Oh, this thing. Ooh, which one of those did you? This one. OK. Yeah, so I don't know if all of you, I know some of you do, but you may all not realize how much at least the large part of NCATS was incubated within genome. I got to genome in 2002 and left when NCATS got formed in 2011, so I was there nine years. And really, a fair amount of what I'm going to show you was sort of a little marsupial in the pouch of genome. And when NCATS got formed, we kind of jumped out and are kind of now on our own. But NCATS is only about two years old. So we are, I often like to say, we're like a toddler who's still learning to walk. We have a lot of potential, but we're still making a mess. And we hope eventually we will do something to really meet our potential. So this is the problem that we work in, and this is the slide I show every time I give a talk. And this is a problem I know that you think about a lot, too, which to me is really the biomedical research question of our era, that is that we live in this really paradoxical time where we know more about ourselves and health and disease, perhaps applied by the genome project, but by fundamental advances in general, but at the same time, we are not making a lot of headway with improvement in health outcomes. And if you haven't read it, if you want to be depressed, you can, if you're not depressed enough from reading the post or something, you can read this as an IOM report that came out about a year ago that was pithily titled Shorter Lives, Poorer Health. And it talks about just how unnecessarily unhealthy Americans are. And a lot of this is because, certainly not all, but a lot of it is because we are really bad at transitioning discoveries over here into health outcomes. And there's a lot of outcomes as a result of this or knock-on effects that the drug device diagnostic development system is a mess. Since I left Merck about 10 years ago, the pharma industry has laid off, I don't know, Lon could probably tell us the up-to-date numbers, but something north of 200,000 people. And a lot of those are researchers because of this problem. The clinical trial system in this country is a mess to put it mildly. And even when we do discover interventions that improve people's health and clinical trials, our system is actually terrible at getting those transitioned to all the patients who could benefit from them.  And not to put too much point on it, funders of the enterprise, public and private, have either gotten in patient with us or have lost patients with us completely. And you see this both in the flight of capital from the life science markets in the private sector and the difficulty of getting across what NIH does in the public sector. And those of you who've been around NIH know, just in the time that I've been here in the last 11 years, that conversation has changed quite dramatically. I think it's fixable, but I think we have to recognize it's a problem. This is the other thing that keeps me up late at night. I don't know if those of you who know this, anybody who works in the drug development field will know this. I show this because it will be a particular currency for you. I know Eric liked showing the Moore's Law and sequencing costs graph. Well, in the world that I live in, we don't have Moore's Law, we have Eroom's Law, which is Moore's Law spelled backwards. And it's spelled backwards because of the dramatically negative productivity growth in therapeutic development over the last 60 years. That is that the cost, the number of drugs produced per billion dollars has gone down monotonically by half every nine years since 1950. So just think about that. This is the reason why pharma's going bankrupt, right? There is no way you can keep this going as a business.  And just in case you're wondering, this number, if you extrapolate out, this goes to zero in 2040. So we'll probably all be retired by then, but going to zero is not good for any of us because we're all gonna have Alzheimer's disease by then and it would be nice to have a treatment which ain't gonna happen at the current rate. So one of the things I find most frightening about this is you think about the ethical changes in biology and medicine that have happened since 1950 and none of them has affected this slope. It's really quite terrifying when you think about it. So one of NCAS jobs is to try to make this flatten out and eventually go back up. I'm gonna make the argument that a lot of this is a scientific problem. The translational space is remarkably empirical. We really do not understand most of what we do in this space. And I think as somebody who comes from a mechanistic background, it's obvious when you move into this space that it's much more like medicine, clinical medicine than it is science. It's, and those of you who are docs will know what I'm talking about. Okay, so this is another graph I show all the time. You know, it's a norm, because that's the problem. Where's the opportunity? You know this one. I need an updated graph here that goes a number of Mendelian disorders identified. It's now north of 4,500. So here are all these rare diseases, the molecular basis of which, thanks to you all, we now know. And I don't know, when are we supposed to know all of them? Thanks to you guys, probably another. What's the number now, five years or something? Yeah, so, but whatever it is, if you go back 15 years ago, the number was less than 50. And now it's 4,500, 5,000 or so. So this ought to give us opportunity and are being, and of course we have this. I always love showing this. You guys still show this, the lollipop graph. I know Terry shows it. But I love this for all the reasons, as you know, this represents opportunity, right? Targets potentially. And I always, I want to go back and show this. This was, those of you who know me for a while know, this is the graph, this is the slide I always used to show when I first got to genome, that the idea was that we were gonna create this translation toolbox when I got to genome. That was, my job was to be the translation guy at the genome institute. And it's interesting to reflect on what we were gonna put into this. One was, by the time I got here, HapMap had started, right? And ENCODE had started. And NGC was had started as well.  We have functional and structural elements. We have CDNAs. And we started, one of the first things I did was to work on transcriptome reference sets. But this was back in 2003. So it was really archaic technology, NPSS technology, if you remember any of that back then. Creating S-Ironase, which I'll tell a little bit more about this knockout mouse project was one of the things that I started along with a bunch of other people here. And then anybody remember this, human base? That was something that never happened. That's interesting. If you get Eric drunk enough, he'll tell you why this didn't happen. It's basically a political issue. And then we put this into the toolbox as well. And when I started talking about this, nobody knew what those things were. And it was really because, if you think about all of them except small molecules, they all operated the gene level, the locus level, or the mRNA level. But one of the things that the genome project told us, of course, is it's really proteins, number of proteins that confer, or not number of genes, that confers organismal complexity and health and disease.  