Good afternoon and welcome to this panel discussion targeting non-coding RNAs. My name is Keay Nakae, I'm the Director of Research and one of the Senior Healthcare Analysts here at Chardan. Joining me on this panel are executives from our participating companies, Dr. Richard Law, CBO of HAYA; Josh Mandel-Brehm, President and CEO of CAMP4; and Dr. Dominique Verhelle, Co-founder and CEO of Alcove Biosciences. As advances in science and technology, including high-throughput sequencing, have increased our understanding of the epigenome, non-coding RNAs, which are generated from the vast majority of the genome that does not encode proteins, or what used to be referred to as junk or dark matter, are now recognized to play important roles in the regulation of genome organization, gene expression, and protein function.
Non-coding RNAs can further be classified by length into either a short or long non-coding RNA or lncRNA, which are longer than 200 nucleotides. Three years ago at this conference, we held our first panel to talk about non-coding RNA. I'm happy to kind of reconvene with these folks from companies that have been pioneering the development efforts in this area. Josh, let me start with you. At CAMP4, the focus is on non-coding regulatory RNAs, and generally it's understood that gene expression is modulated by transcription factors that bind DNA and other co-activating co-repressor protein molecules. However, it's now been shown that transcription factors also bind to RNA, which play a role in gene expression. Can you describe how these regRNAs are implicated in disease states?
Yeah, absolutely. First and foremost, thanks for inviting me here. It's always a pleasure to come. Maybe just briefly, I'll give a little history. One of our co-founders is Dr. Rick Young out of the Whitehead Institute, who is one of the leaders in transcription. Years back, Rick shifted part of his lab to working on condensate biology, which is, of course, another really interesting area of biology. He came to me, and this was about 2021, and said, "You know, there's an interesting insight that we're discovering in our lab, that being that RNA is a really important part of condensate biology and in turn gene transcription." Rick was laughing because he said, "You know, I've been in this field for 40+ years.
I'm having to teach myself the rules of gene transcription all over again." I think that's probably true for a lot of the companies that are working in this field. Long story short, four or five years later, we are, I would say, pioneering this new area of biology called regulatory RNAs. These are RNAs that arise out of promoters and enhancer regions. The novelty of RNA coming out of those regions is 10-20 years old, but the understanding of how they work is emerging, and we're learning more and more each and every day. What we've discovered, as you alluded to, is that they do form three-dimensional structures, these RNAs. They are interesting in that they're not usually polyadenylated. They are not like microRNAs or classic long non-coding RNAs that act more pleiotropically. These act very locally within the locus that the gene is housed.
They do, in fact, form landing pads for transcription factors, activators, and repressors in this kinetic trap that essentially governs transcription in a very specific way.
Okay, great. I guess as you seek to target these things, you want to do that in a systematic way. Talk about your platform that allows you to identify, validate, and convince you that these are the appropriate targets that are the drivers of disease.
Yeah. We have a systematic approach. First and foremost, it starts with enhancers and delineating the genetic locus within any given cell type. I think lots of us have different terms for it, but obviously a gene within a gene loop is essentially a gene control center. The first thing that we do is that we actually go to ask the question of which enhancers are controlling which genes. We have a paper that'll be coming out at some point this year or next, which essentially shows you that even though a gene may have multiple different enhancers that are acting on it, there's typically one to two that dominate it, that control it. You get specificity through these enhancer interactions.
More so, we can actually use our platform to ask the question of, is the enhancer that we care about acting on multiple different genes or the gene that we want to target? If it's acting on multiple different genes, we tend to deprioritize those. We obviously want specificity. We tend to always look for the enhancer that is most impactful and most specific for the gene that we care about. Our platform allows us to do that at scale. Anytime we "map" a cell, we get all that information. It's in our wet labs. Essentially, we have algorithms that allow us to turn that into effectively an in silico exercise. We can essentially pull that down on a computer screen and tell you which enhancers control which genes.
Then we have some other scientific methods we use that essentially allow us to sequence out the regulatory RNA, if you will, that is coming out of that enhancer that is therefore controlling that gene. We can then talk about that more, but essentially we can optimize around that and have a very specific oligo that can act on it and force expression increases in the gene that we care about.
Yeah. Since you are delivering an ASO drug payload, how well characterized is the regRNA such that you can optimize the design of that ASO sequence?
