Differential tissue abundance of membrane-bound drug metabolizing enzymes and transporter proteins by global proteomics.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are critical for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics which relies on synthetic stable isotope-labeled surrogate peptides as calibrators, is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA doesn't consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage (SC-TPA), which was applied to quantify 54 DMETs for characterization of i) differential tissue DMET abundance in the human liver, kidney, and intestine, and ii) interindividual variability of DMET proteins in individual intestinal samples (n=13). UGT2B7, MGST1, MGST2, MGST3, CES2, and MRP2 were expressed in all three tissues, whereas, as expected CYP3A4, CYP3A5, CYP2C9, CYP4F2, UGT1A1, UGT2B17, CES1, FMO5, MRP3, and P-gp were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation (IVIVE) of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic (PBPK) prediction of systemic and tissue concentration of drugs. Significance Statement We quantified the abundance and compositions of drug-metabolizing enzymes and transporters (DMETs) in pooled human liver, intestine, and kidney microsomes using an optimized sequence coverage-informed total protein approach. The quantification of DMETs revealed quantitative differences in their levels in the liver, intestine, and kidney. Further, the analysis of individual intestine samples confirmed high variability in the levels of CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2. These data are applicable for the prediction of first-pass metabolism and tissue-specific drug clearance.

Ontogeny of Human Liver Aldehyde Oxidase: Developmental Changes and Implications for Drug Metabolism.

Despite the increasing importance of aldehyde oxidase (AO) in the drug metabolism of clinical candidates, ontogeny data for AO are limited. The objective of our study was to characterize the age-dependent AO content and activity in the human liver cytosolic fraction (HLC) and human hepatocytes (HH). HLC (n = 121 donors) and HH (n = 50 donors) were analyzed for (1) AO protein content by quantitative proteomics and (2) enzyme activity using carbazeran as a probe substrate. AO activity showed high technical variability and poor correlation with the content in HLC samples, whereas hepatocyte samples showed a strong correlation between the content and activity. Similarly, AO content and activity showed no significant age-dependent differences in HLC samples, whereas the average AO content and activity in hepatocytes increased significantly (∼20-40-fold) from the neonatal levels (0-28 days). Based on the hepatocyte data, the age at which 50% of the adult AO content is reached (age50) was 3.15 years (0.32-13.97 years, 95% CI). Metabolite profiling of carbazeran revealed age-dependent metabolic switching and the role of non-AO mechanisms (glucuronidation and desmethylation) in carbazeran elimination. The content-activity correlation in hepatocytes improved significantly (R2 = 0.95; p < 0.0001) in samples showing <10% contribution of glucuronidation toward the overall metabolism, confirming that AO-mediated oxidation and glucuronidation are the key routes of carbazeran metabolism. Considering the confounding effect of glucuronidation on AO activity, AO content-based ontogeny data are a more direct reflection of developmental changes in protein expression. The comprehensive ontogeny data of AO in HH samples are more reliable than HLC data, which are important for developing robust physiologically based pharmacokinetic models for predicting AO-mediated metabolism in children.

Comparison of Relative Activity versus Relative Expression Factors (RAF versus REF) in Predicting Glucuronidation Mediated Drug Clearance Using Recombinant UGTs.

PURPOSE: Predicting the quantitative fraction of glucuronidation (fgluc) by individual UDP-glucuronosyltransferase enzymes (UGTs) is challenging due to the lack of selective inhibitors and inconsistent activity of recombinant UGT systems (rUGTs). Our study compares the relative expression versus activity factors (REF versus RAF) to predict fgluc based on rUGT data to human liver and intestinal microsomes (HLM and HIM). METHODS: REF scalars were derived from a previous in-house proteomics study for eleven UGT enzymes (UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT1A10, UGT2B4, UGT2B7, UGT2B10, UGT2B15, and UGT2B17), whereas RAF was calculated by measuring activities in rUGTs to microsomes of selective UGT probe substrates. Protein-normalized activity factor (pnAF) values were generated after correcting activity of individual UGTs to their corresponding protein abundance. The utility of REF and RAF in predicting fgluc was assessed for three UGT substrates-diclofenac, vorinostat, and raltegravir. RESULTS: The REF values ranged from 0.02 to 1.75, RAF based on activity obtained in rUGTs to HLM/HIM were from 0.1 to 274. pnAF values were ~ 5 to 80-fold, except for UGT2B4 and UGT2B15, where pnAF was ~ 180 and > 1000, respectively. The results revealed confounding effect of differential specific activities (per pmol) of rUGTs in fgluc prediction. CONCLUSION: The data suggest that the activity of UGT enzymes was significantly lower when compared to their activity in microsomes at the same absolute protein amount (pmol). Collectively, results of this study demonstrate poor and variable specific activity of different rUGTs (per pmol protein), as determined by pnAF values, which should be considered in fgluc scaling.

