Good afternoon. Welcome to Barclays' 26th Global Healthcare Conference. My name is Gena Wang. I'm a U.S. biotech sector analyst at Barclays. It is my great pleasure to introduce our next presenting company, PTC Therapeutics. With us today we have Matthew Klein, Chief Executive Officer; Pierre Gravier, Chief Financial Officer. We also have Kylie O'Keefe, Chief Commercial Officer. So maybe before I dive into the questions, Matt, do you want to give a high-level overview of the company quickly, and then we can dive into the questions?
Yeah, sure. Thank you very much, Gena. So for those of you not familiar with PTC, we are a global biopharmaceutical company that discovers, develops, and commercializes therapies for patients with rare disorders. Last year we celebrated our 25th anniversary and also used that as a time to make important changes to the company, including changes in our leadership, prioritization of our R&D programs, and a significant strengthening of our balance sheet to position us well for the next 25 years. So as we move into 2024, we come with a strong base of revenue from products that we commercialize around the world. We have a strong balance sheet, and we have a number of important catalysts in 2024, including initiating global submissions for our PKU program, which we clearly see as a billion-dollar-plus opportunity.
We look forward to submission of a biological licensing application for our gene therapy program, this month. We are also expecting to submit an NDA for our Friedreich ataxia program by the end of the year and also have important data readouts from our Huntington's disease program with our therapy PTC518, which is now considered to be the leading therapy in development for Huntington's disease, as well as our utreloxastat program for ALS, from which we expect to report top-line data, in the fourth quarter. So a lot of exciting activities this year, and we look forward to answering your questions, Gena.
Thank you, Matt. I think at the earnings call, 4Q earnings call, the most surprising part to a lot of investors was the FDA regulatory update. So maybe can you walk us through, like, what kind of interaction with the FDA that lead to, you know, this potential NDA submission in late 2024?
Yes, absolutely. So we reported data last summer from our placebo-controlled, phase 3 MoveFA trial, in which we had the important findings of a significant effect on the upright stability subscale of the disease rating scale known as the MFARS. This was a particularly important result because in the population that we studied of pediatric and young adult patients who are ambulatory, this is the subscale of the disease rating scale that's most sensitive to change in a trial and also most relevant, given that it predicts time to loss of ambulation. So we had a significant finding with a p-value of 0.021, which equated to about a 40% slowing of progression on this part of the primary endpoint of the MFARS scale.
We also were able to demonstrate that we had improvement on the walk test as well as modified fatigue scale, where we had correlation between the changes that we observed in the upright stability subscale as well as the functional components of the fatigue scale. So we had a number of endpoints that moved together that all told the story of an important functional benefits for ambulatory patients with Friedreich ataxia. The discussions we had with the FDA were really being able to share with them what is relatively new knowledge, which is that this disease rating scale, the MFARS, which has four different components to it, actually has different parts that matter at different stages of the disease. This was something that was not really appreciated when we started this study.
Traditionally, when people did clinical trials of Friedreich ataxia, they would designate the entire rating scale as the primary endpoint, much as we did, because what was not clear was that based on the stage and age of the patients you were enrolling as your primary analysis group, there was probably only one or two portions of that scale that really mattered. So that was relatively new knowledge. In fact, some of that knowledge came from a grant that the FDA itself gave to the Friedreich ataxia community to better understand how to study children with Friedreich ataxia.
So when we met with the agency in this Type C meeting in the first quarter, it was really a discussion of how, when you look at the natural history of the disease, the only progression in the population we studied in the trial that would move over time is the upright stability scale subscale. And if you look at our data in the placebo group over 72 weeks, the only portion of the MFARS where we saw the placebo group progress was the upright stability subscale. And then you look at the magnitude of effect we recorded on the upright stability subscale, we were able to communicate that this was persuasive evidence of meaningful clinical benefit.
I think being able to have that conversation really set us on a path of being able to put together an NDA where we rely on the placebo-controlled data from MoveFA as providing that persuasive evidence of effectiveness, and then so that we have many sources of confirmatory evidence that we could provide, including open-label extension data or long-term data from an earlier clinical trial we completed years back in an adult population. Data that we're collecting now is part of a long-term open-label extension for MoveFA.
We also have some mechanistic data, both laboratory and clinical, that can continue to help tell the story that we have, a data package that communicates the effectiveness of the therapy vatiquinone, for the entire Friedreich ataxia population, but also in particular pediatric and adolescent patients who do not have a drug now because the only approved therapy is for those 16 and older. And of course, vatiquinone, our drug, has a very strong safety record in children. So all of that together really puts us in a position that we are planning to submit an NDA, by the end of the year.
