Great. Thanks for joining us here at the Wedbush PacGrow Healthcare Conference 2022. My name is Robert Driscoll, one of the senior biotech analysts here at Wedbush, and I have the pleasure of hosting Omega Therapeutics here today for a virtual fireside. With us from Omega, we've got Dr. Tom McCauley, the Chief Scientific Officer. Before we get started, I can remind folks, if you have any questions for the company, please feel free to enter them in the chat box, and we'll look to address them. So welcome, Tom. Thanks for joining us here today. Thanks very much, Robert, and thanks to you and the Wedbush team for inviting Omega to be with you here today, and share our vision for epigenomic programming for precision genomic control. Great. So as you mentioned, Omega is developing a new class of mRNA therapeutics that aim to modulate gene expression to treat a whole variety of diseases, actually. So-called Omega Epigenomic Controllers or OECs. Can you maybe start by giving us an overview of what it is you're actually targeting here and the platform that allows you to do this? No, absolutely. So I thought I might project a few slides to. Okay. To walk through that. So here's the customary disclaimer statement. And so in terms of the platform, just to take it one step back, so Omega Therapeutics was founded by Flagship Pioneering in 2017, and is now a clinical stage biotech company, pioneering the first systematic approach, you know, using mRNA therapeutics as a new class of programmable epigenetic medicines. Our platform harnesses the power of epigenetics, which, as you know, is the mechanism that controls gene expression and really every aspect of an organism's life, from cell genesis, growth, and differentiation, all the way to death, throughout the life of an organism. Our goal really is to, again, to take a page from nature and correct the root of disease, particularly the genetic basis of it, by returning aberrant gene expression to a normal range, and to do so without altering native sequence DNA sequences, which we think is a tremendous advantage. So, as a platform, we're a deterministic development platform, and this allows us to prospectively engineer these programmable medicines to control cellular programming and state and function, you know, and to restore gene expression. Our platform, as you can see here, is based on a computational engine that allows us to identify and target specific, unique DNA sequences throughout the genome. These sequence intervention points or epigenomic zip codes, which we call EpiZips for shorthand, represent our drug targets, really, and there are thousands of these throughout the genome. And these are located on and around these loops of chromatin that you see here on the left, called insulated genomic domains, which house essentially all of our genes and the regulatory elements that control those. We then use our computational tools to prospectively interrogate the IGDs and the genes within them to understand how they're regulated, both in a healthy setting and then also in a disease state. And then that leads us directly then to be able to prospectively identify those critical EpiZips from a therapeutic perspective, to control the gene or genes within that loop, you know, in the way that we need to from a therapeutic perspective, and to design the appropriate epigenomic controller, which is, you see a schematic of there in the center, which consists of a DNA binding domain, targeted, designed, to bind that EpiZip that we've selected, and fused to an epigenomic effector, which again, based on the type of epigenetic modulation that we're trying to impart, you know, affecting the DNA or the chromatin in that location to modulate gene expression, you know, we would select an epigenomic effector to have that specific effect, where we can tailor not just the effect and tune gene expression. So as opposed to being a binary effect, this can be tunable, which we think is critical from a therapeutic perspective. But we can also tailor, again, by virtue of design of that effector, in the context of the target, to have a duration of effect that's variable anywhere from a few days to a few weeks to you know, more than a few months, to really target not just acute emergent diseases, but also really chronic diseases as well. And we are currently delivering these mRNA medicines, encoded medicines as in lipid nanoparticle formulations, as you see there on the right, although the platform strictly is delivery agnostic, and you know, we continue to innovate and add additional delivery modalities over time. So at this point, we've mapped, you know, and validated really thousands of EpiZips across the roughly 15,000 IGDs, which constitute the genome, which allows us to very rapidly interrogate and design OECs against, you know, any really any disease condition, or indication that we select. Perfect. So great overview. What are some of the kind of fundamental advantages of this platform versus some of the modalities that also targets, you know, these so-called kind of undruggable targets, such as gene editing platforms, you know, the protein degraders out there, and so on? Yeah, I mean, the sort of advantages depend, you know, very slightly, depending on which of the other modalities you're looking at. But from, you know, starting with small molecules, for example, you know, one of the critical attributes of our approach really is the specificity of targeting that we can achieve, whereas most small molecules are broadly