Good day, and thank you for standing by. Welcome to today's discussion with BridgeBio, which we will cover the company's additional data from ATTRIBUTE-CM, as presented at ISA 2024. At this time, all participants are in a listen-only mode. Please be advised that today's conference is being recorded. I would now like to introduce you to the speakers for today. Dr. Neil Kumar, PhD, CEO of BridgeBio, Dr. Matthew Maurer, MD of Columbia University Irving Medical Center, Dr. Ahmad Masri, MD, MS, Oregon Health & Science University. I will now hand it over to Dr. Kumar.
Thank you, operator, and thanks everyone for joining this call. I'm grateful to be joined today by the BridgeBio team and Doctors Masri and Maurer, two physician-scientist pioneers whose work has helped forge this hopeful moment in the ATTR cardiomyopathy field. All of us around the table have felt the energy these last couple of days at ISA and the advances being made in understanding disease path mechanism, diagnosis, early treatment, matching the right treatments to the right patients, and patient and family support all augur well for a better future. Today, after I provide some brief context, Dr. Maurer will present findings that tie ever higher levels of stabilization, as measured by serum TTR, to ever better clinical outcomes, a core belief underlying the design of acoramidis. And Dr.
Masri will present findings that look at CV hospitalization, a key component of the composite endpoint in modern trials, given the dramatic decrease in mortality events and the lack of impact tafamidis generated on CV hospitalization. Dr. Masri will look at the relationship between CV hospitalization and overall survival in the context of current stabilizers. Before we get into this exciting content, I'd like to provide some brief context, and I guess even before that, I'll remind everyone that I'm making forward-looking statements today. The slide deck associated with this call is publicly available, and we'll refer to specific slide numbers as we progress in this discussion. I'll start with what's always the most important slide in our document, slide 3. A thank you to the amazing and inspiring patients and families, advocates, physicians, clinical research staff, and collaborating research partners that make our work possible.
We've had the privilege of meeting many of you here this week and hope to do even more of that in future conferences. Yours are the efforts that make whatever impact acoramidis is to have possible, and in turn, we recognize our responsibility to you to move expeditiously, to provide this medicine to patients as broadly as possible, and to engage in deep research. Moving to slide 4, I wanted to touch briefly on the design principles that we believe underlying our best-in-class hypothesis. Design objective number 1 was to create a compound that would continually maximize TTR stabilization for all patients and therefore minimize the toxic consequence of the destabilized TTR tetramer.
Several lines of evidence that complement the research being presented this week suggest that maximizing stabilization should lead to ever better benefits for patients, including, first, historical genotype, phenotype data and the disease protective properties of the transallelic trans-suppressor T119M variant, and secondly, the outperformance of the 80 mg tafamidis dose, which is about a 60% stabilizer versus the 20 mg tafamidis dose, as previously published in the ATTRACT trial. Design objective number 2 was to pair toxic monomer minimization with the preservation of the TTR tetramer, a protein responsible for vitamin transport to the eye, a protein that no known species lacks or is haploinsufficient for, and a protein that is maintained at high concentration throughout the course of life. Attendees at ISA heard a pretty interesting talk yesterday from Dr.
Buxbaum, raising the intriguing potential advantages for keeping TTR around in the long term. On slide five, you see much of what the research we are conducting these days relates to. In any disease setting BridgeBio works on, our goal is to connect the dots so we can form a coherent global understanding of how drug, molecular path mechanism, and outcomes all relate. In ATTR cardiomyopathy, we began on the shoulders of tremendous work that had been done linking human genetics to stabilization levels. I referred to some of that on the last slide. Our ATTRIBUTE trial allowed us to connect ever better levels of biochemical stabilization to impact on serum TTR and NT-proBNP. Today, we extend from these measures of evidence of disease and treatment response to hard outcomes and ask how predictive changes are to the well-being of patients.
Furthermore, we ask how these hard outcomes interrelate, making crucial connections between going to the hospital less and surviving longer. In all, our goal is to continue to fill in the blanks so that we can connect more stabilization to more marked improvements in serum TTR and NT-pro, to staying out of the hospital longer, surviving longer, and feeling better. Moving to slide 6. Amidst all of our evolving understanding of the connect-the-dots picture, we tried very simply to articulate the value of a more potent stabilizer to patients and physicians. Simply put, people survive more and go to the hospital less, they improve more, and the impact on composite outcomes begins earlier. Slide 7 contains data we have presented previously, showing survival rates north of 80% and close to that of a similar population without ATTR-CM, and hospitalization rates of 0.29.