Yeah. It's interesting. If you're targeting mRNA for degradation, for example, like the classic ASOs we're doing in siRNA, essentially you can tile across and you can find many different regions where you can get degradation. In our particular case, because this is a three-dimensional structure and because there's different landing spots on it, what we've actually learned is there are rules to these things. Essentially, 95% of the regulatory RNA may not, in fact, matter, if you will, from a drugging perspective. In fact, some places you'll get a decrease in gene expression. But we've gotten very good at, one, being able to use our data to essentially say, these are the areas you want to focus on to get optimized gene expression. Two, once we have that, actually, I think this is a good thing because we're trying to buy down risk.
Because we're using standard chemistry, for example, in Spinraza and other approved drugs, we could take advantage of the rules that have already been developed by other companies in terms of specificity, in terms of how you modulate the chemistry, what can help it essentially have better qualities depending on the tissue you want to go to, to effectively be very deliberate about optimization. That doesn't have to be reinvented. The secret sauce for us is more about understanding the nature of the interaction between the oligo and the regRNA and the gene that we're going after.
Okay, great. Your lead program is now focused on SYNGAP1 related disorders. You did have some preclinical data at ASGCP. Can you talk about some of the key findings from the in vitro iPSC data as well as the mouse model?
Yeah. The way we typically go about doing this, which probably my guess is similar for other companies, is we start with cell lines, although we prefer to work on human cell systems. We have that pretty well characterized. Then we go quickly to the patient cells, as you alluded to. One of the things that we did very quickly for SYNGAP1 is we got iPSC-derived patient cells and we're able to test what we had gotten in terms of our platform in the hits in that, and we saw very robust upregulation. Just to take a quick step back, SYNGAP1 is a genetic haploinsufficiency, learning disability, as well as epilepsy. Essentially, there's no approved disease-modifying therapies there. For haploinsufficiencies, we think that's a sweet spot for our technology because we believe 1.5 to 2-fold increases are going to have a meaningful effect for these types of diseases.
We started with the patient cells, had a really nice response there. But of course, what we always find is to really characterize upregulation, you need to do it in an in vivo setting. You're always going to see more in vitro for a variety of reasons, but in vivo is what really matters. We very quickly went to a humanized mouse model that actually represented many phenotypes of the disease. We were very pleasantly surprised to find that across multiple different domains, we could actually reverse these symptoms that I think are typically very hard, which is anxiety and mood, represented by spatial learning and other metrics you'd look at in these humanized mice. We took the human drug, put it against a human regRNA that was embedded in the mouse, and essentially we're able to show that we could reverse the disease.
I think that really caught people's attention because that suggests it can be a disease-modifying approach for a disease that badly needs it.
Okay. Then I know you also have some NHP data. Maybe this is just more confirmatory of what you thought you should see, but it seems like your intrathecal delivery can achieve decent distribution.
Yeah. I'd say two quick things. One is what I think is some of the power of oligos or siRNAs, any nucleic therapy, is that when you're going after a monogenic disease or a disease where you have a very well-validated gene target and you show pharmacodynamic results in a monkey also that are safe, that tends to translate quite favorably in the clinic. More often than not, that works. That's actually a very important model despite the fact it doesn't represent the disease. What we showed in monkeys was, I think, two very important things. One is that we could increase the gene in a statistically significant way in parts of the brain region that we feel are very important from talking to KOLs and from what's known about the disease.
If you look at the pharmacodynamic data, it is like an overlay on top of Spinraza, which is a pretty good drug as well as some of the other well-characterized oligos. That looked good too. Then very importantly, the safety looks like we have a really good therapeutic index for what we plan to achieve from a pharmacodynamic perspective in the clinic.
Okay. Not to overlook 001, which was your first candidate, you advanced into humans where you did generate SAD and MAD data in healthy volunteers. I know you'll present some additional data later this year. What type of data will you present? Then maybe more importantly, what are you learning from that that's going to help you to go forward in a more intelligent manner?
Yeah. Real quickly, I'm making room for my colleagues here. We did a healthy volunteer study targeting a gene called CPS1, which we believe could have a meaningful effect for patients that have urea cycle disorders. There were three things we cared about for this study. One, which I think reads through to our platform of safety. Can you upregulate a gene? Can you put this type of system in a patient or a person and have a clean safety profile? Despite the fact this is GalNAc and SubQ, it's still helpful in that perspective. Along the way, interacting with different regulatory agencies is very useful because this is a new modality in a sense. Yes, it's an oligo, but it's a new mechanism. There's a lot to be learned there. The second is pharmacodynamic data. What are the properties of this?
