EP 251: Cracking the delivery barrier in genetic medicine with Jagesh Shah of Mirai Bio

Speaker A: Hello and welcome to the Genetics Podcast. I'm your host, Patrick Short. My background is in population genomics and studying the genetic causes of rare disease. I did my PhD at the Sanger Institute and the University of Cambridge and have been in biotech since 2018, when I started Cyanogenetics. Cyanogenetics helps academic and industry researchers to run large scale genetic testing programs that speed up their clinical trials, generate data sets for the next big breakthrough, and give participants the best possible experience taking part in research. Each episode of the Genetics Podcast, we bring you insights from the leading minds in genetics and precision medicine, including household names and Nobel Prize winners, as well as early career scientists and biotechs working on the next big breakthrough. Whether you are a scientist, entrepreneur, executive, patient advocate, or simply someone curious about how genetics shapes our world, you're in the right place. Thank you for listening and let's get started.

Speaker B: Welcome everyone, to the Genetics Podcast. I'm really excited to be here with Dr. Jages Shah, who's the chief scientific officer at Mirai Bio. This is a, uh, conversation I've been wanting to have for a long time because if you listen to this podcast or if you're in the field, you have probably heard me talk about delivery as one of the biggest challenges in gene therapy and advanced therapies more broadly. And Jagesh has spent a major chunk of his career, initially as a systems biology professor at Harvard Medical School and then moving into industry, working on this problem, and it's the focus of Mirai. So we're going to have a great conversation about it today. Thank you for taking the time to join me.

Speaker C: Oh, well, thank you for having me, Patrick. It's a real pleasure to be here and I love to talk delivery for

Speaker B: people who, who are not as in the, in the details. I'd love actually, if you could just start by framing the challenge. When people say delivery is the bottleneck to gene therapy or advanced therapies, what exactly are they they talking about and what's your, your take on?

Speaker C: Yeah, so maybe I'll just reframe this as the nucleic medicine era that we're in now really solves a problem that I thought about quite a bit all over my career is when you identify a molecular pathway and you want to intervene in a disease, you often have to go to what I always call the medicinal chemist that went to Hogwarts. This is a remarkable transition because you're taking your biology that's in the language of proteins and nucleic acids. And now you convert into small Molecules. And this has been a remarkable opportunity for drugs. So I don't want to dismiss that. But the ability to be able to deliver the language of cells has been kind of a transformative element, uh, that I've been trying to push for a long time. And so the real question is what we're trying to do is deliver nucleic acid so that we can reprogram or complement a mutation in a cell. And we're packaging the nucleic acid because the nucleic acids themselves would be degraded by the body. And this is indistinguishable from a viral infection in some respects. So hundreds of millions of years, the human body has been pushing out foreign nucleic acids to prevent that from happening. And so our job is kind of to engineer the delivery system so it stealthily delivers the nucleic acid. And that's the challenge. You're not just trying to understand the chemistry or in some cases the viruses that you can leverage for delivery. You're also trying to overcome hundreds of millions of years of evolution that have prevented nucleic acids from entering cells, because that's something that has historically been bad from the point of view of a viral infection. But now we want to flip that around and try to make it a, uh, therapeutic.

Speaker B: And historically the, the most straightforward places to deliver for different reasons have been the liver and the brain, the eye. There will, there'll be others probably that, that you know better than me. Can you talk a little bit about where, where the low hanging fruits have been and then where's the next frontier?