Again, close to the similar population without ATTR-CM. Rates of 40+% improvement across measures like NT-proBNP and six-minute walk distance are higher than anything we've seen before. And finally, separation at 3 months on the composite endpoint, accompanied by a 42% relative risk reduction and a p-value of 0.0008, stand as the most compelling data we've seen in that arena as well. Slide 8 then builds on these prior data with recent data. I won't walk through it all, but I will say personally that I'm very excited by, although it's small n, the data that were generated separately in our phase 3 Japan trial and the poster that was presented, I believe, yesterday, out of the Maurer Lab that suggests one can achieve 100% survival with acoramidis....
That coupled with, again, with small N evidence of disease regression with acoramidis alone, as presented recently by Dr. Fontana, portends a brighter future for patients with ATTR cardiomyopathy. Slide 9, I won't go through the details here, outlines the work we are privileged to do alongside the clinical community of investigators. As we promised when we started our ATTRIBUTE trial, we're moving fast to interrogate the results of that trial and to launch new studies as well. Moving to slide 10, in that spirit, we are live at the exciting content for today. Today's focus, two compelling presentations from world leaders in cardiovascular medicine. I'll ask Matt Maurer, Professor of Cardiology at Columbia University, to proceed first. He's gonna cover slides 11 through 14, and then we'll have the privilege of hearing from Dr.
Masri, professor and cardiologist at OHSU, who's been a pioneer and whose work I've followed in both ATTR-CM as well as in hypertrophic cardiomyopathy and related areas. Dr. Maurer had to jump on a flight, so he actually recorded his comments a bit earlier. So let's turn around and start those comments, and then we'll come back to Dr. Masri.
On behalf of my colleagues, it's my pleasure to present our poster entitled, "Early Increases in Serum Transthyretin Level is an Independent Predictor of Improved Survival in ATTR Cardiomyopathy: Insights from the Acoramidis Phase 3 Study ATTRIBUTE-CM." The objective of this brief report is to describe the acoramidis-mediated changes in serum TTR, which are an in vivo measure of TTR stabilization and its relationship to all-cause mortality in the ATTRIBUTE-CM trial. As you know, patients with ATTR cardiomyopathy can have lower circulating levels of transthyretin, also known as prealbumin, which are associated with worsening cardiac function and an increased risk of subsequent mortality. Acoramidis is a novel high-affinity TTR stabilizer, which achieves greater than 90% TTR stabilization in patients with transthyretin-mediated amyloid cardiomyopathy. In the pivotal phase 3 trial, ATTRIBUTE-CM, acoramidis met its primary hierarchical efficacy endpoint with regards to mortality, morbidity, and functional components as compared to placebo.
Acoramidis treatment also resulted in a 25% relative risk reduction in all-cause mortality and a 30% relative risk reduction in cardiovascular-related mortality. Details of the ATTRIBUTE-CM trial have been published previously. In this analysis, modeling and simulations were performed to describe the population pharmacokinetics of acoramidis and evaluated safety and efficacy of ER relationships for acoramidis. ER relationships were modeled for all-cause mortality as compared with serum TTR levels. Change from baseline in serum TTR showed observed measurements without any imputation. Baseline clinical and demographics were very comparable between the two treatment groups. Increases in acoramidis concentrations were associated with serum TTR concentrations. Acoramidis treatments increased serum TTR levels, as shown in Figure 1, with elevations remaining stable through month 30.
As shown in Figure 2, serum TTR levels from baseline to day 28 predicted survival in a univariate analysis for the overall population with a highly significant p-value, and the acoramidis treated population. As shown in Figure 2, the probability of all-cause mortality as a function of a change in serum TTR levels was highly and statistically associated with increases in serum TTR being associated with a lower risk of all-cause mortality. For every 5 milligrams per deciliter increase in serum TTR, the risk of death was reduced by 30.9% by the logistic model and 26.1% by the proportional hazard model.
In a multivariate analysis, changes in serum TTR remained an independent predictor of all-cause mortality, even after adjusting for baseline demographic variables, use of diuretics, New York Heart Association class, baseline serum TTR, TTR variant versus wild type, and the National Amyloidosis Center Staging System. So in conclusion, results from these analyses suggest efficacious and protective effects of acoramidis exposure, resulting in increasing serum TTR levels. And the acoramidis-mediated increase in serum TTR levels on day 28 were an independent predictor of improved survival in patients with ATTR cardiomyopathy, even after controlling for baseline covariates. Thank you for your time and attention.
Thank you. Now I'd like to hand the call back to Dr. Kumar.
Thank you, operator. I'll turn it over to Dr. Masri.
Hello, everyone. So I'll be talking about the real-world outcomes of tafamidis. This is a study that we've done across 5 centers. We presented part of it previously at HFSA conference, and then we presented more of this data at ISA. Next slide, please. So these were, again, as I mentioned, 5 centers. Patients were enrolled from 2018 to 2021.... We looked at all-cause mortality and CV hospitalizations. All the patients included were receiving or have received tafamidis, so this was an intention-to-treat analysis for everyone who received tafamidis during those time periods at these 5 centers in the United States. Next slide, please. These are the baseline characteristics. So in total, there were 624 patients. About 109 were variant, and then 515 were wild type.
