AB Science S.A. (AB.PA) Earnings Call Transcript & Summary

[Foreign Language] And in English, welcome, everybody to the web call of AB Science on the latest news, which is the conditional approval of masitinib in amyotrophic lateral sclerosis. My name is Alain Moussy, I'm the CEO of the company. And with me is Laurent Guy, who will facilitate the execution of this call. So we'll present, as usual, the presentation. We'll go through it. And at the end, you will be able to ask your questions that you're going to ask by typing your questions on the computer, and we'll take it and try to answer. We'll try to keep the time to one hour for this web call. Next slide, the disclaimer. Next slide? So the topic is the conditional approval of masitinib with EMA. Again, the objective is to give you details about the objections that has been raised by EMA. And the answers that we have proposed that will also have a [indiscernible] objective, which is to give you more information about the study that substantiated this conditional approval. Next slide. So the first information we received from the CHMP, which is the committee from the EMA, is that it actually validated or confirm the safety of masitinib in ALS, which is deemed to be acceptable. And so in part, having considered the data from EMS team studies, the safety profile is considered acceptable. Next slide. However, the CHMP was not able to conclude on a favorable benefit risk due to the following methodological issues, and we received 4 pending objections at this time of the evaluation of the dose. So -- and we are going to get into the details of each of the objections, of course. So the first objections, I try to explain. I do apologize about the technicity of the presentations. I will try to make it understandable. But I'll do my best, and we'll try to precise later if you have questions, if it was not completely clear, of course. So the first issue concerned GCP. There has been some deviation to the protocol. And the objection that remains at the end is that some identified GCP deviation to the protocol cannot be corrected retrospectively. We are going to tell you more about that. The second objection is concerning on the primary population, which excluded fast progressor, and CHMP disagrees with this exclusion, so we received by writing the standing objection, which is the significant effect was not demonstrated in the total population included in the past, although our protocol I didn't say that, but the primary population, excluding the fast. So we're going to detail that. Then the third objection is more statistical, is the ending of missing data. There are also missing data that we [indiscernible] our study. And according to CHMP, there is a preferred methodology [indiscernible] to reference, and jump to reference should be applied to some discontinuations, and they consider that we should have applied this penalty conjunct reference to all discontinuation but not some of the discontinuation, as I will explain later. And the fourth objection concerns subgroups discussions, where we use the EMA guideline and the thought that this target population, subgroup population was identified post-op, and so cannot be taken into account despite the strong efficacy, as you will see in this subgroup. So that's the issue that we made at this stage of the process. Next slide. Okay. So I'm going to present you the arguments of the key arguments that were used trying to respond to those issues. So the first one, again, is pending GCP issues that cannot be corrected retrospectively. Some can be corrected retrospectively, like, for instance, the collection of OLEs at the site level, but some cannot, for instance, if there has been some deviations of eligibility criteria where the patients have been randomized, and so it is not possible to correct them since they have finished the study. So it's true that not everything can be corrected. We corrected whatever could be corrected. And we apply then the guideline of EMA to whatever could not be corrected. And what the guideline says is that when it cannot be corrected we should -- the sponsor should measure the impact of what cannot be corrected. And this is exactly what we did. And it breaks it down in 2 categories: some findings that potentially could likely influence the benefit risk, for some that may, which is less strong. And as you can see in the green part of the table, this is what the, I would say, findings that were found by the inspections. And of course, we responded to each of them exhaustively where, whatever could not be corrected, we measured the impact using some complex methodologies. So that includes, for instance, the duration of inclusion and exclusion criteria as an example. I don't get into the detail of each of it. But we, throughout the guideline, and we [indiscernible] everything, and we prove and demonstrated that it works, no impact. Next slide? Then we have to apply a second guideline of EMA. That says that -- and I will read the right-hand part of the slide, which is the guideline. So the guideline says in superiority studies -- which is our case -- findings, they do not introduce bias favoring one treatment over the other or relatively and problematic. So when you prove that there is no impact in whatever deviations that have been found, then you match, and you solve this problem. The other point is it is important to assess whether the findings affects the primary efficacy, which is the INSS [indiscernible], the functional score and the -- or the, we can say, and the safety endpoints. Now -- and if those 2 things apply, then