So I think especially topical for early development in oncology is the topic of Accelerated Approval. FDA stance changed on Accelerated Approval. How has it evolved over the years, and how do you factor that into how you choose comparator arms and or natural history data sets? And maybe we could start with Conley.
Sure. So, you know, Accelerated Approval has been around for decades now, and it's done, I think, wonders in terms of getting drugs to market earlier, using surrogate markers. I think some of the FDA's focus recently is there. There's been a few, let's say, failures, if you will, of confirmation. They continue to look at these surrogate markers to ensure that they're true in terms of their relationship to the end goal, which is typically OS in cancer. For us, it was a slightly different focus, I think, for the FDA, and they released some guidance last year. It was more about the pace at which you would get this confirmatory data rather than the surrogate marker itself.
They released guidance last year, talking about sort of the one trial versus having a trial and then a confirmation trial later. Because the speed of which pharmaceutical companies are sort of pacing themselves with the confirmation trials have been a bit slower than the FDA had wanted. For us, at Syros in 2022, we made that shift. We had originally had a trial, our pivotal trial, based on CR, and then we were going to do a confirmatory trial on OS, and instead, we combined that into the one-trial methodology. In fact, I think the FDA really appreciated that, and economically, it's much more feasible for us as well.
Norbert, do you have anything to add?
Yeah, I would just refer you to a paper that came out in New England Journal of Medicine. It was authored by the head of the Oncology Division. They talked about Accelerated Approval, and as Conley correctly said, some of these companies had, there was a 10-year gap between the Accelerated Approval study and the confirmatory trial, and the FDA wants to close that. So they've become a little bit more conservative, but it's, I'm not discouraged at all. I mean, it's still, it still exists, Accelerated Approval.
Marty?
I mean, we're at Mersana, we're not quite yet having those discussions, so I'll go back a little bit. I think one thing that got lost, if you look at the successful accelerated approvals, like Keytruda in lung, one of the things they actually had is the pivotal studies were so well underway that there were opportunities to look at DMC reports, et cetera, and take peeks. So basically, the agency had confidence that it was gonna be confirmed before they issued the approval. And I think what happened were some of these examples, to your point, they got too late.
So whether you do it in a single trial design, an adaptive design, I think the critical thing you have to think about in discussions is: How are we going to give them an assurance, not before we file, but before they issue the approval, that you're not gonna result in a negative-
No
... confirmatory trial? Because I think that really hurt the field, the series of those.
Before we move on to Patrick, I want to ask Marty, since you talked about timing, when is the best time to actually to start interacting with the FDA, really at each stage?
I think there's a couple things you wanna do is: one, you wanna make sure you have a data set that's compelling. And I think that's, and that's back to the nice thing about targets and select-patient selection is, in theory, you should be able to demonstrate numbers that are clear and obvious. In other words, like, if you're looking at triple-negative breast cancer and you have a 5% response rate to chemo, if you have a response rate of 35%, that's obvious. If you're doing another tumor type where it's 20%. So there, you're creating that compelling... That the agency's having, looking at you and going, "Hey, this, there's a drug here." That, and then I think leveraging the breakthrough is probably the most important.
The old school is, you know, thirty for thirty, is 30 patients in the indication that has the high unmet medical need, response rate greater than 30%, you can have a conversation. And then once you get that, then it's a dialogue. What's the best way to get there? But I think in my experience, having dialogues and sharing information on an ongoing basis. I mean, if we go back and look at Keytruda and some of the non-blind approvals we got was Zejula, which unfortunately got pulled subsequently, it was all about an ongoing dialogue as, is it the right patient population? Is it a true unmet medical need, and are we clearly showing evidence that we're superior in that setting?
Mm-hmm. Patrick, maybe you can, you can add on any additional thoughts. I'll add something onto it. Is the FDA guidance clear?
We are not there yet as Molecular Partners, but what the experience we had was actually an even faster towards approval, which was the Emergency Use Authorization in COVID, and that's sort of the extreme form of that. I think in principle, there's a few things you can learn. When the medical need is super high, you do have to have those discussions early, and doors will open that are usually closed.
Yep.
At the same time, you are carrying part of the risk. We went exactly to EUA, and then there was no emergency anymore. So literally, the dossier was there, it was quasi-approved, but then the virus mutated into a less dramatic form, and then nothing happened. So if you're part of the dialogue and risk that you share there, and for me, that was sort of the once being there, seeing you can go fast, you can work very collaborative with the agencies. You're doing the right thing, but you're also carrying part of the risk, and I think that's only fair.
Maybe the one other change that's happened since back then was the dose question has come up, and I think that's been the thing that's, you know, obviously, Project Optimus has come out, is showing, getting an accelerated approval based on a dose that is really high, higher than tolerable because the efficacy was good, and then when you fail, would you replicate, because you ended up testing a lower dose in your confirmatory trial. And I think so one of the key learnings is making sure you do have data to support your dose and get alignment on that, because there's multiple people who had to go back.
Sure.
Mm-hmm.
Because the gap left was, well, is that dose is tolerable and still achieving the efficacy target?
To that point, our EUA, that was 400 patients, 300 per active, three doses, so it wasn't a small trial.
Yeah, yeah.
Mm-hmm.
But it went easily through, and it was at 5 and 25 and 600. So we were spanning-
Yeah.
If you want Optimus, we did what we needed.
Yeah.
But then it was no discussion.
