Wave Life Sciences Ltd.
Q4 2020 Earnings Call Transcript

Published: 4 Mar 2021

[Operator:]

Good morning and welcome to the Wave Life Sciences Fourth Quarter and Full-Year 2020 Financial Results Conference Call. At this time, all participants are in a listen-only mode. As I reminder, this call is being recorded and webcast. I'll now turn the call over to Kate Rausch, Head of Investor Relations at Wave Life Sciences. Please go ahead.

[Kate Rausch:]

Thank you, operator. Good morning and thank you for joining us today to discuss our recent business progress and review Wave's fourth quarter and full-year 2020 operating results.

[Paul Bolno:]

Thanks, Kate. Good morning, everyone, on the call. And thank you for joining us. During the call today, I will provide opening remarks after which Mike Panzara will give an update on our new clinical trials kicking off this year, and Kyle will briefly discuss our financial results. After our prepared remarks, Ken Rhodes and Chandra Varghese will be available for Q&A. 2020 was a productive year for Wave, resulting from focused and deliberate execution that was driven by our commitment to developing transformative medicines for our patients. As we start 2021, I would like to reflect on the progress we made, which has positioned us to have five programs in the clinic this year. In our neurology pipeline, we progressed our PRECISION-HD programs amidst the backdrop of a global pandemic. And we are on track for a data readout at the end of the month. We are delivering on our guidance to advance three new programs into the clinic this year – our SNP3 program in HD, our C9orf72 program in ALS and FTD, and our Exon 53 program in Duchenne, all of which incorporates our new PN chemistry. We also achieved several milestones for the CNS programs we're advancing with Takeda, including the first demonstration of substantial and widespread target engagement in NHPs.

[Michael Panzara:]

Thanks, Paul. We ended 2020 with momentum across development programs as we continued to progress our PRECISION-HD trials towards the upcoming data readout. As Paul described earlier, we anticipate sharing a robust data set for our SNP1 and SNP2 programs by the end of the month. Looking to 2021, we successfully submitted clinical trial applications during December to initiate clinical development for WVE-003, our third allele-selective candidate in Huntington's disease and WVE-004, our variant-selective silencing candidate in ALS and FTD. In addition, submission of a third CTA for WVE-N531, our candidates for DMD patients with mutations amenable to Exon 53 skipping is imminent. All of these compounds incorporate our novel PN backbone chemistry, which has led to favorable profiles in the numerous in vivo studies supporting these programs. We owe the significant progress we've made to our team and our partners in the research and patient communities, who continuously adapted to the ongoing challenges presented by the global pandemic to achieve these objectives. I'll discuss each of these programs in more detail today, starting with our Huntington's franchise. We have purposely built a portfolio of allele-selective HD candidates designed to lower mutant Huntington, while preserving wild type Huntington.

[Kyle Moran:]

Thanks, Mike. Wave reported a net loss of $28.8 million for the fourth quarter of 2020 compared to $56.8 million in the same period in 2019. For the full year ended December 31, 2020, the company reported a net loss of $149.9 million as compared to $193.6 million for the year ended December 31, 2019. Research and development expenses were $30 million in the fourth quarter of 2020 as compared to $49.1 million in the same period in 2019. Research and development expenses were $130.9 million for the full year of 2020 as compared to $175.4 million in 2019. The decrease in research and development expenses in the fourth quarter and full year was primarily due to decreases in external expenses related to Wave's decision to discontinue its suvodirsen program in December 2019 as well as decreases in compensation-related expenses and other external expenses driven by Wave's February 2020 cost reduction plan, partially offset by increases in external expenses related to clinical and preclinical activities related to its HD programs and the C9orf72 program for ALS and FTD. General and administrative expenses were $9.7 million in the fourth quarter of 2020 as compared to $13.8 million in the same period in 2019. General and administrative expenses were $42.5 million for the full year 2020 as compared to $48.9 million in 2019. The decrease in general and administrative expenses in the fourth quarter and full year was primarily driven by the cost reduction plan announced last February. Wave ended the fourth quarter of 2020 with approximately $184.5 million in cash and cash equivalents as compared to approximately $147 million as of the end of December 2019. The increase in cash and cash equivalents year-over-year primarily reflects the $93.7 million in net proceeds from its September 2020 public offering and $59.9 million in net proceeds from its aftermarket equity program. We continue to expect that existing cash and cash equivalents, together with committed cash from our existing collaboration, will enable Wave to fund its operating and capital expenditure requirements into the second quarter of 2023. As a reminder, we do not include potential milestone payments and other uncommitted payments related to our Takeda collaboration in our cash runway. I will now turn the call back over to Paul for closing remarks. Paul?