the benefit risk can be evaluated and there is no more [indiscernible] issues. So we followed this guideline. Now as you have seen, I will start with safety. The safety was deemed acceptable. So it's not a problem. So let's then move to the primary efficacy variable, which is a functional score. The functional score was not even formed by the inspections as a systemic problem. There were very few discrepancies, which had no impact, and we had reassessed anyway this variable, and we found absolutely no impact. So in fact, this primary variable is robust, and the safety is acceptable. So here, we have the cornerstone of the benefit risk. But it's not enough, then we have to go to all other aspects that could impact or favor or create a bias in favor of the treatment. And as I've shown in the previous slides, we demonstrated that the rest, which is less important but still, has no impact. So we think we applied the guideline fully and the guideline says that those [indiscernible] issues, although there were some deviations, have no impact, and so the benefit risk could be evaluated. Next slide. Now we tackle the second issue. The second issue concerns the exclusion of the fast progressors as the primary population of our study. We transition from Phase II to Phase III. And when we transition from Phase II to Phase III, we excluded the fast progressors from the primary in [indiscernible]. Here, it's not really the point of the [indiscernible] note, is the point of excluding the fast itself, which is a debate with the EMA. Why we did that? We did that because the fast progressors, as it is -- actually says progress faster and there is a high risk that they do not reach the time point, which is week 48, which is a long time point. And they will create missing data. And also die, so creating even more, I would say, standard deviations. And in fact, it's exactly what happened. As you can see on the right-hand side, the normal progressors had a 26%, 30% chance to reach the time point, but a fast progressor had more than the 50% not to reach the time point. And so it was a good decision to exclude them to minimize the missing data and also because we then have a more homogenous population as the guideline recommends, to have more homogenous population. So that was justified. Now the counter -- the question we had to answer is the definition itself of the fast. We have to define the cut between the fast and not the fast, and it was based on [indiscernible]. Then the EMA questions, the robustness of the methodology used to define the fast, which is to have a first point, which is the time of the diagnosis. And then the second point, which is the baseline, where we start to randomize the patients. So this is the period where you measure the speed of the progression of the disease. And the question from EMA is to say, but that is not stable through time, which is true. It's -- some patients start as a normal progressor and become fast, right? That happens. However, a [indiscernible] actually say, from onset of the symptoms, the definition of the speed of progression is what is the most robust and what is the most predictive of survival. So in fact, it's backed up by publications. It's also backed up by the practice in clinical trials because it has -- it has been used by most of the clinical trials, and in particular, are the ones that have been registered like edaravone registered in the U.S.A. and tofersen, registered both by FDA and by EMA. So we are not the only one to have used the same definition and to have excluded part of the population on the basis of that definitions. It's also usable in clinical practice. And we have demonstrated in the process that this is robust enough because we don't ask the patients the precise day where, of the onset of symptoms. But in fact, the classification would not change if they remember the month of the onset of symptoms, and of course, the patients, they do remember, normally, the month. And also we demonstrated that this is still successful even in a bracket, which is very large of the cut because we decided based on the actual cut was 1.1. But in fact, the study was successful from 0.8 to 1.4. So in fact, we have a large bracket of flexibility, but still it was a matter of debate. And EMA does not favor or recommend to exclude fast progressors, which, by the way is not a point of discussion, for instance, with Canada or even with FDA. So, it's, we would say, rather a specific find for EMA. But at the end of the process, we were unable to convince them that it was the right decision to make. Next time, next slide, sorry. So next slide is a technical problem either on the statistical treatment of missing data. So as I said, there is a missing data percentage at week 48 because patients progress in their disease, will discontinue, and not reach week 48 or even die. So there is a concern, methodological concerns, as how to treat this missing data because we can't tell that we have missing data. So we have to impute a data, which does not exist and how to do that methodologically? And there is no rules, which is set up by the guideline from EMA or from FDA, which is universally recognized. It's not a medical model. So there are some kind of a recommendation, but not a consensus in the industry. So EMA favors what they call jump to reference. Let me explain. If there is a discontinuation in the de-treatment arm, not the placebo arm, then EMA wants to impute a penalty. The penalty is, immediately a patient that will discontinue under a treatment arm will have to be considered a placebo. So whatever the score at the