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.

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.

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.

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.

Dissecting Parameters Contributing to the Underprediction of Aldehyde Oxidase-Mediated Metabolic Clearance of Drugs.

We investigated the effect of variability and instability in aldehyde oxidase (AO) content and activity on the scaling of in vitro metabolism data. AO content and activity in human liver cytosol (HLC) and five recombinant human AO preparations (rAO) were determined using targeted proteomics and carbazeran oxidation assay, respectively. AO content was highly variable as indicated by the relative expression factor (REF; i.e., HLC to rAO content) ranging from 0.001 to 1.7 across different in vitro systems. The activity of AO in HLC degrades at a 10-fold higher rate in the presence of the substrate as compared with the activity performed after preincubation without substrate. To scale the metabolic activity from rAO to HLC, a protein-normalized activity factor (pnAF) was proposed wherein the activity was corrected by AO content, which revealed up to sixfold higher AO activity in HLC versus rAO systems. A similar value of pnAF was observed for another substrate, ripasudil. Physiologically based pharmacokinetic (PBPK) modeling revealed a significant additional clearance (CL; 66%), which allowed for the successful prediction of in vivo CL of four other substrates, i.e., O-benzyl guanine, BIBX1382, zaleplon, and zoniporide. For carbazeran, the metabolite identification study showed that the direct glucuronidation may be contributing to around 12% elimination. Taken together, this study identified differential protein content, instability of in vitro activity, role of additional AO clearance, and unaccounted metabolic pathways as plausible reasons for the underprediction of AO-mediated drug metabolism. Consideration of these factors and integration of REF and pnAF in PBPK models will allow better prediction of AO metabolism. SIGNIFICANCE STATEMENT: This study elucidated the plausible reasons for the underprediction of aldehyde oxidase (AO)-mediated drug metabolism and provided recommendations to address them. It demonstrated that integrating protein content and activity differences and accounting for the loss of AO activity, as well as consideration of extrahepatic clearance and additional pathways, would improve the in vitro to in vivo extrapolation of AO-mediated drug metabolism using physiologically based pharmacokinetic modeling.

Intestinal Metabolism of Diclofenac by Polymorphic UGT2B17 Correlates with its Highly Variable Pharmacokinetics and Safety across Populations.

Although the United States and Europe have shifted to the prescription use of oral diclofenac due to several serious incidences of cardiotoxicity, it is one of the most commonly used over-the-counter (OTC) pain medicines in major parts of the world. We elucidated the quantitative and tissue-specific contribution of uridine diphosphate-glucuronosyltransferases 17 (UGT2B17) in diclofenac metabolism and pharmacokinetics (PK). UGT2B17 is one of most deleted genes in humans with the gene deletion frequency ranging from ~ 20% in White population to 90% in Japanese population. The human intestinal and liver microsomes isolated from the high-UGT2B17 expressing individuals showed 21- and 4-fold greater rate of diclofenac glucuronide (DG) formation than in the null-UGT2B17 carriers, respectively. The greater contribution of intestinal UGT2B17 was confirmed by a strong correlation (R = 0.78, P < 0.001) between UGT2B17 abundance and DG formation in individual intestinal microsomes (n = 14). However, because UGT2B17 is a minor UGT isoform in the liver, DG formation rate correlated better with the expression of UGT2B7. The proteomics-informed physiologically-based pharmacokinetic (PBPK) model explains the reported higher exposure of diclofenac in women consistent with ~ 3-fold lower expression of UGT2B17. Similarly, our in silico predictions also corroborate with the reported higher exposure and lower standard clinical dose of diclofenac in Japanese population. Therefore, variable UGT2B17 mediated metabolism of oral diclofenac is a cause of concern, especially in the developing countries where it is still used as an OTC drug. The ontogeny data of UGTs in human hepatocytes can be utilized in developing PBPK models for predicting PK in the pediatric population.

Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling.

Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.

Quantification of Accurate Composition and Total Abundance of Homologous Proteins by Conserved-Plus-Surrogate Peptide Approach: Quantification of UDP Glucuronosyltransferases in Human Tissues.

