Bayesian quantitative decision-making framework in cell and gene therapy development for rare diseases

Area of Science:

• Drug Development
• Biostatistics
• Rare Diseases

Background:

• Developing cell and gene therapies for rare diseases faces challenges due to small patient populations and limited clinical data.
• Traditional quantitative decision-making (QDM) frameworks often yield inconclusive results in early-stage proof-of-concept (PoC) studies for rare diseases.

Purpose of the Study:

• To present the first Bayesian QDM framework specifically designed for rare disease cell and gene therapy candidates.
• To improve the robustness of QDM by systematically integrating external data sources.

Main Methods:

• Developed a unified Bayesian QDM framework for controlled and uncontrolled PoC studies, utilizing hypothetical controls.
• Integrated power priors with flexible information borrowing capabilities.
• Provided practical implementation guidance via an open-source R Shiny application.

Main Results:

• The proposed Bayesian QDM framework enables more informed decision-making in early-stage clinical trials.
• Demonstrated potential for reduced trial durations and improved resource allocation.
• Simulation and case studies illustrated the framework's practical application and benefits.

Conclusions:

• The Bayesian QDM framework offers a significant advancement for rare disease drug development, particularly for cell and gene therapies.
• Enhances decision-making robustness by leveraging external data and flexible information borrowing.
• Facilitates accelerated development of critical therapies for rare diseases.