Liquid chromatography - Mass spectrometric analysis of urinary metabolites and their pattern recognition for the prediction of drug-induced hepatotoxicity

Multivariate pattern recognition (PR) analysis combined with LC/MS was utilized to evaluate the feasibility of predicting chemical-induced toxicity in rats. Urine samples were collected from rats treated with vehicles or four hepatotoxins, alpha-naphthyl isothiocyanate (ANIT), carbontetrachloride (CCl4), acetaminophen, and diclofenac, and analyzed by HPLC coupled with electrospray mass spectrometry. Chromatographic data were normalized using modified Z score transformation with those of the control group, to remove the vehicle effects for a further enhanced multivariate analysis. The LC/MS-based profiles of the urine samples showed different levels of endogenous metabolites, which were characteristic of each hepatotoxin. In the principal component (PC) map of the urinary spectra from rats treated with ANIT, the metabolic trajectory moved away from the predose position, reaching a maximum separation at the 32-48 h time period. The metabolic profiles partially recovered to the basal conditions on 7 days postdose. A principal component analysis was performed on the urinary spectra of rats treated with the vehicles or four hepatotoxins. Each group formed a distinct and isolated cluster in the PC map, indicating drug-induced perturbation in the urine profiles. To construct mathematical models for predicting drug-induced hepatotoxicity, supervised analyses, such as linear discriminant analysis and soft independent modeling of class analogy with residual distance (SIMCA-RD), were performed. The SIMCA-RD showed high predictability, over 95%, in the results of cross-validation using the leaving-one-out method. The developed LC/MS-PR approach might be a useful tool for the prediction of drug-induced hepatotoxicity and for the understanding of hepatotoxic mechanisms.