Palatin Technologies, Inc. (0KF3) Earnings Call Transcript & Summary

Good morning, and welcome to the Palatin Technologies KOL event. [Operator Instructions] As a reminder, this call is being recorded, and a replay will be made available on the Palatin website following the conclusion of the event. I'd now like to turn the call over to Carl Spana, Chief Executive Officer of Palatin Technologies. Please go ahead, Carl.

Thank you, Tara. I'd like to welcome everyone to today's event. Can you please go to the next slide? So as a forward-looking statement, we are a publicly traded company. We trade on the NYSE AMEX under the symbol PTM. So this just tells you that you should go look at our filings with the Securities and Exchange Commission. So the next slide. Great. We are very excited today to talk to you about our PL9643 treatment for dry eye disease, which we believe, based on its excellent ocular tolerability, its broad efficacy, has the potential to become a leading treatment for dry eye disease. Before we get started, I'd like to provide a little context. PL9643 is a melanocortin agonist in development for the treatment of the signs and symptoms of dry eye disease. We're currently conducting a Phase III clinical trial called MELODY-1. MELODY-1 is designed as an adaptive clinical study, and it has 2 parts. First part consists of the first 120 patients to complete treatment. They were designated to lead in population. You're going to hear a lot about the analysis of that population today, and they were reviewed by a data monitoring committee. Second part is actually the double-blind part of the study and the population in this point of the study will actually be the analysis population for the final study. After reviewing the lead in population, the DMC recommended that we continue to study, that it was very promising and that we conduct a more detailed analysis of the lead-in population, and that's what we're going to go over today. The speakers and agenda for today are as follows: is myself, Carl Spana, as you know, I'm the CEO. Palatin will be kind of moderating and overseeing the presentation today. First speaker up will be George Ousler. George is the Senior Vice President of Anterior Segment at Ora. Ora is the leading CRO in the ocular space. They're actually helping us conduct the Phase III clinical trial. George is well experienced as a leading expert in the conduct of dry eye disease, their analysis, regulatory aspects of it. I think he's been involved in just almost many of the products that have gotten approved, has had -- George's had a hand in directing those programs or advising those programs through. So he's going to talk today about the regulatory environment for dry eye disease, how we do clinical trials, where the endpoints are. He's going to be followed by Dr. Michael Raizman. He's Palatin's Chief Medical Officer. In addition to that, he's actually a practicing clinician. He treats patients with dry eye disease as well as many other anterior segment conditions. And he's actually been involved in a number of these dry eye disease programs as a clinical investigator, and he will be talking to us today about the results of dry eye disease, but also the implications of PL9643 as a treatment for dry eye disease. And our final speaker today will be Dr. Bruce Stouch. He's a very well-respected biostatistician. Bruce is actually integral in the design of the MELODY-1 study and the design of the process that we use for the lead end analysis, and help present the results, process, and the implications for the double-blind part of the study. Finally, joining us for the Q&A part will be Stephen T. Wills, he's Palatin's Chief Financial Officer and Chief Operating Officer; and Rob Jordan. He's our Senior Vice President of Program Operations; and actually Rob is the one overseeing the clinical operations or the clinical program. So we can move on to the next slide. So just a quick reminder, we have a very deep pipeline. These are all -- all of our programs are based on melanocortin agonist. We have a very strong footprint in that space. In addition to the clinical trial readout for PL9643, which will incur a little later this year, we have 2 additional clinical trials, one in ulcerative colitis and one in diabetic nephropathy. These are smaller Phase II proof-of-concept studies. They also have clinical readouts later this year as well. In addition, as you are aware, we do have an approved product called Vyleesi, which was conducted or developed by Palatin start to finish and that's under the direction of Steve Wills. If you want to find out anything more about these programs, please go to the website, there's lots of information there. And the remainder of today's program will really be focused on PL9643 and the analysis of the lead-in population. Next slide. Just before we go forward, this is the program slide for 9643. We're going to talk about the interim analysis today for -- just in mind, we're actively enrolling patients in the remainder of MELODY-1, so we want to get those double blinded patients in. We expect to have data out in the second half of the year. And based on success, we will have MELODY-2, which will be another efficacy -- Phase III efficacy study followed in MELODY-3 which will be an open-label safety. So I'm done talking. I'm going to pass it over to George and he's going to talk to us about the regulatory implications and endpoints in dry eye disease studies. George?