Yeah. You set a high bar for everybody else now. Three arms at a-
Yeah, but yeah. But then it's a virology trial.
Yeah.
Which is very fast.
The best time to do the Project Optimus, the dose optimization, when is that, like, dose expansion, to actually bring two doses into that early phase two? Or when do you do it?
I would say it's throughout development.
Yeah.
You can do some of it in phase one, two, and some even in phase three.
Mm-hmm. And they would accept really any-
The preference is to not do a lot of doses in phase 3, of course, for obvious reasons.
Yep, yep, yep.
Yeah, I think there's backfill, like, we use backfill cohorts.
Yeah, yeah.
I think if the dose is obvious from those backfills, it's easy.
Mm-hmm.
It's when you have lack of tolerability at that top dose that's showing you the efficacy-
Or you think of combination trials.
Exactly
... and then suddenly you don't know what it's worth, and sort of you're starting again. And then if we always have to do dose finding for every combination, I think it's not going to be a very helpful ingredient for us to be cost effective in making drugs.
Yep.
Mm-hmm. And then, sorry, before I let these other guys talk, which designations are the most helpful in this regard? Marty? Marty, you look like you want to-
I just... Sorry. I feel like I'm talking too much. To me, breakthrough is the most important.
Yeah.
Because breakthrough cuts corner... Well, cuts corner is the wrong word. Sorry if there's anybody from the FDA.
Expedites. There we go.
Well, where it really helps you is there's two places where it really helps you, is, one, from a CMC point of view, because a lot of times what becomes critical path on these accelerated is, is waiting for your various GMP batches, stability, and, and et cetera. You can file with shorter stability data-
Mm-hmm
... and other friendly things if you have breakthrough. And then the other one where it really helps you is from a CDx point of view, there are clearly helps in getting a package through that may have a few deficiencies that get fixed post-approval, that if you were to go through as a diagnostic company in the absence of breakthrough, you would not be allowed to do. So it's, it's— those are probably the two that have expedited timelines. I mean, by, by building on breakthrough, that's been the thing that's been the greatest accelerator. 'Cause the clinical data usually is in... You get in these pathways where all of a sudden, CMC and/or diagnostic becomes critical path-
Mm-hmm
... and breakthrough can help you get those off.
Interesting.
I would say it's helpful, but my experience with FDA was if you have a good drug, they will help you.
Yeah
... get it on the market.
Exactly.
You know, my previous life at Gilead, we had a hepatitis C compound.
Yeah.
I mean, that got, we never had a designation on it, but it got through very, very quickly.
Great. I believe that's...
Thank you.
So this is the Novel Oncology Targets panel, so maybe we could talk about some targets. And just broadly, what's the current state of play when it comes to finding new targets, interrogating new targets, and how has the pace of that process evolved in your careers? Maybe Patrick, we could start with you. Maybe you could take a moment.
Sure.
Obviously, your company is a little bit unique.
Yeah. Maybe just a word to our technology first, because then it makes sense how I answer the question. So we're working with DARPins. These are small protein binders, which are different than antibodies, and we can either use them as mono-domains, so really small, and then we hook them up with a radionuclide to deliver an isotope into the tumor, or we go multi-specific, and we add AND gates and switch concepts to be much more smart how we address a target. And for us, we really go away from novel targets.
And to really state the phrase of Seth here in the room, "We work on clinically validated problems with known targets that don't work." And I think this is sort of where we come in and say, "We want to work on all those targets that were tried and failed for the wrong reason." And then it's less about novel, but it will be a novel target for validating it, but it's not a novel target in the clinic, because where we don't want to build is the biology risk. What is the biology of the target? We always underestimate the biology risk. And with this, we literally look, what is the phenotype of the target, and what can we do with the DARPin to unlock the value of that target? And sort of that's our approach to the target selection.
We have a whole team that is six people just dedicated to this, just to understand the failures in the field and where is there a potential for us to solve it with our technology.
... Yeah, I would say it started a few years ago with DepMap. That was a huge step forward. That's still being refined and worked out. But I think what happened more recently is access to patient samples. We have a collaboration with Tempus, and they have 400,000 patient samples in their database. So you can kind of validate if you have a novel target, validate it there. Like Patrick, we are also not working on novel targets. We have two targets that are well-known. What we're doing differently, we have a completely different pharmacological approach to it. So I would say, the risk that these compounds won't work out efficacy-wise is close to zero in my mind. What is not the risk that still exists is the safety margin, and that's what we have to assess in.
I don't have much to add. We're sort of a slightly different stage company, where we're focused on pivotal trials. But I will say that, you know, our company was built on finding targets that were somewhat unique, somewhat similar to Kronos as well. And just watching the speed of execution in the preclinical phases has been really, really dramatic, I think, a lot because of the information that's available, a lot of AI and so forth. So I think the speed of discovery has really accelerated.
Right. I think for us it's more similar to the targets become what you look for. Ours is, is there off-tumor tissue? 'Cause for the ADCs, if there is an antigen expressed, you're likely to hit that cell. And so for us, the whole concept of targets is there's a zillion targets out, increased antigen expression on tumors. For us, it's all about making sure the more we can do preclinically to ensure that it does not exist on a normal tissue. I mean, NaPi2b is an example of that. We went after NaPi2b with UpRi, and we saw some ILD, and as we're looking through the data, it turns out that NaPi2b expression is specifically on type two pneumocytes, which turns out to be a pre-proliferative cell.
