Bioactivation and reactivity research advances - 2022 year in review‡

With the 50th year mark since the launch of Drug Metabolism and Disposition journal, the field of drug metabolism and bioactivation has advanced exponentially in the past decades (Guengerich 2023).This has, in a major part, been due to the continued advances across the whole spectrum of applied technologies in hardware, software, machine learning (ML), and artificial intelligence (AI). LC-MS platforms continue to evolve to support key applications in the field, and automation is also improving the accuracy, precision, and throughput of these supporting assays. In addition, sample generation and processing is being aided by increased diversity and quality of reagents and bio-matrices so that what is being analyzed is more relevant and translatable. The application of in silico platforms (applied software, ML, and AI) is also making great strides, and in tandem with the more traditional approaches mentioned previously, is significantly advancing our understanding of bioactivation pathways and how these play a role in toxicity. All of this continues to allow the area of bioactivation to evolve in parallel with associated fields to help bring novel or improved medicines to patients with urgent or unmet needs.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

Biotransformation research advances - 2022 year in review

This annual review is the eighth of its kind since 2016 (Baillie et al. 2016, Khojasteh et al. 2017, Khojasteh et al. 2018, Khojasteh et al. 2019, Khojasteh et al. 2020, Khojasteh et al. 2021, Khojasteh et al. 2022). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation.

The role of cytochrome P450 3A4-mediated metabolism in sorafenib and lapatinib hepatotoxicity

Tyrosine kinase inhibitors (TKIs) are increasingly popular drugs used to treat more than a dozen different diseases, including some forms of cancer. Despite having fewer adverse effects than traditional chemotherapies, they are not without risks. Liver injury is a particular concern. Of the FDA-approved TKIs, approximately 40% cause hepatotoxicity. However, little is known about the underlying pathophysiology. The leading hypothesis is that TKIs are converted by cytochrome P450 3A4 (CYP3A4) to reactive metabolites that damage proteins. Indeed, there is strong evidence for this bioactivation of TKIs in in vitro reactions. However, the actual toxic effects are underexplored. Here, we measured the cytotoxicity of several TKIs in primary mouse hepatocytes, HepaRG cells, and HepG2 cells with and without CYP3A4 modulation. To our surprise, the data indicate that CYP3A4 increases resistance to sorafenib and lapatinib hepatotoxicity. The results have implications for the mechanism of toxicity of these drugs in patients and underline the importance of selecting an appropriate experimental model.

Similar 5F-APINACA Metabolism between CD-1 Mouse and Human Liver Microsomes Involves Different P450 Cytochromes

In 2019, synthetic cannabinoids accounted for more than one-third of new drugs of abuse worldwide; however, assessment of associated health risks is not ethical for controlled and often illegal substances, making CD-1 mouse exposure studies the gold standard. Interpretation of those findings then depends on the similarity of mouse and human metabolic pathways. Herein, we report the first comparative analysis of steady-state metabolism of N-(1-adamantyl)-1-(5-pentyl)-1H-indazole-3-carboxamide (5F-APINACA/5F-AKB48) in CD-1 mice and humans using hepatic microsomes. Regardless of species, 5F-APINACA metabolism involved highly efficient sequential adamantyl hydroxylation and oxidative defluorination pathways that competed equally. Secondary adamantyl hydroxylation was less efficient for mice. At low 5F-APINACA concentrations, initial rates were comparable between pathways, but at higher concentrations, adamantyl hydroxylations became less significant due to substrate inhibition likely involving an effector site. For humans, CYP3A4 dominated both metabolic pathways with minor contributions from CYP2C8, 2C19, and 2D6. For CD-1 mice, Cyp3a11 and Cyp2c37, Cyp2c50, and Cyp2c54 contributed equally to adamantyl hydroxylation, but Cyp3a11 was more efficient at oxidative defluorination than Cyp2c members. Taken together, the results of our in vitro steady-state study indicate a high conservation of 5F-APINACA metabolism between CD-1 mice and humans, but deviations can occur due to differences in P450s responsible for the associated reactions.

Bioactivation and Reactivity Research Advances - 2021 year in review

This year's review on bioactivation and reactivity began as a part of the annual review on biotransformation and bioactivation led by Cyrus Khojasteh (Khojasteh et al., 2021, 2020, 2019, 2018, 2017; Baillie et al., 2016). Increased contributions from experts in the field led to the development of a stand alone edition for the first time this year focused specifically on bioactivation and reactivity. Our objective for this review is to highlight and share articles which we deem influential and significant regarding the development of covalent inhibitors, mechanisms of reactive metabolite formation, enzyme inactivation, and drug safety. Based on the selected articles, we created two sections: (1) reactivity and enzyme inactivation, and (2) bioactivation mechanisms and safety (Table 1). Several biotransformation experts have contributed to this effort from academic and industry settings.

Discovery of Novel Reductive Elimination Pathway for 10-Hydroxywarfarin

Coumadin (R/S-warfarin) anticoagulant therapy is highly efficacious in preventing the formation of blood clots; however, significant inter-individual variations in response risks over or under dosing resulting in adverse bleeding events or ineffective therapy, respectively. Levels of pharmacologically active forms of the drug and metabolites depend on a diversity of metabolic pathways. Cytochromes P450 play a major role in oxidizing R- and S-warfarin to 6-, 7-, 8-, 10-, and 4′-hydroxywarfarin, and warfarin alcohols form through a minor metabolic pathway involving reduction at the C11 position. We hypothesized that due to structural similarities with warfarin, hydroxywarfarins undergo reduction, possibly impacting their pharmacological activity and elimination. We modeled reduction reactions and carried out experimental steady-state reactions with human liver cytosol for conversion of rac-6-, 7-, 8-, 4′-hydroxywarfarin and 10-hydroxywarfarin isomers to the corresponding alcohols. The modeling correctly predicted the more efficient reduction of 10-hydroxywarfarin over warfarin but not the order of the remaining hydroxywarfarins. Experimental studies did not indicate any clear trends in the reduction for rac-hydroxywarfarins or 10-hydroxywarfarin into alcohol 1 and 2. The collective findings indicated the location of the hydroxyl group significantly impacted reduction selectivity among the hydroxywarfarins, as well as the specificity for the resulting metabolites. Based on studies with R- and S-7-hydroxywarfarin, we predicted that all hydroxywarfarin reductions are enantioselective toward R substrates and enantiospecific for S alcohol metabolites. CBR1 and to a lesser extent AKR1C3 reductases are responsible for those reactions. Due to the inefficiency of reactions, only reduction of 10-hydroxywarfarin is likely to be important in clearance of the metabolite. This pathway for 10-hydroxywarfarin may have clinical relevance as well given its anticoagulant activity and capacity to inhibit S-warfarin metabolism.

