Crucial role of cytochrome P450 in hepatotoxicity induced by 2,3-dimethoxy-1,4-naphthoquinone in rats

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.

Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches

Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity prediction of organic-type chemicals. Here, classifiers for the prediction of DILI were developed by using QuBiLS-MAS 0-2.5D molecular descriptors and shallow machine learning techniques, on a training set composed of 1075 molecules. The best ensemble model build, E13, was obtained with good statistical parameters for the learning series, namely, the following: accuracy = 0.840, sensibility = 0.890, specificity = 0.761, Matthew's correlation coefficient = 0.660, and area under the ROC curve = 0.904. The model was also satisfactorily evaluated with Y-scrambling test, and repeated k-fold cross-validation and repeated k-holdout validation. In addition, an exhaustive external validation was also carried out by using two test sets and five external test sets, with an average accuracy value equal to 0.854 (±0.062) and a coverage equal to 98.4% according to its applicability domain. A statistical comparison of the performance of the E13 model, with regard to results and tools (e.g., Padel DDPredictor Software, Deep Learning DILIserver, and Vslead) reported in the literature, was also performed. In general, E13 presented the best global performance in all experiments. The sum of the ranking differences procedure provided a very similar grouping pattern to that of the M-ANOVA statistical analysis, where E13 was identified as the best model for DILI predictions. A noncommercial and fully cross-platform software for the DILI prediction was also developed, which is freely available at http://tomocomd.com/apps/ptoxra. This software was used for the screening of seven data sets, containing natural products, leads, toxic materials, and FDA approved drugs, to assess the usefulness of the QSAR models in the DILI labeling of organic substances; it was found that 50-92% of the evaluated molecules are positive-DILI compounds. All in all, it can be stated that the E13 model is a relevant method for the prediction of DILI risk in humans, as it shows the best results among all of the methods analyzed.

Detection of Emodin Derived Glutathione Adduct in Normal Rats Administered with Large Dosage of Polygoni Multiflori Radix

Polygoni Multiflori Radix (PMR) has been commonly used as a tonic in China for centuries. PMR-associated hepatotoxicity has been drawing increasingly more attention in recent years in parallel with its wide utilization. Anthraquinones (AQs) are recognized as the main hepatotoxic components in PMR. However, the exact underlying mechanism of AQs poisoning is still not fully understood. Herein, we proposed a hypothesis that metabolic activation of AQs such as emodin was involved in PMR-induced liver injury, AQs followed to generate the electrophilic reactive metabolites and subsequently formed covalent adduct with cellular nucleophiles in the liver to exert hepatotoxicity. In the present study, the link of cytotoxicity of PMR in primary human hepatocytes and the depletion of glutathione (GSH) was investigated by MTT assay and UHPLC-QqQ-MS/MS analysis. The results showed that PMR depleted GSH and therefore induced cytotoxicity. Then, emodin-GSH adduct was identified in bile of liver injured rats after intragastric administration of PMR or emodin with the aid of UHPLC-QTOF-MS/MS method. Our findings not only provided confirmative evidence that the mechanism of hepatotoxicity induced by AQs in PMR involved key metabolic steps, but also revealed that emodin-GSH adduct had potential to be further developed as a sensitive and traceable biomarker for the assessment of PMR-induced liver injury.

Contribution of reductase activity to quinone toxicity in three kinds of hepatic cells

Two mechanisms have been proposed to explain quinone cytotoxicity: oxidative stress via the redox cycle, and the arylation of intracellular nucleophiles. The redox cycle is catalyzed by intracellular reductases, and therefore the toxicity of redox cycling quinone is considered to be closely associated with the reductase activity. This study examined the relationship between quinone toxicity and the intracellular reductase activity using 3 kinds of hepatic cells; rat primary hepatocytes, HepG2 and H4IIE. The intracellular reductase activity was; primary hepatocyte >>HepG2>H4IIE. The three kinds of cells showed almost the same vulnerability to an arylating quinone, 1,4-naphthoquinone (NQ). However, the susceptibility to a redox cycling quinone, 2,3-dimethoxy-1,4-naphthoquinone (DMNQ) was; primary hepatocyte>HepG2>H4IIE. In addition, the cytotoxicity elicited by DMNQ was significantly attenuated in HepG2 cells and almost completely suppressed in primary hepatocytes by diphenyleneiodonium chloride, a reductase inhibitor. These data suggest that cells with a high reductase activity are susceptible to redox cycling quinones. This study provides essential evidence to assess the toxicity of quinone-based drugs during their developmental processes.