Quantitative Structure-Retention Relationship Modeling of the Retention Behavior of Guanidine and Imidazoline Derivatives in Reversed-Phase Thin-Layer Chromatography

Abstract

The quantitative structure-retention relationship (QSRR) study has been performed in order to investigate the retention behavior of 16 guanidine and imidazoline derivatives in the reversed-phase thin-layer chromatography (RP-TLC) system consisting of RP-8 stationary phase and the mixture of methanol, water, and ammonia as the mobile phase. Statistical results obtained in the one-parameter model with KOWWINlog P indicated that the lipophilicity of the investigated compounds could be determined based on the respective retentions. Three different modeling methodologies such as stepwise multiple linear regression (MLR), partial least squares regression (PLS), and artificial neural networks (ANN) were used in the QSRR approach and for the selection of the most important variables that describe the behavior of the investigated compounds the best. The performance of the developed stepwise MLR-QSRR, PLS-QSRR, and ANN-QSRR models was tested by cross-validation and the external test set prediction. The validated models were compared, and the optimal QSRR model (stepwise MLR-QSRR) was selected. Besides lipophilicity (KOWWINlog P), the number of secondary (aliphatic) amines (nRNHR) among the tested compounds has the strongest influence on the retention in the examined RP-TLC system. The predictive performance of the selected QSRR model suggests its applicability for a reliable prediction of the retention behavior for the congeners.