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.

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 - 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.

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 (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).

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 Kd 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.

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.

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].

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.

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.

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 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.

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.

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.

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.