Immunovant, Inc. (IMVT) Earnings Call Transcript & Summary

Good morning. My name is Michelle, and I am your conference call operator. As a reminder, this call is being recorded. Before we begin, I would like to remind everyone that today's conference call will include certain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements include, for example, statements regarding the potential efficacy and safety to Immunovant product candidates and Immunovant expectations regarding the timing, design and results of its clinical trials, including the timing of future data readouts and the announcement of future indications. These forward-looking statements are not guarantees of future performance and are subject to various risk events and uncertainties, assumptions, known or unknown, which could cause the actual results to vary materially from those indicated or anticipated. For more information, investors are encouraged to review Immunovant's most recent quarterly report on Form 10-Q filed with the SEC on November 9, 2023. Joining me on the call this morning is Dr. Pete Salzmann, Chief Executive Officer at Immunovant. Following his prepared remarks, we will open the call up for questions. With that, I would like to turn the call over to Dr. Pete Salzmann. Please go ahead.

Thank you, Michelle. Good morning, everyone, and thank you for joining our call. At Immunovant, our goal is to develop innovative therapies that address unmet needs of people with autoimmune diseases. Today, I'm very excited to share with you additional clinical data from the Phase I trial of IMVT-1402 in healthy adults. Just as importantly, I want to share why I believe these data matter and why we are so excited about developing a portfolio of indications for 1402. This slide is exactly the same slide I used to open our presentation of the initial 1402 SAD and 300-milligram MAD data at the end of September. Today, I'm happy to report that our 600-milligram MAD data is consistent with the 300-milligram MAD data, and so all the data support these key program goals. Just as important as the data itself is the so what? Why do we believe that a potential best-in-class profile is so exciting in this class specifically? First and foremost, this is a proven category with many, many potential indications. The whole thesis of FcRn inhibition is built around targeted IgG reduction and the advantages of this approach. So the second bullet shouldn't be surprising. In a mechanism built around IgG reduction, data from 5 different indications has shown a correlation between depth of IgG lowering and degree of clinical response. I'm not aware of any exception to this finding. We'll cover the third bullet in a moment. And so putting this all together, we have the potential to create a compelling portfolio of indications for 1402. All right. Let's review the data. The data are really exciting. And so we first summarized everything on one slide where you can see IgG potency similar to batoclimab, while at the same time, there isn't any meaningful impact on albumin or LDL with changes similar to placebo. On the left, you see that we observed a dose-dependent and deep reduction in IgG. You will also note that each successive dose drove additional IgG reduction, which we expected. In the middle and on the right, you see that Albumin and LDL showed up and down variability, staying within the band of placebo variability, including a day 29, where the pharmacodynamic effect of 1402, as shown by IgG reduction was maximal. Let me go over each category in detail. In terms of IgG lowering, we observed a mean IgG reduction of 74% after 4 weekly doses of 600 milligrams of 1402 delivered subcutaneously. As a reminder, with the 300-milligram cohort, we observed a mean IgG reduction of 63%. In both cases, the lines are still heading down during the last dosing interval, which we expected as peak effect is predicted to come a few weeks later with continued dosing. Here, we have overlaid the 1402 IgG MAD data and the batoclimab IgG MAD data. These data are not from the same trial, but the dosing interval was the same. There is a bit of variability in all the dosing arms in the first couple of weeks, which you always see, including across different batoclimab studies. Then around week 4, the curves start to approach the nadir and there is less variability. At day 29, when the pharmacodynamic effect is close to peaking, the observed variability is much lower. At this point, the 300-milligram 1402 arm basically matches the 340-milligram batoclimab arm, while we also observed the 600-milligram arm 1402 matching the 680-milligram arm of batoclimab. In fact, both the 300- and 340 milligram arms achieved an initial reduction of 63% in total IgG, while the high dose arms were also essentially the same at 74% and 76% IgG reduction. Since PK/PD modeling predicts that both of these high doses will fully saturate the Fc receptor with continued weekly dosing, then both should level out at 80% IgG reduction after about 4 weeks -- sorry, after about 6 weeks of weekly dosing. Albumin data is shown on this slide with a side-by-side comparison, the 600-milligram data is on the left and the 300-milligram MAD data is on the right. It is important to note that day 29 corresponds to the time when any potential pharmacodynamic impact would be the highest. In both cases, we don't observe any change at day 29 compared to baseline, and the corresponding p values were not significant. Even more importantly, as we look across the dosing period, we see that with each successive dose of 1402, there is no consistent change in albumin. Instead, we've seen the same slight variability both up and down in the placebo and in the 1402 arms throughout the dosing period. The observations are the same with regard to LDL. As was the case with albumin, we did not observe a statistically significant increase from baseline in LDL values at day 29 in either the 600 or the 300-milligram cohorts. Also importantly, we do not observe any trend upwards with continued dosing over 4 weeks. Instead, we see the same slight variability both up and down in the placebo and in the 1402 arms throughout the dosing period with all the 1402 variability falling within the band of placebo variability. Moving on to safety. IMVT-1402 continues to show a favorable safety profile in the initial Phase I data set. All treatment-emergent adverse events were mild or moderate with no serious adverse events observed across any arm to date. As was previously reported, 1 patient in the 300-milligram cohort discontinued the study due to a mild adverse event that was deemed unrelated to study medication. There were no discontinuations or dose interruptions in the 600-milligram cohort. Okay. With such an exciting profile, I now want to transition to why this profile matters. In fact, we get a lot of questions about the second bullet on this slide, namely the relationship between deeper IgG reduction and clinical efficacy. It turns out that there is a really robust body of evidence across 4 different programs -- across 4 different anti-FcRn programs and across 5 different autoimmune indications that deeper IgG reduction correlates with greater efficacy. The programs and indications are summarized on this slide. Let's go through them in more detail. For Efgartigimod, in their Phase III ADAPT trial, the patient level scatter plot showed that time points with greater IgG reductions correlated with more meaningful MG-ADL improvements. Due to the cyclic dosing schedule used in this trial, a wide range of IgG reduction was observed over time. It also turned out that there was a tight correlation from a timing standpoint between changes in IgG and changes in the clinical response. Based on these observations, I think the graph on the left isn't surprising. While many argenx programs use a single dosing regimen, the Phase II pemphigus study on the right tested a variety of doses and dosing regimens. By a variety of regimens, I'm referring to whether Efgartigimod was weekly or every 2 weeks and also for how long as the duration of dosing varied between the cohorts. You can see a pretty big numerical difference in clinical response when comparing the least intensive dosing regimen Cohort 1, with the most intensive dosing regimen Cohort 4. Although initial IgG reductions observed with a high dose of 25 mg per kg, given weekly weren't much different than those observed with 10 milligrams per kilogram given weekly. You see that cohorts 2 through 4 all yielded IgG reductions in the 60s. The high-dose cohort 4 also dosed patients longer, and so the total IgG reduction over time was greater in Cohort 4. This was associated with numerically higher complete responses and lower patient relapse rates. Switching to nipocalimab, a large nipocalimab Phase II trial in myasthenia gravis tested many different dosing regimens and showed the same correlation between IgG reductions and clinical response that argenx showed. Similarly, in a recent Phase II trial in rheumatoid arthritis, nipocalimab also showed a correlation between autoantibody reductions and clinical response. The auto antibody here is called ACPA. If you look at the dark blue bars, you see that those nipocalimab-treated patients who achieved a meaningful clinical response had numerically much greater reductions in ACPA compared to the patients in the gray hatched bars just to the left of the dark blue bars. The gray hatched bars represent nipocalimab patients who did not achieve a meaningful clinical response. And you can see that their autoantibody reductions were numerically much less. The observation that some nipocalimab-treated patients had small reductions in ACPA and did not observe a clinically meaningful efficacy response was probably due to the fact that the overall IgG reduction at 12 weeks was only 58%. I believe patients with refractory RA will need more than 58% total IgG reduction in order to drive higher reductions in ACPA and to drive better clinical response rates. Similar to some other Phase II programs, batoclimab Phase II program in TED used a wide range of doses. This allowed us to observe the correlation between total IgG reduction, autoantibody reduction and clinical response. Once again, this correlation was positive. It's also very interesting that moving from a 63% to a 79% reduction in total IgG had a very big impact on the percent of patients who seroconverted or normalized they're stimulating auto antibody. You see this in the middle row, which moved from 12% to 57%. Auto antibody titers are sometimes very high in Graves' disease and in TED. And so increasing total IgG reduction from the mid-60s to around 80% is likely required to bring auto antibody titers into a range where they are no longer driving disease activity. Here, we're showing the ITP and POC results for rozanolixizumab. In this unique Phase II trial, a wide range of doses was used and again, correlation between IgG reduction and clinical response. In this case, increasing platelet counts was observed. Okay. Let's turn now to our indication portfolio. With such an exciting class that has an excellent biomarker and with an asset optimized to take advantage of the correlation between depth of IgG reduction and clinical response, this is a really busy time for our clinical teams. Not surprisingly, we're considering the usual factors when prioritizing indications for development. Generally, when this process is applied to a single mechanism of action, you would cut the field of indications down to just a few. But as we all know, that is not the case for the FcRn inhibitor class. So how are we going to choose from so many great options? Well, we can take advantage of the large number of potential indications to diversify our portfolio of indications under development. I believe we'll do this by having indications in each of these categories listed. In the first mover category, we're looking at diseases with a high biologic rationale even recognizing that by definition, there isn't any FcRn -- anti-FcRn data yet for first-in-class opportunities. For these indications, we'll also be looking for very high unmet need at the patient level. Graves' Disease is a good example indication here. For best-in-class indications, there will be 2 flavors. In some cases, we will likely pursue more classical autoantibody indications where strong in-class data and a reason to believe that deeper IgG reduction will really matter, is present. In other cases, we may target more general immunology conditions, where autoantibodies likely play a role and where in-class data from another FcRn inhibitor not only suggests a role for an anti-FcRn, but also suggests a need for greater IgG reducing potency to drive more meaningful efficacy. This slide goes through 2 examples of potential indications to make the points on the prior slide even more tangible. Graves' disease could be a first-in-class opportunity and also a best-in-class opportunity due to a need for deeper IgG reduction. Graves' disease is fairly common with an incidence in the U.S. of about 115,000 patients per year. First-line therapy is pretty straightforward, but second-line options are really drastic. So there's a big gap between first-line and second-line options, and we believe many patients currently undergoing radiation therapy or surgery would prefer anti-FcRn therapy probably for about 18 months or so. Patients failing first-line therapy are quite symptomatic as evidenced by their willingness to undergo radiation or surgery. And even many who refuse one of these 2 second-line options are pretty symptomatic despite treatment with antithyroid medicines. Why do we believe 1402 may be a best-in-class option assuming our batoclimab-POC demonstrates anti-FcRn efficacy in Graves'. While durable symptom relief in Graves' disease, which happens for about half the patients on oral antithyroid drugs is associated with a spontaneous reduction in auto antibody titers, with the other half of patients, the titers don't come down and symptoms remain or keep recurring. Some of these titers are very high, and so we believe that deeper IgG reduction is likely going to be necessary to control symptoms and effectively treat Graves' disease. Moving on briefly to RA again as a potential example of an autoimmune condition where autoantibodies are present and likely meaningful, even if it isn't a classic autoantibody condition. Again, very importantly, there is a very large unmet need in refractory RA. Early line RA treatment is crowded and most patients find a solution. But for those who fail multiple biologics, the unmet need is really high, and the population size here is similar to MG. I already touched on the recently disclosed nipocalimab data that showed a correlation between depth of autoantibody reduction or depth of ACPA reduction and achieving meaningful clinical efficacy. We really believe that overall efficacy in this POC was limited by a total IgG reduction of 58%. Basically, nipocalimab was underdosed in terms of what RA patients need with the dose apparently selected to minimize impact on LDL and albumin. Let me emphasize that we haven't made any final decisions here. In the case of Graves' disease, we're waiting to see the initial results from the batoclimab POC later this year. And in the case of RA, we're still reviewing the publicly disclosed nipocalimab data with experts in the field. Let me underscore that what we have reported today hasn't been easy to accomplish across the entire anti-FcRn class. This is again the same slide we used in September, outlining what we believe to be all the required attributes of a fully optimized anti-FcRn. As far as we know, 1402 is the first FcRn inhibitor to have demonstrated all of these attributes together. The combination of all these attributes is important, and we don't see an easy path for any other known anti-FcRns to get here. Looking ahead, 2024 is going to be a very active year for Immunovant as we continue to progress our development for 1402. While we aren't disclosing specific indication decisions today, our planning is well underway for an aggressive capital efficient development strategy, and we expect to target multiple indications, including some where we begin straight with pivotal programs and others with POCs. Thank you so much for your attention, and let's open the call to Q&A.

