to the pharmacist or to the physician if they use an e-prescribing device. What we've been doing at Metco the last few years is using that same system to propagate a rule that says did you know there is this genomic test for this drug? You might want to consider it. The payer is paying for it. Or if we have the genomic data did you know there's a gene-drug interaction? And here it is and warning the physician or the pharmacist about it. So this is a scalable way in this country to get on board an existing wired system with gene-drug rule sets that don't exist today for systems like this that are resident in every single pharmacy in America. Of course it's all about partnerships and collaborations. Public and private that was the point for those of you who are not born before 1980. This was President Nixon and Elvis Presley, you laughed but I was at a meeting about a year ago using the same slide and a young person came up afterwards and said, who are those two people? So these are some of the folks we've been working with. I just mostly want to acknowledge the very bottom row which is the more than 150 payers and more than 50,000 patients who have participated in one or more studies with us to date. And these are my conclusions and I'm gonna stop there and take any questions. Thank you, fire away. So on your war for an example, what I'm wondering is how you get the other data besides the drug information. So for example, how do you know that the physicians didn't change the dosing because they were doing clotting times? Oh, well we would have, we have the INR tests whether they do or don't do them. So we would know if they did or didn't have an INR test between the data, their genomic test and the data, the drug change. We wouldn't know the INR time, I mean the actual result, but we would know the presence or absence of having a test. So you know they didn't do those tests? That's right. Oh, well I'm surprised actually, that's great. You know what you find in the real world of data like this is people don't behave the way you think they do in academic settings or other places. The frequency with which INR testing's actually done is pretty infrequent after the first week or two of warfarin therapy, it's kind of sad. But a lot of things are like that. Yeah, well we look, well theoretically it would be. Yes. You know, we went to a panel of oncologists asking them, do you think for a mat and abusers a reminder system on BCR Able testing would be a good idea because it's in everybody's guidelines to do that, a certain frequency. And they all said no, no, we don't need to be told because we know to do it, blah, blah, blah. We looked at the data set and 40% of the time that's not being done nationally. So, you know, maybe in your practice you do it, but not happening, yep. You said under most circumstances you're not able to. Oh sorry, sorry, sorry. Could you come in, so you manage this but you have people you report to, companies and employers of various sorts. What's their level of interest in genetics? That's a great question. So about two years ago we actually surveyed 700 payers who represent about $60 billion of prescription drug expenditures in this country and we had them force rank a bunch of different topics and Pharmacogenomics was one on the list. It's the number two topic of interest, do you believe? So, their number one is like benefit designs, co-pays, deductibles, that kind of stuff, but number two was Pharmacogenomics. That beat out generics, biotech drugs, pipeline, all sorts of drug related questions but Pharmacogenomics has the payers' interest right now. They're looking at it as a solution, not so much as an obstacle but a solution to the imprecision that happens today. Could you comment on, oh sorry. I have a quick question, I was interested in the low prevalence or the low comfort level of physicians in ordering tests and you're talking about delegating to pharmacists that advising on the genetic testing. So for you specifically and maybe more generally for this group is what's the role of the medical geneticists and genetic counselors in all of this and I think it's a question specifically here, it'd be really interesting to hear the answer but just in general, how's that community which has traditionally been the experts in counseling going to interact with all this new data? Let me first say that there does not seem to be a natural home for who owns the communication and understanding around all this new science and there needs to be. I mean there's lots of different players in this space. We hire and we have about 30 genetic counselors in our staff today who talk to physicians every day but that's not a scalable solution, there are only 2000 of them in the country. So who should own this information and be the person communicating it and I don't have a great answer for that one. I've been to so many meetings where everybody says it's over there but I'm not sure about the answer to that question but we do need somebody who really owns and keeps people up to date and has a system that learns and educates people because the knowledge is moving so quickly, it's very hard for people who are in practice to keep up. And by the way, I recognize Nixon but I was wondering who that person on the right was. So can you comment on Metcos on DNA direct company and sort of how you think for something that needs to scale at this level doesn't need to be a private entity who can hire 40 people to work on this or can, should it be a government activity? Could you comment along those lines? Sure, DNA direct was a company we acquired a couple of years ago. We started off doing lots of studies and research and helping payers get access to individual tests that were related to drugs of which there may be like 40 total but what the payers told us was the genetic testing in general is so much bigger. You know this, there's 2,000, 3,000 genetic tests and they wanted help managing all of that. So we identified a company called DNA direct who had a sort of a system and software that helps physicians do decision support and figure out are the indications right for that given test. An example of where a finding in that product where it's been used, and I won't say the percent but a surprising percent of physicians order a BCR ABLE test and what they really wanted was a BCR, BRCA one test and vice versa and I'm not kidding. So you're getting a ton of leukemia tests in breast cancer people and vice versa because they start with B. So well, it's the way it's working. I'm serious from real experience. So we do need some kind of decision support tools and I don't know if it has to be the private sector or the public sector but we definitely need to get ready for the future. I think we stop there and move on. You can ask me a question during the break. It's fine, if you want. Thank you very much. Appreciate it.