Good morning, and welcome to the 42nd annual JPMorgan Healthcare Conference. My name is Dave Paharaj, and I'm part of the healthcare investment banking team here at JPMorgan. Today, I have the pleasure of introducing our speaker, Mahesh Karande, CEO of Omega Therapeutics. And also on the podium, we have CSO, Thomas McCauley, who will be participating in the Q&A. In terms of logistics for today, please reserve any questions for after the presentation. A mic will be passed around. With that, take it away, Mahesh. Thank you very much, and thanks to JPMorgan for having me and Omega Therapeutics here. Actually, it's a pretty exciting week for Omega. We are a clinical stage biotech company that is working on the first use of programmable epigenomic medicines, mRNA therapeutics, as programmable epigenomic medicines. And, you know, as a young company that's been around for a little over five years and already in the clinic, it was tremendous that a giant in the field of obesity, which is Novo, signed a deal with us where they chose Omega's platform and technology to tackle some fundamental biology of thermogenesis as the next generational drug for obesity management beyond GLPs. But that's the power of the platform and of precision epigenomic control. So what I'll do today is walk you through the company, a little bit of our science, some data, and then look forward to having a good Q&A session. And, you know, Tom's here as well as our Chief Scientific Officer. So what Omega is doing is... So this is obviously my disclaimer and forward-looking statement. So Omega, as I said, is a clinical stage company pioneering a completely new class of medicine. So they're called epigenomic controllers. These are mRNA medicines, is the first systematic use of mRNA medicines as therapeutics that home into the genome and make epigenetic changes. You know, this is really the promise of epigenetics that has been around for a long time. We're the first company that is delivering on that promise, right? We are clinical stage in a program which is nothing short of, you know, going after a highly intractable oncogene. It's called—it's you know referred to as a holy grail oncology gene, c-MYC. And we put out some tremendous data that we'll walk you through. Pre-transcriptional control of gene expression is what we do, and that allows us to unlock really high-value targets. Very very broad applicability, pretty much to all human diseases. And you know I'm joined here by a lot of Omega team here, members. You know it's a world-class team of very very strong operators who've learned their trade in big pharma and small, and operationally very solid. So let me talk a little bit about the Omega platform. So what do we do? There's two aspects to our platform, and we are a true platform company because everything we do and whatever we're doing in the clinic applies to pretty much every program we have and several programs that we may, you know, not even have right now in the clinic. So let me talk a little bit about our biology. This biology was delineated in 2016 in a Cell paper, and that was the basis of formation of Omega Therapeutics. So, you know, nature has organized our genes and their regulatory elements in these three-dimensional conserved structures that act as control units for genes that sit inside it, right? There could be single or multiple genes in this structure. They are called Insulated Genomic Domains. And here in the middle, you see a two-dimensional depiction of the simplest structure of an IGD. Think of that as a hot air balloon with a couple of genes in there and the regulatory elements that control them. Now, the beauty of an IGD is, first of all, there are 15,000 of these that are distributed across the 23 chromosomes, and they're ubiquitous in every cell of the body, right? All of us have the exact same IGDs. These are evolutionarily conserved across mammalian species. The sequences of these regulators in some lower order mammals might be different, but the beauty of nature is that this is almost intactly conserved between non-human primates and humans. So non-human primates have the same IGDs that we have, right? A couple of things about these IGDs that allow us, allow us to use them as drug targets. First and foremost, the reason it's called Insulated Genomic Domain is because transcriptional control of genes sitting inside an IGD stays within the IGD. That means only regulators inside the IGD can control those genes, and they are free from outside transcriptional control. Secondly, each of those regulators have a genomic sequence which is unique across the genome. So now imagine there are 15,000 IGDs, each one of them with several sequences of regulators that control the genes. These are drug targets that nobody has, and Omega has created a proprietary list of these drug targets. We call them EpiZips, Epigenomic Zip Codes. That's a pretty clever name, I think. They're unique, right? So we go to these with high precision and lay epigenetic marks to simply tune the gene like you would tune a