 Okay. Great. Thank you very much. I actually learned quite a bit about chromatin and transcription these last couple of days, so I appreciate that. And also, because the modern code program is having already in my lab and is going to have in many labs a big impact, and I appreciate the opportunity to talk about how we've been able to take advantage of this. My lab is a fly lab. I should also say before I go on that nothing I'm going to talk about today is relevant to this, but I do want to disclose that I am a co-founder of a company, stock holder, and so on. So here's the basic problem that we have, and that is the lack of success of drug discovery in clinical trials. And this has really been hurting, for example, the cancer field, which has the lowest success rate of drug, success rate of drugs in clinical trials of any major disease, despite the fact that it has been the field of most focus. For example, and I'll talk about colorectal cancer in a bit, 3% of drugs that go into clinical trials for colorectal cancer succeed. In fact, we really don't have much, many good drugs for colorectal, and it's already the second leading killer, cancer killer of Americans. So what's the problem? Obviously all of these drugs have succeeded with our standard mouse models, otherwise they wouldn't have gone into clinics. And I'm going to discuss some of the issues about the complexity of the models that we use, and I'll show you some of the data and some of the things we've found in flies that suggest or indicate what some of the issues are. In a larger sense, these are some of the issues I'm going to talk about. First of all, our tendency to simplify disease, and I think my thing is dying here. Our tendency to simplify disease, we'll see how it goes. That would be too easy because this advances as well. I've got it all worked out. So let's see how far this goes, and then I'll use the other one. So first of all, we have a tendency to simplify disease, and while that makes working on things easier, it has been a problem because cancer and diabetes and so on, they're not simple diseases, and it's okay to embrace that complexity. I'm good, thanks. Second of all, there's been an assumption in the field, and I think not a necessarily correct one, that identifying the driver of the disease is the same as identifying the best therapeutic target. This is a more subtle point and one that we often miss as basic researchers because we really are focused on identifying drivers of disease, and I think we're going to have a panel discussion later where I hope this issue comes up, that there really are differences if you're focused on mechanism versus focused on therapeutics, and this is one of the reasons why there is a difference. And I'm going to also talk about the differences between single and multi-targeting drugs. Each has their advantages and disadvantages. I'm going to show you our efforts to go after multi-targeting drugs, and I think it's obvious where I stand on the cell base versus whole animal screening. Again, each has its ups and downs. Our expertise is in the whole animal. So I'm going to talk about two stories today, or at least two cancers, if I have time. And the first one I'm going to start with is something called medullary thyroid carcinoma. This is, all you need is to know is a couple of things. This is a very, from a genetic standpoint, a very simple cancer. It is due in most patients to an activity mutation in the RET receptor tyrosine kinase. So RET is a classic RTK. It binds GDNF. It has a cofactor. It activates downstream pathways like RAS and SARC and PI3 kinase and so on. I'll get back to those in a moment. Patients that have MTCs have either inherited or spontaneous mutations in either the extracellular domain that drive dimerization in the absence of ligand or the intracellular domain that actually open up the activation loop. And in fact, those receptors can be active as monomers. The result is a proliferation of the paraphilicular cells or C cells of the thyroid. And basically these are the one, this is the one tumor type, thyroid tumor type that can be fatal. These tumors can metastasize and go to distant sites and liver and so on. And if allowed to progress, as I said, to be fatal. And when we started this project, there were no approved chemotherapies for this cancer. And I'll show you our efforts towards that. So how do you model thyroid cancer in a fly? Well, flies don't have a thyroid. To be fair, they have a very skinny neck, so we may have missed it, but I don't think so because they don't have calcitonin. So instead, we took oncogenic RET, the various isoforms, and we targeted them to the eye. And just to cut to the chase, as you can see, compared to a normal eye, the result is proliferation. You can see little tumor-like growths here. Compensatory apoptosis, which is in cell fate if you drive it during development. Many of the aspects that you see in real tumors we see here. Now, this is not a perfect model. As I said, it's not even in the correct cell types. But I'll also show you it's been a useful model. So we've done many things with this model. Perhaps one of the more interesting things is that we've used that to develop a method for screening flies in relatively high throughput using a variety of compound libraries. And what we do is we take liquid handlers that move food and drug into each well. We have a modified FACS order from Union Biometrica that spits 10 embryos into each well. We put an oxygen permeable lid on