Clove Constituents as new leads for the Design and development of Multi-targeted Anti-Alzheimer activity

Objective: To use virtual screening analysis to screen out the phytoconstituents of Syzygium aromaticum against multiple targets of AD and determine its anti-oxidant and inflammatory inhibitory property. Methods: The compounds listed out from Syzygium aromaticum were subjected to virtual screening based on their drug likeness property and bioactivity scores. The molecular docking simulation such as HEX 8.0, PyRx, MVD along with Auto Dock 4.2 were employed to determine the potential candidate for providing activity against multiple targets of AD. The toxicity estimation was also carried out using TEST software. The potential candidate was further evaluated using DPPH, FRAT, Albumin denaturation and Proteinase inhibition method. Results: Only eight phytoconstituents were selected for virtual screening as they possessed drug likeness property and better bioactivity score for inhibition of kinases, proteases and enzymes. The docking results from various tools predicted that Oleanolic acid can be considered as potential constituents for multi-target action against AD. Toxicity estimation was in range. It also exhibited anti-oxidant and inflammatory inhibition providing its evidence for anti-AD activity. Conclusion: Taken together, these virtual screening results and in-vitro assays suggest that Oleanolic acid has multi target action against AD, which can be proved further with in-vivo studies.

Molecular Docking Studies of Phytoconstituents Identified in Traditional Siddha Polyherbal Formulations Against Possible Targets of SARS-CoV-2

The Indian Traditional Medicines System has long used Siddha polyherbal formulations for different viral diseases. The ingredients of these formulas have been proven to be antiviral. The study focuses on in silico computational evaluation of phytoconstituents of the official Siddha formulation Kabasura, Thonthasura, and Vishasura Kudineer, which were widely used in treating viral fever and respiratory infections and may influence the current SARS-CoV-2 coronary virus pandemic. Maestro interface (Schrödinger Suite, LLC, NY) was used for molecular docking studies against MPro (PDB ID 5R82, 6Y2F, and 6LU7), Nsp15 endoribonuclease (6W01), RNA-dependent RNA polymerase (6M71), and spike protein (6VW1) of SARS-CoV-2. In addition, pharmacokinetics (ADME) and safety profile prediction studies were performed to identify the best drug candidates using Qikpro and Toxicity Estimation Software Tool (T.E.S.T). A total of 36 compounds were screened, of which nine displayed strong binding affinity and drug-likeness. Luteolin and chrysoeriol produced stronger results. These nine compounds were free of oral toxicity as evaluated by the Toxicity estimation software. Based on further in vitro, in vivo, and clinical effectiveness trials, these compounds may be used for the prevention or treatment as per the Indian system of traditional medicines.

Metatox - Web application for generation of metabolic pathways and toxicity estimation

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic’s biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite — it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.