Luca Issi, Senior Biotech Analyst here at RBC Capital Markets, and today is our great privilege to have Korro Bio as part of our RBC 2024 Global Healthcare Conference representing the company. We have Ram Aiyar, Chief Executive Officer. Ram, thanks so much for joining us. How are you doing today? Fantastic, except for the Secret Service, but you know, we're getting through it. Always throwing some curveballs. Thanks for having us, Luca. Always keeping things entertaining here. Maybe we have a long list of questions here, but maybe before we ask you about individual programs, can you just maybe talk big picture about what progress has the organization made over the last few months, and most importantly, what's ahead here for Korro? Yeah, thank you for the question. Maybe I'll just take one step back for folks that don't know about Korro, just to, you know, if there are three things you walk out of here today. The first one is Korro, as an organization, works on RNA editing, and by that I mean we have the ability to change a single base on RNA specifically from an adenosine to an inosine. Okay, and we've been able to demonstrate that we can do that with high efficiency, high specificity, and across multiple tissues. So that's the first one. The second one is, you know, we've built a foundation or a platform that has the ability to do that in multiple tissue types, leveraging multiple delivery vehicles, and really be able to expand into common diseases. And so by that I mean we can make a change in a specific adenosine, change the amino acid sequence, and impact biology in a way that nobody else can, right? And the specific focus there is on activation or increasing the activity of a certain protein. The third thing that the takeaway is our lead program is in a rare Mendelian disease. I say rare, even though there are 100,000 patients in the U.S. and about 150,000 patients ex-U.S. That focus is, again, it's a pathogenic G-to-A variant, and we hope to bring a transformative therapy for those patients. So for that lead program, we're going to have a regulatory filing at the end of this year with data coming up at the end of next year, and we have sufficient cash runway to get us beyond all of those milestones, plus additional programs in. So that's the company. So, you know, that's where we're sort of positioned. In terms of over the last, call it, four months, we nominated our first development candidate for Alpha-1 in December. And in a conference in San Francisco, we were able to demonstrate that we have a best-in-class profile for that specific KRRO-110. KRRO-110 is an LNP that encapsulates an oligonucleotide that is our drug product. We've been able to show that we can achieve greater than 50 micromolars of protein with just a single administration in a model that everybody uses. More on that later. I'm sure we'll talk more. Since then, we've also demonstrated outside of Alpha-1, we've also demonstrated that we can leverage additional delivery modalities through conjugates to show that we can achieve greater than 50% editing across multiple targets. As you know, we're the only ones that have been able to show in monkeys for Alpha-1 that we can have the translation from mouse to monkeys and hopefully into humans. So that's also a data set that we shared. Finally, this happened probably a few weeks ago. We had a reverse inquiry for a PIPE financing, and so we closed an additional $70 million that gave us a runway that we needed. Got it. So yeah, a few things here and there. Got it. Super helpful, super helpful, and we appreciate the RBC conference early this year. But maybe if I can stick to Alpha-1, obviously Wave is probably the most relevant comp at this point. Can you just maybe talk about some of the key differentiating features between your program and their program? Yeah. Firstly, I would want to say that I hope Wave succeeds. I think this patient population has a lot of, they're waiting for a therapy, and so there's nothing really out there. Much like Wave, we believe that RNA editing is the way to go. First up, I hope they succeed at showing something. The key differentiators for us, you know, when you think about the two different compounds, I would say that from an intrinsic potency perspective, we're no different. When you look at cells, you break them apart, you look at, you know, how much you edit, the potency with which you edit, we are very, very similar, or at least within error. What is different is delivery. Okay? We made a choice in 2022 to go down a delivery path that involved a lipid nanoparticle. The specific reason for doing that is because this patient population has a lot of turnover on the transcripts, specifically on SERPINA1, and we wanted to make sure we give the highest likelihood of success for that compound. When we spoke and tested on KOLs, the key point that they highlighted over convenience was like getting as much protein in circulation as possible that is active. So that was the