top, the flies hatch out, they eat the food, they eat the drug, and it's a simple like a phase three clinical trial in a dish. We ask very bottom line. We're not making any assumptions about what the best therapeutic target is. We just ask for a drug that makes the tumors go away and the fly lives through the experience. That is, efficacy with minimal toxicity. Also, bioavailability, compound stability, and so on. And I have to say this has worked out great. And I'm just going to cut to the chaser because I'm actually going to a second and more recent story that was just published a couple of weeks ago. So one drug that we hit was particularly notable. And that's a compound originally called ZD6474, which was developed by AstraZeneca. In the presence of this compound and the oncogene, you can see that the eye is almost completely normal. And what was striking about this in the fly is that it could cure the fly tumors at a concentration that was about 30 fold lower than was required to harm the fly. So if you were a human, you would say that's a therapeutic index of 30 if concentration in the food matches concentration in the animal. So I just want to cut to the chaser because I have a lot to talk about. There was important work done by Massimo Santoro showing that ZD6474 also worked well in human cell lines. And then this was picked up by Sam Wells, our close clinical collaborator, longtime collaborator, and brought to clinical trials where last April of 2011 it was approved as the first chemotherapy for major thyracarseanoma. So you can say I just jumped across many years. So what is and it has a commercial name, Capralsa. You can buy it at your local drug store. So how does Capralsa work and what did we learn from that to maybe make a better drug? And to go right to it, Capralsa does not, we think, work primarily or at least exclusively through targeting red itself. In fact, it's a terrible red inhibitor. Capralsa is what I would call a low affinity kinase inhibitor. It hits many targets, but it seems to hit them in just the right ratio that the fly can take that hit, we can take that hit, but the tumor has difficulty selecting around it. And in fact, the best data that Sam tells me to date is that patients that have taken this drug for at least two years, only 20% have become resistant to date to this drug. And I think there's just too many avenues that you need to get around. The tumor needs to get around to get away from this. So we've taken this to heart and I'm going to talk about how we use that idea to go after what I'm going to call rational polypharmacology, whereas this would be, I guess, irrational polypharmacology. And to enter this story, I need to tell you that we did a genetic screen against oncogenic red. And most people here would understand how we would do this as a simple dominant modifier screen. And when I say we, I mean Renee Reed, a former student of the lab, she identified 140 genetic modifiers that are required for RET to drive that tumor. And just to summarize, the majority of them fell into three pathways, RAS, PI3 kinase and SARC. And I have to tell you we've really beaten this to death. There's a lot of signaling both within the tumor and in the neighboring tumor. And so this is why you can't model this well in a dish. But what was interesting is another hit we had, and that brings me to modern code, was a chromatin remodeling, or chromatin remodeling protein, or at least associated protein, called SYN3A. This is homologous to actually two proteins in humans, SYN3A and SYN3B. It has been, it's well known as a transcriptional repressor, okay, as an HDAC. But that's actually not true. Here is a schematic of, and this is work by Der Tedas. Here's a schematic of the complex that it's in. This is the best known complex as an HDAC. But interestingly, when we began this collaboration, or with this work, I contacted Kevin White from modding code. We supplied them an antibody. They did a chip seek for us. And what we found was about 100 targets. So it wasn't many hundreds. That's good. And the targets that it hit were interesting. He'll go away. So here's about half the targets. We validated all these. What's interesting about this is when we knock SYN3A down, if you look here in the red, most of the targets, not all, but most, actually go down. That is, it acts as a transcriptional activator. And I can tell you that 80% of the targets for SYN3A, it acts as transcriptional activator, 20% it's a repressor. And what's more interesting to me is that virtually all of its targets that is an activator are tumor suppressors. They're a who's who of how to make a tumor. And virtually all of the things that acts as an inhibitor, targets acts as an inhibitor, are oncogenes. And it's remarkable how it can activate gaps and inhibit gaps and so on in the same pathways to really drive a pathway in one direction. And just to show you a few examples of many, we hit RAS itself, AKT, and actually multiple things in the RAS pathway, AKT and PI3 kinase, CSK, the major negative regulator, SARC, a host of actin remodeling proteins, regulators of gene kinase. And what's interesting about that is that I had assumed that RET activated these pathways through direct binding of partners that would activate RAS and PI3 kinase and SARC. And what this told us is that a primary, if not the primary regulator of these pathways actually rotates through transcription and then comes back to regulate the receptor. In fact, we think CIN3A is a major regulator of