thermostat. That's what nature does. That's what is happening right now as we sit in this room, right? So how do we do this? We do that by using a technology, which are epigenomic controllers, the class of drugs I was talking about, you know, which are mRNA therapeutics. So the mRNA enters the cell through the ribosomal machinery, goes into the nucleus, expresses two proteins. One is a DNA-binding domain that homes into, with exquisite specificity, homes into that sequence that we targeted, and there it lays an epigenetic mark, and that could be any epigenetic mark. It could be long-acting, short-acting. We choose that based on the disease we are treating, and it modulates the gene either up or down. Right? It changes its expression pre-transcriptionally and brings it back into normal range. Because most diseases are caused by genes either overexpressing or underexpressing. The simple fact is, if pre-transcriptionally, you bring it back into normal, central dogma works perfectly, disease is a result, right? With that, a couple of things in terms of attributes of our platform. You know, I won't go through each of these, charts, particularly, but let me just point out, exquisite specificity is what I was talking about. What you're seeing there is a genome-wide plot, right? This is looking at the entire genome, and what you're looking at is, you know, the dotted line, that's a logarithmic scale. The dotted line is the normal flux of the genome. Anything below that is what is normally happening, right? Genes are expressing, loops are shutting, closing, life is going on. Here, we target, and this is the MYC gene, by the way. We targeted the IGD for the MYC gene, one specific location, and hit that and made an epigenetic change and looked across the genome and didn't touch anything else. Just think about it. We hit 21 base pairs in a particular cell on a particular chromosome, did not touch anything else in the genome. That's how specific these therapies are, right? And then the other ones, control tunability and durability, are imparted by the epigenomic effector, right? What you're seeing there is EpiZip 1 and EpiZip 2, which are two different regulators. We can make different epigenetic changes there, as shown in the histograms, and decide which out of these we choose to finally design a controller, right? So imagine if you wanted to bring it down by, I don't know, 70%, you could add up EC 1 and maybe EC 4 and say, "Well, that's how I get to my 70%," and so on and so forth. That's modular design, and this is, by the way, all rapid. We are able to do all of this within 2, 3 months. We have controllers ready to test. Programmed, prospectively designed with all of these properties. And what you're seeing down there, by the way, is durability. So with one single application, we've been able to downregulate that gene for 6 months. Right? Now, think of the possibilities. In cancer, our drug that we have in the clinic, we are downregulating for 2 weeks. That's because we want to fit in oncologist treatment paradigm. But for chronic diseases, and potentially the deal that we have with Novo, we will use a longer duration controller that you don't have to take a GLP injection every week or, you know, take a pill every day or what have you, right? That's the power of this platform. So now, let me walk you through a couple of more things. Look, all the drugs that we know work, they're beautiful. You know, if you think about small molecules, large molecules, you think of gene therapy, gene editing, all of these have a specific place, but they don't work in every area. And there are, you know, constraints with known modalities, right? Small molecules, large molecules, depend on the structure, chemistry, location of target. You know, drug has to be in the system for it to act, right? If you have a headache, you take a pill, stops till that pill works. After some time, you need to take another pill, right? That's because drug needs to be sitting in the system. All of these, other than gene editing and gene therapy, which create permanent changes in your, you know, nucleic acid sequences, which by themselves have a collateral issue, right? All other drugs need to be sitting in your system for them to act. Our drugs don't. So what we have solved for is all of these constraints, and it's a complete wide space we are operating. We don't depend on the structure or chemistry of the target because we know where to go straight into the genome, and we can home in with the specificity I just showed you guys. Our PK and PD is, PD is uncoupled. What that means is, if you remember, one application, six months, the drug is gone within a couple of days. It's delivered right now with the LNP, mRNA, all of that degrades, gone in a couple of days, but the effect stays on, right? So think, those of you who think oncology, think of the combination potential of this, right? There is really no drug-related side effect other than the LNP, which is the first couple of days, and that is very well characterized. Avoids liabilities of