4-Methyl-1,2,3-Triazoles as N-Acetyl-Lysine Mimics Afford Potent BET Bromodomain Inhibitors with Improved Selectivity

The bromodomain and extra terminal (BET) protein family recognizes acetylated lysines within histones and transcription factors using two N-terminal bromodomains, D1 and D2. The protein-protein interactions between BET bromodomains, acetylated histones, and transcription factors are therapeutic targets for BET-related diseases, including inflammatory disease and cancer. Prior work demonstrated that methylated-1,2,3-triazoles are suitable N-acetyl lysine mimetics for BET inhibition. Here we describe a structure-activity relationship study of triazole-based inhibitors that improve affinity, D1 selectivity, and microsomal stability. These outcomes were accomplished by targeting a nonconserved residue, Asp144 and a conserved residue, Met149, on BRD4 D1. The lead inhibitors DW34 and 26 have a BRD4 D1 K_d of 12 and 6.4 nM, respectively. Cellular activity was demonstrated through suppression of c-Myc expression in MM.1S cells and downregulation of IL-8 in TNF-α-stimulated A549 cells. These data indicate that DW34 and 26 are new leads to investigate the anticancer and anti-inflammatory activity of BET proteins.

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

Novel advances in biotransformation and bioactivation research - 2020 year in review

This annual review is the sixth of its kind since 2016 (see references). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation and bioactivation. These fields are constantly evolving with new molecular structures and discoveries of corresponding pathways for metabolism that impact relevant drug development with respect to efficacy and safety. Based on the selected articles, we created three sections: (1) drug design, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation and safety (Table 1). Unlike in years past, more biotransformation experts have joined and contributed to this effort while striving to maintain a balance of authors from academic and industry settings.

Bioactivation of Isoxazole-Containing Bromodomain and Extra-Terminal Domain (BET) Inhibitors

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD’s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads.

Meloxicam methyl group determines enzyme specificity for thiazole bioactivation compared to sudoxicam

Meloxicam is a thiazole-containing NSAID that was approved for marketing with favorable clinical outcomes despite being structurally similar to the hepatotoxic sudoxicam. Introduction of a single methyl group on the thiazole results in an overall lower toxic risk, yet the group's impact on P450 isozyme bioactivation is unclear. Through analytical methods, we used inhibitor phenotyping and recombinant P450s to identify contributing P450s, and then measured steady-state kinetics for bioactivation of sudoxicam and meloxicam by the recombinant P450s to determine relative efficiencies. Experiments showed that CYP2C8, 2C19, and 3A4 catalyze sudoxicam bioactivation, and CYP1A2 catalyzes meloxicam bioactivation, indicating that the methyl group not only impacts enzyme affinity for the drugs, but also alters which isozymes catalyze the metabolic pathways. Scaling of relative P450 efficiencies based on average liver concentration revealed that CYP2C8 dominates the sudoxicam bioactivation pathway and CYP2C9 dominates meloxicam detoxification. Dominant P450s were applied for an informatics assessment of electronic health records to identify potential correlations between meloxicam drug-drug interactions and drug-induced liver injury. Overall, our findings provide a cautionary tale on assumed impacts of even simple structural modifications on drug bioactivation while also revealing specific targets for clinical investigations of predictive factors that determine meloxicam-induced idiosyncratic liver injury.

Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics

Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spectrometry and quantitate yields during reactions. Of the four quinone adducts, we were able to characterize structures for three of them. Based on kinetics, the most efficient bioactivation pathway led to the monohydroxy para-quinone-imine followed by the dechloro-ortho-quinone-imine. Two very inefficient pathways led to the dihydroxy ortho-quinone and a likely multiply adducted quinone. When taken together, bioactivation pathways for meclofenamate accounted for approximately 13% of total metabolism. In sum, XenoNet facilitated prediction of reactive metabolite structures, whereas quantitative experimental studies provided a tractable approach to validate actual bioactivation pathways for meclofenamate. Our results provide a foundation for assessing reactive metabolite load more accurately for future comparative studies with other NSAIDs and drugs in general.

Significance of Competing Metabolic Pathways for 5F-APINACA Based on Quantitative Kinetics

In 2020, nearly one-third of new drugs on the global market were synthetic cannabinoids including the drug of abuse N-(1-adamantyl)-1-(5-pentyl)-1H-indazole-3-carboxamide (5F-APINACA, 5F-AKB48). Knowledge of 5F-APINACA metabolism provides a critical mechanistic basis to interpret and predict abuser outcomes. Prior qualitative studies identified which metabolic processes occur but not the order and extent of them and often relied on problematic “semi-quantitative” mass spectroscopic (MS) approaches. We capitalized on 5F-APINACA absorbance for quantitation while leveraging MS to characterize metabolite structures for measuring 5F-APINACA steady-state kinetics. We demonstrated the reliability of absorbance and not MS for inferring metabolite levels. Human liver microsomal reactions yielded eight metabolites by MS but only five by absorbance. Subsequent kinetic studies on primary and secondary metabolites revealed highly efficient mono- and dihydroxylation of the adamantyl group and much less efficient oxidative defluorination at the N-pentyl terminus. Based on regiospecificity and kinetics, we constructed pathways for competing and intersecting steps in 5F-APINACA metabolism. Overall efficiency for adamantyl oxidation was 17-fold higher than that for oxidative defluorination, showing significant bias in metabolic flux and subsequent metabolite profile compositions. Lastly, our analytical approach provides a powerful new strategy to more accurately assess metabolic kinetics for other understudied synthetic cannabinoids possessing the indazole chromophore.

Novel advances in biotransformation and bioactivation research-2019 year in review

Biotransformation is one of the main mechanisms used by the body to eliminate drugs. As drug molecules become more complicated, the involvement of drug metabolizing enzymes increases beyond those that are typically studied, such as the cytochrome P450 enzymes. In this review, we try to capture the many outstanding articles that were published in the past year in the field of biotransformation (see Table 1). We have divided the articles into two categories of (1) metabolites and drug metabolizing enzymes, and (2) bioactivation and safety.  This annual review is the fifth of its kind since 2016 (Baillie et al. 2016; Khojasteh et al. 2017, 2018, 2019). This effort in itself also continues to evolve. We have followed the same format we used in previous years in terms of the selection of articles and the authoring of each section. I am pleased of the continued support of Rietjens, Miller, Zhang, Driscoll and Mitra to this review. We would like to welcome Klarissa D. Jackson as a new author for this year's issue. We strive to maintain a balance of authors from academic and industry settings.  We would be pleased to hear your opinions of our commentary, and we extend an invitation to anyone who would like to contribute to a future edition of this review. Cyrus Khojasteh, on behalf of the authors.