So if your lung is undergoing repair, type two pneumocytes become important, and if you have NaPi2b expression or NaPi2b ADC, you're going to get pneumonitis. And that's not a flaw in the design of the molecule. That's an on-target effect. And trying to screen that out across the range of tissues is probably our biggest focus.
Yeah, maybe if I close that part. For us, the novel targets, maybe, the novelty is the combination of these two targets, that if they are co-expressed, we kill the single cells, we don't. So the different way of thinking what the target is. So we bind A, and we only are activated if B is there, too. Sort of, and then you interrogate the system, as Norbert said, with all the clinical samples, to find out what is the Venn diagram of targets and which ones make sense together. And so you can really, with our technology, use what is out there almost better than before because we have a new way of interrogating the target space.
How do you view, kind of patient screening and physician education as they relate to novel target identification? Do you think that patients are being screened early enough, or are they only being screened after standard of care failure?
I think that really begins when you start the project, and we start the project, as we said, on clinical data. So we will be talking to these clinicians and asking: What problems should we help you solve, and what are the patients? And that in the first instance, they would say, "Okay," but then you need target screening if there is a target to screen for. Sometimes you find targets where you need less screening, and, I mean, that's always the best. If you're in an indication, you can more or less run the all-comers, and that must remain the gold standard, so that we don't get too niched. At the same time, the more we can deep dive, we'll need every trial to declare victory. So that trade-off it happens when you start the project, and we involve the clinicians then.
That we already know when we are in the clinics, and we work with them until we are in the clinics, that we have a good understanding how they treat patients and how they look at patients. Because if you come with something that they're not used to not using, it's gonna be very difficult.
I think part of it is, this is where it really kinda depends. If you're in lung cancer now, and patients, a lot of them are getting a wide genetic profiling upfront, and the target you're seeking is identified on that, it's fairly straightforward 'cause you could leverage existing. AML, you can look for, you know, those patients get profiled. If you now try to look at a target that's not captured in standard screening, then you need to go into like, you know, for B7-H4, we go into TNBC because over half the patients have expression. So then you just go into all comers, and then as you're doing that, you'll gather data on expression. But if we were to go into head and neck with B7-H4, that's probably 10% of head and neck patients.
For one in ten on a therapy that you don't know it's going to work is difficult. So, I think to your point, you have to find an enriched population to start or and/or be pursuing a target which routine practice already include screening.
I have to add, in this country, there is unfortunately a big disparity in terms of access to screening.
Yep.
If you live here close to Dana-Farber or MSK or Stanford, you are able to do the screening.
Yep.
But if you live in a rural area, unfortunately, it's not as common. The only other thing I would like to add, if there's a good drug out there-
Yep
... for a new target, that screen will be very quickly incorporated into the panel-
Yep
- Foundation, et cetera.
Yep. Agreed.
... Yeah, I would only add that even in late-phase development, if you have a companion diagnostic, it's not a given that physicians will recruit and use that screen if it's not a screen that's a multiplexer or something that's currently available. You have to consistently and repetitively sort of sell the story of the effects of your drug and the promise of your drug in order to get these sites to really recruit. It's... I think that's a bit of a misnomer sometimes when we have a lot of sites signed up, and we just expect recruitment based on this, and that initial screen is really a challenge, and it has to be sort of proactively discussed with these sites.
Okay, another high-level question, and I know this might be a bit of a jump, seeing as how we're talking about early screening for recruitment. But can we get your thoughts on, like, recent commercial launches in oncology? You know, I know this is uncharacteristic of us as analysts and investors, but are we looking at it too optimistically? You know, should, you know, should. How should we set expectations for launches? I know it differs based on the prevalence that you're going after.
Yeah.
But what do you think is happening with these recent launches, like, you know, LUMAKRAS? It's just been a little bit slower than I think people expected, and why do you think that is?
Yeah. I guess I could start.
Yeah.
I think there's a few things happening. One is it may not have anything to do with the target. The profile of the drug is important, obviously.
Yeah.
The efficacy, the side effects, and it has really little to do with targeted therapies versus a broader treatment. I will say, in the clinician's mind, a large number of them really prefer this targeted theory of personalized medicine, and so you've seen it time and time again, potentially in lung cancer, for example, where you have drugs with similar profiles to the broader treatment. They will gain significant market share because they're a targeted therapy, and physicians really like the theory, so there's a lot of upside to being targeted.
I will say some of the downside and maybe some of the overestimation that you're pointing to is when you have broad, very niche mutations, you know, very small percentages, and some of our panel had mentioned this, it's hard to convince a physician to test for it, to be educated on it, when they may or may not see a patient in their lifetime, right? And so you've seen some of the, all of this sort of tumor-agnostic type of targets, where you have to educate many, many different specialists, and that's a very difficult thing to do. And when you see these projections of launches, where it's basically epidemiology times, you know, price, I think it overestimates the challenges of educating these specialists.
You know, maybe I could add. If you have a 2% incidence, essentially you screen 100 individuals, and you find two positives, and then it depends what the test costs. Let's say the test costs $200. That becomes a difficult equation to support.
The only exception, I would say, is in lung cancer, where you have-
Yeah
... now cumulative mutations that are 2 or 3% on the same panel, on the same-
Yeah
... Then it becomes, you know, psychologically positive-
Yeah
... because now when you test, you could get 30%-40% of patients.
