It's my pleasure to introduce our next guest from Wave Life Sciences, CEO Dr. Paul Bolno. Paul, welcome. Thank you so much for being with us. Maybe to start, provide a few minutes to tell us about the state of the business at Wave and some of your key events in 2024 so far.
First, thanks, Gabe, for having us, and, you know, as I reflect back actually, being here a year ago and looking forward to 2024, you know, I think what we were looking really forward to was a translation on unique chemistry. The work that we had been doing on this convergence of PN modifications, this new backbone chemistry, and what we saw in early looks on clinical data showing better cell uptake, whether that was in CNS, in muscle, would that translate as we move forward? Really, 2024 was going to be, in a forward-looking way, the year that we would see translation of our chemistry into meaningful clinical data.
So as we think about the deliverables over 2024, this summer, we announced the data from our HD Huntington study, showing a 46% reduction in mutant protein, no change in wild-type, a real success in the first human translation of allele-specific silencing for Huntington's disease. Very recently, we just announced the interim analysis from our ongoing 48-week study in WVE-N531 for DMD, where we not just saw 9% dystrophin, but really started to speak to the importance of consistency of response across patients, meaning of that, 89% of the patients had greater than 5% protein. And not only that, potential for monthly dosing and an extraordinarily tolerable safety profile. So great on there, and we'll talk about distribution to heart, diaphragm, and other components of where chemistry is getting us delivered to.
But that still was the first half of the data set, so we're still looking forward as we think about this fourth quarter, to delivering on the first example of human clinical data on RNA editing, so our alpha-1 antitrypsin proof of mechanism data this quarter, which will be the first time to articulate the translation of ADAR from mice, ultimately to a human therapeutic, and how we can extrapolate that beyond alpha-1 antitrypsin to be able to think about additional editing constructs as we move into this quarter, our R&D Day. And then at the end of the year, Inhibin E, the first new genetic target for obesity using our GalNAc siRNA format. As we said, clinical trial submissions as early as the end of this year, clinical first quarter next year.
So I think we're delivering on the translation of chemistry in the clinic, and we still have two more important, meaningful inflections to go as we look forward to new programs.
Great. Yeah, it's been great to follow the progress you're making with your design chemistry knowledge, you know, starting with the stereopure to address chirality, the introduction of PN. And so, as you said, twice this year, we've already seen the benefits from your improved designs. Let's go to the data you presented last week. Pretty impressive data, interim data from your phase II FORWARD-53 , evaluating your drug in patients who are amenable to exon skipping, or exon 53 skipping and DMD. What we liked about it was the safety, obviously, but you also hit your target in terms of dystrophin production. So, you know, give us more color about the data.
Yeah, I think as we have been very clear going into this data readout, there were things that we needed to see to feel comfortable. One, moving forward in the exon 53 amenable space. And then two, from a platform perspective, and platform in this case across DMD, would we build a profile that we believe could be potentially best in class for exon skipping and for the treatment of Duchenne muscular dystrophy? So I think on the former, we said that, you know, yes, we needed greater than 5% dystrophin protein. We also said in that same breath, that achieving greater than that in the absence of a majority of patients on study being able to see that, would also not be a no-go criteria.
I say this because a lot of the discussion in DMD is often focused on purely a mean dystrophin expression. With the realization that then as you extrapolate that out and look at outcome studies, so what happens in confirmatory studies is you run bigger populations. So what you lose in not having the context of looking at that distribution or, in oncology, so as the overall response rate, how many patients are experiencing that greater than 5%? We do believe that probably leads to a lack of translation, ultimately, in clinical benefit as you run larger studies, because only a fewer subset of those patients may actually be getting the benefit. So those two criteria for us were important and not interchangeable.
They were requirements together, which is greater than 5% and a majority of patients, as you said, with 9% on the muscle adjusted, hitting that context, and the fact that 89% of those patients had greater than 5%, you know, achieve that inflection. Now, what was also important to us is the profile of how consistent that response was, not just in expression of protein, but just distribution of drug. And when we went back to our preclinical data, and I think it's an important point of characterization, is we see in our double knockout mouse models, in our non-human primate studies, that we have more drug concentration and more protein in heart and diaphragm than skeletal muscle.