Dual mechanisms suppress meloxicam bioactivation relative to sudoxicam

Thiazoles are biologically active aromatic heterocyclic rings occurring frequently in natural products and drugs. These molecules undergo typically harmless elimination; however, a hepatotoxic response can occur due to multistep bioactivation of the thiazole to generate a reactive thioamide. A basis for those differences in outcomes remains unknown. A textbook example is the high hepatotoxicity observed for sudoxicam in contrast to the relative safe use and marketability of meloxicam, which differs in structure from sudoxicam by the addition of a single methyl group. Both drugs undergo bioactivation, but meloxicam exhibits an additional detoxification pathway due to hydroxylation of the methyl group. We hypothesized that thiazole bioactivation efficiency is similar between sudoxicam and meloxicam due to the methyl group being a weak electron donator, and thus, the relevance of bioactivation depends on the competing detoxification pathway. For a rapid analysis, we modeled epoxidation of sudoxicam derivatives to investigate the impact of substituents on thiazole bioactivation. As expected, electron donating groups increased the likelihood for epoxidation with a minimal effect for the methyl group, but model predictions did not extrapolate well among all types of substituents. Through analytical methods, we measured steady-state kinetics for metabolic bioactivation of sudoxicam and meloxicam by human liver microsomes. Sudoxicam bioactivation was 6-fold more efficient than that for meloxicam, yet meloxicam showed a 6-fold higher efficiency of detoxification than bioactivation. Overall, sudoxicam bioactivation was 15-fold more likely than meloxicam considering all metabolic clearance pathways. Kinetic differences likely arise from different enzymes catalyzing respective metabolic pathways based on phenotyping studies. Rather than simply providing an alternative detoxification pathway, the meloxicam methyl group suppressed the bioactivation reaction. These findings indicate the impact of thiazole substituents on bioactivation is more complex than previously thought and likely contributes to the unpredictability of their toxic potential.

Comprehensive kinetic and modeling analyses revealed CYP2C9 and 3A4 determine terbinafine metabolic clearance and bioactivation

Terbinafine N-dealkylation pathways result in formation of 6,6-dimethyl-2-hepten-4-ynal (TBF-A), a reactive allylic aldehyde, that may initiate idiosyncratic drug-induced liver toxicity. Previously, we reported on the importance of CYP2C19 and 3A4 as major contributors to TBF-A formation. In this study, we expanded on those efforts to assess individual contributions of CYP1A2, 2B6, 2C8, 2C9, and 2D6 in terbinafine metabolism. The combined knowledge gained from these studies allowed us to scale the relative roles of the P450 isozymes in hepatic clearance of terbinafine including pathways leading to TBF-A, and hence, provide a foundation for assessing their significance in terbinafine-induced hepatotoxicity. We used in vitro terbinafine reactions with recombinant P450s to measure kinetics for multiple metabolic pathways and calculated contributions of all individual P450 isozymes to in vivo hepatic clearance for the average human adult. The findings confirmed that CYP3A4 was a major contributor (at least 30% total metabolism) to all three of the possible N-dealkylation pathways; however, CYP2C9, and not CYP2C19, played a critical role in terbinafine metabolism and even exceeded CYP3A4 contributions for terbinafine N-demethylation. A combination of their metabolic capacities accounted for at least 80% of the conversion of terbinafine to TBF-A, while CYP1A2, 2B6, 2C8, and 2D6 made minor contributions. Computational approaches provide a more rapid, less resource-intensive strategy for assessing metabolism, and thus, we additionally predicted terbinafine metabolism using deep neural network models for individual P450 isozymes. Cytochrome P450 isozyme models accurately predicted the likelihood for terbinafine N-demethylation, but overestimated the likelihood for a minor N-denaphthylation pathway. Moreover, the models were not able to differentiate the varying roles of the individual P450 isozymes for specific reactions with this particular drug. Taken together, the significance of CYP2C9 and 3A4 and to a lesser extent, CYP2C19, in terbinafine metabolism is consistent with reported drug interactions. This finding suggests that variations in individual P450 contributions due to other factors like polymorphisms may similarly contribute to terbinafine-related adverse health outcomes. Nevertheless, the impact of their metabolic capacities on formation of reactive TBF-A and consequent idiosyncratic hepatotoxicity will be mitigated by competing detoxification pathways, TBF-A decay, and TBF-A adduction to glutathione that remain understudied.

Biotransformation and bioactivation reactions - 2018 literature highlights

In the past three decades, ADME sciences have become an integral component of the drug discovery and development process. At the same time, the field has continued to evolve, thus, requiring ADME scientists to be knowledgeable of and engage with diverse aspects of drug assessment: from pharmacology to toxicology, and from in silico modeling to in vitro models and finally in vivo models. Progress in this field requires deliberate exposure to different aspects of ADME; however, this task can seem daunting in the current age of mass information. We hope this review provides a focused and brief summary of a wide array of critical advances over the past year and explains the relevance of this research ( Table 1 ). We divided the articles into categories of (1) drug optimization, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation. This annual review is the fourth of its kind (Baillie et al. 2016 ; Khojasteh et al. 2017 , 2018 ). We have followed the same format we used in previous years in terms of the selection of articles and the authoring of each section. This effort in itself also continues to evolve. I am pleased that Rietjens, Miller, and Mitra have again contributed to this annual review. We would like to welcome Namandjé N. Bumpus, James P. Driscoll, and Donglu Zhang as authors for this year's issue. We strive to maintain a balance of authors from academic and industry settings. We would be pleased to hear your opinions of our commentary, and we extend an invitation to anyone who would like to contribute to a future edition of this review. Cyrus Khojasteh, on behalf of the authors.

A Time-Embedding Network Models the Ontogeny of 23 Hepatic Drug Metabolizing Enzymes

Pediatric patients are at elevated risk of adverse drug reactions, and there is insufficient information on drug safety in children. Complicating risk assessment in children, there are numerous age-dependent changes in the absorption, distribution, metabolism, and elimination of drugs. A key contributor to age-dependent drug toxicity risk is the ontogeny of drug metabolism enzymes, the changes in both abundance and type throughout development from the fetal period through adulthood. Critically, these changes affect not only the overall clearance of drugs but also exposure to individual metabolites. In this study, we introduce time-embedding neural networks in order to model population-level variation in metabolism enzyme expression as a function of age. We use a time-embedding network to model the ontogeny of 23 drug metabolism enzymes. The time-embedding network recapitulates known demographic factors impacting 3A5 expression. The time-embedding network also effectively models the nonlinear dynamics of 2D6 expression, enabling a better fit to clinical data than prior work. In contrast, a standard neural network fails to model these features of 3A5 and 2D6 expression. Finally, we combine the time-embedding model of ontogeny with additional information to estimate age-dependent changes in reactive metabolite exposure. This simple approach identifies age-dependent changes in exposure to valproic acid and dextromethorphan metabolites and suggests potential mechanisms of valproic acid toxicity. This approach may help researchers evaluate the risk of drug toxicity in pediatric populations.

CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches

Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.