permanent genetic changes applicable to any human gene, disease process, and, you know, up and down regulation, right? So I'll give you a next slide, which is an eyeful. These are just a non-exhaustive examples of genes that we know we can hit, that we have already done work in. Those of you who are thinking about partnering with Omega, you can pick these and come and talk to us, right? There is no earthly way Omega can actually prosecute everything that we can do. There's just no way. What we did was, out of these, you know, as we started working, we chose a pipeline that allowed us to really exploit the breadth and depth of the platform, and it's a really cool pipeline. It's oncology, obviously. Oncology is the poster child of epigenetic dysregulation. Makes sense. The other end of the spectrum is regenerative medicine, right? In oncology, you are controlling uncontrolled cell growth. In regenerative medicine, you're engendering cell growth, right? We have a really cool program that we put data out on at the liver meeting in HNF4 alpha, where we are upregulating HNF4 alpha, which is a you know, master regulator of hepatocyte function. So think of it in the entire continuum, from NASH to potentially cirrhosis. If you can regenerate the liver, imagine the possibilities. CXCL1, 2, 3, IL-8, there are four chemokines that sit in one IGD that we can control with a single therapeutic across a bunch of diseases. Think of a monoclonal antibody, example, Humira. You would hit one protein of one of those. We hit all four. You would need four monoclonal antibodies to do what we do with one therapeutic, so and so on and so forth. So this is a really powerful platform. We are in the clinic now, and I'm actually going to walk you through some of the clinical data that we put out in September. So that's our lead program in c-MYC for hepatocellular carcinoma, OTX-2002. First of all, you know, I talked about MYC being the holy grail gene in oncology. If you just look at that diagram, you can see where all MYC is implicated in solid tumors. 50% of upwards of 50% of solid tumors, pretty much 100% of all metastatic cancers have a MYC amplification or an overexpression, right? If you can hit that, by itself, this is a tremendous franchise of oncology programs. We have just scratched the surface with HCC, and our next program is in non-small cell lung cancer that we are taking towards an IND, right? Strongly correlated, like I said, with very, very poor patient prognosis. If you can stop this MYC, by the way, controls approximately 15% of the genome. So, you know, if you are able to control MYC, you are able to control a lot of bad things. So that's what excites us. We have a potential solution. It's a historically undruggable target. People have been very, very unsuccessful in targeting this for 40 years. I think this is the way to do it, pre-transcriptionally, because it autoregulates. If you hit it anywhere else, anywhere else, if you hit it with an SI or a small molecule, you kind of need to completely tamp it down. The gene autoregulates, you need to put more drug, you lose therapeutic index. We don't completely tamp it down. We bring it down, you know, by a level of about 70%-90% from an overexpressed state. That kills cancer cells, but allows normal cells to thrive. So this is, this is a game changer in our opinion. So now, this is a study design. We are in monotherapy dose escalation. We just completed dose level 3. You know, we are starting dose level 4. The study is going exactly as planned. At one point soon, we will start a combination. We have some incredible combination data with checkpoint inhibitors, TKIs, and we'll be, you know, our protocol allows us to sort of do combinations with all of these. So more to come on the data, but now let me walk you through some of the data. This slide shows the pharmacokinetics, right? Remember I was telling you about pharmacokinetics and pharmacodynamics being uncoupled, and that the drug disappears from the system very rapidly. Nothing better to prove that than showing you clinical data, which actually shows exactly that. If you look at the LNP and if you look at the controller mRNA, it's pretty much gone very rapidly, but you know, what you'll see on the next slide is that the effects stay. So here, a couple of things, right? Clears rapidly from systemic circulation, no accumulation, no immunogenicity, right? Very, very clean profile. This is exactly what we expected. It's behaving exactly like we predicted in the preclinical work, and we predicted in the clinic. The next slide is super critical. These are the lowest doses of our clinical trial. We were completely blown away by the fact that in eight patients that we studied in the first two cohorts, we saw pharmacodynamic activity. You don't typically see that with drugs, right? The reason you see this, you see it with this drug, is because it's fundamentally going after a mechanism where once you hit a cell, it dies. It doesn't matter what your dose is, right? You need patients to stay on dose longer, and