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

Drug Synergism of Anticancer Action in Combination with Favipiravir and Paclitaxel on Neuroblastoma Cells

Background and Objectives: Favipiravir (FPV) is an antiviral medication and has an inhibitory effect on Cytochrome P450 (CYP2C8) protein, which is mainly involved in drug metabolism in the liver, and the expression of this gene is known to be enhanced in neuronal cells. The metabolization of Paclitaxel (PTX), a chemotherapeutic drug used in cancer patients, was analyzed for the first time in the human SH-SY5Y neuroblastoma cell line for monitoring possible synergistic effects when administered with FPV. Materials and Methods: Further, in vitro cytotoxic and genotoxic evaluations of FPV and PTX were also performed using wide concentration ranges in a human fibroblast cell culture (HDFa). Nuclear abnormalities were examined under a fluorescent microscope using the Hoechst 33258 fluorescent staining technique. In addition, the synergistic effects of these two drugs on cultured SH-SY5Y cells were determined by MTT cell viability assay. In addition, the death mechanisms that can occur in SHSY-5Y were revealed by using the flow cytometry technique. Results: Cell viability analyses on the HDFa healthy cell culture showed that both FPV and PTX have inhibitory effects at higher concentrations. On the other hand, there were no significant differences in nuclear abnormality numbers when both of the compounds were applied together. Cell viability analyses showed that FPV and PTX applications have higher cytotoxicity, which indicated synergistic toxicity against the SHSY-5Y cell line. Also, PTX exhibited higher anticancer properties against the neuroblastoma cell line when applied with FPV, as shown in both cytotoxicity and flow cytometry analyses. Conclusions: In light of our findings, the anticancer properties of PTX can be enhanced when the drug application is coupled with FPV exposure. Moreover, these results put forth that the anticancer drug dosage should be evaluated carefully in cancer patients who take COVID-19 treatment with FPV.

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.

The potential applications of artificial intelligence in drug discovery and development

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.

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.

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.

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.

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.

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.