[Paul Bolno:]

Thanks, Kyle. Wave is entering 2021 with depth and diversity throughout our pipeline and platform. And we look forward to several upcoming milestones. At the end of the quarter, we anticipate sharing data from our PRECISION-HD studies. And as Mike mentioned, our CTA submission for our Exon 53 candidate is imminent. Including this Exon 53 program, we are on track to initiate dosing in three new clinical trials this year. And importantly, we are well capitalized to advance all of these programs through clinical data. Lastly, we continue to generate exciting data for our ADAR editing modality and anticipate sharing preclinical data for our in vivo model and alpha-1 antitrypsin program. With that, we'll open up the call to questions.

[Operator:]

. Your first question comes from the line of Salim Syed from Mizuho.

[Salim Syed:]

Paul, obviously, an important data set coming up at the end of the month. I was just wondering if you can just articulate for us how you're thinking about – this is a very Wall Streety question. I apologize. The bogey here, what's a win scenario, especially in the 32 milligram data? How are you thinking about the various scenarios that we can get? And then, two, more of a higher level question here. Maybe you all just answer the first question first, and I'll follow up with the second one. Thank you.

[Paul Bolno:]

I wouldn't call it a Wall Street question. I think this is the question that's on the mind of our patients and clinicians as well. So, it is an important question for us as a company as we think about the possibility to progress to the Phase III. I'll start and then I'll hand the question over to Mike. But I think what we've continued to look at is a profile that does two things. And I think as Mike alluded to in his section, the wild type, I think, being an important one. I think, one, we want to see – if we see a reduction of 20% to 30%, which is in line with where our peers are in the pan silencing approach and are able to see, in addition, wild type sparing, we do believe that that's a successful profile to advance not just into Phase III studies, but also to think about the pre manifest population more broadly. So, as we think about a profile that could be superior to existing programs, we think that that's a profile that moving forward. Mike, do you have anything you want to add to that?

[Michael Panzara:]

I think that this, as you said, it's a question that is something we've been considering with our partners and clinicians and other people in the field. And I think the allele selectivity aspect of it, we hear time and time again, that even thresholds lower than that could be meaningful if it's shown to be allele activity. But as Paul said, that's where we believe we need to be, and it would be a big success.

[Salim Syed:]

Any comment on NFL expectations? Or how we should be thinking about NFL?

[Paul Bolno:]

I think what we'd like to see, I think like everything else, we need to generate the data and analyze it. But I think if we can analyze the data and see a flat to decrease in NFL, that will be different than other profiles that have been seen. But I think we have to generate the data and analyze it on two fronts. And I think this is the same around the knockdown. I think we're going to look at – and that's why this data set is robust in the sense that we'll be looking at not just the potency, as you pointed out at the 32 milligrams, but we're also going to be able to look at an open label expansion data across two studies at the 16 milligrams and see what happens over time. So, I think we'll get a good assessment in both of those studies at NFL level at the time we have data to share.

[Salim Syed:]

Just a quick follow-up if I can. Strategy wise, which one are you more excited about, the HD data coming up at the end of March or the PN chemistry pipeline that follows subsequent to that?

[Paul Bolno:]

I think we're excited about both, right? I think it's an opportunity. We had an interim look at a partial data set in December of 2019, which was just a snapshot and all patients hadn't received all doses. So, I think for us – and that was just SNP2. We hadn't looked at SNP1. I think this has been a study that we're anticipating and looking forward to reviewing the full data from these studies, and excited about progressing SNP3 and what that brings with PN chemistry, not just in SNP3, but also with C9 and then splicing with DMD. I do think it's important to note that we could have applied our PN chemistry to a pan silencing approach and said, let's just move forward in a way based on following where others have been. And I think our data, as Mike alluded to, in great detail, and I think it's important, around wild type sparing led us apply the PN chemistry to an additional allele specific compound in SNP3. So I think we're looking forward to this data. I think we're looking forward to the upcoming data around the three new programs. And I think the key for us is a commitment to Huntington's disease.

[Operator:]

Your next question comes from the line of Joon Lee from Truist Securities.