you need a higher dose to hit more cells with a single dose. That's what we are actually developing. So this is very different from how we grew up thinking about small molecules, that at one point, you suddenly hit an active dose. Our doses are active right from the get-go. We just want to find the most optimal dose. What you are seeing here, by the way, is, remember I was saying the controller homes into that genomic sequence. This is methylation we are looking at. Controller homes into a genomic sequence and lays an epigenetic mark. In this case, it was... We were looking at methylation around that site. That green bubble you see is like 1.5, you know, KB. It's very, very small, and what you are really looking in those black dots is CpG islands being methylated. That tells you that the drug is getting exactly to the location you want, because pre-dose, you see nothing. Post-dose, right after dosing, two days later, you see a really robust response, and then it tapers off, as you expect, because it's supposed to be active for 15 days. And if you see the dose level 1 and dose level 2, you see a very nice dose response, right? So what you can expect is as we go through dose level 3, 4, you, you'll see something similar. And this is what we saw pre-clinically, so no surprises. Very good that it's actually being replicated in the clinic, right? And then, if you just follow the central dogma, you hit the target, you lay your epigenetic mark. Now you're downregulating MYC, right? Our controller has been designed to downregulate the overexpressed MYC between 70% and 90%, right? So now the next thing that we want to measure is mRNA downregulation, right? Over here, you see mRNA downregulation, you know, exactly as we expected, right? Now, the question you will ask is: Well, you said 70%-90%, why is this showing 50%? Because what we are measuring here is circulating. You know, these are blood biopsy. By the way, the one in the previous figure was also a blood biopsy, where we looked at cell-free DNA. Here we are looking at exosomes that are shed by all tissues of the body in the blood, right? Not just the tissues in the liver, the cells in the liver that are transfected, but all tissues in the body. So mathematically, what you're looking at is a sample of a small sample of liver cells, but an equal or large sample of other exosomes, right? The liver cells are being transfected to approximately 70%-90%, between 70% and 90%, and the others are not transfected. So when you average it out, it comes out to about 50%. So we don't see a classic dose response, but we didn't expect to see it with this measure. Now, in our preclinical work, we saw exactly this, but what we did was we also looked at biopsies. They were mice, we could do that, right? In these patients who are very sick, in this stage of the trial, you cannot do biopsies, right? At some point, when we expand and when we do combinations, we'll be doing biopsies, and we'll be able to correlate. But what you're seeing here is incredible because we have been able to downregulate MYC mRNA, which essentially should tell you that you will eventually lead to activity.... Right? So these data have been great so far, and in the future, you know, what we have publicly committed to is somewhere in the first half, we'll be actually giving another data update. So look out for it. It's incredible data. Let me walk you through a couple of more slides on data. These are preclinical models I was telling you about, combination with checkpoint inhibitors. If you look on the left, you know, we are looking at anti-PD-1, anti-PD-L1, and our combination. And there's a very clear synergy that you see here, you know, with anti-PD-1s, anti-PD-L1, that's not surprising. We expected that we are orthogonal. Also, MYC is a potentiator of checkpoints that allows you... Eventually, our hope is that, you know, we can sort of dose, regulate our drug as well as checkpoint inhibitors. All remains to be seen, right? Because checkpoint inhibitors work in 20%-30%, maybe 40% of patients. Our drug, we expect, should work everywhere because, you know, it's a, it's a fundamental mechanism, but all remains to be seen, right? And on the right-hand side, remember I was telling you about the decoupling of PK and PD? The right-hand side is a great example of that. If you look at body weight of mice, which is the right measure for toxicity, the body weight doesn't change with our drug, right? The curves are overlapping. That tells you it's actually pretty safe to combine. The next slide that I'm going to show you is one of my favorite slides in our preclinical work, and this is showing you the durable and the long-term effect of our drug, potentially, that will prove in the clinic. What you're seeing on the left is a robust sample of immunocompetent mice, right? Their immune systems are intact, right, that they will actually react like normal mice and normal humans should. We took 20 of these, and we dosed them with our epigenomic controller as well as an anti-PD-1. In our arm, we saw 70% complete resolution of tumors, right, complete response. These mice were tumor-free. What we did with those mice, and they were, they were dosed over, you know, a 30-day period, right? At that point, we took those mice, and we put them away, let them thrive for the next 70 days, did nothing with them, didn't touch them with any medicine. 