Wave Life Sciences at Chardan Conference: ADAR RNA Editing Insights

On Tuesday, 21 October 2025, Wave Life Sciences (NASDAQ:WVE) participated in Chardan’s 9th Annual Genetic Medicines Conference, discussing advancements in ADAR RNA editing technology. The conference showcased both promising clinical data and the challenges of translating preclinical findings into human trials. While optimism about the technology’s potential was evident, the need for precise target selection and overcoming regulatory hurdles was also emphasized.

Key Takeaways

• Wave Life Sciences highlighted positive clinical data from their AATD program, showing a shift from Z protein to M protein in patients.
• The potential of ADAR RNA editing to modulate protein function and address liver and CNS diseases was a central theme.
• Panelists stressed the importance of understanding the translatability of preclinical data to clinical outcomes.
• Misconceptions about RNA editing, such as the necessity of high levels of editing, were addressed.
• Optimism was expressed about ADAR RNA editing’s future impact on medicine.

Operational Updates

Alpha-1 Antitrypsin Deficiency (AATD) Program

• Wave Life Sciences is conducting clinical trials, with data from the 200 mg cohort and upcoming 400 mg cohort expected in Q1.
• Aeronaut plans to file a Clinical Trial Application by the end of the year.
• ProQR has received clearance to initiate a Phase 1 trial targeting cholestatic diseases.

CNS Pipeline

• ProQR continues its collaboration with Lilly and the Rett Syndrome Research Trust.
• Wave Life Sciences is building a CNS portfolio with demonstrated potent distribution.

PMPLA3 Program

• Wave Life Sciences is developing a program targeting PMPLA3 for liver diseases, with an R&D day on October 29 to discuss further details.

Future Outlook

Alpha-1 Antitrypsin Deficiency (AATD) Program

• Wave Life Sciences plans discussions with the FDA and GSK regarding the approval pathway.
• Aeronaut aims to support dosing frequencies of more than a month in clinical trials.

CNS Pipeline

• ProQR is advancing its CNS programs with ongoing collaborations.

PMPLA3 Program

• Wave Life Sciences will highlight the PMPLA3 program’s potential at the upcoming R&D day.

Q&A Highlights

siRNA and ADAR

• The timeline for ADAR to reach the stage of siRNAs was discussed, with emphasis on understanding enzyme pharmacology.

Target Selection and Editing Levels

• The importance of selecting the right target and understanding haploinsufficiency and biomarkers was highlighted.

Perceived Misconceptions about ADAR

• Concerns about RNA editing’s upper limit compared to DNA editing were addressed, with a focus on therapeutic benefits.

In conclusion, the conference underscored the potential of ADAR RNA editing to revolutionize treatment for various diseases. For more detailed insights, refer to the full transcript below.

Full transcript - Chardan’s 9th Annual Genetic Medicines Conference:

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Good afternoon. Welcome to this panel discussion, ADAR RNA editing. My name is Kain Akai. I’m director of research and one of the senior biotech analysts here at Chardan. Joining me on the panel are executives from our participating companies, Doctor.

Paul Volno, President and CEO of WaveLife, Dennis Ham, CFO of ProQR, and Doctor. Sriram Sathi, CSO at Aeronaut. So, gentlemen, thank you all for joining us today. I think, most of our audience is familiar that, ADAR, adenosine deaminase is acting on RNA, is an endogenous enzyme that naturally edits one selected adenosine on double stranded RNA, flips it to an inosine, which is then read as a guanosine base during translation. This results in amino acid alterations and often entails changes in protein function, thus opening the door for the development of novel therapeutics.

Two years ago, we hosted our first panel on ADAR, so I’m happy to get the band back together again, two years post, to get an update on what’s happening in the field. So, again, thanks all of you for joining us today. Paul, let me start with you. Your lead program, similar to, Coral and Erna is targeting alpha-one antitrypsin deficiency. It does represent an attractive initial target.

It’s a validated target, caused by a single point mutation. There’s easily measurable biomarkers. You’re the first in the clinic. You’re the first with data. Tell us what you’ve evaluated to date and what you’re seeing.

Paul Volno, President and CEO, WaveLife: Yes. Well, one, thank you everybody for sharing lunch with us, and thank you, Kay, for bringing the BAM back together on editing. I mean, think as you mentioned two years ago, we were all here and you read about adenosine deaminases and flipping bases, and we were talking about the kind of what I call the theoretical, you know, could you drive editing and base correction that could actually make a physiologic protein that could actually recapitulate the human wild type protein and do all of those wonderful functions? I think we had the confidence of this is working in a dish, and it’s starting to work in mice. I think flash forward, over the last year, we took the idea from then preclinical data a year ago at this conference, we’re like, we’re excited to bring this into the clinic and drive data to where we were delivering data a little over a month ago, I think is pretty astounding.

