Welcome to Barclays' 26th Global Healthcare Conference. My name is Gena Wang. I'm a biotech analyst at Barclays. It is my great pleasure to introduce our next presenting company, Alnylam. With us today we have Pushkal Garg, Chief Medical Officer and EVP, Development and Medical Affairs. Pushkal, maybe before we dive into the questions, give a high-level overview of Alnylam.
Absolutely. Well, Gena, thank you first of all very much for, and you and the Barclays team for inviting us here today. It's a real pleasure to be here. Look, Alnylam is now entering its 22nd year as a company, really founded on the promise of RNAi therapeutics. And we're incredibly proud of the progress we've made in terms of bringing an entirely new class of medicines forward that can actually silence specific genes in the genome, and bring transformative medicines to change the lives of patients. We've now, in the last five years, brought five medicines forward to market, and we are on the cusp of doing even more in terms of delivering into new tissues, like the CNS, where we had a major proof of concept this past year.
As we announced at our R&D Day, going into even new tissues, muscle, adipose, and others to bring the promise of RNAi Therapeutics. We're now selling our products, commercializing our products around the world ourselves in over 30 countries, and more with distributor markets. Very proud of the commercial success that we've had. And financially, we're on the path to profitability. We said that we will become profitable by 2025, and we remain on track to do that. We're really proud of what we're doing as a company, and looking forward to some major milestones this year, which I'm sure we'll talk about, and in years to come.
Great. So I would assume you alluded to HELIOS-B.
No, no, no. I was talking about cardio too.
Okay. So HELIOS-B, we got so many questions. And I think of this previously, APOLLO-B was one of very major catalysts for the biotech sector, and now HELIOS-B, again, so important because not just Alnylam, it impacts so many different, also other drugs in development. And so, based on, you know, the, I think in the past you comment based on the three data sets. You revised the statistical analysis, adding monotherapy component. And there's some investor pushback basically saying, "Oh, is that suggesting the concerning of actually detrimental, impact from both silencing together with stabilizer?" From a scientific point of view, do you think that that's at all?
Yeah, Gena. Look, it's no surprise that there's a lot of interest externally on HELIOS-B. We too are very much looking forward to those results. We remain as confident as ever in the design and execution of that study, and we can go into some of the specifics. Maybe one thing I will say is just as a reminder to everyone that we did recently state that the top-line results will be available in late June or early July. Maybe one update to that is just to say that, you know, given how important that readout is, we plan to be entering a quiet period in mid-May in advance of those results. So we'll be engaging with investors up to that point, but in mid-May we'll enter a quiet period until those results become available. With regard to your specific question, Gena, no.
Look, I first of all, I don't think biologically there's any reason that I can think of why there would be a detrimental effect from the combination. And in APOLLO-B, we did have, you know, a modest-sized cohort of patients who were on the combination. And look, I think all told, we saw more or less what we expected, which is that we saw on a number of endpoints in this very small cohort some additive effects. When we looked at the outcomes data in that particular subset of patients, we've seen some encouraging, additive effects there as well.
It remains to be seen what the exact magnitude of that add-on effect is on top of a stabilizer, but we would expect that there will be some add-on effect, and then we think that's exactly what we saw in APOLLO-B, and that will be more further elucidated in HELIOS-B.
Mm-hmm. Very good. So now regarding the monotherapy arm, right, when I do the calculation, the study, you know, by itself could be very similar to BridgeBio ATTRibute Study in a way that you would not allow any tafamidis in the baseline. And maybe when we look at the number of patients, your study actually is has a higher bar to hit a stat. So we're thinking about the number of patients, 60% of your total patients compared to maybe BridgeBio a little bit higher number of a patient. And then also the drop-in rate. The first 12 months they were not allowed tafamidis dropping, you allow basically right at the beginning. And your p-value is smaller, 0.025. So, you know, giving all these, you know, the what, like, do you think that you are setting a very high bar for yourself?
