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. Welcome, Tom. Thanks for joining us here today. Thanks very much, Robert. 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. 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. I thought I might project a few slides. Okay. To walk through that. Here's the customary disclaimer statement. In terms of the platform, just take one step back. 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 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. 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. 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. That leads us directly 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. As opposed to being a binary effect, this can be tunable, which we think is critical from a therapeutic perspective. 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. 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. 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. 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 kinda undruggable targets, such as gene editing platforms, you know, the protein degraders out there, so on? Yeah, I mean, the sort of advantages depend, you know, very slightly on which of the other modalities you're looking at. 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 specific binding domain, which is looking at a particular, you know, typically 21 base sequence throughout the, you know, unique throughout the genome. 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 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. 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 and oncogenes in particular, you know, they tend to be very highly auto-regulated at the mRNA protein levels. 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. 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. 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. The ability to tune gene expression without changing the underlying sequence, we think and also in a redosable way. 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. You know, the approach that we've taken with you know, 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 effector portion of the molecule are you gonna use and so on? Yeah. Absolutely. You know, as I outlined before, kind of at high level, 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. We can very rapidly, you know, 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. As I mentioned, understand, and this is critical, to understand how that gene or set of genes is regulated in a healthy state and then how that goes awry in a disease state. Which leads us then directly, again, in a computationally guided mode to design of the OECs. We can, you know, go from, I would say, concept sort of target ideation to, you know, design and synthesis. 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, putative EpiZips where for example, we could target, you know, we often see a profound effect targeting in the vicinity of the loop anchors. The CTCF molecule's binding and maintaining the IGD loop that you see there on the lower left, as well as regulatory elements, enhancers and other thing sequence elements within the loop itself, and also in some cases the gene itself. We can very rapidly, you know, design and synthesize small, you know, focused panels of those molecules in a matter of weeks, typically. 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. You know, it's a very small matrix. We can very quickly empirically screen through those and optimize in an iterative but rapid way, you know, to get to an optimized molecule. Got it. Maybe just last question on the platform. You know, what's left to really optimize here? I mean, maybe it's work to do around drug delivery for certain targets that you pick. 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 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. Really, you know, I think those are at a mature state now, but, you know, we continue to optimize both on 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 of different programs now that we can get to optimized, you know, putative lead candidate molecules in the matter of several months, you know, a handful of months, really. You know, having now, you know, taken our first program into the clinic, I think we can say, you know, with 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 an IND in about 27 months, which for a novel platform, you know, where everything was being sort of built as you go, is really incredible. 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 initial stages of, you know, going from target ID to an optimized molecule. 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. 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. 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. Yeah. Here we go. Yeah. Awesome. Um. 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 epigenomic remodeling kind of platform. Sure. Just maybe I'll sort of run through them quickly, and then I can specifically answer your question. 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. 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 the ability, you know, by virtue of targeting the nature's operating system, which is, you know, IGDs to target genes and diseases broadly. 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 we believe is likely very challenging with all the other modalities out there that I'm aware of, certainly. You know, is an advantage for us. That's a specific sort of, while not a traditional therapeutic area definition, that's the way that we have described that. 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. 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. We're targeting different epigenomes, in this case, and using a lung targeting LNP. The plan would be to develop these separately as discrete products. 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. In the regenerative medicine space, you know, we're targeting both liver and corneal regeneration. In the monogenic space, we are targeting alopecia in particular. It's you know as you can see the breadth of the pipeline reflects our you know belief in the broad applicability of this. 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 really advanced our first program into the clinic. 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. You know, certainly in the case of oncology, you know, many different cancer types are unknown to be driven, you know, significantly by epigenetic dysregulation. It's, you know, an epigenetic approach from a therapeutic perspective is a very natural fit there. Which 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 particular with regard to, you know, safety in terms of specificity there. You know, 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 beyond the sort of monogenic case. In the regenerative medicine space, in particular, you know, both liver and cornea, you know, the cellular programs that drive the identity and state and function of all of our cells are epigenetic in nature. 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 you know, 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. In monogenic diseases, there are many indications where there's a gene dysfunction, which is not a complete loss of function and all that's required to really rescue a phenotype from a clinical perspective is, you know, a modest increase of, you know, 2x in the gene in either a healthy compensatory allele or some other or the gene itself to really rescue that phenotype. Obviously, all of this is, you know, from the answers I've given you are really focused on, from a sort of, you know, technological and development lens. 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. Hepatocellular carcinoma, huge unmet need there. Mm-hmm. As you mentioned. 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. 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. Really what we've been able to show there, you know, first, that the this platform can really deliver on an optimized product in terms of an Omega Epigenomic Controller that can directly very specifically 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. 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. In vivo, we've shown 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. You know, while we see preclinically and would expect to see clinically, you know, a strong response from a monotherapy perspective. There's also, you know, very 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, including both kinase inhibitors as well as checkpoint inhibitors. We have, you know, MYC targets both cell intrinsic and extrinsic processes in the tumor microenvironment. You know, in the overexpress case, acts to sort of, you know, immunosuppress the tumor microenvironment and upregulate things like proliferation and vascularization. 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, that we do see significant additive effect both in vitro and importantly in vivo, for kinase inhibitors. We 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. There's a strong rationale we believe and our data now are coming out to support that. We look forward to sort of sharing more of that as we go along. 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. Very important for development, kinda going forward. 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? You know, how are you thinking about picking a dose to take forward? Sure. No, it's a good question. Just a point of clarification. The trial design, which I think should be live on ClinicalTrials.gov as of today or very soon thereafter, is a phase I/II. We're looking for, as you noted, 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. In the first part of the study, we'll be doing just a classic 3+3 dose escalation scheme. Yeah. To arrive at, you know, to look for MTD and recommended dose for expansion. Once that has concluded, we would open a monotherapy expansion cohorts, again, to look at monotherapy activity. 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. 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, 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 EpiZips. We're particularly targeting a motif, a sequence motif that's highly conserved across a broad segment of the non-small cell population. We believe that, again, confers some additional cancer-specific specificity to the molecule. Yeah. As well. 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. We'll be bringing forward another molecule in the non-small cell case, as well. I mean, you can see from the pipeline slide here, we're pursuing a number of things in parallel. We've, you know, while we've sort of been first to publish, I would say, in terms of the non-small cell, the next program coming up, there. 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. As of June 30 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. Really we're feeling we have a strong balance sheet, a robust pipeline and really focused going forward on execution. You know, getting the HCC program up and into the clinic and the programs behind that into development. Brilliant. Not seeing any other questions here. Tom, thank you very much for joining us. Great overview and super exciting times. Thank you, Robert. Good to talk to you. Thanks. Bye. Bye-bye.