Lamisil (terbinafine) toxicity: Determining pathways to bioactivation through computational and experimental approaches

Lamisil (terbinafine) may cause idiosyncratic liver toxicity through a proposed toxicological mechanism involving the reactive metabolite 6,6-dimethyl-2-hepten-4-ynal (TBF-A). TBF-A toxicological relevance remains unclear due to a lack of identification of pathways leading to and competing with TBF-A formation. We resolved this knowledge gap by combining computational modeling and experimental kinetics of in vitro hepatic N-dealkylation of terbinafine. A deep learning model of N-dealkylation predicted a high probability for N-demethylation to yield desmethyl-terbinafine followed by N-dealkylation to TBF-A and marginal contributions from other possible pathways. We carried out steady-state kinetic experiments with pooled human liver microsomes that relied on development of labeling methods to expand metabolite characterization. Those efforts revealed high levels of TBF-A formation and first order decay during metabolic reactions; actual TBF-A levels would then reflect the balance between those processes as well as reflect the impact of stabilizing adduction with glutathione and other biological molecules. Modeling predictions and experimental studies agreed on the significance of N-demethylation and insignificance of N-denaphthylation in terbinafine metabolism, yet differed on importance of direct TBF-A formation. Under steady-state conditions, the direct pathway was the most important source of the reactive metabolite with a Vmax/Km of 4.0 pmol/min/mg protein/µM in contrast to model predictions. Nevertheless, previous studies show that therapeutic dosing leads to accumulation of desmethyl-terbinafine in plasma, which means that likely sources for TBF-A would draw from metabolism of both the major metabolite and parent drug based on our modeling and experimental studies. Through this combination of novel modeling and experimental approaches, we are the first to identify pathways leading to generation of TBF-A for assessing its role in idiosyncratic adverse drug interactions.

Biotransformation and bioactivation reactions - 2017 literature highlights *

This annual review is the third one to highlight recent advances in the study and assessment of biotransformations and bioactivations ( Table 1 ). We followed the same format as the previous years with selection and authoring each section (see Baillie et al. 2016 ; Khojasteh et al. 2017 ). We acknowledge that many universities no longer train students in mechanistic biotransformation studies reflecting a decline in the investment for those efforts by public funded granting institutions. We hope this work serves as a resource to appreciate the knowledge gained each year to understand and hopefully anticipate toxicological outcomes dependent on biotransformations and bioactivations. This effort itself also continues to evolve. I am pleased that Drs. Rietjens and Miller have again contributed to this annual review. We would like to welcome Kaushik Mitra as an author for this year's issue, and we thank Deepak Dalvie for his contributions to last year's edition. We have intentionally maintained a balance of authors such that two come from an academic setting and two come from industry. As always, please drop us a note if you find this review helpful. We would be pleased to hear your opinions of our commentary, and we extend an invitation to anyone who would like to contribute to a future edition of this review.

Regioselectivity significantly impacts microsomal glucuronidation efficiency of R/S-6, 7-, and 8-hydroxywarfarin

Coumadin (R/S-warfarin) metabolism plays a critical role in patient response to anticoagulant therapy. Several cytochrome P450s oxidize warfarin into R/S-6-, 7-, 8-, 10, and 4'-hydroxywarfarin that can undergo subsequent glucuronidation by UDP-glucuronosyltransferases (UGTs); however, current studies on recombinant UGTs cannot be adequately extrapolated to microsomal glucuronidation capacities for the liver. Herein, we estimated the capacity of the average human liver to glucuronidate hydroxywarfarin and identified UGTs responsible for those metabolic reactions through inhibitor phenotyping. There was no observable activity toward R/S-warfarin, R/S-10-hydroxywarfarin or R/S-4'-hydroxywarfarin. The observed metabolic efficiencies (V_max/K_m) toward R/S-6-, 7-, and especially 8-hydroxywarfarin indicated a high glucuronidation capacity to metabolize these compounds. UGTs demonstrated strong regioselectivity toward the hydroxywarfarins. UGT1A6 and UGT1A1 played a major role in R/S-6- and 7-hydroxywarfarin glucuronidation, respectively, whereas UGT1A9 accounted for almost all of the generation of the R/S-8-hydroxywarfarin glucuronide. In summary, these studies expanded insights to glucuronidation of hydroxywarfarins by pooled human liver microsomes, novel roles for UGT1A6 and 1A9, and the overall degree of regioselectivity for the UGT reactions.

Computationally Assessing the Bioactivation of Drugs by N-Dealkylation

Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .

Stereospecific Metabolism of R- and S-Warfarin by Human Hepatic Cytosolic Reductases

Coumadin (rac-warfarin) is the most commonly used anticoagulant in the world; however, its clinical use is often challenging because of its narrow therapeutic range and interindividual variations in response. A critical contributor to the uncertainty is variability in warfarin metabolism, which includes mostly oxidative but also reductive pathways. Reduction of each warfarin enantiomer yields two warfarin alcohol isomers, and the corresponding four alcohols retain varying levels of anticoagulant activity. Studies on the kinetics of warfarin reduction have often lacked resolution of parent-drug enantiomers and have suffered from coelution of pairs of alcohol metabolites; thus, those studies have not established the importance of individual stereospecific reductive pathways. We report the first steady-state analysis of R- and S-warfarin reduction in vitro by pooled human liver cytosol. As determined by authentic standards, the major metabolites were 9R,11S-warfarin alcohol for R-warfarin and 9S,11S-warfarin alcohol for S-warfarin. R-warfarin (Vmax 150 pmol/mg per minute, Km 0.67 mM) was reduced more efficiently than S-warfarin (Vmax 27 pmol/mg per minute, Km 1.7 mM). Based on inhibitor phenotyping, carbonyl reductase-1 dominated R-and S-warfarin reduction, followed by aldo-keto reductase-1C3 and then other members of that family. Overall, the carbonyl at position 11 undergoes stereospecific reduction by multiple enzymes to form the S alcohol for both drug enantiomers, yet R-warfarin undergoes reduction preferentially. This knowledge will aid in assessing the relative importance of reductive pathways for R- and S-warfarin and factors influencing levels of pharmacologically active parent drugs and metabolites, thus impacting patient dose responses.

Toxicological Implications of Mitochondrial Localization of CYP2E1

Cytochrome P450 2E1 (CYP2E1) metabolizes an extensive array of pollutants, drugs, and other small molecules, often resulting in bioactivation to reactive metabolites. Therefore, it is unsurprising that it has been the subject of decades of research publications and reviews. However, while CYP2E1 has historically been studied in the endoplasmic reticulum (erCYP2E1), active CYP2E1 is also present in mitochondria (mtCYP2E1). Relatively few studies have specifically focused on mtCYP2E1, but there is growing interest in this form of the enzyme as a driver in toxicological mechanisms given its activity and location. Many previous studies have linked total CYP2E1 to conditions that involve mitochondrial dysfunction (fasting, diabetes, non-alcoholic steatohepatitis, and obesity). Furthermore, a large number of reactive metabolites that are formed by CYP2E1 through metabolism of drugs and pollutants have been demonstrated to cause mitochondrial dysfunction. Finally, there appears to be significant inter-individual variability in targeting to the mitochondria, which could constitute a source of variability in individual response to exposures. This review discusses those outcomes, the biochemical properties and toxicological consequences of mtCYP2E1, and highlights important knowledge gaps and future directions. Overall, we feel that this exciting area of research is rich with new and important questions about the relationship between mtCYP2E1, mitochondrial dysfunction, and pathology.

