Crystal-size distribution-based dynamic process modelling, optimization, and scaling for seeded batch cooling crystallization of Active Pharmaceutical Ingredients (API)

Abstract

Crystallization of active pharmaceutical ingredients (API) is one of the most important and complex multiphase engineering operations in pharmaceutical manufacturing industry. The desired physicochemical properties of solid crystalline product, such as crystal size distribution (CSD), are achieved by optimizing appropriate analyzed process operating conditions. In this application-derived study, an efficient straightforward mathematical modelling approach for the d value targeted CSD (characterized by 10th, 50th, and 90th centiles d10, d50, and d90) optimization of the API fesoterodine fumarate (FF) batches in different solvent mixtures on reactor dimension scales from 0.1 to 15 L is presented. The model is based on energy, mass, and population balance equations, the thermodynamic system equilibrium between solute/solution, and the kinetics of nucleation, crystal growth, and crystal agglomeration. In the first set of two experiments, the ability of the model to predict final CSD under chosen operating conditions was validated applying particular previously estimated kinetic parameters. An excellent statistical agreement between predicted and experimental CSD results was observed. Furthermore, the utility of the model to determine suitable operating conditions for the formation of FF crystals with d value defined CSD is presented. Two additional experiments were designed where stirring, cooling rate, and the amount of seed were optimally regressed. Good agreement between targeted and experimental CSD was shown and depending on the chosen vessel unit, mixing & cooling rates had the strongest relative impact on CSD. Algorithm may be beneficially utilized early during API industrial technological development, intensification, and scale-up, or transferred to continuous flow.