Speaker C: Yeah, so the low hanging fruits have historically been areas that you can access either by putting it in the bloodstream and a lot of it goes there or directly accessing kind of that compartment. So again, if we talk about cns, you have access through intrathecal or intraventricular. So these are the structures that house the csf, which is essentially the blood of the cns. So if you can get it into that liquid, then can you get access to the cells of interest? The eye is an area that I think also has the ability to access, whether through the front of the eye, through the vitreous or subretinal. We've developed the techniques, uh, to do that. So being able to get in there has been an area of real interest. It also has the added element that there's a view that it's immuno privileged. So that idea that if we put material in there that you're not going to see inflammation. Unclear. The human data would speak to the fact that maybe it's not as privileged as we think it is, then ultimately, the liver is a really interesting place because so much of the blood goes there. So much of your blood goes there to be detoxified. And as a result, we can take advantage of the hepatocytes, uh, which basically are cleaning up the blood, and we can make it so that these things look like they should be things to be cleaned up, and then they naturally get taken up by the hepatocytes. So again, you pointed out some really critical areas. The brain, the, the eye, and the, uh, the liver as areas of the. Now where do we want to go? We want to go to the muscle. So on one hand, we have intramuscular injection that we got for a vaccine that's not really muscle delivery per se. It's just getting it into the muscle that then it drains into a lymph node, and then we see the expression of, say, the COVID spike protein so that you can get a, uh, immune response to that. I think that where we really would like to get to is delivery by infusion into the blood or even by sub Q access to the blood, and then really be able to have these particles home to all of the muscles, or in the case of the cns, go through the blood brain barrier so we don't have to do a direct injection into the brain. These are areas that I think will unlock tremendous potential for therapeutics. Just they would dramatically increase. We already know, again, this is the area of nucleic acids. We already know what to deliver in many, many cases, it's just being able to get it there. And the idea that a therapeutic, uh, is stymied by delivery is really kind of what, what the company that I work at, Mirai, is really trying to kind of make sure is no longer barrier.

Speaker B: Yeah, that's a great segue to actually talk about Mirai and the platform. Can you tell us a little bit about how it works? What's the, what's the major focus right now? And what are you all developing?

Speaker C: Yeah, so I guess the word platform means a lot of things to a lot of people. And I think for me, having made the transition out of academia to industry, platform technologies are generally areas where you solve a generalized scientific problem, and it applies to many different therapeutic outcomes. And here, what we're trying to do, and I think you framed it well, is we're trying to build delivery systems that can be modularized so that we can take them and send them a lot of different places simply by just, um, changing a few components. So that kind of Rational design, I think, is, is one critical piece of what we're doing. In addition, we are trying to figure out how to get to these really hard to reach tissues. So we have a core element which is getting to. We have really good liver lipids. So as you pointed out, the lipids, again, the ionizable lipids, is kind of the linchpin of the lnp. So that mixture, getting good ionizable lipids that go to the liver still is a problem. We still want to be able to get to liver. Getting to immune cells, it's an area of great interest. I think probably the, the leader here, the, the tip of the spear is really in vivo car t being able to take that ex. Those ex vivo products that have been remarkably powerful and then move them so that we can reprogram in situ. Then I think we're talking about other organs. Lungs, pancreas, kidney, brain, muscle. So those are the harder ones. And, uh, that's where MRI has spent a lot of its time trying to figure out how to, what I would call broach the endothelial barrier so that we can give it by iv and these things will node a home to a particular tissue and a particular cell type and deliver the genetic cargo of interest. And again, we focused entirely on delivery. So we don't make any therapeutic assets of our own. And so this is maybe a different business model than what we might have encountered in the past. But the goal is people really have good nucleic acid ideas out there and our job is to enable the delivery to the sites that they want to be able to access.

Speaker B: And your focus, you mentioned the ionizable lipids. Your focus is lipid nanoparticles. I would love if you could talk a little bit about lipid nanoparticles versus, uh, vector viral vectors, other strategies that you have considered or might consider also just to help people understand, like delivery is not one, even one. We've got boats, we've got planes, we've got cars.

Speaker C: Exactly.

Speaker B: A lot of different ways to deliver.

