I needed my disclosure statement. There it is. Okay. Thanks for joining, everyone. This is the Fireside Chat with Sana Biotechnology. Happy to have with me CEO Steve Harr. Steve, thank you for joining us. Really appreciate it. Before we get started, just let me read this disclosure statement. For important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. Excuse me. With that, let's go ahead and get into it. Steve, I think at this point, people are likely familiar with the Sana platform, but just to level set for everyone, do you want to just talk for a minute about what you think some of the key milestones have been for the business recently, and just what the Hypoimmune approach generally is conceptually, and then we can go into specific programs from there? Sure. First of all, thank you for having us, and thank you, Morgan Stanley. Thank you, everybody here, for joining us. And, I'll do my quick disclosure, which I'm sure you guys know we're going to do some forward-looking statements. We spent a good bit of time on our risk factors, so take a look at those in our most recently filed 10-Q. So, so just take a step back. The company was founded, you know, really with the goal of making an important company in the era of cell and gene therapy. And, we wanted to go after what we thought were some of the more important challenges. And I, I'd say there are two major platforms inside the company. You know, the one that we're not going to spend much time on today is probably related to cell delivery. And you probably recognize you can do or gene delivery, I should say. You can do pretty much anything you want to a genome in a petri dish. The hard part has been doing it inside of a body. And so we really set out with a goal of being able to deliver any payload, RNA, DNA, protein, to any cell in a specific and repeatable way. And every time you do one of those four things, I think you open up a whole new category of medicine. And we've got this fusogen platform, which really gives us payload diversity and cell-specific delivery. I'm not sure if it'll help us with some of the other aspects of that. And stay tuned. You know, we slowed that down a little bit. We couldn't do so many INDs last year, but we've continued to invest in that on the research side, and we're excited about where it's going or optimistic about where it can go. So you brought up the Hypoimmune platform, and again, if you take it just a broad step back, you know, the goal in cell therapy that we had is to be able to deliver cells that engraft, function, and persist, right? And to be able to do that at scale. And you know, the real challenge to date has been related to, first of all, just persistence, and most importantly, overcoming the barriers of allogeneic rejection. People have approached that problem since the advent of transplant medicine in one of two ways. One is profound immunosuppression, so that the immune system has no ability to find the allogeneic cell, and the other is autologous cells. Both of those have, you know, pretty profound implications on your ability to scale. So we've kind of embarked on a rather audacious goal of being able to overcome allogeneic rejection. We call it the Hypoimmune platform. A step back, very simplistically, there are two elements of the immune system that we have to grapple with. There's the adaptive immune system of B and T cells. It's actually relatively straightforward to deal with. You knock out or in some way disrupt expression of MHC class I and class II, and you won't see T cells recognize these cells, and you don't get B cell, you get antibody production. The challenge is that viruses and tumors figured that a long time ago, and so we've evolved something called natural killer cells that recognize these cells. And so that's been what's really set the field back for the last, you know, several decades. And, you know, our belief is that we've found the key to turning off that innate immune system, natural killer cells and macrophages, and that is overexpression of CD47. And so we make really three gene modifications in this hypoimmune platform. It's relatively straightforward. Disrupt or knock out MHC class I and class II expression, so there's no MHC on the cell surface, not even minor HLAs. And then the other is overexpression of CD47. And we've tested this extensively preclinically. I would even, if I wanted to be bold, say we've solved the problem of allogeneic transplant rejection for mice, for humanized mice, for non-human primates, and the real key is understanding how it translates into humans. And so, you know, we've shown you a little bit of data in people. It was, you know, early this year. It was four patients. You know, it was early in the process. What I would say is it's doing everything we hoped it would do. You know, I think there's still a little bit of work to be done to ensure that what we've seen preclinically fully translates into humans, but there's a lot of reasons to be optimistic, and you know, if we get this right, we think we can have a pretty, pretty big impact, and you know, we've got four different drugs in human testing across, you know, three major areas and seven diseases, right? So it's type 1 diabetes, B-cell-mediated autoimmune disorders like lupus and multiple sclerosis and vasculitis, and then blood cancers, and you know, happy to go into each of those, but those are kind of the big clinical categories that we have, and then we have some things going on earlier stage. Great. Let's try to unpack all of that in the next 30 minutes. But first, Steve, I have a question for you on the platform, generally speaking. One question we've received is: If the secret to the Hypoimmune platform is essentially these