Modelling of UPLC behaviour of acylcarnitines by quantitative structure-retention relationships

In the present work, the retention time (RT) of acylcarnitines, collected by ultra-performance liquid-chromatography after formation of butyl esters, is modelled by quantitative structure–retentionrelationship (QSRR) method. The investigated set consists of free carnitine and 46 different acylcarnitines,including the isomers commonly monitored in screening metabolic disorders. To describe the structureof (butylated) acylcarnitines, a large number of computational molecular descriptors generated by soft-ware Dragon are subjected to variable selection methods aimed at identifying a small informative subset.The QSRR model is established using two different approaches: the multi linear regression (MLR) com-bined with a genetic algorithm (GA) variable selection and the partial least square (PLS) regression afteriterative stepwise elimination (ISE) of useless descriptors. Predictive performance of both models is eval-uated using an external set consisting of 10 representative acylcarnitines, and, successively, by repeatedrandom data partitions between the calibration and prediction sets. Finally, a principal component anal-ysis (PCA) is performed on the model variables to facilitate the interpretation of the established QSRRs.A PLS model based on seven latent variables extracted from 20 molecular descriptors selected by ISEpermits to calculate/predict the retention time of acylcarnitine with accuracy better than 5%, whereas a6-dimensional model identified by GA-MLR provides a slightly worse performance.