An adaptive dose-finding method using a change-point model for molecularly targeted agents in phase I trials.

The paradigm of oncology drug development is expanding from developing cytotoxic agents to developing biological or molecularly targeted agents (MTAs). Although it is common for the efficacy and toxicity of cytotoxic agents to increase monotonically with dose escalation, the efficacy of some MTAs may exhibit non-monotonic patterns in their dose-efficacy relationships. Many adaptive dose-finding approaches in the available literature account for the non-monotonic dose-efficacy behavior by including additional model parameters. In this study, we propose a novel adaptive dose-finding approach based on binary efficacy and toxicity outcomes in phase I trials for monotherapy using an MTA. We develop a dose-efficacy model, the parameters of which are allowed to change in the vicinity of the change point of the dose level, in order to consider the non-monotonic pattern of the dose-efficacy relationship. The change point is obtained as the dose that maximizes the log-likelihood of the assumed dose-efficacy and dose-toxicity models. The dose-finding algorithm is based on the weighted Mahalanobis distance, calculated using the posterior probabilities of efficacy and toxicity outcomes. We compare the operating characteristics between the proposed and existing methods and examine the sensitivity of the proposed method by simulation studies under various scenarios.