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

Developing drugs including cell and gene therapies for rare diseases presents unique challenges, primarily due to small patient populations and limited clinical data. In such settings, traditional quantitative decision-making (QDM) frameworks, which play a crucial role in guiding go/no-go decisions in proof-of-concept (PoC) studies, often lead to inconclusive decisions due to limited information. This paper is the first study presenting a Bayesian QDM framework specifically tailored to cell and gene therapy candidates in the rare disease space, with an emphasis on borrowing information from external data sources to improve the robustness of QDM. While technical components of Bayesian QDM framework have been established in the literature, our contribution lies in (1) a unified framework accommodating both controlled and uncontrolled PoC studies with hypothetical controls, (2) the systematic integration of power priors with flexible control of information borrowing, and (3) practical implementation guidance through an open-source R Shiny application. The proposed framework offers potential advantages, such as more informed decision-making based on reduced trial durations and improved resource allocation, which are critical for accelerating drug development in rare diseases. Simulation studies and a case study are conducted to illustrate the practical application of Bayesian QDM, demonstrating its benefits in early-stage clinical trials.