Speaker C: There's a lot of ways to deliver. And I think at, uh, the core, when you're talking about a nucleic acid, I think the biological precedent is a virus. So we know that in the end viruses are good at this. And we know that there's this, what, this, what's called the Baltimore classification, which is the classification of all viruses. And you just see it's uh, when we were, when I was at my previous company at Sana, we thought about it as almost like a cafeteria where you could say, Oh, I could choose pieces of each one of this virus. Now, the danger, of course, is that the human immune system has now built a lot of defenses against this in terms of immunogenicity. And so what we had seen, and this is probably close to six decades of work on lipid nanoparticles are encasing the nucleic acids instead in chemical entities that we can synthetically generate. And these, these lipid nanoparticles are safe in the blood. They generally will. They don't. We want them to be tolerable and not interact with the blood proteins. And they're not going. They're going to be in stealth mode so they don't get recognized. And they then can enter a cell, usually get into the endosome, which I always think of as kind of the. It's the jail of the cell. It's the place where the cell is sampling and then our escape and deliver the genetic cargo. So in both cases, they're delivering genetic cargoes. The viral vectors take advantage of what every virus has solved, often in a, uh, bespoke manner. So there's many ways that the viruses get in. In the case of lipid nanoparticles, we are using a mixture of different components. The linchpin molecule is this ionizable lipid, and it's the lipid that holds onto the cargo. And when the endosome grabs the lipid nanoparticle, and those of you that are familiar with the endosome biology, it drops the PH as a way to kind of, in a way, kind of freeze whatever's in there. If it's a virus or if it's something they don't want, the drop in PH generally will kind of inactivate. We take advantage of that inactivation by then disassembling and then fusing with the endosome and then getting the, uh, nucleic acid out. So that's kind of an engineering of that system so that we can get that torque. And the lipid nanoparticles have been remarkably successful with the COVID vaccine. But going forward, the things that made a COVID vaccine good, which might have been a little bit of reactogenicity. So turning on the innate immune system because of some of the components of the LNP is not something we'll want for therapeutic usage in other contexts. So our goal is to build in two axes. One is tolerability and one is biodistribution. We're trying to make sure that they're tolerable and that we can send them to a lot of places and have them deliver their nucleic acids there.

Speaker B: You presented data at ASGCT on both, I want to say, adipocytes and CD8T cells. It would be great if you could talk a little bit about what you saw in each of those environments and a little bit about the strategy.

Speaker C: Yeah. So I would say that Mariah's just to take a step back. Mirai's approaching this in kind of two big areas. One is there is a view in the field that ionizable lipids can not just grab onto the cargo and release them, but they can also provide some of the tropism. Now, admittedly that's a lot to ask from a small molecule. It's a lipid, it's got a head group, it's got tails, it doesn't have a lot of information encoded into it. But we have found that that approach has been very valuable to get to sites like the lung and the pancreas. And so we take advantage of those lipid nanoparticles where we see some intrinsic tropism, um, to try to understand how can we leverage that. And one area that we did that in was adipocytes. This is actually a directly direct injection where you can inject into the adipocyte and then ensure that it doesn't leak out and it stays in that compartment of the adipose tissue. And that was actually done. And I think we might get into this later, but I'll just mention right now it was done through a machine learning based approach where we had had good tropism, but we were able to turn it into great tropism by using a, uh, ML based iterative algorithm. So that allowed us to now get molecules and nucleic acids into these adipocytes uniquely after that injection, and then be able to carry out a number of downstream pharmacodynamic readouts. And we have a number of partners interested in that. So again, some of this is partner driven. Somebody said, hey, I'd like to directly inject in adipocyte. So we solve that problem. Yeah, maybe the other side of this is T cells. And we, this is an area that we advanced independently, knowing that there would be partnerships available. But we started the work even without a partner, was we, we saw that there was a lot of interest in getting into T cells. Here we took a slightly different approach. T cells, there's adipocytes, there's a lot of adipocytes in fat tissue, but T cells almost exist in areas where they're not the only cell type. And what we found was that simply finding tropism to a tissue like the spleen or other lymphoid organs wasn't going to get us into T cells. So what we built was a, uh, vehicle that would get to, say, the spleen and detarget from the liver, because you don't want it going to the liver if it doesn't have to. And then we could add in targeting agents on top of the lipid nanoparticle that then specify which cell type you're going to get into. And so the, the T cell demonstration is kind of twofold. One is to show that if we deliver a, uh, partner's cargo that has a chimeric antigen receptor, we can actually get B cell depletion in an, in a monkey. And so that was, I think, an exciting, uh, opportunity. And we can show that it's also tolerable. So you can get it in, you can get sustained B cell depletion for about a week. And then you can also show that it's tolerable. But it's also a proof of concept that this base LNP that detargets the liver and can be programmed to a T cell could be reprogrammed to other cell types. And so we've shown that you can reprogram it to NK cells, B cells. And I think this modular concept sits alongside the intrinsic tropism of the ionizable lipid. These are two different ways to approach designing the lipid nanoparticle and biodistribution for it.