I mean, HER2 is a good example.
Right.
We knew a long time ago that HER2 was a target in lung cancer, but it was less than 5% of patients had high levels of expression. Those trials, actually, Genentech did those trials back in the nineties, and they just really couldn't complete them because back then, you had to do the IHC or FISH. Now, it shows up on a lung panel, and now there's lots of people looking at HER2 in lung cancer.
Yeah
... because you can actually, those patients are selected. So I think factoring in screening and uptake of screening is probably a critical element on a targeted therapy.
I also think the manufacturing part, now speaking maybe a bit more to the radiopharmaceuticals, where the whole supply chain and everything is not to be underestimated. A company that is super organized, like Novartis, wasn't able to do that the way one has to. I mean, let's face it, they have budgets and experience the biotech doesn't have.
Yeah.
We also have to make sure that we pick our battles well and that we partner with those who can help us do it right. Because in the end, it's about how many patients can we help, and then we are also okay to share some of the upside. It is our job to pioneer this. I'm not finger-pointing to Novartis. I'm glad they're pioneering it, that others can follow it and learn from that. It will always be with more hurdles than we think because it is the unknown. So we have to. It's our job to try it and fail, and then fail better, and then make it work for the patients. So just, I think we have to be too optimistic, and then we have to make it happen.
I wanna throw in one positive comment, though.
Mm-hmm
... 'cause we do totally focus on the misses. I mean, if you look at the folate receptor with ImmunoGen-
That's true
... that was a mix-up.
Yeah.
So I mean, there are examples, and that had all the issues. It was a new assay and everything.
Yeah.
I think it's back to your point. There was a strong belief in the data, in a patient population that had no other need, so the gynecologic oncology community adopted the practice and rapidly started treating patients with it.
And to that point, we had tested Radio-DARPin in 2007, and people would say: That's never gonna be a drug. And now we're in it. It's the space-
Yeah
... and it's happening. So if we often are too negative on a space, too, and then it needs one or two examples, and everything changes.
... So, commercial launch is a challenge that I think most folks would like to have. But let's move back a little bit in the development process. How have you seen any evolution in translational science, so preclinical models? You know, a lot of you are already in the clinic, so it's perhaps less relevant. But you know, when you think about your pipeline and your future development programs, have preclinical translational models improved? Is there hope on the horizon for you know, better predictive validity?
I mean, we were kind of touching on this a little bit while we were sitting up here chatting, is one of the challenges of preclinical data is it's always the, this very wonderful, homogeneous world in which all the cells either express the target or they don't. So one of the things we've moved towards is we use much more use of PDXs, where you're basically taking tissue from cancer patients, and you're putting them into murine models. There are still limitations of PDXs, but you get a much more heterogeneous population to test.
So we'll, we'll run experiments in a panel of whatever our indication of interest is across a range of tumors, and I think we get a much better feel truth or the reality in the clinic is gonna work with, as opposed to this, these contrived preclinical models where it's 100% or not. And so that's one area where we've spent a fair amount of focus, and you can run PDX relatively fast.
Yeah, I look at these models as more as directional.
Yeah.
They're certainly not quantitative.
Yeah.
You know, our two compounds or two programs that we have target essential genes, so they are absolutely required. And, you know, because of that, the challenge is more: what is the susceptible population that is more susceptible to inhibition? And number two, how can you modify the therapeutic window? Those, again, are the two questions.
I think from our point of view, what we try to do when we run these experiments is really dissect the hypothesis into questions we try to answer with the models. Like, and we don't try to answer them all in one model, but like, I don't know, does the switch concept work? And then we have 2 or 3 experiments, and then they - that has to prove us. Then we go, does the localization work? Is the co-expression there? Next to the PDX is really ex vivo-
Yeah
... like patient-derived samples directly in the test tube, as simple as possible. I think the other point I want to make is more almost a company cultural one, because you're always tending to want to continue the project. So how do you build a culture of analyzing data and being honest to the data to actually not invest in it?
Yeah.
We live that side that we pitch to the public, and then we have internal reviews of looking at the data together with the teams, and how can we create a culture that we together come to also a no-go decision because we want to invest in something else?
Yeah.
I think that's almost more critical than what experiments do, but how can you have internally the robust discussion, is it worth investing in this project or not? Because you will always face, you want to invest. I think that's, that's almost more critical than the models themselves, that you can draw, that you can draw the right conclusions. We touched on that before.
Yeah, sure.
It's interesting. Maybe I have a question. In today's environment of investors and the scrutiny level, are you finding more deeper conversations in order to convince investors of sort of the path in preclinical data?
I think the good thing is, and it goes a bit to the point, there is more and more understanding of the models and all of this, so I do think the discussions become more educated. And, that's always a good thing because then they are real, and they are not just statements, but there is a real discussion. So I do think that this is much closer. Also, investors are a great source of information.
Mm-hmm.
They see everything. Actually, the idea of a pitch, I prefer a discussion because in a conference like this, when you talk to 15 investor groups in one day, you learn so much what's going on as they are learning from you. And in the end, we're trying to make the best drug. So I think this is really a two-way pitch, if you want, or actually more discussion than a pitch.
Although, I have to say, preclinical data from, for investors, it goes only so far. In the end, you have to show clinical response-
Yeah
... and safety. That's what really moves the needle in the end.