70 days later, we implanted 2 sets of tumors in their flanks. On one flank, we put the exact same tumor, HCC line, that they had seen before, and in the other flank, we put a completely unrelated, lung cancer line. We didn't treat them, and we let them run for the next 30 days. Look what happened on the right-hand side. In the flank where we implanted the tumor they had seen, that tumor didn't grow. In the flank that we implanted the unseen tumor, that grew exactly as expected. We restored immune memory. This is how the drug should work, right? MYC, what MYC does is it, you know, it checkpoints, prevent sort of immune, the host immune system to function. What we did was we opened it up. We allowed T cells to come back in, simply put, right? Cellularly reprogrammed the system. Host immune response was back. That immune response recognizes the tumor, and eventually, you didn't have to treat. Now, mice is not our eventual audience. We want to obviously replicate this in humans. But if we were treating mice, I could tell you that we cured these mice, right? Of course, we have to replicate this in humans, you know, we have to see whether the translation works, all of that, right? We can't claim any of this stuff, but this is incredibly, you know, appetizing data that you can actually try and replicate. So we are super excited about our program. And, you know, just to add, the program, the deal that we did with Novo is very similar in the sense we are actually cellular reprogramming at the, at, you know, at the cellular level when we, brown white fat. But we should discuss that in the Q&A. With that, you know, I would love to leave you with a couple of slides. First and foremost, just to recap the clinical data, 8 on 8 patients, we saw that our mechanism works. We now have clinical proof of principle. Omega's platform has delivered lots and lots of preclinical proof, proof of principle. Biology, technology, everything works. Now, this, this shows that it works in the clinic. This is incredible. This is a first-in-class drug delivering on the promise of epigenetics in nothing short of c-MYC in hepatocellular carcinoma, which is a holy grail gene. We are very, very excited about this, right? And this is just a platform - as, as you know, this is a platform, so everything we do here, other than the biology and other disease, applies to every other program. That's the beauty. It's replicable. One last thing, these are the priorities and the anticipated milestones. You know, as I said, Cohort 3 is done. Cohort 4, the dose range is 0.09-0.125. Sometime in the first half, we are going to come out with a data release. We plan for monotherapy and combination expansion sometime in mid-2024. That's on the lead program. If you look at our pipeline and our platform, that continues to develop really well. OTX-2101 is a non-small cell lung cancer program in with c-MYC that continues towards IND, with IND-enabling work. HNF4 alpha, CXCL1/2/3 that I talked about, we put out some incredible data, and we continue to prosecute those. We are expanding the pipeline, as evidenced by the Novo deal. You know, all of this is possible only because we have an incredible team of people who are brilliant and who have taken this science and created this drug development platform, and we continue to develop the platform into it. But that's it, folks, and, thank you very much for listening. Thank you, Mahesh. I'll kick off Q&A here. So besides representing an entirely new class of medicines, what are the advantages of this approach, particularly compared to other modalities, let's say, including siRNA, gene therapy, or gene editing? Yeah. You know, look, I mean, I think these modalities have limitations like we talked about, right? Gene therapy, gene editing work, and I'm really happy that it works because patients absolutely need it. They come with nucleic acid sequence changes. You alter the gene, genomic code irreversibly. We don't know what that leads to. That's why these drugs have 15-year follow-up, maybe 20-year follow-ups, right? We got our IND cleared in the FDA within 1 month. It took us 1 month to clear our IND. With a completely new modality, all the work that we did, there is no safety concern, no follow-ups, right? That's one piece. And, you know, when you think about siRNAs, et cetera, right, they are, again, great drugs, but, you know, c-MYC is such an important target. Oncology is such an important area. I haven't seen siRNAs work in oncology, right? And they have a 20-year lead on Omega. So that tells you that these are terrific therapeutics, but they don't work everywhere. The big differentiator of Omega's platform is that it pretty much works in every human disease. The