Bayesian Hierarchical Modeling of Dose, Exposure, and Response for Dose Justification in Phase I/II Combination Trials

• Activity Number: 86
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Siyan Xu* and Yu-Yun Ho and Jinnie Ko and Astrid Jullion
• Companies: Novartis Oncology and Novartis Oncology and Novartis Oncology and Novartis Pharma AG/Novartis Oncology
• Keywords: Dose Justification ; Bayesian Hierarchical model ; Dose-Exposure relationship ; Exposure-Response relationship ; Pharmacokinetic

Abstract

In recent years, Health Authorities have put increasing emphasis on the justification of the dose or dose combination used in clinical trials supporting a Marketing Authorization Application. The frequently used rule-based or model-based dose escalation designs rely on dose-response assumption. Although the pharmacokinetic (PK) data is considered in determining the next cohort dose, it is often used subjectively; systematic approach of incorporating PK data in dose escalation is still lacking. We propose a Bayesian hierarchical model which integrates dose, exposure and response into one model in a combination trial setting. Data from previous single agent trials as well as PK exposure from physiologically based simulation which considers drug-drug interaction between two compounds are used in conjunction with the current trial data to inform decision on dose. The proposed method can be used in a dose escalation trial to determine the next cohort dose combination or can be used after completion of dose escalation to recommend phase II dose. We demonstrate the method through a completed dose escalation trial and evaluate operational characteristics of the model through simulations.