Population balance model enabled digital design and uncertainty analysis framework for continuous crystallization of pharmaceuticals using an automated platform with full recycle and minimal material use

Abstract

A systematic digital design framework for the development of a digital twin of a continuous crystallization process was presented using the model compound, diphenhydramine hydrochloride (DPH). The key features of the framework include operating space investigations, kinetic parameter estimation using population balance modeling, and systematic batch-to-continuous crystallization translation of the estimated parameters. An approach that compares experimental uncertainties with model-prediction uncertainties was used to justify the robustness of the parameter estimation procedure. For continuous crystallization development, a continuous configuration with full recycle and dissolution was developed, which enabled rapid process design with minimal material use. The results demonstrated successful model development for both batch and mixed-suspension-mixed-product-removal (MSMPR) crystallization configurations. This experimentally validated model, along with quantified uncertainty space of the kinetic parameters, serves as a digital twin of the process, which was later used to optimize the multistage MSMPR process to produce larger crystals with narrower distributions.