Erasca, Inc. (NASDAQ:ERAS) Q4 2023 Earnings Call Transcript

Jonathan Lim: Yes, I think so we don’t comment specifically on hazard ratios, but what I will say is that the biometrics and and broader Naporafenib and his team at Erasca basically assumed worst case. We really all of our power assumptions and calculations were predicated on the control arm performing on par with what was historically seen with NEMO. And you can see for various reasons that Shannon mentioned during our formal remarks that could be an overestimate of what the quote-unquote natural history of disease could be. So if the control arm performs more in line with benchmarks two and three, which is probably a more relevant benchmark, given that those are post-IO settings versus the NEMO trial, which was pre IO or 80% of the patients received and either Chemo or MEK inhibition from before receiving IO.

But that said, all of our power calculations are predicated on the control arm performing in line with NEMO. So that’s why we’re even more excited about the possibility to show both statistical and clinical benefit. If if the control arm performs more in line with benchmarks two and three, which is that roughly seven months OS range versus the 10 to 11. Does that answer your question?

Operator: Our next question comes from the line of Chris Shibutani with Goldman Sachs.

Chris Shibutani: Great. Thank you. Several questions relating to the study designs have been described, but just remind us, SEACRAFT-1 amongst the potential for tissue diagnostic. We have 1b data coming for the combination at a time from Q2 to Q4. What are the gating factors for that time span? What kind of denominator, what kind of tumor types distribution might you be I’m able to help us with in terms of expecting and then. So obviously that overnight you did the financing, David, if you could just give some clarity in terms of where do you feel that gives you confidence of the runway in terms of supporting your programs? Thank you.

Jonathan Lim: Yes, thanks, Chris. I’ll answer your first question and then David will answer your second one. So there are no real gating factors other than really just enrolling the patients well, operationally, since FPD in Q3 of last year, we’ve really been ramping up the number of sites that have been activated and enrolling. So we’re almost running at full tilt at this point. And so enrollment has been brisk and you would imagine that there’s going to be a range of different solid tumor types that sort of reflect the epidemiology. So there’s sort of the most common tumor types where RAS Q61X shows up is the GI cancers like colorectal cancer and there’s some pancreatic. You also want to look for non-small cell lung cancer, thyroid and some potentially head and neck and other solid tumor types.

And so it’s really all comers. There’s no constraints. It’s really whichever patients show up with a RAS Q61X mutation, irrespective of the solid tumor type, we’re enrolling them. And then it’s really a matter of following them up so that there’s a meaningful length of length of follow-up of those patients so that when we present the data if there’s something meaningful to say. And so that’s really going to dictate the timing within the Q2 to Q4. I would say in terms of this scope it’s going to be dozens of patients. So it will be a meaningful update.

David Chacko: And Chris, on your second question with regard to the private placement that we announced as well, yes, we did do a $45 million oversubscribed private placement with high-quality investors. And with that, we were able to push our runway out from each one of 2026, which was our previous guidance to H2 of 2026.

Operator: Our next question comes from the line of Graig Suvannavejh with Mizuho.

Graig Suvannavejh: Yes, good morning. Thank you for taking my questions and thanks for the presentation today. I had to ask my first first and then I’ll follow-up with the second so and with your Napo OS data in the context of on small patient numbers, were there any patient characteristics across dose cohorts that may have driven the OS results? Or could you potentially see a different OS results? I’ll ask that first. Thanks, sir.

Jonathan Lim: Shannon, you want to take that one?

Shannon Morris: Sure. So we did look for or we did an evaluation of baseline characteristics in things that are prognostic for melanoma and like LDH stage prior lines, you called those sorts of things. And we looked amongst doses across trials and I can say, again, really small sample sizes. So it’s pretty difficult to identify a specific subset that is driving those results. And actually, I would say that the fact that you’ve got two different doses across two studies. You have four different observations here. The fact that all of them were quite similar to one another, I think, really argues that there isn’t a specific subset driving this at this is sort of reflective of the overall population. So, back to Jonathan.

Graig Suvannavejh: Thank you. And my my second question is just trying to get a sense of some OS or read throughs for Napo. And I guess the question is and based on what you might see from OS on in your in RAS melanoma patients? And are there any read-throughs to RAS Q61X mutated solid tumors or any other read-throughs that for other patient populations? Thanks.

Jonathan Lim: Yes. So to my earlier point where there’s that 90% overlap between NRAS and Q61X, I would say that there is a read through for the melanoma cohort within SEACRAFT-1 that has RAS Q61X mutations because all of those patients will have an rash. And so that is almost direct read through. And then in terms of OS implications on other cohorts, solid tumors like non-small cell lung CRC. I think those are going to be context dependent and more dependent on the histology. If there is a read-through, there will be great. But scientifically, it’s tough to say whether there is or isn’t. So that will be a data driven type of analysis.