Thank you so much, Carl. So I want to walk through the regulatory requirements for the FDA for the approval of a dry eye therapy. Our base case is that we demonstrate efficacy in one sign and one symptom. This is the first initial approach here. And the definition of success is to show a statistically significant difference between active and placebo, again on one sign and one symptom. The agency is quite flexible in the endpoints that we choose. So a prespecified symptom can commonly be things like ocular discomfort, pain, dryness, and grittiness and the prespecified sign can be things like ocular surface staining, which is damage of the epithelial layer, tear film breakup time and tear production. We are held to demonstrate this efficacy in one sign and one symptom across 2 Phase III studies to demonstrate reproducibility of the outcome. Due to the fact that there is a lack of correlation at times between signs and symptoms for dry eye, there is what we call a follow-on approach where we can look at signs and symptoms in 2 independent studies for the optimal patient population and the optimal symptom group as well as the optimal sign group. However, this does require a total of 4 studies because you need to show that replication of a sign finding in 2 independent trials and a symptom trial and 2 independent trials as well. So that gives you a total of 4 Phase III studies. Another approach is what we call sign-only, and this is a little bit higher of a hurdle. And it's really based on responder analysis or responder groups. The first one that is commonly used and has brought several products to market is the percentage of patients who exhibit a Schirmer test improvement by at least 10 millimeters compared to baseline. This is a measure of tear production, and we do need to show this also in 2 independent Phase III studies to demonstrate its reproducibility. We can also look at the percentage of patients who exhibit clearing of total corneal staining. And again, a higher proportion of patients in the active group versus placebo that statistically significant would meet that requirement, but also across 2 independent Phase III programs. And finally, we have what we call the symptom only approach, and this is typically not accepted by the FDA historically. The reason is that if a product reduces symptomatology, it may shut down the compensatory mechanisms for the eye to combat against different signs. So the inability to produce more tear or avoid insulting situations, which can put the patient at risk. Next slide. So the PL9643 MELODY program is evaluating both signs and symptoms as primary endpoints. So let's walk through that. What do dry eye disease endpoints evaluate. As we discussed, we're looking at both signs and symptoms and typical measures for signs include things that are looking at ocular surface inflammation or damage to the ocular surface as well as tear production. And then symptom endpoints are patient-reported outcomes or questionnaires that evaluate how the patients are feeling. And this is that subjective analysis, in common terms would be things like ocular pain, dryness, discomfort. And then we think about what is the right patient population. As we just previously mentioned, there can be a lack of correlation in signs and symptoms within the same patient population. So you may have a statistically significant finding on a sign and not a symptom and vice versa. So what do we do? We design clinical trials that enrich our patient populations, and we're very careful in how we select the different patients to achieve a statistically significant finding in a symptom. And lastly, how commercial products achieve approval. As you'll see in the next slide, the most common approach has been that tear production, that responder analysis, the percentage of patients who have a 10-millimeter increase compared to baseline. And that's been, again, the majority of the approval pathways. But also, we do know that Xiidra demonstrate efficacy in a single sign and a single symptom, but that was across 4 different trials to achieve that optimal patient population. But they did have efficacy for 2 sign endpoints as well as 2 symptom endpoints. Next slide. Okay. So the key objective for the PL9643 drive program is the following: in the same Phase III study, achieving statistical significance for both the primary clinical sign and a clinical symptom endpoint as well as potentially achieving multiple secondary signs and symptom endpoints. And how does this compare to other approved products. So as you can see in this list here, Cequa, Restasis and Tyrvaya all were approved with that percent responder rate for tear production, of that 10 millimeters of grade compared to baseline. Xiidra was approved by a single corneal -- single endpoint of inferior corneal staining for the sign and a single endpoint of eye dryness for the symptom. Eyesuvis, it's a little hard to compare because this is a 2-week treatment, but they did demonstrate efficacy on a symptom, which is ocular discomfort as well as a sign, which is conjunctival hyperemia or redness of your eyes. I think it's important to note here that currently there are no approved product that has improvements on multiple signs and multiple symptoms on [indiscernible]. Okay. At this point, I'll turn it over to Michael.