And so small molecules work at the protein level. Okay, so I love this one. Anybody, who do you think said this? Come on, it's gotta be one person. It's gotta be her, right? And here's an example that we all love to quote. How many of these are there? I don't know. But it's one of the things that we're interested in. You're interested in making happen a lot more often. However, I always like to point out that what I learned as a neurologist and then as a geneticist was a very hard lesson, which is that this, so even you cardiologists in the room, I don't know what this is. That's a brain, first of all. And that big black thing, that's not normal. That's a stroke. But I also learned as a geneticist that this, which is the original paper from Linus Pauli on the cause of sickle cell disease in 1949, does not equal this. And just because you can figure out why something is broken doesn't mean you can fix it. It is a completely different exercise and it requires completely different skill set, et cetera. And whenever I go any place, and you probably get heckled with this too, I go places and I talk about how the genome is gonna help us develop new drugs and there's always somebody who says, look, buddy, 1949, you still ain't got a drug based on this insight. So don't talk to me about all these new genetic targets until you fix this one. Now, we're working on that. I'll show you that in a second, but it is a fair point. So what is the NCAT's mission on all of this maelstrom? This is the mission and I just want to emphasize a couple of words. One is we're a catalyst. We're 1.8% of the NIH budget. Eric reminds me that we're now bigger than you guys, ho, ho, ho, as of, I guess this year, because of one very large program that you've all heard of, the CTSA program. But to catalyze, the other reason we're a catalyst is that we're disease agnostic, like you all. And if you're gonna talk about translation to improve health, then we need to demonstrate improvements in health by working in particular diseases.  We have to do everything we do collaboratively. So it's one of the reasons that NCAT's is really fundamentally different from most of the other ICs. So we focus innovative methods and technologies, that's really what we're about, that will enhance the development, testing, and implementation of diagnostics and therapeutics, and we're disease agnostic. Now, when I became director, I actually changed this mission a little bit. I tried to do it officially, but as Eric discovered when he tried to do the reorg of NHGRI that he eventually succeeded in doing, simply to change the words in a mission statement of one IC, of one operational division, and one cabinet department, actually requires an act of congress. So I was told, don't try to change the mission statement, but I do think about it differently. So look, there's two problems I have with this. One is just diagnostics and therapeutics, and I really think about interventions, broadly, behavioral interventions, et cetera. Secondly, it's not good enough to just make diagnostics and therapeutics. You actually gotta show that they do something. So what we really are focused on is interventions that tangibly improve human health. That is a much bigger mission than this, which is much more limited. It's a very important distinction. Okay, so I love this graph, and a lot of you probably seen me, or figure, because a lot of you see me show this before. This was actually made by Francis' office as an organizational chart of the NIH. Now, I like to think about this as a silo, as an aerial view of the silos that make up NIH, really. But you notice in the middle here, NCAS is supposed to be a horse of a different color here, but it's also supposed to be a convener, an adapter, a occupier of the tragedy of the commons, so all the kinds of things that we think about ourselves as. And it really is true that probably a lot like you all, and this is not by mistake, not by happenstance, we don't think about the translational space in a parcellated way. We don't think about what's different about diseases. We think about what's common to diseases, and what's common about the translational space, and we're big on integration and connections, because we're actually confident that that's the way Mother Nature designed the human body, you know, knee bone connected to the leg bone and all that, and if we approach the translational problem that way, we will have better success. But because of where NCAS works, and perhaps best exemplify by the fact that NCAS, unlike any other institute, doesn't do any basic research, we really start at target validation, even though I'm a geneticist by training, we don't do any basic research. As a result, you know, we're sort of right shifted, if you wanna think of it that way, in the development spectrum, and so we have much tighter and more systematic interactions with disease advocacy groups and nonprofits and FDA, pharma, biotech, VC, all those kinds of things. Okay, so I mentioned this before, but it's important to understand, if you're not familiar with this translational vernacular, it's worth knowing about, it has some problems, but it's worth having in your lexicon because it is something that people use, and I actually do find it reasonably helpful. So the important thing is that NCAS works across this space. So what am I talking about here? That T1, in most people's vernacular, goes from a target, a punitive target, through target validation to an intervention which is shown to be potentially useful in a proof-of-concept trial, say a phase two-way trial, if it's a regulatory relevant intervention. Then to go from a proof-of-concept to approval is T2. So that's a bigger population, phase three, et cetera. However, that's really where the next phase of translation starts. So you haven't actually gotten to health because you're working in this hot house environment of a clinical trial, people in the wild, in their wild-type environment, eating all kinds of crazy things and doing all kinds of crazy things, which you don't let them do in clinical trials, does the intervention actually work? And how do you get it to people, all the people who could benefit from this? And then if you get it to all the people who benefit from it, do you actually show that you had an effect on population health? So whenever I'm asked to explain, you know, give me an example of this. So my old company made a drug called Vioxx you may have heard of, and it really had its downfall in T3. It actually got to approval quite nicely. The problem came when it went to many, many, many more patients than had been in the clinical trials, and you see this a lot. And my favorite example of the T4 problem is post-menopausal estrogen. So you're all aware of the fact that in this space, in this space, this looked brilliant. You know, great biological rationale, look great in all the animal studies, all the clinical studies got very widely adopted. And then it turns out, given the results of the women's health study, we were actually killing people. So it's important to not stop here or here or here or here until we get to health. And so that's a really important issue. Now, I will grant you that probably most of what we would do with genome is either down here or maybe down here or here. You know, in the middle, maybe not so much, but you know, that'd be interesting. You know, something that we've been talking about with genome, and