[Operator Instructions] The first question comes from Samantha Semenkow with Citi.

Pete, I just wanted to follow up on the indication strategy nicely outlined in the prepared remarks. Can you talk about how you'll think about prioritizing indications across those 3 categories other than which ones to study first? Obviously, Graves' seems like a front runner assuming the proof-of-concept data goes positively. But outside of that? And also, how are you thinking about your capacity to initiate multiple studies at once? Should we expect more of a steady cadence of initiations over the near to long term? Or could you initiate literally multiple studies all at once?

I appreciate those 2 questions. So across the 3 categories, I mentioned first-in-class, best-in-class classic autoantibody and then third, best-in-class opportunities in autoimmune conditions where autoantibodies play a role. I expect we will have indications in all of those 3 categories right out of the gate. They provide a -- there's really exciting indications in all 3 categories, first of all. And then secondly, the opportunities and risks in the buckets are a little different. First-in-class obviously has a little higher technical risk since there's no FcRn data, but less competitiveness since you're first. So that provides that portfolio diversification that way that I was mentioning. And that sort of leads into your second question, which is our capacity to launch multiple indications in parallel. This is something we've absolutely been building towards for quite a long time now. We have a robust leadership infrastructure, a group of VPs and C-suites that I'm really proud of. You can see them all on our website. And we've organized the company in such a way that we can scale programs underneath them. So I anticipate we'll be able to launch multiple programs in parallel while also, of course, maintaining our batoclimab programs that are ongoing.