Thank you, George. Let's talk about clinical strategy. Based on the Phase II data and the Phase III lead-in population data, we believe PL9643 can be the best approved treatment for dry eye disease. PL9643, as you heard from Carl, is a melanocortin agonist. It works by resolving inflammation and promoting tissue healing. These are unique attributes in the dry eye therapy. They represent 2 new mechanisms of action not found in drugs for this condition. Summarizing the Phase II data in moderate to severe patients, which is numbered 61 in our trial, PL9643 achieved statistical significance at week 2 and week 12 for multiple signs and symptoms, including inferior corneal staining, lissamine green staining of the conjunctiva, ocular discomfort, burning and tear film breakup time. This is a topical formulation administered 3 times a day. The efficacy is broad, covering multiple signs and symptoms. There's a statistically significant co-primary sign and symptom and multiple secondary endpoints. The safety profile of PL9643 is unprecedented. Based on Phase II clinical trial data in the Phase III lead-in population, no safety or side effects are expected in MELODY-1 or in any of our studies with this product. The differentiating factors are superior safety and tolerability to improve treatments with robust and broad efficacy. Next slide, please. Ocular tolerability is a major issue with currently approved dry eye products. For all of the products, and you see them listed here on the left; Restasis, Xiidra, Cequa, Eyesuvis, Tyrvaya; instillation site pain, irritation and burning is noted and is a common reason for discontinuation of these therapies. Other significant adverse events for some of the products include redness, a bad taste in the mouth and sneezing and coughing. In contrast, PL9643 has excellent tolerability. In the Phase II ITT population, there were no treatment-related serious AEs or any ocular AEs. With PL9643 treatment, the drop tolerability is similar to artificial tears. That is to say extremely comfortable and well tolerated. In our leading cases in Phase III MELODY-1 to date, we've confirmed the Phase II study results with no patient receiving PL9643 having any ocular adverse events. As I said, I think this is unprecedented tolerability for a drug. Next slide. So let's talk about the Phase III clinical study and the interim analysis lead in population. Next slide. Here's the study design, a 12-week multicenter randomized, double masked, vehicle-controlled adaptive design study. We evaluated the efficacy and safety of PL9643 and we were looking up to 480 adults with moderate to severe dry eye. That definition is disease duration of greater than or equal to 5 years, an inferior corneal staining score of greater than 1 and an eye discomfort score greater than or equal to 25 as measured by the Visual Analog Scale or VAS. The VAS is a visual analog scale where subjects rate symptoms on a line graph on a score of 0 to 100. As in the previous studies, this study will use the CAE, which is the controlled adverse environment. Subjects are placed in a chamber that tends to maximize the stress on the ocular surface, and this facilitates the capturing of data in the clinical trial. After a 2-week run-in with placebo therapy, subjects are randomized to PL9643 or placebo 3 times a day. It's important to recognize that placebo in these studies is actually an active product, an active comparator. Artificial tears, for example, or any lubricant eye drops cause an improvement in the signs and symptoms of dry eye disease. The subjects in the trial will be periodically subjected to the controlled adverse environment. The primary sign endpoint in MELODY-1 will be inferior corneal fluorescein staining, the primary symptom endpoint will be ocular pain. Next slide. Let me turn this over to Bruce, who's going to go over the statistical analysis and help you to understand how we're moving forward with this trial.