The median age was 78, 20% were Black or non-white as well. About of these patients, obviously, the V122I variant was the most common ones. About a third of our patients had NYHA Class III, and then, as you can imagine, the diagnosis method is non-invasive in the majority of these patients. And then you see this, that the time from ATTR diagnosis to tafamidis start was about 12 months. That's because some of these patients did not receive their diagnosis once tafamidis was commercially available. They received their diagnosis before tafamidis was commercially available, and that's why they were waiting to receive the drug. Next slide. So this is the primary outcome of all-cause mortality.
The median follow-up was 1.8 years, 1.1, 2.5 for the 25th and 75th, for the first and third quartile. Twenty-three percent of the patients died. If you plot this as a probability over 13 months, that would be 70%, with a 95% confidence interval of 65%-74%. Next slide. So how does all of this kind of fit together with our understanding? So we set to do this to understand how, with the evolving landscape of transthyretin cardiomyopathy, things look like compared to ATTRACT trial. And as we were preparing our slides before, also ATTRIBUTE-CM reported last year. And so here you see a reconstructed KM curve. This is not individual patient data, just reconstructed KM curve showing you acoramidis and placebo.
So you can start from the top, acoramidis, placebo in the ATTRIBUTE-CM is the red line, and then you have tafamidis for ATTRACT and for the real world data that we presented, totally overlapping. Both are the blue lines there, and then you have the ATTRACT placebo being shown as the other curve there. Next slide. And so we looked at a few things that you can look up at, you know, from the ISA posters and presentations, but one of the most important things that I think we gleaned from this is: what happens if someone is on tafamidis and gets hospitalized for cardiovascular reasons?
As you can see here, if one does not get hospitalized on tafamidis, there is about 82% in terms of survival over our follow-up period, versus 56% for those who get hospitalized on tafamidis. If you look at the split there for these patients, it's kind of a, an equal split for the numbers of patients, 309 for those who got hospitalized, versus 315 for those who did not get hospitalized. Obviously, this is a real-world evidence, so we don't know what sometimes we're missing. This represents what we are able to ascertain as a cardiovascular admission. Next slide.
These are some of the data we presented at ISA, looking at ATTRIBUTE-CM in this example of acoramidis, looking at non-cardiovascular hospitalized patients versus those who had a cardiovascular hospitalization, showing you that about a survival, about 86.8% for those who did not get hospitalized, versus 62.4% on acoramidis, for patients who got a CV hospitalization before. Then, you know, when you look at the split in the numbers below the curve there, you can see the subjects at risk below there. Next slide. All right. Thank you.
Thanks so much, Dr. Masri. All right, so, slide 22 summarizes the key information you heard today. Our, our belief is, and continues to be, that an agent that better stabilizes the destabilized tetramer and therefore raises serum TTR more effectively, should provide better outcomes, and these outcomes relate to each other. Of course, none of this matters if we don't get the drug to market in an expeditious way, and we continue to work with the FDA to ensure drug approval and launch late this year. With that, let me stop there. Thank Dr. Maurer and Dr. Masri once again, and open it up for questions.
Thank you. Ladies and gentlemen, to ask the question, please press star one one on your telephone and then wait to hear your name announced. To withdraw your question, please press star one one again. Please stand by while we compile the Q&A roster. Our first question comes from the line of Salim Syed with Mizuho. Your line is open.
Great. Thanks for the questions, guys, and congrats on the data. Maybe, Neil, a few from me, if I can, on the Maurer presentation focused on serum TTR increase. So I guess, like, question one is: so you guys presented this data, focused on using month 30 as drawing the relationship between an early increase in serum TTR and month 30 benefit on death and cardiovascular hospitalization. Any work you guys have done on potentially looking at an earlier time point, just given that you have shown separation at month 3? And I think when we looked at the tafamidis data, I think they showed separation closer to month 9, at least on cardiovascular hospitalization. So that's question one.
And then just related to that, just curious how validated this analysis is in the context of maybe discussions with the FDA. And just lastly, just your updated thoughts on potentially using this as a means to do an efficient head-to-head study versus tafamidis. Thank you.
Thanks, Salim. Thanks for the questions. I'll see if I can remember them all. Maybe to start, yeah, good question. I mean, a big part of our analysis is we see the response in terms of serum TTR to be almost immediate. I mean, day 28, you can see a really nice elevation, and you can see that we—you know, that's what allows us to compare, at least in our trial, do those intra-trial comparisons as well. And so—and, day 28 is predictive of the downstream, both mortality and hospitalization, data as well. So yeah, it's a great question. You don't have to wait around 30 months to figure out whether or not you're stabilizing the tetramer.