Computational Approach to Structural Alerts: Furans, Phenols, Nitroaromatics, and Thiophenes

Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites and thereby become toxic. Unfortunately, as useful as structural alerts are, they do not effectively model if, when, and why metabolism renders safe molecules toxic. Toxicity due to a specific structural alert is highly conditional, depending on the metabolism of the alert, the reactivity of its metabolites, dosage, and competing detoxification pathways. A systems approach, which explicitly models these pathways, could more effectively assess the toxicity risk of drug candidates. In this study, we demonstrated that mathematical models of P450 metabolism can predict the context-specific probability that a structural alert will be bioactivated in a given molecule. This study focuses on the furan, phenol, nitroaromatic, and thiophene alerts. Each of these structural alerts can produce reactive metabolites through certain metabolic pathways but not always. We tested whether our metabolism modeling approach, XenoSite, can predict when a given molecule's alerts will be bioactivated. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. Metabolism models accurately predict whether alerts are bioactivated and thus serve as a practical approach to improve the interpretability and usefulness of structural alerts. We expect that this same computational approach can be extended to most other structural alerts and later integrated into toxicity risk models. This advance is one necessary step toward our long-term goal of building comprehensive metabolic models of bioactivation and detoxification to guide assessment and design of new therapeutic molecules.

1,3-Butadiene-induced mitochondrial dysfunction is correlated with mitochondrial CYP2E1 activity in Collaborative Cross mice

Cytochrome P450 2E1 (CYP2E1) metabolizes low molecular weight hydrophobic compounds, including 1,3-butadiene, which is converted by CYP2E1 to electrophilic epoxide metabolites that covalently modify cellular proteins and DNA. Previous CYP2E1 studies have mainly focused on the enzyme localized in the endoplasmic reticulum (erCYP2E1); however, active CYP2E1 has also been found in mitochondria (mtCYP2E1) and the distribution of CYP2E1 between organelles can influence an individual's response to exposure. Relatively few studies have focused on the contribution of mtCYP2E1 to activation of chemical toxicants. We hypothesized that CYP2E1 bioactivation of 1,3-butadiene within mitochondria adversely affects mitochondrial respiratory complexes I-IV. A population of Collaborative Cross mice was exposed to air (control) or 200ppm 1,3-butadiene. Subcellular fractions (mitochondria, DNA, and microsomes) were collected from frozen livers and CYP2E1 activity was measured in microsomes and mitochondria. Individual activities of mitochondrial respiratory complexes I-IV were measured using in vitro assays and purified mitochondrial fractions. In air- and 1,3-butadiene-exposed mouse samples, mtDNA copy numbers were assessed by RT-PCR, and mtDNA integrity was assessed through a PCR-based assay. No significant changes in mtDNA copy number or integrity were observed; however, there was a decrease in overall activity of mitochondrial respiratory complexes I, II, and IV after 1,3-butadiene exposure. Additionally, higher mtCYP2E1 (but not erCYP2E1) activity was correlated with decreased mitochondrial respiratory complex activity (in complexes I-IV) in the 1,3-butadiene-exposed (not control) animals. Together, these results represent the first in vivo link between mitochondrial CYP2E1 activity and mitochondrial toxicity.

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model's performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity.

Kynurenine Signaling Increases DNA Polymerase Kappa Expression and Promotes Genomic Instability in Glioblastoma Cells

Overexpression of the translesion synthesis polymerase hpol κ in glioblastomas has been linked to poor patient prognosis; however, the mechanism promoting higher expression in these tumors remains unknown. We determined that activation of the aryl hydrocarbon receptor (AhR) pathway in glioblastoma cells leads to increased hpol κ mRNA and protein levels. We blocked nuclear translocation and DNA binding by AhR in glioblastoma cells using a small-molecule and observed decreased hpol κ expression. Pharmacological inhibition of tryptophan-2,3-dioxygenase (TDO), the enzyme largely responsible for activating AhR in glioblastoma, led to a decrease in the endogenous AhR agonist kynurenine and a corresponding decrease in hpol κ protein levels. Importantly, we discovered that inhibiting TDO activity, AhR signaling, or suppressing hpol κ expression with RNA interference led to decreased chromosomal damage in glioblastoma cells. Epistasis assays further supported the idea that TDO activity, activation of AhR signaling, and the resulting overexpression of hpol κ function primarily in the same pathway to increase endogenous DNA damage. These findings indicate that upregulation of hpol κ through glioblastoma-specific TDO activity and activation of AhR signaling likely contributes to the high levels of replication stress and genomic instability observed in these tumors.

Subcellular localization of rat CYP2E1 impacts metabolic efficiency toward common substrates

Cytochrome P450 2E1 (CYP2E1) detoxifies or bioactivates many low molecular-weight compounds. Most knowledge about CYP2E1 activity relies on studies of the enzyme localized to endoplasmic reticulum (erCYP2E1); however, CYP2E1 undergoes transport to mitochondria (mtCYP2E1) and becomes metabolically active. We report the first comparison of in vitro steady-state kinetic profiles for erCYP2E1 and mtCYP2E1 oxidation of probe substrate 4-nitrophenol and pollutants styrene and aniline using subcellular fractions from rat liver. For all substrates, metabolic efficiency changed with substrate concentration for erCYP2E1 reflected in non-hyperbolic kinetic profiles but not for mtCYP2E1. Hyperbolic kinetic profiles for the mitochondrial enzyme were consistent with Michaelis-Menten mechanism in which metabolic efficiency was constant. By contrast, erCYP2E1 metabolism of 4-nitrophenol led to a loss of enzyme efficiency at high substrate concentrations when substrate inhibited the reaction. Similarly, aniline metabolism by erCYP2E1 demonstrated negative cooperativity as metabolic efficiency decreased with increasing substrate concentration. The opposite was observed for erCYP2E1 oxidation of styrene; the sigmoidal kinetic profile indicated increased efficiency at higher substrate concentrations. These mechanisms and CYP2E1 levels in mitochondria and endoplasmic reticulum were used to estimate the impact of CYP2E1 subcellular localization on metabolic flux of pollutants. Those models showed that erCYP2E1 mainly carries out aniline metabolism at all aniline concentrations. Conversely, mtCYP2E1 dominates styrene oxidation at low styrene concentrations and erCYP2E1 at higher concentrations. Taken together, subcellular localization of CYP2E1 results in distinctly different enzyme activities that could impact overall metabolic clearance and/or activation of substrates and thus impact the interpretation and prediction of toxicological outcomes.