The next question comes from Louise Chen with Cantor.

Congratulations on the data. So just wanted to ask you a few things here. First, the percent change over time graphs for albumin and LDL have a wide access that extend above and below the valid ranges. So just curious if you could tell us what the numerical percent change was on the last day of evaluation? And then second question is the albumin and LDL data shown as mean. What was the range of change in those parameters?

Thanks, Louise, for those 2 questions. So I think if you -- if I understood your question on the graphs you're asking for some insight in terms of the variability. So what I think what's really important there is the point that -- and this is why we showed the placebo as well as the 1402 altogether. Because when you look at the variability, which we saw for placebo, whether we're talking about albumin or LDL, the variability for 1402 is well within that variability. That's the first important point. The second thing is that as you look at continued dosing, so you keep hitting the target every dark arrow down below, if there were a pharmacokinetic effect on either albumin or LDL, then you should see that impact on the analyte go in one specific direction. In the case of batoclimab, you would be -- you would see the albumin going down and the LDL going up and it would increase with each successive dose. You don't see that. Of course, in the placebo lines, you also don't see it in the 1402 lines where you just you just see the up and down variability. The variability across the analytes was basically the same as what we mentioned in the call last time, which is that for LDL, you see a variability of plus or minus 15%. And for albumin, you see variability of plus or minus about 5%. Occasionally, there are some that are a little bit more than that. You can see that first placebo uptick in the albumin charts probably touching right up against 5% variability. And then similarly, there's a placebo variation in the LDL that's actually greater than the 15%, but the average variability for albumin is plus or minus 5% and the average variability for LDL is plus or minus 15%.

The next question comes from Jason Gerberry with Bank of America.

As you guys look to prioritize indications mindful that like Graves' and RA, you're doing more work or waiting for more data. But I'm curious about your FDA meetings next year, if you could sort of preview the importance and outcomes emanating from those meetings. My sense, and after the last call was really looking to get buy-in to a move straight to Phase III in settings where IgG as a biomarker is highly predictive of efficacy. But is that sort of like the main crux of the meeting? And can you confirm -- you'd be asking the agency with the CIDP study that you'd like to move CIDP responders from batoclimab to 1402 in the randomized withdrawal phase. So just curious where you stand with those objectives?

Yes. Thanks, Jason. Two important question there, one more general one specific to CIDP. So with regard to any program that we're launching as a pivotal program, we would always have an FDA interaction prior to that. You're not -- while there is sort of an official green light that happens kind of at the very end of the process and involves many, many, many different things like CMC package and things like that, generally, the really important part of those FDA interactions is to get conceptual alignment on the key parameters of a pivotal trial, which are dose design and effect size. So for dose, we don't expect to have a lot of debate because they're such a strong biomarker with regard to IgG in this class. And then for effect size, that's something that the sponsor needs to estimate and you don't want to be off because if you are too conservative, the trial is too expensive. And if you're too aggressive, then you have a risk of the trial failing. And of course, you need to get agreement with the FDA, at least in principle, in your SAP, which relates to the effect size. So a lot of the discussion relates to trial design. And for some indications, one of the key tenets of trial design, inclusion, exclusion criteria, primary endpoint, duration of the placebo-controlled period, things like that. For many indications, those parameters are pretty standardized. Several or many programs have used kind of the same rough design and so there's not a lot of discussion unless you're trying to introduce a new twist to the design, which we might be doing in certain cases. For other cases like Graves' disease, where you haven't had a study in a long time, then there'll be a little bit more path breaking to do to define design. And I think a couple of the key tenets of the Graves' design will be, how long do you do their placebo-controlled period. How do you demonstrate durability of response? And how do you taper antithyroid drugs since it's likely, assuming that batoclimab trial shows a benefit of FcRn inhibition in Graves' disease, that the combination of a potent FcRn inhibitor and an antithyroid drug would make many of your Graves' patients actually high both thyroids, you're going to have to take away their methimazole and how you do that exactly as an element of trial design that relates both to showing a clean efficacy signal and also to safety. So there's a lot to discuss around trial design for some of the newer indications. For some of the indications that have been well studied, I think those meetings will be a little bit more straightforward. With regard to CIDP specifically, okay? So this is an indication where we have an ongoing trial with batoclimab that we're excited about because it has 2 different doses in period 1. And that gives us an opportunity to generate data with regard to demonstrating our hypothesis that deeper IgG reduction matters in CIDP. We've also said though that the kind of our -- a priority belief in the importance of IgG lowering, depth of IgG lowering mattering in CIDP is strong. And so the likelihood that you'll need more than 12 weeks of deep IgG reduction, which is what batoclimab can do in CIDP is high. And therefore, we're already thinking that probably that program when it comes to a submission package, a registration trial is going to move to 1402. How we would actually do that and whether you transition patients from the existing batoclimab trial onto 1402 or simply start another 1402 trial in parallel. Those are also the kinds of details that we would look to first develop a sort of a rational plan about in partnership with thought leaders, and of course, our existing team and then get alignment with the agency on that plan. So lots to talk about with them. And I think -- but for the most part, I don't think these are going to be complicated or contentious meetings. I think they'll be really interesting and relate to how we optimize our design, taking advantage of the fact that there's been a lot of trials in the class that we can learn from.

The next question comes from Derek Archila with Wells Fargo.

It's Yvonne for Derek. Congrats on the data. Two from us. First, can you further discuss your take on the nipocalimab RA efficacy data? Is this an indication you're looking to pursue? And if so, what would a proof of concept study look like for 1402? And would you require a partner to run this larger trial? And for the second question, are you currently making investments on the R&D side in order to begin pivotal trials with 1402?

Yes, 2 great questions. So with regard to the nipocalimab data, I think I reviewed with -- I think what we think are sort of the most important points, and that's that there's a -- in terms of their RA data, that there was a correlation, a strong correlation, I think, and I say strong because it was consistent across many different types of analyses that showed a correlation between depth of IgG reduction and clinical efficacy. This was a small proof-of-concept trial. So you wouldn't expect all of these correlations to necessarily be statistically significant. Rather, when you're looking at this type of trial, you look for real consistency across different ways to analyze whether there's a correlation. And in this case, there were several different things that they looked at, including baseline levels of ACPA, depth of ACPA reduction. They looked at different parameters of efficacy, 2 really meaningful ones, ACR50, which is a strong efficacy signal and a DAS28 remission, which is also a strong efficacy signal. So I think there's definitely a biologic signal there. The question is how to optimize it, get the population right so that you can have an efficient program that's likely to succeed and provide a meaningful benefit. RA is one of those areas where there are larger companies that have a lot of experience, and they might be able to accelerate a program in RA or make a contribution that will be incremental to whatever a company like Immunovant could do. So that's something to keep our ears open for. In terms of building our own capability, though, absolutely, we've made a lot of investments to be ready to start quickly with a range of studies in 1402, including both pivotals and proof-of-concepts. And in that regard, batoclimab's clinical trial infrastructure has really helped, and we've tried to design our internal capabilities in such a way that they would be scalable as we add additional programs with 1402 by developing -- or sorry, by hiring leaders at all levels that really have a lot of experience and have the capacity to take on additional trials by adding CRO partners or additional people onto their teams that we don't have to just create a whole bunch of new teams, rather we can expand the existing teams, which will be a more efficient and effective way to scale the company.