time of discontinuation immediately will jump to the reference, which is another term to say placebo. And then we follow the swap of deterioration of the disease of the placebo. So in fact, it's 2 penalties in one. First, you jump to the reference, the placebo. And then you impute the slope of deterioration of the disease of the placebo. That's the preferred methodology used by EMA, which we applied, of course. So the point is not whether we applied it or not. We did. But when we plan it, we have to apply it on some discontinuation. And the discontinuation that usually are used to apply this penalty is on what we call the reasons not at random. So in fact, it's what is related to treatment, like lack of efficacy or toxicity. So when we apply this penalty for those reasons of discontinuation, which is classic, we have a p-value of [ 0.00389 ] which is significant. And that's what we provided to EMA. And EMA challenged that and said, well, the other discontinuations -- it's not on this table, but there are minimum, like less than 10, but still -- those discontinuation, they wanted that we imply, we impute it also penalty, although they are at random. So okay. So we did it. First, we did it on travel because travel, we don't know if they cannot travel because of lack of PKC or because they move to another home. But even if we apply cannot on travel doesn't change P-value, [ 0.00376 ]. But if we apply a penalty on everything, which is what they wanted us to do, then we lose significance, but it's close to significance, the p-value is [ 0.00678 ], as you can see here. Now we received 2 recommendations from EMA in the history of dosing. The first one is when we apply this continuation to penalty -- sorry, to all discontinuations, then you can use a calculation to see what the remaining, I would say, effective treatment affect that is necessary for the studies still to be successful. And if this is less than 25%, then is very good. And in fact, it's 24% in our case. So the recommendation should apply and applying the very, I would say, the very first one, the recommendation from EMA that we received before, the study is successful even in this most conservative pattern. And the second recommendation was to apply another methodology called [indiscernible] reference. Just for you to understand, it's a progressive return to reference, progressing return to placebo, not a brutal jump as the first methodology. And when we do that, the p-value is also significant, [ 0.00477 ]. So applying the most conservative method, the study is in fact, positive. Only when we apply discontinuation in every -- on all discontinuation, we lose it by short, but the recommendation from EMA, the 2 recommendations we received before should have applied. That what we told them, but still, they have at this time, maintained the objection. Next slide. Then, we provided lots of alternative data -- not alternative but supportive data to prove that the product has a robust efficacy. The first data we provided was quality of life. Quality of life's measured by a score called ALSAQ, and we have used the most conservative method to calculate it, which is on the right-hand side, with multiple imputations, the jump to reference, the [indiscernible] as you have -- as I explained in the previous slide. And using that, it's significant. The P-value is below 5%. So we can say that masitinib has a benefit on quality of life, which is very important for the patients, in particular, in the context of the conditional approval. Then we used another methodology for the primary analysis. That is the [indiscernible] a score called [ CAP ]. That is a ranking that FDA, in fact, prefers, not EMA, and this is close to significance. The study was not designed to demonstrate a significance on the CAP. But it's interesting because it's considered as a very difficult variable to reach. And you see that it's close to significance. Next slide. So the next slide is about time to event measures. So in oncology, we use, for instance, the time to progression-free survival. It's called PFS, progression-free survival. And we use, of course, survival. So we did the same because this disease is fatal. So the progression-free survival, which is the other event between sort of progress of the disease or the deaths, which was a prespecified endpoint, is significant. The P-value is [ 0.00159 ], you can see the curve on the right-hand side. It's a very good supportive variable to have in time to event. And then we've been challenged by potential bias from the progression-free survival, which are the use of what is used in the -- against this disease, which is tracheostomy, highly invasive, of course, ventilation, sometimes permanent ventilation, or gastrostomy, which is to have a tube to feed the patients. And that can extend survival. So we need to understand the interaction between that and the PFS. And we first demonstrated those events -- or of course, after progression. So what happens is that there is a progression, then those events come and then death comes. And so it makes the PFS more robust. Then we have showed to EMA, the more, I would say, integrated endpoint, which is called event-free survival, which integrates everything like progression, but also tracheostomy, Ventilation, gastrostomy, or death, so 5 events. And this endpoint is significant, [ 0.00162 ], which proves that masitinib is able to delay those very negative endpoints. Next slide. So the next slide about the survival. Again, this was not the primary endpoint, but still extremely important, of course, to analyze. So the first thing we did is