Good morning, everyone. My name is Ted Tenthoff. I'm a senior biotech analyst at Piper Sandler. Before I begin, I am required to point out certain disclosures regarding the relationship between Piper and our next presenting company, Skye, which are posted at the back of the room and also at the registration desk. Skye is developing CB1 antibody nimacimab for obesity. Here with us from the company is Punit Dhillon, Chairman and CEO, Tu Diep, Chief Development Officer, and also Chris Twitty, Chief Scientific Officer. Guys, thanks for being with us. I love that I've been getting to see you so much recently in person.
Thank you.
It's great. We were just together down at Obesity Society.
That's right.
Not that long ago. So I thought a good place to start would be by describing the cannabinoid system biology and its role in energy metabolism and storage. Walk us through maybe the rimonabant saga and kind of that initial proof of concept in weight loss.
Yeah, I can take the first part of that, Ted.
Sure.
And I'll hand it over to Tu to talk about rimonabant. So the CB1, or cannabinoid type 1 receptor, is one critical piece, along with CB2 and other minor cannabinoid receptors that make up the broader endocannabinoid system. It's really interesting. Like our immune system, our nervous system, it's fundamental to many, many different physiological processes, but really plays a key element in terms of energy homeostasis. So if you step back a moment and think about its role, you think about, certainly, the central compartment in the brain as a driver of finding those calories. And so maybe we go back in time when that was really critical. We needed to find those calories, make things smell better, taste better, generate lipogenesis, so create fat content. You didn't know when the next meal was coming.
We don't live in that type of society anymore, and we're surrounded by calories. This pathway, when engaged, really drives different metabolic pathologies, including obesity. So it's an excellent target that is blocking the CB1 pathway. It can do the opposite. It really can drive, help support satiety. It can help promote fat burning. It plays a role in many related processes. So it's an excellent target. And one, we think we have a really distinct advantage in the way we talk more about that, about how we inhibit it in a very safe and effective way. And in terms of its context.
That's a great, yeah, that's a great overview. And I mentioned rimonabant, which I know you're going to get into that right now, but this sort of provided the initial proof of concept. Tell us a little bit about, on the efficacy side, and then we'll kind of separate out the safety of that.
Sure. Sure. I'll let Tu handle that one.
Yeah, the efficacy side of rimonabant seemed to be very exciting, right? And I think it showed a really meaningful, clinically meaningful weight loss back when it was being developed in the early 2000s, showing about an 8% weight loss, 8%-10% weight loss at 52 weeks, and a relatively good safety profile outside of the challenge they saw with the neuropsychiatric AEs. But when you look at the safety profile of the drug itself, it was relatively well tolerated, in particular from a GI tolerability perspective. You weren't seeing the same sort of GI tolerabilities you see today in GLP-1s, where you're getting 70%+ GI tolerabilities with nausea and vomiting and diarrhea. It was closer to about 30% with rimonabant. So the drug itself was very effective and relatively well tolerated, again, if you take out the neuropsychiatric.
So let's get right to that, because I think this is a really important component for this biology, but specifically for nimacimab and how you're differentiating. So walk us through some of those neuropsychiatric AEs and what's really evolved as our understanding of the cause.
So I think what we've learned is that these neuropsychiatric AEs are certainly due to on-target inhibition of the central CB1 receptors, and again, back to the history of rimonabant. They weren't looking at a differentiation trying to target peripheral versus central. They were really, and I think they actually felt a very strong effect on an affinity towards the central CB1 receptors, because they were really looking at rimonabant at that time, I think, as kind of an anorectic drug similar to GLP-1, trying to reduce caloric restriction and drive weight loss in that way by blocking the central CB1 receptors and maybe having some additional peripheral effects, which they also did see. They saw nice reductions in triglycerides and A1C and things like that, but they were really looking at a central effect.
