Xenobiotics are chemicals which include not only drugs but also dietary supplement, natural compounds, and environmental pollutants. Human exposure to xenobiotics is pervasive, and one might be exposed to 1-3 million xenobiotics in a human life time . These chemicals can be toxic or harmful for the organism when they dramatically disrupt the metabolism and transport of endogenous compounds in the body, such as amino acid, bile acid, lipids, and hormone. Xenobiotics can be eliminated unchanged or metabolized by metabolism enzymes in vivo. Metabolomics, one high-throughput analytical technology, can systematically profile the endogenous metabolites in biofluid, cell, and tissue, which has been widely used to identify the biomarkers for clinical disease [2,3]. It also represents an ideal tool for understanding the impact of xenobiotic exposure on the body , especially for drugs. Multivariate data analysis and LC-MS were first combined for detection of xenobiotic metabolites in 2003 . Since then, mass spectrometry-based on metabolomics has become a powerful tool to identify drug metabolites in vivo, especially for minor metabolites with the potential biological functions [6,7]. Recently, metabolomics has shown its advantage in the prediction of drug efficacy and drug induced side effects as well as personalizes medicine [8,9]. It is apparent that xenobiotic metabolomics has expanded the knowledge of drug and become a leading strategy to understand their metabolism, pharmacological activities, and side effects.
As one high-throughput analytical technology, the wide application of metabolomics is attributing to the development of analytical techniques and data analysis model. The ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-QTOFMS), gas chromatography mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) are the major analytical techniques for xenobiotic metabolomics. Multivariate data analysis, including principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projection to latent structures discriminant analysis (OPLS-DA), has been widely applied in xenobiotic metabolomics. UPLC–ESI-QTOFMS coupled with OPLSDA model are one ideal strategy for drug metabolism to identify drug metabolites and candidate endogenous metabolites. In this method, the drug treatment group and vehicle group are specifically separated in scores plot of an OPLS-DA model . The drug metabolites and increased endogenous metabolites are shown in the top of first quadrant (p(corr)>0.75) in OPLS-DA loadings S plot, whereas the decreased endogenous metabolites occur in the bottom of third quadrant (p(corr)<-0.75). These metabolites can be initially determined to be drug metabolites or endogenous metabolites based on their trending plots. The further identification of these metabolites will be performed through their MS/MS spectrum and comparison with authentic compound.
There are a number of publications reporting the application of UPLC-ESIQTOFMS coupled with OPLS-DA-based metabolomics in drug metabolism. One example is ifosfamide and cyclophosphamide, which are common chemotherapeutic agents . Interestingly, while the two drugs are isomers, only ifosfamide treatment is known to cause nephrotoxicity and neurotoxicity. Metabolomics profiling ifosfamide and cyclophosphamide metabolites (identification 23 metabolites with 5 novel metabolites) revealed that ifosfamide preferentially underwent N-dechloroethylation, the pathway yielding 2-chloroacetaldehyde, while cyclophosphamide preferentially underwent ringopening, the pathway yielding acrolein. Another example is pocainamide, a type I antiarrhythmic agent, which can cause lupus erythematosus in 25-30% of patients in clinic . Interestingly, procainamide does not induce lupus erythematosus in animal.
To explore this difference, metabolomics is conducted on the urinary metabolites from procainamide-treated humans and mice. Thirteen urinary procainamide metabolites, including nine novel metabolites, are derived from procainamide through oxidations and acylation reactions. Significant differences in N-acylation and N-oxidation of the drug between humans and mice largely account for the interspecies differences in procainamide metabolism. Significant levels of the novel N-oxide metabolites in humans might be associated with the development of procainamide-induced systemic lupus erythematosus. It is evident that observations based on the UPLC-ESIQTOFMS coupled with OPLS-DA-based metabolomics can offer clues to understanding drug metabolism and side effects. These observations have provided compelling evidence that xenobiotic metabolomics is an ideal analytical tool that can aid in understanding and revealing mechanism involved in drug metabolism. Through analysis of metabolomics, drug metabolites can be systematically determined and the potential toxic metabolites can be identified. More importantly, the biomarkers induced by drug exposure can be used to predict drug action or toxicity. In addition, inter-individual variation revealed by metabolomics can provide the import information for personalized medicine in the clinic.