Characterization of accurate compositions and total abundance of homologous drug-metabolizing enzymes, such as UDP glucuronosyltransferases (UGTs), is important for predicting the fractional contribution of individual isoforms involved in the metabolism of a drug for applications in physiologically based pharmacokinetic (PBPK) modeling. Conventional targeted proteomics utilizes surrogate peptides, which often results in high technical and interlaboratory variability due to peptide-specific digestion leading to data inconsistencies. To address this problem, we developed a novel conserved-plus-surrogate peptide (CPSP) approach for determining the accurate compositions and total or cumulative abundance of homologous UGTs in commercially available pooled human liver microsomes (HLM), human intestinal microsomes (HIM), human kidney microsomes (HKM), and human liver S9 (HLS9) fraction. The relative percent composition of UGT1A and UGT2B isoforms in the human liver was 35:5:36:11:13 for UGT1A1:1A3:1A4:1A6:1A9 and 20:32:22:21:5 for UGT2B4:2B7:2B10:2B15:2B17. The human kidney and intestine also showed unique compositions of UGT1As and UGT2Bs. The reproducibility of the approach was validated by assessing correlations of UGT compositions between HLM and HLS9 (R2> 0.91). The analysis of the conserved peptides also provided the abundance for individual UGT isoforms included in this investigation as well as the total abundance (pmol/mg protein) of UGT1As and UGT2Bs across tissues, i.e., 268 and 342 (HLM), 21 and 92 (HIM), and 138 and 99 (HKM), respectively. The CPSP approach could be used for applications in the in-vitro-to-in-vivo extrapolation of drug metabolism and PBPK modeling. SIGNIFICANCE STATEMENT: We quantified the absolute compositions and total abundance of UDP glucuronosyltransferases (UGTs) in pooled human liver, intestine, and kidney microsomes using a novel conserved-plus-surrogate peptide (CPSP) approach. The CPSP approach addresses the surrogate peptide-specific variability in the determination of the absolute composition of UGTs. The data presented in this manuscript are applicable for the estimation of the fraction metabolized by individual UGTs towards better in vitro-to-in vivo extrapolation of UGT-mediated drug metabolism.

Dimethandrolone, a Potential Male Contraceptive Pill, is Primarily Metabolized by the Highly Polymorphic UDP-Glucuronosyltransferase 2B17 Enzyme in Human Intestine and Liver.

Dimethandrolone undecanoate (DMAU), an oral investigational male hormonal contraceptive, is a prodrug that is rapidly converted to its active metabolite, dimethandrolone (DMA). Poor and variable oral bioavailability of DMA after DMAU dosing is a critical challenge to develop it as an oral drug. The objective of our study was to elucidate the mechanisms of variable pharmacokinetics of DMA. We first identified DMA metabolites formed in vitro and in vivo in human hepatocyte incubation and serum samples following oral DMAU administration in men, respectively. The metabolite identification study revealed two metabolites, DMA-glucuronide (DMA-G; major) and the androstenedione analog of DMA (minor), in the hepatocyte incubations. After oral DMAU administration, only DMA-G was detected in serum, which was >100-fold compared with DMA levels, supporting glucuronidation as the major elimination mechanism for DMA. Next, 13 clinically relevant UDP-glucuronosyltransferase (UGT) enzymes were tested for their involvement in DMA-G formation, which revealed a major role of UDP-glucuronosyltransferase 2B17 (UGT2B17) isoform with a smaller contribution of UGT1A9 in DMA-G formation. These data were confirmed by dramatically higher DMA glucuronidation rates (>200- and sevenfold) in the high versus the null UGT2B17-expressing human intestinal and liver microsomes, respectively. Since human UGT2B17 is a highly variable enzyme with a 20%-80% gene deletion frequency, the in vitro data suggest a major role of UGT2B17 polymorphism on the first-pass metabolism of DMA. Further, considering DMA is a selective and sensitive UGT2B17 substrate, it could be used as a clinical probe of UGT2B17 activity. SIGNIFICANCE STATEMENT: Dimethandrolone (DMA) is an active metabolite of dimethandrolone undecanoate (DMAU), an investigational male hormonal contraceptive. Previous studies have indicated poor and inconsistent bioavailability of DMAU following oral administration. This study found that UDP-glucuronosyltransferase 2B17-mediated high intestinal first-pass metabolism is the key mechanism of variable DMA bioavailability.

Quantitative Proteomics in Translational Absorption, Distribution, Metabolism, and Excretion and Precision Medicine.

