Welcome to Circio. We are live from our facilities in Stockholm today. My name is Erik Digman Wiklund. I'm the CEO at Circio, and together with me today, I have our Head of Research, Dr. Thomas Hansen. Our plan is to go through with you an update on our R&D strategy, as well as data we have generated to date, and the plans going forward. The presenters you will be meeting are myself and Thomas, and for those of you who don't know, we have a history together. We did our PhDs together back in the early 2000s in a lab in Denmark, and during this period, we actually co-discovered circular RNA and published early work in the circular RNA space. It's great to be back together in Circio and continue to develop novel circRNA therapeutics. Circular RNA is emerging as probably the most promising class of novel RNA therapeutics, following on the success of mRNAs. The reason why there is so much buzz around circular RNA is that circular RNAs are more stable, they are more durable than mRNA, and also they are less immunogenic. This durability and immunogenicity issues have been what's held back mRNA development for a long time. The core problem of mRNA is that they get degraded by exonucleases. These are enzymes inside of the cell that eat up RNAs. You can see it drawn here on the cartoon by these small top hats. They eat the RNA from the end. A circular RNA lacks a free end, so there's nothing for these exonucleases to latch onto, and therefore, the circRNA is resistant to the standard way of RNA degradation inside of the cell. And this means the durability is vastly enhanced. So you get this advantage of an improved durability. With that, you can get more durable protein expression. Also, you can engineer circular RNAs to be more efficient at protein expression than mRNA, so you get not only more durable expression, but higher expression. And this is expected to be a very significant advantage, and therefore, many people in the field believe that over time, circular RNA will replace linear mRNA as the preferred format for long RNA therapeutics. At Circio, we're taking a different approach. When we started this work, Thomas and I sat down and discussed how we could best approach the field of circular RNA. The other players in this space are all doing effectively the same as what BioNTech and Moderna did with mRNA. They're making a synthetic RNA in the lab. They package these in what are called LNPs, lipid nanoparticles, and then they deliver the LNP packaged RNAs for vaccines or for delivery to the liver to express a protein. What we observed is that no one had really taken the approach of making a vector-based expression of circular RNA. So what we are doing when we're doing it this way, is we're building DNA constructs that you can bring into the cell, and then they carry the recipe for the cell to generate their own circular RNAs. Our product is this DNA-based system, can be a DNA or a virus that transports the DNA, carrying the instructions for circRNA biogenesis inside of the cell. Then once this is delivered to the patient, you will get circRNA production and then subsequent protein expression. These are several advantages, and I'll try to explain that on this schematic here. On this slide, now, we're looking at a cell, and then inside of the cell, there is a nucleus, and the nucleus is where the DNA is. When you use the synthetic RNA approach, either it's circular RNA or mRNA, these are LNP packaged. The LNP packaged RNAs, they enter the cell by what's called endocytosis, and then through these endosomes, they get into the cell. However, this process is very inefficient, probably less than 5%. I think the estimates now are 2%-3% are actually released into the cell. More than 95% of the RNA that gets absorbed this way gets degraded. So it's a really inefficient way of getting your circRNA into the cell, and then these copies that are there will then make proteins. Now, the advantage of taking our approach is that we come in with our, what we call circVec, which is our DNA technology, either in the form of a virus or a DNA. This gets transported into the cell, then it makes its way to the nucleus. And once it's in the nucleus, you then have a stable, DNA presence carrying the instructions for circRNA biogenesis, and one copy will make many more copies of RNA. So from just having a single copy inside of the cell, you'll make a bunch of circular RNAs that then will emanate from the nucleus. They'll be natural to the cell, and they get exported into the cytoplasm, where they can make protein. The big advantage is, doing it this way, we can get a higher concentration inside of the cell. We omit this problem of a poor release of the circRNA, which you get with the LNP packaged circRNAs. And also, it's been shown that intracellularly generated circRNAs are even more durable. So the exogenous circRNAs with on the left side typically have a half-life that is 3-5x better than mRNA. With our intracellularly generated circRNAs, that half-life in our hands is about 15x better, and that's because it's produced inside of the cell, it gets certain chemical marks, and this improves the stability. We think our system is probably going to be generating more stable and durable circRNAs. This will translate into higher protein expression and more durable protein expression. Here, we've done a bioinformatic simulation based on our data to date, showing the comparison of a vector expressing mRNA versus a vector expressing a circRNA. The blue line is mRNA, and the red line is circRNA. If you come in with a vector to the cell, you can see with the mRNA, it's produced relatively fast, but then it peaks early, and then it will start dropping because the mRNAs are less durable. The circRNA takes a little bit longer