Cooperativity in CYP2E1 metabolism of acetaminophen and styrene mixtures

Risk assessment for exposure to mixtures of drugs and pollutants relies heavily on in vitro characterization of their bioactivation and/or metabolism individually and extrapolation to mixtures assuming no interaction. Herein, we demonstrated that in vitro CYP2E1 metabolic activation of acetaminophen and styrene mixtures could not be explained through the Michaelis-Menten mechanism or any models relying on that premise. As a baseline for mixture studies with styrene, steady-state analysis of acetaminophen oxidation revealed a biphasic kinetic profile that was best described by negative cooperativity (Hill coefficient=0.72). The best-fit mechanism for this relationship involved two binding sites with differing affinities (Ks=830μM and Kss=32mM). Introduction of styrene inhibited that reaction less than predicted by simple competition and thus provided evidence for a cooperative mechanism within the mixture. Likewise, acetaminophen acted through a mixed-type inhibition mechanism to impact styrene epoxidation. In this case, acetaminophen competed with styrene for CYP2E1 (Ki=830μM and Ksi=180μM for catalytic and effector sites, respectively) and resulted in cooperative impacts on binding and catalysis. Based on modeling of in vivo clearance, cooperative interactions between acetaminophen and styrene resulted in profoundly increased styrene activation at low styrene exposure levels and therapeutic acetaminophen levels. Current Michaelis-Menten based toxicological models for mixtures such as styrene and acetaminophen would fail to detect this concentration-dependent relationship. Hence, future studies must assess the role of alternate CYP2E1 mechanisms in bioactivation of compounds to improve the accuracy of interpretations and predictions of toxicity.

Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

Drug toxicity is frequently caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon-oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule's SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. This network makes predictions at both an atom and molecule level. The final epoxidation model constructed with this approach identified SOEs with 94.9% area under the curve (AUC) performance and separated epoxidized and non-epoxidized molecules with 79.3% AUC. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with AUCs of 92.5% and 95.1%, respectively. Finally, the model separated SOEs from sites of sp(2) hydroxylation with 83.2% AUC. Our model is the first of its kind and may be useful for the development of safer drugs. The epoxidation model is available at http://swami.wustl.edu/xenosite.

Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione

Drug toxicity is often caused by electrophilic reactive metabolites that covalently bind to proteins. Consequently, the quantitative strength of a molecule's reactivity with glutathione (GSH) is a frequently used indicator of its toxicity. Through cysteine, GSH (and proteins) scavenges reactive molecules to form conjugates in the body. GSH conjugates to specific atoms in reactive molecules: their sites of reactivity. The value of knowing a molecule's sites of reactivity is unexplored in the literature. This study tests the value of site of reactivity data that identifies the atoms within 1213 reactive molecules that conjugate to GSH and builds models to predict molecular reactivity with glutathione. An algorithm originally written to model sites of cytochrome P450 metabolism (called XenoSite) finds clear patterns in molecular structure that identify sites of reactivity within reactive molecules with 90.8% accuracy and separate reactive and unreactive molecules with 80.6% accuracy. Furthermore, the model output strongly correlates with quantitative GSH reactivity data in chemically diverse, external data sets. Site of reactivity data is nearly unstudied in the literature prior to our efforts, yet it contains a strong signal for reactivity that can be utilized to more accurately predict molecule reactivity and, eventually, toxicity.

Multiple UDP-glucuronosyltransferases in human liver microsomes glucuronidate both R- and S-7-hydroxywarfarin into two metabolites

The widely used anticoagulant Coumadin (R/S-warfarin) undergoes oxidation by cytochromes P450 into hydroxywarfarins that subsequently become conjugated for excretion in urine. Hydroxywarfarins may modulate warfarin metabolism transcriptionally or through direct inhibition of cytochromes P450 and thus, UGT action toward hydroxywarfarin elimination may impact levels of the parent drugs and patient responses. Nevertheless, relatively little is known about conjugation by UDP-glucuronosyltransferases in warfarin metabolism. Herein, we identified probable conjugation sites, kinetic mechanisms and hepatic UGT isoforms involved in microsomal glucuronidation of R- and S-7-hydroxywarfarin. Both compounds underwent glucuronidation at C4 and C7 hydroxyl groups based on elution properties and spectral characteristics. Their formation demonstrated regio- and enantioselectivity by UGTs and resulted in either Michaelis-Menten or substrate inhibition kinetics. Glucuronidation at the C7 hydroxyl group occurred more readily than at the C4 group, and the reaction was overall more efficient for R-7-hydroxywarfarin due to higher affinity and rates of turnover. The use of these mechanisms and parameters to model in vivo clearance demonstrated that contributions of substrate inhibition would lead to underestimation of metabolic clearance than that predicted by Michaelis-Menten kinetics. Lastly, these processes were driven by multiple UGTs indicating redundancy in glucuronidation pathways and ultimately metabolic clearance of R- and S-7-hydroxywarfarin.

Differences in butadiene adduct formation between rats and mice not due to selective inhibition of CYP2E1 by butadiene metabolites

CYP2E1 metabolizes 1,3-butadiene (BD) into genotoxic and possibly carcinogenic 1,2-epoxy-3-butene (EB), 1,2:3,4-diepoxybutane (DEB), and 1,2-epoxy-3,4-butanediol (EB-diol). The dose response of DNA and protein adducts derived from BD metabolites increases linearly at low BD exposures and then saturates at higher exposures in rats, but not mice. It was hypothesized that differences in adduct formation between rodents reflect more efficient BD oxidation in mice than rats. Herein, we assessed whether BD-derived metabolites selectively inhibit rat but not mouse CYP2E1 activity using B6C3F1 mouse and Fisher 344 rat liver microsomes. Basal CYP2E1 activities toward 4-nitrophenol were similar between rodents. Through IC50 studies, EB was the strongest inhibitor (IC50 54μM, mouse; 98μM, rat), BD-diol considerably weaker (IC50 1200μM, mouse; 1000μM, rat), and DEB inhibition nonexistent (IC50>25mM). Kinetic studies showed that in both species EB and BD-diol inhibited 4-nitrophenol oxidation through two-site mechanisms in which inhibition constants reflected trends observed in IC50 studies. None of the reactive epoxide metabolites inactivated CYP2E1 irreversibly. Thus, there was no selective inhibition or inactivation of rat CYP2E1 by BD metabolites relative to mouse Cyp2e1, and it can be inferred that CYP2E1 activity toward BD between rodent species would similarly not be impacted by the presence of BD metabolites. Inhibition of CYP2E1 by BD metabolites is then not responsible for the reported species difference in BD metabolism, formation of BD-derived DNA and protein adducts, mutagenicity and tumorigenesis.

