Alector, Inc. (ALEC) Earnings Call Transcript & Summary

Ladies and gentlemen, thank you for standing by, and welcome to the conference call being hosted today by Alector's management. [Operator Instructions] Please be advised that today's conference is being recorded. I would now like to hand the conference over to your speaker today, Marc Grasso, Chief Financial Officer. Please go ahead, sir.

Thank you, operator. Good morning, and thank you all for joining us on today's call. I'm Marc Grasso, Alector's Chief Financial Officer. I recently joined the company, and I'm excited to be working with the broader leadership team to advance Alector's mission. We will be reviewing data release today, which will also be presented live tomorrow by Dr. Sam Jackson, our Interim Chief Medical Officer at the AD/PD 2022 International Conference on Alzheimer's and Parkinson's Diseases and Related Neurological Disorders Meeting. We will discuss the rationale of elevating progranulin for the treatment of frontotemporal dementia patients with a C9orf72 mutation, 12-month data results from the INFRONT Phase II open-label study of latozinemab, the generic name for AL001 in FTD-C9orf72 and finishing up with Q&A. This presentation and today's press release are available on the Investors section of the Alector website. On today's call, we have CEO and Co-Founder, Dr. Arnon Rosenthal; our President and Head of Research and Development, Dr. Sara Kenkare-Mitra; and our Interim Chief Medical Officer, Dr. Sam Jackson. Sara will begin with some introductory comments. Sam will then review the data to be presented at AD/PD. And after a few brief closing remarks, we will open the call for Q&A. As a reminder, the information discussed during this call will include forward-looking statements, which represents the company's views as of today, March 15, 2022. We undertake no obligations to update or revise any forward-looking statements to reflect new information or future events, except as required by law. Please refer to today's press release as well as our filings with the SEC for information concerning risk factors that could cause actual results to differ materially from those expressed or implied by these statements. I'd like to now hand the call over to our President and Head of Research and Development. Sara?

Thank you, Marc, and good morning, everyone. Thank you for joining us. Today, we are happy to be reviewing encouraging 12-month data from the symptomatic C9orf72 cohort in our INFRONT-2 Phase II study with our progranulin-elevating antibody latozinemab, the generic name for AL001. At a high level, this new data builds on the earlier results seen in the granulin cohort that we shared last year, and underscores the broad potential for this exciting candidate and our approach. Importantly, for the first time, we had achieved elevation of progranulin levels above normal with an associated trend that suggests latozinemab can slow down disease progression with a clinically meaningful drug effect, if replicated in a placebo-controlled study. These data, therefore, support our therapeutic hypothesis that progranulin is rate-limiting in multiple neurodegenerative diseases and progranulin elevation therapy may be effective in a broad range of indications. With the data emerging from this cohort, we are gaining important insights that will help guide us, as we work together with GSK to expand the progranulin franchise to its fullest potential, including potential applications in ALS as well as Alzheimer's disease and Parkinson's disease. Moving on to Slide 6. As many of you know by now, progranulin is a key regulator of immune response, lysosomal function and neuronal survival in the brain. It is expressed on macrophages, microglia and neurons. And progranulin also controls the function and inflammatory state of microglia, the brain's immune system, which works to maintain brain function and health. Progranulin deficiencies are associated with multiple neurodegenerative disorders, including frontotemporal dementia, Parkinson's disease, Alzheimer's disease and L.A.T.E., which is another type of dementia typified by TDP-43 pathology. Moving on to Slide 7. With our hypothesis around the potential benefit associated with progranulin elevation in mind, our development program for AL001 started with FTD-GRN, but we believe there are opportunities to expand latozinemab as a promising therapeutic treatment option well beyond this first target indication. Our work with FTD-C9orf is the first step in that journey. We know that both decreased progranulin levels and mutations in the C9orf72 gene are associated with abnormal accumulation of the TAR DNA-binding protein 43 or TDP-43. Accumulation of TDP-43 is a hallmark of multiple neurodegenerative diseases, including both FTD-C9orf72 and ALS. Even a small decrease in the level of progranulin in humans accelerates TDP-43 pathology. In preclinical studies using multiple models of acute and chronic neurodegeneration, increasing progranulin levels has been shown to have a neuroprotective effect, particularly in addressing TDP-43 pathology. We now have the first data testing this hypothesis in humans. In the broader set of patients where the progranulin deficiencies either not an issue or is just less than 20% below expected, a sustained increased progranulin level above normal may mitigate the effects of neurodegenerative disease. Let me now turn the call over to Sam for more detail on this encouraging data set. He is joining from the AD/PD meeting where he will present this data as part of an oral presentation tomorrow. Sam?

