[en] Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.
Research Center/Unit :
CIRM - Centre Interdisciplinaire de Recherche sur le Médicament - ULiège
Disciplines :
Pharmacy, pharmacology & toxicology
Author, co-author :
Kumari, Priyanka ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
Van Laethem, Thomas ; Université de Liège - ULiège > Unités de recherche interfacultaires > Centre Interdisciplinaire de Recherche sur le Médicament (CIRM)
Hubert, Philippe ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Fillet, Marianne ; Université de Liège - ULiège > Département de pharmacie > Analyse des médicaments
Sacre, Pierre-Yves ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Hubert, Cédric ; Université de Liège - ULiège > Département de pharmacie > Chimie analytique
Language :
English
Title :
Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy.
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