You could almost immediately get that signal, and from this work, you can assess whether or not you're doing it effectively. And if you're doing it more and more effectively, you do have better outcomes in terms of, obviously, less mortality and, you know, fewer days in the hospital. So, that was the first question. Let me make sure that that answered your question, Salim.
Yeah, I guess, like, versus tafamidis, I mean, is this... At the root cause of this, do you believe, or is there, or what evidence, like, what's the best argument that you can create that this is why you would have an earlier separation in your curve versus in a cross-trial comparison? With ATTRACT, we saw-
Yeah.
the separation, at least on cardiovascular hospitalization. It's a slightly different chart.
Yep.
Right? But they showed it, I think, closer to month 9.
Yeah, and yeah, and on composite, they're like, you know, at a little slightly after month 9, we're at 3, which is mostly driven by hospitalization. So that, that's exactly right, which is a vast majority of the events in our trial. I mean, I'd say the way I think about it is this way: The degree to which you are stabilizing the tetramer, again, going back to our first principles, as reflected to changes in serum TTR, is the degree to which you can have impact across the diaspora of patients we're looking at.
I think the late separations are occurring not because of some biochemistry or something biological that we don't understand, but rather because you're having muted impact across the population, and we're just having more effective impact, just given the rise in serum TTR, and therefore, you can elaborate a signal earlier. That's my guess, but we'd have to, you know, prove that out in further studies. But yes, you're right to say the immediate increase in serum TTR that seems to be higher, at least in our intra-trial comparisons that are post-hoc exploratory analysis in ATTRIBUTE, you know, are accompanied by an earlier separation, both in hospitalization and against the composite endpoint.
Uh-
Maybe secondly, like, what, what, you know, we're, we're not, we're, we're, we're not going to comment, nor is there anything to comment, on specifically about what is in our label or not in our label. Those, those are discussions that are ongoing, with the agency. And, and, and so but whether or not, I mean, I would expect that post-hoc exploratory analyses generally do not make their way into a label, but certainly, they can be related to, to the label, which, which allows, for communication, and it can also be published, which, which will, you know, certainly the latter will be, will be the case here. And, and then your third question was what again, Salim?
Just on your updated thoughts on potentially using early increases in serum TTR-
Oh.
and maybe an earlier
Yeah.
timepoint, not one thirty. If you ever wanted to run a head-to-head versus TAF, how much of this... how much could you rely on that measure? Like, would the FDA be okay with that, or potentially okay, there's a needle moving in that direction, just given the data that you showed?
Yeah, I think the agency would be open to it. I think the question stands as to how the physician community would... I mean, all of these double-blind, head-to-head studies that we're going to do are gonna be based on the level to which, you know, the physician community would take the final answer and say: "Yeah, we believe that this has demonstrated superiority of one drug over the other." So serum TTR is a very clean one in terms of, like, percent covariance is dealable with, and I think, you know, our drug significantly outperforms the other. That would be my ongoing hypothesis, so you'll be able to power a trial to do that study.
But we're gonna need to take some time and understand the degree to which physicians really look at serum TTR as a discriminator. And I would say that ISA is a really nice opening to that because there is just a lot of discussion about what's a better stabilizer? How do we think about stabilization? How do discrepant levels of stabilization lead to discrepant downstream outcomes? Those are all the conversations we're excited to be having, just given the profile of our drug. So, I will probably make that determination closer to launch.
Okay, got it. Thanks so much, and congrats again.
Thanks, Salim.
Thank you. Please stand by for our next question. Our next question comes from the line of Josh Schimmer with Cantor. Your line is open.
Thanks for taking the question, and thanks for joining the call. I just wanna clarify a couple of points about serum TTRs as a potential biomarker. First, do you expect serum TTR to be available as a predictor, or indicator of response at the time of launch, or is it gonna be, is it gonna require additional trials to have a TTR assay in the market?
Jonathan?
Yeah, hi, it's Jonathan Fox, Chief Medical Officer. So, TTR, as was mentioned in the presentation, is the same as serum prealbumin. That is a CLIA-certified standard laboratory test available in any clinical lab, hospital lab. It's been around for decades. It, you know, in the past, it's been used primarily by the surgeons to assess nutritional status in patients both pre-op and post-op, as an indicator of whether they need, you know, to have more intensive nutritional support, for example. But in the modern era, you know, now we know that, you know, the, well beyond it being an index of nutritional status, you know, it's involved as the central player in this pathophysiology of this disease. Does that answer your question?
Yeah, very, very helpful. Thanks for clarifying. Then, how do you kind of envision the assay being used practically to guide therapy? For example, if a patient's on tafamidis, and a specialist trying to decide whether they should consider switching or not, is there sort of a threshold of TTR level below which they should really consider acoramidis? Could it be used in that way? And, if not, how would you see it guiding therapy?