What we learn now, what we know now, is that's when you do block the CB1 receptor in the brain. We see that with rimonabant, and now we see it with the next generation, monlunabant, that that on-target inhibition of the central CB1 receptors certainly leads to the neuropsychiatric side effects that we saw with rimonabant and appears to be seeing with monlunabant as well. Now, what are those? Rimonabant was really pulled not just because of the increases in depression and anxiety, but ultimately they saw a differentiation in the number of suicidal events, suicide ideation, and actual suicide attempts with rimonabant. Ultimately that was the reason it was taken off the market, and that was the reason it was never approved by the FDA.
You can argue, and there have been arguments made, that this might not be as important as the FDA made it. But what you can't argue is that it certainly drives neuropsychiatric events: depression, anxiety, sleep disturbances.
Yeah. And it's really sort of put a wet blanket on the whole CB1 field for a while. But now, because of that weight loss and I think some of the successes, the industry has really looked back to ways to really restrict. And you mentioned monlunabant. Novo Nordisk acquired Inversago for, I think, just under $1.1 billion just last year, probably 18 months ago, not quite, for their peripherally restricted CB1 inverse agonist. They just reported some phase II data. What did you learn from that? And I think this is one thing we've spent a lot of time talking about. How are you really able to dissect out that weight loss to the peripheral CB1 mechanism?
Yeah. So we actually have gone through that just recently. I think the first important thing is that the monlunabant data, in our eyes, is actually a very strong data set in the non-GLP-1 space. It's very competitive to semaglutide. It's competitive to oral semaglutide alone or injectable semaglutide at 16 weeks, showing about 6% placebo-adjusted is a great number. The concern is what Tu highlighted in terms of the activity that we saw in the central compartment. When we kind of peel back the onion and look at the PK analysis that our team, led by Chris, has been able to pull together, it really revealed that you're getting a really good, in the case of monlunabant, in the case of nimacimab, really good saturation of the receptor in the periphery. No problems there at all these different doses.
But unfortunately, on the small molecule side, these incremental increases in dose also increase the central compartment activity, and leading to what Tu pointed out in terms of you're getting a level of central inhibition. That's what's creating the safety issue. So for us, that's the most paramount product profile marker that we're trying to address. We came at this from that lens when we acquired Bird Rock, saying, "How do we look at CB1?" We were already experienced in the CB1 target. But how do we look at CB1 inhibition and get over the challenge that the FDA has had, patients have had, clinicians have had, and not open up that door? And we're really lucky to have a drug that is differentiated that shows about 600-fold below in terms of its competitors, in terms of brain exposure. So I think that that's a key thing.
Now, coming back to your question about monlunabant data, data is strong. Safety issues are the issue with the central concern. I think overall, the street's interpretation of that was a little bit knee-jerk, in our opinion. Maybe, as Tu's pointed out, it was a bit of a botched PR strategy by Novo. Whatever it is, unfortunately, it was interpreted on two points kind of knee-jerk. One was the number that was highlighted for efficacy, I think, under the guise of what's happened in the GLP-1 space, and where everyone's been focused on these double-digit weight loss numbers. I don't think everyone's taking into account that, look, these other mechanisms are driving these other components, as Chris has pointed out. Number two is the safety issue, that these small molecules weren't able to shake off the neuropsychiatric adverse event. So where do we go from here?
Skye's laid out a really solid story here in terms of how we're differentiated on the central versus peripheral, that peripheral is where we're driving weight loss. With the rimonabant backdrop, we've been clear that rimonabant even has a peripheral-driven mechanism. Now we added our DIO data that showed that nimacimab can show a dose-dependent weight loss. Now you're going to see the most important clinical data set in the CB1 space coming up with.
So let's get into that in more detail. This is a perfect background, because tell us about nimacimab. Walk us through, I mean, you guys just said some really, really interesting data with a human CB1 receptor knockout, DIO mouse model. Tell us about nimacimab and how it differs. Because in a way, I think of that monlunabant data as almost like the optimal outcome for you guys, where it validates CB1, but still shows the essentiality of staying out of the central CNS.
