Quantitative Dose Optimization for Gene Therapy Trials: Implications for Project Optimus and Early-Phase Design

Area of Science:

• Pharmacology and Clinical Trial Design
• Biopharmaceutical Development
• Regulatory Science

Background:

• Quantitative dose optimization is increasingly used in early-phase clinical trials for new drugs.
• The FDA's Project Optimus Initiative promotes dose optimization over maximum tolerated dose (MTD).
• Gene and cell therapies present unique challenges for traditional dose-finding methods.

Purpose of the Study:

• To describe the current state of quantitative dose optimization for cell and gene therapies.
• To identify challenges and limitations of existing methods in this context.
• To present an adaptable Bayesian framework for dose optimization in early-phase trials.

Main Methods:

• Review of current quantitative dose optimization strategies.
• Adaptation of a Bayesian logistic regression framework for toxicity class distinction.
• Illustrative tutorial example for safety-constrained decision-making in sparse data settings.

Main Results:

• Established Bayesian logistic regression can distinguish clinically distinct toxicity classes.
• The framework demonstrates safety-constrained decision logic in early-phase trials.
• The approach is a component of broader dose optimization strategies.

Conclusions:

• Quantitative dose optimization frameworks are advancing for gene therapies.
• Bayesian methods offer a viable approach to characterize safety and efficacy.
• Further integration with pharmacodynamic and efficacy data is recommended for comprehensive optimization.