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Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model
Citation
Wen, Y and Talebi, M and Amos, RIJ and Szucs, R and Dolan, JW and Pohl, CA and Haddad, PR, Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model, Journal of Chromatography A, 1541 pp. 1-11. ISSN 0021-9673 (2018) [Refereed Article]
Copyright Statement
Crown Copyright © 2018
DOI: doi:10.1016/j.chroma.2018.01.053
Abstract
Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η' H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.
Item Details
Item Type: | Refereed Article |
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Keywords: | quantitative structure-retention relationships, QSRR, RPLC, hydrophobic subtraction model, retention prediction |
Research Division: | Chemical Sciences |
Research Group: | Analytical chemistry |
Research Field: | Separation science |
Objective Division: | Expanding Knowledge |
Objective Group: | Expanding knowledge |
Objective Field: | Expanding knowledge in the chemical sciences |
UTAS Author: | Wen, Y (Mr Yabin Wen) |
UTAS Author: | Talebi, M (Dr Mohammad Talebi) |
UTAS Author: | Amos, RIJ (Dr Ruth Amos) |
UTAS Author: | Haddad, PR (Professor Paul Haddad) |
ID Code: | 124439 |
Year Published: | 2018 |
Funding Support: | Australian Research Council (LP120200700) |
Web of Science® Times Cited: | 35 |
Deposited By: | Austn Centre for Research in Separation Science |
Deposited On: | 2018-02-22 |
Last Modified: | 2022-08-22 |
Downloads: | 0 |
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