[Joon Lee:]

If the results of the upcoming Huntington's updates are below your threshold of 20% to 30%, but there's some signal that you still want to proceed with the SNP1 and 2 programs, would you go up in the dose or switch to PN backbone all together? And related to that, what proportion of SNP1 and 2 patients also have SNP3 that could be treated with your SNP3 program? And up to what dose are you planning to test for the SNP3 program? Thank you.

[Paul Bolno:]

I'll take part of the question and hand it to Mike. And I think the question I'll take is, the reason we chose SNP3 at the beginning was because, in and of itself, given the overlaps where there's SNP1, 2 and other SNPs, SNP3 covers 40% of the Huntington's Disease population. It gives us an incremental 10% on and above SNP1 and 2. But it is a substantial portion of Huntington's Disease by itself. Mike, do you want to take the question as it relates to dose escalation?

[Michael Panzara:]

I think that if we saw some very promising target engagement in a safety profile and allele specificity that all lines up, we would consider going up higher and starting up planning for Phase III at the same time because if we have some robust engagement and a good profile and allele specificity, and we know we can go higher based on our existing preclinical data in terms of the range we can go, there's no reason we wouldn't do that. But we would do that as we were beginning the planning for that next phase of development. And I think you asked one last question about in terms of the dose in SNP3, we are going to base that starting dose on our preclinical data. And as we said that, we will then be from there guided in a very data-driven way by our independent Data Safety Monitoring Board.

[Joon Lee:]

But your preclinical data implies that you use a lot higher dose for SNP3 than you did with SNP1 and 2, is that correct? If so, does that mean that you're also going to test a lot higher dose than what you did for SNP1 and 2?

[Paul Bolno:]

What's interesting, remember, about SNP1 and 2 is we didn't have a preclinical model that guided dosing. So, a lot of the dose exposure work was done in potency in vitro and then exposure in vivo. The data that's going to guide the starting dose, which is why we believe we could start today if relevant dose in SNP3, and as Mike said, be able to accelerate that study in terms of exploring doses beyond that. It is because we have in vivo PK/PD modeling on target engagement with SNP3. So, we have to look at those programs differently around where we can guide our starting, and ultimately, where we get to. They are different drugs. And I think SNP3 leveraging our PN chemistry, we have a lot more data, in vivo data, as we alluded to earlier, that guide those dosing decisions. Mike, is there anything you want to add to that?

[Michael Panzara:]

No, only one thing you alluded to, is that the beauty and the power of this platform is each one of these compounds are different from one another, and that they have their profiles and they are rationally designed individually. So, there is, by definition, just like with any compounds, you're going to have different numeric doses to achieve what you're looking for. So, that would be the only thing I'd say, is that the beauty is having a single compound that we can then optimize in preclinical, each individual preclinical study.

[Paul Bolno:]

And I think it's important to – and I think the previous question addressed this, I think that's why we are excited about this data set. In the absence of preclinical PK/PD modeling, we'll have answers around human data at various dose – higher doses, for long doses in terms of accumulation. And so, I think, again, this being a robust data set, it's really going to tell us where we are.

[Operator:]

Your next question comes from the line of Mani Foroohar from SVB Leerink.

[Mani Foroohar:]

I guess the first question is around this upcoming Huntington disease readout. Should we expect that the assays being used in this readout should be modestly different in subtle ways from what was used in the last time we saw data from this study in May 2019? I think you mentioned something about your pull-down assays with magnetic beads and assay, as you think about those changes as we interpret these data sets.

[Paul Bolno:]

I think the most important piece is the mutant Huntington assay and the neurofilament assay are the same assay we used in the previous readout. Those are assays that have been used by others. And so, no change in the interpretation of how those assays. What's new is really the work and the team has done an amazing job this year of really working through how to develop an assay to assess wild type. And so, Mike, do you want to take the second piece because that is in a different assays than the total assay?

[Michael Panzara:]

I think the work that's been done has to take these assets and the available reagents that have already been used in the field and already what we used in the initial assessment and modify them in a way that allows us to directly measure wild type protein. So, the antibodies being used in this pull-down approach are the same antibodies that would be used in the measurement in the mutant Huntington assay, except that they're now applied to beads, and we use that approach to pull out the mutant, so we can directly measure wild type on a total assay that is also already been developed. In terms of interpretability, with mutant, it's going to be the same. And the wild type now is a new approach that we'll be presenting in the context of the mutant. But it's using available reagents, it's using techniques that have been deployed before.