No, not at all. Look, I think what it really shows is the confidence that we have in the mechanism of action and what we expect to see in the monotherapy, as well as in combination. I'll remind you that the combination of the overall population remains as part of the primary endpoint. I think I would maybe say that I'm not sure that BridgeBio would be the appropriate reference point. I'll remind you that, with 600 patients, it wasn't able to show actually a significant effect on a six-minute walk test of 12 months, which vutrisiran, a similar mechanism, was able to show in 360 patients in 12 months, right? So I think comparing to the ATTRibute Study may not be the best benchmark there.
What I'll say is that, look, our enthusiasm about the monotherapy effect comes from first and foremost what we saw in APOLLO-B, where we saw over 2+ years, including after crossover, that we saw this maintenance of effect which was quite durable, where we saw evidence of empowered endpoints of stabilization of disease, both on functional status and quality of life, biomarkers, etc. All of that's incredibly encouraging to us and suggests potentially a very differentiated effect on these diseases. Now, I'll remind you, those are all predictors of outcomes, and so that sort of suggests to us that we may be having an outsized effect. We did see encouraging trends in terms of outcomes as well, although that past study wasn't powered for it.
When we look at it, and, um, I'll just as a reminder that what we've done in this study is that we've, you know, while that monotherapy cohort's about 400 patients, we've enriched for those patients who are most likely to benefit or show the largest therapeutic effect. NYHA Class I and II, that's been shown in two large pivotal trials now, is having the largest treatment effect. With the changes that we've made in terms of duration of follow-up, we ostensibly have, for all intents and purposes, more or less a 36-month study. So it's about 20% longer than both ATTRibute and ATTR-ACT. And so and then when I look at the sample size, it's about the same sample size as ATTRACT. So I think all of those things give us the confidence in the monotherapy arm.
Mm-hmm. So maybe, like, you do have the opportunity to extend all the way to 36. So why you choose one month more?
Yeah. Look, I think what we did when we looked at that is we, you know, took advantage of some aspects of how the trial was done. I'll just remind you that we did not change the structure of the trial in any way. The patient experience on the study remains unchanged. What we did do was take advantage of the fact that we had patients in time before patients were crossing over into the open-label extension, and so we could add some extra exposure into the double-blind placebo-controlled exposure in the study. When by doing this, we actually felt like we actually have brought over 20% additional patients to the full 36 months. We think we're getting the lion's share of the benefit that we were hoping to get, and that's really the critical factor for us.
In fact, ostensibly, this is more or less patients who continue into the study, more or less a 36-month study. So we're really, you know, we think that we've gotten the lion's share of the benefit, and it didn't make sense for us to sort of go out farther, you know. And I'll remind you that this is in the period of the study which is the most critical, which is when you actually see the greatest separation or the greatest number of events occur, particularly in your placebo arm. And so we think this substantially adds to the powering of the study, and really helps us. Maybe one other point I'll just bring up, Gena, that, you know, I've heard, through some conversations that there's some speculation that somehow, this was informed by the DSMB or DMC, something like that.
I just have to say the comments and the questions are a little bit farcical, frankly. This is not a change in any way in the design or structure of the study. This is a change in the analytic approach. It's not something that the DMC needs to is based on some review of DMC data or blinded data, etc., etc., etc. This was really just a change in the analytic plan. So definitely want to dismiss those concerns or those questions and idle speculation.
Okay. So you mentioned that, you know, the adding additional 3 months or reach as many patients as possible, reach 36 months, that will make a very big difference in terms of initial like, the final 3 months is the event.
Mm-hmm.
Could you provide a little bit quantitative, numbers, like, in terms of, like, how much more percentage events we should be able to see in that the last additional 3 more months?
Yeah. Look, I can't give you an exact number, Gena. I think I would just take.
Or some reference point.
Yeah. No, I would just look at the existing curves that you see from placebo-controlled trials in heart failure in general or HFrEF/HFpEF. You can look at ATTRibute. You can look at ATTRACT. And you can see what's happening in terms of placebo rates over time and how they're increasing, and how active drugs really are able to slow that trajectory. And so you can kind of follow those lines forward. But certainly, as patients advance in their disease, they have more time; those placebo patients are going to accrue a lot more events the farther they are out from randomization.
