Developing predictive models for batch cooling crystallization of APIs with limited data availability

The objective of this work is to investigate strategies for the calibration of crystallization models aimed at predicting particle size distributions (PSDs) of active pharmaceutical ingredients (APIs) when using industrial datasets, which are limited in terms of number or information for the modeling exercise. In this work, the calibration task relies on two kinds of measurements, commonly performed in industrial crystallization practice: offline measurements of PSDs and API solute concentration carried out only at the beginning and at the end of experiments, and online measurements of chord length distributions (CLDs). Particularly, a strategy is proposed to use CLDs data from focused beam reflectance measurement (FBRM) probes as proxies of the PSD, which is the main key performance indicator for the model exercise. Industrial data concerning a seeded batch cooling recrystallization of an API in an organic solvent are used as a case study. The PharmaPy process simulator is used for parameter estimation and process simulation. Results demonstrate that, with proper data processing and feature extraction, all parameters can be estimated with sufficient precision. The model performance is satisfactory for most of the batch duration, even though some shortcomings highlight possible limitations in the data and/or in the model itself. From the industrial perspective, results pave the way for a quantitative usage of FBRM probes to enhance process understanding and to guide process development and scale-up.