I think what we’ve learned along the way and what we’ll continue to learn, I think is really stepping back and thinking about this target. And I think it’s so important when we do think about target biology to really think about what’s the driver for treating the disease and what’s the modality and the mechanism with the modality treats that disease. And I think coming into the treatment of Alpha-one, as you pointed out, I think this community, patient community, physician community, investor community, had a lot of different sets of expectations based on IV protein replacement therapy. So if we think about the gold standard of IV protein replacement therapy, it’s really pouring water into a bucket that has holes in it. So this notion of how do you get from a ZZ phenotype, so these are patients who are going be at risk of lung disease and liver disease who go on to progress, how do you transition them phenotypically to an MZ patient, so a patient who now has low risk of lung disease, low risk of liver disease, that’s where when we think about protein replacement therapy and a number of companies in the space, people have in their head 11 micromolar, and then suddenly with inhibericks people are like, do you need 15 or 20 micromolar?

And this notion of what I call kind of rising this bar on protein replacement, that was always a notion of how do you put protein into a patient’s body who can’t make that protein so when they do have this acute phase response, they’re going to consume that protein and it’s going to go away. And if you think about the 11 micromolar and why there’s been debate of whether or not that’s the right bar, it’s because if you assume that a patient has 11 micromolar because you infused it and they have an acute phase response, they may actually have zero at the time of the event. There’s nothing. We have to step back and when we look and think about our data in the context, which is super exciting, we’ll talk about the acute phase response and the ability to generate more protein than seen with inhibericks. The notion in editing has to be a different mindset.

The mindset in editing is you’re actually correcting that mutation on the transcript, so you’re fixing it within the patient, so that now truly a ZZ patient becomes an MZ patient. What does that mean? That means they should have, you know, still to be MZ above 11 micromolar. In these data that we presented, we saw 12 micromolar of total protein. We’re up to 13 micromolar as we continue the study.

So we saw MZ like protein total levels. But most importantly, we saw the exact correlation between what was supposed to happen with editing, meaning a shift from Z protein to M protein. And we saw that where patients went from 0% M protein at the beginning of the study to 44% of the protein being M protein after the single dose. This is still the lowest dose cohort to ultimately about 65% M protein at the multi dose, still in the lowest first cohort. Our approach using novelty of chemistry gives us stable drug that gets in the cell, stays there subcutaneously because it’s GalNAc conjugated, and can be there to drive editing in a highly durable feature.

So if we think about what editing is supposed to do, it’s supposed to correct Z protein to M protein, And then most importantly, and this gets to the fundamental treatment of alpha one antitrypsin deficiency, is that when a patient has an acute phase event, meaning when they have an IL-six mediated response that turns on transcript, that is trying physiologically, normally these patients would then produce protein to protect their organs. These ZZ patients have nothing. If you get to MZ, you can start mounting a response, and that’s the exact response we saw. These patients went from, you know, zero ms to now nearly 11 of m protein and over 20 micromolar of endogenous protein production, stayed there for about a month, then came back down to their baseline levels from the study. And so I think this notion of being able to think about the treatment of alpha-one antitrypsin deficiency in the setting of editing is highly important in ultimately driving a therapeutic forward.

We saw we were there in the clinical data, as I was saying, at the lowest dose cohort, so that’s the two hundred milligram cohort. And we’re excited about the four hundred milligram cohort, which will come in Q1, where, again, it’s about driving how can we get not just more M protein, but really durability. We’re probably now at monthly, bimonthly, we think quarterly. By the time we get to the first quarter at 400, we’re going to be able to see how long we have editing. But we were sustaining editing on the single dose out past two months.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Well, great. Yeah, and I think, with a little bit of time, people are starting to appreciate what actually happened in that one patient with the exacerbation. And saw that, your drug is allowing it to do basically its day job. So what will you report next for six?

Paul Volno, President and CEO, WaveLife: The next step for six is again two hundred mg, to your point, doing its job responding to those and again, highly important we talk about these acute phase events because in the absence we don’t use lipid nanoparticles. By using GalNAc there’s no risk of the irritation being driven by the nanoparticle. So it was nice to see that editing happens very, very quickly. I mean, two weeks after that first dose, that patient could mount a 20 micromolar response to an acute phase event, and it was stable and durable past that. To your point, the four hundred milligram gives us a real opportunity again to continue driving editing efficiency, where and how much more M protein do we make.

