Quantitative structure-retention relationships of cannabimimetic aminoalkilindole derivatives and their metabolites

Development of chromatographic analyses of synthetic cannabinoids is complicated by the lack of commercial reference standards, especially for new analogues introduced in the clandestine market to bypass legal controls and for their metabolites. In the present work, we explore the possibility of predicting the retention behaviour of the cannabimimetic aminoalkilindoles and their urinary metabolites in high-performance liquid-chromatography using a quantitative structure-retention relationship (QSRR) generated by multilinear regression. To represent the structure of the 43 investigated analytes, 617 computational molecular descriptors are subjected to genetic algorithm variable selection aimed at identifying a small but informative subset. Predictive performance of the QSRR model is evaluated on an external set consisting of 10 representative compounds, including both drugs and their metabolites, and, successively by a Monte Carlo validation method. The best QSRR model, based on six molecular descriptors, exhibits a promising predictive performance and robustness.