(625a) Solvent Mixture Design for the Integrated Purification in Pharmaceutical Manufacturing.
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Pharmaceutical Discovery, Development and Manufacturing Forum
Development of Pharmaceutical Processes for Drug Substance and Drug Product Manufacture
Thursday, November 19, 2020 - 8:00am to 8:15am
Several solvent mixtures design approaches [6-9] are based on the computer-aided mixture/blend design (CAMbD) framework [10] and are applied to the crystallisation of active pharmaceutical ingredients. To date, most of the proposed methods consider optimising a single purification process unit (e.g., crystallisation) with fixed operating conditions (e.g., fixed temperature). Recently, this has been broadened to the consideration of temperature as a variable, to investigate hybrid anti-solvent and cooling crystallisation [9]. In this work, the scope of design is extended further and a comprehensive solvent mixture design methodology for the integrated crystallisation and isolation processes of pharmaceutical compounds is presented. The proposed integrated approach considers a combined cooling and antisolvent crystallisation process, where optimal solvent and antisolvent mixtures, their proportions in solution, and optimal process temperatures are determined simultaneously, while maximising crystal yield and reducing solvent consumption [9]. In addition, critical interlinked design decisions, such as API solubility across both crystallisation and isolation stages, as well as the miscibility of crystallisation and wash solvents, are taken into account. Furthermore, environmental, health and safety performance measures are considered in the comprehensive model, so that only safe and environmentally friendly solvents are identified.
The design method is applied to identifying high-performance solvent blends for the crystallisation and isolation of mefenamic acid, while removing an impurity, chlorobenzoic acid, from the system. This work demonstrates that optimising simultaneously several design decisions and different performance criteria can lead to significant improvements in overall process efficiency over conventional designs.
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