So again, these numbers that we're citing may very well be underestimating the amount of protein expression in two very important organs in the DMD pathology, meaning heart and the cardiomyopathy that patients have, and diaphragm, as we think about respiratory dysfunction. So the ability to know that while we can't image that or biopsy that to assess it, we have good data coming from our preclinical data to assess that. I think beyond that, what was really compelling to learn on the histology, and I think more than looking at dystrophin protein, is what's it doing inside the muscle. We did have the ability to look at patient followed from part A at the six weeks to part B at six months, and could see improvement in muscle fiber health, so that we could see less inflammation, muscle constructs were doing better.
We also saw on immunohistochemistry that dystrophin that was being made was actually in the right place. It was in the sarcolemma, meaning it was functional, it was working in the functional capacity. And we saw the corresponding reduction in CKs that were as good, if not more, than what you'd expect on steroids, except for the fact that these patients were already stable on baseline levels of steroids. So a bunch of confounding continued biomarkers that were very supportive of progress. And I think the last component of differentiation that we saw consistently from preclinical to our early clinical data, to now in a larger sample size, is the distribution to the regenerative cells in the muscle, those, muscle stem cells.
And that's really important as we think about ongoing treatment, the potential for more dystrophin to continue to be produced, but also thinking about treatment paradigm. Meaning, you know, if we think about the treatment paradigm now, we're in ambulatory boys. The ability to get to heart diaphragm definitely extends as we think about the non-ambulatory setting, where at that point, being able to rescue muscle is probably less feasible as much as it is protecting heart and diaphragm. But stem cells open up a unique opportunity to really think about earlier in the treatment paradigm, meaning at a point in time with newborn screening now in the U.S. and Europe, where patients are identified earlier, if you can get to those regenerative cells before muscle has become fibrotic and fat infiltrated and be able to rescue them, you could really think about changing the disease paradigm.
So I think these data in their totality, coupled with a safety profile that, you know, had three mild events, I think creates a very different profile with, you know, again, dystrophin expression, consistency, where it's expressed and functionally doing, safety, and now, based on our pharmacology potential for monthly dosing.
All right, so let's talk about what's next, and we'll start with tee up what we'll see when you report 48-week follow-up data.
So I think, and it's very important. I oftentimes think with data, people think it's the end. This, again, was an interim analysis, 48 weeks. So what we'll do is have another opportunity to look for the dynamics of dystrophin. So we still have that opportunity in the subsequent biopsy. But also with longer follow-up, the opportunity to look at the clinical measurements in that study. And one of the important measurements, you know, we believe, is the 95% stride velocity. So being able to follow that as an endpoint, not just in this study, and thinking about the dynamics of regulatory environments, the U.S., while dystrophin is the core regulatory endpoint, at least for accelerated registration that people think about, in Europe, it's not.
And so in our conversations with the community, we often hear companies, you know, you'll get their approval in the U.S. and then never migrate beyond. And so I think our real opportunity to think about our DMD programs as global programs is being able to build in endpoints that are acceptable outside the U.S. So 95% stride velocity represents one of those, and so it does open up the opportunity for conversations with regulators outside the U.S. as we generate those data. So that will be important as we generate the 48-week data set. I think our other opportunity is, as we engage regulators on next steps, a lot of what we're thinking about is how do we approach what's not new to us. So, you know, we have had regulatory discussions around confirmatory trial designs in DMD before.
It was built around Suvodirsen, which had a very different outcome, but what was successful, and we actually published along with the FDA, was we brought forward this idea of an augmented placebo design, so working with the agency to say: How do you, and can you run placebo-controlled studies where you can minimize the number of patients on placebo 'cause of natural history? That we agreed on a Bayesian design that enabled that. I think what's happened subsequent to that, which has been great for the field, is there's now more platform guidance from the agency emerging on how to run platform-based studies, meaning thinking about how we could run multiple exons in an umbrella study together with shared placebo arms.
So if we take that in totality, one of the areas that, you know, we want to engage the agency on is this platform umbrella design around multiple exons predicated on dystrophin, but using a limited match placebo arm, using our prior design, so that we can do that in a capital- and patient-efficient manner to ultimately deliver, you know, hopefully a beneficial outcome. But that'll be a key point of the discussion.