I think what happened was we got a little, 2, 3 years, well, 4 years ago or whenever, we got a little too treating preclinical data like clinical data, and basically, people were making major investments on, you know, a couple mice. And I think then it obviously swung the opposite way, where you could put out good clinical data, and you still got punished. And what I think probably for us, the most exciting is it's almost getting back to a balance of you're getting the, the privates and the VCs, and everybody is investing in the preclinical, but you're not, you know, you're not IPO-ing on a DC, which was probably a little bit extreme.
Yeah.
To your point, the clinical data is then where you really need to do the major investment. So I think, hopefully, we're getting to the right place. Hopefully, getting to the right balance.
All right, I think we should switch to company-specific questions. It's like lightning round. We'll start with Syros. Do you want to-
Yeah. So this one's for Conley. So, Conley, you are evaluating a lead candidate, tamibarotene, in combination with azacitidine in the phase III SELECT-MDS trial in high-risk MDS. So I guess, just for those who may not be aware, can you just kind of give a brief overview of the trial design and then maybe kind of shift focus a little bit to talk about the CR primary endpoint, what the rationale is there?
... and, kind of what, increase would be considered clinically meaningful in that endpoint?
Yeah, so our lead candidate, as you said, is tamibarotene. It's for RARA overexpression in MDS. It's a double-blinded, 2-to-1 randomization. The first 190 patients will be focused on the primary endpoint of CR, and we're hoping that the 190th patient will be this month, actually. So exciting times for the company. If it's positive, we'll file on that. And the FDA has confirmed over and over that a CR endpoint is valid for either accelerated approval or full approval in MDS, in higher-risk MDS, and we're in front line, by the way. And then the trial will go on to 550 patients to confirm the secondary endpoint of OS. So we're excited by it. You know, in MDS, the current standard of care is azacitidine alone.
The CR rate is 17%. So the bar is quite low, I would say, and these patients are in tremendous unmet need. There hasn't been a treatment in 20 years or so, so I think the community is extremely excited for us to hopefully succeed here. I started at Syros about 2 years ago, just to reflect on a personal note, and at the time I joined the company, I think there was 5 phase 3s in MDS, and now, with the recent delay in VERONA, and we'll see what their data looks like, but it could be almost a simultaneous sort of launch of the two compounds, and that's kind of exciting for physicians, but it, the landscape has changed so much. It's really dramatic in MDS, not in a good way.
I guess what, specifically, I know we haven't seen data yet in MDS, but what gives you confidence of tamibarotene's potential in this indication?
Like, like I said, the bar is very low for azacitidine. It's 17% CR, and what we, I think the most compelling data that we have is when we looked at lower blast count MDS. So as you may know, it's a continuum of disease, MDS and AML. And so when we looked at AML patients that were 20%-30% blasts, and these were patients that were deemed actually MDS patients a few years ago, and the classification kind of changed. In that group of patients, we had 66% CR rate. So, 17 versus 66, we're, we're pretty optimistic that, that we could potentially have quite a significant drug here. And as, as the clinical relevance of that number is a pretty low bar because there, there's really not any treatment for these patients.
Great.
Norbert, can you talk about your lead CDK9 inhibitor, KB-0742? What is differentiating about it relative to other historical CDK9 inhibitors?
So CDK9 has long been known to be a target in oncology. There's nothing novel about this, but why almost all the previous compounds have failed because they didn't have what we call the three things that you really need. Number 1, oral bioavailability. Number 2, long half-life. Some of these compounds were dosed once a week, and you had a half-life of 5 hours, and we believe that the high Cmax that you get when you dose when you dose IV, that that leads to neutropenia, and all of the compounds have failed because of Grade 3 or neutropenia. And finally, the other key profile is it's specific for CDK9. There were a lot of, a lot of other kinds of CDKs, the cell cycles 2, 4, and 6. So we have a really good compound.
It's orally bioavailable, 24-hour half-life, very specific CDK9 inhibitor, and we have now proven clinically that our hypothesis was correct. We see responses, but we don't see Grade 3 or 4 neutropenia, and by the way, we see responses at a dose of 60 mg, given three days a week, and we now know that that's not the optimal dose. So we have cleared the 80 mg dose, and now we made the decision to go to 80 mg, four days on, three days off, and I am convinced that will be a winner. And by the way, the safety is manageable. We had some GI upset. That's kind of almost expected, but you know. So I'm pretty hopeful this is gonna be a compound that will help a lot of patients.
Great. Marty, you're next. Okay. A lot of places where I would like to start, but I think it's important for us to hear what—I mean, at a high level, the improvements that-
Yep
... the Dolasynthen platform makes over Dolaflexin, and then in particular, this week, it's gonna be data from-
Yep
... the 1592.
Yep.
And what can we learn from that, that we can take forward to XMT-1660?
Thank you. So for the day she's alluding to is we have our Dolasynthen on NaPi2b data is presented at ESGO. Maybe you want to go to Barcelona on March 7. And then our UpRi, which was our 1536, which was NaPi2b, with our original platform, Dolaflexin, will be presented on the 10th at ESGO. And why is that relevant? Because they're both discontinued programs. Well, one of the things we set out to do as a company is we're strong believers that while vcMMAE, the Seagen platform, and the Daiichi topo, great platforms, lot of benefit for patients is both of those platforms are limited by off-target toxicity. Our goal as a company has always been to eliminate or mitigate those off-target toxicities. And with Dolaflexin, we thought with our original approach, we had an opportunity to do that.