Thank you, Michael, and good morning. So there are 6 sections for the supporting Phase III program that I'd like to review with you this morning. The first 5 are focused on increasing the probability of success for the Phase III program. So when we look at this slide, the purpose is really to describe the process we went through to identify the most sensitive population for both a clinical sign and a clinical symptom. Starting at the very top box, the Phase II moderate-to-severe patient population, through this, we were able to identify a combination of a disease characteristic, clinical sign at baseline and a clinical symptom at baseline. The identification of this refinement has served as the inclusion criteria into the MELODY-1 Phase III program, again, looking specifically middle box here at the sign endpoints. Finally, we have had an additional refinement looking at the time in the chamber to receive or to actually achieve the highest level of response, and that is how we are identifying our symptom endpoint. Next slide, please. So as Carl alluded to, following the DMC meeting where the results were promising, the DMC recommended that we examine a lead-in population for optimization of additional refinement regarding these clinical signs and the clinical symptoms. I'd like to have you draw your attention to the bottom left-hand side, the text in blue. So there are 4 primary research questions we're attempting to answer going through this analysis process using the lead-in population. The first is identification of the symptom population. And for that, we are using the chamber that Michael referred to. The second is identification of the sign and symptom optimal endpoints, exactly at what point coming either into the chamber or out of the chamber, finding the greatest separation between PL9643 in vehicle. The third is the optimal time point, and now we have identified it as 12 weeks. And finally, the identification of the analysis method that will be used for the Phase III analysis. The middle part of this slide has 4 boxes, and these are actually broken into 2 groups. The 3 boxes on the left-hand side really deal with the process we have been going through dealing with the lead-in population. The fourth box on the right has to do with the ongoing surveillance of the Phase III program. We will get into that later. So starting on the far left-hand side, we started with the lead-in population, the goal being to assess the sample size needed for success, and we were able to identify that for a clinical sign, and we tied to that a sample size that was required of 350 patients. However, the symptom data was inconclusive and required a more refined analysis, again, specifically looking at the time in the chamber, the CAE, to identify the best symptom. So this leads to our second box, the detail of analysis. And the results of this have been very fruitful for us because we've identified a symptom endpoint of ocular pain, again, measured on the VAS, the 0-100 scale, at 12 weeks. And so we have identified the symptom population and a sign population through mining of the -- going through this process of the lead-in population. The third box refers to the independent validation we went through. We then took all of the analysis we did. We contracted with an outside independent group of analysts that have confirmed not just the sign, but also the symptom that we're going to be looking at moving forward. The fourth box, as I alluded to, is the blinded study surveillance. And in this case, the goal is really to compare the blinded means and standard deviations from the initial completers in the Phase III double-blind study to the lead-in population. And what you'll see later is very similar means, standard deviations, which means that the patients that we are enrolling in the double-blind study are lining up nicely with what we're seeing with the lead-in population. Next slide, please. So the next 3 slides that I'm going to cover deals specifically with the clinical signs with inside the lead-in population. And PL9643 was found to be superior to vehicle in all of the 13 assessed clinical signs based on the change from baseline. And we're defining in this context, superiority to be greater improvement within the PL9643 group compared to vehicle. These results demonstrate improvement in the ocular surface inflammation, as Michael has covered, with daily use of PL9643. Of the 13, you can see a number of these have 1 or 2 asterisks after it, 2 asterisks refer to a probability value less than 0.1 in a lead-in population, and that is considered to be highly suggestive of a significant difference. Next slide, please. This is the second of the 3 clinical sign slides I'll be presenting. The clinical sign endpoints have been ranked for the final analysis in the double-blind study. And what we have calculated based on the change from baseline is the minimum required sample size to achieve 80% power. So in order, you see the 4 signs that will frame the testing strategy for our clinical sign. I'd like to speak just for a moment about power. What does power mean? So if we repeated the study 5,000 times, what that means is an 80% of the simulated studies, the primary sign endpoint would be met. So you can see that the sample size is required for each of these individual 4 clinical sign endpoints is below the target sample size that we're enrolling of a maximum of 480 patients. Next slide, please. So we've turned this around a little bit and said, "Well, let's apply the least square mean estimates from each of these individual parameters and assume that we've enrolled just 450 patients." What would be the power coming out of that analysis? So you can see for each of the clinical signs, again, ranked in the order of testing that they will be evaluated in the double-blind portion of the Phase III study, you can see the actual power calculation is well north of 85% here. So this gives us great comfort moving forward that our sample size is certainly of 450 patients, up to 480, certainly robust enough to go ahead and find significance, meaning separation between the vehicle and the active treatment group. Next slide please.