three edits, what stops another company from being able to make these same three edits, and what keeps your approach proprietary to Sana? What keeps it proprietary to Sana? I can't imagine there's only one way to do this, right? And I think to the extent that there's more than one way, people will figure it out over time, right? You know, the most important thing we can do to kind of exploit the advantage of this system for patients is to move quickly and urgently in developing medicines. You know, we do have a very robust intellectual property estate. You know, this has been a challenge people have gone after for years. This is clearly something that's pretty very, very proprietary to the company, where we've built a nice patent portfolio. But you know, I mean, I think one of the things you have to recognize over time is that if you build something important, people will try to join the club. So, I think the most important thing for us is to make sure it's important, and then we'll work really hard on defending our position over time. Got it. Got it. Okay, with that, maybe we can start Type 1 diabetes first. I know that you have, potentially a data readout coming out from an investigator-sponsored study there. So let's talk about that program first, before we go to autoimmune and oncology. So Type 1 diabetes, you presented non-human primate data earlier this year. Could you just unpack that for people just to kind of explain what you thought were the key de-risking events from that data set? And then what are the open questions now when you try to translate that signal into humans? Like, what are the leaps of faith that you need to make to bet on the signal in humans? So Type 1 diabetes, it's a very simple disease, right? I mean, it's very complex biologically, but at the end of the day, what's happened is the immune system has specifically recognized one cell in our body and killed them all, the pancreatic beta cell. So the pancreatic beta cell is what makes insulin in a glucose-dependent factor and really keeps all of us in homeostasis. Up until 100 years ago, it was a death sentence for patients, and at that time, there was the advent of porcine-derived insulin. And actually, the first biologics were human insulin. And so, it's you know, the innovation in this space has been important over time. So what we're trying to do is very simply make a replace the cell that's missing, right? And so that is the pancreatic beta cell. You know it works because for the last 20+ years people have been transplanting cadaveric-derived islets in patients with Type 1 diabetes. And in many of them, what you find is that they have normal blood glucoses without needing any insulin. It's a pretty profound effect. Two issues, though. The first issue is that that's not a very replicable or scalable manufacturing source, right? And the second is that there are only a limited number of patients for whom lifelong immunosuppression is better than lifelong insulin, right? And so, others in the field now have subsequently shown that you can take stem cells and make them into a pancreatic beta cell and transplant that. And in the context of immunosuppression, see really, you know, again, profound clinical effects. You know, that is probably almost certainly a more replicable and scalable manufacturing source, but you still have the challenge of, you know, the immunosuppression. So really, what we're trying to do in the short term is understand, can you get rid of the immunosuppression and overcome allogeneic and autoimmune recognition and killing of those cells, right? Our long-term goal is very simple. You know, it's a gene-modified, stem cell-derived beta cell, where with a single treatment, patients are euglycemic, normal blood glucoses, with no insulin and no immunosuppression. So it's kind of like a functional cure. Give them back their normal life, and give them back their normal life expectancy, and give them back their normal organ function expectancy. So that's the goal. What we showed last year, or what we put in, that was published earlier this year, was a non-human primate model Type 1 diabetes, where we Type 1 diabetes chemically, right? So as a STZ, we'll knock out the pancreatic beta cell. And these monkeys had full-blown diabetes, or this monkey had full-blown diabetes, difficult to control, but doable with insulin. Received, after several months, a stem cell-derived. Sorry, a islet, a gene-modified islet transplant into the muscle of the animal. And the animal was euglycemic, normal blood glucoses, off insulin, went from living in a cage and very difficult life to going back into its colony. We followed that animal for six months. To ensure that it was our cells that were having the effect, we then gave it a kill switch, and we killed those cells. They went away, and the animal went right back into full diabetes. So a wonderful proof of concept that this works in non-human primates. So we're basically replicating that experiment in humans with these investigator-sponsored trials. You know, with really three important differences. So these are leaps of faith, probably. One is, it's a human instead of a non-human primate, right? And so the leap of faith here is that the immunology is pretty similar. The second is that the mechanism for diabetes is different, right? So in the human, it's autoimmune, meaning the immune systems attack the beta cell. In the monkey, we chemically induced it, right? And so you have to believe that we've overcome the autoimmune component as well as the allogeneic component. Let me get into why we believe that. The third is that it's just a lower dose, right? So don't expect to see, most likely, euglycemia off insulin, right? I mean, I think