RTKs in general. And if you're going to have a tumor, you need to get out away from CIN3A. And our data in eight different human tumor lines shows that all eight of those, a variety of tumors, CIN3A is consistently and strongly down-regulated in these tumors. So we postulate that you need to drive CIN3A down just to be able to escape this repression and actually have a full-blown tumor. Now, how do we use this to get into drug discovery? That takes me here and a collaboration with our lab, especially Derr Tadas, and Kevan Schokat's lab with his postdoc, Arvind Dar. And to get further with this, to develop compounds, to take a process further, we set up a screen against oncogenic rep model, not in the eye anymore, but actually several places in the fly, causing tumors to develop in multiple places in the fly, and it actually kills the animal. And so Derr to set this up so that half of the animals make it a pupation and half die, none of them make it to adult. Now, if I was a pharmaceutical company, the simple thing to say is, all right, let's make a clean red inhibitor. Everything that's wrong in this fly is due to oncogenic rep. We know this because we made the fly. So Kevan and Arvind developed one called DLO6, that's as close to a clean red inhibitors I'm aware of. It works beautiful in cell lines, in a dish. However, in the animal, at the highest dose we can give it before we kill everything, it basically does nothing. And we have a lot of data, and I'll show you more, that there's no correlation between activity against ret, generally in our screens, and utility as a drug. And that's probably because ret, like many oncogenes, is important for cell viability, for organism viability, that's why they drive tumors, because they're fundamental to the biology of the cell. Hitting them with drugs will often cause talks. All right, so let me just cut to the chase here, and say that we screened through a library developed by these guys, a library that's specialized in hitting multiple targets. We had one hit, which I call AD1 for Arvind R1, and you can see it's a pretty good hit. That more flies make it to pupation, and now we're beginning to get adults. Now interestingly, and here's the here's the composition of matter here. Interestingly, if you look at close analogs of AD1, AD2 and AD3, AD2 is highly toxic, and yet the only difference chemically between two and one, is just this trifluoromethyl group on the terminal phenyl group here is lost, is missing in AD2, that small change causes this to become highly toxic. Despite the fact that this would look the same in cells in a dish and so on. The animal is telling us it has marked differences. 83 has this extra methylene group in at the center core here, that tiny difference causes 83 to be almost inactive. All right, so it's very striking how the animal gives you a very different result in this too, I think is a key point. But also remember that I told you there were three pathways that mattered, RAS, PI3, kinase and SART. So what are this, what does these drugs do to the rate limiting enzymes in these pathways? RAP, TOR and SART. If you look at in vitro kinase data, we find that 81 hits all of them. It hits RET, but notice they all hit RET. There's no correlation between activity and RET. But 81 hits SART, it hits BRAP, it hits MTOR, so you're good to go. Gets all three pathways that are genetic said were needed. 82, interestingly, hits SART, it hits MTOR, but it doesn't hit BRAP. Now why would not hitting a target actually make you toxic? That was counterintuitive to us beginning and just a cut to the chase. We weren't the first to show this, a number of laboratories including ours have evidence for this, that there's a feedback loop between TOR and BRAP. If you don't, that's required to suppress this pathway. If you inhibit TOR, this feedback inhibition is relieved. RAS signaling goes up throughout the animal and that's toxic. And we have a lot of data showing that when you give this drug, RAS signaling goes up everywhere in the animal. And the tumors actually get worse. Now, how do we know the toxicity is due to this? Well, let's look at 82. Let's remove one genomic copy of the downstream target, ERC. There's all your toxicity. So from these sorts of experiments we realized we could actually begin to walk through the Kynome and identify better targets and also identify what we call antitargets. Those targets that need to be left alone or the drug becomes toxic. And so we played this game, for example, with 81, remove a copy of ERC. Now that's a drug I'd love to have. Okay. And that tells you that we can get a better balance between these two things. So we played through this game quite a bit. I'm not going to show you all the data. And we went back to cave on an ARVN and said, okay, we need a drug. It needs to hit SARC. It needs to hit BRAP. It needs to leave M-Tor alone. But it still has to knock the pathway out so you have to hit the next step down below the feedback loop, which is S6 Kynos. Can you build that drug? And to my continually stunned amazement, they can do this. They sent back two drugs after they did computer modeling. They sent back two drugs, which I'll call AD1B. Oh, and I should say if you remove a copy of Torr here, AD1 becomes AD2. Also evidence of the importance of leaving that alone. Okay. And they sent two drugs back that have that profile, AD1B and AD1C. And you can see these are drugs that we really are interested in. And they've been refined to maximize therapeutic