to generate. It's a more complicated process of biogenesis, but then once it starts building up, it's more durable, and you reach higher steady-state levels. So it means you can get your higher levels, more durable levels, and this gives you what we say, larger area under the curve. The total amount of the protein you make is much higher. This is looking at the RNA level. On top of this, you can make circRNAs better at protein expression. We achieve up to five times better protein expression from our circRNA versus an mRNA, and this means you add that on top of this, you have now a system that can give you ten, maybe fifteen times better protein expression, that also is more durable versus mRNA. And this is potentially a massive advantage for any DNA-based or virus-based therapeutic in the future. The next question is: what type of vector do you use? Well, we take two approaches. We are looking at viral vectors, and we're looking at synthetic DNA vectors. If we start with a virus approach, viruses have evolved to be very efficient at delivering DNA into the nucleus of a cell. It's a natural way of getting your DNA into the cell. It's maybe the simplest, lowest technical hurdle approach to get our circVec, circRNA instructions into the cell nucleus. We are focusing on two different virus types. AAV, this is a type of virus that is typically used for gene therapy. It has advantages in terms of being very low, have low immunogenicity, and it's very durable, so it's good for expressing a protein over a long period of time. The other approach is adenovirus. This comes from our ONCOS program, for those who remember that product. Here we can adapt our ONCOS virus to make circular RNA. Adenovirus has certain different features. It's much more immunogenic, so this is suitable for vaccines, and it can also be used in cancer. So these are the two viruses we are developing, carrying then our circVec insert to make circRNA. The disadvantage of using viruses is that because they are immunogenic, the patient gets an immune response to the virus over time, and it makes repeat dosing difficult. So if you want to do gene therapy, most gene therapies today that are AAV-based can only be dosed once. So this is a drawback of gene therapy today. Synthetic DNA has the potential to solve this problem. If you can use a synthetic DNA, you can potentially avoid this immune response, and you can redose. So going in this angle, you have something that is simpler, it's simpler to manufacture, and it can be repeat dosed, which would be a great advantage over circular over virally delivered gene therapy. Now, the drawback with synthetic DNA is that cells have defense mechanisms to prevent this from happening, to sense foreign DNA and break it down. And therefore, you need a delivery tool to get your synthetic DNA into the nucleus of the cell and not just being degraded. And this is still a challenge in the field. So I think most people believe that in, say, the next 10 or 20 years, synthetic DNA will be the format that is preferred for gene therapy. Still, the chemistry and technology to get it effectively delivered in patients remains to be solved. Therefore, we believe it's wise to pursue both avenues. Virus, technically more straightforward, well-established, manufacturing is there. Second step, synthetic DNA. Technically more challenging, higher risk, but also higher reward and what's expected to be the future. We announced yesterday a collaboration with a Korean company called NeoRegen, and this is precisely to enable us to do this delivery of synthetic DNA vectors. So we're working on two different formats of novel DNA vectors. We plan to deploy these for gene therapy and vaccines, but we need some chemistry to get them efficiently delivered into the cell, and this is what NeoRegen offers. So this is a simple research arrangement to begin with. NeoRegen will adapt their peptides that they are developing to match with our circVec synthetic DNA vectors. We will then combine the two, and then in our labs, we're going to test whether this can be used to efficiently transfer DNA circVec into the cell nucleus. So at this point, this mutual 50/50 type preclinical collaboration, and it gives us access to an important potential technology to deliver these synthetic DNA vectors in the future. How are we gonna deploy this? Well, the current priorities we are looking at is rare disease and vaccines. So rare disease are genetic disorders where you have a missing protein, and you can use gene therapy to replace that protein. And we see this as a major long-term potential of our technology. The second avenue we're pursuing is vaccines. So we're using our adenovirus to begin with to generate what we believe can be a single dose vaccine format. If this works, we will have a platform that is highly potent and has a big advantage in that it doesn't need repeat dosing. As you recall, we have also a cancer program, and this maybe is what we've pursued most actively in the past. We still have several ideas we are working on in the oncology space, but for now, this is being deprioritized and the lead projects internally are in the rare disease and vaccine space. First step in rare disease gene therapy is to show that we can improve on existing AAV gene therapy. So I mentioned before, AAV is a vector. AAV is the standard vector for gene therapy today. Almost all of the approved gene therapies are AAV-based. The The disadvantage of AAV is that you need to give really high doses. These high doses, they lead to toxicity for the patient, which is a major challenge, and also they really drive up cost. You may have heard some of these gene therapies come at a very high price tag, several million dollars in some cases. We anticipate that