Sequential Designs for Individualized Dosing in Phase I Cancer Clinical Trials

• Activity Number: 507
• Type: Invited
• Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Xuezhou Mao* and Ken Cheung
• Companies: Sanofi-Aventis U.S. and Columbia University
• Keywords: Adaptive design ; Coherence ; Eigenvalue constraint ; Personalized medicine ; Pharmacokinetic variability ; Repeated least squares

Abstract

This research presents novel dose-finding designs that adjust for individual pharmacokinetic variability in phase I cancer clinical trials. Extending from a single compartmental model, we postulate a linear model to describe the relationship between the area under concentration-time curve, dose and predicted clearance. We propose a repeated least squares procedure that aims to sequentially determine dose according to a subject's ability of metabolizing the drug. To guarantee consistent estimation of the individualized dosing function at the end of a trial, we apply repeated least squares subject to a constraint based on an eigenvalue theory for stochastic linear regression. We empirically determine the convergence rate of eigenvalue constraint using real data from an irinotecan study in colorectal carcinoma patients, and calibrate the procedure to minimize a loss function that accounts for the dosing costs of study subjects and future patients. As compared to the traditional dosing method using a patient's body surface area, our simulation results demonstrate that our proposed procedures control the dosing cost and allow for precise estimation of the individualized dosing function.