Mm-hmm. So regarding the upright stabilities, you know, how much information's already there, you know, predicting, say, the natural history decline? And I'm kind of thinking similar analogies are North Star versus time to rise. But, you know, before that, we already saw the time to rise can predict the patient who's ambulation got already evidenced a long time ago. Like, here's, like, try to avoid, say, data mining because I saw this, and therefore there could be correlation. What is the precedent of the natural history data predicting, you know, this could be a good endpoint to predict the disease progression?
I think that's what the beauty of all this is, Gena, is that that was all going on outside of us. The Friedreich ataxia community and the researchers have done a lot of work, I would say, in many ways equivalent, if not stronger, than what's been done in the DMD world, which we know very well. And what they've been able to show is that on this upright stability scale, there's certain items that begin to that are lost in a predictable sequence that eventually then ultimately predict time to loss of ambulation. And there've now been, at least two papers that have been written on this, again, independent of us. It was all knowledge that emerged while we were doing the MoveFA trial. So it was in no way data mining. It was established independently from us, from a very robust and trusted data source, for natural history.
It's one that the FDA itself has leveraged in the approval of Skyclarys, seeing that this is a robust, reliable natural history registry, that has a great deal of data, that could support regulatory decisions.
Mm-hmm. I think when I look back, you know, at the data you presented last year, we did not see the baseline score for upright stability. And, like, can you do some comparison, like, comments there? Because when we look at the MFARS data, it seems like a little bit higher for the placebo arm than the drug arm.
Yeah, I think for the MFARS there, it's about a point difference, which is actually pretty good in these studies. I think the experts would consider that very well balanced. And we similarly see very good balance between the groups on upright stability in the overall population. It was 23.6 in the placebo group and 22.9 in the treatment group. So a 0.7 difference over 100+ patients, I think, is pretty, pretty good. The randomization worked very well in terms of balancing the groups so that we can make a reliable assessment of treatment effect.
Okay, good. And then, you know, when we go back to look at the data, I think now everyone took a closer look at the data because of the new update, right? So, like, can you remind us why 20 of the enrolled 143 patients were not included in the primary analysis?
Yes. So the primary analysis population for this study was the group of patients who were aged 7- 21. Interestingly, that was done to ensure that we had a population of ambulatory patients who were relatively young because what was known at the time that we designed this study several years ago is that younger patients tend to move at a bit more of a uniform pace. And we also knew that we had a drug in vatiquinone that had a very strong safety record. So this was an opportunity for us to capture to increase our probability of capturing a meaningful clinical effect in a particularly important and vulnerable population with Friedreich ataxia, 7- 21-year-old patients. But we also knew that there'd be a number of additional patients who wanted to be in the trial.
So while the primary analysis population of 7- 21-year-olds was roughly 123 of those who were enrolled, the additional 20 were older than 21 years of age. So it was just a matter of having an opportunity for more patients to participate in the trial. We nonetheless, when we looked at the primary analysis population and the overall ITT population, we in both groups saw a significant effect, a nominally significant effect, on upright stability. And in fact, I think as we look through the remainder of the data points, we see strength in that overall population as well. But it was really a matter of just being able to segment out a younger group that we wanted to focus on that we thought would be more homogeneous.
Mm-hmm. I see. So then, when, you know, you do have a study as an open-label extension phase, and how many of these, say, 143 patients, and also how many of these out of 123 patients enrolled to the OLE part?
So, there are two different aspects of OLE data. Within MoveFA itself, MoveFA was designed as a 96-week study with 72-week placebo control and then 24-week open-label extension. We had the vast majority of patients who completed the study moved into that 24-week period. We then, after that ended, initiated another open-label long-term extension study, as we often do, just to allow patients to remain on therapy if they wish and also allow us to collect valuable long-term data. From the overall 143, we have well over 100 patients who enrolled in that separate long-term open-label study.
Okay. So for the first embedded crossover 24 weeks, how many of these patients?
I think virtually all of them. We had.
All of them. Okay.
Virtually, there were a few who didn't finish. It was all that study was conducted during COVID, so we had a few patients who did not finish the study due to COVID. But of those who finished the 72 weeks, we had virtually all of them move over to the additional 24 weeks.