that, it's the upper part of the slide, is that we -- there is, in the design of the study, an open-label phase, where we unblinded the treatment, and we proposed the placebo to switch to masitinib. So it was important to verify what was the impact of the switch because we measure the survival long term. And so that analysis there is the switch, the difference between the placebo switch to masitinib and the placebo, we still under placebo and it was done 3 years roughly, in average, after they've been randomized. And you can see here statistics in each one. It means that masitinib was able to give more survival, even to placebo who started the drug 3 years later. Now that is important to analyze the survival as a whole because it creates a bias against masitinib because we switched from placebo to masitinib . So we have to retreat this bias. This bias is retreated. And when it's retreated -- that's the table on the bottom part -- you can see that survival as a trend. It goes to significant [ 0.007% ], and it brings 6 months additional survival. So in the population of the primary analysis, it's not significant, but it's a trend. And the guideline of EMA as a trend does not count significance. It has the trend in time to event. So here, we have significance on PFS, significance in EFS and a trend on survival. Next slide. So then we move to the fourth objection. And that objection is because we proposed a -- to use a guideline from EMA. The guideline is on the subgroup. The said, this guideline cannot apply. They cannot take our data on the subgroup that you will see are extremely robust, but they cannot take it for a registration. It's not that they ignore the data completely, but for the purpose of the registration, they think they cannot choose it. And why? Because they say that these subgroups, I would say, has been identified exposed, which means it was not prespecified in the protocol, which is true. It was not in the protocol. It has been determined after an exposed analysis. However, the guidance says that it is -- this guideline can apply even from postdoc identification of subgroup. It is written black and white. It says it is of interest, in fact, postdoc subgroup where efficacy and risk benefit is convincing. So that's what we need. And in fact, the guideline says another thing. It says that when the clinical data is statistically persuasive, which is our case in the primary population, but there is a subset of patients, a part of the population, where there is a bias -- that's what we have put in bold -- then this is of interest to identify this subgroup, the subgroup being the full population minus the subset. And that's what we did. So next slide, I will show you where is the bias and subset. So this subset of patients is the most severe patients. It's the patients loss of function of -- identified by the score, the functional score called ALSFRS. A loss of function means what? The score will measure 12 functions. The function is what the patients can do. And the loss of function means that it's at 0. So patients cannot do, for instance, cannot speak or cannot walk. They cannot do something. And so when there is a loss of function, it means part of the motor neurons and the patients will probably die very soon. Of course, those patients are extremely severe, the most severe of the populations. Now when we take this subset of patients, which was presented in our protocol. They were eligible. We see there is a huge dis-balance between masitinib and the placebo. The placebo, they were 8% and masitinib, they were 20%. And so that creates a bias against because it's the most severe population. And in fact, when we look at the severity of this subset of patients, we can see in the table below, that it's not one of -- in average, for the placebo, there were 9 patients with a loss of function, and only 1 out of 12 possible loss of functions and 1 to 4. But in the [indiscernible] group, there were 21, and 10 has 1 loss of function, but 11 had 2 or 3 or 4. So they were even more severe than the placebo. Next slide. And so as per guideline, it is of interest, as the guidance says, to look at the data when you remove this bias subset. And so it becomes a subgroup that is called patients prior to any complete loss of function, which is the column in the middle. The left is the primary analogous population. And the middle is the subgroup, okay? And the right-hand side, it's another subgroup. It is the subgroup where we have added the fast because we have, of course, integrated the fact that CHMP or EMA does not like that we excluded the fast. So it's important to include the fast in some analysis to see if it is still beneficial. So in this subgroup, prior to any complete loss of function, you can see the efficacy variables. So the functional score, calculated with [indiscernible] is significant. The [ CAP ], which was not significant. You remember it was close to significance, now is significant. The quality of life was significant, remain significant. The first vital capacity, which is the respiratory function, is significant. The PFS moves from 4 months to 9 months benefit and the OS, which was just a trend is now significant with a significant benefit of 1 year of additional survival, which in the context of the ALS is, of course, of extreme clinical relevance. And when we add the fast progressor, which is so important for EMA, you can see that the data remains very strong with a nonsignificant but close to significance benefit in survival, which is of interest. Next slide. So unfortunately, and despite our arguments at that time, 4 objections