U-DESPE: a Bayesian Utility-based methodology for dosing regimen optimization in early-phase oncology trials based on Dose-Exposure, Safety, Pharmacodynamics, Efficacy

2025-11-21

AI Summary

Traditional “maximum tolerated dose” paradigms fail in dose optimization for novel anticancer agents (e.g., targeted therapies, immunotherapies). To address this, we propose U-DESPE—a Bayesian framework integrating pharmacokinetic, safety, pharmacodynamic, and efficacy data into an exposure–effect nonlinear mixed-effects model. Crucially, U-DESPE introduces a utility function to jointly optimize multiple endpoints via weighted trade-offs, enabling precise identification of optimal dose–regimen combinations. It supports flexible dose-escalation and confirmation cohort designs, overcoming the limitations of toxicity-centric approaches. In real-world trial applications and extensive simulation studies, U-DESPE demonstrates superior operating characteristics—enhancing the scientific rigor, robustness, and personalization of dose-selection decisions in early-phase oncology trials.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With the development of novel therapies such as molecularly targeted agents and immunotherapy, the maximum tolerated dose paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel therapies may be inadequately characterized and oncology drug dose-finding approaches should be revised. This is increasingly recognized by health authorities, notably through the Optimus project. We developed a Bayesian dose-finding design, called U-DESPE, which allows to either determine the optimal dosing regimen at the end of the dose-escalation phase, or use of dedicated cohorts for randomizing patients to candidate optimal dosing regimens after that safe dosing regimens have been found. U-DESPE design relies on a dose-exposure model built from pharmacokinetic data using non-linear mixed-effect modeling approaches. Then three models are built to assess the relationships between exposure and the probability of selected relevant endpoints on safety, efficacy, and pharmacodynamics. These models are then combined to predict the different endpoints for every candidate dosing regimens. Finally, a utility function is proposed to quantify the trade-off between these endpoints and to determine the optimal dosing regimen. We applied the proposed method on a clinical trial case study and performed an extensive simulation study to evaluate the operating characteristics of the method.

Problem

• Optimizing dosing regimens for novel oncology therapies beyond maximum tolerated dose
• Developing Bayesian methodology integrating dose-exposure-safety-efficacy-pharmacodynamics relationships
• Determining optimal dosing regimens using utility-based trade-off between endpoints

Innovation

• Bayesian utility-based dosing regimen optimization
• Combines dose-exposure-safety-efficacy-pharmacodynamics models
• Uses utility function to determine optimal dosing regimen