Right. Okay. Yeah. So a few things there, Ted. Thanks for the question. Our inhibitor is an antibody. And so by virtue of that, we are incredibly differentiated in our restrictions. So we're only in the periphery. We have a lot of different non-human primate studies demonstrating that. And I think something that's getting more and more clear is that the small molecule is going to have to wrestle. Even the second generation, the so-called peripherally restricted small molecules are still getting exposure in the brain. And we can walk you through, as Punit did, in more depth why we think that's critical. So if we pivot back to how our antibody works, it's really interesting. It binds allosterically. So there's a distinction mechanistically how it works and how it interacts with the receptor. It's non-competitive. There's some distinct advantages there in terms of competing for the endogenous CB1.
It acts as an inverse agonist as well. So even without any endocannabinoid present, it can back signal. And that can be quite important when the receptor is on state and kind of reverse that pathology. So it's really interesting. One of the limitations of our antibody, however, is that it does not cross-react with the mouse CB1. So we had to build out, as you noted, a mouse that is expressing the human CB1. So we generated a knock-in mouse that exactly a wild-type mouse, but expresses human CB1. And that allowed us to build out a diet-induced obese model. So the DIO models are very much standard now. They're essentially looked at as the benchmark in terms of understanding efficacy with these obesity drugs. And as you noted, we were able to generate our first data set.
This is really important, not only from a company, but from a broader standpoint. This is the first data set that we're aware of that are truly restricted, that is no meaningful 600-fold below the IC9. This is essentially peripheral only. We were able to demonstrate dose-specific weight loss. This was really encouraging, up to 16%. We noted that there was fat mass loss with lean mass preservation, as one might expect with the CB1 inhibitor. We saw really nice changes in the glycemic indices, such as clearance of glucose in line with the semaglutide. We have more data sets that are ongoing right now. The model's established, and we're looking at many different combinations and comparators. It's really exciting for us.
I mean, the work, it was very elegant, the design of that human CB1 knockout. I mean, that alone was, I think, a really masterstroke, but just essential for really delivering important data for nimacimab. One of the other competitors out there is Corbus, and they presented some preclinical data at ObesityWeek. Not being an antibody here, 913, how does it differ? I mean, there's a lot, but what did they present, and what does that kind of teach you more about nimacimab?
Yeah. I can take a swing at it and then Tu, weigh in. So it's encouraging data from Corbus. Some of the takeaways there, they had a nice dose range. So they dosed up, and they don't quote me on the final body weight change, but I think they edged up into the high 20s, early 30% loss. But the one thing to make note of, that dose range is quite high. So we're very mindful of looking at doses and exposure. When we think about the exposure in these DIO models, that can translate back to the trial we're running, right? So we're very mindful of that.
And when we think about an 80 milligram per kg dose, that was the high dose in the Corbus preclinical study, and we think about the difference in what that equates to in terms of an antibody versus small, it's a huge amount of small molecule. Just to put that in context, 10 milligram dose used in the monlunabant study is significantly less. And yet they're still seeing exposure. So I think Corbus data is very interesting. I think it, again, validates the pathway. We're all on the same side here. This is a great pathway. Very interested in seeing that develop, whether small molecule or antibody, but just being mindful of the safety risk. And I think that exposure is fundamental no matter what type of small molecule development.
If you're going to a dose that big, some of it's going to get across. I think they were like 15-fold lower, but there's still some getting across. And especially over time as it accumulates. Sorry, Punit.
