Computer-Aided Solvent Selection for the Design of Integrated Synthesis, Crystallization, and Isolation Processes.
5 affiliations
- 1Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K.
- 2Lilly Research Laboratories, Indianapolis, Indiana 46082, United States.
- 3Department of Chemical and Process Engineering, University of Strathclyde, Glasgow G1 1XQ, U.K.
- 4EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation, University of Strathclyde, Glasgow G1 1RD, U.K.
- 5Department of Chemistry and Institute for Molecular Science and Engineering, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London W12 0BZ, U.K.
Abstract
The extensive use of solvents in pharmaceutical manufacturing has major impacts on overall process performance and final product quality. Solvent selection often requires tedious and resource-intensive experimental investigations. Nevertheless, the growing interest in embedding sustainable practices in process development workflows has motivated pharmaceutical manufacturers and researchers to deploy digital tools for enhancing process understanding and guiding the design of greener materials and processes. In this work, a computer-aided mixture/blend design (CAM b D) methodology is developed to identify simultaneously the optimal solvent or solvent/antisolvent mixtures, mixture compositions, and process conditions in end-to-end processes, considering integrated synthesis, crystallization, and isolation. Within the proposed approach, thermodynamic models are used to couple property prediction with process modeling to describe pharmaceutical process systems, while mathematical optimization techniques are applied to search the large design space of potential solvents and process conditions and identify the most promising designs among thousands of possible options. Further consideration is also given to the integrated design of reaction, crystallization, and wash solvents, where design decisions across the entire process are interlinked. The methodology is applied to the synthesis, crystallization, and isolation of mefenamic acid as a case study, while considering a broad range of process-wide key performance indicators (KPIs) that quantify product quality and resource efficiency, such as product purity and process energy consumption. In order to explore the trade-offs between competing KPIs, the proposed approach is extended to solve multiobjective design problems and generate a list of Pareto-optimal designs. The results highlight that the optimal designs are strongly dependent on the criteria considered and motivate the need for an integrated approach to solvent selection and process design. The findings of this study are expected to contribute to the digitalization of solvent selection for the accelerated development of sustainable pharmaceutical processes.
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BibTeX
@article{muhieddine2026computeraided,
title = {{Computer-Aided Solvent Selection for the Design of Integrated Synthesis, Crystallization, and Isolation Processes.}},
author = {Mohamad H Muhieddine and Suela Jonuzaj and Oliver Watson and Sara Ottoboni and Jan Sefcik and Chris J Price and Shekhar K Viswanath and Alan Armstrong and Amparo Galindo and George Jackson and Claire S Adjiman},
journal = {Organic process research \& development},
year = {2026},
month = sep,
doi = {10.1021/acs.oprd.6c00168},
url = {https://europepmc.org/article/MED/42921585},
note = {CC BY},
}
RIS
TY - JOUR
TI - Computer-Aided Solvent Selection for the Design of Integrated Synthesis, Crystallization, and Isolation Processes.
AU - Mohamad H Muhieddine
AU - Suela Jonuzaj
AU - Oliver Watson
AU - Sara Ottoboni
AU - Jan Sefcik
AU - Chris J Price
AU - Shekhar K Viswanath
AU - Alan Armstrong
AU - Amparo Galindo
AU - George Jackson
AU - Claire S Adjiman
PY - 2026
DA - 2026/09/24
JO - Organic process research & development
DO - 10.1021/acs.oprd.6c00168
UR - https://europepmc.org/article/MED/42921585
AB - The extensive use of solvents in pharmaceutical manufacturing has major impacts on overall process performance and final product quality. Solvent selection often requires tedious and resource-intensive experimental investigations. Nevertheless, the growing interest in embedding sustainable practices in process development workflows has motivated pharmaceutical manufacturers and researchers to deploy digital tools for enhancing process understanding and guiding the design of greener materials and processes. In this work, a computer-aided mixture/blend design (CAM b D) methodology is developed to identify simultaneously the optimal solvent or solvent/antisolvent mixtures, mixture compositions, and process conditions in end-to-end processes, considering integrated synthesis, crystallization, and isolation. Within the proposed approach, thermodynamic models are used to couple property prediction with process modeling to describe pharmaceutical process systems, while mathematical optimization techniques are applied to search the large design space of potential solvents and process conditions and identify the most promising designs among thousands of possible options. Further consideration is also given to the integrated design of reaction, crystallization, and wash solvents, where design decisions across the entire process are interlinked. The methodology is applied to the synthesis, crystallization, and isolation of mefenamic acid as a case study, while considering a broad range of process-wide key performance indicators (KPIs) that quantify product quality and resource efficiency, such as product purity and process energy consumption. In order to explore the trade-offs between competing KPIs, the proposed approach is extended to solve multiobjective design problems and generate a list of Pareto-optimal designs. The results highlight that the optimal designs are strongly dependent on the criteria considered and motivate the need for an integrated approach to solvent selection and process design. The findings of this study are expected to contribute to the digitalization of solvent selection for the accelerated development of sustainable pharmaceutical processes.
SN - 1083-6160
SN - 1520-586X
ER -