[Mani Foroohar:]

And then, considering WVE-003 or SNP3, whichever you want to call it, considering time to data, how should we think about the sites in which patients are being enrolled and rate of enrollments and the impact of screening around SNP3 to determine when we might see that?

[Paul Bolno:]

Step 1 is, obviously, we'll set the framework once we dose and how to think about going forward. I think what we've leveraged and we've said this a couple of times, which I think is really important, is we're able to leverage the infrastructure, the clinical trial infrastructure, the SNP phasing and screening infrastructure, running the assays, I think there's a lot of can take from SNP1 and 2 in terms of infrastructure, architecture, and execution, and be able to apply that rapidly to SNP3. Mike, I don't know if there's any additional insights you want to do add to that.

[Paul Bolno:]

I think that the sites we've been working with for years now know how to do this. And then, it's just a matter of getting those prospective numbers on SNP3 from patients that they have in their practices. So, no, I think once we get the first patients in, we'll be able to really see where we are.

[Paul Bolno:]

To echo and add on to that, that is the advantage of having run the SNP phasing studies over time, is we can identify the SNP3 patients to get a start, the clinicians know how to run the studies. And as we said before, around having the models to predict where we can begin the study, there's a lot of features that we think are in place now for this study to execute a lot more quickly than the first two where we had to start .

[Operator:]

Your next question comes from the line of Paul Matteis from Stifel.

[Thor Nagel:]

This is Thor on for Paul. I think you mentioned previously that it was a COVID dosing delay, but can you just give us a little color on why we won't have 32 milligram data in HD1? And then kind of a follow up to that, is there any reason that this 32 milligram data in HD1 will be meaningfully different than HD2 data at 32 milligrams? Thanks.

[Paul Bolno:]

No. And as we said last year on our call where we provided that update, there were two patients that had to be rescheduled for their dosing related to COVID. And because of that, they'll get their last dose in the – I think we said end of March. So, given that shift, they're already scheduled, they'll be dosed, and that was unfortunate last year, but that's what we are dealing with in terms of a global pandemic. In addition, to your point on what we see differences, that's why we think this is a robust data set and we'll be able to make an interpretive decision with that because we'll have matched cohorts up to 16 mgs where we can compare that pharmacology between SNP1 and 2, knowing that the SNP1 32 milligram data will be following on that. So, as we're always saying, different molecules, there could be differences, but we'll have the data to determine where we are, and we'll have that data on the other side. But given past experience and understanding, what we don't want to do is cut a partial data set. We'd rather give out the data set be intact and evaluated. And based on the other data we'll have, including the open label extension data sets, 16 across SNP1 and 2, we believe we'll have the data that's necessary to relate SNP1 and 2 pharmacology.

[Operator:]

Your next question comes from the line of Luca Issi from RBC.

[Lisa Walter:]

This is Lisa Walter on for Luca. Just again on our HD program, we recently saw some data from the open label extension trial for Ionis and Roche's tominersen on the European clinical trial register. The data here was early, but it suggested no cognitive benefit by HD-CAB, nor a benefit in ventricular volume. Just wondering what was your reaction to that data? And how should we think about implications for your program? Thank you.

[Paul Bolno:]

This is a question that we focused on actually years ago when we began our program. If we think about the early days of when we decided to go into Huntington's Disease with our oligo approach, our view was to take an allele selective approach because we believe the biology supported that. We always said, as both studies were kind of moving forward, that the reason we were taking an allele-selective approach and sparing wild type is that Huntington's is both a toxic gain of function and a toxic loss of function disease. Both are important. I think Mike eloquently shared this tug of war concept that I think is just so critical, at least to try to imagine this really is a two-pronged disease. Our view was that sparing wild type and wild type or wild type reduction wasn't going to be equivalent to a safety signal. But what would probably be the manifestation of treating the toxic loss of function would be that it would become harder to see a clinical benefit with a requisite amount of knockdown. And so, therefore, our approach was remove the mutant toxic gain of function protein, but preserve the toxic loss of function, so that you have the protein there to help. We have to see this study go out. This thesis, we're running the allele specific silencing clinical studies. It's interesting to watch what pan silencing does. But there's two different biological approaches that we're taking in the treatment of Huntington's disease. So, we have to watch the data emerge and be prepared. But I think this is aligned with some of our thinking around what happens when you deplete wild type. Mike, I don't know if there's any anything you want to add or…?

[Michael Panzara:]

No, nothing to add, Paul. That captures it nicely.

[Operator:]

Your next question comes from the line of Eun Yang from Jefferies.