Mm-hmm. Okay. So, with updated study, so then really, like, the monotherapy part, the one we were talking about, the high bar.
Yes.
P-value low is under the condition that you fail overall patient population, right? Otherwise, the p-value would be 0.05 for monotherapy component. Is that right?
Yeah. So the way to think about the way that we've controlled for alpha in this study is basically that, look, if both the overall population and the monotherapy hit, then the p-value for both has to be p of 0.05. If only one of the two hits, it has to be p of under 0.025.
Mm-hmm.
Yeah.
Okay. So would that update?
The way to think about it, right, is that we've basically, in terms of the endpoints, we now have the overall population. That's a blended population, the monotherapy group, as well as the combination group, that constitutes 40% of it. We've seen encouraging data on both of those subsets out of APOLLO-B. We remain confident about that. That's 100% of the sample size, all 655 or 60,160 patients, roughly, in there. And then we've got an enriched subset of 60%, 400 patients. It's the monotherapy group that has the potential to show an outsized benefit, and we've elevated that into the endpoints trial.
Mm-hmm. So would this updated study do you think that you will have a, oh, maybe I saved that question. Why for the secondary endpoint, why, you know, the order you give, you know, when we look at it, you know, the six-minute walk, you know, test that we the first, the all-cause mortality actually is relatively lower after KCCQ. So why that order?
Yeah. Look, I think what we did when in revising the secondary endpoint structure is really focused on those endpoints that we think are going to be most important to showing differentiation, of with vutrisiran, relative to other available therapies that might be out there. And really most informative for physicians, in their prescribing and patients, in terms of deciding which therapy to get on. And what we saw is, you know, very encouraging data out of APOLLO-B that really is recapitulation of what we saw in the neuropathy setting, that by silencing TTR upstream through an RNAi mechanism, that we can result in rapid knockdown of TTR and result in stabilization of disease. And we've seen that for 2+ years in the APOLLO-B study in the cardiomyopathy setting. We've seen that previously in the polyneuropathy setting.
And the best ways to demonstrate that, we think, are through what patients experience, which is their functional status every day and their quality of life every day. And that's why those two endpoints are in there. You know, the primary endpoint is, as everyone knows, is on death and recurrent all-cause mortality and recurrent CV events. That will be split out in the primary endpoint structure. The two components will be shown. But all-cause mortality, of course, is a higher bar, and so we've captured that in the secondary endpoint structure as well. The ordering is really that we think that with the sample size and the duration that we have, we should have a tremendous amount of power for 6-minute walk test and KCCQ, so we put them higher in the hierarchy, and then we put all-cause mortality, the highest bar, after that.
Mm-hmm. So for primary endpoint, you also will be able to show if you were able to hit a statistical significance for the all-cause mortality part, right? That would be a statistical hierarchy, testing all-cause mortality, then move to the hospitalization events.
No. So the way that, you know, we're using an Anderson-Gill approach, so it looks at the blended of mortality and recurrent CV events in the primary endpoint. But the results will be reported out as showing the two components of that as well.
I see. So, when the top line release, you will be able to show both, like, p-value for each one, or will be blended together?
Yeah. No, it'll be for the primary endpoint, which is the blend of all-cause mortality and recurrent CV.
Mm-hmm. I think the reason I'm asking is we're thinking, would you be able to have a chance to show, say, stat the all-cause mortality, which, you know, in the BridgeBio case, they did not?
Yeah. So, look, in the primary endpoint, it's a blend of these two components.
Mm-hmm.
The actual effect sizes and the confidence intervals will ultimately come out. That, that'll be part of the endpoint structure. Then there is a separate endpoint for the higher bar of all-cause mortality, and that was included because, you know, we think as a standalone endpoint, it obviously is important. While the study was not formally powered for that, it's possible that we may see a stat-sig effect, and we wanted to be able to attribute a p-value to that. So that's why it's included in the hierarchy, in an alpha-conserving strategy.
