Xenobiotic metabolism, a ubiquitous natural response to foreign compounds, elicits initiating signals for many pathophysiological events. Currently, most widely used techniques for identifying xenobiotic metabolites and metabolic pathways are empirical and largely based on in vitro incubation assays and in vivo radiotracing experiments. Recent work in our lab has shown that LC-MS-based metabolomic techniques are useful tools for xenobiotic metabolism research since multivariate data analysis in metabolomics can significantly rationalize the processes of xenobiotic metabolite identification and metabolic pathway analysis. In this review, the technological elements of LC-MS-based metabolomics for constructing high-quality datasets and conducting comprehensive data analysis are examined. Four novel approaches of using LC-MS-based metabolomic techniques in xenobiotic metabolism research are proposed and illustrated by case studies and proof-of-concept experiments, and the perspective on their application is further discussed.
Bibliographical noteFunding Information:
We thank Kristopher Krausz and Yueying Zhen for providing debrisoquine data for multivariate data analysis. Research described in this manuscript was supported by the NCI Intramural Research Program of the NIH. JRI is grateful to US Smokeless Tobacco Company for a grant for collaborative research.
- Drug metabolism
- Multivariate data analysis
- Xenobiotic metabolism