Sure. Well, you know, at a first level, it is a very clear reflection of the pharmacology of the drug. So, we have lots of data that we've published, both in vitro and in vivo, that upon initiation of treatment with acoramidis, that there's a prompt and sustained increase, as was shown in the graph that was in the presentation. We actually observed this even in healthy adult volunteers going way back to phase one, that it's, you know, essentially within, like, even with the first dose, we saw increases in serum TTR.
So, as a sort of an ability to reassure the patient, in particular, that, yeah, we started this new medicine, and we know it's working because, you know, we can check this biomarker, this widely available lab assay. We, you know, we've published and presented a lot of data showing, especially in vitro and ex vivo, comparisons of patient samples that have been exposed in the laboratory to different concentrations of the two stabilizers, and with very consistent results showing that acoramidis is a better stabilizer. Does that help?
But. Well, I guess, you're positioning it as, I guess, a tool to illustrate to physicians the more powerful stabilization of acoramidis. I'm kind of coming more from the clinical decision-making tree, especially if they have a patient on tafamidis in front of them. They're trying to decide, well, can I get further improvement? Can they use TTR as some kind of indicator that they could do better in choosing which patients on tafamidis might be suitable for a switch to acoramidis, or is kind of the idea, well, you don't know until you try it, so try everybody and see what kind of incremental benefit there may be? If that was clear.
Yeah, no, that's, that's, that's very clear. The short answer is yes, that there's plenty of data to suggest that even in the individual patient, there's a high likelihood that, moving from tafamidis, I mean, the situation that I, that I heard you describe sounds like a switch, and whether or not you, the physician might, might want to consider doing that. They can, they can measure the serum TTR while on the prior therapy, and then, again, on, on a, on a new therapy and do the comparison in the individual patient. I would hasten to add that the data from ATTRACT, from some of the real-world evidence trials that Dr.
Masri presented, as well as from our clinical trials program, that the, you know, we usually present either means or medians, and we give confidence intervals and so forth, but we're describing the, the behavior of, of populations, not necessarily, you know... You could easily go into the population and pick out the, the best-responding patient and the least-responding patient and try to make a case, but that's not particularly scientific. However, you know, you can take the population-based data and make some reasonable predictions about how it might apply to your patient sitting in front of you in the clinic.
Okay, got it. Thank you. Very helpful.
Jonathan, if I may add one more thing.
Yeah.
Hi, this is Uma Sinha, Chief Scientific Officer at BridgeBio. We've also presented data that individual subjects who, during our double-blind study, the individuals who were in the placebo group and got tafamidis during the open label extension, their TTR rose to meet up with what the acoramidis group active moiety had in sustaining TTR circulating level. We've also presented the data that within the double-blind period, the placebo plus tafamidis group had 42% lower levels of change from baseline in circulating TTR relative to the active group.
Mm-hmm. Got it. Thank you very much.
Thank you. Please stand by for our next question. Our next question comes from the line of Anupam with Cantor. I'm sorry, Anupam with J.P. Morgan. Your line is open.
Hi, guys. This is Priyanka on for Anupam. We just have one question. So we noted that the tafamidis label was updated in 2023 to list the DDI, DDI potential of BCRP substrates, including Crestor. Given that many of the older patients require concomitant lipid-lowering therapies, would that DDI potential be anticipated for acoramidis? Thanks.
Yeah, thanks for the question. Yeah, we did note that the DDI with certain types of statins associated with tafamidis, and I'm not an expert as to why that's occurring. But certainly, we don't see anything of that ilk with acoramidis. It is a clean drug.
... Thanks so much for the answering my question.
Thanks. Actually, before we get to the next question, I wanted to go back, because Salim asked an interesting question about separations of curves over time. And one of the interesting things about this space is time is a little hard to judge since of the left shift that occurred between the original, you know, ATTRACT study and the ATTRIBUTE study. So another way actually to look at separation is the percent of events and what it—where drugs separate the curves, if you look at the KM curves at those various percents. So, like, if you take mortality, we're actually separating the curves at 10% versus where tafamidis will separate at 20%. That's just another way, is actually the number of events that have occurred if you think about time as an event-driven thing.
And so it's very interesting to note that a sicker patient population is where they start to see the separation. We are seeing it at an earlier and healthier time point, quote, unquote, in terms of events. But another interesting way to think about that data. Sorry, next question.
Please stand by for our next question. Our next question comes from the line of Paul Choi with Goldman Sachs. Your line is open.
Hi. Thanks, and good afternoon, and thanks for all the data presentations. My first question maybe will be for Neil, since Dr. Maurer is unavailable. Can you maybe just remind us if in this analysis of serum TTR changes, is this inclusive of the CKD population? And if not, what does that data cut look like for that CKD population? And also, if you added into the to the modified intent to treat population, in terms of the survival changes in other metrics?