A reliable translation of in vitro and preclinical data on drug absorption, distribution, metabolism, and excretion (ADME) to humans is important for safe and effective drug development. Precision medicine that is expected to provide the right clinical dose for the right patient at the right time requires a comprehensive understanding of population factors affecting drug disposition and response. Characterization of drug-metabolizing enzymes and transporters for the protein abundance and their interindividual as well as differential tissue and cross-species variabilities is important for translational ADME and precision medicine. This review first provides a brief overview of quantitative proteomics principles including liquid chromatography-tandem mass spectrometry tools, data acquisition approaches, proteomics sample preparation techniques, and quality controls for ensuring rigor and reproducibility in protein quantification data. Then, potential applications of quantitative proteomics in the translation of in vitro and preclinical data as well as prediction of interindividual variability are discussed in detail with tabulated examples. The applications of quantitative proteomics data in physiologically based pharmacokinetic modeling for ADME prediction are discussed with representative case examples. Finally, various considerations for reliable quantitative proteomics analysis for translational ADME and precision medicine and the future directions are discussed. SIGNIFICANCE STATEMENT: Quantitative proteomics analysis of drug-metabolizing enzymes and transporters in humans and preclinical species provides key physiological information that assists in the translation of in vitro and preclinical data to humans. This review provides the principles and applications of quantitative proteomics in characterizing in vitro, ex vivo, and preclinical models for translational research and interindividual variability prediction. Integration of these data into physiologically based pharmacokinetic modeling is proving to be critical for safe, effective, timely, and cost-effective drug development.

Exploring the Interactome of Cytochrome P450 2E1 in Human Liver Microsomes with Chemical Crosslinking Mass Spectrometry.

Aiming to elucidate the system-wide effects of the alcohol-induced increase in the content of cytochrome P450 2E1 (CYP2E1) on drug metabolism, we explored the array of its protein-protein interactions (interactome) in human liver microsomes (HLM) with chemical crosslinking mass spectrometry (CXMS). Our strategy employs membrane incorporation of purified CYP2E1 modified with photoreactive crosslinkers benzophenone-4-maleimide and 4-(N-succinimidylcarboxy)benzophenone. Exposure of bait-incorporated HLM samples to light was followed by isolating the His-tagged bait protein and its crosslinked aggregates on Ni-NTA agarose. Analyzing the individual bands of SDS-PAGE slabs of thereby isolated protein with the toolset of untargeted proteomics, we detected the crosslinked dimeric and trimeric complexes of CYP2E1 with other drug-metabolizing enzymes. Among the most extensively crosslinked partners of CYP2E1 are the cytochromes P450 2A6, 2C8, 3A4, 4A11, and 4F2, UDP-glucuronosyltransferases (UGTs) 1A and 2B, fatty aldehyde dehydrogenase (ALDH3A2), epoxide hydrolase 1 (EPHX1), disulfide oxidase 1α (ERO1L), and ribophorin II (RPN2). These results demonstrate the exploratory power of the proposed CXMS strategy and corroborate the concept of tight functional integration in the human drug-metabolizing ensemble through protein-protein interactions of the constituting enzymes.

Interindividual Variability and Differential Tissue Abundance of Mitochondrial Amidoxime Reducing Component Enzymes in Humans.