distributed throughout the body and have relatively short pharmacokinetic half-lives, which necessitate, you know, frequent relatively high doses. You know, we have sort of exquisite both cell type selectivity through the delivery, but also genomic specificity through, again, through that very, very specific binding domain, which is looking at a particular, you know, typically 21 base sequence throughout the, you know, unique throughout the genome. So, you know, that combination of specificity and another key attribute that has implications for both potency but also safety, is that because we're working sort of one level above the genome, at the epigenomic level, we can, you know, we're delivering our medicines in lipid nanoparticles, which are taken up into the cell. The mRNA is released, engages the ribosomal machinery, and is translated into the therapeutic, which is itself a protein, which then homes to the nucleus, binds the DNA, you know, again, at that cognate locus for the targeted EpiZip and imparts the epigenetic state change to reprogram that cell as we designed. But then, you know, both the mRNA that is used for delivery as well as the protein OEC itself degrades really over the course of, you know, hours to a few days. Whereas that epigenetic state change that we've imparted can really last for again, for days or weeks or even months, you know, after a single treatment. And so what it allows us to do is decouple the pharmacokinetics from the pharmacodynamics for these molecules, which so you can have the benefit of the modulation of gene expression from a therapeutic perspective, without the liability of having drug on board for that entire time. I mean, thinking about sort of other RNA, you know, RNAi and other RNA-based therapeutics and things, another key attribute we believe from a platform approach is that we're targeting really at the highest level of the central dogma, at the pre-transcriptional level, which is really the root of, you know, the genetic basis of any disease. And so certainly, you know, as we've found and others, in that, you know, targeting genes that are essential for cell function, like MYC and other pan-essential genes in, you know, and oncogenes in particular, you know, they tend to be very highly auto-regulated, at the mRNA protein levels. And so, you know, trying to target them at those levels is fraught in that sense. You know, the therapeutic attempts, you know, to do so, certainly in the context of MYC, have been, you know, stymied, we believe, you know, because those approaches are rapidly overcome by the mechanisms that tend to keep the expression of this, in the oncogenic case, those genes very, very high, in the case of cancer. And then, you know, with an eye towards the. You spoke about gene therapy and gene editing. I mean, again, you know, not to impugn, you know, gene therapy, it's been a tremendous advance from a medical and patient perspective, and, you know, all to the good in the sense of, you know, gene replacement for, you know, really some complete loss of function, you know, devastating monogenic diseases, for example. But, you know, in terms of broader application to more common diseases, the permanent nature of editing and gene transfer approaches, you know, has some long-term safety concerns associated with it. And so the ability to tune gene expression without changing the underlying sequence, we think, and also in a redosable way. So, you know, with the gene therapy, certainly an AAV directed gene therapy approach, for example, you know, if the initial dose isn't correct or if there's some loss of expression down the line, redosing can really be a challenge. And so, you know, the approach that we've taken with mRNA-encoded therapeutics delivered as LNPs with an epigenetic mechanism of action here really allow us to redose as necessary. Perfect. Could you just maybe walk us through at a high level, you know, just development of a therapeutic for a certain EpiZip that you've chosen? You know, where do you start? How do you design, you know, where that controller binds? You know, what effective portion of the molecule you're going to use, and, and so on? Yeah. Absolutely. So, you know, as I, as I outlined before, kind of at high level, once we have. And we take both systematic approach to, you know, broadly from a computational perspective, we have, as we've mapped IGDs across the genome, you know, we have classification system and can very rapidly screen for IGDs that are likely to be dysregulated in a particular disease state. And so we can very rapidly, you know, if we start with a disease indication in mind or a set of target genes, we can very rapidly zero in on the IGD that's relevant and the genes inside it. And then, as I mentioned, understand, and this is critical, to understand how that gene or gene-set of genes is regulated in a healthy state, and then how that goes awry in a disease state. And which leads us then directly, and again, in a computationally guided mode, to design of the OECs. And we can, you know, go from, I would say, concept, sort of target ideation to synthesis, you know, design and synthesis of, you know, really, typically, we would test, you know, handfuls of molecules. This is very much a rational design approach. This is not high throughput in any sense. We can very quickly sort of zero in on, you know, EpiZips, where, for example, we could target, you know, we often see a profound effect targeting in the vicinity of the loop anchors, so the CTCF molecules