Thank you for that introduction, Sara, and good morning to all of you who have joined us for today's call. It's a pleasure to be here. Let's start with FTD. FTD is a rare neurodegenerative disorder characterized by a rapid and progressive decline in socially appropriate behavior, judgment and language. A fatal disease with no approved treatment, FTD is the most common form of dementia seen in people under the age of 60. And once symptoms present, the average life expectancy is only 7 to 10 years. A large portion of cases run in families and various genetic mutations have been linked to FTD. Last year, we presented encouraging data on latozinemab in FTD patients with a mutation in the granular gene. The most common genetic cause of FTD, however, is the mutation in chromosome 9 open reading frame 72, or C9orf for short. This mutation leads to a protein with excess hexanucleotide repeat that become pathologic. Although its function is not completely understood, the C9orf72 protein plays an important role in the processing of RNA as well as in endosomal trafficking and autophagy. We also know that C9orf72 repeat expansion mutation leads to the accumulation of misfolded TDP-43 protein and are an important cause of ALS. Let's move on to Slide 10, where we can talk about progranulin. As Sara noted, progranulin is a secreted protein that modulates inflammation, regulates lysosomal function and promotes neuronal survival in the brain. I want to call your attention to the figures in the slide. To study the therapeutic potential of elevated progranulin, mice designed to express human mutant TDP-43 have been crossed with mice designed to overexpress human progranulin. The figure on the left illustrates higher levels of insoluble TDP-43 were found in mice expressing the TDP-43 mutant alone when compared to mice with this mutant who also had the transgene for human progranulin. As shown in the figure on the right, in these double mutant mice, overexpression of progranulin significantly slows down disease progression, extending their median survival by approximately 130 days. Now on to Slide 11. There is genetic evidence that low progranulin levels exacerbate FTD and ALS. A study of C9orf carrier population who also carry the rs5848 single nucleotide polymorphism in the GRN gene found in association with lower progranulin expression and decreased survival after onset. In the figure on the left of the slide, you can see that carriers homozygous for the minor allele of rs5848, that is the TT genotype who have lower levels of endogenous progranulin, demonstrated increased FTD risk when compared to homozygous major allele carriers. The rs5848 single base change is associated in a predicted binding site for human-specific microRNA. Changes in microRNA binding may alter expression, leading to lower progranulin levels and increased risk for these diseases. On the figure on the right, we see a similar effect with respect to age of onset in ALS. Carriers of the rare intronic progranulin SNP IVS2 + 21G>A develop ALS at an earlier age than carriers of the GG allele. Conceivably, the function of progranulin in neuronal survival is altered or reduced in carriers of this rare variant gene leading to a more rapid loss of motor neurons. This single nucleotide polymorphism is also associated with decreased survival after the onset of ALS. Moving on to Slide 12. Many listeners are probably familiar with the mechanism of latozinemab. But as a quick reminder, the transmembrane receptor sortilin is the main degradation pathway for progranulin. The human monoclonal antibody, latozinemab, blocks sortilin, increasing the half life of progranulin, which results in sustained high levels of this important protein in the plasma and in the CSF. It's worth noting that there is an abundance of literature that has found that ablation of SORT1 in animal models does not prevent the entry of progranulin into the lysosome. Slide 13 describes INFRONT-2, a Phase II open-label study assessing the safety and tolerability of chronic dosing of latozinemab. Pharmacokinetic and pharmacodynamic endpoints, as well as the CDR plus NACC FTLD sum of boxes are also being assessed. INFRONT-2 includes 3 cohorts. We previously reported data from the symptomatic GRN cohort at both AAIC and CTAD last year. In that cohort, treatment with latozinemab resulted in an improvement from baseline across multiple biomarkers of disease as well as a slowing of clinical progression relative to a match control cohort. Today, we will be presenting 12-month biomarker and clinical outcome assessment data from the cohort of symptomatic FTD-C9orf72 patient, here seen highlighted in blue. On Slide 14, we focus on the safety data. Latozinemab has been generally safe and well tolerated in 28 patients treated to date with 21 patients treated for 12 months or more. In the C9 cohort, there were a total of 7 treatment-related adverse events, and each was mild or moderate in severity. The most commonly reported adverse events in all 3 cohorts were fall, rash, urinary tract infection and headache. Moving on to target engagement in Slide 15. At baseline, all C9orf patients had progranulin concentrations in plasma and CSF that were similar to those of aged-match control. Treatment with latozinemab led to a rapid and durable increase in progranulin level. And this is the first time that Alector has presented data in patients in whom progranulin levels are above normal after treatment with latozinemab. Levels of progranulin in the C9orf patients in INFRONT-2 are consistently at 2x the mean levels in age-matched control, and we are reassured that there are no concerning safety findings to