maybe you'll see this as I go on. Okay, so sometimes people ask me, well, you know, translation's pretty straightforward, right? So what's your problem? You know, what are the problems that you guys work on? And these are some of the problems that we work on. And if you look at all of these problems, they are the reasons that intervention development fails, either in the preclinical stage, the first three, or four, depending on your point of view, or in the clinical stage, or in later translational stages. And you'll notice that there's no disease listed here. And so this gets to a point that I mentioned before, that these are all problems that are everybody's problem in general, but nobody's problem in particular. I mean, what I see is responsible for these things, which is why nobody's solved them. I mean, that they are scientific and organizational problems. They are nobody's problem in particular, so they're tragedy of the commons problems. And if you're familiar with that term. So virtually everything NCATS does is in the tragedy of the commons space. And if I thought about things that really intersect with what you do at genome and what I used to do, these red ones are particularly relevant. Why do drugs have, or interventions have toxic effects that they're not supposed to? Why do they not have efficacies that they're supposed to? How to de-risk undruggable targets, state or interoperability, biomarker qualification, clinical diagnostic criteria. How do we find the appropriate patients to get an intervention to, pharmacogenomics if you wanna call it that. Okay, so what's within NCATS? There's, you could divide this roughly into three pieces. 80% of the budget is in the CTSA program. And if you don't know what that is, I'll tell you in a second. About 15% of it or so is in rare diseases. And I, there's a lot of reasons for this. Part of it is because rare diseases are most often multi-system disease diseases. And so they don't fit nicely in any one institute. I also like thinking of this, not to be pejorative. I like to think about them as a sort of model organisms of the translational space. If you're gonna try to work out novel ways of doing translation, you don't wanna start with type two diabetes. You wanna start with a relatively simple system and rare diseases represent that. And then we have about 5% of the effort, maybe a little more, is focused on technology development without a specific disease implication as an approximate outcome. So one of the things that I'm, sort of when you become director of an institute and those of you who run things will know this, you always have to have things that people can remember and put on their t-shirts and stuff like that. So this is what I've been beating into the heads of everybody at NCAS, is that it's not good enough to simply develop new ways to solve that long laundry list of problems that I gave you. We have to demonstrate in individual use cases that they actually work better than the previous methods or else nobody should believe us. But if they do work better, then we can't assume that everyone will stop all ways of doing things and all of a sudden see the light and start doing what they're supposed to and whether these are scientists or physicians or patients. And so we have a lot of emphasis on dissemination science as well, which is, you know, probably science in its own right. Okay, so I'm gonna start at the clinical part and work backwards now. So the division of clinical innovation. So this is currently, it's mainly the CTSA program. And here's the vision. This will not surprise you that the first is development, demonstration, implementation of methods of technologies. And this next word is important. Logarithmically improve the efficiency of clinical research. Translational problems are so large that it is not good enough to think about arithmetic improvements. I think about this really, you know, I think about everything in terms of genetics and genomics, because it's my background. Just think if sequencing technologies had gotten 10% better, 20% better, 30% better. Wow, great. You know, we'd be toast. We wouldn't be anywhere. It's only because the people at the institute said, no, no, no, logarithmic improvements, $1,000 genome. And so, because that's suitable to the scale of the problem we're dealing with here. Secondly, there's a whole nother area that we can talk about if you're interested. This, what I've realized, taking on this job after being away from academic medicine for almost 20 years, is that what I grew up with, the tradition of clinical investigation and phenotyping, which was the heart of what people did in academic medical centers, a lot of that has withered because of the relentless pressure on reimbursements and academic departments having to make money, et cetera, et cetera. And so, if you think about trying to make genotype-phenotype correlations, we are now really good, thanks to you all, at doing genotypes. We are actually terrible at doing phenotypes. And we've actually lost a whole generation of people who never really learned how to do this. And so, it's something that we're doing within the CTSA program. So, that gets into the training programs that we spend a lot of time doing. I think what we really need to have over the long term is something that I think about a lot is a robust academic discipline of translational research, which is gonna have different metrics. It's not gonna be papers in the cover cell. They're gonna be different metrics, but, and something I talk to the CTSA heads a lot. And then, we're really big on novels of engagement, models for engagement of the various communities that we work with. And really, if you think about this, why do we do this? Translational research by its name means we are carrying something from a place to a place, which indicates that where we're going ought to be of interest to somebody. So unless we know what that person is interested in, whether it's a clinical scientist or a practitioner or somebody in the community, whatever, patient, advocacy group, what have you, we're not gonna understand what those problems are. So we like to have those partners involved in every project we do from the beginning. So this is the CTSA program. It's a National Consortium of Medical Research Institutions. This is the current map. It's probably one of these that I would guess, probably most are all of the institutions that you come from, if you come from an academic center. So there's 62 of them now. These are legacy, large parts of these are legacy GCRC programs, if you know what that is. And we're still dealing with some of that legacy. But the vision here is that this program will go from being what they have been for the last 20 or 30 years or 40 or 50 years, in some cases, which are essentially glorified core facilities to do clinical research within academic medical centers, which is not bad. It's just not enough. It's not commensurate to the scale of a problem to a national network for translational medicine. And actually this committee that I'm going back to talk to after I leave here is a new steering committee which is focused very much on this question so that one would be able to recruit for clinical trials across this network through a common electronic health record infrastructure, have a common IRB structure, be able to recruit PIs because most of the key opinion leaders are at