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

Message Passing Neural Networks Improve Prediction of Metabolite Authenticity

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely react with protein and DNA and prompt drug candidate attrition or market withdrawal. Previously developed models help understand how these enzymes modify molecule structure by predicting sites of metabolism or characterizing formation of metabolite-biomolecule adducts. However, the majority of reactive metabolites are formed by multiple metabolic steps, and understanding the progenitor molecule's network-level behavior necessitates an integrative approach that blends multiple site of metabolism and structure inference models. Our previously developed tool, XenoNet 1.0, generates metabolic networks, where nodes are molecules and weighted edges are metabolic transformations. We extend XenoNet with a bidirectional message passing neural network that integrates edge feature information and local network structure using edge-conditioned graph convolutions and jumping knowledge to predict the authenticity of inferred Phase I metabolite structures. Our model significantly outperformed prior work and algorithmic baselines on a data set of 311 networks and 6606 intermediates annotated using a chemically diverse set of 20 736 individual in vitro and in vivo reaction records accounting for 92.3% of all human Phase I metabolism in the Accelrys Metabolite Database. Cross-validated predictions resulted in area under the receiver operating characteristic curves of 88.5% and 87.6% for separating experimentally observed and unobserved metabolites at global and network levels, respectively. Further analysis verified robustness to networks of varying depth and breadth, accurate detection of metabolites, such as d,l-methamphetamine, that are experimentally observed or unobserved in different network contexts, extraction of important metabolic subnetworks, and identification of known bioactivation pathways, such as for nimesulide and terbinafine. By exploiting network structures, our approach accurately suggests unreported metabolites for experimental study and may rationalize modifications for avoiding deleterious pathways antecedent to reactive metabolite formation.

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.

CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs

Diphenylamine NSAIDs are taken frequently for chronic pain conditions, yet their use may potentiate hepatotoxicity risks through poorly characterized metabolic mechanisms. Our previous work revealed that seven marketed or withdrawn diphenylamine NSAIDs undergo bioactivation into quinone-species metabolites, whose reaction specificities depended on halogenation and the type of acidic group on the diphenylamine. Herein, we identified cytochromes P450 responsible for those bioactivations, determined reaction specificities, and estimated relative contributions of enzymes to overall hepatic bioactivations and detoxifications. A qualitative activity screen revealed CYP2C8, 2C9, 2C19, and 3A4 played roles in drug bioactivation. Subsequent steady-state studies with recombinant CYPs recapitulated the importance of halogenation and acidic group type on bioactivations but importantly, showed patterns unique to each CYP. CYP2C9, 2C19 and 3A4 bioactivated all NSAIDs with CYP2C9 dominating all possible bioactivation pathways. For each CYP, specificities for overall oxidative metabolism were not impacted significantly by differences in NSAID structures but the values themselves differed among the enzymes such that CYP2C9 and 3A4 were more efficient than others. When considering hepatic CYP abundance, CYP2C9 almost exclusively accounted for diphenylamine NSAID bioactivations, whereas CYP3A4 provided a critical counterbalance favoring their overall detoxification. Preference for either outcome would depend on molecular structures favoring metabolism by the CYPs as well as the influence of clinical factors altering their expression and/or activity. While focused on NSAIDs, these findings have broader implications on bioactivation risks given the expansion of the diphenylamine scaffold to other drug classes such as targeted cancer therapeutics.

Impacts of diphenylamine NSAID halogenation on bioactivation risks

Diphenylamine NSAIDs are highly prescribed therapeutics for chronic pain despite causing symptomatic hepatotoxicity through mitochondrial damage in five percent of patients taking them. Differences in toxicity are attributed to structural modifications to the diphenylamine scaffold rather than its inherent toxicity. We hypothesize that marketed diphenylamine NSAID substituents affect preference and efficiency of bioactivation pathways and clearance. We parsed the FDA DILIrank hepatotoxicity database and modeled marketed drug bioactivation into quinone-species metabolites to identify a family of seven clinically relevant diphenylamine NSAIDs. These drugs fell into two subgroups, i.e., acetic acid and propionic acid diphenylamines, varying in hepatotoxicity risks and modeled bioactivation propensities. We carried out steady-state kinetic studies to assess bioactivation pathways by trapping quinone-species metabolites with dansyl glutathione. Analysis of the glutathione adducts by mass spectrometry characterized structures while dansyl fluorescence provided quantitative yields for their formation. Resulting kinetics identified four possible bioactivation pathways among the drugs, but reaction preference and efficiency depended upon structural modifications to the diphenylamine scaffold. Strikingly, diphenylamine dihalogenation promotes formation of quinone metabolites through four distinct metabolic pathways with high efficiency, whereas those without aromatic halogen atoms were metabolized less efficiently through two or fewer metabolic pathways. Overall metabolism of the drugs was comparable with bioactivation accounting for 4-13% of clearance. Lastly, we calculated daily bioload exposure of quinone-species metabolites based on bioactivation efficiency, bioavailability, and maximal daily dose. The results revealed stratification into the two subgroups; propionic acid diphenylamines had an average four-fold greater daily bioload compared to acetic acid diphenylamines. However, the lack of sufficient study on the hepatotoxicity for all drugs prevents further correlative analyses. These findings provide critical insights on the impact of diphenylamine bioactivation as a precursor to hepatotoxicity and thus, provide a foundation for better risk assessment in drug discovery and development.

