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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.

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 inter-laboratory variability due to peptide-specific digestion efficiency leading to data inconsistencies. To address this problem, we developed a novel universal 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) fractions. The relative percent composition of UGT1A and UGT2B isoforms in 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 show unique compositions of UGT1As and UGT2Bs. The reproducibility of the approach was validated by assessing correlations of UGT compositions between HLM and HLS9 (R20.91). The analysis of conserved peptides also provided the absolute 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), 138 and 99 (HKM), respectively. In summary, the CPSP approach could be utilized for applications in the in-vitro to in-vivo extrapolation (IVIVE) of drug metabolism and PBPK modeling.

This article puts forth all the existing methods proposed by the various authors of the Stack Exchange community to find all the edges on any shortest path between two given nodes of a directed acyclic graph. For a directed acyclic graph with N number of nodes, an exponential number of paths are possible between any two given nodes and, thus, it is not feasible to compute every path and find the shortest ones in polynomial time to generate a set of all edges that contribute or make any of the shortest paths. The methods discussed in this article are not limited only to this specific use case, but have a much broader scope in graph theory, dynamic programming and counting problems. Generally, various other questions and answers, raised on the community portal having similar scope to those that the users specifically seek, do not receive sufficient hits and, hence, enough attention and votes for various reasons worth contemplating. Therefore, this article also aims to highlight the various scopes of the methods discussed in this article and acknowledge the efforts of the authors, moderators and contributors of the Stack Exchange community for their expertise and time to write precise answers and share their opinions and advice. Finally, it also appeals to all the other beneficiaries in the community to use their privileges responsibly and upvote the posted answers, if they helped solve their queries, as one upvote is free of cost.

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.

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.

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.

The algorithm aims to find the K\({\varvec{th}}\) max sum pair of two indices of an array of N (N \(\ge \) 2) distinct elements [a1, a2, a3, ..., an]. If the sum of values represented by the 2 indices of a single pair in array A is the same as that of any other pair, i.e., if P(i, j) and P(m, n) are 2 distinct pairs and if \({(\mathbf{A} [\textit{i}] + \mathbf{A} [\textit{j}] = \mathbf{A} [\textit{m}] + \mathbf{A} [\textit{n}]),}\) then the pair containing the index which represents the maximum of all 4 values represented by indices of the 2 pairs in the array obtains the highest priority, i.e., if \({(\mathbf{A} [\textit{m}] {>} \mathbf{A} [\textit{i}] {>} \mathbf{A} [\textit{n}] {>} \mathbf{A} [\textit{j}])}\), then the pair containing the index m obtains the highest priority. The purpose of this algorithm is to optimize the computation of recommendations on real time platforms. At the time of making a purchase on e-commerce platforms, with millions of options available in the product catalog, the algorithm can be used to recommend the best complementary product that can be bought as a pair with the main product or two all together different products of same type as of main product which can be bought as a combo or a pair. Not only the top recommendations, but random recommendations are also necessary so that the customers get a good breadth or variety of the available products in the catalog. In this paper, we propose an algorithm which can be used to address both the scenarios in real time and conclusively, it is evident that the time and space complexities are independent of K.

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.

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.

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.

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.