date. Now moving on to Slide 16. Since INFRONT-2 is an open-label study, we relied on historical data from the ALLFTD group to help estimate the effects of latozinemab treatment. We engaged in a process to fund the best matches among the patients in the ALLFTD database to create a matched historical control group. This process was blinded, meaning that when we selected the matches, we did not know the 12-month outcomes of any of the patients in the sample. This is the same process we used in our CTAD and AAIC presentations last year. Here though, instead of GENFI, we used ALLFTD due to their larger and more comprehensive FTD-C9orf72 data set. ALLFTD is a comprehensive study of frontotemporal dementia, collecting cognitive and behavioral assessment data in addition to imaging, blood and CSF biomarkers. The goal of this matching process is to find the best matches for our patients in a blinded manner. And the most important factor is severity of disease, which is represented by the baseline CDR sum of boxes score. In the ALLFTD data set, there are 84 C9orf patients with at least 1 post-baseline CDR plus NACC score. This group of 84 patients is represented by the circle on the left of the slide. Using the propensity score matching method to analyze baseline CDR sum of boxes score, the most important indicator of disease severity, we found 29 matches for our patients, here represented by the middle circle. Out of those 29 individuals with similar disease severity, we identified 10 patients who are the best matches for the 001-2 patients by age, sex, neurofilament level at baseline and diagnosis or variant of FTD. These 10 final patients are represented by the circle on the right of the slide. On Slide 17, we see the outcome of this 2-step matching process. The AL001-2 patients are on the left side of the table and the matched historical controls from ALLFTD are on the right. As you can see, the 2 groups aligned well when considering baseline CDR sum of boxes score, age, sex, baseline neurofilament level and diagnosis. Slide 18 has the most interesting result that we are sharing with you today. To assess the effect of latozinemab treatment on clinical disease progression, we compared the CDR plus NACC FTLD sum of boxes scores in the Phase II patients to those of the matched historical control group from ALLFTD. As you can see in the thick blue line, the predicted annual change in the latozinemab Phase II cohort is 1.6 points. When we put the observed 12 months' ALLFTD cohort scores into the same model, represented by the thick gray line, we find that the CDR sum of boxes scores in these untreated matched control patients increased by 3.4 points from baseline over a year. This difference of 1.9 points represents a 54% reduction in the rate of disease progression. These results are an encouraging trend, and a verified in a placebo-controlled study would represent a clinically meaningful treatment effect. These are patients with no real treatment options. To measure how sensitive our result was to the clinical adjudication process, i.e., the matching of patients by age, sex, baseline neurofilament level and diagnosis, we conducted an analysis on the ALLFTD 29-patient group produced by using the propensity score technique alone prior to clinical adjudication. This group is not as good a match as our final group of 10 that includes several patients who have diagnoses other than FTD and who have lower plasma neurofilament level. Nevertheless, this analysis resulted in a similar trend towards benefit, a 36% delay in disease progression. Now moving on to Slide 19. We also measure neurofilament light, a marker of the axonal damage that we know is useful in detecting disease, but that may be slower to respond to treatment in the setting of FTD. During the 12-month period, C9 patient neurofilament levels were generally stable in both plasma and CSF. Moving on to Slide 20. We assess the activation of astrocytes by measuring GFAP or glial fibrillary acidic protein. Astrocytes are responsible for regulating and maintaining an optimal milieu for neuronal functioning. When there is insult or injury to the brain due to a traumatic event or due to chronic disease, astrocytes proliferate and become reactive in response, a process known as astrogliosis, for which GFAP is an established biomarker. We know that elevated GFAP has been associated with faster rates of brain atrophy in the setting of FTD. As you can see from the graph, treatment with latozinemab leads to a decline in GFAP in both plasma on the left and CSF on the right. At baseline, symptomatic FTD-C9 patients don't have elevated levels of GFAP compared to age-matched control. But the decline does suggest a reduction in astroglial activity. We look forward to developing a greater understanding of this biomarker in this disease. Moving on to Slide 21. In summary, the scientific rationale for latozinemab in FTD caused by mutations in the C9orf gene is based on genetic and preclinical evidence. Thus far, latozinemab has been well tolerated in C9orf patients in the study. When we compare patients in the 001-2 trial to a set of matched historical controls from ALLFTD, we observed a trend that suggested that latozinemab can slow down disease progression by 54%, a clinically meaningful drug effect, if replicated in a placebo-controlled study. We also saw encouraging biomarker results in the 001-2 patients, including a stable neurofilament level and a decline in GFAP in both plasma and CSF. This is the first example of an increase in progranulin levels above normal that has been associated with a potential benefit. And with that, I'll turn the call back to Sara.