Mitochondrial amidoxime-reducing component (mARC) enzymes are molybdenum-containing proteins that metabolize a number of endobiotics and xenobiotics. The interindividual variability and differential tissue abundance of mARC1 and mARC2 were quantified using targeted proteomics in three types of tissue fractions: 1) pediatric liver tissue homogenates, 2) total membrane fraction of the paired liver and kidney samples from pediatric and adult donors, and 3) pooled S9 fractions of the liver, intestine, kidney, lung, and heart. The absolute levels of mARC1 and mARC2 in the pediatric liver homogenate were 40.08 ± 4.26 and 24.58 ± 4.02 pmol/mg homogenate protein, respectively, and were independent of age and sex. In the total membrane fraction of the paired liver and kidney samples, the abundance of hepatic mARC1 and mARC2 was comparable, whereas mARC2 abundance in the kidney was approximately 9-fold higher in comparison with mARC1. The analysis of the third set of samples (i.e., S9 fraction) revealed that mARC1 abundance in the kidney, intestine, and lung was 5- to 13-fold lower than the liver S9 abundance, whereas mARC2 abundance was approximately 3- and 16-fold lower in the intestine and lung than the liver S9, respectively. In contrast, the kidney mARC2 abundance in the S9 fraction was approximately 2.5-fold higher as compared with the hepatic mARC2 abundance. The abundance of mARC enzymes in the heart was below the limit of quantification (∼0.6 pmol/mg protein). The mARC enzyme abundance data presented here can be used to develop physiologically based pharmacokinetic models for the prediction of in vivo pharmacokinetics of mARC substrates. SIGNIFICANCE STATEMENT: A precise targeted quantitative proteomics method was developed and applied to quantify newly discovered drug-metabolizing enzymes, mARC1 and mARC2, in pediatric and adult tissue samples. The data suggest that mARC enzymes are ubiquitously expressed in an isoform-specific manner in the human liver, kidney, intestine, and lung, and the enzyme abundance is not associated with age and sex. These data are important for developing physiologically based pharmacokinetic models for the prediction of in vivo pharmacokinetics of mARC substrates.

Probing functional interactions between cytochromes P450 with principal component analysis of substrate saturation profiles and targeted proteomics.

We investigated the correspondence between drug metabolism routes and the composition of the P450 ensemble in human liver microsomes (HLM). As a probe, we used Coumarin 152 (C152), a fluorogenic substrate metabolized by multiple P450 species. Studying the substrate-saturation profiles (SSP) in seven pooled HLM preparations, we sought to correlate them with the P450 pool's composition characterized by targeted proteomics. This analysis, complemented with the assays with specific inhibitors of CYP3A4 and CYP2C19, the primary C152 metabolizers, demonstrated a significant contrast between different HLM samples. To unveil the source of these differences, we implemented Principal Component Analysis (PCA) of the SSP series obtained with HLM samples with a known composition of the P450 pool. Our analysis revealed that the parameters of C152 metabolism are primarily determined by the content of CYP2A6, CYP2B6, CYP2C8, CYP2E1, and CYP3A5 of those only CYP2B6 and CYP3A5 can metabolize C152. To validate this finding, we studied the effect of enriching HLM with CYP2A6, CYP2E1, and CYP3A5. The incorporation of CYP3A5 into HLM decreases the rate of C152 metabolism while increasing the role of CYP2B6 in its turnover. In contrast, incorporation of CYP2A6 and CYP2E1 reroutes the C152 demethylation towards some P450 enzyme with a moderate affinity to the substrate, most likely CYP3A4. Our results reveal a sharp non-additivity of the individual P450 properties and suggest a pivotal role of P450-P450 interactions in determining drug metabolism routes. This study demonstrates the high potential of our new PCA-based approach in unveiling functional interrelationships between different P450 species.

Ultrasensitive Quantification of Drug-metabolizing Enzymes and Transporters in Small Sample Volume by Microflow LC-MS/MS.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are broadly applicable to the characterization of in vitro and in vivo models, in vitro to in vivo extrapolation (IVIVE), and interindividual variability prediction. However, the emerging need of DMET quantification in small sample volumes such as organ-on a chip effluent, organoids, and biopsies requires ultrasensitive protein quantification methods. We present an ultrasensitive method that relies on an optimized sample preparation approach involving acetone precipitation coupled with a microflow-based liquid chromatography-tandem mass spectrometry (µLC-MS/MS) for the DMET quantification using limited sample volume or protein concentration, i.e., liver tissues (1-100 mg), hepatocyte counts (~4000 to 1 million cells), and microsomal protein concentration (0.01-1 mg/ml). The method was applied to quantify DMETs in differential tissue S9 fractions (liver, intestine, kidney, lung, and heart) and cryopreserved human intestinal mucosa (i.e., CHIM). The method successfully quantified >75% of the target DMETs in the trypsin digests of 1 mg tissue homogenate, 15,000 hepatocytes, and 0.06 mg/ml microsomal protein concentration. The precision of DMET quantification measured as the coefficient of variation across different tissue weights, cell counts, or microsomal protein concentration was within 30%. The method confirmed significant extrahepatic abundance of non-cytochrome P450 enzymes such as dihydropyridine dehydrogenase (DPYD), epoxide hydrolases (EPXs), arylacetamide deacetylase (AADAC), paraoxonases (PONs), and glutathione S-transferases (GSTs). The ultrasensitive method developed here is applicable to characterize emerging miniaturized in vitro models and small volume biopsies. In addition, the differential tissue abundance data of the understudied DMETs will be important for physiologically-based pharmacokinetic (PBPK) modeling of drugs.