Does it act like other oligos systemically, for example? The third was, which is a little bit more of, I don't want to say a moonshot, but something where we were hoping we're hopeful that we might get some data was, can we take an assay, this sodium acetate, which is a harmless assay, and in a completely healthy volunteer show that this drug could have some activity, which I'd say is a pretty high hurdle, but there's no downside to trying it because what you really care about is the first two things. If those look good, you're going to go into patients. That's the type of data that we're going to put out there. I can tell you we already feel pretty good about the safety just based on what we know. We already shared some SAD data.
Whether or not we see a result with that assay, I think it'll be important to test this in patients based on the properties we've seen. Then lastly, what I'd say operationally for a company our size is we ran the study in Australia. We're going to be doing more stuff in Australia as well as in the EU. We're opening a site as well. From a regulatory perspective and operational perspective, hugely valuable for SYNGAP and the next programs that are coming behind it. Then I'd also say if you look at the data package for what we have on SYNGAP, it's not only compelling, but it's exactly the type of data package we want to keep replicating for future programs. I think it's a bit above and beyond what we did for urea cycle.
There's just a lot of learning in there in terms of how we test it, where we go, how efficiently. There's lots of nuances on the preclinical side and how we develop our drugs that we've learned a lot from.
Okay, great. Richard, let me switch to you and HAYA. I guess the first question, again, similar to what I asked Josh about your platform, is as you're targeting an lncRNA, how does your platform allow you to do that in a systematic way so that you identify the disease-causing target and can be confident that that's the appropriate thing to go after? Again, especially for you folks, whether it's a cell state that's quiescent or one that's perhaps implicated in a disease state.
Yeah. Josh did a great job kind of introducing some of the physics of how this works. But I think HAYA has the first platform that's truly multimodal in trying to understand the role of these long non-coding RNAs in actual cell programming. Not considering disease at a protein or single gene level, but actually seeing the cell as the unit of disease. What we're able to do with this multimodal platform is decode which of the, say, 200,000 RNAs in a cell are actually long non-coding RNAs responsible for holding a cell in a specific disease state and be very specific to that cell state. Therefore, this is a relatively first in biology approach, but it means that we can design incredibly specific ASOs then.
Taking a first in biology approach, but relatively established chemistry approach, whereby we're also finding and intentionally discovering lncRNA targets that have this very high-level control over the entire transcriptional state of a cell, but do so only in that specific cell state. When we discover these targets, they are not pleiotropically expressed. Therefore, it's much easier for us to control the therapeutic window and delivery.
Okay, great. Your initial target 001 is targeting an lncRNA Wisper. It's a validated target associated with cardiac fibrosis. Describe the role it plays in that when the cell's in this disease state.
Yeah, absolutely. Yeah. Wisper was discovered in the Samir Ounzain's lab many years ago prior to the founding of HAYA and HAYA was founded on the basis of the discovery of Wisper, actually. Kind of like what we've done since is made this systematically and work repeatedly in different cell tissues. But fibrosis is an incredibly important driver of heart failure. Just 5% increase in fibrosis means that you have 50% worse outcomes for patients post-MI or in heart failure conditions. Wisper, it turns out, is a long non-coding RNA responsible for holding myofibroblasts in that fibrotic state. When you knock down Wisper, the cells reprogram back into healthy fibroblasts. Fibroblasts are roughly 25% of the cell content of the heart.
Not only can we predict this in vitro, but we've now seen this in vivo that we can truly control this cellular reprogramming and the reversal of the fibrotic state.
Okay. I know you've done some extensive preclinical work here. Maybe highlight some of the key findings there and especially in your pig model.
Yeah. The platform is multimodal in that there are multiple assays required in order to try to understand exactly what's happening at a transcriptional level that's controlled by these lncRNAs. We can now predict just straight out of the functional genomics which lnc RNAs are causal in terms of cellular reprogramming. We then obviously see that in vitro and we prove it with in vitro proof of concept with tool ASOs that we design. Kind of as Josh was saying, you find that there are very specific, what we refer to as hotspots when you're designing these ASOs that can control the knockdown. Then yes, we've then seen for HTX-001, our lead candidate in heart failure, we've seen this effect in vivo, both in mice with functional equivalents and in pig models as well.