these places as well, have innovative clinical trial designs embedded into the metrics of the program and overall improve the efficiency and quality of clinical research. And I'm hoping that a few years from now, when I come back, I'm gonna be able to talk to Lon and Lon will not say to me, we're going overseas to do our clinical trials because we would love to do them here, but you guys are so inefficient and so costly that we can't afford to do it. That's what I hear from farmers all the time. I probably don't need to tell you what the statistics are in NIH funded trials. They're actually quite bad to put it mildly. So there's a lot of room for improvement here and we're gonna use the CTSAs to drive that. Part of the help here in doing this was an IOM report that came out about six months ago on the CTSA program. If you're interested in it, you could read it, but one of the important things was to really strengthen leadership of the program by NCATS and to focus on clear deliverables and outcomes. There's a committee that we have that's a working group of our council, which is helping us with this. And you can look for names that you recognize. I just wanna point out too, Ron Bartek, some of you probably know who runs the Friedreich's Research Alliance and Lynn Marks, who runs clinical research at GSK, as well as other folks that you probably know, Gary Gibbons, among others. So stay tuned on that. They're gonna report in May at our council. So it'll be interesting to see what they come up with. And what I've asked them specifically to do is to focus on outcomes, clear outcomes that address critical translational, general translational questions to improve the quality and efficiency of translational research through the CTSA program. How do we do that? Okay, so if we move backwards now from the clinical space into the preclinical space, this is all of what I built when I was a genome, actually. So some of you've probably seen this and it really hasn't changed much in the last couple of years. And the idea here is that most academic investigators who want to get into the translational space just don't have the experience, the expertise, the facilities, the knowledge, et cetera to do this. And so what we did when I came to genome now almost over 10 years ago, the idea was that we would set up an industry standard, industry-scale translational operation, which would recruit mainly people from biotech and pharma. But the model is that all of those people would work in project teams and they would work collaboratively with academic investigators who were disease experts or target experts. So though this is our intramural program, it is the weirdest intramural program in NIH because it has no tenure, it has no tenure track, it has no independent PIs, everybody works in project teams, every project has a collaboration with somebody somewhere in the world. So it's very unusual program. So what happens is collaborators come to us, stuck at various stages, and they go into one or another of these programs and what comes out the bottom are deliverables and those deliverables are either a physical entity, a drug, a lead, a repurposed drug, an S-I-R-N-A probe and or data that goes into public domain. And those deliverables move the project forward down the translational pipeline. But the other thing is we like every project to be dual use projects. So they tell us, they not only move a project forward, but they have a paradigm technology development component. So they tell us how to do one or more of these processes better. I probably don't need to remind you, but the current success rate going from here to here is about 0.1%, takes about 13, 14, 15 years and costs, depending on how you do the math, between two and six billion dollars. So as I often say, something which fails 99.9% of the time cannot be said to be optimized, so we are spending a lot of time working on this problem. And if you ask why, how is NCATS different from all of the farmers like the farm I used to work in, there's a critical difference and that critical difference is we do not have a short-term commercial imperative.  Somebody's got to do that science and it can't be done in a for-profit environment. The other thing is that we work on the 95% of targets and diseases that are not de-risked enough to work on in a private sector environment. Okay, so every project is a collaboration with people all over the country. These are the places that these folks came from. Just imagine the model here is in order to get hired you have to know the state of the art at one or more of these places, but the state of the art is terrible. So I don't want you to put in state of the art, 99% failure rate, right? So you get hired, you gotta tell me what the state of the art is but you gotta tell me how to do it better. And when you bring in 150 people which we have from all these different places and you set them loose that way to focus on the science only and do it in a collaborative way where they are tied in with academic opinion leaders, you can make a lot of headway quite rapidly. So this is the first center that I started back in 2003, currently has 200 collaborators in the target to lead space. Since I said about when I did what screening, informatics, mid-chem focused on sort of one minus pharma, the universe of targets and diseases that exists, you subtract out what bio pharma works on, that's what we work on. And the mission is chemical sRNA probes, new technologies. And just to give you a couple of examples of how we've done this. So one of the things that we started doing a number of years ago was to make genome-wide RNAI screening actually work. Those of you who've done this know that it's actually quite easy to go out and buy an sRNA library, but the results you get for the most part are junk. And I have data that would curl your hair to show it to you. And I didn't bring these, but you can often pick out individual genes that will allow you to move forward, but overall the data are simply not reliable. And when we looked at the more we looked into this, the more actually shocked we were by this. So we started this center about five years ago to work to reduce the practice to doing genome-wide RNAI screening. Secondly, to develop a collaborative resource to do individual projects. And thirdly, to create the first public sector database of RNAI data. It's amazing, 10 years after the genome project, there is no such database, right? Where you can go in and look at every gene in the genome knocked down one at a time and be able to mine those data. It's quite remarkable. And the reason is that the companies who make the sRNA oligos would not agree to make the data public. So being kind of using our convincing power and genomes familiar with this, we were able to get life technologies to agree to do this. And so just last December, we had a couple of great papers, this one, and I don't show this just because Bob's here. There's a really great paper on mytophagy, genes involved in mytophagy and Parkinson's disease that was in nature. And then the same month was this press release having gene silencing data available for the first time. So you can now get into these data all in Pubcam, put them elsewhere too, get in and look at every oligo and what its effect is in these screening systems. Just the one little tickler I'll give you is that the results do not correlate with the