only place it may not work is you have complete loss of function, no allele that works, right? There, only gene therapy works because you have to literally put in the gene, but otherwise, we work everywhere else. Tom, would you like to add anything more, or did I miss something? No, that's exactly right. Thank you. And so honing in on, some of the recently disclosed data for OTX-2002, how should we understand the decoupled PK/PD? You know, what makes you believe there's greater potential for clinical activity, with higher dose or repeat dosing, as you've reached, target PD? Great. I'll... Tom, go ahead. No. Thanks. Happy to take that one. Yes, I mean, from, you know, from the data that Mahesh showed, you know, and based on our preclinical data, we're already, even at these initial low doses in the beginning of escalation, in the zone from an activity perspective, in terms of the level of MYC downregulation that we know we need to hit to drive antitumor activity. So at, you know, at a molecular level, we have achieved, you know, the appropriate activity. Now, it's simply a matter of, you know, hitting enough cells to drive, you know, at a macroscopic level, to drive objective responses in terms of regressing tumors. And so, you know, the two sort of levers to do that are number of dosing cycles and the absolute dose. You know, as we continue to escalate up in dose, you know, we're putting more drug molecules into the system, you know, that will transfect more cells. The other thing to mention, too, in terms of, you know, we showed very robust, you know, pharmacodynamic activity and PK and safety in 8 out of 8 patients early on. But again, being so early in dose escalation, those were very, very sick patients, really a salvage setting. And so, just given the severity of their underlying disease at enrollment, on average, they were only able to stay on therapy for about 4 doses, which, you know, frankly, is not really enough drug on board to fully reap the benefits, frankly, of any therapeutic modality. We believe strongly that as we move up in dose, as well as, you know, as we move towards the expansion phase, where we're, you know, we'll get to more second-line type patients that should be able to stay on study longer, you know, we hope to see objective responses there. I guess, you know, with that additional monotherapy dose escalation, is that how you should frame the expectations around the next data set? Yeah, I mean, I think, look, you know, let's step back for a second, right? I think there's a penchant in all of us to try and figure out whether anytime we see clinical data, whether there's objective response and efficacy. Let's be clear, this is a phase I study. It's not set up to study efficacy, right? But we are hopeful based on what we see, right? So if we do see objective response, and we have our CMO sitting here, and, you know, he lives and breathes for that, right? If we see partial response or complete response, that'd be amazing. But this is a phase I study, so I want to set the right expectations that we, you know, we would continue to see what we have seen. That leads us to believe that there will be responses, but I don't know any other drug in phase I HCC that is approved that showed responses or efficacy in phase I. So it's a pretty high bar. Time will tell. As we put out data, you know, if that happens, we'll be as ecstatic as you guys. Got it. And you touched a little bit on this. You know, last week you announced a collaboration with Novo Nordisk on obesity. Can you tell us a little bit more about the program and how, how that all- Yeah. came together? No, absolutely. I'll talk a little bit about it. I'm sure Tom will definitely want to add a little bit more. So look, I think, you know, we had been talking to Novo, and there's... Novo is an expert, obviously, in obesity. And there is this concept of thermogenesis. So if you think about obesity drugs today, right, the GLP-1s have really taken off, and people have been losing significant amount of weight. But GLP-1s, what they really do, in, you know, very simple terms, is that they trick you into believing that you are not hungry and you lose weight by starving or gastric emptying.... Once you stop taking those drugs, the weight typically returns, right? So, you know, Novo has been looking at a sustainable solution and a more durable solution as the next generation of therapeutic. The science that allows you to do that is thermogenesis, which is essentially, you know, allowing brown fat to burn itself off, right? Which is metabolically active. What happens is, when we are babies, you know, we predominantly have brown fat, which over time converts into white fat and some brown fat. The white fat is inert, it doesn't burn off. So if there was a way to, convert white fat, you know, to brown fat by cellular reprogramming or going after genes and tuning their expression, that would be an incredible program to have and an incredible drug to develop. This is the science Novo was looking for, looking to address, and