Automated Protocol Optimization for CAR-T Cell Manufacturing Through Bayesian Reinforcement Learning and Digital Twin Simulation
Abstract: Current CAR-T cell manufacturing protocols are complex and sensitive to variations in raw materials, equipment, and operator skill, leading to significant batch-to-batch variability and impacting therapeutic efficacy. This paper proposes a novel framework leveraging Bayesian Reinforcement Learning (BRL) integrated with a Digital Twin (DT) simulation to dynamically optimize CAR-T cell manufacturing protocols. By continuously learning from historical data and simulating the impact of process parameter adjustments, our system reduces process variability, increases cell yield and potency, and ultimately streamlines CAR-T cell production. This framework promises to significantly reduce manufacturing costs and accelerate delivery of life-saving therapies.
1. Introduction
CAR-T cell therapy represents a revolutionary advancement in cancer treatment; however, the man