Okay. Very helpful. So now, say, if we fast-forward, so if you only show, say, monotherapy part, so what kind of label you were thinking that could be, you know, this current data, like, whatever the data, if it's positive, what kind of label that could be? And how would you be competing with, say, for example, other drugs, stabilizers, maybe already two on the market?
Yeah. Look, you know, we've talked about what we're doing from both a development perspective, but we also think that the changes and optimization that we've done really aligns very much with where we see the market being as well, the external landscape. If we, you know, if again, we remain confident about the overall effect and the overall population, and seeing combination effect, but if only the monotherapy were to hit, you know, we think we'd be able to show that there is a reduction that would imply that there is an improvement in both death, recurrent CV events with monotherapy and Amvuttra. And, you know, where we see the market evolving over the next several years is there's multiple new entrants, that at the time, until Tafamidis goes generic, we think that this is largely going to be a monotherapy market.
That's not to say there won't be exceptions. By and large, we think that from an access perspective, a pricing perspective, it's going to be quite challenging to be on multiple therapies at the same time. And so we see this largely being a monotherapy market. We see AMVUTTRA, therefore, in, even in that construct, being positioned either as a first-line agent or as a monotherapy that can be used in patients who progress on a stabilizer. We know from a lot of reports, both data from the clinical trials, both ATTRibute and ATTR-ACT.
Mm-hmm.
as well as physician reports and real-world evidence, that, you know, a majority of patients on tafamidis do continue to decline over time. And so, we think there's a very rich opportunity for a new therapy that might come along, like AMVUTTRA, hopefully showing stabilization of disease that might be used, again, as a differentiated, as a front-line therapy or for those patients who progress on tafamidis. Once we expect, you know, once tafamidis does go generic, we expect there'll be more combination use at that point.
Okay. So maybe regarding these two scenarios, first-line or second-line post-stabilizer progression.
Yeah.
Based on your clinical trial design, like, there's no evidence showing whether it should be first-line or second-line, right, because there is no tafamidis progressor or, you know, so, like, what will be the data sets? You do have the other two subgroups, with the combination with tafamidis. So what kind of data sets? I know it's not powerful, to detect stat significance, but what kind of, say, data sets or trend that will warrant for the first-line versus, say, second-line post-progression?
Yeah. Look, I think, as we've been talking about, I think the data coming out of APOLLO-B in the cardiomyopathy setting, as well as what we've seen in polyneuropathy, really suggests that, you know, RNAi-mediated silencing of TTR and the rapid knockdown that that causes can really result, we believe, in a differentiated effect on this disease from what other therapies know. So while there's no head-to-head study, you know, as I just said, we know that patients on stabilizers, while those drugs have done an amazing job with tafamidis in helping patients with this disease, there's a lot of unmet need. Patients do continue to decline based on the clinical trial data month on month in terms of losing some functional status and quality of life. We've seen that recapitulated when, you know, based on, physician data, etc.
So we think that what we're seeing in APOLLO-B that needs to be shown now in HELIOS-B is that we may be stabilizing this disease by targeting TTR upstream. And we think we may, you know, we'll see what the mortality and the hospitalization curves, you know, it right now, we know on the stabilizers, it can take up to 18 months for those differences to emerge. It may be that we see some earlier separation. We certainly saw that in APOLLO-B as well. So we think there could be some differentiated effects that may emerge in the data set that, you know, we've been excited about from what we've seen in APOLLO-B.
And if HELIOS-B shows that differentiated profile, we think that that will allow physicians and patients to make those choices, potentially, to use AMVUTTRA upfront and/or to switch tafamidis patients who aren't doing well onto that. I'll just remind you, perhaps, of the greatest proof point of this, which is, you know, tafamidis was approved for polyneuropathy in hereditary patients in Europe. When the APOLLO data came out on ONPATTRO and the HELIOS-A came out for AMVUTTRA, what we saw in both settings is and there was no head-to-head data. There was no patients who were sort of stabilizer progressors. What we saw was what looked, I think, many people thought was a very differentiated profile from what tafamidis had shown in that population.
