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

Abstract

ABSTRACTBackgroundLate-stage cancer immunotherapy trials strive to demonstrate the clinical efficacy of novel immunotherapies, which is leading to exceptional responses and long-term survival in subsets of patients. To establish the clinical efficacy of an immunotherapy, it is critical to adjust the trial’s design to the expected immunotherapy-specific response patterns.MethodsIn silico cancer immunotherapy trials are virtual clinical trials that simulate the kinetics and outcome of immunotherapy depending on the type and treatment schedule. We used an ordinary differential equation model to simulate (1) cellular interactions within the tumor microenvironment, (2) translates these into disease courses in patients, and (3) assemble populations of virtual patients to simulate in silico late-stage immunotherapy, chemotherapy, or combination trials. We predict trial outcomes and investigate how therapy-specific response patterns affect the probability of their success.ResultsIn silico cancer immunotherapy trials reveal that immunotherapy-derived survival kinetics – such as delayed curve separation and plateauing curve of the treatment arm – arise naturally due to biological interactions in the tumor microenvironment. In silico clinical trials are capable of translating these biological interactions into survival kinetics. Considering four aspects of clinical trial design – sample size calculations, endpoint and randomization rate selection, and interim analysis planning – we illustrate that failing to consider such distinctive response patterns can significantly reduce the power of novel immunotherapy trials.ConclusionIn silico trials have three significant implications for immuno-oncology. First, they provide an economical approach to verify the robustness of biological assumptions underlying an immunotherapy trial and help to scrutinize its design. Second, the biological basis of these trials facilitates and encourages communication between biomedical researchers, doctors, and trialists. Third, its application as an educational tool can illustrate design principles to scientists in training, contributing to improved designs and higher success rates of future immunotherapy trials.