The next question comes from Colin Bristow with UBS.

Congrats on the data. First question, I think the presentation on the sort of correlation of IgG lowering, this clinical effect was very helpful. Obviously, this is going to vary across disease states. But do you have a view on what's the approximate minimum difference you need to see in IgG lowering to drive a meaningful clinical difference? And then just secondly, any thoughts on the argenx subcu ITP failure, that would be helpful?

Yes. Thanks, Colin. Two really important questions. And I think you kind of hinted that the most important part of the answer in the first question, which is it's likely to vary by condition. So maybe first, let me answer which types of conditions are most likely to be most influenced by deeper IgG reduction. That's an important question as kind of part of your first question. So those indications where you see a greater ramping up of the immune system, more inflammatory processes beyond just a single auto antibody being present I think are one group of diseases where deeper IgG reduction is likely to matter. So what are examples of that? Rheumatoid arthritis, CIDP, if you contrast those 2 conditions with myasthenia gravis, in the case of myasthenia gravis, there's not a lot of inflammation at the neuromuscular junction. Yes, you do have some complement activation, but you don't have a wide-ranging cellular infiltrate at the neuromuscular junction. Contrast that with the areas of disease activity in CIDP around the Mylan sheath or in rheumatoid arthritis in the synovium and joints. And in addition, in those 2 conditions, in addition to the autoantibody, you see a lot of other immune cells and cytokines. So in a situation where the immune system is just much more active, if you're taking a very targeted approach, which is to reduce the autoantibody, I think it's logical that you're going to have to go a lot deeper. And how deeply you need to go? I mean this is a little bit of a question like sort of related to the effect size question. There's a first order question, which is if you reduce IgG by 65%, what effect size will you get? And then a second order question, if you reduce it by 80%, how much more will that effect size be. When does an effect size go from being good to great? That depends a little bit on the condition. Let's take RA for an example. So the effect size in the nipocalimab trial for ACR50 was about 15%, I think. And if you look at other agents in -- that have been studied in a similar population, there's been a lot of those studies. The average effect size in that group is closer to 20% or 25%. So in the case of refractory RA, looking at the outcome measure, ACR50, if you move from a 15% effect size to a 25% of effect size, you made a huge, huge step forward. In other conditions like myasthenia gravis, I think what might be more important is how many deep responders you get rather than the population level change in MG-ADL from 4% to 4.5% or 4% to 5%. I think the bigger point will be what's the percentage of people who get a very, very deep response. And if you look in the efgartigimod label in the U.S. for MG, 40% of people got a 6 point or greater improvement in MG-ADL, those people are getting a dramatic improvement. So compared to the people who didn't get such a dramatic improvement, but just improved, those 40% really got dramatically better. So I think in the case of MG, if you can move those deep responder rates from 40% up to 50% or so, then that's going to be really meaningful because you're looking at a number needed to treat of just 10%, which is a very low number needed to treat. There's a competing call right now that argenx is holding for their ITP trial. I've only seen the press release. So -- and I'll let them discuss their trial results. But we have thought a fair amount about ITP and we've looked a lot at their previous data where the effect size was modest in a pretty late line group of patients. That's a very different group of patients than the rozanolixizumab POC. But then the other thing that was very different in the rozanolixizumab POC versus the argenx study is you had a wide range of IgG response. And you saw that strong correlation at the lower levels of IgG reduction in the rozano POC you had a pretty modest improvement in platelets. When you got to a high, high dose, a dose that's too high for them to use probably because of the headaches where the IgG reduction was lower, you had a high effect size. So I think it's very -- it's too early to say for sure, but it could be that ITP is one of those conditions where to get any meaningful effect size in a later-line therapy, you need to maximize the IgG reduction. Whether 80% is enough in that population, I think we'll need to learn more over time. But my guess is that's probably -- those are probably the 2 factors. The refractory patient population and the need for deeper IgG reduction.

The next question comes from Sam Slutsky with LifeSci Capital.

Congrats on the update. Just a couple for me on the upcoming Graves' readout. Just first, just remind us what level of efficacy would be deemed as clinically relevant in the ongoing study? Second, is it expected that the primary efficacy endpoint in this ongoing study would be similar to Phase III for the primary endpoint? And then third is for the ATV reductions in the current study. Remind me what the protocol is in terms of how often Q3 and Q4 are being monitored? And then at what point can patients be lowered in their dose?