index. Let's look at AD1B. It hits SARC. It hits BRAP. It leaves M-Tor alone. It hits that S6 Kynos. We're good to go. All right. How does this work in a mammal? Is it just a fly phenomenon? And the answer is no. So here's the drug that was approved last year, Capralsa. And it works well in human cells in a dish. These are ME and 2B cells. But you can see AD1 and AD1B work much better, about 500 fold better in a dish. If you look at a xenograft and I'd love to take better models, but on oncogenic ret put in a mouse really doesn't do much. So we looked at xenograft models. We actually forced the tumors. We forced the issue by growing the tumors for 46 days, full-blown tumors, and then ask the drugs to actually reverse it, which is different from previous studies. And in this more stringent case, Capralsa really struggles to deal with those, but AD1B does well. And importantly, if we look at body weights of the mouse, based on those body weights at high doses, AD1B shows little difference to vehicle, whereas Capralsa shows the expected toxicity. So this drug is now being licensed. And we're hoping that with lock this will be into clinical trials within the year. So to summarize the section, to wrap this up, we think that model organisms using sophisticated network analysis like modern code, which we were able to take advantage of because it was there was essentially off the shelf information. Combined with sophisticated modeling of medicinal chemistry can be very useful for identifying both targets, things that need to be knocked out, but in a complex way, not single targets, but also what we call anti-targets, things that need to be left alone, or they can drive toxicity, and only in these whole animal approaches, in my opinion, can you actually do this to generate better, more sophisticated, and hopefully see more useful drugs. You guys with me because I'm going to finish with one last little story. And that's this. And this takes me to the flip side. So I showed you what we think is the importance of developing polypharmacology, rational polypharmacology. Let's go to the other side and talk about models, and the importance of embracing complexity, which I believe is exactly what modern code is about, or at least stepping towards. And I mentioned at the beginning colorectal cancer. We've been modeling breast, lung, and colorectal, as well as thyroid cancers. And I want to show you this work by a really terrific postdoc in the lab, Erdem Bangi. And what he did is he went into the human sequencing data. Sorry, am I standing in your way? He went into the human sequencing data and asked what are the most common triple, quadruple, quintuple combinations of oncogenes and tumor suppressors that you see in currently sequenced patients. So we worked with the Vogelstein group. And I can tell you that to date, the most common quadruple combination of mutations you see in patients is RASP P10 APC P53. This is the second most common quadruple. This is the third. Erdem built all of these, targeted the transgene to the gut, using conditional activation turned it on only in the adult. So no cheating in that. He also, in addition to the quadruples, he built all the subset triples, all the subset doubles all the singles. That's 15 fly lines for each quadruple. And for those of you who are wondering why we stopped at four, there are no two currently sequenced tumors that share five genes. Okay, the statistics fall off sharply. All right. So we've learned quite a bit about this. We don't have time to get into all the very details. Erdem has done an impressive job. Am I feeding to that? All right. Could it be my phone? Can you hold that? I actually did that once. It's a true story. And my mother called and I handed it to somebody right there. And they chatted for a little while with my mom. So at least my mom now believes me when I actually do give talks. So so Erdem took these four hit models and he's done a very extensive analysis and we're pushing towards a network analysis. I really don't have time to get into this. But the some of the phenotypes he finds are hyper proliferation, multilayering and release of cells from the tissue and distant migration to form secondary metastasis like growths, I'll say. Also aspects of senescence and apoptosis. And what's really fascinating about this is that different aspects of these emerge as we look at different combinations of oncogenes and tumor suppressors. All right. And from a network analysis, this is really an interesting problem and one we're trying to grapple with. Just to show you what a tumor looks like. So and also to be clear that this four hit fly has 10 trans genes in it. So this is very difficult to build in a mouse. I know because we're trying. Here is part of the gut that includes GFP. This is part of the gut here. Here's the muscle wall around the gut. This is the hind gut. This is what's called the trachea for those of you aren't familiar. This carries oxygen in the fly. And what happens is in addition to a polyplike formation and growths and so on and so forth is that periodically cells will actually pop out of the tissue. They'll push their way through the muscle wall. This one has extended a process. It's found its source of oxygen and corkscrews around it. And the cell will walk right out of the gut. And in many cases just walk along the trachea off into distant sites really throughout the animal as far as the head. Okay. Here's another example, a bird's eye view. The gut is actually below the surface