Computer Aided Design of Solvent Blends for Hybrid Cooling and Antisolvent Crystallization of Active Pharmaceutical Ingredients

Abstract

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAM b D) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

Experimental and Theoretical Screening for Green Solvents Improving Sulfamethizole Solubility

“…In general, many multicomponent liquid mixtures, such as NADES [27][28][29][30][31][32], ionic liquids [30,[33][34][35], and organic solvent mixtures [36][37][38] are considered as promising green solvents. Another reason for binary solvents research is the optimization of reactants concentrations and crystallization efficiency [39][40][41][42][43][44].…”

Simultaneous Solvent Selection and Process Design for Continuous Reaction–Extraction–Crystallization Systems

“…Solvent selection is generally challenged by its discrete nature and a required trade-off between various performance criteria. Computational approaches for solvent selection can be essential tools during the early stages of process development by identifying a list of attractive solvent candidates to be tested so that the overall experimental efforts can be minimized …”

“…A constraint on the product crystal morphology was incorporated into the optimization problem based on prior knowledge. Watson et al developed a computer-aided solvent blend design framework based on statistical associating fluid theory (SAFT)-γ Mie for combined cooling and antisolvent crystallization. The same group developed a computer-aided solvent blend design strategy for a crystallization process with a washing stage .…”

“…Computational approaches for solvent selection can be essential tools during the early stages of process development by identifying a list of attractive solvent candidates to be tested so that the overall experimental efforts can be minimized. 4 A typical sequence of processing steps during the manufacture of high value-added chemicals involves a reaction followed by extraction and crystallization as work-up steps. Solvents are often used for each of these steps.…”

The green solvent: a critical perspective

“… 5 Two very recent studies of the design and selection of solvents used in the isolation of paracetamol (Ottoboni et al 2021 ) and ibuprofen (Watson et al 2021 ) highlight the associated complexities. …”