remains. And because of objections remains, we don't have the conditional approval at that time. We -- okay. So what are the next steps? First step is that we have not received the full feedback from the EMA. And we will have more information in the coming weeks, I would say. Also, I prescribe that the next step for the CHMP is to have a vote, a final word. We received the feedback, which is a trending vote. The trend was negative. So I have to be very clear with the audience when the trending vote is negative, the final vote, which comes 1 month later, is usually negative -- not always, but in a large proportion negative. So I have to be very clear with the audience there. But we are going to have a discussion with EMA, and we receive more information about the reasons why those 4 objections given, despite our arguments that we objectively think we're strong. So let's wait. And then what we know because it's the process, is that there is a reexamination possibility, which is a second cycle. So we are here at the end of the first cycle, but there is a possibility of a second cycle called reexamination. Now, how it works is that this reexamination is done -- will be done by a new team, which is always -- which is an opportunity because it's a new, I would say, look at the same data. So new opportunity [indiscernible]. Then in the reexamination, there is the possibility to ask a scientific advisory group, which is an external advice on precise forms, which is not really possible in the first cycle. So first, if we go to reexaminations, we would be very interested to have the opinion on the SAG because CHMP usually follows the opinion of SAG. So we can imagine that in an option of a re-examinations, we could maybe treat those 4 questions that we have, which is -- relates to why at that time, the arguments that we have developed have not been, I would say, successful. So for the GCP, we apply the guidelines. But still, it has not been, I would say, a recognized as successful. We don't know why exactly at that time, okay? But we can go to reexamination, for instance, to debate that. The guideline on the subgroups, it seems that we have applied a fairly and objectively the guideline of the subgroups, the objection being post-op, but the guideline didn't it's impossible for post-op. And it says, it's very -- it's of interest, the precise wording is of interest in case there is a bias on the subset of patients. So while this guideline is not applied in this case at that time, okay? Then we received 2 recommendations for the missing data and the statistics, as I have explained. We applied, objectively, those 2 recommendations, the [indiscernible] and the TP. And it has not been at that time recognized -- we can, of course, ask the opinion of the scientific advisory group. And then we're left with the last objections. We do not refer to guideline and not refer to produce recommendation, but we refer to a position of DMA, which is not, I would say, universal because we have seen it's not the position of other agencies, but still is a position of EMA, which is the exclusion of the fast progressors. They don't like that we -- that the studies exclude fast progressor, although in some circumstances, they have accepted it. So it's not entirely clear. And we have heard their argument about the suitability through time of the definition, how you define a fast progressor, which is a pure technical point, but there are arguments and counter arguments. So it's not obvious. It's a point of debate. So that's what we could discuss, but I tried to explain you that it's very technical. It's extremely technical. It refers to guideline, it refers to definition, it refers to statistics, and that is complex and can be debated as it is sometimes the case when experts treat a complex problem. Next slide So I wanted in this presentation also to give you some comparison with Canada, where we receive also objections. Next slide, between EMA and Canada. So was it the same? No, it was not the same. It was the same topic, I would say, same section, but not for the precise same reasons. Let's try to explain, for instance, the primary analysis population, [indiscernible], EMA is not available to assume the fast progressors. Okay? But what does the Canada say? They have no objections on that. They actually understood the transition from Phase II to Phase III. They understood that when we transition and when one could be inevitable and not data driven. They understood that there was a necessity, as I explained, to select a more homogeneous population to limit heterogeneity. They understood the cut of 1.1%, but they had still at the time of the process, some concerns about whether the amount month was not too late and whether it was scientifically justified. So in the justifications, as we have done, we have explained it by the necessity to minimize machine data. If we do not exclude the fast progressor, then we will have lots of missing data. Although, of course, they are not at week 48. They are at week 24. So maybe for other protocol, the necessity to minimize -- to exclude fast at week 24 is less necessary. In fact, it's a score, whether for a score at week 48, it becomes, of course, much stronger. So that's the main justification. The second -- then whether the [indiscernible] is late, too late or not too late or acceptable, it's a matter, of course, of debate. So we have arguments. The argument is that it was done when we transitioned from Phase II to Phase III. So there were obviously some patients already included. But still, we were 2.5 years from the end, so -- which