binding and maintaining the IGD loop that you see there on the lower left, as well as regulatory elements, enhancers and other things, sequence elements within the loop itself, and also in some cases, the gene itself. So we can very rapidly, you know, design and synthesize small, you know, focused panels of those molecules in a matter of weeks, typically. And then we would very quickly be able to empirically test the computational predictions of both from the standpoint of looking both at the accuracy, if you will, of our prediction in terms of the purity of EpiZip sequence targeting, but also the appropriateness of the effector. We would generally create a matrix of EpiZips on one axis and again a putative set of epigenomic effectors that we think would be most relevant in a particular case. And you know it's a very small matrix, so we can very quickly empirically screen through those and optimize in an iterative but rapid way to you know to get to an optimized molecule. Got it. And And maybe just last question on the platform. You know, what's left to really optimize here? I mean, maybe there's work to do around drug delivery for certain targets that you pick. Mm-hmm. You know, I guess, you know, what's left to optimize? Yeah, that's a great question. I mean, we have, you know, done a tremendous amount of work over the past couple of years, really building computational tools and algorithms to, for, identification and assessment, interrogation of IGDs, predictive models to understand what the perturbations both from the disease state, as well as, you know, the effects of our OECs will be on those. And really, you know, we, I think those are, those are at a mature state now. But, you know, we continue to optimize both on the, the sort of computational side, you know, employing, new aspects of artificial intelligence and machine learning, you know, to really, speed up. I mean, for example, you know, I mentioned we can go from a sort of a target concept to molecules to be screened initially in a matter of several weeks. We can really, you know, and have shown in the context of a couple different programs now, that we can get to optimized, you know, potent lead candidate molecules in the matter of several, you know, several months, you know, a handful of months, really. And, you know, having now, you know, taken our first program into the clinic, I think we can say, you know, with, with, you know, some definition, that we're able to do this in a reproducible way now, you know, where we went from, you know, initial, you know, inception of the program for the MYC HCC program, for example, you know, to, to an IND in about 27 months, which for a, a novel platform, you know, where everything was being sort of built as you go, is, is really incredible. And so we... In the spirit of continuous improvement and optimization, you know, we continue to refine the tools that allow us to really compress, you know, the, the initial stages of, you know, going from target ID to, to an optimized molecule. And then, you know, there's always more to be done in terms of, you know, sequence engineering and protein engineering around effector domains and binding domains. You know, and, I mean, the ones we have are, you know, we've tested them head-to-head, and we believe are best in class, but there's always innovation to be had there. And as you mentioned, you know, we continue to innovate, internally, you know, to really develop the, you know, additional best-in-class lipid nanoparticle technologies to target sort of a, you know, ever-increasing breadth of cell types and tissue. Perfect. I don't know if you have a pipeline slide, but maybe just. I do. I do indeed. Okay. Yeah. Here we go. Yeah. Awesome. Um. So, maybe just at a high level, just thinking about how you've put these programs together, you know, why maybe you've chosen them for the epigenetic-- epigenomic remodeling kind of platform? Sure. So just may I also sort of run through them quickly, and then I can specifically answer your question. So just to orient you in terms of the portfolio, I mean, as you can see, it's quite broad, and we've, you know, organized it into oncology, multigenic disease, diseases with an initial focus particularly on immunology, and then regenerative medicine, and then select monogenic diseases, both rare and non-rare. So I mean, these really, you know, as just discussed, these really span the breadth and depth of the platform, in terms of showcasing our capabilities. One of the things I didn't elaborate on significantly, but which we think is a significant benefit for our approach over all the other modalities out there, is in the ability, you know, by virtue of targeting the nature's operating system, which is, you know, IGDs, to target genes and diseases broadly. We can target. By targeting the IGD, we can in parallel target a number of genes, and we have data that, you know, we've shown that we can target, you know, as many as eight or more genes, you know, in a loop with a single therapeutic. You know, that kind of inherent ability to multiplex is something that's gonna be, we believe, is likely very challenging with all the other modalities out there that I'm aware of, certainly. And you know, is an advantage for us. And so that's a specific sort of, while not a traditional therapeutic area definition, that's the way that we have described that. And so in oncology, you know, as you've heard recently, we have a number of programs in the oncology space, first in hepatocellular