Yeah. Hi, it's Jonathan Fox, Chief Medical Officer again. I mean, basically, we did the analysis both ways, and there's no difference in the outcome. Just to remind you that the CKD population with the eGFR less than 30 was a rather small subgroup of 20-odd people. So, you know, the... You wouldn't expect it to have any real impact, including them in or not. But, yeah, the results across this analysis, as well as across the analyses of the primary endpoint when we went in back and did that on the ITT population, as opposed to the MITT population, gave the same result.
If anything, there were the measurable survival benefit, even in that sick population with low renal function, actually boosted the statistical significance of the all-cause mortality signal when looked at by the Mantel-Haenszel statistical analysis.
Okay, great. Thanks for that. My second question is for Dr. Maurer, if he's available. Just in terms of the slides, you know, it does indicate-
No, they had to jet back to the conference, so, yeah. Sorry.
Okay. Then to the team. I guess, you know, the data set was from 2018 to 2021, and, you know, Neil, you mentioned a bit of a time shift in terms of the treatment landscape. So I guess the question here is, you know, if you think about this curve, these curves, what they look like in a current population, given the changes in care for this patient population, just kind of, you know, how do you think the curves, how much separation, you know, if you had to project here, what it would look like at the various time points for a truly contemporaneous, contemporary real-world population or experience?
And then, can you maybe just remind us on slide 19, what sort of the reasoning behind the placebo curve in ATTRIBUTE going flat lining for a while, let's say between roughly months 12 and 18 was obviously, you know, there was introduction to tafamidis, but just any other explanations for that? If you could just remind us maybe what happened there.
Hi, it's Jonathan Fox again. You know, I think you're seeing a treatment lag in things like all-cause mortality because it's a multi-step process to, you know, reducing toxic circulating toxic monomers and their ongoing deposition versus any sort of alleviation of that, I'll call it, disease pressure or pathological pressure on the ongoing deposition and impairment of both structure and function. That whole process, you know, sort of takes time to respond, if you will. I mean, one good analogy would be the treatment lag that's been observed many, many times with statin therapy in terms of cardiovascular outcomes with that class of agents.
What I think I would direct your attention to is, you know, not only do we see the early separation in cardiovascular hospitalization, but that's actually also mirrored by early separation in NT-proBNP decline and or an increase in the proportion of people who made it to month 30, who actually had a reduction in proBNP being 45% of those individuals, as well as an early separation in KCCQ quality of life. So, you know, our sort of interpretation or hypothesis generated by those observations is that the prompt reduction of circulating toxic monomer aggregates is having a more immediate effect on function, if you will, and on feeling in terms of quality of life than on the, you know, the ultimate heart endpoint, as I said, a multi-step process leading to mortality. Does that help?
... Yes, it does. Thank you.
Paul, this is Julie Miller, our Chief Business Officer. I'm gonna add one more point, too. One thing that was just presented this week at ISA, poster number 118, coming out of Columbia, Dr. Maurer's team, they actually did a matched population comparison between acoramidis and tafamidis, where they matched patients among all sorts of factors to make sure that they were really eliminating that contemporary cohort question. So they matched along age, gender, race, genotype, and disease severity. And when they actually did that comparison in that matched population, acoramidis had 100% survival at 36 months.
When you looked at the win ratios, they were in favor of acoramidis in both the total cohort, as well, as well as the matched cohort, with the win ratio of 2.6 in the total cohort and 1.88 in the matched cohort. So again, that was a matched population, eliminating that question of the contemporary population.
Okay, great. Thank you very much.
Thank you. Please stand by for our next question. Our next question comes from the line of David Lebowitz with Citi. Your line is open.
Thank you very much for taking my question. On slide 19, you had indicated that you did not have individual patient data, that this is deconstructed. Could you just run us through the process of how the chart was constructed?
It's Jonathan Fox again. So this work was done by Dr. Masri and his colleagues. We didn't have any role in the analysis. But basically, they took the Kaplan-Meier curves from the publications and sort of placed them onto a combined chart that they generated.
And got it. Got it. And then could you run us through the - you were talking about the matched analysis. Could you, the matched population was not specifically inclusive of Class III patients. How did this influence the results?
No, what I mean, what they did was they, you know, the sort of baseline characteristics that seemed to matter are things like age, renal function, New York Heart Association class / NT-proBNP or NAC stage or Columbia stage. You know, the sort of the clinical characteristics that, in fact, in our trial, we, in order to ensure that we had a good balance between the two treatment arms, we stratified for some of those same factors to make sure that equal numbers or roughly equal numbers of people with the same severity of illness at entry were equally represented in the two arms. So that's kind of what they did.
I mean, there's a methodology that some refer to as propensity score matching, so it's similar to that in terms of looking at the baseline characteristics in those two sets of patients and, you know, basically choosing individuals out of the total population, where those clinical characteristics and laboratory characteristics roughly match. Does that help?
