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Activity Number: 272 - Statistical Innovations in Regulatory Science
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #312631
Title: A Semi-Mechanistic Dose-Finding Design in Oncology Using Pharmacokinetic/Pharmacodynamic Modeling
Author(s): Yisheng Li* and Xiao Su and Peter Müller and Kim-Anh Do
Companies: University of Texas-MD Anderson Cancer Center and PlayStation and University of Texas Austin and The University of Texas MD Anderson Cancer Center
Keywords: Area under the concentration-time curve (AUC); Dose response; Maximum tolerated dose; Pharmacologic effect; Phase I trial; Toxicity

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 incorporating dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. This modeling framework naturally incorporates the information of dose, schedule and method of administration. The resulting design is an extension of the existing designs that make use of pre-specified summary PK information (such as AUC). Our simulation studies show that, with moderate departure from the hypothesized mechanisms of the drug action, the performance of the proposed design on average improves upon those of the common designs. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable to that of the other designs. We illustrate the proposed design by applying it to a phase I trial setting for a gamma-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide an R package to implement the proposed design.

Authors who are presenting talks have a * after their name.

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