Yeah. So I... You know, again, what I've asked some of the other small RNA companies who've reported positive DMD data this year, who also have, obviously, other programs to address other exon mutations, you know, for Wave, what's the strategy to put the pedal to the metal for your other DMD targeted programs?
Yeah, I think I look at, you know, stages and steps, which is having that feedback is gonna be important in picking the designs and making sure we do that efficiently. I think what's nice from a platform perspective is, you know, we don't have antibody or protein engineering in addition to the synthesis of an oligonucleotide. So the simplicity, for us, at least from the CMC side, 'cause we have to remember all these discussions are not just about the clinical trial designs, there's also the manufacturing component of these studies. Being able to know that each one of the subsequent exons we move forward with is using a common manufacturing plan, right? There is synthesis of an oligonucleotide. So we don't have the complexity of antibody or protein synthesis, then conjugation to an oligonucleotide, and then kind of the characterization of the product.
I think there is an efficiency with which we engage around the platform approach around, again, single oligonucleotides. But I think our plan is, we have, you know, as we said on the prior call when we announced data, we do have our preclinical data on exons 45, 52, 51, and 44, all of which have more dystrophin than what we've seen with 53, and 44 consistently, which I think is important in the field, remains a very differentiated outlier from the other exons, both what we know on baseline data, but also in terms of the ability to splice and generate dystrophin protein.
... Okay, well, let's move on to your other asset, 003 for Huntington's. And again, just another example of how your latest design approach is generating a better drug candidate. We saw the results that clearly were better than what you had shown previously with some earlier candidates, 101 , 102 . So talk about the highlights and what you're most excited about from those findings, Paul.
I mean, obviously, we showed for the first time that one could exquisitely knock down the mutant toxic gain-of-function protein in Huntington's, and do so at a clinically relevant threshold, and importantly, not knock down the wild-type healthy protein-
Yeah
... that's important to be spared. It's never been demonstrated before, and was consequential. I think what's also important pharmacologically, if you look at the PK, is it does support quarterly dosing, so the ability to extend that dosing interval out even farther without losing efficacy, 'cause we did do the follow-up. So I think we come out - came out of that data set, you know, I think, very conscious that the data on silencing is important. I think what was also highly compelling was, for the first time ever, we showed that reduction of mutant protein correlated with slowing of caudate atrophy. You let that sit, and you kind of think about this field, the field was really driven from an extrapolation on the other side, which is, as mutant protein increases and as patients do worse, the caudate atrophy increases.
You know, I don't think it was wrong for us as a whole field to say: Well, obviously, if you can slow it, then you'd slow caudate atrophy. That correlation has never been established in the clinic. I think being able to demonstrate that was incredibly powerful. I think that also coincided with an important moment, I think, for the HD field, which is TRACK-HD and the investment that the community had been making for a very long time in natural history studies, was really starting to continue to bear fruit. The fruit that it delivered was this notion now on TRACK-HD, that now with longitudinal imaging data, you had, for the first time, the demonstration that caudate atrophy could correlate statistically with clinical measurements like TMS, Total Motor Score, TFC, and others.
That's a big frame shift, because what you now have are clinical endpoints that the FDA looks at, correlating with an anatomical change that's standardized and being able to be measured on an MRI. Being able to put those two together really defines kind of a core criteria of what meets the definition of a clinical surrogate endpoint. There have been multiple discussions with HD-RSC and other organizations really working on: Could that be a potential clinical surrogate endpoint? I think as all of these things always come to a conclusion of, somebody has to be the driver of sponsoring that discussion. We're at a point, as we've been very clear publicly, that we are engaged with regulators. We'll have feedback by the end of this year.
And really, our defining path forward in HD is predicated on the alignment around a clinical surrogate endpoint in HD that we can and believe we can tractably pursue. I think the advantage of that as an endpoint versus the historical, you know, Generation HD-like clinical outcome studies in HD means you can run a much smaller focused study, about 70-75 patients on a treatment arm, and over about 18 months to see changes on imaging endpoints. So as we think about the tractability of that to really validate a potential treatment endpoint for HD, I think it's an exciting prospect, and for us, you know, we're interested in pursuing that. But obviously, it'll be predicated on the discussion with regulators.