it's what we're looking for success is to show that these cells survive with no immunosuppression and function, right? And so detectable C-peptide is the biomarker we're looking for. And for those of you who forgot your basic biology, the beta cell makes proinsulin, and it then secretes it as C-peptide cut, cleaves it as it secretes it, and you get C-peptide and insulin. So when you see C-peptide, what you know is that the patient is making their own insulin, right? So that's what we're really looking for, is evidence of that, and that would be very clear to us that we'd overcome, we've overcome the allogeneic and autoimmune mechanisms of rejection. So hope that's helpful. It's a long-winded answer. No, that's helpful. So, cell survival, safety, C-peptide levels, those are kind of your thresholds for, I guess, a viable data readout from this, from the initial IST dataset, is what you're saying? I think to the extent that you've shown that you have cell survival, a cure for Type 1 diabetes becomes inevitable, right? I mean, it may not be us that gets there. I sure hope it is, right? But at that point, given the things that we brought up earlier around what you've seen with cadaveric islets, what you've seen with stem cell-derived beta cells, you know, a functional cure, meaning, you know, euglycemia off insulin it's going to happen, right? And then our goal is our job is to make sure that we're a part of that. Got it. Is there any way that there could be a false positive in either the cell survival component of the readout or the C-peptide expression levels of this readout? Is there any reason why, if you were to see both of those things, that you would still have doubt that these cells are doing what they're supposed to do, b iologically speaking? Maybe just to add, so yeah. So is there any way that this doesn't translate into what we're trying to do, and you say, "Boy, they really haven't overcome allogeneic and autoimmune rejection"? We've never been able to come up with one. You come up with reasons for false negatives, but you can't come up with one for a false positive. It's really unprecedented to transplant. It's basically an organ transplant, right? You're just transplanting a cell with no immunosuppression and to see cell survival. It's unprecedented, so it would be transformative, I think, for the risk of the company. We like to think it'd be transformative for patients over time as well, and what we can deliver for them. Got it. Got it. And I know you've mentioned previously that you don't need to see very many patients' worth of data to get proof of concept from the IST. So for this initial dataset, I know you're not guiding the specifics, but do you think it's a handful of patients at most that we would see data for, one patient? I think if you saw one, it would be all you need to see. Okay. If it works. If it doesn't work, we understand why it doesn't work. You know, some patients don't get a primary islet transplant. They just don't graft, right? That just happens, and we need to redo it again. You know, it may be that there's immune rejection of our cells, right? I mean, that would be, I think, really important. Again, that would be where n of one would be very instructive, right? We really want to understand that. You know, it's probably a true negative, right? And that's something we need to better understand and discuss with you and others. If it's a true positive, we'll also wanna discuss it. Got it. Got it. Okay. So I know you've mentioned that seeing a patient get off insulin is not reasonable to expect for this initial data cut. But if you have a patient with cell survival, C-peptide levels, would you then follow them? Would you keep following them i n the IST to see, if they actually get off insulin? We'll follow them, and the longer it lasts, the better we all feel about, you know, how this will play out over time. Got it. So we'll definitely follow them for a long time. And you know, it you know, just having detectable C-peptide is a clinical benefit for patients. They. You know, historically, that's led to you know, very clearly lower risk of both lower hemoglobin A1c and lower risk of severe hypoglycemia. So it isn't no clinical benefit for the patient if they have detectable C-peptide. They're making insulin. You get a basal level of glucose sensor of insulin secretion, right? That's a really nice thing to have. But, you know, it would be unreasonable or unlikely to think that in the first study of a, you know, first in human experience, that the doses we're gonna start with, that this person would have a normal blood glucose without insulin. It could happen. But again, my general view is, if that's your investment thesis, don't invest. Fair enough. All right, so let's say that this initial data readout hits and meets your expectations on safety, C-peptide expression. What does that mean for SC451, your internal T1D program? Yeah. So SC451 is a gene-modified, pluripotent stem cell that we make into a pancreatic islet, right? Mostly beta cells. And so the reason I like to define that. So an islet has alpha, beta, and delta cells in it. They're endocrine cells, and you put all of them in there, but the beta cells that we really care about here. So you know, so to make that product work, we've kind of thought about, is there are four major scientific questions to really get through. One, and this is the part that, you know, probably proved a bit more challenging than we expected early, but, and that is to make a gene-modified, pluripotent stem cell master cell bank, where you're comfortable with the genome integrity or stability over time. I think we got it. I wouldn't guarantee that to you yet, but we think we got it, and that took