focus. So we are an LNP delivery, and they deliver via a sub-Q. Got it. Got it. Super helpful. They obviously have data at the end of this year. What do you think is the worst-case scenario, base-case scenario, and slam dunk scenario for Korro into their data? Yeah, there have been a couple of read-throughs over the last six months in terms of competitor data having an impact on companies. So listen, I think all I can talk about is our data set and the confidence that we have in terms of the translational pieces that we put together, the therapeutic index that we are seeing, and the likelihood of success. But, you know, let's play devil's advocate. Let's see, you know, there are three scenarios we're likely going to end up with with the Wave data. The first one is that they'll take their low dose, and you will see some movement in M protein. Okay? There is no M protein in these individuals. You're going to see some improvement in M protein. That's the base-case scenario for me because the dose that they have, and assuming that they go in the low dose, they're likely not going to see much. So they'll see some movement in M protein, and that'll take time. It'll probably take, you know, two to eight doses to actually show enough oligo in the hepatocyte and an impact on the protein. So that's what I think that they will demonstrate. That's one scenario or base-case scenario that they will show from a low dose or a middle dose this year. If you see nothing, right? I think that's a downside scenario in terms of you're not going to see any change in the M protein. You're not going to have any activity so far. You're not going to even see a bump in total protein that pushes the Z out. Then you have to ask the question like, how do you know that you got the drug there at the right concentration? And unfortunately, there's no way to answer that question because you're not going to take biopsies in this patient population, and you're not going to know what the distribution is. Okay? That's where we're slightly different because in the context of an LNP, we know we can get the drug to the liver. Many people have shown that. And we can have an endosomal escape mechanism that's 100 times more potent than Wave is. And so you combine the two together, we feel pretty confident that we'll be able to see some activity even at the lowest doses. The bold case scenario for them is they hit 11 micromolar of protein in circulation at their highest dose. I don't know if they can show that this year because from a timing standpoint, how many doses, the dose escalation in the MAD study. But eventually, if they show that, that works in our favor as well, because that means that it'll validate our thesis that we'll be in the normal range of the protein and likely improves the dosing frequency that we would see in humans. So I think in all cases, I have a sense that our data should work. So fingers crossed. Got it. Got it. Super helpful. Like I said, I hope they succeed, right? Because I think if they do, it shows that the mechanism works and proves out the thesis that we have on editing. Sure. And obviously, it could be very critical. It's the very first time we're seeing any data for ADAR in humans, right? So obviously showing that concept and your ability to move some of the levels in the serum would be critical for the broader field, not only for you, but for many other players in the field. Maybe if I can circle back on the PiZ mouse data that you have, I think you have very impressive 50 micromolar levels, I think after a single 2 mg per kg infusion there. And actually pretty, pretty compelling ratio between the wild type and total protein there. However, when I look at that experiment, I think the control level was actually pretty elevated to begin with. I think you have 20 micromolar to begin with. What drove that? Is that potentially confounding of your data, or how should we think about that part? So let me sort of level set, right? So when you think about the three companies in the space, all of us have shared data in the NSG-PiZ mice, which is the mice that you're talking about. We've also shared data in the Jeffrey Teckman mouse model, which is the C57BL/6 mouse, right? And so in both of those cases, when you think about the percent editing that we've achieved and what we've shown is about 50% in both of them, right? So which means of all the transcripts that is in the liver, we've edited greater than 50%. When you think about the specificity around the controls in the NSG-PiZ mouse, you don't see any M protein. Like there's zero M protein in circulation. It's all Z protein. So for me, it doesn't really matter what those control levels are to a certain extent. It may indicate the severity of the model more than anything else. But at the end of the day, it's, you know, all of these mouse models are just POC in the context of do you see activity and what's the activity translate to from a PKPD standpoint. What I would ask you to focus on is