Herein, a population balance model (PBM) for a combined cooling and antisolvent crystallization process for an active pharmaceutical ingredient (API) has been developed and utilized to predict the product particle size distribution (PSD) for two sets of four ∼35 kg scale plant batches, with good agreement to data. The PBM was constructed from lab-scale (∼10 g) crystallization runs using seed and product PSD measurements along with concentration measurements of the API during batch desupersaturation experiments. The PBM was then used to predict the product PSD for two sets of four plant batches, run using different reactors equipped with different agitator types operated at different agitation rates. Analysis of the crystallization kinetics reveals that secondary nucleation due to attrition has a strong influence on the PSD in the crystallization process of the API, and thus mixing conditions (agitator type, agitator speed, pumping, and power numbers) have a strong effect on PSD. The model provides a more robust particle size control strategy than design of experiment (DOE) studies alone by incorporating fundamental crystallization kinetics, with data from a small set of lab experiments in lieu of extensive DOE studies. This first-principle-based approach was useful for enhancing the robustness of the technical transfer process by accounting for impacts on product PSD stemming from process scale-up and parameter changes.

• Bristol-Myers Squibb (BMS)
• PSE Ltd.

• Modeling

1. crystallization
2. modeling
3. particle size prediction
4. population balance modeling
5. process modeling
6. Research Article
7. Scale-up
8. secondary nucleation