Speaker B: Yeah, I think this is probably a good time to drive in, to dive into the ML approach. What I'm really interested in learning is like, yeah, what is the starting point? And then how do you manage all of these different potentially competing vectors you're optimizing on? What do you, what can you fully predict computationally? Where do you need a wet lab in the loop? Like, if you take us into the ML feedback loop, I think that, yeah,

Speaker C: happy, happy to do that. And what I'll say at the outset is, of course, uh, I think what we all want at some point is what I call chat lnp, right, where you just type in, I want to go here with this potency, I don't want to go there. We're not there yet. And so what we decided early on at Mirai was that the data, if we wanted to build out a strong ML platform in our hands, had to be in vivo. If you want to go extra hepatic, you need a circulatory system, you need a way to biodistribute you need kind of to avoid the liver, so you need all of these pieces. And so we decided to embark on a campaign where we had smart chemists that could, uh, define different ionizable lipid kind of chemistries. And those properties then would be tested in vivo to see where they, where they distribute. And that is one good starting point for any place that we might want to go. So we look at all the major tissues. We do this in a mouse right now, and we look at kind of liver, spleen, pancreas, lung, heart, bone marrow, pancreas, so a number of sites. But the most important thing is we get that view of all of those tissues with each different ionizable lipid. The second feature of that is now that's a base by which to have ML navigate chemistry as well as formulation. I'll just say one word about formulation. These lipid nanoparticles are put together with four or sometimes more components. And the proportion of each one of these is what we refer to as formulation. So the formulation of those and the individual chemistry of each of the elements is something that we can give the machine access to as a lever. And what we'll do is we'll give it this database and then we'll give it an objective and say, listen, I want to go to the spleen, but I want to avoid the liver. So what it's good at is saying, oh, you've got a few good examples here. What I'm going to do is I'm going to push the boundary of those either through formulation or through chemistry. And I'd like you to formulate those for me and tell me how it goes. And so not to make it sound like it's an ML overlord or anything, but this is a collaboration with the machine, um, where it then gives us formulations, we formulate them, we put them back in vivo, and now the machine can laser focus on that objective, and then it'll take those answers and it'll say, oh, that's not the right direction to go in. This is the right direction. Oh, I've made some progress here. And the goal is to each time make some progress. And what we found, and this is, I think perhaps for me was a little bit surprising, is that within three or four rounds we're, we're at a, uh, very high forming lipid nanoparticle. And that in vivo work is done quite efficiently and the formulation done quite efficiently, mri. So we can. Four rounds is about six months. So six months they go from kind of something that's good and kind of looks like it gets there, but maybe has other places. It goes to something that's selective for that tissue. That's pretty fantastic. And we use that in both the adipocyte, uh, work where we wanted to screen out any of the delivery to say a liver or spleen when you inject it in the fat pad, and also for that common base lnp to get it to go to the spleen, but then eliminate the liver. And so both of those were designed using this ML based approach. And it's something that we are really excited about using over and over again. And the other key thing here is this term. I'm, um, reticent to use the word flywheel because it's used overused. But it really is the case that every in vivo study we do goes back into the database. So it's learning and it's gathering data. And even if it makes a bad choice, that might be a bad choice for that objective, but it could be a good choice for a different objective that we may use down the line.