What happens? We saw a 60% decrease in Z protein. How does that impact the decrease and continuing decrease in Z protein, which protects the liver? And then ultimately, lets us test durability. So again, do we see this now going from bimonthly to even less frequently?

And then we still have one more cohort above that. So again, highly encouraged given that again, this is the first lowest dose and we could see activity at the single dose portion too of that two hundred mg low dose, but four hundred mg gives us another opportunity to look at more efficiency.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: And Paul, since you are furthest along, give us your view on where you think the bar is for an FDA approval.

Paul Volno, President and CEO, WaveLife: Yes. And I think if we think about the approval thresholds, everybody has in their head 11 micromolar. I think the field got a lot of consternation going with the inhibericks discussions of, does it need to be more? I think we have to look at inhibericks being a little bit different in that it’s a dimerized protein. And so this idea was the real debate wasn’t of how much protein, it was as you created this larger protein, is it going to behave the same way?

So it didn’t necessarily, in our minds, change the threshold for the protein as it’s endogenously made. I think the key is really thinking about phenotypic registration. The whole premise on nineeleven was all grounded on the human clinical genetics of an MZ phenotype. And I think the benefit we all have in the RNA editing space, and frankly editing space more broadly for AATD, is really one for thinking about what is the phenotype of an MZ patient where really they have better clinical outcomes, whether that’s focusing on M protein, the totality of reduction, Z protein, which potentially allows you to build liver into labels. And I think this acute phase events, being able to demonstrate because it always comes up where they say, great, you get this MZ like phenotype, it’s all in preparation for what?

It’s all in preparation for an acute phase event. And you can see people asking the question of, well, how do we know how your drug is going to respond in the setting of an acute phase event? And I think the fact that we’ve demonstrated that we get high levels of protein, high levels of M protein in response to an acute phase event, durability of response during that event, and really protect, we think we’ll continue to support that and, you know, we plan to engage the agency along with our collaborators at GSK.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Alright. Well, great. Shuram, let’s, switch over to Aaron now. And, you know, while your company itself is relatively young, people involved with it have been in this space for a while. So maybe highlight who who they are and what they’ve done.

Sriram Sathi, CSO, Aeronaut: Yeah. I think the field goes back to 2012 when, Tharston for the first time showed that you could generate, you know, DMNAs fused to an RNA and edit RNA in an effective manner in a test tube. And of course, now we have clinical data saying that this could be actually proved in the clinic, and it’s working. And, it’s interesting that it’s the exact same era where CRISPR, showed that it can be evaluated, in preclinical models, and, of course, we’ve advanced much more rapidly in terms of CRISPR, generating modalities moving into the clinical proof of concept and approval with Kashyavi ex vivo editing approaches. So I think the company is really founded based on the foundational work from, Tharsten, and and as you’ve seen more recently, it’s now shown that you could use endogenous enzyme and an oligo to do it without requiring the stereopurity of the oligo.

We have also presented some of the data in most recent conferences early this year preclinically, but we are excited to move this into the clinic with CTA the filing expected end of this year.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Yeah. And so you showed some data, ASGCT for your lead, again targeting AATD. Maybe share some of the key findings there.

Sriram Sathi, CSO, Aeronaut: Yeah. I think three key findings consistent with the field is really understanding what kind of chemistry backbone modifications and what kind of optimization of engaging all of the different isoforms of ADAR, especially ADAR exists in this P110 as well as P150, which is what we talked a lot about the response to the infection, and we can see that you could, even in a dish add interferon alpha and induce the production of this cytosolic form of ADAR P150. We showed that with our oligo we can engage both of these isoforms effectively, and this provides, I think, a clinical patient standpoint, that ability to respond to that infection in a mechanistic manner. And so the data that we showed at ASGCT not only highlighted the platform in its ability to engage both of these, isoforms of AR, but also demonstrated in vivo that you can reach, much higher levels than what would be a theoretically meaningful levels, in terms of MAAT reaching up to 39 micromolar, total AAT reaching up to about 63 micromolar in those mouse models. And NHP data demonstrated that durability could be expected with a half life extending more than thirty days in monkeys, demonstrating that the molecules will potentially substantiate less frequent dosing in the clinic.

You know, we want to support dosing frequencies of more than a month.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: So, you know, again, as you finalized your drug candidate, you’re preparing to enter the clinic, what do you think, number one, you’ve learned over the last, say, twelve to twenty four months in that process? And how do you think that plays into your company’s ability to differentiate itself based on your platform and design approach? You know, not just in this initial indication where there’s multiple drugs entering it, but you know just in a broader landscape of AR.