Thank you, Sam. The data we shared today are encouraging and highlights the broad potential of our progranulin franchise. We have demonstrated that elevating progranulin above physiologic levels can drive a potential benefit, significantly broadening the potential for latozinemab beyond FTD-GRN and into other indications. Our development program includes our ongoing Phase III study in FTD-GRN and Phase II study in ALS. We expect to present data from AL101 later this year. This candidate is designed to address the larger indications like Parkinson's disease and Alzheimer's disease. Following the execution of our transformational partnership with GSK last year, we are working closely with them to initiate studies in additional indications to unlock the full potential of our progranulin franchise and maintain our leading position as we continue working to combat neurodegeneration. We look forward to providing further updates, and now we'd be happy to take your questions. Operator?

[Operator Instructions] Our first question comes from Matthew Harrison with Morgan Stanley.

I guess 2 for me, if I could. So first, just as we think about C9orf patients more broadly, it looks like the trends in the biomarkers are fairly similar to your other FTE cohort. Maybe you could just compare and contrast that. And given that we get Phase III data from the FTD cohort first, what are your thoughts on translatability of clinical benefit if you do observe that? And then second, could you just comment on what the path forward here is in C9orf patients in terms of either some sort of larger Phase II or a pivotal study?

Matt, this is Sam. I'll take -- I can take both those questions, actually. Yes, I agree that certainly the trends that we're seeing in the biomarkers are largely similar. I think in general, the context with GRN patients is that the disease is faster moving. We see that both clinically and likely in the same biomarker context as well. So you wouldn't see absolute differences that are as large in GRN as you would see in C9orf. Also another important point is that C9orf is less well characterized, I think, than FTD-GRN. So a lot of these biomarkers are still -- we're still learning, right? And I think your second question, we are developing this molecule in various indications in concert with GSK, our partner. So we're evaluating what our next moves are. And I don't think we're going to guide to that right now. But you can imagine that we're contemplating what is the -- what are the criteria for go/no go for larger studies. We're really excited by the data we're presenting, well, technically tomorrow.

Our next question comes from Greg Harrison with Bank of America.

So how does this update impact your view of the therapeutic potential of increasing progranulin above normal levels, both in this indication and FTD, and also in other indications that you're looking at? Do you think that this -- that there's good read across to those other indications based on this data set?

Go ahead, Arnon.