Maybe highlight the idea that you've got this excited state. In your case, you're delivering a gapmer ASO, let's call it naked. But because of the specificity to the excited state, the amount you need to get to the heart as opposed to what's going to end up in the liver, let's say, is fairly minimal based on what you've seen.
Yeah. This is kind of an advantage of the lncRNA target space that it exerts its mechanism, probably due to these condensates that Josh was talking about, with incredibly low copy number of the target. It is incredibly cell state specific, or at least that is what we are determining for the targets that we go after. What that means is that yes, a naked [goat] mode gapmer will get to the target in the myofibroblasts in the heart at very low dose. In our pig study, 1 to 5 mg/kg dosing once every two or three weeks will enable on-target activity, reduction in fibrosis, increase in ejection fraction in the heart. We don't require any specific targeting to that tissue to enable that.
Okay, great. As you're moving forward towards your first in human study, beyond evaluating the safety, how will you design for maybe the types of disease patients to target? Again, we think of fibrosis being a real problem post-MI. Maybe talk about who's an ideal patient to evaluate first.
We are going into non-obstructive hypertrophic cardiomyopathy. This is a wide-ranging heart condition where fibrosis is a major driver. We can determine the level of fibrosis and we will select that based on MRI and on NT-proBNP as a biomarker. Then that will be starting in healthy volunteers. We will start our phase I beginning of next year. That will extend into a phase I-B in patients and open-label extension. Then in the future, we'll look to go into heart failure with preserved ejection fraction, which is really the big heart failure indication.
Okay. Well, great. Dominique, let me pull you into the discussion here. At NextRNA, you were also focused on targeting lncRNA. However, you're going to do so with a small molecule. However, you did a lot of the same discovery work that these folks did. First of all, tell us about the platform you had. Again, I just keep repeating this theme. How did you identify the appropriate target that gave you confidence that this was the right thing to target because of its implication as the driver of disease with that platform?
Sure. NextRNA was created and launched almost five years ago. The mission here was to identify novel lncRNA for oncology. We were using, and I say were because the company wound down the operation this summer. NextRNA does not exist anymore, at least doesn't have operation anymore. But the objective was really we develop a big computational capability like many of my colleagues here, where we were identifying lncRNA that were expressed uniquely into the cancer that we were interested in. The way we developed the platform was first part was identifying lncRNA with patient sample, then identifying which protein is important for the function of the lncRNA. We discussed here about transcription factors. Many transcription factors are actually responsible for the function of the lncRNA by interacting. But also there is an example of epigenetic complex that are also very important to regulate gene.
Our approach was to identify which protein is important for the function of the lncRNA. Then the modality that we were using to inhibit the function of the lncRNA was developing a small molecule. The small molecule was about inhibiting the interaction between the lncRNA and the protein that is responsible for the function of the lncRNA. We had developed capability where we developed small molecule binding the lncRNA at the site of the interaction or small molecule binding the RNA binding protein.
Beyond the fact that you were developing the payload as a small molecule and all of the factors involved with that, from your perspective and your experience, what are still some of the biggest challenges in targeting a non-coding RNA?
I think I would start by saying that I am convinced that lncRNA are driving disease. At least we show it in oncology. Globally, we have one program, actually two programs that we developed a collaboration with Bayer. These two programs have been moved into the hands of Bayer. The two programs that we did were in two different oncology indications and we validate deeply the role of the lncRNA in the disease. We also validate lncRNA in neuroscience. We identify three very interesting lncRNA for ALS when we were using some iPSC cells. I would say I am convinced after spending five years in this company and also working with other people in the field of lncRNA that they are drivers. However, drugging them from my perspective, it's still very difficult.
We encounter, and I would say what was the most challenge is time and money. It's always the same when you are in very innovative space. It always takes longer than what you anticipate and it always costs more money. But let's say for the lncRNA, we were focusing on lncRNA exclusively expressed in the cancer that we were interested in patients. However, there were a lot of different isoforms that we have to identify. The main objective was which isoform is a disease isoform because this is the one that we want to develop a drug against. Just identifying isoform, we had to have a lot of the sequencing capability, even if we were outsourcing. It's not just normal sequencing. We have to have the long read sequencing where we have the sequencing of all the isoforms. Some of these isoforms were pretty long.