genes that the companies say are being knocked down. They are oligo specific, they are sequence specific, but those sequences have almost nothing to do with the genes that are identified. And so I'll just leave that with you and we figured out why that is too. I don't have time to get into it, but it's a really interesting story. Okay, so if I then move on to the small molecule side, we did exactly the same thing on the small molecule side. This is a project we started years ago with Alan Suderansky who you know is a tenured investigator at the genome institute working on small molecule chaperones for misfolded glucose herbicidease and Cauchy disease. Without just skip through about six years of work, we've identified compounds that bind these, that bind to mutant glucose herbicidease are biochemical inhibitors, so they bind and inhibit the enzyme in a tube in a cell-free system, but in a cell-based system, they actually increase glucose herbicidease activity exactly as you'd expect with a chaperone. And these have now been licensed to do different companies, Biogen and LTI for further development. And we're now working on the alpha-synuclein connection to see how these compounds might affect alpha-synuclein levels. Another thing which was another technology development area is working on the area of drug combinations. You're all aware that there's a lot of promise in drug combinations, a lot of diseases which can't be treated with single drugs. It's been very hard to do efficient screening of combinations for a variety of reasons. A number of companies tried to do this, combinatorics, just a lot of you probably remember. Again, really smart people, but they were limited by the technology available at the time. So, and also the need to make money because they were a company. So we had the opportunity now about 10 years later to say, well, gosh, isn't there a better way to do this? And so, without skipping through all the data, what was required was getting a high value library of small molecules, which I'll show you in a second, an effective planning process based on acoustic dispensing rather than contact dispensing and automated data analysis methods and really sophisticated bioinformatics. And bottom line is, that's not been done. And the first example of this was just published last month in a paper with Lou Stout looking at Lou's most favorite type of lymphoma. And you can go read the paper. It's quite a beautiful paper, but what it's doing is identifying compounds that might be synergistic with a rutinib, which is one of the typical compounds used in this kind of B-cell lymphoma. And we're applying this to literally 20 other projects now. And the reason this is possible is that we spent about two years on technology development, getting the compound acquisition, the dispensing, the compound management, the informatics right. One of the things that also made this possible is we'd spent five years before that developing this, which was a complete non-redundant list of every compound that were approved for human use worldwide. Amazingly, when we started doing this back in 2007, I thought there must be such a list, a complete non-redundant list of every compound approved for human use, right? It's gotta be available, just Google it. Took them five years to come up with this, variety of reasons. This sort of reads like a Tom Clancy novel, why it was so hard. But it's all in this paper. The important thing is, and this is really, this is a page right out of the genome book, we did this once to high quality and then we made all the data public. So it's now in a public database, anybody can access it. We've got a physical collection of compounds that we collaborate with people all the time to screen. Moving on to another problem now. Another problem in the translational space is unanticipated toxicity. This data a little bit old now, but toxicity accounts for about a third of failures when you're trying to develop a novel drug. And one of the reasons is that toxicity is really, toxicity testing for the most part is really stuck in the 1950s. That is what you do is if you have a novel drug or a novel chemical, you expose an animal to a certain amount of compound, has a certain tissue dose, and then you pretty much close your eyes and wait for what's called an apical endpoint. That is an observable endpoint. If the animal gets cancer or dies, something really obvious. And then you say, hmm, that was bad. We shouldn't do that again, kill that compound or go on to the next one. But you very seldom know why it actually caused that problem. So the next time you don't get any better at predicting which one is gonna be effective. And this gets back to this empiricism problem I was talking about before. So about six or seven years ago, we in the EPA and the FDA and the National Toxicology Program came up with this idea that, well, could you do the mother of all systems biology experiments to solve this problem? And metaphorically what we're doing, and this will be near and dear to your hearts because I know this is the way you all think too, we thought, well, okay, instead of giving these chemicals, which are either drugs or environmental chemicals, to the rat and then watching what happens, we're gonna metaphorically dissect the rat or the human into its component cell types. And then we're gonna dissect that into its pathways within those component cell types. We're gonna treat all of those pathways within those cell types with all of those different chemicals. Look at the effects on the pathways in the cell types and the phenotypes and then computationally put the rat back together again. So we're actually doing now 10,000 different drugs and chemicals in triplicate 15 point dose response in a different pathway assay every week. So it's about 600,000 data points every week. All of this goes in the public data, in the public domain, you can access it. And we're gradually coming up with predictive models using the historical data that we have from animals to try to predict eventually what compounds might have adverse effects. So this is the collaboration. Interestingly, it has gotten me involved in a world that I never knew before, which is the EPA and the NTP, people who run the Superfund sites, but they're very much the same problem. They're just chemicals that have an adverse effect on human health. Somewhat different, for those of you who are chemists, there's some what different kinds of chemicals. And this program called TOX 21. And these are the goals, identify patterns of compound-adduced biological response to identify the toxicity or disease pathways and eventually develop predictive models for biological response in humans. And it just makes the data point, it makes the point that in the last six months we've deposited 33 million data points. That is a data point, in this case, is a chemical in a given concentration with a pathway into PubChem. Something that I knew would be interesting for you is that we've also in this project begun to think, well, we know that humans and animals vary quite a bit in the response to chemicals. It's otherwise known as pharmacogenetics, right? But in the chemical world, in the environmental chemical world, you see the same thing. So the concept was, hmm, well, could we use, could we study, would it be possible to study the effect of human-inherited variation on response to environmental