Speaker B: Yeah, absolutely. Are you able to multiplex them in some way? Like, because I'm imagining you have this. When you use LLMs, you can set the heat, which will tell you just how out of the box you want it to think. Right. I imagine you probably have some. This is the best, this is the hill climb best choice. But you may want to go left field intentionally to see if there's a new, A new peak.

Speaker C: Yeah. And I guess the term that kind of most scientists use is exploitation versus exploration. And so we can tune that in. So exploitation is just looking around things that already work and just fine tuning and exploration is like you said, left field. Let's go over there and see what that path looks like. And uh, if we get some traction, we'll go over there and test some more so we can tune that in. And usually you're doing a lot of exploration early and you're doing a lot more exploitation later on to be able to fine tune when you've got good nanoparticles.

Speaker B: Are there parts of the process where the computational methods are resisting biology in some ways where it's hard to predict and you need a lot more wet lab data to, to build up that machine, human intuition in some way.

Speaker C: Yeah. So I would say that we are at the early stages of how to turn a chemical entity into like numbers. Right. That ultimately that's what the machine deals with. So being able to do that encoding, I think, is the term that we would use in, in the ML language is, is I think still a challenge for us is what's the right way. And as you can imagine, this is a, again you mentioned that, you know, kind of I was a systems biologist. I think we call this a multi scale phenomenon. Right. You go from a tiny little lipid, it becomes a nanoparticle, it then gets injected into the bloodstream and it's got to get into a cell. So that's a lot of scales for a machine to learn. So what we're trying to do is try and understand how we could chunkify and really modularize the study so that we can try to have the machine learn something about the lipid, then have something uh, learn about how LNPs are made and then something about the NVivo. That's something we're trying to move towards. I think if you had a big enough LLM, maybe people think that you can just learn everything. This is maybe where my age shows a little bit. Kind of like I still, at some point we want to crack the box. If we get good performance out of these kind of multi objective optimization problems, we'd like to know what it learned. And I think that that's another reason to kind of maybe, maybe modularize it. But to your broader point, chemistry and ML, we've seen some great successes. So I don't want to certainly in the context of proteins and I, I, I think this is an area that you kind of, I think your audience will know something about. But I think when it comes to chemistry and particularly ionizable lipids and lipid nanoparticles, uh, I think we're just at the beginning and yeah, it's an exciting beginning and we're making progress, but we're going to need to understand chemistry a little bit better. Nanoprecipitation, which is the term given to the formulation approach and then really what happens whenever you put any body into a, uh, it's a journey as you go into the blood and then wander around and find the cell of interest. There's a lot of biology that you need to interact with there. And integrating all of those is kind of really the key.

Speaker B: Yeah, and that modularity you talk about is it, is it kind of literally in the physical geography of the, of the lipid nanoparticle, the locate, like when you're thinking about learning something about tropism, for example, which, which compartment or which organ system it it naturally traffics towards, is there a location on the lipid nanoparticle that tends to drive that or, or Is it not such a one to one mapping in that way?

Speaker C: Yeah, I think there's a lot of, I think there's a prevailing theory which is called the corona hypothesis. So again, as you put material into the blood, there's blood is full of proteins and all those serum proteins and other cells and the coding that occurs as a result of that I think is thought to drive a lot of this tropism. So I think that that's where kind of the goal is to have the outside tell you where to go and then the inside be protection and the delivery mechanism inside the cell. That would be the ultimate go. This is that modularity. You can't have modularity without abstraction. So I would like the outside to just be the directional vector and then the inside be the one that when you get to your destination, then it takes over to deliver the nucleic acid. That's what I'd like. That's not how chemistry, uh, and biology work, but that's what we're trying to design towards.

