Formation of a Toxic Quinoneimine Metabolite from Diclofenac: A Quantum Chemical Study

Machine Learning Enables Accurate Prediction of Quinone Formation during Drug Metabolism

Metabolism helps in the elimination of drugs from the human body by making them more hydrophilic. Sometimes, drugs can be bioactivated to highly reactive metabolites or intermediates during metabolism. These reactive metabolites are often responsible for the toxicities associated with the drugs. Identification of reactive metabolites of drug candidates can be very helpful in the initial stages of drug discovery. Quinones are soft electrophiles that are generated as reactive intermediates during metabolism. Quinones make up more than 40% of the reactive metabolites. In this work, a reliable data set of 510 molecules was used to develop machine learning and deep learning-based predictive models to predict the formation of quinone-type metabolites. For representing molecules, two-dimensional (2D) descriptors, PubChem fingerprints, electro-topological state (E-state) fingerprints, and metabolic reactivity-based descriptors were used. Developed models were compared to the existing Xenosite web server using the untouched test set of 102 molecules. The best model achieved an accuracy of 86.27%, while the Xenosite server could achieve an accuracy of only 52.94% on the test set. Descriptor analysis revealed that the presence of greater numbers of polar moieties in a molecule can prevent the formation of quinone-type metabolites. In addition, the presence of a nitrogen atom in an aromatic ring and the presence of metabolophores V51, V52, and V53 (SMARTCyp descriptors) decrease the probability of quinone formation. Finally, a tool based on the best machine learning models was developed, which is accessible at http://14.139.57.41/quinonepred/.

The Phytochemical Profiling, In Vitro Antioxidant, and Hepatoprotective Activity of Prenanthes purpurea L. and Caffeoylquinic Acids in Diclofenac-Induced Hepatotoxicity on HEP-G2 Cells

Oxidative stress is a common phenomenon of many liver disorders; it both affects patient survival and directly influences the applicability, effectiveness, and toxicity of drugs. In the pursuit of reliable natural remedies for hepatoprotection, this study reports on the complete phytochemical characterization, antioxidant, and hepatoprotective activities of the Prenanthes purpurea methanol-aqueous extract in an in vitro model of diclofenac-induced liver injury (DILI). An ultra high-performance liquid chromatography-high-resolution mass spectrometry analysis (UHPLC-HRMS) was conducted, delineating more than 100 secondary metabolites for the first time in the species, including a series of phenolic acid-hexosides, acylquinic, acylhydroxyquinic and acyltartaric acids, and flavonoids. Quinic acid, chlorogenic, 3,5-dicaffeoylquinic and 5-feruloylhydroxyquinic acid, caffeoyltartaric and cichoric acids, eryodictiol-O-hexuronide, and luteolin O-hexuronide dominated the phytochemical profile and most likely contributed to the observed hepatoprotective activity of the studied P. purpurea leaf extract. The potency and molecular basis of cellular protection were investigated in parallel with pure caffeoylquinic acids in a series of pretreatment experiments that verified the antiapoptotic and antioxidant properties of the natural products.