by switching from mRNA-based AAV expression to circRNA-based AAV expression, as I showed you before, we can drive a 10X-15X improved protein expression, and it will be even more durable. If this actually works, it would mean that you can reduce the dosing of AAV-based therapy simply from switching to circRNA instead of mRNA-based expression cassettes. If this can be demonstrated, you could drive much improved safety, you can reduce the dosing, and you can reduce the cost. This would be massive advantages in AAV gene therapy, and we think this is the quickest route to demonstrate a proof of concept and an advantage of our approach. If we can show it works in this setting, I think it's likely that anyone doing any DNA-based therapeutic are going to want to switch to a circRNA-based expression system rather than mRNA in the future, simply due to this superiority. Where can we deploy this? Well, we've done an extensive in-house screen looking for suitable genetic disorders where we think our technology fits. We've had several input criteria for this filtering process, such as which tissues do the disease manifest itself? Is the protein suitable for expression with our technology? How many patients have the disease? What are the symptoms, et cetera, and how large is the gene that you need to deliver? And also the competitive situation. So after this analysis, we now arrived at a short list of 6 candidates that we are exploring in more detail, and we've picked the top candidates. The lead disease that we're planning to develop a circVec candidate for is called alpha anti one, alpha-1 antitrypsin deficiency, or AATD for short. In this disease, the AAT protein is missing. This is a protein that's expressed in the liver and secreted into the bloodstream. And AAT is primarily active in the lung. If you lack functional AAT, you get inflammation and lung symptoms. Over time, these can be very serious. You get emphysema, developing chronic bronchitis, permanent inflammation in the lung. In parallel, you get problems in the liver. The mutant form of the protein accumulates in the liver, causes protein deposits to form, and over time, these are toxic, can cause cirrhosis, eventually cancer. You have then a disease here where both the liver and lung is affected, and you have a problem both with a missing functional protein as well as accumulation of the mutant form. This is something we can uniquely solve with our approach, because our circVec approach allows us both to remove the toxic protein as well as replace the functional protein. Thomas will come back to this in his part of the presentation. No one else is doing this or has successfully done that so far. So we can potentially develop the first product to do what we call remove and replace. Remove the mutant form, replace the functional form using our circVec constructs. Currently, there is nothing approved for dealing with the liver-associated AATD, and there are only really inefficient technologies for the lung problem. So this is an area of high unmet medical need where we think circVec can provide a massive advantage for patients. Vaccines is the second program, and here we're building on our adeno experience. We're making a first generation of non-replicating adenoviral vectors that then have our circVec inserts to make circRNA, and these circRNAs will drive expression of the antigen that you're vaccinating against, as well as additional immune response boosters. This is the first generation that's been in the first in vivo experiments, and we're already at the process of developing second generation pro-products. The aim for vaccine concept is to build a preclinical proof of concept and then out-license to a partner before entering the clinic. Rare disease, we're probably planning to take forward ourselves in the to towards the clinic. You can see rare disease, in-house vaccines, preclinical proof of concept, aim to do a partnering deal. Hopefully, we can achieve that next year. We have also formed a collaboration in the vaccine space. In this case, it's with an academic lab at Washington University. This is a lab that is one of the world leaders in Adenovirus developments. It's led by Professor David Curiel, and what we're doing here is developing a novel adenovirus-based concept for flu vaccination. This work will largely be done in the lab in Washington University, and it's about to start right now. So this means we will get an active flu program going in the near future, managed externally, so that also means we don't spend as much resources ourselves internally. So with that, I hand over to Thomas to take you through some of the data that we have generated so far. Good morning, and thanks for watching, and thank you, Erik. I will try to brief you on the R&D development here at Circio. I will skip to this slide. Of course, I started, did my PhD with Erik on circRNA biology. Been working on this topic for a decade now and, you know, have a- ... a great deal of expertise, at least when it comes to the naturally occurring circRNA. So I could talk about this at length, but I'll try to be somewhat brief. So as you can see at the top here, this is Molecular Biology 101. You have the conventional gene expression. So this is basically a piece of DNA from the genome. This contains exons and introns, and if you splice these exons together, this will produce the mRNA. That's the conventional way of gene expression. Occasionally, at certain positions in our genome, we have these naturally forming circRNA by a process called back splicing. So this has been the object of but most of my academic research. But what is very interesting, at least for a subset of these circRNAs, is that so these critical elements just flanking the exon that's shown