Yes. We think that the read-through, basically the concept of elevating progranulin above physiological level is sort of reading through to AD, to PD, to ALS, to FTD with C9orf mutations to 50% of the FTD with TDP-43 pathology. And basically, the C9 data support our hypothesis that progranulin is rate limiting even when it's at a physiological level. And the C9 data that sort of Sam just presented are really supported by animal model data showing that elevating progranulin just two to threefold above normal is protective in animal models, again, for ALS, for Parkinson's disease, for Alzheimer's disease. So we think that conceptually, there is sort of read-through and some risk mitigation for the other indications where we are going to elevate progranulin two to threefold above normal level. And it's important to know that we think that there is like a Goldilocks level that sort of elevating progranulin two to threefold above normal is still beneficial with minimal adverse effects. If you elevate progranulin a lot beyond that means we don't really know what the safety profile will be. And as you know, our drug has an intrinsic mechanism. We are not going to elevate progranulin more than two, threefold above normal because of our mechanism of action.

Got it. That's helpful. One follow-up maybe. Could you comment on the difference in the level of severity for the C9orf72 patients versus GRN and what that means for how we would interpret the CDR data? How noticeable is this change in this population compared to your previous updates?

Yes. Greg, it's Sam again. By level of severity, I guess you mean the level of dysfunction. I think the C9orf population does progress more slowly, as I said in the response to your question or to another question earlier. But nevertheless, we are hearing from KOLs that anything around a 30% slowdown in the rate of disease progression would be very clinically meaningful in this population. I mean neither GRN nor C9orf, I mean, basically any FTD has a real treatment. So if we were to slow down progression by 30%, that would translate to basically 1 or 2 points on the CDR and that would be pretty impressive. So we are definitely in that range with these data.

So as Sam said, the disease progressed slower, but the lifespan of these patients is very similar to progranulin FTD. It means a very short lifespan. And the clinical readout, the CDR is the same CDR for -- the same clinical readout for FTD and C9. So people who are at a certain level of CDR with progranulin mutation of C9 have the same disease severity.

Yes, that's an important point, Arnon. I just want to expand on that. So the CDR NACC FTLD sum of boxes is the instrument that measures FTD no matter what the genetic mutation or sporadic FTD. So when you think about disease severity, the most important aspect or descriptor of disease severity is your baseline CDR sum of boxes. So Arnon is exactly right. So I mean these are -- we're looking at the same kinds of systems in these patients.

Our next question comes from Neena Bitritto-Garg with Citi.

So I just have a question about the clinical endpoint data. It looks like at an individual patient level, it's a little bit choppy in there, some patients that did have substantial gains after the first 3 to 6 months in the study. And I'm just curious if you can comment on that. I mean is that just a reflection of kind of heterogeneity or some consequence of the behavioral variant being very variable? Or do you think that those are real sustainable gains?

Neena, this is Sam. I think when you look at the individual patients, you have to acknowledge that the endpoint is fairly variable. That's why we're really focused on the model output, which tries to dampen some of that variability. These patients don't get better spontaneously, so that certainly doesn't happen. I think as we look at more patients, this message will certainly clarify, but we're kind of focused on the model. I'll point the [ thicker ] lines on the slide, blue and gray.

Our next question comes from Yaron Werber with Cowen.

This is Brendan on for Yaron. I wanted to first ask about the comparative control database you used here. I know you mentioned -- you had the ALLFTD versus the GENFI that you used. I guess maybe just to clarify what some of that reasoning was. Is it really because they had more of the biomarkers in the ALLFTD database that you guys are looking at? I guess -- I'm just curious if there -- or if there's any kind of issue maybe with GENFI in terms of patient numbers for C9orf72, or if it really just was because these are more of the biomarkers that you guys are looking at here in the study? And I guess, along those lines as well, do you have some of these, like the graphs, the NfL and GFAP levels over time from the matched controls? Is there any way that we could -- are going to be able to see kind of how the changes in these biomarkers that you guys have here match up with the natural-history controls, if that's kind of the guiding force behind using the ALLFTD patients?

