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Wiley, Clinical Pharmacology & Therapeutics, 2(115), p. 221-230, 2023

DOI: 10.1002/cpt.3055

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Comparative Intra‐Subject Analysis of Gene Expression and Protein Abundance of Major and Minor Drug Metabolizing Enzymes in Healthy Human Jejunum and Liver

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This paper was not found in any repository, but could be made available legally by the author.

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Abstract

First pass metabolism by phase I and phase II enzymes in the intestines and liver is a major determinant of the oral bioavailability of many drugs. Several studies analyzed expressions of major drug‐metabolizing enzymes (DMEs), such as CYP3A4 and UGT1A1 in the human gut and liver. However, there is still a lack of knowledge regarding other DMEs (i.e., “minor” DMEs), although several clinically relevant drugs are affected by those enzymes. Moreover, there is very limited intra‐subject data on hepatic and intestinal expression levels of minor DMEs. To fill this gap of knowledge, we analyzed gene expression (quantitative real‐time polymerase chain reaction) and protein abundance (targeted proteomics) of 24 clinically relevant DMEs, that is, carboxylesterases (CES), UDP‐glucuronosyltransferases (UGT), and cytochrome P450 (CYP)‐enzymes. We performed our analysis using jejunum and liver tissue specimens from the same 11 healthy organ donors (8 men and 3 women, aged 19–60 years). Protein amounts of all investigated DMEs, with the exception of CYP4A11, were detected in human liver samples. CES2, CYP2C18, CYP3A4, and UGT2B17 protein abundance was similar or even higher in the jejunum, and all other DMEs were found in higher amounts in the liver. Significant correlations between gene expression and protein levels were observed only for 2 of 15 jejunal, but 13 of 23 hepatic DMEs. Intestinal and hepatic protein amounts only significantly correlated for CYP3A4 and UGT1A3. Our results demonstrated a notable variability between the individuals, which was even higher in the intestines than in the liver. Our intrasubject analysis of DMEs in the jejunum and liver from healthy donors, may be useful for physiologically‐based pharmacokinetic‐based modeling and prediction in order to improve efficacy and safety of oral drug therapy.