[Suji Jeong:]

I have a few questions about the HD program. The first one is about the wild type Huntington assay that you're going to be using. Just wondering if that assay has been validated with external party? And also, for the OLE data that you're expecting to report, how do you expect to utilize the data to guide the next step? And I have a follow on question on the alpha trypsin deficiency program. Thank you.

[Paul Bolno:]

Mike, would you like to take the first two Huntington's questions?

[Michael Panzara:]

Regarding the assay, we've been working with the developers of the original assays that we employed to develop this particular assay. We've been working to get patient samples that have allowed us to go through the validation process for the assay. And our intention is to work with those external parties to publish the assay and share the results and share it with the community at large, so that it can be used. So, this has been – we couldn't have done what we've done with this assay without the collaboration of external parties, and we're really pleased where we are, and they're really pleased where we are. In particular, our partners at CHDI who – without their work, we wouldn't have been able to leverage and adapt to take this next step. Regarding the OLE data, that's a really important data set. When you think about the PRECISION-HD studies, you think about any proof of concept study, you want to get that short-term exposure to try and get a nice, clear short-term dose response. The OLE gives you that added power. Even though it's open label, in the setting of a biomarker like we're doing, it really allows you to see not just a dose response, but the impact of chronic longer-term dosing, which gives you a sense of not just an effect, but the depth of the effects with chronic treatment, as well as safety, of course. But what we've seen from the Roche/Ionis studies is that the degree of mutant knockdown, the degree of Huntington knockdown, I should say, in a planned selective way, it increased with the extended duration. So, it's a really important data set that is going to help us in our decision making as we look at dose selection for future studies. So, it's very valuable to be able to have this and we're grateful that patients have opted to continue in the studies.

[Suji Jeong:]

And my follow-up question is with AATD. Based on the non-human primate, what is your reasonable expectation ?

[Paul Bolno:]

I think there's two things. This is why we actually want to run the mouse model data and the work twofold on the mouse model. One is to make sure that not only are we looking at the SERPINA1 animal model, but we're doing it in the crops with the human ADAR mouse, is that we can expedite the work that we do on optimization, so that we're not optimizing a sequence to edit mice, but we're optimizing a sequence that can be clinically translated to patients because it expresses human ADAR at the appropriate level. I think what's interesting and exciting about using the mouse work is, while we see substantial editing efficiency in NHPs and we can edit mice, we can move from just looking at a percent editing of a number because we know that just like a number of oligonucleotides, it's not a one for one ratio with an editing generating protein, but in the animal model, really be able to start measuring the protein level. So, this correlation looking at editing percentage, but really starting to establish biomarker threshold in terms of how much protein do you need. As we think about in the clinic, that restoring of levels greater than 11 micromolar of AAT protein, but we know that the protein infusions there are leaving patients uncovered for a good amount of time. And so, our belief is by using the model, we can really do several things. We can continue to characterize the protein production, the fact that it's functional and be able to start establishing dose responses as we have for a number of our other programs in silencing and splicing that we can then translate those molecules to the clinic. So, I think we've got great data around GalNAc conjugation and exposure and NHPs. We've got exposure in mice, using – getting the human ADAR coupled with the target. So, I think if we can see durability and duration, we can see PK/PD and generation of protein, those will be the features that we'll be looking for as we set up a transition to the potential to have a clinical program here, but we're very excited about ADAR as a space and how we can apply it broadly. And we're excited about the AAT program specifically.

[Suji Jeong:]

[Paul Bolno:]

I apologize. You broke up a little bit on…

[Suji Jeong:]

Sorry. What is the expected dosing frequency for AAT…?

[Paul Bolno:]

That's what we're going to establish as we – that is the primal example when we look at duration too. So, when we think about the dosing frequency, that's going to be driven based on our preclinical models. Obviously, now with GalNAc, we have subcutaneous administration that we've looked in both large animals and small. So, we know that subcutaneous administration with GalNAc is efficient. But we need to do that work in the in vivo models to explore that PK/PD relationship to be able to move forward.

[Operator:]

And presenters, there are no more phone questions. I'll now turn the call back over to Dr. Paul Bolno.

[Paul Bolno:]

Thank you, everyone, for joining the call this morning to review our fourth quarter and full-year 2020 corporate update. And thank you to our Wave employees for their hard work and commitment to patients, especially the global pandemic. We look forward to speaking to you again soon. Have a great day.

[Operator:]

This concludes today's conference call. You may now disconnect.