chemicals and drugs? So what we did was, we took the 1086 lymphoblastoid cell lines from the 1000 Genomes Project and we screened them in a 15 point dose response across about 250 different chemicals. So imagine that, so now we're doing 250 chemicals in duplicate at 15 concentrations times 1086 different cell lines. This is the kind of thing you can do when you have big honking robots and you don't have a drug you have to make out of the end. You can really do these kinds of massive experiments. And then you can use response to the chemical or lack thereof, this is a simple site of toxicity assay in this case, as a quantitative trait that allows you to map the loci responsible for differential sensitivity to the drug or the chemical. And we did a lot of work before this to test whether this crazy idea could even work and whether we have the power. Turns out it did, it did work and the paper is now in preparation but another thing we did, which again was take a page out of your book was to say, well gosh, this is a huge amount of data and we have some really smart informatics people but wouldn't this be a great thing for a challenge? So we teamed up with DREAM who are the challenged people and with Sage Bio Networks and Steve Friend's operation. And we did this challenge with them to use crowdsourcing to better predict the toxicity of chemicals, both the chemical response and the genetic loci responsible. And this paper is now, I should tell you that the winner of these, which actually the same team from UT Southwestern, the winner of this challenge didn't get money, they got a guaranteed paper in nature biotechnology. And we got about a hundred submissions to each of these as a result of that without offering anybody any money which I thought was interesting, just a paper and bragging rights, which is important. Another thing we're doing, which I threw in here for Dee Dee's benefit because I know she's a techno geek and I say that with all affection as a fellow techno geek. Another thing we're doing in the toxicology world which you may have heard of is this tissue chips for a drug screening project. This is a classic NCAS project in a couple of ways. First it addresses a critical bottleneck in the translational process, that is how do you test for toxicity. Secondly, it's a novel kind of collaboration. This is a collaboration with DARPA. And thirdly, it's focused on logarithmic improvements. This is focused on fundamentally changing the way that we test for effects of drugs. A different approach from the TOC-21 approach which is a very reductionist pathway systems biology based. This is an experimental approach using microfluidics. So the idea here is that we would have instead of a modeling in cells, we would model in three dimensional organoids of in this case, 10 different human organs. And these would all be represented on microfluidic platforms that would allow us to infuse theoretically artificial blood into say an artificial intestine which would then the drug would either get absorbed or not go through a microfluidic channel to an artificial liver and get metabolized or not and then go say to an artificial kidney and have a toxic effect or not. That's the model. And so when this started about two years ago, NIH funded 19 awards in 10 different systems. And these are just two examples. This is a lung chip from the V center blood brain barrier chip from some folks at Vanderbilt. And then DARPA focused had two rather large awards focused on the microfluidics and the engineering. I must say when I first started working on this, I thought this was completely nuts. I thought there's just no way they're gonna make they're gonna make this work. I must say I was wrong. At least and I'm glad to say I was wrong. So why was I wrong? It's because I underestimated the value of convergence. So this is a convergence of IPS ESL technology, biosensor technology, microfluidic technology and tissue printing ESS cell technology. All of these are milestone driven projects. Every one of them without exception is ahead of its milestones. And so what we're doing now is now that the individual microsystems of the organs are seen to be working reasonably well with the positive controls are starting to be put together in groups of two or three. If I move on to a more therapeutically directed program, this is a program that we started in 2009. Again, when I was a genome focused on rare diseases and the model here was the same model as the NCGC model that is these are obligatory collaborations between academic or small company investigators who have expertise in a disease or a target have partially developed intervention in this case and they collaborate with NCATs, intramural scientists who have expertise in drug development to move these projects to the point where they are adoptable by an outside organization, usually a biotech or a pharma for completion or development usually after two way. And so these are the kinds of things the trend does. If you look at all of these things, they are very difficult to do or impossible to do within a normal academic environment for a lot of reasons. Hard to get grant support to do this. These are very expensive. They're not hypothesis driven programs, et cetera, et cetera, et cetera. So these are all things that trend does. Essentially they go from where the NCGC ends, which is a lead to first in human trials. If we can offload these to a company first before that because they're de-risked enough, great. But often that doesn't happen. So this is the portfolio and I don't want you to read this. I just want you to see, if you could see it, that the therapeutic area is very broad. Therapeutic areas, everything from hematologic disease to cancer to infectious disease, nerdy generative disease, et cetera. Why do we do that? Because we're interested in, remember, general principles which will allow us to improve the efficiency of the translational space. In order to have general principles, you have to go across therapeutic areas or else by definition you can't argue it's generalizable. Look at the collaborators, about half of them are academics, half of them are small companies. There are three, actually, with genome. And these all went through peer review, I should say. They were not picked by me. So I think this says something about the quality of the people at genome because there is a lot of competition to get into this program. And so I'm just going to very quickly tell you what they are and who they're from. So this is a project on a rare myopathy with Bill Gaul. This is a classic rare disease to a single gene mutation of G and E. It's a disorder of the sialic acid pathway. The important thing is, in this case, Bill was stuck because he didn't have the capacity or the knowledge to do all the IND-enabling TOC studies and hadn't done a natural history study, so he didn't know how to approach this problem. So we teamed up with Bill. And actually, with a year, we're off of clinical hold into patients. This project you may have heard about because it's gotten a lot of notoriety in the press. This is a very unusual development project with a drug which has been used as an excipient in the past, never an active drug. Given it drantically, the collaborators here are all of these folks, including Bill Pavin from Genome who's one of the people involved in cloning the FPC gene about a decade ago or a little more, along