Yes. Those are really good questions, Sam. I appreciate them. Thank you. So of course, the backdrop of all this is we have, as you mentioned, batoclimab POC data later this year. And first of all, what is -- what's the efficacy measure and then what is the bar we believe, for success in this POC. So the efficacy measure is normalization of thyroid hormones without increasing, of course, your antithyroid drug. So patients come in, they have to be on an antithyroid drug at a reasonable dose and still be hyperthyroid, so they're uncontrolled in spite of antithyroid medication in terms of their Graves hyperthyroidism. So for somebody like that, how many of them would spontaneously become euthyroid over a 12-week period. There's really not good data on that. By the way, these patients are all antibody positive and people who are antibody positive are sort of the least likely to spontaneously remit. As I mentioned, some Graves patients become an antibody negative in terms of what you're measuring over time. So this is a group of patients that we have an expectation that if there were a placebo arm, you'd see a very, very low number of the placebo patients hitting the efficacy endpoint, which is becoming euthyroid, might be 5% or 10%. Let's just be conservative for the purposes of this little thought experiment and say it's 15% if we had a placebo arm. So then we look at what's a meaningful placebo-corrected delta in autoantibody studies. And I think that is 35%, an absolute difference between the active treatment and placebo of 35% on your primary endpoint. That's a really solid result. So 15% plus 35% would get to 50%, again, in this open-label trial. So we have a 50% of the patients become euthyroid due to batoclimab therapy, then I think we've got a really, really good result. There could be some secondary endpoints like some patients might reduce or even stop their antithyroid drug. You might have more than 50% convert to becoming euthyroid, but 50% is sort of the bar we use for you to be excited about this indication. In terms of the primary endpoint that we're using in Phase II and whether that would be the one for Phase III. I think roughly, it would be the same. Sometimes there's a little bit of a debate on the analysis. Like in MG, you can use a responder analysis or you can use a percent change from baseline. But at the end of the day, everything kind of centers around MG-ADL and MG. In Graves' disease, you have a -- not a biomarker but actually a blood test for the condition, which is your T3 and T4 which are your thyroid hormone levels There are well described normal limits for T3 and T4 in any lab in the world. So it's easy to define what is euthyroid and what is hyperthyroid? -- we've defined becoming euthyroid without any increase in your antithyroid drugs and I think that's a pretty reasonable primary endpoint. It could be that it's again slightly different in terms of using a responder analysis or something like that once we have discussions with the agency taking a look at our batoclimab data and assuming that's positive and then having that discussion with them. You asked the third question about antithyroid drug tapering, and this is really important because antithyroid drugs inhibit the production of thyroid hormone, whether or not you're hyperthyroid or euthyroid. So if the person becomes euthyroid by an anti-FcRn removing their auto antibody then the -- and they continue taking antithyroid drug, then they're likely to become hypothyroid. So how do we manage that in the POC. T3 and T4 are very easy to measure. So we measure those weekly. And in this POC, it's an open-label trial, so we don't have to worry about unblinding and the primary investigator has a lot of experience. So he will take a look at the trends in the T3 and T4. So for example, if the T3 and T4 are trending down, then he may start to taper the antithyroid drug. He's going to use his clinical judgment in terms of the pace at which he does that, and he has a lot of experience treating people with antithyroid drugs and we'll be recording information every week. So we'll get a lot of data about well, how to optimize the tapering of antithyroid drugs that we can then build into our Phase III protocol. In some cases, if you taper it a little bit too quickly, then people might start to become a little more hyperthyroid. If he tapers too slowly, then people might become hypo. And by having weekly measurements of what dose was the person taking of antithyroid drug, what was their T3, T4, week-to-week, I think we'll get a really good picture of how to optimize tapering in the setting of a potent anti-FcRn inhibitor.

Got it. And if I can ask a quick follow-up. Is it known what proportion of Graves' patients are autoantibody positives?

Yes. So I mean, I guess, at time zero, like at the time of diagnosis, that's pretty much a part of the diagnosis. So if you have -- if you're antibody negative, but you otherwise look -- everything else looks like Graves' disease, the other thing it could be, there's a Hashimoto thyroiditis, which is a different autoantibody. And then there are some other sort of less common forms of autoimmune thyroiditis. But sort of 0 negative Graves' is -- which I'm using air quotes, as I say that. It doesn't really exist. I'm sure there are some patients that where they -- they're autoantibody is unique enough that it's not detected by the standard assays. But for the most part, you need to have a positive antibody to be called Graves'. Otherwise, you'd worry that there's something else going on that's causing the hyperthyroidism. And there are some -- there's a bunch of corner cases of things that can cause hyperthyroidism that an expert endocrinologists would want to exclude. Once you're diagnosed with Graves', let's say you have somebody who -- their antibody positive classic Graves' presentation. Over time, some people, for unknown reasons, really, their antibody titers go down, and eventually, they don't need treatment anymore. They're the lucky ones. Many, many other people for, again, unknown reasons, their antibody titers stay high, and so they're not able to stop their antithyroid drug. And if they couldn't be controlled, if you -- the sort of the double whammy is the people whose antibody titers stay high and they can't be controlled with an antithyroid drug because they're sort of stuck. And that's the group that will either go on to a second line therapy, which is pretty drastic or they just suffer from their hyperthyroidism for quite a while until maybe after a couple of years, then they do have a reduction in their autoantibody titers.

The next question comes from Douglas Tsao with H.C. Wainwright.

Pete, congrats on the MAD data. Just maybe following up on the earlier question in terms of ITP and maybe not specific to that indication. But just how do you think about an indication like this where there is certainly evidence that the mechanism can be relevant, although the data is a little mixed with other agents. Does that give you any hesitation or just given your profile in terms of superior IgG lowering, perhaps you can enhance the opportunity because that means that you would be that much more differentiated?

Yes. Thanks, Doug. Kind of for me just a little bit at the beginning. So you were speaking generally of the category of sort of autoimmune but not classic autoantibody conditions?

No, just an indication like ITP.

ITP.

Right, where we see evidence that the FcRn mechanism should be relevant. But obviously, the clinical data suggests that it's not as easy as perhaps other indications that are more straightforward, perhaps more IgG lowering is needed.

Yes, yes. Sorry, I missed the ITP piece. Right. So look, I think one of the huge advantages and sometimes small challenges associated with the FcRn inhibitor classes are so many indications. But on days like today, it's an advantage. So look, ITP just dropped down. It wasn't, I don't think, at the top of anybody's list in terms of the most exciting indications because the prior data was kind of modest. And because, frankly, there are reasonably good therapies for many, many people with ITP. There's still an unmet need and people who don't respond to those anti-TPOs and other agents. But -- but many people do respond to that TPO agent. So we started out with kind of ITP in the -- maybe in the middle of the pack of a long list of indications. And I think after today's argenx data where I've just seen in the press release, it probably ticks down a notch in general. That would be one where if for whatever reason, as more data comes out, where we believe like, well, okay, there's definitely an opportunity here. You just need to go to deeper IgG reduction. That's the kind of thing where we'd want to test that in a POC. Back to dose design and effect size, I don't think we can sit here today and be convinced that there's a good enough effect size in ITP even with deep IgG reduction to jump right into a pivotal program. Even though you could otherwise start a pivotal program, I think it would just be too risky and just financially, not very smart. However, running a small proof of concept to see whether -- you're looking basically for a strong signal, you're not looking for a weak signal. So you don't need such a large study to do that to determine whether deep IgG reduction, deepest, 80% IgG reduction can matter in a particular population of ITP patients. That's a kind of POC that would be reasonable to consider. Again, I don't think it's probably at the top of the list, but it's definitely an example of the type of question, which you can answer with a 1402 POC that might be interesting.

The next question comes from Alex Thompson with Stifel.

This is Patrick for Alex on. Just going back to RA, I guess, and kind of in light of running a capital-efficient development program here, what does the path forward look like for 1402 in RA? Do you run a Phase II? Or is there a possibility that you could go right into a Phase III?

So first of all, I don't want to over-index on RA. There's some new data there, which is really interesting and illustrative in terms of how we think about more is better. What I'm not saying today is we're launching an RA program. I don't think RA is ready for a pivotal program, but it might be ready for a carefully designed POC. Certainly, Janssen that has a lot of experience in RA and even with an asset that's not able to get deep IgG reduction without hitting albumin and LDL, they've chosen to launch another proof of concept. They're using -- their second proof of concept is with combination therapy with a pegylated interferon, anti-interferon. So that's -- I think there's reason to think that a POC could make sense. For us, whether we end up doing a study in RA or not, will be part of an output of our strategic process over the next couple of quarters where we look across all the indications back to that slide I had in terms of where we weigh the commercial opportunity with the probability of technical success, the unmet need and then what a regulatory path would look like and an execution plan. You put all that together and then we'll weight the various opportunities based on how much value they can create. So whether RA is a yes or no after that process is run over the next months, we'll have to wait and see. If we end up doing something in RA though, I would think it's much more likely to be a proof-of-concept study than a straight to pivotal.