Yeah. I think the key takeaway, Tu wants to probably comment on this as well, the key takeaway for us is that there's such a large therapeutic window with nimacimab that differentiates it from small molecules, so we have room still in our clinical study, and in fact, we've alluded to that the next program that we have, we're looking at two important things of dose optimization. One is, can we concentrate the dose higher? And the other is, can we look at a monthly or less frequent dosing? Which is ultimately, we've got to remember, we're taking this back to the patients. You don't want to have safety issues. You want to have an improved GI tolerability, and you want to make patients a drug, a pharmacotherapy, on top of these lifestyle changes easier for them to take.
So a monthly dosing, we think, is a very competitive drug relative to the small molecules.
Yeah. Let's talk about the phase II CB1 study design. Walk us through that design. What data should we be expecting next year?
Yeah. So it's a four-arm study, Ted. We've got two arms that are conducted as a fully blinded, double-blinded portion of the study. And it's really where we're focusing the stats around, which is the single-agent nimacimab arm in roughly 40 patients versus placebo arm, which will also enroll 40 patients. And what we're looking at from a primary endpoint perspective is to demonstrate essentially an 8% placebo-adjusted weight loss between nimacimab and placebo. The other two arms are really looked at as kind of control arms. And these two arms are conducted what we call partially blinded, because patients will be receiving Wegovy, but they'll know that they'll be receiving Wegovy. They won't be blinded to Wegovy, but they will be blinded to whether or not they're receiving either placebo or nimacimab on top.
We'll use these arms as really direct comparators to our active arm, our nimacimab arm, really to show, is there a difference? What is that difference between nimacimab and Wegovy? To Punit's point, what we're really excited about is, can we show that our drug will drive still clinically meaningful weight loss, but much more tolerably, right, without the GI issues, without the neuropsych issues? Ultimately, is that weight going to be maybe potentially more of a quality weight loss? Can we have better lean mass preservation? Ultimately, will that weight loss be more durable? We will have a 13-week follow-up period after patients come off drug after 26 weeks. We will be able to see after patients come off drug, is that weight loss more durable with the nimacimab group, or even potentially with the nimacimab plus Wegovy group?
Lastly, with that combo group, I think that's a really interesting exploratory arm. It'll be the first time clinically anybody's going to be able to show a combination of a GLP-1 with a CB1 inhibitor. Again, is there an additive? Is there a synergistic effect? We think the mechanisms of these two drugs are really complementary in that CB1 drives things like leptin sensitivity and insulin sensitivity and improvements in triglycerides, and adding to what GLP-1s do with increasing insulin secretion and things like that, we think that combination could potentially add to the synergistic effect. That data is going to be really exciting. The interim data we're going to report at Q2 will be at 50% enrollment, where all patients have completed their 26 weeks of dosing. We'll be presenting sort of top-line weight loss data at that point.
Yeah. I'll just add to the bottom line for us is that this is. I've said this before, and I'll just say it again, that this is like a no-bullshit trial where we're asking all the right questions in a robust study where there's no noise to try to decipher of comparing 12-week or 16-week. Like, this year in 2024, what's been the frustrating thing is we have had these small studies and random data points where a lot of people are trying to interpret what does this all mean. Here we have one study. We're going to have three active arms, and we're comparing against 26 weeks, and you're going to have a robust data set based on a strong number of patients. So I think for us, it's the most exciting non-incretin data point that's coming, the non-GLP-1 data point that's coming in 2025.
In terms of validated mechanisms, you have GLP-1. We've seen a lot of that data, so we know what that's looking like. We've seen some amylin data, and we've seen now pure-play CB1 inhibition data. But we haven't seen what the CB1 antibody data looks like until this nimacimab data.
Nor the CB1 clip.
Exactly. Yeah.
It's a really good point.
Yes.
Ted, I want to dig in a little bit more on this muscle preservation and sort of these secondary endpoints. Because I agree that everyone focuses on the weight loss, and that's what everyone compares about. I think more and more the focus is going to shift to the quality. So let's just talk about it a little bit more. What are those secondary endpoints? How do you capture that? And mechanistically, you started to touch on it. Why could we see that really improve with CB1 in terms of muscle preservation?