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.

XenoNet: Inference and Likelihood of Intermediate Metabolite Formation

Drug metabolism is a common cause of adverse drug reactions. Drug molecules can be metabolized into reactive metabolites, which can conjugate to biomolecules, like protein and DNA, in a process termed bioactivation. To mitigate adverse reactions caused by bioactivation, both experimental and computational screening assays are utilized. Experimental assays for assessing the formation of reactive metabolites are low throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. In contrast, computational methods are high throughput and cheap to perform to screen thousands to millions of compounds for potentially toxic molecules during the early stages of the drug development pipeline. Commonly used computational methods focus on detecting and structurally characterizing reactive metabolite-biomolecule adducts or predicting sites on a drug molecule that are liable to form reactive metabolites. However, such methods are often only concerned with the structure of the initial drug molecule or of the adduct formed when a biomolecule conjugates to a reactive metabolite. Thus, these methods are likely to miss intermediate metabolites that may lead to subsequent reactive metabolite formation. To address these shortcomings, we create XenoNet, a metabolic network predictor, that can take a pair of a substrate and a target product as input and (1) enumerate pathways, or sequences of intermediate metabolite structures, between the pair, and (2) compute the likelihood of those pathways and intermediate metabolites. We validate XenoNet on a large, chemically diverse data set of 17 054 metabolic networks built from a literature-derived reaction database. Each metabolic network has a defined substrate molecule that has been experimentally observed to undergo metabolism into a defined product metabolite. XenoNet can predict experimentally observed pathways and intermediate metabolites linking the input substrate and product pair with a recall of 88 and 46%, respectively. Using likelihood scoring, XenoNet also achieves a top-one pathway and intermediate metabolite accuracy of 93.6 and 51.9%, respectively. We further validate XenoNet against prior methods for metabolite prediction. XenoNet significantly outperforms all prior methods across multiple metrics. XenoNet is available at https://swami.wustl.edu/xenonet.

Feasibility of the use of Lentinula edodes mycelium in terbinafine remediation

A detailed understanding of the fate of xenobiotics introduced into the environment and the mechanisms involved in their biotransformation, biodegradation, and biosorption is essential to improve the efficiency of remediation techniques. Mycoremediation is a form of bioremediation technique that has become increasingly popular in recent years as fungi are known to produce various effective extracellular enzymes that have the potential to neutralize a wide variety of xenobiotics released into the environment. Hence, mycoremediation appears to be a promising technique for the removal of a wide array of toxins and pharmaceutical residues from a damaged environment and wastewater. This study primarily aimed to investigate whether white-rot fungus (Lentinula edodes) can be utilized for the bioremediation of common antifungal agent terbinafine, which is mainly available in the market as powder or cream. The cultures of L. edodes were cultivated in the medium containing terbinafine powder or terbinafine 1% cream, each at a final concentration of 0.1 mg mL^-1. The addition of terbinafine in powder form have a negative effect on biomass growth (p < 0.05). The total amount of terbinafine in the dry weight of mycelium after culture was estimated to be 7.63 ± 0.45 mg and 12.52 ± 2.46 mg for powder and cream samples, respectively. In addition, there were no traces of terbinafine in any of the samples of medium used for culturing L. edodes after the experimental duration period. The biodegradation products of terbinafine were identified for the first time using UPLC/MS/MS. The biodegradation of terbinafine resulted in the loss of 1-naphthylmethanol, which occurred via oxidative deamination, N-demethylation, or tert-butyl group hydroxylation. The results of the study demonstrate that L. edodes mycelium can be effectively used for the remediation of terbinafine.

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.

Examining the Potential of Developing and Implementing Use of Adiponectin-Targeted Therapeutics for Metabolic and Cardiovascular Diseases

Cardiometabolic diseases encompass those affecting the heart and vasculature as well as other metabolic problems, such as insulin resistance, diabetes, and non-alcoholic fatty liver disease. These diseases tend to have common risk factors, one of which is impaired adiponectin action. This may be due to reduced bioavailability of the hormone or resistance to its effects on target tissues. A strong negative correlation between adiponectin levels and cardiometabolic diseases has been well-documented and research shown that adiponectin has cardioprotective, insulin sensitizing and direct beneficial metabolic effects. Thus, therapeutic approaches to enhance adiponectin action are widely considered to be desirable. The complexity of adiponectin structure and function has so far made progress in this area less than ideal. In this article we will review the effects and mechanism of action of adiponectin on cardiometabolic tissues, identify scenarios where enhancing adiponectin action would be of clinical value and finally discuss approaches via which this can be achieved.