here, that's required, and sufficient for this back splicing. So that you can utilize to build our circVec cassette. So the first approach we basically did was to try to get a good overview of what's naturally occurring in our, you know, the human cells. So taking advantage of a lot of deep sequencing datasets, you can profile circRNA expression in tissues and cell lines. And what you can get here in this plot is basically circRNA expression and the corresponding sort of host gene mRNA expression. And from that, you can sort of derive high expressed circRNA that seems to have a very high efficient biogenesis rate as well. So from that, we basically, in addition, try to identify these critical features in the flanking regions. So if you have a high expressed circRNA that comes along with these elements in the flanking regions that enables the back splicing process, we could basically take out natural regions of our genome, put it into our little expression cassette, and see whether we get circRNA expression. So that you'll see here, we tested a small subset of highly expressed, naturally occurring circRNA, how do they perform in a sort of a vector system? And at least one of them performed, or outperformed all the other ones consistently. This is the one we refer to as L1, so that's basically a naturally occurring sequence that we build our circVec cassette upon. You can see here, it's much better than all the other ones we tested. We believe we have a starting point here based on the best performing, naturally occurring circRNA taken from the human genome. On the next slide here, we then build upon that and have been conducting extensive research on how to optimize this sequence to enable, first, better biogenesis, more effective biogenesis. This is what you see up here. We have done extensive rational design, optimization, development, and this L1 is the one from before. This is a naturally occurring sequence, but by doing different approaches, you can get up to almost 10x improvement in terms of biogenesis. Other optimization features has not worked as well as you can see here, but at least we have a setup that consistently outcompetes or outperforms the natural sequence. So we can do slightly better than nature by doing some rational design optimization steps. And maybe what's even more critical in terms of designing your circRNA specifically for protein expression, we actually developed and realized at an early time point that how you design this protein coding cassette is critical for protein yield. So this is a somewhat systematic screen we've done, where we did different design rules. As you can see, there's a subset of designs here from D2- D7 that works quite effectively. Here, D4 is the most effective design, where in contrast, there's other design rules that are completely blank in terms of protein expression. So if you have intensity here, that correlates with protein levels. So I think that's what we filed some of our early IP on, basically, what how to design your circRNA to enable high-yield protein expression, and that seems to be extremely critical to achieve the protein yield that we are getting from our circVec system. So that was a very, you know, a hallmark, a key, key data point going forward. So this is sort of the design that goes into what we are calling the circVec 1.0 cassette. And as you can see, when we try to compare what is the protein yield then for this, first-generation cassette compared to a conventional vector expressing an mRNA-based cassette. And there you can actually see we get higher protein yield, with the 1.0 system here. So we are already... And this was at an early time point, and maybe to me, it's slightly surprising, but we're already, on par or outcompeting the conventional mRNA-based vector designs. Then what Erik also mentioned, the intrinsic benefit of using circRNA is basically the longevity of the RNA molecule. It's extremely stable. We have, Actually, it's stable to the extent that we have had technical issues measuring the half-life, but we've come up with an approach that actually gives us the half-life estimates, which in this case, comes up to around 130 hours for the circRNA, and that's in contrast to a 9-hour half-life for mRNA. So I think that's pretty much aligns with what's in the literature, and we see then a 15x improvement in terms of half-life. So that also goes into that simulation we showed you previously, and that of course translates to not only longer expression, but also accumulation of RNA over time, so it will translate into higher protein yield. And the data that we have so far is actually, if you do a relatively short time course experiment shown here, you have circRNA levels at the early time point, 48 hours after you introduce the vector system into cells. Lower circRNA expression compared to mRNA, due to the fact that the biogenesis is slightly more complex, so it's a little slower, but this is also being optimized as we speak. But then the critical point here is if we move forward to 96 hours, the RNA molecule accumulates due to this long half-life, the mRNA declines. So in the long-term process, long-term window, circRNA will prevail. So this we see consistently. What's also interesting, and this Erik also mentioned, for the protein yield, so this is RNA we measure here, protein being measured. Despite the fact that we have lower circRNA levels at 48 hours here, we actually see, actually, higher protein yield. You can actually, I'm sorry, we can actually design the circRNA to, on the translational rate, outcompete mRNA translation due to these IRES elements that we insert in the circRNA cassette. That seems to also accumulate over time. The mRNA declines here, we have a protein stability in addition to