Okay. So -- I'm sorry.

This slide goes to you.

Yes. Thank you, Bruce. So I'm going to walk through the clinical symptoms and the analysis of this lead-in patient population and what we learned. So as previously discussed, following advice from the DMC and an independent data analysis by [ SEC ], we have 3 key findings. The first is that we identified this symptom responder subpopulation using the controlled adverse environmental model. As described, the CAE model is a well-established model that were created more than 25 years ago, and it's used to help identify what we call modifiable patients and patients that have to show a worsening in both a symptom as well as a sign when challenges -- in this acute challenge. So when we look at the patient population in this first lead-in population, we did see that there were patients who are what we call hypersensitive to the challenge where they become symptomatic very quickly. This is an indication of a patient population that will be more sensitive to symptom endpoints and help to demonstrate greater treatment effect, but also minimize any sort of vehicle or placebo effect. So the CAE chamber identified this subgroup, and it also retained the greatest number of patients for the final analysis based on the time point chosen. And as I just said, we optimized the treatment effect between PL9643 and vehicle. The second key finding is that we identified what would be the most appropriate primary symptom endpoint for the final analysis. And this is pain as measured by the VAS at week 12. Third, the team is able to identify the optimal calculation for this primary symptom endpoint. And what this is, is it's an analysis of a change from pre to post CAE at week 12 compared to baseline. And this is an analysis that's been well established for many years in many programs in the dry eye space. So I will turn it back to you, Bruce.

Thank you, George. So there's a slide -- the single slide I want to present here on clinical symptoms has to do with the power estimates, assuming 450 patients are enrolled. And I want to draw your attention to the 2, the text in blue here, that the current FDA-approved products have an effect size of 0.25 in one symptom or one sign. Now what is effect size? So effect size, and I will describe this mathematically for you, it's the arithmetic difference of the least square means, okay? So it's the difference between the mean value from PL9643 minus the mean value from vehicle divided by the common standard deviation. Effect size is not just the arithmetic difference between the active and the vehicle results. And again, the FDA-approved products are coming in with an effect size of 0.25. Based on the estimates that we have generated looking at the lead-in population, we're looking at effect sizes in the range of 0.47 to 0.69, which are strong effect sizes. And these translate into a power calculation of over 94%. So in summary, with the 450 patients, we feel that we have excellent power for ocular pain measured on the VAS. The FDA also views positive secondary endpoints is very supportive for approval. And our 2 secondaries that we will be testing are burning, again, measured on a VAS and foreign body sensation. And these effect sizes are both 0.31, again, over the effect size that we're seeing with the approved products. Next slide, please. This is the fifth of the 6 sections that I will be reviewing today. And this specific is for the pain symptom endpoint, looking at the lead-in refined population based on the chamber results and the double-blind population, which, of course, we are still blinded to. So in the top box here, what we're seeing is there are 2 rows here, PL9643 and vehicle. And if you look at the least score mean values here, we're seeing a drop in PL9643 in terms of the pain at 12 weeks and an increase in the pain in the vehicle arm. Now this difference is 14.5%, and this 10-point change is considered clinically meaningful. So we have eclipsed this 10-point change. In the bottom box, we're looking at now, in aggregate, the results from the lead-in population, again, coming out of the chamber, 61 cases. So in the leading cases, there are 61 and the least score means value is 41 -- I'm sorry, 45. In a double blind, we're seeing with these cases at least square means of 45. So we are seeing very similar results in terms of the leading case results relative to pain compared to the patients that are being enrolled in the double-blind study. And again, when we look at the standard deviations, these are very, very close as are the standard errors. Next slide, please. So my final slide is really a review of where we are here, and there are 5 points I'd like to make. Based on the statistical separation, the lead-in population, we have identified a primary sign and a primary symptom for testing in the double-blind study. The primary sign endpoint is the inferior corneal fluorescein staining. The primary symptom endpoint is ocular pain measured on the VAS. Point number 2 is a statistical point in terms of the testing strategy here and to preserve the Type 1 error rate at 5% for evaluating the primary clinical sign and symptom, we are going to employ a stepdown approach. This means that we will first test our primary clinical sign. And if that passes at a probability value less than 0.05, we will then test the primary clinical symptoms. Point number 3 is because we have multiple clinical signs that are very encouraging from the lead-in population and encouraging clinical symptoms that we are going to employ the same hierarchical testing strategy that we will continue to test the endpoints until an endpoint fails to be less than 0.05. At that point, we can continue to test. However, it's not going to be considered significant because we failed to be less than 0.05 in terms of separation from vehicle. The fourth point is the target sample size for the Phase III study has been fixed at 450 patients. We have the ability to enroll 480, but we certainly have sufficient power, as you've seen today, with 450 patients. And finally, the Phase III double-blind study will be run as a fixed design. There will not be an interim assessment or an interim analysis performed in the double-blind study, patients being enrolled currently. So I'd like to turn the presentation back over to Carl.