Utility of Quantitative Proteomics for Enhancing the Predictive Ability of Physiologically Based Pharmacokinetic Models Across Disease States.

Disease states such as liver cirrhosis and chronic kidney disease can lead to altered pharmacokinetics (PK) of drugs by influencing drug absorption, blood flow to organs, plasma protein binding, apparent volume of distribution, and drug-metabolizing enzyme and transporter (DMET) abundance. Narrow therapeutic index drugs are particularly vulnerable to undesired pharmacodynamics (PD) because of the changes in drug PK in disease states. However, systematic clinical evaluation of disease effect on drug PK and PD is not always possible because of the complexity or the cost of clinical studies. Physiologically based PK (PBPK) modeling is emerging as an alternate method to extrapolate drug PK from the healthy population to disease states. These models require information on the effect of disease condition on the activity or tissue abundance of DMET proteins. Although immunoquantification-based abundance data were available in the literature for a limited number of DMET proteins, the emergence of mass spectrometry-based quantitative proteomics as a sensitive, robust, and high-throughput tool has allowed a rapid increase in data availability on tissue DMET abundance in healthy versus disease states, especially in liver tissue. Here, we summarize these data including the available immunoquantification or mRNA levels of DMET proteins (healthy vs disease states) in extrahepatic tissue and discuss the potential applications of DMET abundance data in enhancing the capability of PBPK modeling in predicting drug disposition across disease states. Successful examples of PBPK modeling that integrate differences in DMET proteins between healthy and disease states are discussed.

Identification of novel glutathione conjugates of terbinafine in liver microsomes and hepatocytes across species.

1. Terbinafine (TBF), a common antifungal agent, has been associated with rare incidences of hepatotoxicity. It is hypothesized that bioactivation of TBF to reactive intermediates and subsequent binding to critical cellular proteins may contribute to this toxicity. In the present study, we have characterized the bioactivation pathways of TBF extensively in human, mouse, monkey, dog and rat liver microsomes and hepatocytes. 2. A total of twenty glutathione conjugates of TBF were identified in hepatocytes; thirteen of these conjugates were also detected in liver microsomes. To the best of our knowledge, only two of these conjugates have been reported previously. The conjugates were categorized into three groups based on their mechanism of formation: (a) alkene/alkyne oxidation followed by glutathione conjugation, with or without N-demethylation, (b) arene oxidation followed by glutathione conjugation, with or without N-demethylation, and (c) N-dealkylation followed by glutathione conjugation of the allylic aldehyde, alcohol and acid intermediates. 3. Differences were observed across species in the contributions of these pathways toward overall metabolic turnover. We conclude that, in addition to the glutathione conjugates known to form by Michael addition to the allylic aldehyde, there are other pathways involving the formation of arene oxides and alkene/alkyne epoxides that may be relevant to the discussion of TBF-mediated idiosyncratic drug reactions.

Use of Hybrid Capillary Tube Apparatus on 400 MHz NMR for Quantitation of Crucial Low-Quantity Metabolites Using aSICCO Signal.

Metabolites of new chemical entities can influence safety and efficacy of a molecule and often times need to be quantified in preclinical studies. However, synthetic standards of metabolites are very rarely available in early discovery. Alternate approaches such as biosynthesis need to be explored to generate these metabolites. Assessing the quantity and purity of these small amounts of metabolites with a nondestructive analytical procedure becomes crucial. Quantitative NMR becomes the method of choice for these samples. Recent advances in high-field NMR (>500 MHz) with the use of cryoprobe technology have helped to improve sensitivity for analysis of small microgram quantity of such samples. However, this type of NMR instrumentation is not routinely available in all laboratories. To analyze microgram quantities of metabolites on a routine basis with lower-resolution 400 MHz NMR instrument fitted with a broad band fluorine observe room temperature probe, a novel hybrid capillary tube setup was developed. To quantitate the metabolite in the sample, an artificial signal insertion for calculation of concentration observed (aSICCO) method that introduces an internally calibrated mathematical signal was used after acquiring the NMR spectrum. The linearity of aSICCO signal was established using ibuprofen as a model analyte. The limit of quantification of this procedure was 0.8 mM with 10 K scans that could be improved further with the increase in the number of scans. This procedure was used to quantify three metabolites-phenytoin from fosphenytoin, dextrophan from dextromethorphan, and 4-OH-diclofenac from diclofenac-and is suitable for minibiosynthesis of metabolites from in vitro systems.