Thanks for taking my question.
Sure.
Thank you. Please stand by for our next question. Our next question comes from the line of Cory Kasimov with Evercore. Your line is open.
Hey, good afternoon, guys. Thanks for taking the question. Most of my questions were for the docs, but if they're not there, I'll ask one of the team. About the data you had at ISA showing that 12% of acoramidis patients demonstrated late amyloid regression from month 24 to month 30. Curious if there's any particular baseline features or characteristics or anything you've been able to tease out that are common among these patients that could have potential predictive value. Thank you.
Yes, Jonathan Fox again. So the short answer is no. I mean, it's a, it was a pretty small sub-study, and, obviously, people who didn't survive to month 30 are not represented in the final analysis. You know, just as I was saying earlier about population-based data, you've got variability on any one of these measurements across the population, and sort of expecting them to somehow segregate into a small group of people together, is probably asking a little too much of the data. What I would point out, however, is the consistency in the different measurements that were taken by CMR.
So not only did we see evidence of regression in total amyloid burden by extracellular volume, but we saw a reduction in left ventricular mass index, which is LV mass that's indexed to body surface area. Similarly, left ventricular stroke volume, which is basically the amount of blood that's pushed out with each heartbeat, went up, and it was flat in the placebo group. We also saw an increase in ejection fraction, which is another measure of overall cardiac performance. So to see those sorts of changes in cardiac structure and function, as well as amyloid burden, to me at least, is pretty remarkable and hasn't really been seen before. There have been a couple of case reports with some of the other agents of one or two patients here and there that showed some interesting trends in the same direction.
So it seems that this idea that, you know, once you've got amyloid in the heart, there's no turning back, maybe have to be revisited as we accumulate more data.
Yeah, very interesting and helpful. Thank you.
Sure.
Thank you. Please stand by for our next question. Our next question comes from the line of Jason Zemansky with Bank of America. Your line is open.
Perfect. Thank you. Thank you for taking our questions, and congrats on the data. Just a few follow-ups. Slide 13, I was curious, is regarding the serum TTR levels.
... it seems to be a bit of variability month-over-month, yet the error bars are pretty compact. So wanted to ask about that, you know, especially given the statistic, I think, on the next slide regarding, you know, for every 5 mg per deciliter increase, you know, if the variability is 1 or 1.5 here, you know, that takes about 20% of that away. So any comments on why we're seeing kind of that fluctuation?
Hi, this is Uma Sinha again. Just want to comment on that first part. You know, when you're looking at the early time points after the twenty-eight-day first snapshot, that's the COVID period, so some of the sample sizes are a little bit smaller there. So we have tested the data three separate ways. In the New England Journal publication, the missing data has been imputed. In this particular slide, which is the real data, this is the MITT population, and we have also presented the ITT population. The conclusion is pretty much the same, which is there's a rapid rise at the earliest time point, which is the 28-day blood draw, and then the extent sustains throughout the 30-month period.
Okay, thanks. And then on slides 20 and 21, the delta between the non-hospitalized patients seems to be much smaller than that between the hospitalized patients. And so I'm just curious, is the implication that acoramidis is more potent in more advanced patient, where there's maybe greater need of stabilization? Or is this just an artifact of, you know, maybe there's just fewer rates of hospital - of survival issues in non-hospitalized patients, and we're just not tracking the same here?
Yeah, I think it's definitely not that acoramidis is in any way showing a signal that's unique to late-stage patients. In fact, you know, as I've mentioned earlier, it's likely the opposite, that acoramidis is starting to show impact in all patients, but actually for early patients, even earlier than what we've seen before. This is a little artifactual because you've got to remember, 75% of our events were a CVH, and you can see on the non-CVH survival curve here, we're close to 87% survival, which is, I mean, close to the tipi-- I mean, we've seen evidence of 100% survival in some cases, but for patients of, with this disease baseline characteristics, that's very, very high.
So you're probably just getting up to the, toward the top of where you could get any dynamic range in terms of comparisons.
Yep, makes sense. And then I, I know this was kind of asked earlier, but I, I wasn't sure quite on the response, so maybe I'll kind of ask it in a different way. But, you know, on the, the slide 18, you know, imputing tafamidis versus acoramidis is, you know, survival. You know, if approved, you know, is this the sort of thing that you can go into a prescriber and, and just, you know, lay out the two different mortality curves, you know, side by side and, and kind of point out this difference?
You would never be able to do that unless you ran a double blind head-to-head. What you can say, and you know, what we fully expect to be able to talk about are the absolute levels of survival that have been observed and the absolute levels of hospitalization, as well as the relative risk reductions. You know, just go back to the fact that, again, the number of actual mortality events in our trial was, you know, maybe 38% of what you saw in ATTRACT, with 75% of our events coming from CVH.