Sriram Sathi, CSO, Aeronaut: Yeah, think in terms of the design space as we sat around looking for molecule optimization, there are probably a billion different molecules you could design, given just that one edit that you need to make, given the different options of the backbone, the sugar, the base modifications that you could make. And, one of the seminal work from, Jim Billy’s lab was to show that actually, base pairing of the oligo is not optimal for maximizing editing efficiency. There has to be some more mismatches that are required for optimizing the editing efficiency. And so we really incorporated all of those principles, which could be translated from target A to target B to target C. And, we have now progressed on our pipeline as well to show in vivo proof of concept, which hopefully we’ll be able to release, sometime next year.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Okay. Great. So Dennis, you know, let’s move over to ProQR. And, you know, one of the things we like most about the potential for ADAR is the ability to affect protein protein interactions and to translate a protein variant in applications for that. So we’re really happy to see that that’s where you guys are initially starting.

So talk about your lead eight ten. I know you just had some news about it. But, talk about that program and your targeting of cholestatic diseases.

Dennis Ham, CFO, ProQR: Sure. Thanks, Kaye, for inviting, myself. I’m excited to be here, and I I I guess, like the rest of the panel, really excited about the broad potential of RNA editing. And as you pointed out, our lead program, does indeed introduce a protective sorry, not a protective variant. A variant to modulate protein function, not just correct protein, mutations like, what they’re doing.

So a x eight ten is designed to edit NTCP and specifically block bile acid transport into the liver for cholestatic disease. So as you mentioned just yesterday, we announced, clearance to initiate our phase one trial in healthy volunteers. It’s a big step forward for ProQR, but I think also for the field because it’s the second target to enter human studies and help validate AR editing as a field. In terms of mechanism, I mentioned that the edit is designed to block bile acid transport, but just as important, the edit is designed to allow the normal transport of other molecules. So this is where the modulation of protein function comes in.

Interestingly, this is also a potential for eight ten to be disease modifying because it is designed to reduce the toxic accumulation of bile acids, which is a common pathology amongst, cholestatic disease. Before I go into some of the preclinical work that we’ve done, there’s quite a bit of genetics and pharmaceutical targeting around NTCP. So it really helps validate this approach for us. In terms of genetics, there are naturally occurring variants of NTCP where individuals show a impaired transport of bile acid function and naturally have quite high, like you can see up to 40 times elevated levels of serum bile acids with no negative consequence, which is very interesting. In particular, no pruritus.

Additionally, there is a peptide inhibitor of NTCP that’s been approved for hepatitis. In that phase three trial, those patients showed elevated levels of bile acids. So showing that indeed they were able to block NTCP transport of bile acids into the liver, and that resulted in improvements in liver health, even without virologic response. So it’s important to consider that without the removal of the virus, you’re still seeing liver health improvements with an NTCP blocker. So preclinically, we ourselves, we’ve demonstrated that about, a 15% level of editing in both mice and in non human primates has translated into roughly a two fold increase in serum bile acids, which we know from the literature and from the clinical studies, done in, this other blocker, can translate into functional effect.

So, from just to wrap up from a safety perspective, our GLP tox studies have not shown anything concerning. It’s essentially a profile consistent with other GalNAc conjugated oligos.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Okay. And not to front run your event coming up to talk about this, but, you know, there previously was some guidance about when you would show first data. So maybe update or tell us about that.

Dennis Ham, CFO, ProQR: Yeah. Great question. As you mentioned, I can’t front run too much, but we have an event scheduled for November 3, which will walk the street through the specific trial design, biomarkers that we expect to read out, that sort of thing. But at a high level, I can tell you, as all Phase one studies, primary endpoint is safety and tolerability. But this particular mechanism lends itself to measure of PD markers of target engagement in a healthy population which will be treating healthy volunteers.

So, in that case we’ll be able to look at whether blocking or editing this target will indeed block bile acid transport into the liver by looking at increases in serum bile acid which I talked about already. So we will be looking at potentially a threshold of two times the level of serum bile acids and increase. And that should tell us do we have target engagement. Great.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: You know another area of focus for your pipeline is the CNS. You guys have presented preclinical data on some of your axiomar candidates, which show good editing efficiency, good distribution. So why more specifically do you guys believe the CNS is an attractive, place for

Dennis Ham, CFO, ProQR: That’s a that’s a great question. As you know, RNA editing is not limited to the liver, naturally. Right? So the brain has robust, endogenous ADAR expressions. So in some ways it makes it, an ideal target organ.