Next slide. Can I have the next slide? Can you hear me? Hello?

Hello, Carl. The next slide is up.

I didn't see a change on my screen. I'm sorry. Great. Thank you, Bruce. I hope you could understand our great excitement around PL9643 based in the presentation on the extent speakers today. We've got a very robust Phase III clinical program. We've used an adaptive design. It's given us lots of flexibility. I think we really reduce or mitigate the risk of this particular program. So right now, the ocular tolerability, the broad efficacy really makes us excited about the potential for PL9643 to be a leading treatment for dry eye disease patients. I'd like to thank the speakers today, LifeSci for setting this all up. And I'm going to turn it back over to Tara so she can start the Q&A.

[Operator Instructions] So our first question comes from Joseph Pantginis from H.C. Wainwright.

Thanks for all the details today, and nice signals with regard to the lead-in data. So I guess my questions are going to focus on building the profile for your asset here. And I guess, first off, the confidence that you have that you picked the right primary sign and symptom and how you sort of gauge that with the hierarchy you've designed after that? And expectations where you think then where you think 9643 might not work from a mechanistic standpoint. That's the first aspect of building the profile.

Okay. Bruce, do you want to take a stab at this.

Sure. I'd be more than happy to. So again, when we look at the clinical trial program, our focus is on the regulatory application. And what we are looking for is to optimize the strongest signal we can to have the highest probability for success in terms of having a positive study. So when you're seeing real concentration in just a couple of signs and a couple of symptoms, as you saw with the 13 clinical signs, everything was moving in the right direction. So if I interpret your question correctly about where does this work, where does it not work? We're not seeing a scenario where this drug is not having effectivity across multiple areas of the eye. And to that, I'd like to move over and ask George to address this and please, Michael, because we've discussed this multiple times about how unique it is to see clinical signs, having a drug having an effect on the clinical signs in multiple areas of the eye.

Happy to share some thoughts here. As you point out, there are efficacy signals in multiple signs and multiple symptoms, and this is challenging to achieve. But what it does suggest to us is that there is a broad acting molecule here, and the team has been very methodical in understanding and determining, which is the best endpoint to move forward with. I would also add that a presentation that was recently done at ARVO demonstrated that when you look at the Phase II data in conjunction to this lead-in population that you see a lot of replication. So that's another thing that's encouraging is when you have 2 independent studies showing the same outcome and then you have a lot more data to make wise decisions moving forward.

That's very helpful. And then I guess the second question having to do with building the profile. I'll just use MELODY as an example with regard to your inclusion criteria and where the drug might fit in if approved in the initial parts of commercialization. So I'll just use one of the aspects as a specific example, and that disease duration of greater -- or greater to 5 years. So does that imply that you'll have to have patients that have had dry eye for a while, if approved, would physicians use it earlier on, mechanistically, would it not work earlier on or even in patients that have had dry eye for a much longer period. So I guess how would you talk about the addressable population if approved?

Quick second. I'm going to ask both George and Mike will comment on that. George, can you just talk about what these labels look like, what a dry eye disease label looks like? You'll give some results that we are getting?