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User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using...

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Autores principales: Van Laethem, Thomas, Kumari, Priyanka, Boulanger, Bruno, Hubert, Philippe, Fillet, Marianne, Sacré, Pierre-Yves, Hubert, Cédric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735675/
https://www.ncbi.nlm.nih.gov/pubmed/36500399
http://dx.doi.org/10.3390/molecules27238306
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author Van Laethem, Thomas
Kumari, Priyanka
Boulanger, Bruno
Hubert, Philippe
Fillet, Marianne
Sacré, Pierre-Yves
Hubert, Cédric
author_facet Van Laethem, Thomas
Kumari, Priyanka
Boulanger, Bruno
Hubert, Philippe
Fillet, Marianne
Sacré, Pierre-Yves
Hubert, Cédric
author_sort Van Laethem, Thomas
collection PubMed
description In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure–retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user’s decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.
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spelling pubmed-97356752022-12-11 User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules Van Laethem, Thomas Kumari, Priyanka Boulanger, Bruno Hubert, Philippe Fillet, Marianne Sacré, Pierre-Yves Hubert, Cédric Molecules Article In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure–retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user’s decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps. MDPI 2022-11-28 /pmc/articles/PMC9735675/ /pubmed/36500399 http://dx.doi.org/10.3390/molecules27238306 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Van Laethem, Thomas
Kumari, Priyanka
Boulanger, Bruno
Hubert, Philippe
Fillet, Marianne
Sacré, Pierre-Yves
Hubert, Cédric
User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title_full User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title_fullStr User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title_full_unstemmed User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title_short User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules
title_sort user-driven strategy for in silico screening of reversed-phase liquid chromatography conditions for known pharmaceutical-related small molecules
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735675/
https://www.ncbi.nlm.nih.gov/pubmed/36500399
http://dx.doi.org/10.3390/molecules27238306
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