RNA editing can target transcripts precisely and reversibly. I think everyone knows that and therefore somewhat unique amongst genetic medicine approaches. In terms of what work we’ve done, as you mentioned, we’ve shown that our editing oligonucleotides are distributed broadly across the CNS, both cortical and subcortical regions, and continues to redistribute over time. So that speaks to durability. And from an efficiency standpoint, we’ve actually seen up to 60% editing in vivo.

These data come, from, I would note, intrathecal delivery of a tool EON that we use into nonhuman primates. We’ve gained a lot of this knowledge through our partnership with Lilly. So which is focused primarily on CNS but also liver diseases, and it covers 10 targets in both, as I mentioned, liver and CNS. So in addition to that, we have a collaboration with the Rett Syndrome Research Trust. So together, these two collaborations have sort of enabled us to do quite a bit of work in CNS.

We haven’t publicly been able to talk too much about it because those targets are still confidential to to Lily. So

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Yeah. I think, I know for Rett’s, your lead candidate there, again, I think interesting in that it’s trying to, translate a wild type like protein. So, again, a very interesting application for ADAR. Paul, let me, switch to your pipeline. Again, I don’t want to front run your R and D day, but you’ve also talked about, PMPLA3 an attractive so tell us why.

Paul Volno, President and CEO, WaveLife: Yeah, mean, can talk about PMPLA3 in terms of size and correction. Mean, I think the benefit of several research days, so we don’t have to front run the ones we’ve done, you know, really do demonstrate the breadth of editing. It’s interesting, you know, I always say, taking clinical data now and applying what do we learn in the clinic and how does that derisk what we’re doing next is really the fundamental feature, I think, of thinking about translation. And I think the real opportunity, I’m fixated on that picture where I look like I had a lot less gray hair back then, the translation on being able to take preclinical data and translate it is really important and fundamental. I think we learned a lot if we take AATD to a mouse that has 12 copy numbers that, you know, why we wanted to wait for the mouse to actually get well below 11 to treat it, taught us a lot that actually shows us modeling really well the mouse model to the human model.

And I think sometimes we have to be really careful because the data we use for a lot of these extrapolations are always preclinical systems that we’re trying to predict clinical benefit from. So the more time we have to take learnings from our preclinical data, generate human clinical data, and bring that human clinical data back creates the efficiencies that ultimately release a flywheel. And I think our now translation from clinic, from target to clinic, I mean for our obesity candidate in siRNA is eighteen months from generating mouse data to human clinical trials. To your point on PMPLA3, I think stepping back, we’ve generated editing data in lung as it related to CFTR and fixing mutations with substantial editing there without conjugates. We’ve demonstrated the potent distribution in CNS around editing and building out a portfolio.

So to not front end Research Day, I think one of the highlights for October 29 is really going to be, what did we learn on alpha-one antitrypsin, how has that derisked the ability to think about our chemistry, which is not just stereochemistry, I know that’s back in that time. Stereochemistry is important to rationally design single drugs. I think the real evolution wave back in 2020 to 2021 is the advancement of phosphoriguanidine that’s independent of stereo control, we control it so we make a single drug, but really the intellectual property around how do you design medicines that can stabilize oligonucleotides, give them the ability to distribute, have good retention and potency, and ultimately the N3uridine, which is our modification for how do you get site specific base editing. So if we translate that to this research day, it’s going be a continuation of the clinical data. How do we build beyond alpha-one antitrypsin with PMPALA3?

How do we look at extrahepatic and the growth of the pipeline there? As it relates to PMPALA3, this is really an exciting opportunity to think about correction, the environment where it’s about kind of what I call the dynamics of an acute phase response and trying to see how do you fix something so that it can respond to these acute events, but here’s how do you fix an enzyme that actually is responsible for a good portion, about a nine fold increase in hepatic diseases, liver diseases across all causes, alcoholic cirrhosis, NASH. And so it’s really a unique genetic population that happens to be substantial in size. It’s about nine million patients who are homozygous for this particular mutation. If you can fix that mutation you decrease their risk of liver disease, and the ability to demonstrate how we can drive that forward.

In a way that’s going to be important, and we’ll reiterate this at research stage, this isn’t front running, look at this as kind of sensitizing, is this notion that siRNAs, so there are targets where, and this is really I think the advantage we have as we think about an armamentarium of different modalities, and how do you pick the right modality for the right indication. This is one where if you knock it out, actually you could reabsorb fat in the liver. You actually need this protein to be physiologically functional to protect the liver. How do you do that? You edit and correct it.

