(656b) Using Bayesian Modeling and Crystallization Fundamentals to Develop a Robust Particle Size Control Strategy

Bayesian statistical modeling was used in a risk-based approach to develop a particle size control strategy of an Active Pharmaceutical Ingredient (API) made via crystallization with rotor-stator wet milling. Tight particle size control was required to obtain desired in-vitro dissolution and in-vivo performance, but earlier iterations of the API process resulted in inconsistent particle size control during bulk manufacturing. A new API process was developed by optimizing crystallization and revising the wet milling equipment selection to best suit the particle size goals. Then, using scale-down experiments representative of the manufacturing equipment train, particle size data was collected within the parameter space to build a probabilistic robustness model. The robustness model enabled calculation of probability of achieving a range of upper particle size targets, which was used to balance process capability and the desire for small particle size. The redeveloped process and updated targets were then successfully transferred the manufacturing site. In this presentation, the experimental and modeling methods, rationale, and key assumptions are discussed along with equipment selection and scale-up considerations that led to the successful technology transfer.