with three other universities, four ICs, and two companies involved in this, including J&J, who makes this. And the important thing is we went through all these milestones in a rather rapid fashion. And this trial is now in progress at the Clinical Center as we speak. One of the important things is one of the reasons it's worked so well is that the disease foundations were involved from the very beginning. So they were very clued in and hooked into what was going on here. This kind of leukemia that you probably heard about from Paul Hulou, he's been working on this a large amount of his career. This is a fusion that results in a particular rare type of leukemia. And through an earlier NCGC project, we'd actually identified this compound, which is an old Roche compound, believe it or not, which seemed to have an effect on improving or inhibiting the action of this fusion protein. And so this program has now moved into trend as well. This one I show not because it's an NHGRI program, but because I want to get back to that Linus Pauling paper, that if this works, this will be the first drug developed against the mechanism of sickle cell disease. But it's a classic example of why these projects are so hard to do. So this is a collaboration with a company called SRX. It's a virtual company in Boston. This compound right here, any of you who are chemists, you'd be having a stroke at the moment because that is a really nasty-looking compound if you're a chemist. It also has a really problematic, potentially mechanism. It binds irreversibly to sickle hemoglobin. When it does that, it shifts the oxygen association curve to the left and allows the sickle erythrocytes to get through the postcapillary venules, the hypoxic environment of the postcapillary venules without sickling. However, the concern was if it covalently binds to sickle hemoglobin, doesn't covalently bind every other protein in the proteome and cause all sorts of problems. Doesn't, but that was one of the reasons why this company could not get support for this program. It's also regulatory issues and clinical risk issues. But we partnered with this company and within a year got all the work done, all the tox work, the CMC work, and the regulatory work to be in the patients.  It's now in a phase 2B trial now. And the biomarkers are great, that's all I can say. So we're really excited about this. OK, so I'm going to skip through this one. OK, I'm just going to finish with O-R-D-R. So you're all aware of this because genome has been a partner of O-R-D-R since it started. So R-D-R is now part of NCATS. And there are a number of things I just want to point out. One is this rare disease clinical research network. It's a rather remarkable network of 17 different consortia, working at over 200 institutions. Important things about these is that they don't work on individual diseases. They insist on grouping diseases that is either by cell type or by pathway or something, so as you don't work on one disease at a time. And secondly, every one of them has to have a patient advocacy group as a critical and integral member of the consortium. And this repository is something that we're actually very excited about, too. One of those things that's sort of similar to the problem in the old days of doing genetic linkage studies. You remember how it was very hard to get funding to acquire and phenotype patients that do linkage studies? And it really held back the field. That's what's going on in therapeutic development now. So it's one of the things we're working on. And this genetic and rare disease information center that, of course, has been funded with genome for many years now. And this is an information center that gets about 500 calls a month, generally from patients and parents who have just gotten a very bad diagnosis to help them with what this disease is, how to find advocacy groups and practitioners, experts in the field, et cetera. This is the consortia that are part of the rare disease clinical research network. And if you just look at the names of a few of these, like the Lysozomal Disease Network, these work on the 40 Lysozomal Diseases. So this is organelle. There are other ones that work on phenotypes. This is rare kidney stones. And then this one is a clinical syndrome, nephrodic syndrome. And then this one is more of a syndrome as well, autonomic rare diseases of the nervous system. So I just want to finish with this, that overview of what NCATS does. So a lot of you know Steve Groft, who has been a real icon in this community for the last 30 years, was actually at the FDA when the Orphan Drug Act was passed in 1983. And shortly thereafter, left the FDA to go to what then was the Department of Health Education and Welfare to get Orphan products, Orphan products started, and Orphan research started within HHS, what's now HHS. He retired on Saturday. And so this is a big change for us. Those of you who know Steve know he is an absolutely extraordinary visionary and advocate for rare diseases, rare disease research, understanding and treatment. He is not replaceable, clearly. And so we are going to have him stay on with us as probably a half-time consultant, because he's really critical for what we're doing. But we are going to be recruiting for a replacement for Steve. And so if you know folks who are in the rare disease community, I'd like to hear about them when we put the ad out. What I'm looking for is really to go genome-wide for this problem. And the way I describe this is Steve and his incredible efforts have focused attention on the problem of rare diseases. But for the most part, they've been on individual diseases. And we now have the opportunity to globalize this question. Because rare diseases, there are 6,000 independent diseases, but of course they're connected with each other in ways that we don't understand. And so the next director of this office, I want somebody to be somebody who thinks of the rare disease problem as a problem and probably has a heavy interest in knowledge and informatics and thinks about systems not on individual diseases. So it's really an opportunity to take this whole effort to the next level. However, it's important, and there have been some questions actually, oh, gosh, is NCATs going to abandon rare diseases because Steve's leaving? Give me a break. No, we're not. As a matter of fact, we are, as I've often told Steve, I think in many ways NCATs is a validation of all the things Steve has done over the last 30 years. So it's going to continue its important work. But I love this picture. As Steve rides into the sunset, those of you who are physicians will know why he's riding a zebra. If you don't know what that is, I will ask you to talk to Bob Nussbaum. He can tell you. And with that, we are done. So I'll just leave you with this. You're going to have these slides. Certainly, I'm glad to hear from you about anything. But if you're interested in any of these projects, any of these areas, I want you to have the contact information for the people who do these. And be glad to take a few questions before I run back and try to deal with our steering committee. OK, thank you, Chris. Jim. So that was great. I was just wondering, it seems like a lot of problems with therapeutics boils down to engineering and delivery. That's why we look for small molecules, because while we can get them into places, do you have a concerted effort looking