So by correcting the protein you actually restore its physiologic function back and therefore can think about treating patients that are genetically stratified based on their status and ultimately be able to think about treating them for a variety of liver diseases, you know, MASH being one of the ones that’s interesting, in a way that’s distinct from, you know, how we think about medicines that treat obesity and actually decrease fat to treat this, but this is actually an underlying genetic driver for these patients. So we’re excited about the opportunity on research data to highlight, obviously, PMPLA3 beyond alpha-one antitrypsin and really go on the journey of how do we continue to derisk the modalities both in the liver and very importantly outside the liver for both editing and siRNA.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: So Shiro, let’s talk about how you think about your pipeline. You know, obviously, again, walk before you run. So AATD, point mutation, validated target, large enough population, you can enroll the trials in a reasonable amount of time, and there’s biomarkers. What does the next thing look like for you that kind of meets those same kind of criteria?

Sriram Sathi, CSO, Aeronaut: Yeah. Think, really, AATD has really allowed us to build a platform as we play with different chemistries and different delivery optimization to really we anchored ourselves on GalNAc because we wanted this to be Alnylam has taught us through multiple clinical development phases that that is actually the better, approach. And, you know, it’s really good to see that, you know, three different companies are really, focusing on GalNAG to, deliver to the lever. And since we are still a private company, we have really remained, focused on unlocking the liver. If you think about the white space in liver, you know, people say, hey, for a knockdown approach, liver is saturated.

For an editing approach, I don’t think so. You know, we’ve identified more than a dozen different targets, that are either a gain of function mutation, that could be introduced into the population, or a specific protein protein interaction. One example that we’ve announced, outside nonconfidentially is E3 ubiquitin ligase binding pocket of an important protein, where you can just block the E3, epigridine ligase binding itself and not affect the unintended consequence of knocking down the E3 epigridine ligase. And so those type of precision edits are now possible. But we are, for for the investor dollar concerns, really going to remain focused on on the white space in the loop.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Okay. Great. Well, let me take advantage of the collective brainpower up here and have each of you ask somebody else a question. So Dennis, why don’t you start?

Dennis Ham, CFO, ProQR: I guess I would be curious to see what the panel thought about how long it will take for us to get to the stage of siRNAs, where it’s now seen broadly as, applicable across targets and not just narrow, mutation correction for example.

Paul Volno, President and CEO, WaveLife: It’s an interesting way of stratifying because I think you could say, and I like it of like, you know, how do we think about SI and I because we’re working on SI, so we’ll have data next quarter on our

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: So you

Paul Volno, President and CEO, WaveLife: have a unique perspective. So I think a lot we we spend a lot of time when I’m looking at our team thinking about balancing the ADR portfolio and editing and correction and where is that tool useful and then where is the tool useful as it relates to SI. And so I think to your point, the similarities on those are, once you understand, because I think if we are really saying how do you get to the stage that SI is in, it means that you understand how the enzyme behaves between a mouse and nonhuman primate and a human. Once you canO but once you understand the pharmacology of how that enzyme is going to translate, right, like we know if we see X amount of time in silencing of a protein in a mouse for siRNA and AGO2 that should reasonably translate to X amount of time in NHP, and we’re seeing actually really reproducible translation of what’s supposed to be happening with AGO2. I think the same is true now for ADAR, where what is that catalytic efficiency of that enzyme look like.

I think what we’re going to realize, and we see this, and I think we need to be careful also creating generalizations. I mean, case in point for inhibiting in obesity is a program we have now where it’s been intractable by those who are really viewed as, I think, be given, I think being viewed as experts in the field, in terms of knockdown of the target protein and duration. So the only ones who’ve demonstrated potent durable silencing of Activin E, which is this biomarker, this protein that’s produced for obesity in the liver, and shown in a mouse that that reduction translates to fat loss, so no loss of muscle but just fat loss in a mouse and ultimately generating that data in a human. Others who’ve targeted AGO two haven’t been able to see that level of reduction of the protein. So I think the caveat in thinking about ADAR is realizing that there’s a when we talk about the generalization, we got to think about it in terms of the protein, and then how do you and each company I think that’s the benefit of each company, how do we each learn systematically about how our chemistry interacts with a given protein and how that interaction with a given protein translates to patients?

And once we can establish that longitudinally, I think then we unlock what we’re kind of saying is the power of SI, which is how do you quickly go from a target to human clinical trials in a reproducibly fast way, And that’s about understanding the platform capability as well as then the biology and target biology. And I think, I mean, we’ll talk about this at our R and D Day soon. I think we’re getting pretty close. I mean, I think our understanding of the catalytic efficiency of the enzyme, both between mouse to nonhuman primate. Now with human data, how well that’s happening, I would say it takes two to feel like it’s not one target translating in a unique way, but how does that happen across multiple programs.

