Pharmacometrics-Enabled DOse OPtimization (PEDOOP) for Seamless Phase I-II Trials in Oncology

TL;DR

The paper introduces PEDOOP, a novel dose-optimization design for early-phase oncology trials that integrates pharmacokinetics and pharmacodynamics data to improve dose selection and transition to late-phase development.

Contribution

PEDOOP is a new seamless phase I-II trial design that incorporates patient-level PK/PD data for more accurate dose optimization in oncology.

Findings

PEDOOP outperforms existing seamless designs in simulation studies.

It effectively integrates PK/PD data for dose escalation and selection.

The design facilitates seamless transition from phase I to phase II.

Abstract

We consider a dose-optimization design for first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design, incorporates observed patient-level pharmacokinetics (PK) measurements and latent pharmacodynamics (PD) information for trial decision making and dose optimization. PEDOOP consists of two seamless phases. In phase I, patient-level time-course drug concentrations, derived PD effects, and the toxicity outcomes from patients are integrated into a statistical model to estimate the dose-toxicity response. A simple dose-finding design guides dose escalation in phase I. At the end of the phase I dose finding, a graduation rule is used to assess the safety and efficacy of all the doses and select those with promising efficacy and acceptable safety for a randomized comparison against a control arm in phase II. In phase II, patients are randomized to the selected doses based on a fixed or adaptive randomization ratio. At the end of phase II, an optimal biological dose (OBD) is selected for late-phase development. We conduct simulation studies to assess the PEDOOP design in comparison to an existing seamless design that also combines phases I and II in a single trial.