Speaker B: When, what actually attracted you to join Mirai in the first place? What, when was it? What had you thinking about it and how. Yeah, what was the concept at the time? Tell me a little bit about that, that decision to join.

Speaker C: If, yeah, if I don't go mind, if you don't mind, I'll just go back a little bit farther. Just. I was a faculty member, I, I was a basic scientist and we ended up working with a number of companies and I identified some of the language and processes that they used that they termed drug development, which frankly I had never been exposed to, even as kind of, kind of a more senior person. And so, so what you do in academia and you're familiar with this is you start a course. If you don't know something about it, you start a course.

Speaker B: No better way to learn. It really is, it's free to learn.

Speaker C: There's nothing like the, you know, kind of having to show up for students and know a little bit more than they do. That is a powerful, uh, motivator. So we, we started a course and I had a lot of people from the outside world come in and that turned into a sabbatical for me. And that then turned into the first company that we started, which was Cobalt at flagship Pioneering and then merge into Sauna. And that was a delivery play and that was using a viral vector. And I think that having thought about nucleic acid delivery for many years, as a basic scientist, it was just a natural way to say the tools that we use for basic science should Be the tools of therapy.

Speaker B: Yeah.

Speaker C: And when I left uh, Mirai in 2024, I was, or no, I left Sana in 2024. I found that that itch for in vivo delivery was just something that I had not. And we knew that lipid nanoparticles weren't fantastic part of this. And there were things that you could do with lipid nanoparticles that you couldn't do with viral vectors. And so came back to flagship. The Mirai had been going for a little while already and I came in and we really tried to reinforce this idea of the in vivo delivery, the ML and try to really bring that to the table so that we could bring all the tool, all the modern tools to The NVivo and LNPs are different to enviro vectors. But I think in many ways there, I always like to think of it as the Armamentarium. If you're Batman, you have a utility belt. Right. You have lots of tools. And so the goal is to have as many tools as possible. And I think lipid nanoparticles have some real benefits that I wanted to be able to try to exploit. And the idea of a focused on a delivery asset only and then partnering with and so enabling many, many therapeutics, I think was kind of a platform within a platform. So the idea that you could make one thing that goes to T cells but serve many people who had different cargoes, that was a very exciting prospect as well. So I think we're at the early stages of that, but we look, I think we're very excited about the data we have and the interest that we've generated.

Speaker B: Yeah, I think the Bill Gates definition of platform, if I've got the history right, which is a little bit more commercial, but it's basically about making the people you work with a lot more money than you make yourself. And that's obviously the case with Excel or Word or something like that. But I think the, and this isn't a comment on the commercial potential, rather the size of the market of if I would imagine the uh, adipocyte work can serve GLP1s and some of the largest markets on Earth and you can help a lot of people deliver a lot more effectively. Likewise, the NVivo Car T is just too big of a sector for anybody to, to do all the work. So being able to enable everybody who's in the space or, or a big chunk of it is, is probably a, uh, a really obvious platform in the, in the truest sense of the word.

Speaker C: Yeah, well, I hadn't heard that definition but yes, that's that the goal is certainly to enable others. And I think maybe in therapeutics we might add one component to that which is, but it from our point of view it also allows us to diversify the, the, the bet as it were. Right. So you have T cells and then the, the active pharmacological ingredient as it were kind of is the nucleic acid. And so we don't know what's going to happen when you get to humans. And there's always a lot of challenges that of biology that we can't model. And so if you now instantiate a large number of people and enable them to be able to put in again from a business perspective now Mirai is placed a lot of bets and again to your point, we're seeing some of that come to us. But of course the goal is to enable partners so that they can be remarkably successful kind of not just in early, not just an NHP study, but clinical evaluation and hopefully commercial success. And we do our best to make sure that that's the piece that we, that we can control.