is a long period of time. When we did it where we exited the fast progressor, there were only 8 fast progressors, and they were spread across 3 arms, which is very, very minimal. We have included 12% of the data already of the patients, and we had acquired 12% of the data. But when we remove those 12%, the study is still successful. So it has absolutely no impact. So we have provided those arguments to Canada, as you know, and they have accepted to go to reexamination. The second objection is the statistical method for amputations. You have seen that EMA has [indiscernible] to impute the penalty. And the discussion is even not on whether to include a penalty or not. We do it. It's on whether it could be imputed on all discontinuations or not all discontinuation. That's -- rather that, okay? So it's not the point of Canada. Point of Canada is a different methodology to analyze missing data. And this methodology is very complex because it says that it should be nonlinear, Just for you to understand, the -- some patients discontinue and some die. And because some die, the distribution is linear, Canada is right, okay? But that statistical specificity, which is extremely complex and specific, I would say, is only for Canada and FDA, they follow, in fact, the FDA guidance. EMA doesn't care of that. So they have no objections about the linearity or nonlinearity of this solution. But Canada, yes, because they follow the FDA guidance. So they ask us to prove that all what we -- all our analysis were also substantiated by positive data. When we use different methodology, which are nonlinear, okay? That's -- it's complex. But we did. We actually did. And we have positive data statically significant in nonlinear that we presented at the time of reconsideration and we hope that it can make the point. So then -- and by the way, just for you to know, they didn't ask us those data before. So unfortunately, otherwise, we would have done it before. But we actually added at the very end, when we receive the conclusion, so to speak. Otherwise, we would have provided it before. So that's why reexamination sometimes is useful because it can respond to questions that could not have been responded. And not because we responded wrongly, just because we didn't have the question. So then we have a subgroup analysis. So the subgroup analysis is the same issue as for EMA. At that time, BPS because they have the same problem, they say post-op. And we say, yes, it's post-op, but still the guideline -- we do not say should apply. It's not a must. But can apply. It is of interest, in particular, when there is a bias in a subset. So you see nothing is of use. Everything is technical. And the -- it's an interaction with the agencies. It takes several steps. Sometimes, unfortunately, at the end of the cycle, not everything is responded or completely clear. And this is why sometimes reexamination is of some value. Reexamination has no value if it's to repeat the same argument because we know already their positions, but we need to have a second look, sometimes, because the amount has not been entirely considered and then might be worth to be considered another time and sometimes it could use the help of Scientific Advisory Board. So this is what we have today. It's extremely complex, usually sponsors don't do that, what I have done right now. But we think it is of merit because what we want is to explain the audience that the data are good despite the fact that today, at this stage, we have not been able to convince either Canada nor EMA. Next slide. So as a conclusion, I have only one more slide. I would like to tell you why we think the data of the program are robust because there is a possible confusion to conclude that because and Canada and EMA at this stage do not give a conditional approval, then the data are bad. And this is important to address this question. Next slide. So the question of the conditional approval is to ask an agency whether a single study is acceptable for registration, and there a guideline and single study approval, and that requires very compelling evidence. So I know that a lot of people in the audience feels that because ALS is devastating fatal disease and there is no drug, the agency should be very flexible, and the hurdles should be low, and it's not the case. They apply the guideline the same way for ALS as for in other indications. And their guidelines is very compelling data to -- what the definition of very compelling is not known -- it's there, I would say, power to judge what is very compelling and what is not. But that's the question we asked. So to some extent, what I would like you to understand is that we asked the question whether the first evidence we have in hands is very compelling. So the hurdle is very high. That's why it's very difficult to get an approval. Now we, as a sponsor, when we decide to go for conditional approval, we, of course, considered that hurdle. We think that the data are strong enough to try to pass that hurdle, but it's our opinion. Maybe the agencies have a different opinion. So maybe we will not convince neither Canada nor EMA that it is very compelling enough. But that doesn't mean that the data are bad. That's my point. It's just a question of hurdle with only a first piece of evidence. So we think that the data so far of the masitinib program, preclinical [indiscernible] are robust and are completely effective to support a full approval, not a conditional approval, when we will have a confirmatory study, which we hope it will be positive. So the preclinical data, I'm not going to present the details of