Quantitative Proteomics of Clinically Relevant Drug-Metabolizing Enzymes and Drug Transporters and Their Inter-correlations in the Human Small Intestine

2020 
The levels of drug metabolizing enzymes (DMEs) and transporter proteins in the human intestine are pertinent to determine oral drug bioavailability. Despite the paucity of reports on such measurements, it is well recognised that these values are essential for translating in vitro data on drug metabolism and transport to predict drug disposition in gut wall. In the current study, clinically relevant DMEs (cytochrome P450 (CYP) and uridine 59-diphospho-glucuronosyltransferase (UGT)) and drug transporters were quantified in crude membrane preparations from the human jejunum (n = 4) and ileum (n = 12) using QconCAT-based targeted proteomics. In contrast to previous reports, UGT2B15 and OATP1A2 were quantifiable in all our samples. Overall, no significant disparities in protein expression were observed between jejunum and ileum. Relative mRNA expression for drug transporters did not correlate with the abundance of their cognate protein except for P-gp and OST-α, highlighting the limitations of RNA as a surrogate for protein expression in dynamic tissues with high turnover. Inter-correlations were found within CYP (2C9-2C19 (p = 0.002, R2 = 0.63), 2C9-2J2 (p = 0.004, R2 = 0.40), 2D6-2J2 (p = 0.002, R2 = 0.50)) and UGT (1A1-2B7 (p = 0.02, R2 = 0.87)) family of enzymes. There were also correlations between P-gp and several other proteins (OST-α (p SIGNIFICANCE STATEMENT A number of drug transporters were quantified for the first time in this study. Several inter-correlations of protein abundance were reported. mRNA expression levels proved to be a poor reflection of differences between individuals regarding the level of protein expression in gut. The reported abundance of DMEs and transporters and their inter-correlations will contribute to better predictions of oral drug bioavailability and drug–drug interactions by linking in vitro observations to potential outcomes through physiologically-based pharmacokinetic models.
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