there's nothing that addresses it. small molecules can't. I don't think siRNA can, right? That's why they did the deal with us, because our platform absolutely can do that. Because that's if you saw the oncology data that we showed, there's a parallel, right? That you reset cellular programs. Here, we'll reset cellular programs. And the beauty of this is we can create a durable controller, like you saw, right? Which you don't need to dose very often or chronically. And if you actually do the process of converting white fat to brown fat and it burns off, then the question becomes: how often do you want to be treated, right? Pick your number, and after that, you're done. And then maybe get into maintenance, because we are not disciplined, you know, animals, right? We will put on weight. So that's kind of how I look at it. Tom, anything you want to add? Yeah, no, you hit most of the really key points. I mean, just to amplify that a little bit further, I mean, you know, the process of thermogenesis, as you know, one of the key pillars of you know, tackling obesity, has, I think, been on people's radar for a long time, but has not been effectively drugged till now. And I think, you know, Novo was just... To your point, you mentioned the example of MYC. We also have data from our HNF4 alpha program in liver regeneration, you know, that speaks to the ability to epigenetically reprogram cells, to rescue both, you know, change cell state, but also rescue cell function. I think, you know, we, we showed the clinical data here, but as you mentioned, these conversations, you know, with Novo began, you know, quite a while ago, and I think they were really compelled by, you know, the compilation of our preclinical data and our ability to do this, even in advance of the, the clinical data we've shown. Thank you. I guess considering the wide range of applications across various therapeutic domains, what are the ongoing, partnering plans, if there are any? Yeah. No, look, that's a great question. If you saw what we can do with this platform, right, it is humanly impossible for any one company to actually prosecute this platform across all of these therapeutics. Look, I worked for Novartis for a long time, and I can tell you, with even Novartis, there's no way Novartis can prosecute this platform alone. So partnering has been a mainstay of our strategy for a long time. Now, we inked the first partnership. The interesting thing about partnering and having been on the other side of the fence, you know, in big pharma, I can tell you that, you know, unless you understand, big pharma understands what exactly they're dealing with, it's hard to wrap your head around it, right? Because if we were the next ADC, great, I understand how ADCs work, right? Because somebody did a deal. Hopefully, this precipitates other partnerships. We have a partnering team here that is very busy, was busy at this meeting, has been busy for the last three years, right? You know, we just expect that, as this platform develops more and more, you know, we work on the Novo thing, we, clinical data comes out, that we should be able to ink many more partnerships. That's what we intend to do. There is no way that we can actually prosecute all of these things. We'll play in areas like oncology, regenerative medicine, but we are going to partner... Oncology is also very vast. We can partner in there as well. So we are absolutely open to partnerships. The reason for that is not so much... You know, partnering is not an end goal in itself. These drugs are game-changing potential. They have game-changing potential, and it would be, you know, very unfortunate if we can't develop this platform as a biotech community, because I think these drugs really will change the game of disease. So that's one of the reasons why, you know, developing this platform as an ecosystem is super important. That's our view. Thank you. Finally, one last question from me. Can you just remind us of your cash runway? Yeah. Outline the, you know, just reiterate the major 2024 catalysts. Yeah. No, absolutely. So our cash runway that we last, you know, disclosed was close to $90 million. And what we have said is cash into the third quarter of 2024. And, you know, you saw the milestones. I mean, the clinical trial continues to develop. We'll be giving our next data update sometime in the first half. We will also start our expansion phase and combination sometime around the middle of the year. And then, you know, if you followed us over the last 3, 4, 5 months, since September, we put out clinical data, and then we have been at every major meeting with really incredible preclinical data. You should look at that. You should look at the preclinical data that we put in, even HCC, non-small cell lung cancer, HNF4 alpha, CXCL, and that just tells you that it's broad and it's incredible data all around. We'll continue doing that. Awesome. Any questions from the audience? It's a, it's a three-part question. One is, market, one is tech, and one is, disease. So the first about