that, and we'll [indiscernible] it anyway. But what I want you to understand is that -- there has been recently 2 drugs developed at Arbonne, and [indiscernible] and that one is still being registered and [indiscernible] registered, and the mechanism of action was unknown, but still registered. Here we have an advantage. We understand how masitinib, and the mechanism of action, which is to target the microenvironment of the motor neuron and in the system in this microenvironment, and in these, in the [indiscernible] mast cell and in microbia has been published, validated by scientific community. And that is a strong point because we know that, that has an impact on the disease. Now, whether the impact is in one study, very compelling or not very compelling is another story. But the mechanism of action, we understand, and we think is very valid and can create a benefit in clinical. Then we have the clinical data. So we have only one piece of evidence, which is not a lot, but still it's good. And what we have seen in this study is that, provided that we exclude the fast, which is a matter of debate for EMA, the study is successful. Then there is a debate on penalty -- full penalties. But still, we can see a lot of variables to transform. And there was a bias with the subset of patients, we have at least 1 loss of functions. And when we remove this bias, the study is extremely strong, and that gives the idea of the ultimate benefit of masitinib. So, that benefit needs to be replicated -- but provided we replicate, then we have a drug which is very strong. So what I would like you to know and to understand is that, yes, it's a disappointing news. And yes, we did all efforts to try to register conditional approval, and we will still continue, apparently. But you should know that masitinib has robust data, which is recognized as such by the scientific community. So we try to ask you to not -- to differentiate the points of the conditional approval, very high bar from good data, robust data for the future. And with this presentation, I thank you very much. I have taken a lot of time, but it's technical consideration, which is hard to explain, and we will take maybe not all of your questions, but some of your questions because... Laurent?

Yes, we'll take your questions that are really related to this conditional approval procedures. So first question, if data is strong, but agency are not convinced, what can a science do?

Discuss with the agency, maybe Laurent, if you can go back to the reexamination, the next step slide. Before. That's for Canada. Yes. Thank you. So when we are -- we finish a cycle and it's not successful, we have to discuss with the agencies because, obviously, we have a meeting, then we have to leave the meeting, and then we receive an email, okay? So it's not enough given the -- what is at stake. So before that there are other steps which are unforeseen, which is, of course, a discussion with the agency. So we have to take the necessary time to understand. So the first step is to understand here, we do a difficult exercise, which is to give you immediately all information -- not all, but the key information that we have. And -- and like me, you would like maybe to know more, but we have to wait the interaction that we will have with the agency. So we will do that. And we work closely with agencies with EMA and with Canada, of course. So there is an interaction to understand better and take the time to understand. Then we will now have a possibility of reexamination. So to your question, is it worth to go to reexaminations, but you have seen our arguments. They seem to be valid. If they are not valid, we have to understand why. And sometimes we disagree. And if we disagree, we have the possibility to ask a reexamination. So let's wait what we are going to do, but please, we share with you the possible arguments for reexaminations.

Next question. Have the associations of patients provided any feedback to the decision of the CHMP?

Hello. We were supported by the patient associations before we entered in the call. And feedback after, yes, it's a disappointment like us.

I understand that EMA wants a robust sample for this study. And the only way is to have a robust Phase III. The current study is not enough and cannot be enough. What is your strategy to tackle this?

Well -- hello, let's move forward on GCP slide. It depends what you mean by robust, but behind robust, there are everything. No, just GCP, to the Slide 7, Slide 7. So it depends what you mean by robust. Could be robust statistic is that questions, but we can start by robust in terms of GCP, for instance. So we have to convince the agency that the study is robust enough, first in terms of conduct of the study, and so GCP issues. As you have seen in the guideline, we have to go step by step through all the steps. At that time of the process, they still have concerns, okay? However, according to us, ALSFRS is robust. They themselves, EMA agreed on that, and the safety is acceptable. So why it's not considered robust enough, we have to discuss with the agencies. It's a [indiscernible]. It's a matter of judgment. Just for you to know, the analytics, for instance, study, that was approved by FDA and a greater number of deviations to the protocol that study, but they are 3x less patients. So in fact, by patients, we have 3x fewer deviations. So it's not that it's unusual to have deviations to the protocol. But still, we need to convince the agencies that it's robust enough. So we are going to discuss with them to see what's the point behind that conclusion from the EMA.