Well, we had an auristatin payload, and in fact, we eliminated neutropenia, neuropathy, and ocular tox. So from that point of view, objective achieved. But unfortunately, we discovered novel off-target platform toxicity with Dolaflexin. So what's really important for us, and that for those of you who don't know our entire story, that was the miss on our pivotal study last year. So what we've gone back and looked at all the data, and we realized we could do this experiment, where we had the same antibody, NaPi2b, same payload, but the scaffold linker was different. One was Dolaflexin, one was Dolasynthen.
The data this week we're sharing shows that not only does Dolasynthen solve the problem of neuropathy, neutropenia, and ocular tox, but the novel off-target platform toxicity we saw with Dolaflexin, thrombocytopenia, which was associated with the bleeding events, the elevated AST and some of the fatigue and some of these other things are night and day difference. So we can't really go into the numbers yet 'cause the data is coming out soon, but I think there's, our 10-K has some of the data. So I think this is something that we're very optimistic that we now are down to true, pure, on-target tox.
For that, we need the 1660 data, but based on the B7-H4 from Seagen and now Pfizer, and from Hansoh, and from our own preclinical work, we think that's much less likely that there's a B7-H4 mediated toxicity outside of the tumor. So all in all, that all puts together, and we're leading to our mid-year, and we're very excited about the data, too. Sometime soon.
So, Patrick, you have an embarrassment of riches. Maybe you could introduce for folks that don't know the story, MP0533.
Yes.
Describe the technology, the target, the approach, and the data, and when we can expect that data?
Sure. I think I'll just start with on the MDS story with kind of nothing has happened in AML in a long time, if you want. And there is really no T-cell engager or other approach that was very successful. And we went back and looked at, again, these, call it failed targets, and we found that there is just not a clean expression of these targets, but they're all preferentially expressed on AML cells. They're even more preferentially co-expressed on AML cells. So our team went through thousands of iterations of tri-specifics binding to CD33, CD70, and CD123, and then hooked that up to a DARPin that binds CD3, so a T-cell engager and half-life engineered that. So in the end, it's a tetra-specific with HSA binding properties.
We looked at that and found, to Norbert's point, it's opening a therapeutic window on existing targets, and pre-clinically, this window opened around a hundredfold, and that was kind of the molecule we then brought into the clinics. Roughly a year ago, dosed the first patient, and now we're almost done with our planned dose escalation. And literally, when we open a new dose level, it sort of recruits in no time. To your point, there's just nothing there. And at the same time, it's not that we're just dosing. We actually saw first responses at rather low doses, and that then sparks this enthusiasm with the investigators to really try your product. And we're now hitting sort of the almost top dose. The safety is still looking very good.
We're planning to add additional doses on top because, I mean, higher is better. Sort of, that trial is recruiting, and we're very happy with the progress of it.
When might we see some of these data?
That's a very fair question because we wanted or we are still planning to give an update, first half of this year. And just from the plan and recruitment speed, we thought we would be done with dose escalating. And now, if we add another 3-4 dose cohorts at even higher doses, that will just be an interim update. So that's a good problem to have. At the same time, it's not gonna be the final data, and we want to understand obviously, also the high dose data before we come out with a response rate or a duration. So there's just a bit more work to be done, but in a good way.
Great. Maybe I can take delivery of one more question, then we can move back down.
Sure.
Yeah, yeah.
So you also have mentioned your Radio-DARPin efforts. Maybe you could elaborate-
Sure.
On those efforts.
So the radio space is super interesting, and you saw that you had a target PSMA and a few others that are very targetable with small molecule binders. They are perfect. They go to the tumor, they accumulate, you put an isotope on it, you get great responses. But the target space is very limited. It's. If you're generous, you give it 30% of the targets, extracellular targets you will be able to approach, but 70% of targets are not druggable with that approach. If you take antibodies, they have a super long half-life, you will just toxify the bone marrow with isotopes, so that doesn't work. So what you need is a small protein binder that can bind these other 70% of targets. The problem is, with small proteins, they get reabsorbed in the kidney, so you'll just toxify not your bone marrow, but the kidney.
So our team spent two years engineering DARPins that are stealth to the kidney. So the kidney does not recognize them as proteins. It lets them through, they end up in the urine, and with that, the isotope gets flushed out. This is real platform improvement, and it took us literally a lot of trial and error to get there, because you can't really just - and maybe that's the next question, the AI. You can't apply AI with - if there's no data, so you have to generate the data on it. And then, that's what we did. We're - We think we're there on that platform now. We're at - We are improving the tumor uptake, so that's a bit half-life engineering, bringing up the tumor load.
The goal is to have the first candidate first half of this year and then have a suite of follow-up candidates, moving in that, because we do believe that the mode of action, the efficacy, will be really stronger to anything else out there, and the new targets are, for us, the exciting part.
Moving back to Conley. So, Conley, you recently shared positive early data from the phase 2 SELECT-AML study of the venetoclax... Or sorry, the tamibarotene triplet with venetoclax and azacitidine in early unfit AML. So I guess, what is your, how do you view that data, both in terms of efficacy and toxicity, and what would be the next steps for this program?