us a bit of time. The second is to make drug product at a purity, potency, and yield that is adequate for phase I testing, right? Just to get going. Again, we think we got it. I wouldn't, you know, lose a ton of sleep over that if I were in people's shoes. The third is, can you overcome allogeneic and autoimmune rejection? That's what we're gonna learn, right? So I think that's something that will be majorly de-risking for the program. And then the fourth is, can you make drug at a purity, potency, and yield to be really commercially important, right? And we have a ways to go to be there, to be very clear. And so, that's kind of how we thought about it. We haven't really gated any of the investments in phase in those first two questions around master cell bank and STZ and phase I scale on success of this study. I think we'll feel more confident. We're underinvesting right now, I think, realistically. And truly, the science of scale, this isn't just, well, you know, put more manufacturing suites together. This is really, it's a scientific question. We need to put more resources towards that. I think that'll be easier to do with positive data, and so that's the part of the SC451 program that I think would accelerate with some good data. Got it. From a regulatory standpoint, thinking a couple of years ahead, assuming SC451 is progressed, it's going through initial studies, what is the best way to think about how much data the FDA might wanna see for this kind of a product in this disease space to feel comfortable to grant an accelerated approval? Yeah, I'd have to say we don't have a good answer for that yet, is probably the best answer to the question. If you're looking at proxies, I mean, one of the things that we have in the field is there's a company that's ahead of us that's, you know, doing non-gene modified, but pluripotent stem cell-derived islets. I think that that's at least a guidepost to think about. Our program could be very different. We might have different safety, we have things to worry about. We might have different efficacy. But it's a better guide than anything else that's out there right now because there haven't really been many things out there to date. Fair. Okay, fair enough. Any final, I guess, thoughts or guidance you provide on Type 1 diabetes and the upcoming readout before we switch gears to autoimmune? Any other thoughts or guidance? There's been a good amount of focus on how to best i nterpret the data sets, yeah. I would say it's. You know, we're really optimistic that we can do something important here, and if we get this right biologically, it's gonna be, we think, a very important drug, you know, but it really will be dependent. Its importance will be dependent upon our ability to scale it, right? If we really nail this, it's hard to imagine that, you know, our supply isn't the rate limiter, you know, kind of for success for a prolonged period of time, so we need to really start investing more heavily in getting that part of it right, but that's one that we really think can be, you know, something very important for the company and for patients. Got it. Got it. Sorry, final question on this program, and then we'll move on to others. Snapshot of where the IST currently stands. You're enrolling patients. Have you dosed a patient yet? Have you disclosed that? Sorry, what? Have you dosed a patient yet in the IST? Have you disclosed that? Oh, we just told you that we had patients that are on the transplant list, and that was as of early August, and that we weren't gonna provide any update until we had data, most likely. I mean, we don't need people hanging out outside the hospital where we're doing this, trying to figure this thing out. Fair enough. Okay. That's helpful and probably a good segue to start talking about your autoimmune efforts then. But before we get into your trial, maybe you could talk a bit about your view of just autoimmune CAR T in general. We've had a couple of data sets come through from other companies now, also from the academic setting. What is your view of what we're seeing from an efficacy and safety standpoint what's your view of the third-party data sets out there in autoimmune CAR T? So, I think it's been really kind of, encouraging, surprising in many regards, and, almost thrilling to see the clinical benefit that, you know, first of all, Georg Schett has found, right? In his single center experience, for these B-cell-mediated autoimmune diseases. And, you know, it seems like what you get is a control-alt-delete of the B-cell repertoire, right? And, you know, and so when you think about the competitive dynamics, you have to kind of think about that biology. So what's really important in that biology? You know, first is just what cells are you trying to get rid of, right? And so you need to get rid of B-cells, memory B-cells, and tissue-resident plasmablasts. The challenge with that is, to do all of that, you actually have to have a volume of distribution of your drug that gets into tissue and allows you to get into the tissue, the tissue germinal centers, right? And so there are a host of things that you could do that just can't do that, right? The second is you have to get rid of, you know, it seems like pretty much every single cell. I mean, you don't really have to, you have to get rid of the pathologic cells, but it's just luck if you don't get rid of every cell. So when you think about that, that means you have to estimate of how many cells are there. There are 3 times 10 to the 11 B cells, right? That's kind of the estimate, right? So 300 billion. So it's very difficult to do that and to dose anything that's that potent unless these cells, unless you have something that logarithmically