Speaker B: What if you have a collaboration with a drug developer, what is the nature, uh, if in its best sense what is the nature of that collaboration look like? Because there's probably data they're generating that really helpful to your process and, and vice versa. Like how do you run those, those thread those two processes together?

Speaker C: Yeah, I think it really depends on how they enter. So if we have something that I would call cargo ready, one thing that just take a moment, one thing about being in the flagship ecosystem is access to a lot of different nucleic acid cargoes. So it's uh, not just MRNA anymore. Now every form of RNA is being used in some way to create a therapeutic opportunity and so we've been able to encapsulate many of those. So we tend to call the things where we've got a lot of data, NHP tolerability, a cargo ready vehicle. So there the idea is that a partner will bring already an established cargo and our job is to do an evaluation with them and then hopefully get them enough data to uh, indicate to kind of be have some confidence about nominating a drug candidate. And once that happens then I think part of our role is to ensure that they can take that material form, uh a good therapeutic asset with it, help them with manufacturing by tech, transferring the asset into say a CDMO so that they can make it at scale along the way. What we've done is we've asked them to take a License to our lipid. So that's really a little bit of the kind of the underlying business model of how we would then make money ultimately that we have to stay in business and so keep doing the, to keep making innovations. We want to be able to be able to bring that money back and that's really how we enable the uh, partners on the other end. You can imagine somebody says hey, we really love the tools that you've developed, kind of the machine learning, the ionizable lipids. We want to go somewhere where it doesn't look like you're there, but we think if we work together we'll be able to get there. And that's a very different, that's a much more kind of research activity that hopefully turns into ultimately NHP data, which is kind of some of the most confidence building data that then goes in there. So a longer path. But I think that we can really, we're excited about bringing enabling partners at every road, every part of the, part of the development pipe, the early research pipeline so that they can move on.

Speaker B: Amazing as, as we start to wrap up here. What are the big things that are on the horizon for you in the next couple of years? What are you most focused on that we haven't talked about yet?

Speaker C: So I think one of the exciting things is what we've been uh, deeming kind of what we call trans endothelial delivery is that as you put stuff into the bloodstream through an infusion, I think that it's an exciting opportunity to be able to now take advantage of that trafficking system as it were, the circulatory system, and then be able to exit the highway at the tissues you're interested in. And being able to do that in a manner where we can get to the brain or the muscle so that we can get to cell types of interest. That's I think one of the areas that we're going to see the most excitement and we have these partner meetings where we introduce the technology. I often measure it by the amount of time before somebody says muscle or cns. Right. I think people have really good idea, therapeutic ideas. We just, they're stymied by delivery and our job is to kind of lift that barrier. So we're working on that. And I think that's an area where again I think we have some biological precedent. There are particles that are transported through the endothelium to the back to the parenchyma behind the endothelial cells. And so we just need to take advantage of that and find that so That's a uh, very basic science activity that's ongoing at Mirai. To be able to break open those tissues and uh, those unlocks I think will really enable many, many different kinds of therapeutics.

Speaker B: And the reason you're hearing muscle so much is that skeletal muscle, cardiac muscle, is it both an equal measure? Is it more cardiac?

Speaker C: It's, I think it, I think it's both. Yeah, both. I think in the context of cardiac we see a number of, again it's all kind of backward from an indication, the kinds of indications that you might want to be able to do where you know as something like you want to make VEGF locally so that you can revascularization say post myocardial infarction all the way to a skeletal muscle where you're talking about Duchenne genetic disease diseases where you may want to just complement dystrophin and be able to put it, put that gene back in. So I think there's just what's exciting for me has been for a while is that delivery. I gotta be careful how I say this. I feel like it's future proofed against what comes next. Yes, it's gonna be something like Cas9 that is gonna be equally amazing. We've pulled it from somewhere or some piece of, and it's still gonna be encoded with an RNA or DNA and we can still put it into lipid nanoparticles. So this is I think something where if you get the delivery system right, no matter what innovations happen out in the world in terms of therapeutics, I think we can keep, keep that, keep the access open for people. So uh, I think that that's what delivery is exciting for me is that kind of we can innovate so that enable many different kinds of biology can be delivered.

