In silico Docking, ADME and Toxicity studies of Aryl Glyoxamide Derivatives as Anti-Virulence Agents

The expression of phenotypic characteristics in bacterial species is regulated by the signalling mechanism called quorum sensing (QS). In current scenario, the quorum sensing inhibitors (QSIs) have established themselves as attractive leads which can be exploited to overcome antimicrobial resistance exhibited by various pathogenic bacteria. Aryl glyoxamide derivatives belong to one such class among several chemical classes which are known to inhibit the quorum sensing in P. aeruginosa (MH602) and E. coli (MT102). These derivatives are mostly designed using amino acid esters and found to exhibit fairly good activity and can act as promising leads for QSIs. However, in the field of drug design, the optimization of lead compounds with their activity profile plays a very crucial role. Due to ever growing demand of lead optimization/modification, the use of in silico drug design techniques proves to be most economical and best high throughput screening methods. In present study, QSTR (Quantitative Structure Toxicity Relationship), pharmacokinetic profiling (ADMET) studies and molecular docking studies were carried out on 21 N-Aryl glyoxamide derivatives. The studies implied that these derivatives had less probability to show hepatotoxicity and found to have good oral absorption profile. QSTR (Quantitative Structure Toxicity Relationship) studies performed by using TOPKAT (Toxicity Prediction Komputer Assisted Technology), showed that the compounds devoid of nitro substitution are non-carcinogenic and possess least probability of producing carcinogenicity and mutagenicity among computational models. The molecular docking studies suggest that autodock Vina and gold scores are comparable in determining the biological activity of synthesized compounds. The results indicated that the aryl glyoxamide class of compounds has substantial potential which can be exploited for the development of lead optimization in the field of QSIs