carcinoma, where we've just had our IND cleared and are working rapidly now to enroll the first patient in that trial. We also have a separate program targeting MYC as well. So this is the same, targeting the same IGD, but the dysregulation of the IGD in the context of non-small cell lung cancer is different. So we're targeting different EpiZips, in this case, and using a lung-targeting LNP. So the plan would be to develop these, you know, separately as discrete products. And then we also have a program targeting small cell lung cancer. In the multigenic space, we have two different programs targeting severe inflammatory disease, both in the lung, in this case, acute respiratory distress syndrome, as well as idiopathic pulmonary fibrosis. And then in the regenerative medicine space, you know, we're targeting both liver and corneal regeneration, and in the monogenic space, we are targeting alopecia in particular. So it's you know, as you can see, the breadth of the pipeline reflects our you know, belief in the broad applicability of this. And we've demonstrated now, you know, broad proof of platform across multiple really uncorrelated disease targets and indications, disease models, across species, you know, in some of the preclinical data that we've presented and published recently, and we really advanced our first program into the clinic. And so, you know, to address your question in terms of how we approach these and sort of why what's up here is listed. I mean, we're our first goal was to really demonstrate the breadth and depth of the platform, but also really to address diseases where we believe our approach can have a really meaningful impact. And so, you know, certainly in the case of oncology, you know, many different cancer types are known to be driven, you know, significantly by epigenetic dysregulation. And so it's, you know, an epigenetic approach from a therapeutic perspective is a very natural fit there. Which, you know, certainly I think that's been shown, at least conceptually, with small molecule, you know, epigenetic drugs. You know, we believe we can, you know, do much better, in particularly with regard to, you know, safety in terms of specificity there. You know, in the, again, in the multigenic disease space, you know, we believe our data support that, you know, we can, again, potentially with a single therapeutic target, really complex multigenic diseases, real-world diseases, far, far beyond the sort of monogenic case. And in the regenerative medicine space, in particular, you know, both liver and cornea, you know, the cellular programs that drive the identity and, you know, state and function of all of our cells are epigenetic in nature. And so the concept of reprogramming either senescent or dysfunctional cells, you know, to a functional state or reversing degenerative processes, you know, in a regenerative sense, including, you know, to include diseases not just in the liver and cornea, but ultimately, you know, in the CNS and diseases of many of the diseases of aging, with which we're all familiar. You know, it's really kind of a natural fit from an epigenomic programming standpoint to apply this technology to regenerative approaches in, you know, in all of these areas. And then again, in monogenic diseases, there are many, many, indications where there's a gene dysfunction, which is not a complete loss of function, and, and all that's required to really rescue a phenotype from a clinical perspective is, you know, a, a modest increase of, you know, 2x in, in the, you know, gene, in, in either a, a healthy compensatory allele or, or some other, or the gene itself, to, to really rescue that phenotype. So, you know, we've, we've, obviously, all of this is, is, you know, from the answers I've given you are, are really focused on from a sort of, you know, technological and, and development lens. But obviously, we begin, as well as most others do, from an unmet need perspective as well, where we're obviously trying to target indications where there's either no existing standard of care or, you know, or really, you know, unmet or undermet need from a patient perspective. Got it. Let's move to your first program. So hepatocellular carcinoma, a huge unmet need there. Mm-hmm. As you mentioned. And we talked a little bit about why you're targeting MYC here, which is just, it's really one of, you know, the driver of disease in a majority of maybe liver cancer cases. Could you talk about some of the preclinical data that you've generated here? Yeah, no, absolutely. So we've published several tranches of data now at the first coming at AACR earlier this spring, and then just recently again at ESMO-GI. So really, what we've been able to show there, you know, first, that the, you know, the, this, this platform can deliver on an optimized product in terms of an Omega epigenomic controller that can directly transcriptionally target MYC. We've shown in vitro and in vivo data showing downregulation of MYC expression, you know, at the mRNA and protein level, and associated changes in vitro, in cell viability. Again, from a selectivity and safety perspective in cancer cells, specifically, where MYC is dysregulated and overexpressed, you know, but also being sparing of healthy cells in liver. And we've shown this also in the context of the lung, for the non-small cell program, with some data we published at ASGCT recently. And then in vivo, we've shown very, very strong effect, in terms of tumor growth inhibition in a number of