Then the other part is the ASO. My colleague here, they are developing ASO for medicine. We were using ASO to validate the lncRNA. I would say even if you don't develop the ASO as a medicine, you have to really understand how to design your ASO to be able to show that the lncRNA is driving disease. This is what I'm hearing from you and I'm glad to hear. Depending which ASO you are using, you see very different effects. This is a little bit confusing because you expect that if an ASO degrades a lncRNA, then you should have the same kind of effect. That was something that we took a long time to develop ASO and we had to start to develop ourselves the ASO because not just having a company that are designing an ASO based on the sequence was sufficient for us.
That was one of the challenges that we encountered. This is why it took longer time. But I would say the program that we have in the hands of Bayer, we trust and believe the lncRNA.
Okay. What we have is this very interesting technology. We've seen interest in big pharma, Bayer in your case, Lilly in your case. But Josh, you're now the CEO of a public company. You know all about cash runway and trying to get that next financing in the door to keep the ball moving down the field. Talk to me about focus in strategy. Let me start with you, Josh. I know you again switched your lead to SYNGAP. That's part of your strategy. But tell me, how are you implementing your strategy going forward, especially with the additional pressure of being a public company in an emerging space?
I'm laughing because my CFO is sitting out there and is probably like, does he know about that? Careful. I guess a comment and then an answer. I think that the history of platforms, if you kind of look at them, watch them, as you alluded to this a bit, they take time and they take money. I think that's because it's a lot of different reasons, but it takes time to understand how your technology works and where it works best. Unfortunately, you also have to probably test things beyond just in vivo models and in the clinic to learn as well. That also means markets need to cooperate or business development needs to be in the equation. I think Alnylam failed its first three programs in the clinic. I forgot cancer and RSV and another.
Huntington's.
Yeah. That's obviously not to diss on them. It's just to say that there's a lot of companies that can rattle off like that. At CAMP4, we have been around a long time and there has been a lot of money come in. I think in some ways we've been deliberately loose about trying to understand where our technology works best while we understand our technology and find the right indications. We thought UCD was a pretty clever approach and we still like it. But we found our way to the CNS, which has a multitude of haploinsufficient disorders where we believe we can achieve expression through intrathecal delivery, which is highly amenable to these very bad diseases and getting to the regions of the brain that matter, not the deep brain regions. We also think that SYNGAP1 is one of these rare diseases.
Having been in rare diseases for 20 plus years, every five or so years a rare disease comes along and it's met with, I've never heard of that. Fast forward, it becomes the next cystic fibrosis, DMD, what have you. When I went to Biogen from Genzyme, nobody at Biogen had heard of SMA except for three people, the people who had in-licensed it. That's true. Now it's a very important disease. I remember TTR back in the day. I think SYNGAP1 is going to be like that. To answer your question, we just did a financing and we repositioned around SYNGAP1 because the data is so compelling we can't not move that forward given the unmet need and the opportunity.
We think it's not only a big commercial opportunity, but a strong read-through of the platform for other diseases that are going to follow that that make a lot of sense. The agreement we have with our investors right now is you should expect from CAMP4 laser focus on execution there. I think all of our investors that are in the stock want us to maintain a platform and a pipeline. But I think these are short-term, medium-term, long-term decisions. In the nearer term, the majority of our capital is going to get put to work on SYNGAP and making sure that gets done the way it needs to get done because that's going to lower our cost of capital. That's going to, I think, validate our platform. Along the way, you should expect to see us do more business development. Stay tuned for that.
We're getting a lot of really good discussions and a lot of action happening there. We are doing some early stage stuff, but we're being very quiet and thoughtful about that because again, it's not just about money and resources. It's about focus. That's something that I think investors, I would say, want to see that you have to be a platform company with a product, not the other way around in this type of market. That's how we think of ourselves. We think we have a platform to make products, so we got to show the product works. That is our strategic prerogative right now. We have the capital to do that. We're managing it. I'm looking at my CFO very, very closely. We're not spending money where we don't need to. We're going to make sure that we execute on that and use BD dollars prudently.
Okay, great. Richard, let's switch to you folks and your strategy. Maybe first talk about the partnership with Lilly, just kind of the basic parameters of that to start.