RNA stability. Here, there'll be some delay before we see this level of delta between circRNA and mRNA. At least this is a consistent expression profile over time that we observe from our circVec system. Here's another sort of time course experiment where we also look at the ratio between circRNA and mRNA over time. This goes to 144 hours, so this is six days after we introduce the vectors. Again, at the very early time point here, one day after we introduce the vectors, it's a very slow, or not a very slow, it's a slow process for circRNA biogenesis, so it's below mRNA, but already after 48 hours in this experiment, we see on par expression comparing mRNA and circRNA, and then it goes to above, twice the yield using the circVec design. This is again, just to emphasize that this is circVec 1.0 first generation cassettes. This actually fits nicely with the simulation and what we would expect, because you would expect sort of a logistic curve, which is roughly what we get. So this will probably go asymptotically towards between 2X or 3 X compared to mRNA in this system. So as we mentioned, we are developing this. One thing is the vaccine track that we are working on, and for proof of concept, we have developed a non-replicating adenovirus vector expressing the spike antigen that we all know from the COVID pandemic. Again, we see from the RNA level, mRNA-based expression is very high at the early time point. It drops circRNA expression a little low by the early time point, but increases dramatically. We see roughly the same from the protein levels, low levels at the circRNA after 48 hours, but then it accumulates quite dramatically and outcompetes the mRNA at 96 hours. Yeah, this is, again, the first generation adenovirus vector system that we are using, and this will be introduced into an in vivo model very, very soon. We are very excited to see how that will translate in terms of immunogenicity. I'll come back to that. Finally, as in research, you consistently optimize your system. We do the same. This is, of course, a never-ending process, but luckily, it's going in the right direction. We started off here, some very early data that we called 0.1 version. That's sort of the pre-generation circVec, didn't perform very well. The 0.0 that I've been showing you so far, all the data so far is based on this cassette design. So this is a level of expression you get sort of relative, so let's just put to one in this bar chart here. And we've done a series of incremental improvements on that vector system. So now we recently, you know, sort of established the next paradigm for our cassette, which we call circVec 2.0, which roughly has a 10X, up to a 10X increase in yield compared to the vector system that we have been working on so far. So there's a... Already we've come a long way. We have this vector system that we're very excited about to test in vivo as well. I think that looks very promising, but going forward, we will of course likely optimize this even further, getting a 3.0 at some point, hopefully with a similar x improvement. For the in vivo data, we have been working hard to generate this data package. We have been... Maybe the data so far has been a little technically variable, so we have not been able to reach any sort of solid conclusions when we benchmark to an mRNA-based cassette. What we at least have been able to conclude is that based on this 1.0 cassette, we can get circRNA biogenesis, we can get protein expression in a mouse model. That's established. We can also do intratumoral injection of a replicating adenovirus vector that expresses a circRNA, and we can also detect the circRNA expression, the protein expression from that circRNA in the tumor. We have also done a preliminary circVec, a vaccine experiment where we also got positive immunogenicity confirmed. But this is, as mentioned down here, further optimization is unfortunately required. We learn a lot from these early experiments, and of course, from a scientific and ethical point of view, we wanna set up the best possible design going forward in vivo. But there's a lot of planned experiments, for the time being, so there will be critical readouts here towards the end of the year on all three, or at least on the biogenesis and protein expression of reporter genes and on the vaccine track here. We'll generate more in vivo data. So that sort of concludes the section on these highlights in terms of the R&D packets that we have, specifically also for the rare disease or alpha-1 antitrypsin. I think we have a very, very unique opportunity here with our circVec platform. As Erik mentioned, we have a remove and replace option for AATD. It has these two manifestations. Due to lack of AAT expression, you have a lung manifestation, and due to this toxic accumulation of the mutant form-... You have a liver toxicity. So what we can basically do, what this platform allows us to do is to, first of all, obviously use a circRNA to get high and durable expression of the functional AAT protein, which likely could solve or treat the lung manifestation. In addition, we can design our vector that will actually allow us to remove specifically the mutant variant of this AAT, and that will then alleviate this liver toxicity. So that's why we call this remove and replace. We replace with the functional protein, and we remove the abnormal protein. So we hit two birds with one stone, basically, and treating the lung and the liver manifestation at the same time with one drug product. So the data that we have in vitro so far is basically getting AAT expression from circVec, and in this case, it's also the 1.0 generation. This will be repeated now with the 2.0. But what we see consistently high levels of AAT from mRNA, but it's dropping quite