Using MCP-MOD in dose finding for Phase 3

Dear all, I am writing to you as we are currently discussing the implementation of the MCP-MOD approach for dose finding based on Phase 2B results and would like to hear your opinion on this approach. It would be good to get feedback from both statisticians and classical modelers. I have thought about the approach, and have a few problems about seeing the advantage of the approach over complete population-PK/PD modeling. From what I understood, I can see the following issues: MCP-MOD · Only uses trial endpoints, i.e. it ignores the time course of the treatment effect. I have a problem with this because there might be noise in the endpoint (e.g. if the effect has reached a plateau), which might potentially lead to the selection of the wrong model structure. Including the time-course like in PKPD modeling approaches would detect that the deviation is just noise, and thus probably be able to identify the right model structure despite this. · Uses dose-response models instead of exposure-response models · Pre-specifies the model structure. While I understand that for pivotal trials prespecification is crucial, I would assume that Phase 2 is performed to allow exploration of the data to come up with the best model given the data we have. What happens if the true model is not part of the tested ones? What if we have new physiological insights that tell us about the model structure after we have seen the data? Do we then ignore what we know and fit all bad models, and if none gives a good description we do model averaging of bad models? · If we include a model with many parameters in the prespecification and only have a few dose strength, wouldn't the model with more parameters be more likely to give a good fit (e.g. when comparing Emax to logistic), with the consequence that a wrong dose might be selected? Colleagues from statistics recommend to cover all potential models with different shapes in the candidate set to avoid potential bias in dose selection, but they argue that post-hoc model fitting leads to data-dredging and over-fitting, does not account for model uncertainty and gives overly-optimistic results. I am wondering however what the difference in the approach is if anyway ALL potential models are considered (which can lead to overfitting as well)? Might a good solution be to combine PKPD modeling with MCP-Mod? Your opinion will be highly appreciated, and I am looking forward to receiving comments both in favour and against the approach :-) Best Nele ______________________________________________________________ Dr. Nele Mueller-Plock, CAPM Associate Scientific Director Pharmacometrics Global Pharmacometrics Translational Medicine Takeda Pharmaceuticals International GmbH Thurgauerstrasse 130 8152 Glattpark-Opfikon (Zürich) Switzerland Visitor address: Alpenstrasse 3 8152 Glattpark-Opfikon (Zürich) Switzerland Phone: (+41) 44 / 55 51 404 Mobile: (+41) 79 / 654 33 99 mailto: [email protected]<mailto:[email protected]> http://www.takeda.com/ -------------------------------------------------------------------- The content of this email and of any files transmitted may contain confidential, proprietary or legally privileged information and is intended solely for the use of the person/s or entity/ies to whom it is addressed. If you have received this email in error you have no permission whatsoever to use, copy, disclose or forward all or any of its contents. Please immediately notify the sender and thereafter delete this email and any attachments.