Okay. And then for the OLE study, do you look at a 143 patient, or you will be focusing on 123 patients?
It's gonna be all patients who participate in the OLE will follow.
Okay. No, like, the demo denominator, would that be intended to treat 123 patients, or will you be also including 20 patients that was older than 21?
Yes, we'll include those as well.
I see. So you include all of them.
Because what we're gonna do in the OLE is we're gonna look at the natural history database and do a propensity match for those who on whom we have data so that we can separate out the trajectory between those who received drug versus what would have occurred, as in natural history.
Okay. So then, you know, given its crossover nature, so how are we looking at the OLE part and then you have two phases, right? There's 24, and then you have additional OLE part.
So it's unclear how they're going to look at it. I know we look back to the experience with Skyclarys where they had asked for an analysis of open-label data relative to natural history. And I think if you look at the label itself, they commented that there was a benefit shown relative to natural history, but these are difficult to interpret. So we see that as the only formal opinion they've given in the past in this disease with open-label data. It's hard to know what they will specifically look for.
What we intend to do is to look at the patients who started on drug for whom we would have 72 weeks of treatment in the trial and then additional time on open-label and see over that longer period of time of 2-3 years what would have happened in natural history. So that's one difference relative to natural history. And then I think the placebo group gives us a couple of different things to look at. One is, certainly, the period that they received drug; we can compare their trajectory on drug to natural history. And then we can also look at how that group changed when they went from being on placebo to being on drug, which gives us another way to try to capture treatment effect, within the same group of patients.
Okay. So I think that's tons of additional analysis and additional data. Will you be able to share those data with us before you submit to the FDA?
Yes. I think the way we're gonna do this. I mentioned we have two sources of open-label data we're gonna look at. There was an older study where we have a 24-month we had previously published from the initial placebo-controlled study, a few years back that we had followed all the patients in that trial for 24 months. And then we did a matched natural history comparison where we had a highly significant benefit relative to natural history. We're gonna repeat that analysis, with a pre-specified analysis plan so that it can really serve the regulatory purpose we're looking for. Since that study has been completed for a number of years, I think that's something we can do relatively quickly, and we'd look forward to sharing those data when we had them.
And then of course, as we move forward and have more analyses as we prepare for the submission, we certainly will share when possible.
Okay. And then, you know, when you compare to the natural history, like, what type of are you only focusing on the upright stability, or were you also looking for other endpoints as well?
So I think we will look across all of the MFARS for a number of reasons. One of the lessons here is that as patients progress through the disease, you start seeing other components of the MFARS start to come into play. So I and the reason I raise that is because while we said upright stability is really important for the first 72 weeks of the study, we know that as the patients start to move from 72 weeks to 96 weeks and even longer, you're gonna start seeing other components of the MFARS move. That's really important. Again, borrowing a page from Duchenne Muscular Dystrophy, which you raised with North Star and Time to Rise, we know in DMD that if you delay time to, say, loss of ambulation, you will then delay next milestones of disease.
It's likely going to be a similar thing in Friedreich ataxia, that one of the things we may very well observe as we move into the open-label extension is that on the treated patients who you preserve upright stability function for longer, you're likely also delaying the onset to the other components of the scales deteriorating. So that's gonna be a very important data point to understand. So we'll be looking at, yes, at upright stability, but also importantly at the overall MFARS, at the other components of the MFARS as well.
Mm-hmm. I think also another layer is, are these data well collected in the natural history study?
I would say that, we've done a lot of work in rare disease. I, I think that the Friedreich ataxia natural history data registry is probably one of the strongest, in existence. This has been an incredibly robust and reliable and thorough data collection, global data collection effort. I, I think that even FDA has previously, when Reata was doing their NDA preparations, I think the FDA has pointed to this registry as an example of a kind of natural history data that they can use to help inform regulatory decision-making. We're very happy to be able to have that as a resource, that we can do the robust type of analyses using propensity matching and the appropriate statistical analyses to truly inform the therapeutic benefit we're having relative to a well-collected and well-characterized natural history.
Mm-hmm. So going back to 72 weeks and 24 weeks, and then additional OLE, so I assume nearly all patients complete the first 96 weeks?
Right.
Okay. And then for the additional OLE, how many patients?
I think we had over 100 entered at additional OLE.
Oh, yes. Yes. Okay.
Yeah.
Okay. That's good. And then, maybe one question is the timing of the data sharing. Since you will have NDA submission, you hopefully will be by the end of this year. Is it fair to say, like, we should see the data 3Q, late 3Q timeframe?
