A Bayesian Adaptive Design in Cancer Phase I Trials Using Dose Combinations with Quasi-Continuous Toxicity Index

• Activity Number: 355 - Contributed Poster Presentations: Biopharmaceutical Section
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Sungjin Kim* and Zahra Razaee and Andre Rogatko and Mourad Tighiouart
• Companies: Cedars-Sinai Medical Center and Cedars-Sinai Medical Center and Cedars-Sinai Medical Center and Cedars-Sinai Medical Center
• Keywords: Cancer Phase I trials; Maximum tolerated dose; Continual Reassessment Method; Escalation With Overdose Control; Drug combination; Toxicity index

Abstract

We propose a Bayesian adaptive design for early phase drug combination cancer trials that incorporates all toxicity grades information for each patient by quantitatively measuring the severity of a patient’s toxicity response using the toxicity index (TI). Parametric models are used to describe the relationship between the dose combinations and the probability of dose limiting toxicity (DLT). A quasi-Bernoulli likelihood approach is used to account for the contribution of the toxicity index when estimating the risk of DLT. We investigate several algorithms to design the trial that are based on the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC). At the end of the trial, we estimate the MTD combinations as a function of Bayes estimates of the model parameters. We evaluate design operating characteristics in terms of safety of the trial and percent of MTD recommendation by comparing this design to the one that uses a binary indicator of dose limiting toxicity (DLT).