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372 – Contributed Oral Poster Presentations: Biopharmaceutical Section

Dose-Finding for Drug Combination in Early Cancer Phase I Trials in the Presence of a Baseline Binary Covariate Using Conditional Escalation with Overdose Control

at Cedars Sinai Medical Center

We present a Bayesian adaptive design for dose finding of a combination of two drugs in cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose (MTD) as a curve for continuous dose levels of the two agents for patient's specific baseline covariate value. Parametric models are used to describe the relationship between the doses, baseline covariate, and the probability of dose limiting toxicity (DLT). Trial design proceeds using univariate escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current dose of the other agent and the next patient's baseline covariate value. At the end of the trial, we estimate MTD curves as functions of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of dose recommendation at dose combination neighborhoods around the true MTD by comparing the design that uses the covariate to the one that ignores the baseline characteristic.