The next question comes from Thomas Smith with Leering Partners.

Let me add my congrats on the data. Just on the albumin and LDL changes, I think the mean changes look really good. But can you describe some of the patient-to-patient variability and how that compared versus your expectations? And can you just clarify what the change was from baseline on LDL at day 29? And then just a quick one on the Graves' proof-of-concept data set, can you just describe where you are with the study progress at this point and remind us what we can expect in the top line data release by the end of this quarter?

Yes. Thanks, Tom. I'm just taking a couple of notes here. So I don't forget those questions because they're good ones. All right. So patient to patient variability, there's nothing surprising. So whether we're looking at albumin or LDL, you just -- you see sort of the same thing that you see in the averages. You just see more ups and downs at the patient level, but there aren't outlier patients that are surprising and cancel each other. Like that's not the case, meaning somebody goes way up and somebody goes way down, and that's -- it averages to a flat middle, which is what we see. Rather you just see choppiness at the patient level. So nothing surprising there. In terms of the absolute changes, you could probably draw a line across and guess what they are. They're very small. We're not -- we didn't list them because we don't want to create an anchor point for people to I think this is the change because the change is -- there isn't a change from my perspective. And I say that because, again, we're seeing variability in the 1402 arms for albumin and for LDL that is the same variability that we see for placebo. And just as a single data point in the placebo group, which is over or above the baseline, wouldn't convince you that there's a placebo impact on albumin and LDL. So the small changes in the 1402 arms that are well within the placebo variability don't convince me that there's a change in the albumin and LDL. So that's my perspective on the albumin and LDL curves. In terms of the Graves' POC, that will -- we'll be releasing that data by the end of the year. And our main goal with this initial data and the Graves' POC is to answer the question, does FcRn inhibition have a meaningful impact on Graves' disease, particularly in the first 12 weeks. Because I think there's going to be an impact, we're going to see it right away. If we've got to wait for a long time, then you're probably looking at subtle impacts that aren't going to be exciting. So we're going to be looking for an impact in the first 12 weeks. Actually, the way the program is designed, the second 12 weeks is a little bit unique, and it is designed specifically with our portfolio of 2 assets in mind. So we have a hypothesis and I kind of hinted at it earlier that Graves' disease is going to need deeper IgG reduction. One way to test that hypothesis would have been to have a 680 mg arm and a 340 mg arm right out of the gate. Because we felt pretty strongly about the 680 mg arm, and that if we were to run that experiment it would do a lot better than the 340 mg arm, we decided to -- rather than running them in parallel, do sort of -- start with 680 mg and then go to 340 mg. There's a chance that in the second 12 weeks, the patients who gain efficacy in the first 12 weeks, will lose it in the second 12 weeks, which if we only had batoclimab, that would be a negative. But since we have the profile described today with 1402, we have an opportunity to treat Graves' patients with a more potent IgG suppressing agent for longer than 12 weeks, if that's what the batoclimab POC shows is necessary. And actually, I'd say we're uniquely positioned to be able to do that. So in some ways, if patients lose the effect in the second 12 weeks it's actually favorable from a competitiveness of 1402 in Graves'. And then in terms of what data we'll show basically, it's an open-label study, so we'll be -- we're collecting a lot of different information. And we'll want to show enough information that if there's an effect size that we can convince you and others that, okay, yes, that's a real effect size, while retaining as much patient level information as possible because this is as a first-in-class study highly competitively sensitive. So it will be a little bit of a balance there in terms of what we share to make the case that there's an effect size that's meaningful and what we maintain to retain some strategic advantage in terms of designing a pivotal in that scenario where we're sitting on good data.

The next question comes from Danielle Brill with Raymond James.

This is Daniel Mink calling for Danielle. I have 2 questions. Given 1402 superior profile, is there any remaining scenarios where you would still continue to advance 1401? The second question is like on LDL. Is there any quantification if you use the baseline on batoclimab rather than screening? And does that matter?

Thanks, Daniel. These are 2 really good questions. So in terms of batoclimab programs, I don't anticipate that we'll initiate any new programs with batoclimab rather any new programs that we initiate will be with 1402. And we already talked about Graves' and CIDP pretty extensively. In terms of the albumin and LDL changes, you asked about LDL, but it could apply to either one. So just taking a step back. In this Phase I study, we measured several values prior to dosing. And that was something which we added to this Phase I trial based on the learning that we've had over time, these are modestly variable analytes, particularly LDL. And so because there was more variability this time in the LDL and Albumin kind of out of the gates, we -- this time, we're showing all the data from the very beginning, including for the 300 mg cohorts and the 300 mg placebo. So I think you can kind of look at the data however you like there. But I think the most important thing is when you look across the line, as I mentioned earlier, every time you continue to hit the target with a dose, if you look on the slide that has the 3 graphs with the IgG, you see boom, boom, boom, every time you hit the target, it goes down, down, down. With albumin, and I'm talking about the 1402 lines, obviously, the placebo goes up and down because you're not hitting the target in the placebo curves in the IgG figure. And the albumin and LDL, everything looks the same as placebo in the IgG figure, which is you hit the target and nothing specific happens. Sometimes it goes down, sometimes it goes up. You can see that, as you pointed out, from screening to -- the very first screening value to the last value prior to treatment. There's some movement up and down. So it's a variable assay. But maybe the most important thing I haven't said yet on this call is every piece of information we have from batoclimab, where we have a lot of human data suggest that the reason for LDL change with batoclimab is because of a reduction in albumin. And we now have a couple of other pretty comprehensive data sets. So it was a while before nipocalimab released any information. But in their -- one of their posters at ACR, they also showed their albumin LDL curve, and you saw approximately 9% reduction in albumin that led to an approximately 5% increase in LDL. Then you have the UCB FDA review of their rozanolixizumab Phase III submission, where they had approximately a 4% reduction in albumin that yielded a 0% change in LDL. Those numbers are pretty consistent with what we see in the low end on the batoclimab side as well. So that's why we really spent a lot of time emphasizing albumin specifically because if you see no change in albumin or 1% or 2% reduction in albumin, you're just not going to have an LDL. The LDL is so variable that it's hard in a small study to convince yourself totally one way or the other. But the albumin is not as variable. And I think there it's very convincing that there's really just no change or at best a super tiny change, which is the same you saw in placebo, and therefore, it's probably nothing. And that gives us confidence that we're not seeing analyte changes with -- that are 1402 induced in albumin or LDL.

The next question comes from Yasmeen Rahimi with PSC.

This is Jung Yu on for Yas. Congratulations on the data. First, how soon could you engage with the regulatory agency to discuss next steps? And second, on the basis of the data, what are additional key decisions or determining factors you're considering before disclosing the final indications?