Yes. Thanks for that question. This is Sam again. The ALLFTD database had more C9orf patients. It was a little more comprehensive for us, and we wanted to have as large a database as we could when we were assembling our synthetic control cohort. Unfortunately, they did not have some of those biomarkers that we'd like to look at. We're certainly looking for other databases that might have comparisons that we can use, but they weren't available in ALLFTD.

GENFI means sort of -- there are really now like GFAP biomarker do not exist. They are just doing that. And yes, GENFI did not have like a meaningful number of C9orf mutation carriers within 2 sort of cognitive readouts that we were able to use. Sort of GENFI had a better cohort that we could select controls from.

Our next question comes from Tom Shrader with BTIG.

Congratulations, and thanks for the clear presentation. I'm wondering if you measured markers of inflammation, lysosomal function. I'm curious in this disease if you have a sense of what marker is most likely to definitively change, even if it's maybe a little removed from clinical benefit.

Yes. Arnon, do you want to take that one?

Yes, sure. So I mean the data on -- the biomarker data on C9 is still not clearly [ felt ], data in animal models that lysosomal proteins are misexpressed, that complement proteins are misexpressed. But there is no clear information that what we've seen in animal models with either C9 knockout mice or C9 repeat mice is rarely seen in human. We are looking at that. We didn't sort of have solid enough data to present. Again, the animal models argue that there is up-regulation of inflammatory biomarker, PMF, IL-1, IL-6. There's no clear data in human that this is really what happens. Overall, sort of the notion is that the C9 repeats -- the pathology is very similar to progranulin loss of function, that the lysosomals are dysfunctional, the immune system is hyperactive, that there is secretion of complement proteins that destroy a nerve ending and sort of nerve connections. But this is all done in different types of animal models. There are really no validating data in humans. So I mean we are still looking at human biomarkers, and we don't really have a clear picture yet what would be a definitive biomarker.

Okay. If I can follow up with kind of a background question. So the treated patients, the data are so all over the place. I mean it's just really noisy data. Is it as noisy in the control group? You only gave us the average control group. And really, the remedial question is, this is a hexanucleotide repeat disease. Does the severity correlate with numbers of repeats? Is there a hope the data can be cleaned up that way?

Yes, I can take the second part of that. So the hexanucleotide repeat part is interesting, that you have to get above a certain number to achieve pathology. Various sources say that number is somewhere around 30 or slightly higher. But the best correlate for disease is the -- or sorry, for severity is the baseline CDR sum of boxes. So that's really how these patients are performing at baseline on this instrument, which is designed again specifically for FTD. Go ahead, Arnon.

Yes, there is no clear correlation between disease -- like the rate of disease progression and the number of repeats. So if you have a repeat above 30 or most people have repeats between 66 and over 4,000, you get the disease, but it seems to be like a threshold effect. There is no good data that disease severity is -- or disease -- the rate of disease progression are really determined by the number of repeats. So it's hard to stratify by that, but that's an interesting experiment to do. Regarding the viability in the control, unfortunately, ALLFTD and GENFI are just measuring cognitive decline, I think, once a year. So we don't really have the interim analysis. We measure cognitive decline more frequently, but we currently compare it to the ALLFTD there because the data do not exist like for shorter intervals.

And our next question comes from Paul Matteis with Stifel.

I wanted to clarify something and then ask a question related to it. So just to clarify, the 12-month data here are -- is this analysis based on projections for 4 of the 10 patients? And given how variable the pace of progression is, can you talk about how you made those projections and what assumptions went into it? And then separately, I guess if you were to exclude those 4 patients and just look at the 6 completers at 12 months, what's the output of that analysis versus natural history?

Yes. Paul, this is Sam. There is a model output for the patients in the trial. I believe we have 7 out of 10 of them completed 12 months, and then there are a few patients, 3 that did not complete all 12 months. But the model uses all of the measurements. So it's actually more than a simple extrapolation. It is a model that gives you an output that is making some assumptions. I think if you wanted to go into detail, we probably have to talk to one of our statisticians. The data from ALLFTD though are actual data in 12 months. So there when you plug those data into the same model, right, then you get a number that you can compare apples-to-apples. And I don't believe we've done that analysis, that completer analysis. Again, as you get to a smaller and smaller number of patients, then you get to the law of small numbers that starts to dominate everything you do, right? If you have 1 outlier, then that can make things pretty problematic. Did I answer your questions? Sorry, there's a little bit of delay on my line so.