market, you- early phase I company, new modality. In the last few years, company that have went public with new tech like these have commanded billion-dollar market cap. If you think about Protagonist, and they haven't showed anything more in the clinics than you did. So that's the first thing about why suddenly your new modality, which is also a first in class, does not command that kind of firepower? The second, in term of tech, this is, I mean, lipid nanoparticle outside liver is everyone's dream in genetics, so how do you plan on tackling this one? And the last one, if I think about disease, I often think about induced pluripotent stem cells. This is just a cocktail of transcription factors. And when I think about skin aging, that just... This would be a market like obesity- It sold itself. Like, you don't have to prescribe it, people will just run for it. Yeah. So I'll take the first question first, right? Your guess is as good as my guess, my friend, because, look, when we went public, we went public, you know, at a very good valuation, $17. We went to $32. We were at $1.5 billion, and the whole market collapsed. Look, look, in 2021, 2022, right, there was nowhere to invest. 2020, so every generalist investor came into biotech. All biotech stock grew. This is very similar to, you know, way back when, you know, if, if anybody remembers the dotcom era, right? Anything got funded, whether it was good or not. Eventually, there were some companies that survived, some that didn't. There is a self-correction that's happening, and what happens is, as I understand, you know, the long-term buyers who understand this, and look, we had, we have had great conversation with JPMorgan, right? And before. So we understand the market, that at some point people will start coming. A company like this, with the data that we have and the potential we have, I mean, there's nowhere else to go but go up. So, you know, I can't answer. I don't have a crystal ball, but that's the answer I will give you. I don't know whether there's something to add to that. The second part of your question was about, Tech for- Tech. Delivery. I'll actually hand it. So by the way, here, in Tom and his team, you have one of sort of leading experts in lipid nanoparticle and delivery technologies. I'm gonna ask Tom to actually answer the question about why LNPs beyond liver. Yeah. No, absolutely. I mean, if you think about it, people have been, you know, the sort of clinical application of all genetic medicines essentially have relied on some sort of delivery, you know, be that, you know, initially viral through conjugation or, or most recently with mRNA therapeutics through lipid nanoparticles. You know, the, the, the liver at this point is, you know... I think there's tremendous potential still yet to be gained in terms of potency and targeting the liver, but, but that has been achieved, certainly. And we, and the, you know, this is not a program, or, or excuse me, a problem unique to Omega. I mean, the entire field is hammering very, very hard, as you said, to unlock delivery to essentially every cell type throughout the body. And I think, you know, if you look in the literature and at some of our, you know, flagship and other, you know, corporate entities in this space, there has been tremendous progress in targeting, you know, circulating immune cells, for example, and other tissues. We are internally developing technology. We have, you know, where it makes sense in terms of speed of development, we have partnered, for example, the liver targeting LNP for our lead program, and for our lung program. However, you know, for, in the oncology setting, for example, a systemic approach to lung delivery makes a lot of sense in terms of targeting tumors. For other indications in either rare disease or inflammatory conditions in the lung, for example, you know, an inhaled approach probably makes more sense. And so we are, you know, well along, I would say, and you know, pushing the field in terms of both- Yeah ... systemic and inhaled approaches to lung delivery as well as to tumors. You know, we have nascent efforts in- And so on and so forth. other tissues as well. I mean, you should look up on the website. We have a slide, actually, in our corporate deck that shows you all our internal efforts. You know, by the way, we are delivery agnostic. We went with lipid nanoparticles simply because mRNA and LNP, as a modality, is in more patients today than all of the biotech modalities combined, right? So that's why we went with LNPs. You know, it works. Privileged compartments, we're happy to go with viruses, all of that. And, you know, I'll segue into your last question. When you look at that slide, you'll see that we can target the skin, right? We'll figure out whether we want to go into aging of skin or not. I mean, there's a lot to be done. That's obviously a serious condition for many people. You know, it's a balance. I think we're at time for today, but that concludes our presentation. Thank you, everyone. Thank you. Thank you, everybody.