As a result, we've had, you know, a lot of front-line use, and frankly, a lot of the tafamidis patients have switched over to a silencer. So I think that, again, it'll remain to be seen what the HELIOS-B data set looks like. It certainly has to proof point that, you know, physicians and patients are going to be going to the therapy that, you know, has the greatest beneficial effect for the disease.
Okay. I will also save the competitive landscape, the question later. Before we move that, maybe wanted to ask you housekeeping questions. For NYHA Class 1, 2, for HELIOS-B, should we expect similar range or percentage of inclusion comparable to APOLLO-B?
Yeah. Look, I think for all intents and purposes, the inclusion criteria for HELIOS-B and APOLLO-B were very, very similar.
Mm-hmm.
Yeah. So I think I would look to the APOLLO-B baseline demographics to give you some sense, what HELIOS-B would look like.
Okay. And then the other question is the dropout rate, you know, as in the past. I know you will not disclose the specific numbers, but we do know, say, APOLLO-B study was also similar time frame enrollment. Like, is that a good benchmark regarding the dropout rate for tafamidis?
Yeah. What I'm going to say is, as we've talked about in the past, Gena, that, you know, in terms of we took pretty conservative assumptions, when we powered and designed HELIOS-B. And, you know, we're happy that the dropout rates are significantly less than we had sort of factored in our calculations, so, we feel good about the design and the execution of the study.
Okay. And then one last question. For top-line data, what exactly will we share on the?
Yeah.
Yeah.
Yeah. So look, I think, you know, based on the endpoints that we've shared with you all, over the last month or so, we will be providing for the primary endpoint, which is now in these two populations, as well as the secondary, the p-value. In the top line, we expect to show give some data and information around safety. And then we will also provide some information around subgroups, and including the tafamidis subgroup. And then we'll present a more whole picture of the data at a medical conference thereafter.
Okay. So when you will share some information for subgroup, will you just, like, high level, or there is a trend separation or not, or what addition?
Yeah. I think we're just going to have to pay attention when we put it out. But I think all I can say today is that there will be information around subgroups in the top line.
Okay. Okay. That's very good. And I think we have a little bit over one minute. I wanted to ask, you know, you do have an ALN-TTRSC04, which could be, I think, very exciting. And then giving all the, you know, the dynamic evolving base and also hATTR, you know, clinical trial design, and then by the time, how many drugs will be on the market, what kind of, say, three-study design for ALN-TTRSC04 that will make your drug competitive once it's launched? And then giving also the dynamic tafamidis could be, become generic by the time the drug launches.
Yeah. So look, I think we're very excited about ALN-TTRSC04. I think if you take a step back, really what we're trying to do at Alnylam is we really see the opportunity to be leaders in this space, TTR proteins, and continue to bring innovation and transformative therapy to these patients. We've done that with ONPATTRO, then with AMVUTTRA, and now ALN-TTRSC04 gives us the opportunity to bring forward a therapy that may allow for more for continued rapid but even more durable knockdown, potentially even once a year, and even deeper knockdown. The phase 1 data that we presented at our R&D day showed up to 97% knockdown of TTR, which is really unprecedented. And so, we're really looking forward to bringing that forward as the next level of innovation. Basically, we want patients to feel more or less free of their disease and unburdened by their disease.
We think we can do that with AMVUTTRA and then with ALN-TTRSC04. It's a little early to talk about exactly what the design of that study's going to look like. Certainly, we, you know, we're going to be informed by the continued phase one data, but most importantly, by the HELIOS-B results. So once we see those results, we'll be in a better position to talk about what that phase three program will look like. For ALN-TTRSC04, we've said that we plan to start a study at or around year-end, and we'll share that information. But, you know, certainly, you can imagine that HELIOS-B will be very informative to that.
And look, we're going to bring forward a development program, as I think we've done in the past, which hopefully is robust, but also, highlights the urgency of bringing forward innovative therapies to this population. And so we'll be using the wealth of, you know, patient-level data that we have now from multiple therapies in-house to sort of accelerate the HELIOS-B program as well or the, ALN-TTRSC04 program as well.
Great. Thank you very much, Pushkal.
Thank you, Gena. Great seeing you.
Thank you. Thank you, everyone.