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

Abstract

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 interindividual 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 [Spearman rank order correlation (Rs) = 0.73, P < 0.0001; R 2 = 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; R 2 = 0.34-0.58; n = 59) to strong correlations for UGTs 1A1, 1A3, 1A4, and 2B7 (Rs = 0.79-0.90, P < 0.0001; R 2 = 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.

Determining Allele-Specific Protein Expression (ASPE) Using a QconCAT-Based Proteomics Method: a Novel Approach to Identify Cis-acting Genetic Variants

“…Combined with the use of heavy isotope-labeled internal standards, targeted proteomics approaches, such as the sMRM-HR method used in the present study enable the most accurate and reproducible protein quantification relative to other untargeted and/or unlabeled proteomics methods. QconCAT-based targeted proteomics haves been used for the quantification of many proteins including UGT2B15 26,27 . The QconCAT protein standard we designed differs from conventional QconCAT standard in two important aspects.…”

Toward a Consensus on Applying Quantitative Liquid Chromatography‐Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

“…Consensus suggests that these methods should be improved or replaced with a more robust approach because of reported technical variability. 91 A more recent alternative, which relies on tryptophan fluorescence, allows assessment of protein and peptide yield. 46 Tissue mass can also be used for normalization but has the limitation of being less applicable to in vitro systems.…”

Mechanistic Assessment of Extrahepatic Contributions to Glucuronidation of Integrase Strand Transfer Inhibitors

“…This tendency was observed across several UGT substrates from other therapeutic classes (Boase and Miners, 2002;Soars et al, 2002;Laufer et al, 2009). This underprediction may be due to inadequate applications of the mechanistic and physiologic characteristics of the glucuronidation pathway and an inadequate understanding of the contribution of UGTs other than hepatic UGT1A1 (Izukawa et al, 2009;Court et al, 2012;Achour et al, 2017). Multiple UGTs, including UGT1A1, are expressed in multiple tissues at varying drug-metabolizing capacities, such as the liver, kidney, and intestine (Court et al, 2012;Gill et al, 2013;Margaillan et al, 2015;Drodzik et al, 2018).…”