Structure of pyrazole derivatives impact their affinity, stoichiometry, and cooperative interactions for CYP2E1 complexes

CYP2E1 plays a critical role in detoxification and carcinogenic activation of drugs, pollutants, and dietary compounds; however, these metabolic processes can involve poorly characterized cooperative interactions that compromise the ability to understand and predict CYP2E1 metabolism. Herein, we employed an array of ten azoles with an emphasis on pyrazoles to establish the selectivity of catalytic and cooperative CYP2E1 sites through binding and catalytic studies. Spectral binding studies for monocyclic azoles suggested two binding events, while bicyclic azoles suggested one. Pyrazole had moderate affinity toward the CYP2E1 catalytic site that improved when a methyl group was introduced at either position 3 or 4. The presence of methyl groups simultaneously at positions 3 and 5 blocked binding, and a phenyl group at position 3 did not improve binding affinity. In contrast, pyrazole fusion to a benzene or cyclohexane ring greatly increased affinity. The consequences of these binding events on CYP2E1 catalysis were studied through inhibition studies with 4-nitrophenol, a substrate known to bind both sites. Most pyrazoles shared a common mixed cooperative inhibition mechanism in which pyrazole binding rescued CYP2E1 from substrate inhibition. Overall, inhibitor affinities toward the CYP2E1 catalytic site were similar to those reported in binding studies, and the same trend was observed for binding at the cooperative site. Taken together, these studies identified key structural determinants in the affinity and stoichiometry of azole interactions with CYP2E1 and consequences on catalysis that further advance an understanding of the relationship between structure and function for this enzyme.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

Cooperative effects for CYP2E1 differ between styrene and its metabolites

Cooperative interactions are frequently observed in the metabolism of drugs and pollutants by cytochrome P450s; nevertheless, the molecular determinants for cooperativity remain elusive. Previously, we demonstrated that steady-state styrene metabolism by CYP2E1 exhibits positive cooperativity. We hypothesized that styrene metabolites have lower affinity than styrene toward CYP2E1 and limited ability to induce cooperative effects during metabolism. To test the hypothesis, we determined the potency and mechanism of inhibition for styrene and its metabolites toward oxidation of 4-nitrophenol using CYP2E1 Supersomes® and human liver microsomes. Styrene inhibited the reaction through a mixed cooperative mechanism with high affinity for the catalytic site (67 µM) and lower affinity for the cooperative site (1100 µM), while increasing substrate turnover at high concentrations. Styrene oxide and 4-vinylphenol possessed similar affinity for CYP2E1. Styrene oxide behaved cooperatively like styrene, but 4-vinylphenol decreased turnover at high concentrations. Styrene glycol was a very poor competitive inhibitor. Among all compounds, there was a positive correlation with binding and hydrophobicity. Taken together, these findings for CYP2E1 further validate contributions of cooperative mechanisms to metabolic processes, demonstrate the role of molecular structure on those mechanisms and underscore the potential for heterotropic cooperative effects between different compounds.

CYP2E1 metabolism of styrene involves allostery

We are the first to report allosterism during styrene oxidation by recombinant CYP2E1 and human liver microsomes. At low styrene concentrations, oxidation is inefficient because of weak binding to CYP2E1 (K(s) = 830 μM). A second styrene molecule then binds CYP2E1 with higher affinity (K(ss) = 110 μM) and significantly improves oxidation to achieve a k(cat) of 6.3 nmol · min(-1) · nmol CYP2E1(-1). The transition between these metabolic cycles coincides with reported styrene concentrations in blood from exposed workers; thus, this CYP2E1 mechanism may be relevant in vivo. Scaled modeling of the in vitro-positive allosteric mechanism for styrene metabolism to its in vivo clearance led to significant deviations from the traditional model based on Michaelis-Menten kinetics. Low styrene levels were notably much less toxic than generally assumed. We interrogated the allosteric mechanism using the CYP2E1-specific inhibitor and drug 4-methylpyrazole, which we have shown binds two CYP2E1 sites. From the current studies, styrene was a positive allosteric effector on 4-methylpyrazole binding, based on a 10-fold increase in 4-methylpyrazole binding affinity from K(i) 0.51 to K(si) 0.043 μM. The inhibitor was a negative allosteric effector on styrene oxidation, because k(cat) decreased 6-fold to 0.98 nmol · min(-1) · nmol CYP2E1(-1). Consequently, mixtures of styrene and other molecules can induce allosteric effects on binding and metabolism by CYP2E1 and thus mitigate the efficiency of their metabolism and corresponding effects on human health. Taken together, our elucidation of mechanisms for these allosteric reactions provides a powerful tool for further investigating the complexities of CYP2E1 metabolism of drugs and pollutants.

Metabolism of R- and S-warfarin by CYP2C19 into four hydroxywarfarins

Coumadin (R/S-warfarin) is a highly efficacious and widely used anticoagulant; however, its highly variable metabolism remains an important contributor to uncertainties in therapeutic responses. Pharmacogenetic studies report conflicting findings on the clinical relevance of CYP2C19. A resolution to this controversy is impeded by a lack of de tailon the potential role of CYP2C19 in warfarin metabolism. Consequently, we assessed the efficiency of CYP2C19 metabolism of R- and S-warfarin and explored possible contributions in the liver using in vitro methods. Recombinant CYP2C19 metabolized R- and S-warfarin mainly to 6-, 7-, and 8-hydroxywarfarin, while 4'-hydroxywarfarin was a minormetabolite. Over all R-warfarin metabolism was slightly more efficient than that for S-warfarin. Metabolic pathways thatproduce R-6-, 7-, and 8-hydroxywarfarin in human liver microsomal reactions correlated strongly with CYP2C19 Smephenytoinhydroxylase activity. Similarly, CYP1A2 activity toward phenacetin correlated with formation of R-6 and 7-hydroxywarfarin such that R-8-hydroxywarfarin seems unique to CYP2C19 and possibly a biomarker. In following, CYP2C19 likely impacts R-warfarin metabolism and patient response to therapy. Intriguingly, CYP2C19 may contributeto S-warfarin metabolism in patients, especially when CYP2C9 activity is compromised due to drug interactions orgenetic polymorphisms.

The role of ERp44 in maturation of serotonin transporter protein

In heterologous and endogenous expression systems, we studied the role of ERp44 and its complex partner endoplasmic reticulum (ER) oxidase 1-α (Ero1-Lα) in mechanisms regulating disulfide bond formation for serotonin transporter (SERT), an oligomeric glycoprotein. ERp44 is an ER lumenal chaperone protein that favors the maturation of disulfide-linked oligomeric proteins. ERp44 plays a critical role in the release of proteins from the ER via binding to Ero1-Lα. Mutation in the thioredoxin-like domain hampers the association of ERp44C29S with SERT, which has three Cys residues (Cys-200, Cys-209, and Cys-109) on the second external loop. We further explored the role of the protein chaperones through shRNA knockdown experiments for ERp44 and Ero1-Lα. Those efforts resulted in increased SERT localization to the plasma membrane but decreased serotonin (5-HT) uptake rates, indicating the importance of the ERp44 retention mechanism in the proper maturation of SERT proteins. These data were strongly supported with the data received from the N-biotinylaminoethyl methanethiosulfonate (MTSEA-biotin) labeling of SERT on ERp44 shRNA cells. MTSEA-biotin only interacts with the free Cys residues from the external phase of the plasma membrane. Interestingly, it appears that Cys-200 and Cys-209 of SERT in ERp44-silenced cells are accessible to labeling by MTSEA-biotin. However, in the control cells, these Cys residues are occupied and produced less labeling with MTSEA-biotin. Furthermore, ERp44 preferentially associated with SERT mutants (C200S, C209S, and C109A) when compared with wild type. These interactions with the chaperone may reflect the inability of Cys-200 and Cys-209 SERT mutants to form a disulfide bond and self-association as evidenced by immunoprecipitation assays. Based on these collective findings, we hypothesize that ERp44 together with Ero1-Lα plays an important role in disulfide formation of SERT, which may be a prerequisite step for the assembly of SERT molecules in oligomeric form.

