Adaptive designs for identifying optimal biological dose for molecularly targeted agents

Area of Science:

• Oncology
• Biostatistics
• Clinical Trial Design

Background:

• Traditional dose-finding focuses on maximum tolerated dose (MTD) using toxicity.
• Molecularly targeted agents may have limited toxicity and non-monotonic dose-response curves, challenging the 'more is better' principle.
• Finding an optimal biological dose (OBD) balancing efficacy and safety is crucial for targeted therapies.

Purpose of the Study:

• To propose and evaluate novel adaptive dose-finding designs for molecularly targeted agents.
• To identify the optimal biological dose (OBD) defined as the lowest dose with maximum efficacy and safety.
• To address challenges posed by unimodal or plateaued dose-response curves.

Main Methods:

• Developed three adaptive dose-finding designs: parametric (logistic model), nonparametric (isotonic regression), and semiparametric (local logistic model).
• Utilized extensive simulation studies to assess the operating characteristics of the proposed designs.
• Focused on binary endpoints for dose-finding and optimal biological dose identification.

Main Results:

• Simulation studies demonstrated that nonparametric and semiparametric designs exhibit good operating characteristics for finding the optimal biological dose.
• These designs effectively balance efficacy and safety, outperforming traditional MTD-based approaches for targeted agents.
• The proposed methods provide a robust framework for dose optimization in targeted cancer therapy trials.

Conclusions:

• Nonparametric and semiparametric adaptive dose-finding designs are recommended for practical application in targeted cancer therapy trials.
• These methods offer a superior approach to identifying the optimal biological dose compared to traditional toxicity-focused designs.
• Free downloadable software is available for implementing the recommended nonparametric and semiparametric designs.