that we start with 0, and we end up with 35 micromolars of protein in circulation. Yeah. Yeah. Okay, with a single dose. I think that that in and of itself demonstrates that not only do we get the editing that we need, but we also get the protein translation of the amount of editing that we see. The other note to point out is that, again, irrespective of levels, when you look at the ratio of Z to m, right? We show greater than 75% m to Z. That will only occur if we're editing at greater than 50% during that timeframe. Although we show 50% at that time point, the data suggests that we're actually much higher than that. Got it. Got it. Got it. Super helpful. I think you already talked about it earlier in the conversation, but can you maybe expand a little bit more on the IV administration? Obviously, Wave uses GalNAc, so it's subcutaneous. It feels to me that you took more like the pragmatic decision to do an IV approach with lipid nanoparticle first. One, why is that the right decision? And two, is there a vision down the line to actually pivot to GalNAc, or do you think you can put this all the way to potential approval with an IV that obviously will require steroid premedication and the whole nine yards? So any thoughts there? Yeah. So unfortunately, I'm an engineer by training. So it's, you know, you modify one variable at a time. In this case, the thing that we want to show in humans is that this modality works. So you have to give it the highest likelihood of success that you want to move forward with, right? And so in that context, you know, we've been able to show in vitro studies that we can actually edit the Z protein. We can show that the secretion of the Z protein is possible. We've then showed in mice that we have a correlation between the amount of tissue versus protein in circulation. We've then done it in monkeys with a surrogate, one versus the other, all of this with an IV LNP particle, right? What that tells me is that the choice that we made around going down an LNP and generating protein that's going to be at a high level, I think at least based on the preclinical data, we believe that we've made a good decision. When we've tested with physicians, categorically, we've gotten a response that more the better, right? One, I mean, when you think about the protein in circulation from a concentration standpoint at baseline, this is the fifth most concentrated protein in plasma. Okay? And it's all the half-life of this protein is maybe five to seven d ays, okay, in plasma. So a lot of transcripts are being produced to produce this amount of protein. So you want to give it the highest likelihood of success. When you think about convenience versus GalNAc, subQ versus an LNP IV, with the LNP IV producing 100-fold more into the cytoplasm and nucleus versus the GalNAc needing a lot more compound to get to that same level. For your first compound for a novel technology, I'm not sure that's a bet I wanted to take. I wanted to show that this will succeed in the clinic. You think? Go ahead. Sorry. So that's for our first generation product, right? We've also shown that there are differences in targets from a GalNAc standpoint that if you achieve potency in vitro, you can get to those levels. But it is target dependent. And it is dependent on the potency. This is no different than siRNA. siRNAs are picomolar potencies, right? We're not there yet from an ADAR editing standpoint. We will get there. But it's a question of, you know, time to iterate on the technology. So you are potentially ready to push that all the way to commercial with an IV, and you're envisioning GalNAc possibly being a lifecycle management type of an approach more than pivoting as you're in the clinic. So you're fully committed to push this potentially all the way to approach. That is correct. The reason for that is, you know, you want to see there are some aspects of the LNP that are very unique than GalNAc, right? So you actually get the opportunity to go and distribute to immune cells that you don't necessarily do with GalNAc. So does that have an impact on SERPINA1, especially on macrophages? Does that have an impact on SERPINA1 in terms of, you know, resident tissue expression? We don't know these answers, right? So I think that for us, we have committed to 110, taking it all the way. We believe that we have a best-in-class product. We will then be able to show what is the level of potency we need to achieve in humans so that we can come back from a lifecycle management standpoint with a GalNAc to then provide convenience. That convenience today, maybe once a month, but if we know that we want to achieve once in three months, that's something that we can achieve down the line. That's the kind of data set I think would be important for the company. Okay. Got it. Super helpful. How do you think about a filing strategy? I think we've seen Beam actually prioritizing a CTA versus an IND. You know, they make an argument that in the U.S., augmentation