But I think we’re right at that precipice.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: So, Sriraj, let me modify that question a little bit and then have you answer it. So, and it goes to what Paul said, how do we engage this endogenous enzyme? Because for an siRNA, when you’re going after a validated target, we assume you’re going to knock it down sufficiently. And because it’s a validated target, knocking it down sufficiently, we kind of know what the result should be. So then it’s just a matter of, okay, whatever your tissue target is, can you get enough of it there?

And we’re not so much worried about safety anymore either. So what do we need to know better as a field, or you guys need to know better as a field, to get to that kind of same point where an siRNA is?

Sriram Sathi, CSO, Aeronaut: Yeah. Think it really goes back to picking the target, right? We don’t want to make an edit to kill an enzyme activity where you could just take a siRNA and knock down the protein if that is viable, right? You have to really identify that white space. And second, understanding the haploinsufficiency of the transcript, understanding that maybe 50% edit is sufficient.

That’s where the human genetics for alpha-one is very, very strong, right? We understand the phenotype of the SZ, we understand the phenotype of the MZ, we know the problem associated with the ZZ, we know exactly what is the median levels of the circulating biomarkers are, and then we can establish a relationship between the drug’s potency in the mouse model, the PK duration in NHBs, and then the now emerging human clinical data start to draw that association between these molecules, how they are behaving in mouse to monkey to human. And that translation really will help us accelerate this process, but the front end needs to be very clear. There has to be a target where, you know, the level of editing of that target needs to be clearly associated with what is the level of, therapeutic benefit that’s needed, and that should be a circulating biomarker, like what we are talking about, bile acids, or it is AAT, wild type AAT levels, that should be associated with clear clinical benefit. And therefore, you know, we don’t need to wait for a randomized placebo controlled study to understand that this is actually trackable or not.

Paul Volno, President and CEO, WaveLife: I think sometimes the models, and it’s an interesting point, we have to be highly selective about how we use the models to translate. I think our learning out of the gate is I think, if I had to look back, I think the Serpent A1 model itself, as we think about the extrapolation, probably created more consternation about thinking about that initial translation. Again, the benefit in hindsight of clinical data and how do we all learn from clinical is like this notion of when you have 12 copy numbers in a mouse and depending on when you treat and how you treat and the response rate of the mouse, you could actually in some ways think you’re gonna see more and really not getting people indexed on what are you trying to do in terms of this concept of editing efficiency, how efficiently are you converting it from Z to M. And I think a little bit of that model got everybody kind of, I mean I think about conversations we were having with you and your peers coming into like alpha-one antitrypsin data where the field is going like, we should be seeing 20 micromolar, 30 micromolar proteins.

That’s why I remember and remind everybody, anytime you go into any therapy it’s really about thinking about whatis biologically relevant, what do you actually need to treat the human disease, whatis the drivers for that, and so I think thereis ample opportunities to do that, but a lot of this translation, so I get to some of it is what do you learn about the enzyme and how the enzyme behaves and how does that translate. The other side is how do we think about targets that are moving forward that best underline and validate the capability? And then ultimately how do you quickly do you learn from clinical data to imply back to preclinical data?

Sriram Sathi, CSO, Aeronaut: Just one addition to that, I think all of the work that, Chen Bai Lee has shown so far is that humans have evolved to have an active AR function. We all, benefit from that natural ability because unlike mice, humans and primates have a lot of endogenous retroviral transcripts that are expressed at high levels. And when you have mutations in those ADOR enzymes, you see that there is an autoimmune consequence. That is the inability of those, ADOR to edit and repair these double stranded RNAs that are naturally produced because of the pseudo retroviral, elements that are present in our body is, is an advantage that we are are seeing that translation from mouse to human happen because of this natural biology of this enzyme, and and also, biggest advantage of is the precision. Because we are using the endogenous enzyme, the adenosine, the target adenosine, has to flip out of the double helix for it to be deaminated, and that provides a very precise chemistry that we can apply around that edit size to make this exquisitely precise compared to a bacterial protein engineered to have that precision with the which is a big advantage for us to think about other targets where, you know, we can introduce temporally, transiently gain of function mutation and withdraw the drug, and the patient goes back to natural state without having, any of those deleterious consequences of editing out the DNA.

