Mass spectrometry-based absolute quantitative proteomics of drug-metabolizing enzymes in human liver

Mass spectrometry-based absolute quantitative proteomics has emerged as a powerful method for accurately quantifying hepatic drug-metabolizing enzymes, which play a crucial role in drug disposition and therapeutic outcomes. Understanding the absolute drug-metabolizing enzyme protein concentrations and the associated interindividual variability in the liver, a primary organ for drug metabolism, is essential for developing predictive models for personalized pharmacotherapy. Over the past few decades, the rapid advancement of mass spectrometry-based proteomics has enabled the application of various techniques to study drug-metabolizing enzymes, significantly enhancing our understanding of their isoform composition in the liver. However, a focused review on mass spectrometry-based absolute protein quantification of human hepatic drug-metabolizing enzymes remains lacking. This review introduces commonly used strategies in mass spectrometry-based absolute protein quantification and summarizes the absolute quantities of Phase I and Phase II hepatic drug-metabolizing enzymes. It also updates the isoform compositions of cytochrome P450s and uridine diphosphate glucuronosyltransferases and explores factors contributing to variability in quantifications across studies. Additionally, we discuss the genetic and non-genetic regulations of hepatic enzyme protein expressions, as revealed by mass-spectrometry based-proteomics. Despite its potential for clinical applications, MS-based proteomics faces challenges, such as sensitivity limitation, significant inter-study varibility, cellular heterogeneity, and a lack of integration with other omics data. Future advancements in mass spectrometry-based quantitative proteomics, including single-cell proteomics, multi-omics integration, and artificial intelligence-driven data analysis, hold promise for better understanding of drug metabolizing enzymes, improving predictions of drug responses, and optimizing therapeutic outcomes for patients.