dramatically early on. So you can see these are the time course again. circRNA starts off a little lower, but it accumulates over time. So that's basically the expression profile that we get consistently using different payloads, and I think that's a promising readout for long-term expression of AAT going forward. Then in addition, we can remove specifically the mutant AAT by some proprietary designs that we have in our vector. Here we just see an experiment where we measure the level of mutant AAT, and then we try different design rules, and we have one that's specifically for what's called a Z allele, allele, and that seems to also be the only one responding in this experiment. This is a very exciting technology to us. We are going to develop this even further to improve the yield and the knockdown specificity and efficiency, but we are looking... We are setting up in vivo data as well to measure what is the temporal profile of AAT production from our circRNA vector compared to mRNA vectors. That's AATD. The summary here, I think we've come a long way. We've basically been working on this for maybe a little more than one and a half years, setting this up from scratch. It's a very time-consuming process to develop, you know, good science. Basically what we've achieved, as I showed you, the circRNA biogenesis, we have 10x biogenesis rate compared to the best design in nature, we believe. We have this 15X extended half-life compared to mRNA, and we have confirmed that we can express the circRNA in vivo. That's established. For the vector functionalities we have shown, sorry, we have shown that it works from a conventional DNA format, it works from viral-based format, so we believe it's a vector-agnostic approach. We can basically plug and play into the vector system of choice here. We will probably move forward with, as I mentioned, two different synthetic circDNA formats, as well as AAV, at least for the rare disease program. We have now an optimized circVec 2.0 that outperforms the 1.0 tenfold, roughly, and we can express circRNAs at least up to a 5 kb in length, or 5,000 nucleotide circRNAs. This is... We have not attempted yet to go beyond that, but that at least works in the lab. Of course, the next step is to test this circVec 2.0 in multiple vector types, both in vitro and in vivo. For protein expression from the circRNA, we get 3-5x enhanced protein expression per RNA molecule compared to mRNAs. We have validated this for several different protein payloads, including AAT alpha-1 antitrypsin, and we have also confirmed that it works when introduced into mice. We will, of course, continue our work on establishing an in vivo package for protein expression, both the durability and expression level. Then we can add in, on top of that, I didn't touch so much upon this today, but we can add in other regulatory functionalities in the same vector system. So we can have a multifunctional cassette, basically, that will co-express the circRNA in addition to other functionalities. So this we're also developing and working on to get that as effective as possible. So some of the readouts that we expect going forward in the rest of the year here, we have the three different vector formats that we're working on. AAV is high on our priority list, so we will be testing the first vectors very soon, where we will compare the circRNA-based expression from the AAV, comparing that to an mRNA. And of course, if that looks encouraging, we'll go directly to in vivo studies as well for an AAV-based circVec format. Then we have two very interesting DNA formats that we'll be testing also throughout the end of the year here. So that's basically the same setup. Of course, for this DNA system, we need to identify delivery in the sort of delivery-enabling technologies, and we will of course be testing the NeoRegen approach as well. So this could be highly applicable to some of these vector systems. So that will also be tested in vitro first, as always, and then we'll go to in vivo data afterwards. Lastly, for the experimental readout, as mentioned, we are setting up a COVID spike vaccine program, where we will inject mice with the adenovirus vectors expressing a circRNA encoding the spike protein and compare that to a similar vector, but expressing spike from an mRNA vector. And then that we will expect to get the readout of that towards the end of the year. So it's a somewhat lengthy protocol, 6-8 weeks to evaluate the full potential of the immunogenicity. So it takes a while to read out to finalize that study. In addition, which maybe is a little quicker, is the reporter expression durability. So expressing reporter setups such as the firefly luciferase in mice, here you can track the expression, both the biodistribution and the expression profile over time. And here we can actually utilize just synthetic DNA, the circVec 2.0 design, and then see how that performs over time in mice using different routes of administration and different delivery technologies. And similarly, we'll test specifically AAT expression that you can pick up in the bloodstream. It's a secreted protein, so there you are, also have the possibility to track the expression over time in mice that have been treated with the circVec 2.0 vector encoding AAT and comparing that to an mRNA equivalent. So that brings me to basically the last slide. So we believe that we have a very unique position, not only in the circRNA field, because we are probably the only significant players in the DNA format space where all the other players are synthesizing the circRNA in vitro. But we're probably also the only circRNA player in rare disease space, to our knowledge. And I think that gives us a very unique approach. I think what we have shown is that we