Yeah, so, you know, you've kind of guided to maybe having some more information about that around the end of the year. And I know it's independent, but it seems like that could align nicely with the deadline for the opt-in by your partner, Takeda.
Yeah, I always say two trains running on two tracks, but the trains are running very close to each other. And I truly, you know, and I say that in, with a high degree of seriousness. I think our decision tree is always predicated on what do we think is right, right for patients, right for development, right for investment. And I think, you know, having a now potential accelerated registrational endpoint based on a clinical surrogate, like imaging, is highly tractable for Wave. You know, Takeda will, you know, make their decision for a whole variety of reasons that they will. But I think it's important for us independently, and I say that because on the other side of independence, if Takeda were to say, you know, "We're in, but the, it...
And we have to go run a generation HD-like study." I don't think that study is the right one, given our other investment opportunities that we have for Wave, and there's other alternatives. So, you know, I remain focused for the company on what's the right study. We think that's the right study, and, you know, as we've learned, there'll be ample folks interested in working with us along that if we have that pathway, and obviously, we're waiting Takeda's decision.
Great. Let's shift to ADAR. Here you've been a leader in the field, first in the clinic. You know, you were a participant in our panel last year. It was great to have you and talk about the field of ADAR. It's nice to see your progress. We know that we are anticipating some data from RestorAATion-2 in actual patients. Talk about what you have seen from RestorAATion-1, and tee up what you're going to report on RestorAATion-2.
Yeah, it's amazing, since sitting here last year, you know, we're in, we're generating clinical data, and we'll have our research day coming up this fall, and it's a great opportunity for us to think about this data in the context of building out an editing portfolio, in much the same way in the siRNA space. Once you had GalNAc validated pharmacologically in humans, it became a lot easier to de-risk the other components of the portfolios. So as we think forward, you know, RestorAATion-1 gave us great insights into pharmacology, right? We could predict pharmacology translation in healthy volunteers. Was it behaving as we expected? Were we getting exposures that we were expecting?
I think what's also encouraging about RestorAATion-1 is it continues, so we've got ample safety margin, and it tells us about the constructs and safety, and I think, you know, that's, that's always important to make sure we've got ample margins. I think also important was the decision when we announced the transition to RestorAATion-2. So RestorAATion-2, we've always said that transition would happen at a dose that we believe to be therapeutically relevant, and really, that to be defining as kind of the, the first way of unlocking alpha-1 antitrypsin. I want to say that separately, 'cause as we think about dose two and three, so there's two other cohorts, we need to think differently about how we define cohorts. Everybody tends to think about them as each one.
That, the next one has to obviously be a higher dose, and then the next one, higher dose. We've established this study because we do have RestorAATion-1 to really be defining of what we kind of call both dose and frequency established, and so Cohort Two and Three don't have to be about dose escalation, but really about extending the dosing interval. But the upcoming Q4 is proof of mechanism. I think it's very important to continue to reiterate: proof of mechanism is not a, a goal-line end to the study. So proof of mechanism is not historically how we would disclose DMD data. Here's the interim analysis, you know, all patients, HD data, end of the multi-dose study. This has a unique definition of proof of mechanism, which is really a window into...
This is the first time that anybody has looked in humans at this enzyme to see how it behaves, be able to identify how is it behaving relative to what we've already learned about the animal model and its production of protein and editing efficiency, and how can we correlate where we think we're going to be on the journey to, you know, a therapeutically relevant, potentially registrational amount of protein. So I think, you know, to answer the question that goes in people's heads, and they're thinking like, "Well, is it 11 micromolar and 50% editing?" The answer is no. That's not the target for this particular proof of mechanism data point. That's success at the end of a study. If I see that, I'll call that a grand slam.
