In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Jeroen H. A. Creemers, Ankur Ankan, Kit C. B. Roes, Gijs Schröder, Niven Mehra, Carl G. Figdor, I. Jolanda M. Vries and Johannes Textor ()
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Jeroen H. A. Creemers: Radboud university medical center
Ankur Ankan: Radboud University
Kit C. B. Roes: Radboud university medical center
Gijs Schröder: Radboud University
Niven Mehra: Radboud university medical center
Carl G. Figdor: Radboud university medical center
I. Jolanda M. Vries: Radboud university medical center
Johannes Textor: Radboud university medical center

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials – simulated trials based on three different mathematical models – to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design – sample size, endpoint, randomization rate, and interim analyses – we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.

Date: 2023
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DOI: 10.1038/s41467-023-37933-8

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