A novel Bayesian decision procedure for early-phase dose-finding studies

Phase I first-in-man studies in normal, healthy volunteers are performed to define a maximum safe dose and to identify a range of acceptable doses for later drug development studies in patients. Analysis of pharmacokinetic and pharmacodynamic data using mixed-effects modeling can be used to fit an overall dose-response relationship. By expressing prior information as pseudodata, the same methodology can be used to perform a Bayesian analysis and to determine posterior modal estimates for the model parameters. Decision theory can then be applied to maximize a chosen gain function, utilizing real-time data capture for choosing safe doses in a way that will provide more informative responses, thus accelerating study completion. The methodology is introduced elsewhere (1). The purpose of this paper is to describe software currently in development and to illustrate the method using an example from a recent study.