Yes, yes. So the Phase I trial is now completing the post-treatment period, which involves the recovery of the IgG curves. And although those data points aren't so interesting, from an investor standpoint, they're important from kind of total study package standpoint. So that's all finishing up between now and the very early part of next year. And that, together with other things we've already been working on in parallel like what our approach would be to different indications based on assuming success, which we've been doing for a while, it'll all be ready for FDA interactions starting in the very, very early part of next year. The -- what we learned from those FDA interactions, combined with kind of some other scenario planning and financial planning and the prioritization across the indications using that process I mentioned earlier, will put us in a position to make our final decisions and then begin to move forward aggressively in 2024. So this is going to be a fast path.

The next question comes from Robyn Karnauskas with Truist.

So I guess my first one is a follow-up to the first question you got, which is on indications you might go after. How do we think about spend and how you think about how much money would cost to really go after all these indications given how competitive the landscape is? And second, I think Argenx said at our conference that they are being very quiet in how they actually disclose data going forward and also trial design. How are you thinking about how you can compete with the competitive landscape for trial design? And how much you'll disclose for Graves? And then I have one follow-up.

Yes. Those are really, really good questions, Robyn. So in terms of our capacity to begin and continue, which is the heart of your first question, we have a lot of -- a ton of capacity to get started, okay? We've -- as of our last financial filing, taking into consideration the financing that was closed right at the time of the quarter close, we have $737 million. I've mentioned a couple of times, we've got a strong, high-performing leadership team that's ready to scale up. And we've been working hard to make sure that CMC is off the critical path, and we've got a great CMC team and strong partners there. So whether we're thinking about human resources, drug substance or dollars, we got a lot of capacity to get started. And then we're going to be building a -- make sure we maintain -- build and maintain a catalyst-rich time line such that over time, we can continue to raise the additional capital we would need to fully maximize this opportunity. So that's on the first question. Can you give me your second question again, Robyn, I want to make sure I get it right?

So I mean, first -- follow up to the first one. So should we up our spend, given all these data readouts you've seen with RA? And should we increase our spend? Or how are you thinking about spending and starting proof of concept like next year. You said 2024, you'll give us some more guidance on how many trials you'll run? I'm just concerned that we're underspending -- we're under modeling spend. And the second one is more about competitors, look, do you think that you can be -- how much will you disclose for Graves', for example? Because I think that a lot of the competitors are saying they're not going to disclose a lot because they don't want to tell anyone anything. So how do we think about that?

Yes, yes. No, that's really -- it's a fabulous question. In terms of the spend, I think the first -- I realize you're asking a multi-quarter, multiyear question, which is important for the models that you and others build. I think in the short term, in the near-term quarters, a lot of the work that we have to do isn't that capital intensive. It's when you actually begin to start those trials middle of next year or something where you're engaging with CROs and that kind of thing where the spend goes up. And by then, we'll have disclosed more clarity in terms of what we're doing where and what pace and how many in parallel. So I think those kind of inputs will become -- will come in enough time to add them to your model. So in terms of Graves', it's a really, really interesting question, and I'm not at all surprised. I haven't heard that yet, but I'm not at all surprised to hear that others in the space are starting to say they're going to be a little bit more parsimonious with what they share externally. It makes a ton of sense, particularly with regards to data because the patient level data you have, if you're the company that's run the only trial or the largest trial in that indication, then you've got a big, big head start over your competitors in terms of knowledge. And even disclosing group mean data can provide some insight that would allow a competitor to accelerate or deaccelerate a program and be strategically disadvantageous for the disclosing party. So we'll think about those things carefully. As an example, I think how to optimize methimazole dose tapering. That might be an example of something which we keep a lot closer to the vest. One thing, though, that I have really appreciated in being close to trial design now for the last, I don't know, 5 or 6, 7 years of my time in biotech and pharma is how much you can learn from experts in the field, even without them disclosing any competitors' confidential information. They just have an intuition. And the other interesting thing is a lot of times the first couple of trials are pretty vanilla because in the first time you run a trial, you just really need to show an effect size and you don't want to add a lot of bells and whistles. -- whereas once the effect size is established, then you have the ability to use that confidence to develop a more creative, patient-focused, physician interesting type of study. I put our MG trial in that category. We get tremendously positive feedback from investigators around the world in terms of our MG trial design when they think about what kind of information it can deliver to help run their practice. That's not a design that anybody would have run at the time. Argenx was running their initial trial in MG because it's nuanced and it builds on the fact that we already had a good sense for the effect size, which they didn't have the advantage of knowing. So sometimes all you need is the effect size and that one is pretty hard to hide unless you're a really, really large company like J&J to then take the next step of working with thought leaders to be really creative. And that's an area where I think as a small company, sometimes there can even be some advantages because you have kind of a closer link from those key world experts to not just the study team, but senior management since study teams and senior management are a little bit closer at smaller companies, and that information can flow a little bit more quickly. So bottom line, I think we're in a good position to continue to design really creative and meaningful studies. And with regard to Graves' specifically, we will be thoughtful about how much we disclose for the reasons you mentioned. Great questions, Robyn.

And a follow-up on the Graves'. The comments you made about like maybe saying like an S&P and then maybe there's a possibility with the lower dose maintenance that you actually see a rebound. Like how comfortable are you that the efficacy will be maintained if you see that?

Yes. Yes. So the super interesting thing about Graves' is how quickly things change because T3, T4, it's a pretty sensitive hormone and the half-life of methimazole is very short. So if you make a change to methimazole, if you increase it or decrease it, whatever impact that antithyroid drug is having on the T3, T4, within a week, basically, you're seeing the effect. Within 2 weeks, you're very confident you're seeing effect. So you don't have to wait a long time. So in a hypothetical scenario where let's take it just like an individual case patient, just to kind of make something up. You have a patient who's hyperthyroid on methimazole, that's our entry criteria. They start batoclimab, and let's take the golden example, they become euthyroid and they're able to stop their methimazole. So that's really a strong evidence of clinical efficacy. Then they -- and they maintain that while they're on 680 mg. Then if in the second 12 weeks, that hypothetical person switches to 340 mg and their T3, T4 starts clicking back up, their auto antibodies are going to click back up because we know that happens with 680 mg to 340 mg, 680 mg is more suppressive. And they need to go back on methimazole, and it all happens not just like in one random week, but you kind of see it over the course of time, then that type of a profile would tell me, huh, it looks like you need to you need to be on the more potent agent for longer. And I guess to your point around, well, how confident would you be that the more potent agent would work, I guess it kind of mattered like how long were they controlled? Did they have just 1 week of control at the end of 12 weeks of dosing? Or did they have many weeks of control on 680 mg? And these are the kind of things, I'm being hypothetical here, but it's possible that some of that information, we might say, wow, this is super interesting. And there are certain patient types who do have one profile or the other, and that could cause us to keep some of that information close to our vest rather than disclose it since it's going to be very, very strategically interesting.

The next question comes from Delma Kaioti with Guggenheim.