Yeah, thanks for the question. It was an exciting time. In December, we released the data, as you pointed out, with a triplet versus a doublet in RARA overexpressers in AML, and it was the first cohort of our phase 2, which is scheduled to be 80 patients, and so it was the first 20 patients or so. And we saw a 100% CR/CRi rate, so it was a bit of a drop-the-mic moment, to be honest. We were pleasantly surprised by that efficacy, and we had a lot of interest based on that. I will say one of the key things that we were looking at and looking for was actually the side effect profile, because as you may know, venetoclax and azacitidine are fairly toxic combinations to use in AML, and it's not well-tolerated.
And so in a triplet form, we really didn't want to add any more toxicity, and we felt good about it going into the trial based on our current AEs of tamibarotene, and it's quite tolerable. And what we saw was that there was no increased signals of hematologic effects or side effects. And in fact, early data suggests that it may actually lower the hematological side effects, and we'll wait to see if that holds out. But there's some mechanistic rationale as to why that may be occurring. So, super excited by the data. We'll continue the trial through this year, and we'll share some data later in the year and an update to that with some durability, because that'll be the next question.
Yes, we'll go to Norbert next. So for the 0742 data coming, it's mid-year, right? What do you see as success here?
Pardon?
What would you view as successful data for the next update?
Well, we will give an update on whatever data we have. We have a fair number of patients now in our expansion cohort, and then keep in mind, this is 60 milligrams, and there are only a few patients on 80 milligrams, so it's not the optimal dose yet. But we have a lot more patients with longer follow-up, and the data look interesting. I mean, just the compound clearly works. You know, if you look at sarcoma in particular, by RECIST, by the way, we see tumor shrinkage, and that's remarkable because you wouldn't normally expect sarcomas to shrink because they're full of collagen, et cetera. So RECIST is not the best way to look at it, but that's what we had in our clinical trial sites.
Marty, so kinda left you hanging on the last one, but now let's talk about the 1660 data that's coming up. So how should we set the bar for this? I know many are using the Seagen and the Hansoh data that were presented at ESMO as the bar, but is that the right way of thinking about it? How should we go about that?
Well, I think the first thing we need to remember is if we're looking at triple-negative, so we have to think about which indication we're looking at.
Right.
Chemotherapy is 5%, so that's really a high-end medical need. I think Pfizer just put out some data, and I think we're probably focused there because with the Hansoh data, it's China. We really don't know what those patients were in the treatment paradigm. If you think about the data they just set out was 20% in TNBC. What was not in there was how many of those patients had seen Trodelvy and/or Enhertu. Now, being the trial was done with the U.S. patients, there probably was that usage. So, so for us, instead of getting too caught up in the details of the exact response rate in these limited kind of expansion backfills, it's really the types of patients. But we should see meaningful objective responses. I mean, we're, we're-- if we don't see any responses, we're not gonna see that as positive.
I mean, that's obviously negative. But, but I think we're really not trying to get too precise on the bar, other than we should see activity in these patients post the current standards of care.
Can you get into the idea, though, of... We've talked about this multiple times, and I'd like for you to share your thoughts on this because it's very interesting, like the idea of topo resistance and how—I mean, XMT-1660 has a really unique opportunity here-
Yeah
... where other topo products don't. So could you-
Sure
- Elaborate on that?
Yeah, and that's. Yeah, I probably should have finished the answer on the previous one. Why are we interested in post-Enhertu and post-Trodelvy is, for those of you who don't know, Enhertu is a HER2, but it's a topo payload, and if you look at Trodelvy, it's a Trop-2, but it's a topo payload. And there's data emerging, and this was one of the themes at ASCO, ESMO, and San Antonio Breast Cancer. Physicians give them after each other, and the reason they do that is because chemotherapy has a 5% response rate, so you give it. But as they're collecting real-world data, what they're realizing is the first therapy will work, and then the second one won't. And at first, that was a little confusing because but they're different targets, so you don't have you shouldn't have target cross-resistance.
But there's anecdotal data now, where if you go in and look in the tumors, and there's some, a couple of really nice papers on it, that they have identified that the patients have switched from a topo one using topoisomerase one to topo two. The tumors flip because they can survive on that pathway just as well, and so they've overcome that. So one of our key attributes is a different payload is going to be critical for success in TNBC.
And not only, though, from this initial approach of this first data set is going to be in these late-line refractory patients, but that takes us where we really, really want to go, which is if you have a non-cross-resistant payload and a different target, and oh, by the way, you have a safety profile that allows you to give a combination, we can envision a world in which we move upfront with another ADC. If you think about the current, you know, Pfizer data with the neutropenia neuropathy, the ability to give a B7-H4 with vcMMAE on Trodelvy is just not going to happen. I mean, or if it's going to happen, you're going to have significant compromises in dose.
So kind of our vision is not only is it the ability to go into resistant patients or refractory patients, but it's the ability then to combine upfront. And why do we care about that? If you really think about our progress in oncology, the places where we get the magic CRs and that are long-term durable is really where we get combinations. So as much as the monotherapy data is a good place to see, do we have activity? Our goal is move these upfront earlier in these combinations. So seeing a good safety profile that says you're combinable, combined with activity in these refractory settings, those are kind of the signals you look for to move up into those settings.
So, Patrick, in addition to the multi-specific DARPin and the Radio-DARPin, there's a third leg to the Molecular Partners tool in the Switch-DARPins. If you could describe that technology and its current phase.
