Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial

Immunotherapy, especially checkpoint inhibitors, have transformed the treatment of cancer. Unlike chemotherapy, checkpoint inhibitors modify and enable the patient's immune system to fight cancer, thus prolonging survival. The conventional maximum tolerable dose finding designs were used for dose-finding in checkpoint inhibitors studies. These proved to be unsuitable as in the majority of checkpoint inhibitors there was no appearance of toxicity. Hence doses were selected using pharmacokinetic and pharmacodynamic modelling. However, these doses produce plasma levels of the drug, which are far higher than the levels required for its optimal action. Further increment in dose in phase 1 settings was not associated with an increment in response or survival. Considering the cost implications and scarcity of these resources probably a dose much higher than necessary is administered. The need of the hour is to define a dose beyond which in the majority of patients, there won't be an incremental benefit in cancer-related outcomes. The current challenge is that to best of our knowledge, and no statistical model exists to find the minimally effective dose of the checkpoint inhibitors. Therefore, here we propose a Bayesian design to determine the effective biological dose (EBD) for immunotherapy trials. This work is about the preparation of methodology with two scenarios, (1) EBD of checkpoint inhibitors administered as monotherapy (2) EBD of checkpoint inhibitors administered as a combined therapy.