I'll speak to sort of the operational, how we're capturing some of that, and Chris can jump into some of the biomarker mechanistic stuff. Yeah, in terms of lean mass preservation and how we're going to measure that, we are measuring that by DEXA, which is pretty standard across the industry right now. We're taking three measurements, one at baseline, one at 12 weeks, and one at 26 weeks. We'll be able to see sort of that progression and if we're seeing any significant sort of improvements, I guess, in lean mass preservation. Obviously, that's important, as you said, to sort of, I think, when you think about sort of longer-term healthier weight loss, right?
If you just talk to, let's say, a personal trainer, let's say, they want to make sure when you're working out, you're gaining muscle mass, or at least you're maintaining your muscle mass, because it's your muscle that's really your metabolic engine, right? So if you're losing muscle mass while also losing fat mass, your metabolism really isn't going to keep up with the weight loss, and ultimately, your metabolism slows down, and you can easily just rebound back and gain that weight back, which is what we see with the GLP-1s and what we see with yo-yo dieting, essentially, when you don't sort of really focus on the broader sort of metabolic profile of the individual when they lose that weight, so we think that's really important.
And we'll be measuring sort of that lean mass preservation, sort of that more phenotypic lean mass preservation by DEXA and really looking there. But I think more from a biomarker perspective, and what that means, I think, is also really important to the story.
Yeah. We're really excited about the biomarker program to kind of get at some of the underlying mechanisms that might help explain the difference in rebound, the muscle mass preservation, even the anorexigenic effects. And so we're looking at it in a few different ways. We're certainly interested, and we know that CB1 inhibition can impact the broader coordination of the different hormones. So we're looking at sort of, if you will, different organ systems and what they impact there. So from a GI perspective, there are specific hormones that CB1 can regulate that help curb sort of the need to bring in calories. We also are looking at inflammation and adipokines. Importantly, we're really looking at leptin, not only sensitivity production. So we understand that these are both in the CB1 inhibition pathway.
Even things like fibrosis and energy expenditure, mitochondrial health, so uncoupling proteins, all these pieces work in concert, we believe. And there's not necessarily one magic bullet, if you will. We're not just limiting calories. We're really looking at mechanisms that help coordinate and create a more productive type of weight loss that ultimately will sustain, I think, more durable and meaningful weight loss. And so we'll be really teasing that apart.
Great. So we only have about a minute left, and probably one of the most important questions. What do you guys see as sort of the regulatory path with the huge demand from big pharma? And really, no one yet really being able to unseat Novo or Lilly. Is this something that you guys are going to take forward yourselves? Do you think this would be something you would partner?
Yeah. So look, for us, at the moment, we think that the regulatory path is fantastic for the whole metabolic or obesity space, specifically 5%, 52 weeks, but the placebo control is the bar. And we feel that this mechanism, it's a validated mechanism that can achieve that. So this trial is geared towards giving us these important data points that help us keep the gas pedal down and pushing forward on that regulatory development plan. Overall, 2025 is really well situated for that because you have the interim data that allows us to start explaining what the phase II-b is going to look like. And then in the background, what's happening when Tu is leading the charge on this is the CMC work.
So we're not losing any pace here in terms of the manufacturing and other things that are the other regulatory part when it comes to getting to an auto injector. All of these things that make these products come to fruition, they have to be happening in parallel. So we're moving that along. And then Chris has got this wonderful DIO model now that is robust. So he's just talked about what we're doing on the mechanism stuff, which we've already started to share. We have more data coming out of that. Then we have additional lifecycle management and then other opportunities in terms of combination. So the combination data that we see in clinical is also coming back and full circle in terms of the translation that we're doing in other pipeline work, which is exciting.
Yeah. It is a really exciting time for the space, and I think you guys are going to fit into this expanding market. It's really cool. Thanks so much for being with us today.
Thank you. Appreciate that.