Maybe one last question. How long does the reexamination process last?

Hello. Let's go to slide -- yes, the slide on reexamination. But -- so the reexamination -- yes, thank you. So the reexamination takes, we have to wait for the vote first. Then a reexamination takes 4 to 6 months. So we can expect [indiscernible] but then it takes 4 to 6 months. So we can expect an answer by the end of the year, if we go to reexamination.

If you have the support of a large pharma partner, could that impact positively the process of acceptance of masitinib from the EMA?

That's a frequently asked questions. The data on the data. Now you have seen in the field of amyotrophic lateral sclerosis that they are not big pharma. Biogen biting the biggest, but still not -- it's probably the smallest among the big, and they have registered tofersen and sold it in a very specific population, which is only 2% of the patients, the patients who bear the sub-1 mutations. Big Pharma first of all, it's rather an innovation -- innovation companies like us who try in one of the most complex diseases of the world to bring a relief. So it's -- first of all, we have to start with what exists. It's small companies, or Biogen, which is the biggest and not. Now if a big one would come, would it change the decision? It's unknown. Of course, it's -- it do not change the data. Will it change something from the perception of the CHMP? I cannot answer this question.

I would like to take maybe one last sentence, which is not a question. So I would -- this is not a question, but a comment which I would like to be addressed also in the report about the presentation of this evening, each of us was investing in the science because we trust the research being done since years. I would like investors to have not a purely short-term financial behaving, but to be an actor of medical progress, and be a partner of medical progress tomorrow and let's not think in terms of financial profitability and behaving. AB Science support for medical progress and let each of us be part of this challenge and trust.

I think the person who did this comment, of course, it's comforting when we have not the expected good news. I would like to conclude maybe on the benefit that we showed here -- or yes, the other table is the same, but you can leave it here. So what can I say? The fight against ALS is a crusade, and it has been 30 years that everybody tries and fails. And 30 years ago, there was riluzole, and 30 years after, there is still riluzole. And riluzole is considered by most of the people as even not very effective or not effective at all. So it is 30 years of fail. Now we have the chance to have a promising compound with a very good mechanism of actions. With data, we show what they are. They might have limitations. We did an amount. We need the fast progressor, exactly. You can find the limitations you want to the study, but it's promising. Now I understand the frustration, and I'm the very first one to be most frustrated among you, that it's hard not to be recognized by the agencies in an early approval process when we see people dying like that. But it's the way the industry is structured we have to accept it, okay? It's tough, but that's it. So okay, it's not the good news, but -- do you have something better in the market? No. Can something come with a solution to ALS, which brings exceptional survival and improvement to the people. No. So we have to be realistic. It's very tough. Some indications, some diseases are extremely tough. AB Science has decided through the mechanism of action of its compound masitinib to try to make a progress, medical progress in those indications and because it's extremely difficult because people fail, because it's extremely risky. And because the agencies are a little cautious because they have seen so many failures, we pay the cost of the innovation today. And you can conclude that it's unfair or that we as managers are not good, but it's very difficult, but it's the way it is, and we are going to continue to because it's difficult [indiscernible] to drop. We think the compound is good. You have seen the objections. And that's why I shared that with you. It's technical. And it's not other than that. And we can maybe convince them -- but it takes some time. It takes different cycles. Keep in mind, Amylyx succeeded to convince FDA after 3 cycles, first one they fail, they appeal. They fail, and exceptionally, FDA gave them a third cycle because at the request of probably, I don't know, the lobby or whatever. But they succeeded in the third cycle. We are here at the end of the first cycle for EMA and the beginning of the second cycle for Canada. It's not successful so far. And it's not a reason to conclude the data are bad. It's not a reason to conclude that we should drop the development. It's -- we need to stay [indiscernible] and analyze the data. It's difficult, but we have to continue. And I thank you for your support and keep you informed about the next development, of course, of our compound. Thank you for this attention -- for your attention and time for this very difficult, complex presentation, and we'll close there.