here. Here's the muscle wall. Here's the trachea. And here's the cell. It's found its target. It's now hopped up onto the trachea here. And it's off to the races. All right. It'll go off to distant sites. And at least some of our tumor models, we also just for those of you who are interested, we do see the equivalent of neo-angiogenesis where it's neo-tracheogenesis of oxygen bearing trachea back into the growing tumors and so on. Okay. So many of the aspects that you see in human tumors we see here. Now here's my final question. What's the difference between a one hit and a four hit colorectal model? All right. And what can that tell us about why drugs have failed at such a high rate in clinical trials? There's many differences and we're really trying to wrap our heads around it through network analysis, RNA-seq analysis and so on. But let me just show you something, one thing that we've learned. First of all, if you just look at the cells, here's a one hit model, the equivalent of the mouse KRAS model. So if you put RAS into the fly gut, you will get cells coming out of the gut, you'll get proliferation, they will go to distant sites. But if you just look at the cells, they're quite different, they're smaller and less robust than the four hit mouse, okay? But here's a more interesting, I think, and a more practical difference. And that is that Erdem took 16 drugs, many of them clinically relevant, many of them failed in clinical trials for colorectal cancer. And he asked, what is the sensitivity of a one hit model versus a four hit? And that's shown here. Of the 16 drugs he tested, 13 of them worked fine in a one hit fly to knock those tumors down. But zero of 16 worked in the four hit mouse, in the four hit fly. And I think that's really telling us exactly why these drugs have failed. Because the complexity of our models, our models that just don't have the sufficient complexity. And remember, we've made all the two and three hit combinations and so on. We've mapped out the resistance for multiple drugs. And using that information and biochemical analysis and so on, we've now begun to identify combinations of drugs. For example, these combinations, that is actually successful at knocking these tumors down. And Erdem has done a lot of biochemical analysis. This is really a fascinating story on why particular combinations of drugs will work on these and why others won't and so on. And this is something we can talk about in the discussion section. So let me finish up by telling you what I told you. Excuse me. What I told you is that we've taken a whole animal approach to try to build complex drugs. So we're not focusing on making the drug cleaner and cleaner. We're actually going the other direction. The problem with polypharma oncology is that you need to know all the targets for medicinal chemistry regions so you can modify these drugs without messing with the active sites. And so we're basically offering the pharmaceutical industry the opportunity to develop them in a rational way so you can keep track of the activities so you can modify them for PKPD and so on without messing with what's important in the drug. We've used mod and code to really explore, I call it the epigenetics I'm not sure that's quite right, but to explore the transcriptional control of the factors that actually driving this tumor genesis and that has been fundamentally useful for our ability to identify the key pathways and once we have those key pathways in hand that puts us in a position to work with medicinal chemists to attack those pathways in a way that's useful. And through chemical genetics we've developed a method we call rational polypharmacology that we hope will be useful. And finally I finished by talking about the importance especially with model organisms of taking advantage of these model organisms they're really the only ones that can embrace this complexity so readily and so quickly and cheaply. So to develop complex models to take advantage of the sequencing data that's going on and not to focus on a single target but to go ahead and embrace that complexity and I showed you reasons for that and to cut to the chase a four hit model is nothing like a one or a two hit model. And they're nothing like them in the ways that matter like drug sensitivity. Okay. And this is my lab and the guys who have done this work and also my thanks to a mod and code as well as NIH and ACS. So thank you very much. Hey Russ. Breathtaking presentation. Really, really enjoyed it. So I have several questions here. So how do you go I mean in a way that part of the challenge is translating all that to human. So what are some of the challenges here? First of all, the networks themselves might not be conserved. Second, you actually need to build a model for the disease in Drosophila. So my question is from your own experience. Are these sort of limiting the number of diseases the number of pathways for which this will be you know, this type of success story will be possible and now conversely if you want to now take these approaches and apply them directly to mammals, for example, the two rate limiting steps seems to be the complexity of the networks that we can build and that complexity is now increasing with you know, encode mouse encode and so on where we can build these more complex networks. Of course, building the mice with two and three and four and five hits can be can be much more difficult. But I'm just wondering if you see other limiting steps that you know that go