get enhanced durability, enhanced protein expression from the circRNA, and I think that then translates, that Erik also mentioned, into that we can actually reduce the dose level and reduce toxicity as a consequence. And that may actually solve some of the most critical issues currently with gene therapy going forward. So we believe, of course, in our technology. We think that going forward, the vector-based circRNA approach is probably gonna outcompete mRNA-based vector approach for all DNA-based therapeutics in the future. Just as we believe for the synthetic RNA space, circRNAs will probably outcompete mRNA. So, for the applicability of a synthetic RNA approach, this will probably be circRNAs going forward, and the applicability of a DNA-based approach, it will likely be vector-based circRNA going forward. So with that, I'll thank you for listening. This is the last slide, and we will proceed with the Q&A. Thank you, Thomas. We've received some questions, and we can move to tackle these. Let's see. First one is, typically, there is always interest and enthusiasm around getting data and impatience. There's a question here relating to, to see. There seemingly are several delays in the neural development. Can you comment on this? You want to start? Yeah, I mean, we can, we can, of course, discuss whether we have delays or whether this is just a natural process of, of, of research. Of course, we would always hope to, to advance quicker. We're working very hard to advance as, as quickly as we can. I think some of the things that may have, you know, been suboptimal is that some of our early design that we used for our reporter, the reporter gene that we focused on works extremely well in vitro. Did a lot of work on that. Turns out in vivo, it was a sort of a suboptimal choice of payload. So that is not, you know... So that was somewhat negative data, both for mRNA and circRNA, so it wasn't specific for the circRNA. Because that was simply just a suboptimal design. That's where we constantly learn and then going forward, so we can make, you know, clever decisions. And I think we've, as mentioned, come a long way. We have a very good in vivo program coming up, where we've learned from some of the suboptimal designs we may have had. And that's-- I think that's a process you do the data-driven decisions, and then that will guide you, I mean, in your next experiment. Anything to add? I would argue that we have progressed very quickly in terms of what we've been able to build of constructs. We very rapidly were able to establish a vector format that outcompete the mRNA-based vectors. As we've shown today, we already brought this to a next generation, where we have a 10X improvement, up to 10x better protein expression, our second gen compared to our first gen, and this has been achieved. No one else has done this before. It's the first time it's being done, and we've done it in a year and a half. We achieved something that potentially can outcompete mRNA-based DNA therapeutics in the future. We think it's likely that this over time will become the preferred format. In that context, I think we've moved very quickly. Now, of course, there are... We're guiding on what we're doing, and we've been guiding on the in vivo readouts, for example, and these are real experiments that have been ongoing. As Thomas said, the experiments are not always behaving the way you expect. We have to do certain optimizations before we're able to make any definitive conclusions, but we're learning from what we're doing, and we're optimizing experimental setups, and we're now moving to test our circVec V2.0. Drug development research is often unpredictable. You do an experiment, you learn some things, you get more questions, and then you adapt. I think what we've learned so far has put us in a context where we now have good setups, multiple functionalities. We've been able to identify what are suitable disorders to move forward with. When we started this, we simply did not know that AATD, alpha-1 antitrypsin deficiency, would be a good target disease for us, and that's something that has emerged through the research and development that we have done. We understand people are impatient, and we will keep you updated as we progress. It's also important that we maintain certain know-how internally. We don't release too much information. Also, it's important for future partnerships, future IP, that you kind of hold close what you're actually doing from both the patenting as well as the competitive side of things. Second, we have a question regarding the NeoRegen Biotech deal. How will Circio use the NICT technology from NeoRegen Biotech in its preclinical development? Will it be used for the entire platform or certain indications? So yeah, I, I can comment on this one. As we said before, the NeoRegen technology enables delivery of synthetic DNA vectors. So this does not apply to the viral delivery, only to the DNA. In that case, you, you can't just give the DNA to a cell and expect that it will go in. You need the chemistry and technology to make it enter the cell, invade the immune system, and then travel to the nucleus. And this will—this is what we'll be testing. At this point, this is at the early research stage, so it's a relatively low-cost experiments. We will simply test the chemistry of NeoRegen in our lab and see how efficiently we're able to get the nuclear transfer of our synthetic DNA formats. And then if that is successful, we'll bring it forward into in vivo work. But from what we've seen so far, this is a highly promising approach that we believe can overcome some important challenges facing the DNA gene therapy space. And then we have a question on our scientific advisory board and whether an individual