Certain aromatic nitriles are well-known inhibitors of cysteine proteases. The mode of action of these compounds involves the formation of a reversible or irreversible covalent bond between the nitrile and a thiol group in the active site of the enzyme. However, the reactivity of these aromatic nitrile-substituted heterocycles may lead inadvertently to nonspecific interactions with DNA, protein, glutathione, and other endogenous components, resulting in toxicity and complicating the use of these compounds as therapeutic agents. In the present study, the intrinsic reactivity and associated structure-property relationships of cathepsin K inhibitors featuring substituted pyridazines [6-phenylpyridazine-3-carbonitrile, 6-(4-fluorophenyl)pyridazine-3-carbonitrile, 6-(4-methoxyphenyl)pyridazine-3-carbonitrile, 6-p-tolylpyridazine-3-carbonitrile], pyrimidines [5-p-tolylpyrimidine-2-carbonitrile, 5-(4-fluorophenyl)pyrimidine-2-carbonitrile], and pyridines [5-p-tolylpicolinonitrile and 5-(4-fluorophenyl)picolinonitrile] were evaluated using a combination of computational and analytical approaches to establish correlations between electrophilicity and levels of metabolites that were formed in glutathione- and N-acetylcysteine-supplemented human liver microsomes. Metabolites that were characterized in this study featured substituted thiazolines that were formed following rearrangements of transient glutathione and N-acetylcysteine conjugates. Peptidases including γ-glutamyltranspeptidase were shown to catalyze the formation of these products, which were formed to lesser extents in the presence of the selective γ-glutamyltranspeptidase inhibitor acivicin and the nonspecific peptidase inhibitors phenylmethylsulfonyl fluoride and aprotinin. Of the chemical series mentioned above, the pyrimidine series was the most susceptible to metabolism to thiazoline-containing products, followed, in order, by the pyridazine and pyridine series. This trend was in keeping with the diminishing electrophilicity across these series, as demonstrated by in silico modeling. Hence, mechanistic insights gained from this study could be used to assist a medicinal chemistry campaign to design cysteine protease inhibitors that were less prone to the formation of covalent adducts.

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.

RATIONALE Several mechanisms have been anticipated for the toxicity of amiodarone, such as oxidative stress, lipid peroxidation, phospholipidosis, free radical generation, etc. Amiodarone is structurally similar to benzbromarone, an uricosuric agent, which was withdrawn from European markets due to its idiosyncratic hepatotoxicity. A proposed reason behind the toxicity of benzbromarone was the production of a reactive ortho-diquinone metabolite, which was found to form adducts with glutathione. Therefore, taking a clue that a similar diquinone metabolite of amiodarone may be the reason for its hepatotoxicity, metabolite identification studies were carried out on the drug using liquid chromatography/mass spectrometry (LC/MS) tools. METHODS The studies involved in vitro (rat liver microsomes, rat liver S9 fraction, human liver S9 fraction) and in vivo (rat feces, urine, plasma) models, wherein the samples were analyzed by employing LC/HRMS, LC/MSn and HDE-MS. RESULTS AND CONCLUSIONS A total of 26 metabolites of amiodarone were detected in the investigated in vitro and in vivo matrices. The suspected ortho-diquinone metabolite was one of them. The formation of the same might be an added reason for the hepatotoxicity shown by the drug. Copyright © 2013 John Wiley & Sons, Ltd.

Drug-metabolizing enzymes (DMEs) are primarily expressed in the liver but their role in the extrahepatic tissues such as gastrointestinal tract (GIT), pulmonary, excretory, nervous, cardiovascular system, and skin cannot be neglected. Generally, the expression of DMEs in extrahepatic tissues is quantitatively lower than that in the liver, but there are a few enzymes such as CYP1A1, CYP1B1, CYP2F1, and CYP2U1 that are more abundant in extrahepatic organs. As many extrahepatic organs are portals for administered drugs, DMEs expressed in these organs can be responsible for significant metabolism, leading to first-pass effects and lower bioavailability. Extrahepatic DMEs are also involved in bioactivation of prodrugs and formation of reactive metabolites that may interact with cellular components, resulting in organ-specific toxicity. Activity and expression of extrahepatic DMEs is often altered by coadministered drugs, leading to drug–drug interactions. Expression of DMEs in living beings affected by a host of environmental and genetic factors such as genetic polymorphism, age, gender, pathophysiological conditions, inborn errors in metabolism, food habits, and environmental pollutants, contributing to varied drug effects and idiosyncratic toxicities. Keywords: extrahepatic metabolism; extrahepatic organs; drug-metabolizing enzymes; xenobiotics; drug–drug interaction; drug bioactivation; drug deactivation; genetic polymorphism; cytochrome P450; non-CYP enzymes; bioavailability; toxicity