grows, right? So those parts, so I think that's the first thing to think about within the competitive dynamics. The second is that these are complicated patients, and giving these drugs is very complicated. So, you know, I think this is an area where, in particular, the allogeneic platform can offer a substantial benefit, even over, you know, even more so than in oncology, over autologous CAR T cells, right? Patients have to come off of, if they want to do an autologous cell, any type of immunosuppressant, which they're all on, 'cause they all have a severe disease, they're not gonna take this stuff, before they even get plasmapheresed, then they have to be put back on while the drug product's made. They have to take back off once the drug's made. So the allogeneic really simplifies that, right? Take them off, and you dose them. Very simple. It's a lot lower risk for the patient. And the risk to the patient as they go through all of these changes is otherwise they'll flare, right? And you've seen some examples of that in the field, right? So, you know, the second is just scale, right? We can do this at a scale and a, you know, that, that is very, very different. We'll make hundreds and hundreds of doses, you know, per manufacturer. If you use a number of 500, I think that's just a nice round number. Put into context, you know, a hundred manufacturing runs of an autologous product is you get, at best, a 100 patients that you can dose. If we do a 100 manufacturing runs, that's 50,000, right? And so when you kinda go through the scale of the autoimmune diseases, it really gives us an opportunity, both simpler and also just more readily available for patients. We would love to be better as well. I'm just gonna hold off hope on that and see where we end up. You know, when I think about where we are and the data we've disclosed, so then we gave 4 patients of cancer data. You know, we deplete B cells, right? Yeah, we showed you that, right? So if you have a question, really left around, okay, does that translate into the autoimmune setting? I would argue it's way easier to deplete B cells than to deplete B cells and get rid of tumor cells, which is what you have to do, right, in oncology. And so then the second question then is, you know, what's your clinical benefit long term really look like, right? And, you know, I think it's very reasonable to say, well, maybe we won't be as good as some of the other things out there, and we'll have to find our niche, right? But, you know, and it's very reasonable to say we have the category killer, right? And then the clinical data will prove out which one of those we end up with. But, you know, we do have a scale and hopefully a potency and durability that will give us the chance to do that. You know, I would guess that, you know, if you look at autoimmune versus oncology, you know, you probably don't need the cells to stick around quite as long, right? 'Cause all you do is trying to get rid of the last cell a fter that doesn't matter. And again, it just takes less time, most likely, to get rid of B cells than to get rid of B cells and every tumor cell, right? Just common sense tells you that, right? Sure. 'Cause on that point, what is the minimum amount of durability you think an allogeneic CAR T needs to show for a disease like lupus? What is it? Minimum, like, amount of durability that an allogeneic CAR T therapy would need to show for a disease like lupus, to be considered commercially viable in your perspective? I would like to think that there isn't a big difference between what the allogeneic CAR T cell and the autologous CAR T cell delivers. There is. It will probably have a, you know, a commercial place if it's not quite as durable. But, you know, some of the rate limiter for patients right now in terms of market size is the lymphodepleting chemotherapy. So while it's easier to retreat with an allogeneic CAR T cell, it's really simple. It just sits on the shelf and off you go. You know, you would have to, you know, lymphodeplete the patient again, and that's something we'd prefer not to do. So, I think we can get away with, you know, some variability around that, and I'm sure there will be variability across different studies that just isn't even real, right? Whether we're better or a little bit better or a little bit worse, I mean, I think that is just patient population and luck, but I think you probably wanna be pretty similar. I don't think you wanna... We don't really start out trying to be worse. Fair enough. So coming to your program then, for the data set expected this year, what can people expect in terms of number of patients, amount of follow-up, the number of indications that could be represented? I can't really answer that except for one thing, and that is that the first patient was dosed around May. You know that from ClinicalTrials.gov. So it's not really realistic to think they're gonna be a whole bunch of patients with one-year follow-up, right? And, you know, we're limited in dosing early on because we're going through dose finding, right? So, you know, the most you can do is one per month until you find a dose, and I haven't yet been a part of a study where, in CAR T cells, where we're good enough to enroll every 4 weeks, right? Usually, there's a little bit of time lag in there, just, you know, screening, scheduling, lymphodepletion, then you dose the CAR T cell, and then you wait another 28 days before you start that cycle again. So, you know, you're not gonna get, you know, tons of patients. But, you know, our goal is to say, okay, you know, first off, does what we see on B cell depletion translate into what you see in the autoimmune space? I think that's very low risk. Then you say, okay, does that