This is Thelma for Yatin. Congrats for the update. So about Graves', what are you reading from KOL about unmet need in this indication because we heard mixed feedback, and it would be very helpful if you could comment on that? And then I have another question on RA. So definitely, nipocalimab showed encouraging results there. But do you think they are robust enough to support [indiscernible] development in this indication? In other words, how strong is the biological rationale there?

Thanks, Thelma, two really good questions. So with regard to Graves', I think you're hearing what everybody is hearing when they talk with thought leaders. And -- but I don't think it's the reality. So if you call a few endocrinologists, even thyroid specialists, you will get a sort of a top line opinion that Graves' is pretty easy to treat. Most people respond to methimazole. A few people don't respond to methimazole, they need to go on to more aggressive therapy, okay? That's kind of what you hear. And I think the reason for that is not because that's the reality, but that because they don't have very good tools to treat refractory Graves' patients today. And so those patients are often suffering at home. They're easy to find. So we've done patient market research and the perspective you get from people with Graves' disease is totally different than the perspective you get from physicians. And it is true that a good number of patients, definitely the majority, maybe not anywhere close to all of them, but 50% of Graves' patients are pretty easy to treat. So that gives you a sense that this is a pretty easy condition. And the hard patients you don't keep seeing back because you're not stepping them through different therapies. But from a patient perspective, the 2 data points that are really strong for me is you have people -- a lot of these patients are younger women who are willing to be irradiated and commit themselves to a lifelong therapy with Synthroid. It's a pill and everything, but still, you're going to have to take thyroid replacement for the rest of your life almost guaranteed after you irradiate your thyroid gland or have it surgically removed. Okay, if you weren't having serious symptoms, you're not going to do that. You're just not going to sign up for that. And although the percentage of people going through radiation therapy or surgery is going down, it's still a large number. And then you have a lot of other people that are hemming and hawing for good reason and debating whether they really want to do that. That's another large group. The fact that they're even considering it again suggests their symptoms have to be pretty severe. When you talk to them, it's not subtle, okay? They're not doing well. So I think the opportunity for a therapy between the first line and second line is really strong. With regard to the nipocalimab data and what do we think about RA? I agree with you, it's encouraging, but it's not good enough to say, okay, it's time to start a pivotal trial in rheumatoid arthritis because the effect size -- the predefined outcome effect size was modest, sort of even if it wasn't statistically significant. But if you even assume that, okay, with a larger number, you could make that same effect size, which we've seen in kind of the all-comers population statistically significant, you'd still say that effect size is too modest to be competitive. The ACR 50 or DAS28 remission that they got in the all-comers analysis. However, when you look at what about those patients who got a deeper reduction in IgG or a deeper reduction in ACPA. Even though the total reduction was 58%, there were some people who did a little bit better in terms of IgG reduction and ACPA reduction. And those patients did a lot better clinically. Where you look at patients who started out with high antibody titers that are more likely the ones who are actually going to respond to an anti-FcRn, those patients did a lot better. So there's a lot of things that our hypothesis generating suggests that you could build a program to deliver a robust effect size if you could select the right patients and if you could deliver deep IgG reductions. And we think we might be able to do both of those. The second one for sure, and the first one maybe. And maybe the first step is a POC to demonstrate that. We'll see, again, we're thinking carefully about rheumatoid arthritis. It's very exciting from an unmet need and a population size standpoint. And by population size, I mean specifically that the group is not too big. So refractory RA is not this huge population that's going to totally reset the competitive and price dynamics for the class. Refractory RA is similar in size to MG and the unmet need in that group is high. So we're motivated to try to crack that nut, but I don't think it's been cracked yet.

The last question comes from Yaron Werber with TD Cowen.

So I'm getting a lot of questions on kind of a couple of things. I think the first one is definitely the more on point one. When you look at Phase I healthy volunteer data, including yours, argenx, et cetera, they're looking at MAX mean IgG reduction, whereas when you're looking at sort of Phase III data, it looks at sort of total IgG reduction. Is that just semantics or there is a slight difference there? I'm just trying to understand a little bit why VYVGART sort of went down from low 73 to the 60s? And then secondly, and I think you've answered it, but I just want to hear it sort of in more fine detail. On the 600 mg, there's just a little bit of a bump up at the end on the LDL measurement. Is that just variability? Or because it doesn't seem to be correlated to albumin at all. What do you think? And congrats on the data, by the way.

Those are really good questions, Yaron. I appreciate it. So in terms of what we've reported, sometimes we reported mean and sometimes we reported median, but they're almost always very, very, very similar for IgG we're talking about now. And we have mean here in this trial. So the group mean, the group median at the -- here, we're reporting it at the -- or showing it by time point and some of our other trials we've reported it at multiple time points and at the final time point. If you have a -- so what others have done, which we have not done is occasionally reported like the max IgG, which relates to an individual patient. So like what was the max observed at the patient level. We've never done that. We've just reported the mean, the group mean. There is a little bit of variability, and it's particularly more present when your IgG suppression is less because you don't have the receptor saturated, so you're going to get more biologic variability person-to-person when you're below saturating. So if you have an agent that delivers 65% IgG reduction in a large population, there'll be the occasional patient who gets an 80%. So if you report the max IgG reduction at the patient level, I don't know that's not very meaningful. I think the comparative number is what's your group mean or group median. The mean and median are going to be very similar for IgG. I went back and looked at a lot of the Phase I data from VYVGART because we were getting a lot of questions and they did have like a very, very old press release that said something like that they saw a sort of a 73-or-something 70s-percent reduction across the study. But that related to, again, it was kind of the max reduction in a particular isotype. They studied different doses. I don't think they were trying to do anything untoward. It was just -- this was a long time ago and they were just kind of summarizing the data, which included a lot of different isotypes and a lot of different doses. But when you look at the dose that they're actually studying 10 mg per kg IV or the 1,000-milligram Halozyme-enabled VYVGART, I think -- and they've studied it now in good size groups, it's pretty much always in the 60s. And that's true for batoclimab, 340-milligram has a lot of data, which is pretty much always in the 60s. And then if you look at batoclimab 680 milligrams, it's -- once it gets past 6 weeks or so, it's pretty much always in the '80s. So I think those numbers are pretty solid and the comparability for the 300 mg and 600 mg of 1402, over to the numbers we saw with batoclimab, because you're at the trough there, when you get at the end of the dosing period, there's less variability and they're pretty much right on top of each other. With regard to the LDL in the 600 group, where you have a little bit of uptick there on day 29, that's well within the band of placebo variability, I actually went back and looked at that yesterday. We have the data for some of these cohorts QC'd out to day 36 and the day 36 LDL in the 600-milligram cohort is back at baseline. So it's just variability. And again, it's not a surprise because you can see that the amount of uptick. You got it, I don't know, the same basic uptick around day 6 or 7 and then it went down and then it went up. And in placebo, you see it up and down. So we're confident that we're not seeing a signal in Albumin and in LDL.

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