Metabolite Identification, Reaction Phenotyping, and Retrospective Drug-Drug Interaction Predictions of 17-Deacetylnorgestimate, the Active Component of the Oral Contraceptive Norgestimate.

Ortho Tri-Cyclen, a two-drug cocktail comprised of ethinylestradiol and norgestimate (13-ethyl-17-acetoxy-18, 19-dinor-17α-pregn-4-en-20yn-3 oxime), is commonly prescribed to avert unwanted pregnancies in women of reproductive age. In vivo, norgestimate undergoes extensive and rapid deacetylation to produce 17-deacetylnorgestimate (NGMN), an active circulating metabolite that likely contributes significantly to norgestimate efficacy. Despite being of primary significance, the metabolism and reaction phenotyping of NGMN have not been previously reported. Hence, detailed biotransformation and reaction phenotyping studies of NGMN with recombinant cytochrome P450 (P450), recombinant uridine 5'-diphospho-glucuronosyltransferases, and human liver microsomes in the presence and absence of selective P450 inhibitors were conducted. It was found that CYP3A4 plays a key role in NGMN metabolism with a fraction metabolized (fm) of 0.57. CYP2B6 and to an even lesser extent CYP2C9 were also observed to catalyze NGMN metabolism. Using this CYP3A4 fm value, the predicted plasma concentration versus time area under the curve (AUC) change in NGMN using a basic/mechanistic static model was found to be within 1.3-fold of the reported NGMN AUC changes for four modulators of CYP3A4. In addition to NGMN, we have also elucidated the biotransformation of norgestrel (NG), a downstream norgestimate and NGMN metabolite, and found that CYP3A4 and UGT1A1 have a major contribution to the elimination of NG with a combined fm value of 1. The data presented in this paper will lead to better understanding and management of NGMN-based drug-drug interactions when norgestimate is coadministered with CYP3A4 modulators.

A Novel Liquid Chromatography Tandem Mass Spectrometry Method for the Estimation of Bilirubin Glucuronides and its Application to In Vitro Enzyme Assays.

BACKGROUND: Bilirubin is a toxic waste product of metabolism, eliminated mainly through UGT1A1 mediated conjugation to mono- and di-glucuronides. Due to the potentially low Km value of bilirubin glucuronidation, the quantitative sensitivity obtained with most UV/visible light detection methods are not sufficient to accurately calculate UGT1A1 enzyme kinetics at low bilirubin concentrations. In addition, bilirubin, as well as its metabolites, are unstable during sample preparation and bioanalysis. This necessitates the need for a rapid, sensitive and robust assay to measure bilirubin glucuronides. METHODS: A robust LC-MS/MS method was developed to measure low levels of bilirubin glucuronides accurately from in vitro incubations, as well as stabilizing the analytes during sample preparation and analysis. The metabolites were quantified using a qualitative/quantitative approach utilizing UV to MS correction, thereby eliminating the need for synthetic standards. RESULTS: The method was sensitive enough to quantify mono- and di-glucuronides as low as 3 nM from in vitro incubations, and kinetic data was determined for total glucuronide formation. The Km and Vmax values for total bilirubin glucuronide formations were determined to be 0.05 ± 0.01 µM and 181.9 ± 5.3 pmol/min/mg-protein, respectively, in human recombinant UGT1A1, and 0.23 ± 0.05 µM and 875 ± 45 pmol/min/mg protein in human liver microsomes (HLM). CONCLUSION: We have developed a sensitive LC-MS/MS based method for the quantitation of bilirubin and its glucuronides from in vitro incubations. This method was successfully utilized to determine bilirubin glucuronidation kinetics in HLM and human rUGT1A1.