beyond these the simple view. OK, so that's a lot of questions. So let me let me see if I can remember and walk through them. The first question I believe was conservation between flies and mammals. And and also you had a bigger larger question, which if I can rephrase it, which is essentially when are flies and model organisms good and when aren't they. And I think that that's where you have to choose carefully and we try to choose carefully. Excuse me, in terms of what we can model because flies are not good for many diseases that we consider, actually, and have turned down in terms of. So there are some playgrounds that you can play in with flies that work well. Kind of the kind of is one of them. There may be an exception, but I can't think of any. Every chemical kinase inhibitor that we have tried and in flies and also look to validate has played out as hitting its target. And I'm talking about functionally looking at, you know, phosphoantibodies and so on. Okay. So I think that those work well. Also, Erdem Bangi, who's postdoc in my lab now, was at Novartis and they've published a paper looking at a number of various pathway inhibitors, not pathway inhibitors, hedgehog and so on, and shown that flies work well for those. So I would not suggest that it's going to be successful for everything. For example, those drugs that need to be metabolized, some will work. And actually, my company has checked one or two of those and the cleavage for the ones we checked is fine. But I would guess, in most cases, that's not going to work because P450 is going to be different. So you have to be smart about what you model, all right? Maybe in retrospect, it was done with us to model thyroid cancer in a fly. I was too naive at the time and it worked out well because it was such a simple signaling pathway. And as you say, at the end of the day, you have to carefully validate what you're looking at in flies. So we've done what I'm not showing you is the parallel work that, for example, Erdem has been doing with flies and human cell lines, all right? So we knock this out or we add this drug. We see this change. We better see the same thing in human. And to a remarkable extent, he has. I wouldn't push the point that we have a perfect gut model. Fly gut has many differences than human. But again, I think it's a useful one. So it's a little bit of a rambling, but to answer your question on that. Now, I would love the community to take up the question of network analysis, which was, I think, was your last question. So what we're doing now, and in fact, I just finished my part of it at 7 o'clock this morning and it's due today, is we are working with network analysis people in the Eric Chakrup, Jun Zhu and Rui Chang. And for example, with colorectal cancer, we're doing RNA-seq against all 15 sub-combinations. That's our goal. So we have drug sensitivity although we need to expand that. We have phenotypes. We want to get the RNA-seq and we can plug all of that into network analysis and then compare that to the analysis that has already been done for colorectal cancer in humans. So this is where we're going with that part of it. There will be, I predict, similarities and differences. And what will be useful for us is that will point us in directions that we can be useful. I have a question. Can these fly models be utilized for studying disease recurrence and drug resistance? Disease? Recurrence? Reoccurrence? Yeah. And drug resistance? And resistance. So the question was, can flies be used to study disease recurrence and resistance? Yes. And I mean, yes, it can. And in fact, I have had some conversations with pharmaceutical companies when they've given inhibitors of certain pathways and resistance emerges quickly and they want to know what those resistance pathways are. I have to say I'm a little up and down about that because we can find pathways that you can hit that could potentially drive resistance. But that doesn't mean they actually are driving resistance in patients because there are many ways around these initial pathways. So to answer your question, yes, you can do it in principle. But I expect, and what we've seen from the sequencing data of patients is that it has been either particular to the patient or there have been mutations that were either so, they were obvious and you didn't need flies like an activating mutation would crop up in the receptor or it was something surprising on some downstream thing that again flies I don't think would be helpful. So I would say where flies can be helpful is this, is this polypharmacology? Not so much predicting resistance because there are many roads to resistance, but developing a drug that's so complicated for the tumor to get around that it would have difficulty selecting around it. That I think would be more reasonable as a utility for drugs, for flies. So a lot of small molecules we work with tend to have multiple targets. So I was wondering if you had tried anything to, I know you may have tried to increase selectivity with your design, but of course you could get unwanted targets and things as well and it seems like flies would be a great system to sort that out as well if you had a suite of these heterozygous kinase mutants that you could just toss these on. Have you tried anything like that? You mean with the low specificity kinase inhibitors to try to find things that work? Yeah, or even the ones you think are high specificity toss them on a suite of, you know, you tested them on the obvious candidates, which