Yeah. I'll just say upfront, like I a million percent agree with what Josh just said. It's really about focus and how you balance asset versus platform. HAYA, I think, have done a brilliant job in creating a platform that is efficient and repeatable. That's why we now have multiple development candidates. But I think they had the advantage that when they were a seed funded company that was led by Broadview Ventures who were very heart failure focused and made them focus on the lead asset. Then the platform became repeatable in the background. Therefore now, given that it's so repeatable, it's a huge opportunity for business development. That's why we signed the deal with Lilly about a year ago. That is going fantastically well. Unfortunately, I can't really say too much about it. The collaboration is built around obesity. Obesity is not a cell state.
You have to imagine which cell states are involved in that. But if you did, you'd probably be right. But what we found is, and what we've now done a couple of times, is that because this platform that is multimodal in terms of assays and then both completely decoding what the transcriptomic state of cells is and how we then design ASOs against it, you're talking about billions of data points in these atlases. Therefore, we absolutely require AI/ML to understand that and be able to design exactly the right ASO. But we're now finding we can go from completely novel tissue to targets which we're designing ASOs against in about nine months.
Then we can design ASOs that are super selective and safe in probably another 12 months and therefore be in the clinic in less than two and a half years on tissue types that we've never seen before. This is a fantastic opportunity for the platform to build out the value of HAYA beyond HTX-001.
With Lilly, beyond the upfront, what other benefits are you deriving as a company from their knowledge, their vast amount of knowledge?
Yeah. This is weird to say, but I can't speak too highly of them as a company, like how they've engaged with us in terms of trying to understand this exciting novel space and actually how they've really bought into it in terms of teaching us about a therapeutic area that we knew nothing about when we started this. Then also buying into it in terms of helping us with resources on the project. Honestly, obviously, well, not obviously, the HAYA platform is delivering in this collaboration. But actually, the way that Lilly are collaborating with us is a huge part of what we are getting out of it. They are enabling the success of it for themselves.
Okay. Then as you move beyond Wisper and again with your platform, you can identify attractive targets. But as far as a disease opportunity, what are some of the key considerations that it has to have for you to want to move forward with that opportunity and dedicate your precious resources towards that effort?
We actually have a really, now that we realize how repeatable this is, we've established quite a rigorous and systematic process internally for deciding what are we going to work on. Now, obviously, unmet medical need is part of that. We are essentially going after chronic diseases of aging where you get this, what Altos have called mesenchymal drift. Essentially, as we age, our tissue cell types become less specific versus what they're supposed to be. We now have a development. We're about to nominate development candidates in our pulmonary programs, in our oncology programs. We're always looking for, can we isolate and understand how those cell types differ so that we can then find the lncRNA that's responsible for locking those cells in that disease state and reprogram them.
Okay. Dominique, you've heard both of them talk about their strategy. Based on your experience, what other words of advice could you offer to them as they move forward?
Validate deeply your target. I mean, sometimes we want to go fast to the next stage and then mainly use seems to be something more validated. But in the case of HAYA, right, it's like you discover this novel lncRNA and they are changing and it changed the fate of the cell. It's like they are affecting several different mechanisms. I think we have a tendency to try to go quick, quick, quick and then get to the drug. But then you get to the drug and then the drug doesn't do what it's supposed to do. Then you have to come back to the beginning. It's like I would say that's probably my advice.
Okay, great.
That seems to be a little bit academic work. But it is when you are developing and have a platform of novel target, you have to do it.
Dominique, you're now moving forward with a new company, Alcove. Maybe briefly tell us about what you're trying to do there.
Yeah. Alcove Biosciences, it's a new company that I co-founded a few months ago. It's coming from the work that we've done at NextRNA. At the end, I would say the last year, we were seeing the challenge that we were having to develop small molecule targeting RNA, which we are not the only one. We were more looking at and see small molecule targeting RNA binding protein. We had done some initial work actually in the kidney disease field in ADPKD and we had shown some very interesting data when you use small molecule modifying one of the RNA binding protein that I'm not disclosing. When we wind down the company, I actually work with the board of NextRNA to transfer the data to Alcove Biosciences. The objective of Alcove Biosciences, it's very simple compared to what we were doing at NextRNA.
But I feel this is what investor want right now, which is an asset-centric company close to clinic with some kind of validation out there. It's still a very new and exciting protein, but it's not like new like a lncRNA. I would say when I am talking about it, I feel like it's a simpler story than NextRNA. I am currently fundraising, so let's see if it goes. But it's exciting and I am excited about the initial data that we have. Let's see what happens.
Okay. Very good. Well, I think we've reached the end of our time here. I want to thank all of you for joining us today.
Thank you.
Thank you.