Yeah. So we haven't guided to exactly when. The one thing that I can say is that we expect we'll be able to share the data from the older dataset, that open-label extension analysis, probably sooner.
Sooner than 3Q?
Yes.
Okay.
As soon as it's done, if it's re.
Okay.
One thing we have to do, but, you know, again, we haven't given specific time guidance. We have to, we wanna prepare a statistical analysis plan, submit that to the agency, get feedback on that, and we move to do that as quickly as possible. So there's a little bit of the timing element that's out of our control, which is why it's hard to give any specific timeline guidance right now. But what I can say is those data are collected and clean, so it's just gonna be a matter of moving through the correspondence on the analysis plan before we can do the analysis and have the results.
Okay. And I think, you know, given the complexity of the data and you compare it to the natural history, and I think statistical methodology becomes very important. Like, so will you like, at what point you would align with the FDA regarding what exactly, you know, statistical methodology you can use so that they do not need to do another analysis and then say, "Hey, is your analysis legit?
Yeah. I, I think, we're working on the analysis plan as we're sitting here, the team back in New Jersey working on that. But I would also say that we have a little bit of guidance from what we can read about in the review of the Reata data package, right? They did a very similar propensity score matching with the natural history. They did a mixed-effects modeling to compare the two populations. And we can obviously use that as a starting point so that we have, as you say, a, a the best chance for rapid alignment with the agency on the appropriate statistical methods so we can run these analyses.
Mm-hmm. Okay. Last question regarding FA, the EMA scientific advice recommended additional study. You know, do you see any read-through to FDA or vice versa regarding regulatory decision there?
No, yeah. I think those are different. I would say the written scientific advice we got was a written response. We didn't have a chance to have a dialogue with them. We do know that it's always very helpful to be able to have a live discussion to exchange viewpoints. I think we got the feedback from EMA that suggests another study would be best. We have feedback from FDA saying that we could be in a position to submit an NDA, so we'll focus on the FDA for now and then, as we move forward, sort out the European strategy.
Okay. Good. So now switch gears for your, you know, sepiapterin PKU. Maybe, you know, how's the NDA submission, the MAA submission? Is that on track? And also launch preparation as well.
Yeah. So I can, I'll hit on the regulatory aspects, and I can, like, highlight the launch aspects. Look, we're incredibly excited about this program. I think the opportunity is tremendous. The data package is incredibly strong and supports the real differentiation of our therapy versus what's available and really supports the ability of sepiapterin to meet the significant unmet medical need that remains for the majority, the vast majority of 50,000 patients with PKU. We are planning to submit the MAA this month, which is great. The FDA says the NDA will be submitted no later than the third quarter and possibly in the second quarter. So everything is moving forward as planned, and we really look forward to this being a successful global launch. Kylie, I wanna talk to you about the launch preparation.
Yeah, absolutely. From a launch preparation perspective, we're in a unique position where a lot of the hard work that you commonly do upfront has been done. So when you think about finding patients, there's well-instituted newborn screening in most major markets around the world, and so patients are diagnosed at birth. Well-known treatment centers of excellence. So we know that the prescribing physicians are medical geneticists in the U.S. and pediatric metabolic specialists outside of the U.S. that work hand in hand with dieticians in this space. We know where they are, the individual clinics, and the team has already begun engaging with them. We know from a payer point of view what's required from the data package, and we know that payers have a strong understanding of disease pathology and the value of treatment in this space, which is important.
Then lastly, it's a well-coordinated and well-connected patient advocacy community. Again, the teams have already begun engaging with them. From that perspective, as we look toward launch, it's truly around differentiation and also the understanding of how we can meet the needs of all those patients that have unmet medical needs, which there's a vast majority of those.
Mm-hmm. And regarding the pricing, you know, given now the Valsemolares already could be on the market and generic, you know, so, like, what, what would you be thinking about the,
From our pricing perspective.
Final pricing point of view?
We're not there yet on sort of final pricing, but what we have shared is that we expect to be in the range of Palynziq-like pricing. We think with the ability to treat a broad range of PKU patients across all ages, across all severities, and bring the vast majority of those patients into the range that's expected in guidelines to be able to look to start to liberalize their diet, we think Palynziq-like pricing in that range is something that we think we'll be able to look to.
Okay. Good. We have a few more minutes. I do wanted to ask, Huntington update, which is very important. So I assume data's still on track, 2Q24? Yeah. Okay.