called Alex Wesselhoeft has joined the scientific advisory board. So Alexander Wesselhoeft is the founder of Orna Therapeutics and the person who did the early research that led to the foundation of that company. So Alex recently has left Orna Therapeutics and gone back to academia, and we can confirm that we've signed him on as a consultant for Circio. He's now helping us with some of our experimental designs and analysis, and it's also an intention that he will join our scientific advisory board. That's correct. We're currently working on setting up a new scientific advisory board. Our previous scientific advisory board was more immuno-oncology, oncolytic virus, clinical development focused, so we need to find individuals now with relevant competencies in rare disease, vaccines, DNA delivery, and Alex will be one of those people, and we'll update the market once we have fully completed this new scientific advisory board. We have a question for Thomas. What is the biggest surprise or most interesting data you have generated so far? Yeah, I believe that as I showed you, we had these design rules that we also filed IP on that basically dictates whether you get high protein expression or basically zero protein expression from the circRNA vector. I think that was, to all of us, a big surprise that it was so black and white, the picture, and I think that has now enabled us to move forward with a very effective, you know, vector design, irrespective of what payload we actually want to express. I think this also puts us in a good position from, you know, from an IP perspective because we have good protection for this design rule, so that has been filed, as mentioned. I think that was quite surprising that it, we saw this night and day, and it was very consistent throughout all the different experiments we conducted. On top of that, as Erik mentioned, we were able, already at the 1.0 generation, to actually compete with a good old mRNA optimized approach. Of course, what you should understand is that the circRNA technology basically initiated 10 years ago, a little bit more, you know, and Erik and I were sort of humble contributors of some of the early research within the circRNA space. It's a very, very young technology, and, you know, you've seen biotech now spin out within the last three or four years, or more recently even. So it's a young technology, and I think it has extreme potential when already at this stage, we can actually be competitive with a technology that's been developed for a long period of time. So I think that's maybe the second thing that we've already come to this stage in the circRNA space, that we can actually be competitive with established technologies. Yeah, and I agree. I will reiterate this point. Certainly, maybe the most surprising was that we found certain design features that appear, appear to be completely critical for the protein expression, and we had no idea which one would work or not in, in... At starting up, we just tried different designs. One is very effective, one is completely ineffective. And this design is crucial, at least in our hands, and we've created IP around that. We think it will be challenging for anyone moving into this space to circumvent that, that IP. From what we see, we may have discovered certain aspects in the design area that will be crucial. If, as we expect, this will become the future preferred format for DNA-based therapeutics, I think we sit on some very important know-how and intellectual property. So the final question we've received is: When can we expect the next value inflection point from Circio? I think we've achieved many important value inflection points so far in terms of value inflection points for investors. I think very important readout will be to show that we can get prolonged, higher and more durable expression of a SEAP-based reporter in the mouse model using our circVec 2.0. Probably the lowest hanging fruit will be doing that with a simple DNA system first, and then the key experiment will be doing it from AAV. AAV, as we said before, is the standard approach for gene therapy. So if we in the mouse model can show head-to-head, classic AAV versus our circRNA AAV expression of a suitable reporter in the mouse model, that will be, I think, a crucial data point to attract partners as well as prospective specialists, investors, and kind of firmly validate our technology against benchmark system in gene therapy. And we're already now—we've done our first gen version V 1.0 experiments. We've seen the expression. Now we're, based on what we learned, we're optimizing those experiments. We're testing it with the DNA format 2.0, and then after that, we will do the AAV. So I think likely, later this year, we'll have the first data. Probably in Q1, we'll start seeing potentially some of these key experiments pan out. And of course, drug development takes time. Getting this forward into the clinic will take still several years. We're in the early, early development phase. Shorter term, in terms of value inflection points, I think what to look for is that we're able to do business development deals based on our technology. We think this is a suitable platform that can be partnered either for specific therapeutic areas or vector uses. Once we establish this robustly, shown some of these key data points, we aim to do multiple partnering transactions in the future, and hopefully, we can achieve that already next year. I think with that, we've dealt with the questions that have come in. Thank you all for attending. We will also be posting this webcast onto our webpage. And as always, don't hesitate to reach out to us if you have questions. Always happy to speak with our investors. So thank you again, and bye from Circio.