Asthika Goonewardene: Good morning. And thanks for taking my question. You guys just very nicely laid out how some of the design features for 1660 could be making a difference. And we look at things like duration of response and duration of therapy as good measures just to confirm if that’s actually had their clinical difference. So maybe I’ll ask you guys this. Given what we know about some of the B7-H4 ADCs, what kind of duration of response or duration of therapy would you be looking for in 1660 to say, aha, this is actually makes a difference in the clinic but with the patients treated with 1660?
Martin Huber: It’s Marty. One, I would like to put a little caveat on looking at duration of response in Phase 1 data sets even with pretty robust fulsome data sets is always challenging just because the number of responders to get a precise point estimate is always relatively limited. So we want to be a little careful about getting too obsessed on that early on in the development program. However, we do agree it’s a very important question. And if you think about it, the standard of care, chemotherapy has a 5% objective response rate. And by the way, that’s not even in a – that’s the control arm from the current ADCs. That’s not post TRODELVY or post in HER2. And importantly, the duration of response for that control arm was less than four months.
So I think a DOR, usually, when we think about these things, you’d like to see a 5, 6. One of the things that we were very excited about from UpRi is, while overall, the response rate was lower, the – one of the things you’ll see in the data is that the duration of response for those patients who did respond to UpRi was over seven months. So we think there’s an opportunity to increase DOR. I think it’s just going to be challenging to clearly demonstrate that in the initial data set.
Asthika Goonewardene: Got it. That’s very helpful. I’m also wondering, you talked about how resistance emerging from prior payload exposure is an issue that’s emerging a lot more in the breast cancer patient population. Will the Phase 1 data set — the dose escalation data set give us any sort of clues or be able to parse out when patients who are developing resistance to prior payloads and show us what the efficacy of 1660 looks like in that? Or is that just too much to try and piece out of that data set that’s coming up?
Jason Fredette: Yes. So this is Jason again. So again, I think it would be a little premature for us to pigeonhole ourselves into specific cuts of the data at this stage. We did note neither Hansoh nor Seagen showed any responses transparently, at least in post-TOPO treated patients. But again, we’re not going to commit to that today.
Asthika Goonewardene: Got it. Thanks for taking my questions, guys.
Operator: Thank you. We have the next question from the line of Brian Cheng with JPMorgan. Please go ahead.
Brian Cheng: Great. Thanks for taking our questions this morning. Maybe first one is on from a modeling perspective, how should we think about the expense trajectory given your plan to move forward into potentially larger studies across a number of indications? And I have a quick follow-up. Thank you.
Brian DeSchuytner: Sure. Well, our cash runway guidance is based on our current operating plan commitment. That does include the early clinical development of both 1660 and 2056. But if I sort of double-click on your question, as you know, we don’t provide forward-looking financial guidance, but you’ll note in our press release and our remarks in the K that we’ve reported a significant reduction in OpEx in Q4 and have since then substantially completed our UpRi wind-down. So we think this meaningfully simplified cost structure is going to enable our available funds to support our operating plan commitments into 2026.
Brian Cheng: Great. And then second is on the dose escalation work that you’re doing for 1660. How does the latest escalation to 59 mg per meter square compare to your peers who are also targeting B7-H4? Maybe you can also provide some color on the expected therapeutic window that you expect to see compared to your peers. Thank you.
Martin Huber: We want to be careful on directly comparisoning across the ADCs, for there are several differences in these molecules. I mean not only do they have different amounts of payload, we have DAR 6, but in addition, the potency of the payload is different. And then one fundamental difference is because of our scaffold, our Dolasynthen platform, with the approved drug-like properties and an antibody like half-lives, we end up having – allowing a less frequent dosing either Q3 or Q4. But what that’s associated with, and you think about it is, it’s a slower clearance of the molecule because it has an antibody like half-life. So it gets very complicated to try to do a direct detailed comparison until we get the full data disclosure, and then we can start having that conversation.
Brian Cheng: Great. Thank you, Marty. Thanks.
Operator: Thank you. This concludes our question-and-answer session. I would like to turn the conference back over to Dr. Marty Huber for any closing remarks.
Martin Huber: Thank you, operator, and thanks, everyone, for dialing in. We hope to see some of you in the next couple of weeks at ESGO as well as Cowen and at Leerink. So that concludes the call, operator. Thank you.
Operator: Thank you. The conference has now concluded. Thank you for attending today’s presentation. You may now disconnect.