Sure, sure. No, that, that's almost the most exciting part of protein engineering, which is also my background. And DARPins, I was telling you before, are small protein binders. And now one trick you can do with them is you can put two DARPins in one DARPin, and then they have a shared binding site. So this binder can either bind target A or target B, but not at the same time for steric hindrance. So you have created a logic gate with that DARPin that it has to decide. By tuning the affinities, you can create on and off switches, where this DARPin now it can bind, let's say, another DARPin, which is CD3, or in one case, it's a CD47 binding DARPin, and in circulation, this is off. Another DARPin, let's say, in our case, it's c-Kit.
The c-Kit binder brings this to, in this case, leukemic stem cells and hematopoietic stem cells. The either or DARPin will switch also to c-Kit binding, then the CD47 is opened. It blocks CD47, and in the initial DARPin, the CD16A engages macrophages and then NK that then kill these cells. And something like that just has never been around. That you can do something like that, that's new. And that's our first example, and we're getting a lot of inbound requests, what else one could do, and it is a, an open question to everyone. So if you have good ideas, please do approach us if you need this gate system. And I do think we saw good, approaches. I mean, the other way to do it was protease cleavage sites.
That was a bit of, let's say, underwhelming data in the past until Janux now presented beautiful data. So for us, that's great because people start to believe in these gates again. So we definitely see this as, A, for our product, but also for the platform that this, the switch idea really can now get more attention and also more products. And for us, where the company started from protein engineering, it's really about finding the problems you want to solve with this technology, and the first one is the c-Kit.
Briefly, uh...
Yeah. Okay. Just one for Conley. So, Conley, Syros is trading at a valuation that's below that of other companies, the pivotal stage candidate in oncologic cancers. Why do you think that is, and what do you think is kind of being misunderstood about Syros?
You sound like my board. So here's what I tell them.
Can you answer ours?
Yeah. It's a tough question. No. So I think there's some technical reasons, and then maybe just to share with you our current state. So you know, we're a company of... If you look at our top 10 investors, they're really the who's who of investors, right? And they're long-term players and so forth, and they own a majority of our company, and so we don't have a lot of volume. In fact, you know, we heard a lot when I first started this in December, was, you know, "We'd like to get in, but we can't.
Like, how do we do that?" And so you don't see a lot of volume trade until the events of December where we announced data, and then we announced an equity raise, and then it started flowing after that. The other piece of it, I would say, is we entered as a company, this, I'll call it sort of the-
... the silent value, right? So for many years, we've been sort of talking to investors about our potential phase 3 and so forth. There was really very little value inflection points, and so I think partly folks started to kind of forget about us, and well, we'll kind of revisit it when we get close. And now we are close. And so you're starting to see activity pick up, and fortunately for us, since December, our company has, from a market value perspective, quadrupled. And we still think, to your point, we're quite undervalued, and hopefully, as we sort of get the message out about our current status of our company, it'll pick up.
helpful. Thank you.
Norbert, let's end on you. So you guys recently announced a really interesting new candidate that I'm hoping you can discuss, this IRF4 targeting drug. So, why is IRF4 important in multiple myeloma, and, you know, what's unique about this product?
So if you were to ask scientists: What's the number 1 target in multiple myeloma? Most of them would say IRF4. And because it's the, the transcription factor that defines plasma cell identity. But the usual story, as with most transcription factors, it's difficult to drug 'em. But we are a computational biology company, really, at our heart. We mapped the transcriptional regulatory network, and we found out that P300 is a close neighbor to IRF4. And lo and behold, when we drug P300, the KAT domain, by the way, it has a number of domains, KAT, is lysine acetyltransferase, we get a very potent, very specific compound.
What that compound does in multiple myeloma cells, in patient samples, it, IRF4 goes down, MYC goes down, IRF4 protein goes down, and at the same time, these cells undergo apoptosis. So it's a great drug, preclinical compound, oral, of course, in animals, I have to say. And what we're currently doing is IND-enabling studies, and hopefully, by the end of this year, early next year, we can initiate the IND.
Great. Okay. Yeah, what, what have you shown preclinically so far? I know you guys have, have-
So we have a collaboration with the Dana-Farber group, and we have done PDX models, we have done patient samples, and the compound clearly causes apoptosis of multiple myeloma cells.
Mm-hmm
... while not touching the other cell,
Mm-hmm
... stromal cells. So it's all. It looks. I have to say, it's preclinical. You know, I'm always a little bit skeptical until I see clinical data. There's one saying that I often use, it's, "The less you know about something, the more excited you are about it.
Mm-hmm.
This looks as good as it can at this point in time, at this stage in development.
I guess we have one more minute left. I got dibs on it. I'm gonna ask Marty, why are you targeting HER2?
Well, first of all, there's a lot of HER2 out there, and we get that.
Yes.
We heard that question all the time. I think one of the reasons we're looking at this, we have our—for those of you who don't know, it's on Immunosynthen, which is our STING payload. One of the reasons we picked that is, we wanted no target risk whatsoever. And so we were looking for an opportunity to really bring a novel payload. So you, with a STING agonist now, they can have all the cytotoxics in the world, but you still haven't exhausted, like, an IO path. And if you think about that, we also did it on a different epitope, so that, in theory, back to the combination theory, we could give two HER2-targeted, one with a traditional payload, as well as then with an immuno-oncology target.
So if you wanna go when you're really doing true, true, true novel platform payload work, you wanna make sure you have a valid target.
Absolutely. All right. This is beeping at us right now, so I guess that, that is time. I really appreciate you all for being here and giving us your very interesting answers, and for everyone for watching us, thank you very much.