therapy is obviously widely available, and that could be potentially a confounding factor. And that's why they decided to go to CTA. Are you going to do CTA or IND? Why make that choice? Why not both? So I think it's just going to be a question of timing. We have strong, very strong relationships with the Alpha-1 Foundation here. We've demonstrated the data with them, and they're working with us, and we're working with them over the last three years to build that network of physicians that can advise us on development here in the U.S. There's nobody in the U.S. at the current moment from a development standpoint. I think the question for us is that, you know, when do we enter the U.S. and how quickly do we enter the U.S. versus something else? And that will be dictated by the study design that we haven't really shared with the external community. So the bookends are, can we go fast, much like Wave, and show healthy volunteers and get to, you know, safety and then come into the ZZ individuals in a MAD study? Or is there anything other creative that we can do? Remember, our onset of action is pretty rapid. So we could do a SAD study in ZZ individuals and get the numbers that are very, very meaningful. So that's really the balance. The other aspect is that from a regulatory standpoint, I don't think we have any challenges to come to the U.S. It's just ensuring that we have alignment across the different agencies. Got it. Helpful. Maybe can you talk briefly about timelines? When do you think is the earliest you can have either CTA or an IND open? I think you're guiding the second half of the year. Is that July 4th or Christmas? It's definitely not July 4th. It's somewhere in the middle. It's not Christmas either. So I think the question is that we need to complete a couple of studies from an IND enabling standpoint. We've done them from a dose range finding standpoint, from a tox, as well as from a manufacturing standpoint. It's now just building the dossier to file. Got it. Got it. Helpful. Maybe going back to delivery, given such an important part of your strategy and lipid nanoparticle, obviously, we've seen some AEs from Verve. And you know, they claim that they don't think it's the base editor. They think that that's really driven by lipid nanoparticle with, you know, some AEs also, you know, that we haven't seen for a while, including thrombocytopenia. Does that keep you up at night? Safety always keeps me up at night. We're going into humans. We got to do this carefully. And we got to do it thoughtfully. So, you know, better get it, you know, in this case, better right than vast. From an LNP standpoint and from the use of lipids, now all of us have been exposed to lipids. So we know what it feels like. If you've got a Moderna shot, you know, it pains a little bit more than the Pfizer shot. So there are differences in terms of when you think about the lipids versus not. When I think about our choice of going down the Genevant path, we actually in 2022, we did a comparison of lipids from multiple vendors to find out what is the composition and the lipid profile that gives us the best therapeutic index. You know, there are instances like Generation Bio and others that have shown that, you know, just because you change the cargo and you put a lipid that you're used to, you may not get what you're looking for, right? And so we wanted to make sure that we have the right substrate to actually do that. So through that comparison is what we picked Genevant. Our base case competitor was MC3. MC3 is in Onpatro. And when we look at the TI relative to Onpatro for multiple targets, we have a 3 to 5x better therapeutic index with the Genevant lipid than we did with lipids that are in Patisiran. Okay. And so that's one. The second reason why we went down the Genevant path from a lipid standpoint is they've taken these lipids into humans. We know what the non-clinical package and the TI looks like. We are mimicking some of those. Then in humans, when they've taken it in, they've seen no LFTs at pretty high doses that they've gone in. I wish I could tell you with which partner they've done it and what those programs are. But unfortunately, I won't be able to do that. They haven't shared that with us. So from a clinical precedent, pretty confident that, you know, I think we're headed in the right direction. There will be LFT increases with lipids. It's a question of at what dose. And it's a question of what the efficacy profile looks like at those toxic doses. So when you think about Verve, for instance, which is what started a lot of these conversations, at 1.5 mgs, they showed a 15x increase in ALT/ASTs, right? This is there in their filing in non-human primate studies, right? So it was not like it was not there. So that 1.5 mgs is both the mRNA as well as the 100-mer guide, right? So when you think about weight to weight ratio, they have a lot more lipid to product ratios, a 10:1 in terms of lipid to product. Our molecular weight