Phase I Dose Escalation trials in cancer immunotherapy: Modifying the Bayesian Logistic Regression Model for Cytokine Release Syndrome

Abstract

AbstractWe extend Bayesian Logistic Regression to model the dose-toxicity relationship in the setting of phase I dose-escalation/ dose-finding trials for cancer immunotherapies. Immunotherapy drugs are associated with Cytokine Release Syn-drome, a systemic immune system reaction that can be mitigated when initial lower doses of the drug are administered to generate immune tolerance. This changes the classic dose-finding problem of determining an optimal safe dose, to a more complex problem where the search is for both the optimal safe dose and the dose regimen that allows patients to quickly and safely reach that dose without CRS. As part of solving this methodological challenge, we show how to jointly model CRS and non-CRS toxicities, which have distinct mechanisms, while controlling for the overall toxicity rate to make dose-escalation decisions.