Speaker B: There's a, with, with viral vectors like aav, there's a pretty well known cargo size limit that, that is very limiting in a lot of these cases. Do you have any analog in lmps or.

Speaker C: Not so much, not so much that we've seen again we've played with some fairly big mRNA so self amplifying mRNA which has been the basis of a recent uh vaccine that is about 12 to 16 KB of MRNA. So you can, you can put that in. So I think we haven't seen a size limit. I think the other big thing that comes of course is immunogenicity with viral vectors. And yes we have a good sense that there are varying views of kind of what immunogenicity is going to look like for LNPs. Particularly now if we put proteins on the outside to target them. But I think we're going to find out very soon some of that human data. And I think we're all doing our best as a field to try to mitigate that by using humanized binders, binders that look like they're of human origin to mitigate some of the immune response. But, yeah, I still think we need all of these in the bucket. And so we need to be able to make sure that we can bring whatever is necessary for any particular tissue. AOVs have been really good at getting to the brain from the. From the vascular space. So that's something that we still can't do. So we wouldn't want to. We don't want to take it off the table, certainly.

Speaker B: Yeah, yeah. I mean, you anticipated one of my questions, which was going to be, how, how does this all shake out? And my sense is that you're going to have some things that naturally lean towards virus, some that naturally lean towards nanoparticles. And also on the modality point. Right. Not everything is going to be gene editing, not everything's going to be rna. Ah, you. Like you said, you have to play for an uncertain future.

Speaker C: Yeah. And I do want to feel like it's not that we're agnostic, but I think you want to be educated about which one is the right choice, just as you said. And we need everybody to kind of work together to do this. And I think the real key is how do we all meet down at the regulatory pace? Because if it is. So if we have so many, uh, ideas about nucleic acid cargoes, we should really be generating a lot of therapeutics. Right. We should be able to get into people to evaluate and resolve some of their diseases, but we're not seeing at the pace that we expect. So kind of that's the other place where we want to see more of this stuff. And so you see, uh, the two possibilities are Covid where you have a pandemic worldwide and then you see baby KJ N of one type and need to figure out how to bridge these so that we can bring that level of scale to even things like rare diseases, so that people can have the access to the technology quickly.

Speaker B: Well, thank you so much. I said at the start of the episode that I've been wanting to have a conversation about opportunities in delivery for a long time. And, and you delivered. So. So thank you. I really appreciate you taking it out.

Speaker C: Yeah. Well, thank you for having me, Patrick. This is a wonderful opportunity. And, uh, yeah. Um, excited about what's going to happen in the field.

Speaker B: Likewise. Thanks so much and everyone, thank you for listening. We will see you next time.

Speaker C: Thank you very much.

Speaker A: Thanks as always for tuning in to the Genetics Podcast. If you enjoyed today's conversation, the best way you can support the show is by sharing it with a friend or colleague who might find it interesting as well. We'd also really appreciate if you could subscribe, rate and review us on Apple Podcasts, Spotify or wherever you listen to podcasts. This helps other people discover the show when they're searching for content on genetics, precision medicine or biotech. We always want to hear from you as well.

Speaker B: If you have feedback or questions or

Speaker A: want to be featured on the show, you can email us@podcastsonogenetics.com you can find me on LinkedIn. Patrick Short, you can find us as well Sonogenetics on LinkedIn or reach out on Instagram at the Genetics Podcast. And finally, a special thank you to the team behind the show who makes this possible. Joy Ismail produces and manages the show and James Pierce from Scott Selective Frequencies for his expert audio engineering. I'm Patrick Short, your host. Thanks again for listening and we'll see you next time on the Genetics Podcast.