different tumor xenograft models. You know, spanning the sort of clinical subtypes of anticipated tumors, that we expect to encounter clinically, in HCC. We've also done a significant amount of work looking at the. So, you know, while we see and preclinically and would expect to see clinically, you know, a strong response from a monotherapy perspective, there's also, you know, various just again, from, particularly in the case of MYC, looking at, you know, the biology of MYC, there's a very strong hypothesis and rationale around how this should combine from an efficacy perspective with the current standards of care, both kinase inhibitors as well as checkpoint inhibitors. And so we have, you know, MYC targets both cell intrinsic and extrinsic processes in the tumor microenvironment. So, you know, in the overexpressed case, acts to sort of, you know, immunosuppress the tumor microenvironment and upregulate things like proliferation and vascularization. And so, you know, we have now generated data and presented some of that, and we'll be presenting more, showing that in the context of kinase inhibitors in particular, as well as checkpoint inhibitors, you know, that we do see, you know, significant additive effect, both in vitro and importantly, in vivo, for kinase inhibitors. And have also shown in the ASGCT work that direct inhibition and downregulation of MYC with our OECs leads to downregulation of PD-L1 on tumor cells. You know, and so there's a strong rationale we believe, and our data now are coming out to support that. So we look forward to sort of sharing more of that as we go along. But you know, all of which supports our hypothesis from a clinical perspective, that you know, we should see, you know, expect and hope to see activity and ultimately efficacy. We hope from a monotherapy perspective, but also that there's a very strong rationale, you know, and support for our proposal to study OTX-2002 in combination with all of the current standard of care agents. Got it. And very important for development, kind of going forward. So IND recently cleared by the FDA. Let's talk a little bit about the design of the clinical trial. You know, obviously, it's a phase I. We're looking for safety and PK. Mm-hmm. How is the dose escalation designed here? And then, you know, how are you thinking about picking a dose to take forward? Sure. No, it's a good question. So just a point of clarification. So the trial design, which I think should be live on clinicaltrials.gov as of today or very soon thereafter, is a phase I/II. So we're looking for, as you noted, we're, you know, initially, safety and tolerability, pharmacokinetics, but we will also be in both the latter part of part one and in part two of the trial, we will also be looking at antitumor activity. So in the first part of the study, we'll be doing just a classic 3 + 3 dose escalation scheme. Yep. To arrive at, you know, to look for MTD and recommended dose for expansion. And then, once that has concluded, we would open a monotherapy expansion cohorts, again, to look at monotherapy activity. And then, in part two of the trial, we'll be testing OTX-2002 in combination with both, you know. We've not specified sort of which of the various agents we'll be looking at, but kinase inhibitors or tyrosine kinase inhibitors, as well as checkpoint inhibitors. Yep, that makes sense. Except we talked about next steps. Maybe just since we've only got a couple of minutes left, just quickly on the non-small cell lung cancer program. Mm-hmm. So the same IGD, just maybe a different kind of, you know How are you targeting this differently, I guess, is the question? Yeah, I mean, so again, we haven't disclosed sort of what the specifics of the molecular composition for either molecule is at the moment. But as I say, we're in the context of non-small cell lung cancer, we're targeting same IGD, different epitopes. So we're particularly targeting a motif, a sequence motif that's highly conserved across a broad segment of the non-small cell population. And so we believe that, again, confers some additional disease, you know, cancer-specific specificity to the molecule. Yep A s well. So we, you know, we are, as I say, you know, currently planning to develop both OTX-2002 for HCC, as we've, you know, as laid out. But we'll be bringing forward another molecule in the non-small cell case, as well. I mean, I would note we are. I mean, you can see from the pipeline slide here, we're pursuing a number of things in parallel. So we've, you know, while we've sort of been first to publish, I would say, in terms of the non-small cell as our next program coming up, I would say we are, you know, there are a couple of programs under consideration in terms of which one of those we would choose to advance immediately after the HCC program. Got it. Looks like we're pretty much out of time. Just maybe the standard cash question, runway question, where are you guys here? Yeah. So as of June thirtieth of this year, we had $174 million in cash. You know, we filed our 10-Q last week, and you know, our cash equivalents and securities. You know, we believe will last us for at least 12 months. So really, we're you know, feeling we have a you know, strong balance sheet, a robust pipeline, and you know, really focused going forward on execution, you know, getting the HCC program up and into the clinic, and the programs behind that you know, into development. Brilliant. Not seeing any other questions here. Tom, thank you very much for joining us.