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Article

Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis

1
Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, UK
2
Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
Academic Editor: Josef Jampilek
Int. J. Mol. Sci. 2021, 22(8), 3848; https://doi.org/10.3390/ijms22083848
Received: 26 March 2021 / Revised: 4 April 2021 / Accepted: 6 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Chemistry towards Biology)
Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters. View Full-Text
Keywords: Quantitative Structure Retention Relationships; chromatographic method development; pharmaceutical analysis Quantitative Structure Retention Relationships; chromatographic method development; pharmaceutical analysis
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MDPI and ACS Style

Szucs, R.; Brown, R.; Brunelli, C.; Heaton, J.C.; Hradski, J. Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis. Int. J. Mol. Sci. 2021, 22, 3848. https://doi.org/10.3390/ijms22083848

AMA Style

Szucs R, Brown R, Brunelli C, Heaton JC, Hradski J. Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis. International Journal of Molecular Sciences. 2021; 22(8):3848. https://doi.org/10.3390/ijms22083848

Chicago/Turabian Style

Szucs, Roman, Roland Brown, Claudio Brunelli, James C. Heaton, and Jasna Hradski. 2021. "Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis" International Journal of Molecular Sciences 22, no. 8: 3848. https://doi.org/10.3390/ijms22083848

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