This is an early look at: are we really generating the amount of protein that puts us on the curve, and are we seeing editing of M protein? Because M protein is only produced in these patients by the ability to edit those hepatocytes. So I think we're getting a window into that data, the pharmacology of the enzyme, the translation back to SERPINA1 models about the production of protein, and then, most importantly, how we could be thinking about the subsequent programs that we'll be introducing of potential- I mean, these are indications that are as large, if not larger, than alpha-1 antitrypsin. How can we translate that pharmacology back to those? So it's going to be a meaningful update for the field of ADAR.
It's going to be a meaningful update for alpha-1 antitrypsin deficiency treatments, but it's also going to be, you know, an important update on the subsequent programs that, you know, we'll be giving more guidance to as we approach the clinic.
Yeah, and I know last year you did a good review at your R&D day of your EditVerse. So, maybe we'll hope to see an update there with your R&D day this year and how that's possibly informing your next targets for ADAR.
Yeah, I mean, I think, you know, the EditVerse wasn't a theoretical concept. I mean, we were looking at it as: How do we best predict identifying new biology that we can target with editing? I think the team's done a lot of work to turn those tractably into medicines and make it tangible. I think the other opportunity we're going to have at R&D Day is to give some more insights around chemistry. I mean, it's interesting. We talk a lot about the challenges of delivery in oligonucleotides, and, you know, the first jump that people make is, well, we need to put something on the backbone to deliver it to these cell types. And I think if we step back and look at the clinical data sets that we've delivered on, we haven't had to put exogenous moieties to get them into cells.
We see high levels of muscle distribution in DMD. You know, we can extrapolate that to what we've seen in our siRNA constructs to muscle delivery for silencing. You know, we see high levels of tissue penetrance in CNS without having to put conjugates on them. We can do the same thing with siRNAs and splicing. And so I think being able to step back, our R&D Days are always nice opportunities to remind ourselves that, you know, chemistry is a massive differentiator as it relates to delivery and ultimately opening up biology for therapeutic effects, and so we'll have that opportunity.
Yeah, well, we could spend another 15 minutes talking about your first siRNA candidate, 007 , targeting Inhibin E, but let me squeeze in this final question, Paul. You know, you announced your data last week. Your stock had a nice bounce. What do investors really need to understand still, or perhaps even still missing, about the Wave story, to really appreciate what you're doing?
I mean, I think I go back to that last statement I was making around chemistry as a differentiator. I think still in this space, there's still this kind of, you know, what do we need to solve in those different problems? And I think they're still being looked at from how other people are trying to solve them. I think about what we've done in, again, HD with the first demonstration in the clinic of allele specificity, delivered DMD, muscle distribution, and protein. We've delivered first RNA editing program into the clinic, and, you know, we'll get proof of mechanism data.
And as you pointed out, I mean, I think sitting there just at the end is probably the largest opportunity within Wave, which is Inhibin E for obesity, where here's the first new genetic target that comes out of clinical genetic database, UK Biobank, that shows loss of function, builds better cardiovascular outcomes, better type two diabetes outcomes for these patients, low waist to hip ratio, meaning low visceral fat, low lipid profiles. And we were the first last year at R&D Day to demonstrate that we could recapitulate that phenotype in the diet-induced obesity mouse. We could see weight loss similar to semaglutide. We've shared publicly on data that's continuing to emerge and, you know, stay tuned for Research Day, where we'll give an update on obesity, that we could stem rebound weight gain from removal of, GLP-1s.
So kind of an off-ramp to GLP-1s and, you know, the ability to improve on and reduce GLP-1s to see weight loss. And I think the important piece around this is that it's a non-centrally acting approach. The beauty of this target really is you've got this ligand that's produced in hepatocytes in the liver, and its receptor sits on adipocytes or fat cells. So the ability to turn off the spigot right at the source, and therefore induce lipolysis, reduce fat, and improve outcomes with an siRNA format where we have published our siRNA format. We've got 30 times the AGO2 loading efficiency than the best-in-class state-of-the-art siRNAs. So now we're looking at the potential for this, which will start in the clinic first quarter of once a year to twice a year dosing.
So really a differentiated profile from a target perspective, but also really importantly, from a pharmacologic perspective. And so I think as people learn more again about the translation of our pharmacology, I think there's a lot of opportunity for people to learn more about Wave.
Great. Thank you so much for being with us today.
Thanks for having us again.