A0932
Title: A semi-mechanistic dose-finding design in oncology using pharmacokinetic/pharmacodynamic modeling
Authors: Yisheng Li - The University of Texas MD Anderson Cancer Center (United States) [presenting]
Abstract: While a number of phase I dose-finding designs in oncology exist, the commonly used ones are either algorithmic or empirical model-based. We propose a new framework for modeling the dose-response relationship, by systematically incorporating the pharmacokinetic (PK) data collected in the trial and the hypothesized mechanism of the drug effects, via dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. The resulting design is an extension of the existing designs that make use of pre-specified summary PK information (such as the area under the concentration-time curve [AUC] or maximum serum concentration). The simulation studies show, with moderate departure from the hypothesized mechanisms of the drug action, that the performance of the proposed design on average improves upon those of the common designs, including the continual reassessment method, Bayesian optimal interval design, modified toxicity probability interval method, and a design called PKLOGIT that models the effect of AUC on toxicity. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable with that of the other designs. We illustrate the proposed design by applying it to the setting of a phase I trial of a $\gamma$-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide an R package to implement the proposed design.