is great. Oh no, I didn't, I'm sorry, I didn't show this. I'm sorry I wasn't clear on that. When I showed the in vitro kinase data, what I'm not showing is it's the full in vitrogen panel. Okay? Okay. And so we actually know quite a bit and I'm not showing you all of its targets. So in vitro you test them first and then you go in in vivo. And then we go in in vivo and validate the in vitro chip, because the in vitro chip from in vitrogen is human. And then we go into the flies and we say, okay, it says it's gonna knock out SARC so we look at the downstream target and yes it does, that kind of thing. So to answer your question, you could do this with a low specificity library, absolutely. And yeah, let me just leave it at that. You can do it with that. What I think is powerful is if you can then work with chemists to consider that a starting material. So the drugs I showed you were not high specificity or a fairly high specificity, the AD one and so on and so forth. They're fairly high specificity in that they don't hit 20 targets. It may be hit six or seven. But what's really powerful is if you can then whatever drug you start with, I don't care how many targets it hits because in flies you can check them all. Is to be able to work with a chemist to flush some of those out. The chances you'll find a drug that's gonna get everything just the way you want it is possible, but then. You know, your multi-genic model of the disease causation or response to the drugs. The data you presented or the information presented is essentially a validation information. You have known genes which you have put together and studied the response of the drugs. I would assume it will be at least possible, may not be easy, to include knock out or knock in of genes which are distantly suspected to be related to the disease. That's right. If I can rephrase the question, we were focusing on obvious targets. What about not obvious targets? So what I probably didn't make clear is when I said there were three pathways that mattered and so on, that was a little bit of cherry picking but actually not entirely. So we had done an unbiased question at X screen across the genome to ask for all targets required, not all, but to the level we did it because when we did it not, we didn't have access to mutations in every gene but we had access to mutations in thousands, bless you. And so we did an unbiased screen and of course I'm presenting a simple talk where I said what I'm not going through is that there are a number of other pathways. For example, wind signaling is key for here. Hedgehog signaling, we also hit that as well. And I haven't talked about those in part because we don't have a lot to say about those. So you're absolutely right. And we do have that information in hand by our functional screen. And you could decide in principle to match this initial drug with a wind inhibitor, for example. And that would be reasonable as well but we do have that information. We didn't pick those three just because they were obvious. We picked them because our genetics pushed us in that direction. And actually to finish that point, when you look at SYN-3A and its targets, it's astonishing how it hues to those three pathways. Not 100% but it's astonishing the high percentage that really hue to RAS, SARC, PI3 kinase, actin remodeling, a little bit of junk. It's one notch target sort as suppressor of hairless. That's kind of it. So we are getting focused back over and over again but I agree with you that. So in principle, your multigenic model could be used to sort of make a decision whether a given gene inclusion or exclusion is a driver gene or just a passenger gene. Absolutely. And yes, so that's a great point. And on that point, we have a work we've been doing with Francis Collins on the diabetes side. I didn't talk about our diabetes work but we just feed flies a high sugar diet and they become diabetic. That's all you need to know. I love this model. And we actually went through their GWAS studies and in each region we said, okay, there is five genes that we've defined if we just make an arbitrary interval of 100 kb. Which one's the driver? So we tested the fly orthologs to each of those and in many cases we were actually able to say, yeah, it's probably that guy. In some cases we were even able to say, is this guy plus these two flanking guys which you would miss in standard analysis because the statistics of the neighboring genes are swamped by the peak of the one next to it and so on. Absolutely, I think flies are terrific for that and just to finish this point, in that thyroid cancer we actually published a study where we used the fly genetic screen to predict susceptibility low side to secondary tumors in a related cancer syndrome called multiple endocrine neoplasia type two which the major problem they have is the thyroid tumor. And we were actually able to identify two that were at least markers and possibly the drivers of susceptibility to these secondary tumors. So I absolutely agree with you, flies and worms have great potential if they are used that way to really explore various aspects of drivers and susceptibility low side and so on. I think they have a lot of promise for that for sure. So I assume standing up means I need to sit down, yep. Two things before break. We'll have coffee outside and also upstairs and I'd like to ask people to be back at 10.45. I'd also like to thank our morning group of speakers for a set of excellent talks. So please join me. Thank you.