Abundance of Phase 1 and 2 Drug-Metabolizing Enzymes in Alcoholic and Hepatitis C Cirrhotic Livers: A Quantitative Targeted Proteomics Study

Abstract

To predict the impact of liver cirrhosis on hepatic drug clearance using physiologically based pharmacokinetic (PBPK) modeling, we compared the protein abundance of various phase 1 and phase 2 drug-metabolizing enzymes (DMEs) in S9 fractions of alcoholic ( = 27) or hepatitis C (HCV, = 30) cirrhotic versus noncirrhotic (control) livers ( = 25). The S9 total protein content was significantly lower in alcoholic or HCV cirrhotic versus control livers (i.e., 38.3 ± 8.3, 32.3 ± 12.8, vs. 51.1 ± 20.7 mg/g liver, respectively). In general, alcoholic cirrhosis was associated with a larger decrease in the DME abundance than HCV cirrhosis; however, only the abundance of UGT1A4, alcohol dehydrogenase (ADH)1A, and ADH1B was significantly lower in alcoholic versus HCV cirrhotic livers. When normalized to per gram of tissue, the abundance of nine DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, CYP1A2, ADH1A, ADH1B, aldehyde oxidase (AOX)1, and carboxylesterase (CES)1) in alcoholic cirrhosis and five DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, and CYP1A2) in HCV cirrhosis was <25% of that in control livers. The abundance of most DMEs in cirrhotic livers was 25% to 50% of control livers. CES2 abundance was not affected by cirrhosis. Integration of UGT2B7 abundance in cirrhotic livers into the liver cirrhosis (Child Pugh C) model of Simcyp improved the prediction of zidovudine and morphine PK in subjects with Child Pugh C liver cirrhosis. These data demonstrate that protein abundance data, combined with PBPK modeling and simulation, can be a powerful tool to predict drug disposition in special populations.

Age‐ and Genotype‐Dependent Variability in the Protein Abundance and Activity of Six Major Uridine Diphosphate‐Glucuronosyltransferases in Human Liver

“…For zidovudine, first a minimal PBPK model describing adult PK (200 mg, oral dose) was developed using the population‐based Simcyp simulator (v. 15, Sheffield, UK) similar to that described elsewhere . The model consisted of a liver compartment and a single adjusting compartment connected to a systemic compartment.…”

“…For morphine, an approach similar to zidovudine was used for PBPK modeling with few exceptions . A previously published adult PBPK model was used with input parameters described in Table S11 .…”

“…For zidovudine, first a minimal PBPK model describing adult PK (200 mg, oral dose) was developed using the population-based Simcyp simulator (v. 15, Sheffield, UK) similar to that described elsewhere. 48 The model consisted of a liver compartment and a single adjusting compartment connected to a systemic compartment. Physicochemical and blood binding parameters such as molecular weight, lipophilicity (log P), acid dissociation constant (pKa), blood to plasma ratio (B/P), and fraction unbound in plasma (fu) were used directly from the Simcyp library (Table S10).…”

“…For morphine, an approach similar to zidovudine was used for PBPK modeling with few exceptions. 48 A previously published adult PBPK model 50 was used with input parameters described in Table S11. Because OCT1 is involved in hepatic uptake of morphine, 50 a full PBPK model was employed to describe the permeability-limited uptake.…”

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

“…Despite routine application of quantitative proteomics and its integration in translational modeling in certain areas, including prediction of clearance, 2-6 DDI, 7 and effects of covariates, such as age, [8][9][10][11] disease, 12,13 ethnicity, 14,15 and genetics, 9,14,16 lack of harmonized practices in the implementation of the technique (and, hence, variability in reported abundances of the same proteins 17 ) has led to some uncertainty in the outcomes of such applications, especially when there are insufficient clinical data for verification of predictions. Some variability can be due to differences in sample matrices used (e.g., cell lysates vs. membrane fractions), whereas other differences are potentially related to the measurement technique (e.g., targeted vs. global proteomics).…”