So for us and all other trials that have been run these days, people are going to look at that composite outcome and look at the relative risk reduction of 42% that we were able to see, which far outstrips, I think, the relative risk reduction that has been shown by others against the composite endpoint. And I'd say also that, you know, the lack of impact that you see until you actually do some statistical transformations in the ATTRACT trial on hospitalization and the data you see here, that is quite compelling, suggesting that you want to keep people out of the hospital.
In doing so, and where obviously that's a characteristic of people who are able to live longer, I think furthermore makes the case for an agent that's been able to have that type of relative risk reduction, which is around 50%, that acoramidis did have on hospitalization.
Got it. Appreciate the color. Thanks, guys.
Thank you.
Please stand by for our next question. Our next question comes from the line of Eliana Merle with UBS. Your line is open.
Hi, this is Jasmine on for Elli. Thanks so much for taking our question. So how well do the serum TTR increases correlate with the level of repeat hospitalizations compared to that first hospitalization event? And how important do you think it is to physicians to prevent that first hospitalization versus lowering the total amount of repeat hospitalizations? Thanks.
Sure. Jonathan Fox again. So just to remind everyone, you know, what we reported in the New England Journal paper back in January was the CVH endpoint component, was the frequency or cumulative hospitalizations. And again, that was reduced by half compared to placebo. And the reason why we did the subsequent analysis of time to first was to basically combine those two hard endpoints of mortality, all-cause mortality and CVH. You know, in order, those obviously are a lot more important to patients and to physicians than, you know, changes in biomarkers, which, you know, they, most patients, you know, probably don't have a great grasp of, you know, what N-terminal pro-BNP means, for example.
You know, the other thing that we thought about more recently, and is the topic of the data that we showed today, was, well, to what extent are those two things actually linked in terms of as an index of overall mortality risk? Can it be presaged, if you will, by the earlier someone undergoes a hospitalization? Obviously, you know, people are at different levels of their disease journey when they enter the trial. Different physicians may be a little bit more or a little bit less skilled in outpatient management that might trigger, you know, where patients might decompensate more easily and be hospitalized. I mean, I'm not trying to cast any aspersions on anyone's clinical skills, but, you know, some patients are just more fragile than others, so there is some variability there.
So really it's more about you know, if you can keep somebody out of the hospital, that's actually a reassuring message that the treating physician can take from that observation and actually share with the patient and their family that, "Look, you know, we're keeping you out of the hospital," which is a good thing, and it's not just, it's good to stay out of the hospital, which of course it is, but that in fact, it's an index of overall likelihood of survival. Does that help?
Yeah, awesome. Thanks so much.
Sure.
Thank you. Please stand by for our next question. Our next question comes from the line of Daniel Brill with Raymond James. Your line is open.
Hi, this is Daniel on for Danielle. We are curious if you explored into the shape of the dose response of the increase of serum TTR with respect to CVH and CVMs. And if you also see, like, a monotonically increase of benefit dose response like we showed for the all-cause mortality in slide 22. Thank you very much.
We studied a single dose in this trial, so you know, as Jonathan indicated, we have done the... In the responders who have the elevation in TTR, it's very good prediction of survival as well as the hospitalization, both time to hospitalization as well as the totality of CV hospitalization. But because we studied a single dose, it's kind of difficult to do the fractionation that you're asking about.
Yeah, if I could just add that-
Let me clarify my question. Yeah.
Sorry, go ahead.
I was referring to, like, the change in serum TTR level with respect to the probability of the other metrics, such as, CVH and CVM, as you showed in slide 22 for the all-cause mortality.
Yes, absolutely. Hi, this is Julie again. So we did that analysis, and there are actually two posters also presented at ISA that cover these, in addition to Dr. Maurer's analysis. So looking through 30 months at cardiovascular mortality, for every 1 milligram per deciliter increase, it led to a 5.5% risk reduction in cardiovascular mortality. Looking at cardiovascular hospitalization, every 1 milligram per deciliter increase led to a 4.7% lower risk of first cardiovascular hospitalization over 30 months. So we are very encouraged to see the consistency in this correlation between early increase in serum TTR and hard clinical outcomes.
Julie, this is Uma Sinha. I'd also like you to refer to slide 14. In Dr. Matt Maurer's analysis, every 5 mg per deciliter increase reduced the risk of death by about 31%, and we did the analysis two different ways. 5 mg per deciliter reduced it by 31% by one method of calculation, and by Cox proportional hazard, it reduced it by 26%. So it's linearly very concordant with what Julie referenced to in the 1 mg per deciliter reduction.
Very helpful. Thank you.
Thank you. Ladies and gentlemen, due to the interest of time, that concludes our Q&A session, and that will conclude today's conference call. Thank you for your participation. You may now disconnect. Everyone, have a wonderful day.