Paul Volno, President and CEO, WaveLife: Yeah, think simple, and I completely agree. I mean I think ADAR, I have every confidence now that ADAR in humans translates as a therapeutic enzyme. I think unequivocally like it’s endogenous, it’s catalytic, it’s functioning as predicted. So I do think to that, you know, to bring it back to your question, I actually, you know, I think we’re there in the context of what we would think about AGO-two and nobody, to your point, having this debate of is AGO two a therapeutic enzyme in the cell that’s going to do what’s predicted, I have a high degree of confidence that ADAR as an enzyme will be able to do what it needs to do. And then to that point, it comes down to how you harness it, how you think about targets, how you advance it.

But the enzyme, I think compared to like two years ago where there was more of at least where I was sitting theoretical saying this should work, like this is all of the premises of what a therapeutic enzyme should do, I think two years later can say the enzyme is doing exactly what it should do. It’s remarkable when you have human data and you step back and you go, the enzyme did exactly what it’s supposed to do, which is pretty incredible.

Dennis Ham, CFO, ProQR: So for the investment community when you think about the timelines of getting ADAR to where siRNA was, I think it’s going to happen much faster, right? It’s not going to take was it? Fifteen years. Yeah, fourteen, fifteen years from the time, yeah, so the time the first siRNA was approved. So it shouldn’t take us fifteen years, right?

Sriram Sathi, CSO, Aeronaut: Yeah. Yeah. And that’s largely because the chemical toolbox is quite well validated now clinically. We understand all of the modifications that people are using, and we know how to not trigger the immune response, by doing that.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Paul, you want to squeeze in a question?

Paul Volno, President and CEO, WaveLife: Sure. Look, I and I think it’s not dissimilar, but, know, we’ve got a lot of colleagues that are out there, and Kate, you can participate too. You know, it’s really, I mean, frankly a question as much for the audience is. I do think that all of us spend a lot of time with this community, and I think as things evolve at various stages, I do think there’s some misconceptions about the enzyme. Think you’re pointing it out on the time horizons and the translatability.

Curious, you know, as you hear, where do you think or hearing where you think the misconceptions still exist for RNA editing and ADAR?

Dennis Ham, CFO, ProQR: When I have investor discussions, they center not around the ADAR editing anymore. They center around the indication. So have you picked the right indication to demonstrate proof of concept here for the technology?

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: And that’s specific to you folks because you’re not going after initially a point mutation.

Paul Volno, President and CEO, WaveLife: Right. Right.

Dennis Ham, CFO, ProQR: But I’m assuming these guys only get questions about alpha one, not about how does this work.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Right? Yeah.

Sriram Sathi, CSO, Aeronaut: I think for us, the biggest questions that we are being asked is, is there an upper limit to how much editing can you see with RNA editing compared to DNA editing? I think the conception is DNA editing is reaching 90% in humans, which is not, I think, mathematically true from the models that we see, because if they are getting 90% editing, then the levels of AAT should be 27 or 30 micromolar and not 12. So I think it is that comparison, is inevitable given the amount of clinical studies that are happening, in parallel, in alpha-one. So I think really convincing the investment community that RNA editing is here to provide therapeutically meaningful benefits to these patients and has a broad breadth to go beyond multiple targets is, to me, the next key step.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Yeah. I think what’s interesting is that I think investors sometimes overemphasize where you’re at today. You’ve gotten there very quickly and to the point you made, Dennis, this stuff usually doesn’t happen that fast. So maybe take a step back and let them flesh it all out before we set the bar too high. But all that said, Paul, to all of your points, theoretically, a lot of this stuff should work.

And we’re building on, you know, the shoulders of other RNA medicines, but you do have to prove it.

Paul Volno, President and CEO, WaveLife: And I think that piece, and you brought up in a great way, I I think the field now, which is great for an indication, means we’re talking about targets as opposed to an enzyme, one is a lot more important. It’s what hopefully all do, is make medicine. So I think the ability to shift the framework from enzymatic biology to actually therapeutic indications and how do you unlock them is frankly I think a lot more exciting for all of us and a lot more beneficial to patients. It’s a good place to be.

Dennis Ham, CFO, ProQR: But I would comment just like how siRNA evolved, the first generation and the second generation. Our technology, RNA editing technology is evolving quite rapidly and we’re learning quite a lot in a short period of time. So maybe it could be that the first drugs that go into the clinic won’t be the best, there’ll be subsequent ones. I think it’s I shouldn’t be stronger than maybe. Right?

For sure.

Kain Akai, Director of Research and Senior Biotech Analyst, Chardan: Yeah. Yep. So Alrighty. Well, we’ve reached the end of this session. Thank you all for the great discussion.

Paul Volno, President and CEO, WaveLife: Thanks.

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