Data-Driven Modeling for the Enhanced Understanding of the Crystallization of an Active Pharmaceutical Ingredient

In this study, we developed a data-rich experimentation workflow that can be used to characterize and model the dynamic behavior of an antisolvent crystallization on an active pharmaceutical ingredient (API). First, automated, parallel experiment technology and empirical models are used to describe the API solubility as a function of temperature and solvent composition. Next, an efficient protocol that leverages laboratory automation and process analytical technology (PAT) was used to generate an accurate chemometric model that quantifies API supernatant concentration with in-situ FTIR spectroscopy. Using a 2^2 full factorial design and data-rich experimentation (DRE), supernatant concentration profiles were obtained to characterize the impact of crystallization temperature and antisolvent charge rate on the isolation procedure. 

These multivariate, time series data profiles were subsequently modeled using Dynamic Response Surface Methodology (DRSM). This provides a comprehensive understanding of the sensitivity of the operation and highlights how the combination of DRE and process modeling can lead to valuable process knowledge for optimization and control strategies for crystallization in pharmaceutical manufacturing.