is much closer to an siRNA or an antisense nucleotide. So when we say 1 mg per kg or 2 mg per kg, we're talking about a lot more product relative to lipids. So it's a 2:1 or a 3:1 ratio from lipid to product. There is a publication out there that Genevant put together comparing a lot of the lipids. You should have a look at that. And they looked at both the ALT/AST increases in different species. They also looked at cytokine increases across different species. By far, I mean, it's self-serving for them, but the Genevant lipid looks good. They also compared Moderna and Novartis and the Acuitas LNPs. So you'll get a sense for where that fits. Got it. We'll definitely check that out. We're running already out of time here. But maybe on the competitive landscape, when you think about Alpha-1 more broadly, obviously multiple players really talk about Wave, which is probably the closest combo. We have other, what do you call it, orthogonal approaches like the Arrowhead approach, which obviously can tackle only the liver and not lung, but we have others in the space, including obviously Beam doing DNA editing. Like, what's your Vertex still, the mix of the small molecule? Like, what's your big picture thoughts on how the competitive landscape will evolve and why you're differentiated versus any of the other players out there? You know, I've been thinking about this question for a while because I keep getting it. It's like saying, you know, once you develop a statin, you're done, right? You know, all cardiovascular disease is solved. But clearly, that was not the case. Then, you know, the PCSK9s came in, then Inclisiran came in, and now Verve is doing something. And in each of those cases, the severity of the patient population is very, very different, right? Verve is looking at, you know, familial hypercholesterolemia. And statins are for everybody, right? And so that spectrum is also there in Alpha-1, right? You have patients that are super severe. And then you have patients that are not. You have patients that are 20 years old, and you have patients that are 80 years old. So my personal belief is I don't think there's a magic bullet in terms of, you know, one technology sort of taking it all. There is a spectrum. And that spectrum is going to depend on the severity of the patient population. And so when you think about a DNA edit, I think you better be careful in terms of, you know, diagnosed COPD has high risk. Those are the patients you want to go after with an edit that touches your genome. When you don't need to, and you have other safer modalities, why wouldn't you do it? So I think that's the balance. So I don't think that there's a winner takes all. I think it's a question of, you know, where these products fit in the convenience and severity profile of each of these physicians. You know, as we've tested physicians, there is a lot of enthusiasm about, you know, having a transient method to go and edit RNA without touching the genomic material. Gotcha. Gotcha. Maybe last question. We talked about obviously Alpha-1, but I do want to talk maybe really briefly about the platform. I think I was interested in this tantalizing signal that you've seen for your transcription factor where you actually extended half-life. One, can you talk more broadly about that? And two, how do you do that? Did you maybe eliminate the ubiquitination sites? And so like the transcription factor had extended half-life. Like, walk us through that experiment in the context of the broader modularity or platform. So we've highlighted two targets in the pipeline, which is TDP-43 and Nav1.7. In our S-4 filing, we've highlighted one other protein-protein interaction, which is a transcription factor that you talked about. We knew enough about the structure of those two proteins that we made an amino acid change based on genetics that prevents binding to this other protein that's its regulator. And so by doing that, you can now take away this transcription factor and the half-life lasts longer in terms of minutes. And therefore, you see increased activity. There is genetics behind it in terms of those specific domains leading to increases in protein signaling and therefore downstream events. So we leverage that. There are other targets where we've removed lysines to remove the site that could be ubiquitinated and therefore increase the half-life. So the exact opposite of what molecular glues do. For Nav1.7, we've created a structural change that constricts the ion channel. So less current flows through. And thereby, you don't feel sensation or pain. And bring it back to physiological levels. So there are many, and these are all protein changes, right? This is another reason why we're different from Wave is our focus is on the protein side of things rather than on the transcription and gene expression side of things. Super helpful. I have another 300 questions, but no more time. So Ram, thanks so much for joining us. And best of luck for the rest of your.