Yesterday, we expect to share data from 12-month data from that initial cohort of patients on whom we shared 12-week data last year, and then 12-week data on the vast majority of the remaining enrolled patients, both stage II and stage III disease.
Okay. And then I think, in the past, Matt, you said the CSF Huntington protein level is not very important. So maybe I mean, from the blood level and also the CSF level, like, what should we expecting, you know, these two data? Should we still predicting, you know, the ratio depends on the blood-brain barrier crossing capability and to predict, you know, the CSF Huntington protein level reduction? Is that the right way to think about it?
No, I honestly think we're not sure what to make of the CSF huntingtin protein. I think no one's quite sure how to interpret that. I mean, we're in a situation with Huntington's disease where we know that the disease is caused by a mutant protein in the brain cells.
Mm-hmm.
We know that if we wanna affect the disease, what we need to do is have it if we're gonna favorably affect the disease through huntingtin lowering, we need a drug that can first effectively lower the production of mutant huntingtin protein. Two, the drug needs to reach the cells of the brain so that it can exert its effect. It needs to get there in sufficient quantity so that you can get the benefits of that exposure on the brain cells. In an ideal world, we'd be able to take biopsies of brain cells and be able to say, "Aha. Prior to treatment, we had this level of huntingtin protein, and following treatment, we had this level of huntingtin protein." But for obvious reasons, we're not in a position where we can take biopsies of brain cells.
So what can we do to try to understand what's going on in the brain? Probably the best reflection is to look at another cell in the body because we know that the genetic machinery that we're targeting works the same, whether it's a fibroblast, whether it's a lymphocyte, whether it's a neuron. And so by looking at the peripheral blood cells, we can get that first important checkpoint, that we have a drug that is effectively altering the genetic machinery so that we're lowering Huntington protein production. Okay. Next, we have to say, are we fulfilling the other part of this? Is this drug getting across the blood-brain barrier, and are we getting exposure in the brain? And the best way we can learn about exposure, again, since we can't biopsy the brain, is to look at the CSF because you can measure drug levels in the CSF.
We saw that. We saw that we're getting higher exposures in the CSF in the CSF in the blood. We already have the data from the first 12 weeks that we are doing what we think we need to do to affect the disease.
Mm-hmm.
Now, the question comes up, well, how do we interpret huntingtin protein that's in the CSF? Well, the first thing is we know it's not there because there's cells in the CSF. There aren't. It's coming from somewhere else.
So I think of the reason why, you know, investors maybe focusing on CSF because we have a precedent. You know, the Roche trial is, you know, they had a long study.
Yeah.
And then showing always below market is a CSF Huntington's.
Right.
Right? And then now, you know, why they were able to do it, and now it's not, you know, applicable now?
Right. Great question. So two reasons. One is they had to look in the CSF because they couldn't look in the blood cells because it doesn't get there. The other is that they were giving very high doses of a drug where you had the highest exposure in the cells that line the CSF. It was intrathecally administered. So it may very well be that what they were observing was related to a function of it being intrathecally administered and affecting a whole bunch of cells that line the CSF more than elsewhere. The other question is they also had a high degree of toxicity. So it's hard to interpret whether their reductions were happening in the face of a therapeutic benefit or in a toxic event.
So, is it possible if you're damaging a whole bunch of cells, maybe that's why protein levels are going down. You're damaging the cells that line the CSF that were secreting their protein into the CSF, and now a lot of them are injured, and maybe you're not seeing as much. That's one possibility. The other is we don't know ultimately what we're gonna see with 518. Maybe over time.
Mm-hmm.
We see a similar thing. Our point is that we don't know yet how to interpret CSF at this stage of the game.
Mm-hmm.
We know that we're in a much different framework than tominersen, which was intrathecally administered.
Mm-hmm.
For us, we look very much forward to seeing what we see in the CSF, seeing NFL, seeing continued safety, and starting to see the clinical markers move.
Mm-hmm.
So I think the one thing you're hearing me pushing back on a little bit is today, I can't tell you what meaningful results we're gonna see at 12 months, just that I think what we really wanna do is start to learn about the effects of the therapy that's demonstrating to be safe, that holds a lot of promise based on its mechanism of biodistribution. And now we'll start to understand what that looks like from a biomarker and clinical scale standpoint.
Mm-hmm. Okay. Very good. Well, thank you very much.
Thank you.
Thank you. Thank you. Thank you, everyone. Thank you.