Assays and applications in warfarin metabolism: what we know, how we know it and what we need to know

INTRODUCTION

Coumadin (R/S-warfarin) is the most widely prescribed oral anticoagulant in the world; nevertheless, its clinical use is complicated by unpredictability in dose requirements to achieve and maintain optimal anticoagulation. Variations in warfarin metabolism among patients contribute to unpredictability in therapeutic responses. Studying the clinical relevance of warfarin metabolism poses a significant analytical challenge. Warfarin is given to patients as an equal mixture of R and S enantiomers. Both drugs undergo extensive metabolism through different pathways to generate > 20 structurally similar isomeric metabolites.

AREAS COVERED

The article discusses how analytical methods have evolved to effectively resolve and quantify individual metabolites. The authors also discuss how the application of these methods has identified clinically relevant metabolic pathways for warfarin and fostered the investigation of clinical biomarkers for patient responses to therapy. The article additionally presents the power of these methods and how aspects of warfarin metabolism have led to the use of warfarin as a phenotyping probe for multiple drug metabolizing enzymes.

EXPERT OPINION

Progress in these areas has been hampered by shortcomings in analytical methods and a narrow focus on one metabolic pathway. Recent advances in liquid chromatographic-mass spectral methods can rapidly analyze most warfarin metabolites. It is now possible to effectively assess alternate metabolic pathways and expand biomarker analyses for clinical and phenotyping applications.

Contribution of three CYP3A isoforms to metabolism of R- and S-warfarin

Effective coumadin (R/S-warfarin) therapy is complicated by inter-individual variability in metabolism. Recent studies have demonstrated that CYP3A isoforms likely contribute to patient responses and clinical outcomes. Despite a significant focus on CYP3A4, little is known about CYP3A5 and CYP3A7 metabolism of warfarin. Based on our studies, recombinant CYP3A4, CYP3A5 and CYP3A7 metabolized R- and S-warfarin to 10- and 4'-hydroxywarfarin with efficiencies that depended on the individual enzymes. For R-warfarin, CYP3A4, CYP3A7, and CYP3A5 demonstrated decreasing preference for 10-hydroxylation over 4'-hydroxylation. By contrast, there was no regioselectivity toward S-warfarin. While all enzymes preferentially metabolized R-warfarin, CYP3A4 was the most efficient at metabolizing all reactions. Individuals, namely African-Americans and children, with higher relative levels of CYP3A5 and/or CYP3A7, respectively, compared to CYP3A4 may metabolize warfarin less efficiently and thus may require lower doses and be at risk for adverse drug-drug interactions related to the contributions of the respective enzymes.

Novel multi-mode ultra performance liquid chromatography-tandem mass spectrometry assay for profiling enantiomeric hydroxywarfarins and warfarin in human plasma

Coumadin (R/S-warfarin) is a commonly prescribed anticoagulant for over ∼20 million Americans. Although highly efficacious, positive clinical outcomes during warfarin therapy depend on maintaining a narrow therapeutic range for the drug. This goal is challenging due to large inter-individual variability in patient response, which has been attributed to diversity in drug metabolism. Warfarin is given as a racemic mixture and evidence suggest differences of R and S-warfarin in their therapeutic activities and metabolism. Previous investigation of warfarin metabolism has been hampered by the inability to quantify the individual enantiomers. To overcome this limitation a multi-mode LC-MS/MS method is reported. This strategy combines phenyl based reverse phase chromatography with chiral phase chromatography prior to quantitation by liquid chromatography tandem mass spectrometry. This approach was made possible through advances in UPLC technology producing narrow peaks suitable for transferring to a second column. The reported method separated individual R and S enantiomers of hydroxywarfarin and warfarin. All four possible isomers of 10-hydroxywarfarin were resolved to reveal unprecedented insights into the stereo-specific metabolism of warfarin. Characterization of the method demonstrated that it is robust and sensitive with inter-day coefficients of error between <7% and a detection limit of 2 nM in sample or 10 fmol on column for each analyte. Individual metabolites may be suitable surrogate biomarkers or predictive markers that predict warfarin dose, adverse interactions, or other important clinical outcomes during anticoagulant therapy. Consequently, the metabolite profiles obtained through this dual phase UPLC-MS/MS method are expected to increase our understanding of the role warfarin metabolism plays in patient response to therapy and yield new strategies to improve patient outcomes.

Hydroxywarfarin metabolites potently inhibit CYP2C9 metabolism of S-warfarin

Coumadin (R/S-warfarin) anticoagulant therapy poses a risk to over 50 million Americans, in part due to interpersonal variation in drug metabolism. Consequently, it is important to understand how metabolic capacity is influenced among patients. Cytochrome P450s (P450 or CYP for a specific isoform) catalyze the first major step in warfarin metabolism to generate five hydroxywarfarins for each drug enantiomer. These primary metabolites are thought to reach at least 5-fold higher levels in plasma than warfarin. We hypothesized that hydroxywarfarins inhibit the hydroxylation of warfarin by CYP2C9, thereby limiting enzymatic capacity toward S-warfarin. To test this hypothesis, we investigated the ability of all five racemic hydroxywarfarins to block CYP2C9 activity toward S-warfarin using recombinant enzyme and human liver microsomes. We initially screened for the inhibition of CYP2C9 by hydroxywarfarins using a P450-Glo assay to determine IC(50) values for each hydroxywarfarin. Compared to the substrate, CYP2C9 bound its hydroxywarfarin products with less affinity but retained high affinity for 10- and 4'-hydroxywarfarins, products from CYP3A4 reactions. S-Warfarin steady-state inhibition studies with recombinant CYP2C9 and pooled human liver microsomes confirmed that hydroxywarfarin products from CYP reactions possess the capacity to competitively inhibit CYP2C9 with biologically relevant inhibition constants. Inhibition of CYP2C9 by 7-hydroxywarfarin may be significant given its abundance in human plasma, despite its weak affinity for the enzyme. 10-Hydroxywarfarin, which has been reported as the second most abundant plasma metabolite, was the most potent inhibitor of CYP2C9, displaying approximately 3-fold higher affinity than S-warfarin. These results indicate that hydroxywarfarin metabolites produced by CYP2C9 and other CYPs may limit metabolic capacity toward S-warfarin through competitive inhibition. Subsequent processing of hydroxywarfarins to secondary metabolites, such as hydroxywarfarin glucuronides, could suppress product feedback inhibition, and therefore could play an important role in the modulation of metabolic pathways governing warfarin inactivation and elimination.