Quantitative Characterization of Major Hepatic UDP-Glucuronosyltransferase Enzymes in Human Liver Microsomes: Comparison of Two Proteomic Methods and Correlation with Catalytic Activity

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Alyssa Dantonio
  • Mark Niosi
  • Jonathan J Novak
  • John K Fallon
  • P C Smith
  • T C Goosen


Quantitative characterization of UDP-glucuronosyltransferase (UGT) enzymes is valuable in glucuronidation reaction phenotyping, predicting metabolic clearance and drug-drug interactions using extrapolation exercises based on pharmacokinetic modeling. Different quantitative proteomic workflows have been employed to quantify UGT enzymes in various systems, with reports indicating large variability in expression, which cannot be explained by inter-individual variability alone. To evaluate the effect of methodological differences on end-point UGT abundance quantification, eight UGT enzymes were quantified in 24 matched liver microsomal samples by two laboratories using stable isotope-labeled (SIL) peptides or quantitative concatemer (QconCAT) standard, and measurements were assessed against catalytic activity in seven enzymes (n=59). There was little agreement between individual abundance levels reported by the two methods; only UGT1A1 showed strong correlation (Rs=0.73, p<0.0001; R2=0.30; n=24). SIL-based abundance measurements correlated well with enzyme activities, with correlations ranging from moderate for UGTs 1A6, 1A9 and 2B15 (Rs=0.52-0.59, p<0.0001; R2=0.34-0.58; n=59) to strong correlations for UGTs 1A1, 1A3, 1A4, and 2B7 (Rs=0.79-0.90, p<0.0001; R2=0.69-0.79). QconCAT-based data revealed generally poor correlation with activity, whereas moderate correlations were shown for UGTs 1A1, 1A3 and 2B7. Spurious abundance-activity correlations were identified in the cases of UGT1A4/2B4 and UGT2B7/2B15, which could be explained by correlations of protein expression between these enzymes. Consistent correlation of UGT abundance with catalytic activity, demonstrated by the SIL-based dataset, suggests that quantitative proteomic data should be validated against catalytic activity whenever possible. In addition, metabolic reaction phenotyping exercises should consider spurious abundance-activity correlations to avoid misleading conclusions.

Bibliographical metadata

Original languageEnglish
Pages (from-to)1102-1112
Number of pages11
JournalDrug Metabolism and Disposition
Issue number10
Early online date2 Aug 2017
Publication statusPublished - 1 Oct 2017

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