Completer analysis, we are not going to discuss now, but the effect is actually significantly larger than what we presented. But because of some said -- so the number is a little bit smaller, and we don't want to bias the data we have not presented it. But sort of the data we've completed is at least as good and actually significantly better than with everyone. And you were right, like the patients that did not complete 12 months, sort of the data from the shorter, like 9 months and 6 months, were utilized. So it's a model that actually used the time point that we measured.

Okay. And can you just clarify if it's 6 people at 12 months or 7? Because we had read 6 in the press release.

Yes, I think it's 6 patients.

[Operator Instructions] Our next question comes from Myles Minter with William Blair.

I had another clarification question between the PR and what I'm seeing in the presentation. Just in the press release, it says at 49 weeks the CSF levels of progranulin is 1.7 nanograms per ml, which would imply a decrease. But in your presentation, I mean, that same data point on Slide 15, looks like it's coming in at around about 9 or something like that. So I'm assuming that the presentation is correct and maybe this version of the press release that I'm looking at has an error in it. Can you just clarify that? And then I have a few follow-ups.

Yes. I'm sorry, this is Sam. Could somebody address that if they've got the presentation in front of them?

Yes, maybe we can get back to you on this. Basically, you said...

Yes. It's just the week 49 data point in the press release that I'm seeing that 1.7 nanograms per ml. The standard error of the mean of 0.9. And on Slide 15, I think the correlating data point looks like it's coming in, I'm just reading off the graph, looks like it's coming in around, I don't know, 8.5 or 9 nanogram per ml. I'm assuming the presentation is correct and that the press release version that I've seen here is...

Yes, that presentation is the correct one. It's around 8.7.

Okay. Cool. And then I did have a question, again just relating to the CDR sum of the boxes data. I'm curious, again, on like the use of the repeated measurements model here, just given the variability specifically at that 3-month -- the patients that we have data points for 3 months. Like if the data cutoff for data that I'm seeing here is June 15, 2021, like why wouldn't you just use the 9-month data that you probably should have for that patient and use that instead of a repeated measures model?

Well, I think -- this is Sam again. If you're talking about variability in data, actually using all of the measurements is a better way to approach the variability. If you were to use one measurement, an anchor on that sort of like MMRM approach at 12 months, then that relies solely on the results at 12 months, right? So actually, you're getting more data using all of the data with the repeated measurement model. I'm not exactly sure what you're looking at, though, so going to make sure -- we're not all in the same place.

No, no, no, it's still good. It was just Slide 18, there's definitely 1 patient that you're showing 3 months' worth of data for in the CDR, NACC FTLD. Like if the data cutoff was June 15 last year, you should probably have internally at least the 9-month real data for that patient. And I'm just wondering why in today's data cut, we're not seeing that and that's leading us to questions about the validity of the statistical model here when you probably have the real data internally. So maybe I can reshape that question. When will we see that data?

Right, right. Well, I can say that the data that you're seeing here, they do take quite a bit of time to produce. So -- and it takes a while for these data to make it from the clinical sites and then through our various processes and then trying to align them with biomarker data that there is a process there. I don't see where the data cutoff is referenced. So that's where I meant a little bit of a disadvantage. But that's the message.

Yes, we will present data as we get them. But some -- I mean, yes -- I mean there is some time before the data are collected, probed and analyzed, so there is a delay.

And I'm currently showing no further questions at this time. I'd like to hand the conference back over to Mr. Marc Grasso for closing comments.

Thank you, everyone, for joining us today. As a reminder, the data reviewed on this call are posted in the Investors section of the Alector website. We will be participating in person at the upcoming Barclays Healthcare Conference and virtually at Stifel's CNS Days event, and we hope to see many of you there in the near future. We look forward to continuing to update you on Alector's progress. Thank you.

Ladies and gentlemen, thank you for your participation in today's conference. You may now disconnect. Everyone, have a wonderful day.