translate into clinical benefit for a patient? I mean, I think that's an important question. And once you have that, it's actually pretty straightforward, then you're more or less de-risk until you get a ton of data, right? And a ton of data would be your question is, how do you compare to others in the field in a very competitive space? And that will take, you know, a lot more than we can generate this year. Got it. Got it. Okay. You think it's reasonable to expect data for indications outside of lupus this year, or? Sorry, from what? Reasonable to expect data for indications outside of lupus for this year, or? Well, I think it's reasonable to expect data. I think that's about as much as I'd be willing to get into, and Fair Even that has some variability of timing that's beyond our control. Sure. So I think last year, you probably remember, we thought we'd have data at ASH, and we didn't get an ASH abstract. So we, you know, put it out the first week of January. So there are elements of this that we don't control. We'll do our best to. I kind of look at this, we're building. We want investors to come along with us on a journey in building, you know, something very valuable over time, and we have to make sure that we have transparency around what we think are the important, you know, kind of de-riskers along the way. Part of that is always going to end up being early clinical data to see, does it do what you hope it would do? And then it takes a lot of information to, say, truly define the drug characteristics after that, right? Right. But we should be in a place, I think, this year, we can start to tell you, does it smell like we think it should? Sure. Okay, fair enough. We have three minutes left. I want to make sure we touch on oncology before we close out. Yeah. So, ARDENT, you've reported some initial data from ARDENT, like you mentioned earlier this year, and I think you've guided to more, like, a further data update, I think, later this year as well. The first what? A further data update for ARDENT later this year as well, right? I guess, what are you looking to see from the next update to kind of get conviction that this is a differentiated CD19 therapy? Is it durability? Is it depth of response? Like, what do you hope shows that t his is differentiated? To be very clear, our goal has been to do two things, right? What you've seen in the allogeneic CAR T cell field to date is they get pretty good early responses, right? But they're not very durable generally, because the cells don't last post the time of the immune system recovering. Either that or the immune system's really heavily knocked out, and that's come with its own toxicities, right? So our goal would be able to show with kind of normal lymphodepletion that we can you know really. Our goal is to see durability that is you know better than what's been seen in the allogeneic space and really more comparable to autologous, right? And it's available and off-the-shelf and scaled, right? And so it becomes to smell more like a drug and our ability to deliver it for patients. We're not trying to be better than autologous. We would love that if that happened. We're trying to, as you know, look the same. So we'll see where that ends up. You know, it's a competitive space, and so, you know, we need. You know, in some of these other places, like, if we're not quite as, you know, good in the autoimmune space, it's like, I think we'll find a place for the drug. It works, it will find a place where it's really important. I think in this space, with the establishment, you got to have a higher bar for what you expect from us. So that's kind of. At least we have a higher bar for what we expect to justify, you know, ongoing investments in this space. So that would be what you should be looking for, and say, "Hey, this is starting to smell like something that can, you know, really compete and be similar to autologous cells or not," right? I think that's the important element of what we're trying to establish. Okay, understood. Thirty seconds left. Maybe we can just touch on Sana's cash balance, your current cash balance, the associated runway, and then importantly, how much, I guess, pipeline development is contemplated in that runway. So, like, where does your current cash get you for T1D, oncology, autoimmune? How far can you get those programs? Yeah, we're going to need more money, I would just say, right? And we're going to need more money in the not-too-distant future, right? When we raised money earlier this year, we kind of what we told investors, we were going to raise more or less as little as we needed to. We wanted to kick the can down the road to get more data, right? And so we wanted to kick the can far enough down the road, we didn't trip over it, right? But really, we thought the next time we raised money, it should be with some clinical data that helped to you and, and us to define kind of what is what the company's future looks like, and what areas and what level of investment we're going to need. So, I mean, our... You know, we have a lot of levers we could pull to extend the runway, but our goal is that we get good clinical data, and we kind of push forward in all these areas, right? It may not be possible, right? We may have to make some choices, you know, I'd like to think they would make those choices based on clinical data, not finances, but cell therapy is expensive, and we'll live in the real world. We'll figure that out. Got it. Got it. Great. And with that, we're actually at time, so we'll go ahead and close out. Thank you, everyone, for joining. Thank you, Steve, for being here. Really appreciate your time. Thank you, Vikram, and thanks, everybody, in the audience. Appreciate it.