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A Novel Integrated Workflow for Isolation Solvent Selection Using Prediction and Modeling
[Image: see text] A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally select...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American
Chemical Society
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289338/ https://www.ncbi.nlm.nih.gov/pubmed/34295140 http://dx.doi.org/10.1021/acs.oprd.0c00532 |
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author | Ottoboni, Sara Wareham, Bruce Vassileiou, Antony Robertson, Murray Brown, Cameron J. Johnston, Blair Price, Chris J. |
author_facet | Ottoboni, Sara Wareham, Bruce Vassileiou, Antony Robertson, Murray Brown, Cameron J. Johnston, Blair Price, Chris J. |
author_sort | Ottoboni, Sara |
collection | PubMed |
description | [Image: see text] A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying. |
format | Online Article Text |
id | pubmed-8289338 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American
Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-82893382021-07-20 A Novel Integrated Workflow for Isolation Solvent Selection Using Prediction and Modeling Ottoboni, Sara Wareham, Bruce Vassileiou, Antony Robertson, Murray Brown, Cameron J. Johnston, Blair Price, Chris J. Org Process Res Dev [Image: see text] A predictive tool was developed to aid process design and to rationally select optimal solvents for isolation of active pharmaceutical ingredients. The objective was to minimize the experimental work required to design a purification process by (i) starting from a rationally selected crystallization solvent based on maximizing yield and minimizing solvent consumption (with the constraint of maintaining a suspension density which allows crystal suspension); (ii) for the crystallization solvent identified from step 1, a list of potential isolation solvents (selected based on a series of constraints) is ranked, based on thermodynamic consideration of yield and predicted purity using a mass balance model; and (iii) the most promising of the predicted combinations is verified experimentally, and the process conditions are adjusted to maximize impurity removal and maximize yield, taking into account mass transport and kinetic considerations. Here, we present a solvent selection workflow based on logical solvent ranking supported by solubility predictions, coupled with digital tools to transfer material property information between operations to predict the optimal purification strategy. This approach addresses isolation, preserving the particle attributes generated during crystallization, taking account of the risks of product precipitation and particle dissolution during washing, and the selection of solvents, which are favorable for drying. American Chemical Society 2021-05-05 2021-05-21 /pmc/articles/PMC8289338/ /pubmed/34295140 http://dx.doi.org/10.1021/acs.oprd.0c00532 Text en © 2021 The Authors. Published by American Chemical Society Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Ottoboni, Sara Wareham, Bruce Vassileiou, Antony Robertson, Murray Brown, Cameron J. Johnston, Blair Price, Chris J. A Novel Integrated Workflow for Isolation Solvent Selection Using Prediction and Modeling |
title | A Novel Integrated Workflow for Isolation Solvent
Selection Using Prediction and Modeling |
title_full | A Novel Integrated Workflow for Isolation Solvent
Selection Using Prediction and Modeling |
title_fullStr | A Novel Integrated Workflow for Isolation Solvent
Selection Using Prediction and Modeling |
title_full_unstemmed | A Novel Integrated Workflow for Isolation Solvent
Selection Using Prediction and Modeling |
title_short | A Novel Integrated Workflow for Isolation Solvent
Selection Using Prediction and Modeling |
title_sort | novel integrated workflow for isolation solvent
selection using prediction and modeling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289338/ https://www.ncbi.nlm.nih.gov/pubmed/34295140 http://dx.doi.org/10.1021/acs.oprd.0c00532 |
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