at ways of targeting things? Yeah, I actually should have said this. So yes, one of the things, and you probably didn't see it because I flipped through it so fast in the NCGC slide, the only way we're going to get to predictability and being able to target is to understand what the general principles are that govern small molecule-target interactions. We don't understand that. And it's really interesting to think about. If you think about genetics, if we did not understand sense ionisense, that A goes with T, G goes with C, if you didn't understand that, how would you do genetics? But we don't understand that in this space. So everything's empirical. So because this space is so much more complicated, there are three-dimensional structures that are floppy and chain shape and all that stuff, we really need to generate massive amounts of data and then work backwards to identify what those principles are, what those patterns are. And that's a lot of what the NCGC now is going to be able to do. It's been sort of distracted from doing this for a variety of reasons for the last couple of years. The other thing that we're doing is working a lot with a structural biologist to see how we can marry those a little bit better. It's made a little bit more difficult by the fact that the PSI has gone away, but we'll deal with that. We're also working very closely with engineers both at DARPA and at Pharma's about novel ways to identify compounds more efficiently. But I would say overall, it's a matter of understanding what the general principles are. I often say that the robots that we have can screen. The big one screens about 3 million wells a week, which is great. But the fact that we are screening 3 million wells to find a compound that might work is prima facie evidence that we have no idea what we're looking for. So eventually, I want to put the screeners out of business because we'll be able to target them. So thanks, Chris. Behind your e-rooms law there, of course, as you know very well, there's a sort of a flat rate of new drugs. The problem is, well, that that's flat, but also the problem is that the cost is going up and therefore the overall slope is low. Part of the reason that cost is going up so much is because things are failing really late in the process. And part of the reason there is because 15 years before, the things they chose to work on, we're never going to work. The targets, we're never going to get you there because they weren't relevant to the human disease they were being developed for for a lot of money. So with all that logic, it seems to me your position to do something nobody else on the planet could be, that is sitting amongst the other institutes with access to the extramarital expertise who know more about those targets than anybody else. Anywhere. What are you doing in that regard to really draw out the expertise that exists at the early end amongst the institutes? Yeah, it's a great question. And you're absolutely right. It is a unique advantage that we have. And we use it a lot. We also have the FDA on speed dial, and that helps. Doesn't help with the target validation question, but it helps with others. You might have noticed this AMP thing. I know you're part of this, this Advanced Medicines partnership that came out last week. It's really a target validation effort, is really what it is. And so we are very deeply engaged in conversations with the institutes about doing this in individual projects. I was just at a meeting not long ago on Alzheimer's disease in this arena. And I would say the other thing we're focused on is general enabling validation technologies. And how do we make those work better? And the genome-wide RNA, I think, is a perfect example. I think that'll be a great technology now now that it's been worked out how to do it. So I realize you inherited 40-year GCRC and several years of CTSA. But I would like to encourage you to keep your logarithm measure with that group. Having been amongst two CTSAs, the goal is not always logarithmic advances. The goal is sometimes sustaining the 40-year legacy. So I would just encourage you to keep pressing on with that because we all need it. Thank you so much. Can I ask where you were? What were the two places? No. No. Yeah, so you, as Eric was telling me before, we are pushing that agenda quite aggressively now because it absolutely has to be done for all kinds of reasons. The opportunity is huge. And so I got a lot of people mad. And so as Eric said, hmm, must be doing something useful if you got a people mad. So yeah, it is. I think the good thing about it is that the PIs really understand the opportunity here. But what I often discovered they were lacking is a clear mission from NIH about what they were supposed to do. And so I would say the vast majority of them are really excited about this. Well, I think Lon's comment really comes back to part of the problem, too, is working on things that never should have been started in the first place. And if you have a hammer, everything looks like a nail. Well, the answer to every question is the assay that's running in my lab. And just throw it on through. And so we need to get past that. And like you said, someone's going to be mission-led, and certainly at University of North Carolina and at Washington University, where I was previously. There was a lot of good things happening. A lot of it wasn't. So while we're talking about, one last question, just before we go. So you want to say anything about some of the conversations we've had thinking about as we're looking at opportunities? And we are funding programs moving into the clinical arena, the application of genomics, and often at their institutions that have CCSAs. Yeah, so one of the things that I'm most excited about as you might imagine is that the very things that I think are needed in the genomic medicine space actually at least are theoretically available through the CCSA program. It really is complementary to what I did at Genome and a lot of the early preclinical things that we do at NCATS. And so I think these questions of not only genetic disease therapeutic development, which is kind of a minor part of what we do, but on a more fundamental level, for instance, how is it that genetic circuits are put together as analyzed through the lens of knocking down every gene one at a time? There's a lot of really cool experiments to do in that space. When you think about one of the major problems that we work in the clinical space, which is, how do you identify people via biomarkers, which could be genetic biomarkers, for treatment, and then test those hypotheses in a rigorous but efficient way? Those are things which are of great interest to many of the people at the institutions, which have CCSAs. So if we can harness that, then I think there's some really remarkable things that we can do together. I think from my own point of view, the limitation at this point is not genome. It's us, because we have some work to get our own house in order to be good partners with you. But I really think there are enormous things that we should do together. And it will not surprise you to learn that as somebody who's spent nine months in genome, this is how I think. Nine years. Yeah, nine years. Thank you. And nine months. There you go. OK, any last quick question before Chris to raise out the door? OK. Good. Thank you so much. OK, Terry, you're up next.