Promiscuity and Quantitative Contribution of UGT2B17 in Drug and Steroid Metabolism Determined by Experimental and Computational Approaches.

Uridine 5'-diphospho-glulcuronosyltransferase 2B17 (UGT2B17) is important in the metabolism of steroids and orally administered drugs due to its high interindividual variability. However, the structural basis governing the substrate selectivity or inhibition of UGT2B17 remains poorly understood. This study investigated 76 FDA-approved drugs and 20 steroids known to undergo glucuronidation for their metabolism by UGT2B17. Specifically, we assessed the substrate selectivity for UGT2B17 over other UGT enzymes using recombinant human UGT2B17 (rUGT2B17), human intestinal microsomes, and human liver microsomes. The quantitative contribution of intestinal UGT2B17 in the glucuronidation of these compounds was characterized using intestinal microsomes isolated from UGT2B17 expressors and nonexpressors. In addition, a structure-based pharmacophore model for UGT2B17 substrates was built and validated using the studied pool of substrates and nonsubstrates. The results show that UGT2B17 could metabolize 23 out of 96 compounds from various chemical classes, including alcohols and carboxylic acids, particularly in the intestine. Interestingly, amines were less susceptible to UGT2B17 metabolism, though they could inhibit the enzyme. Three main pharmacophoric features of UGT2B17 substrates include (1) the presence of an accessible -OH or -COOH group near His35 residue, (2) a hydrophobic functional group at ∼4.5-5 Šfrom feature 1, and (3) an aromatic ring ∼5-7 Šfrom feature 2. Most of the studied compounds inhibited UGT2B17 activity irrespective of their substrate potential, indicating the possibility of multiple mechanisms. These data suggest that UGT2B17 is promiscuous in substrate selectivity and inhibition and has a high potential to produce significant variability in the absorption and disposition of orally administered drugs.

Physiologically-based pharmacokinetic modeling of prominent oral contraceptive agents and applications in drug-drug interactions.

Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.

Developmental Expression of Drug Transporters and Conjugating Enzymes Involved in Enterohepatic Recycling: Implication for Pediatric Drug Dosing.

Around 50% of the drugs used in children have never been tested for safety and efficacy in this vulnerable population. Immature drug elimination pathways can lead to drug toxicity when pediatric doses are determined using empirical methods such as body-surface area or body-weight-normalized adult dosing. In the absence of clinical data, physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to predict drug pharmacokinetics in children. These models utilize developmental physiological data, including age-dependent differences in the abundance of drug-metabolizing enzymes and transporters (DMET), to mechanistically extrapolate adult pharmacokinetic data to children. The reported abundance data of hepatic DMET proteins in subcellular fractions isolated from frozen tissue are prone to high technical variability. Therefore, we carried out the proteomics-based quantification of hepatic drug transporters and conjugating enzymes in 50 pediatric and 8 adult human hepatocyte samples. Out of the 34 studied proteins, 28 showed a significant increase or decrease with age. While MRP6, OAT7, and SULT1E1 were highest in < 1-year-old samples, the abundance of P-gp and UGT1A4 was negligible in < 1-year-old samples and increased significantly after 1 year of age. Incorporation of the age-dependent abundance data in PBPK models can help improve pediatric dose prediction, leading to safer drug pharmacotherapy in children.

Sex and the Kidney Drug-Metabolizing Enzymes and Transporters: Are Preclinical Drug Disposition Data Translatable to Humans?

Cross-species differences in drug transport and metabolism are linked to poor translation of preclinical pharmacokinetic and toxicology data to humans, often resulting in the failure of new chemical entities (NCEs) during clinical drug development. Specifically, inaccurate prediction of renal clearance and renal accumulation of NCEs due to differential abundance of enzymes and transporters in kidneys can lead to differences in pharmacokinetics and toxicity between experimental animals and humans. We carried out liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based protein quantification of 78 membrane drug-metabolizing enzymes and transporters (DMETs) in the kidney membrane fractions of humans, rats, and mice for characterization of cross-species and sex-dependent differences. In general, majority of DMET proteins were higher in rodents than in humans. Significant cross-species differences were observed in 30 out of 33 membrane DMET proteins quantified in all three species. Although no significant sex-dependent differences were observed in humans, the abundance of 28 and 46 membrane proteins showed significant sex dependence in rats and mice, respectively. These cross-species and sex-dependent quantitative abundance data are valuable for gaining a mechanistic understanding of drug renal disposition and accumulation. Further, these data can also be integrated into systems pharmacology tools, such as physiologically based pharmacokinetic models, to enhance the interpretation of preclinical pharmacokinetic and toxicological data.