Bioactivation and reactivity research advances-2023 year in review.

Advances in the field of bioactivation have significantly contributed to our understanding and prediction of drug-induced liver injury (DILI). It has been established that many adverse drug reactions, including DILI, are associated with the formation and reactivity of metabolites. Modern methods allow us to detect and characterize these reactive metabolites in earlier stages of drug development, which helps anticipate and circumvent the potential for DILI. Improved in silico models and experimental techniques that better reflect in vivo environments are enhancing predictive capabilities for DILI risk. Further, studies on the mechanisms of bioactivation, including enzyme interactions and the role of individual genetic differences, have provided valuable insights for drug optimizations. Cumulatively, this progress is continually refining our approaches to drug safety evaluation and personalized medicine.Shuai Wang and Cyrus Khojasteh, on behalf of the authors.

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.

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 (see references). 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.[Table: see text].

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.[Table: see text].

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 STATEMENT: Meclofenamate bioactivation may initiate hepatotoxicity, yet common risk assessment approaches are often cumbersome and inefficient and yield qualitative insights that do not scale relative bioactivation risks. We developed and applied innovative computational modeling and quantitative kinetics to identify and validate meclofenamate bioactivation pathways and relevance as a function of time and concentration. This strategy yielded novel insights on meclofenamate bioactivation and provides a tractable approach to more accurately and efficiently assess other drug bioactivations and correlate risks to toxicological outcomes.

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.

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.

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.

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 (Vmax/Km) 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.

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.

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.

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.

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.

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.