Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry.

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (t) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure-ion intensity relationship (QSIIR) model.