Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are critical for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics which relies on synthetic stable isotope-labeled surrogate peptides as calibrators, is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA doesn9t consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage (SC-TPA), which was applied to quantify 54 DMETs for characterization of i) differential tissue DMET abundance in the human liver, kidney, and intestine, and ii) interindividual variability of DMET proteins in individual intestinal samples (n=13). UGT2B7, MGST1, MGST2, MGST3, CES2, and MRP2 were expressed in all three tissues, whereas, as expected CYP3A4, CYP3A5, CYP2C9, CYP4F2, UGT1A1, UGT2B17, CES1, FMO5, MRP3, and P-gp were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation (IVIVE) of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic (PBPK) prediction of systemic and tissue concentration of drugs. Significance Statement We quantified the abundance and compositions of drug-metabolizing enzymes and transporters (DMETs) in pooled human liver, intestine, and kidney microsomes using an optimized sequence coverage-informed total protein approach. The quantification of DMETs revealed quantitative differences in their levels in the liver, intestine, and kidney. Further, the analysis of individual intestine samples confirmed high variability in the levels of CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2. These data are applicable for the prediction of first-pass metabolism and tissue-specific drug clearance.

In this study, to better understand the mechanisms of the profound impact of alcohol consumption on drug pharmacokinetics, efficacy, and toxicity, we characterized the alcohol-induced changes in the ensemble of drug-metabolizing enzymes and transporters (DMETs) in the human liver by performing global proteomic analysis of human liver microsomes from 94 donors. DMET protein levels were analyzed concerning alcohol consumption, smoking history, and sex using non-parametric tests, which were further strengthened by correlational analysis. To this end, we used a provisional index of alcohol exposure formulated based on the relative abundances of four marker proteins best correlating with the level of alcohol consumption. Alcohol-induced changes in the cytochrome P450 pool include significant increases in CYP2E1, CYP2B6, CYP2J2, and NADPH-cytochrome P450 reductase levels and the lowering of CYP1A2, CYP2C8, CYP2C9, CYP4A11, and cytochrome b₅. Changes in UDP-glucuronosyltransferase (UGT) abundances comprise elevated UGT1A6, UGT1A9, and UGT2A1, and reduced UGT1A3, UGT1A4, UGT2B7, UGT2B10, and UGT2B15 levels. Tobacco smokers showed elevated CYP1A2, UGT1A6, and UGT2B4 and reduced FMO3, FMO4, and FMO5 levels, while in females, CYP1A2, UGT2B17, and UGT2B15 levels were lower, and UGT2A3 and STS were higher compared to males. The alcohol-induced changes in the DMET ensemble at the protein level reported herein provide deep insights into how alcohol impacts drug and xenobiotic metabolism.