Imidazolium ionic liquids as effective antiseptics and disinfectants against drug resistant S. aureus: In silico and in vitro studies. (April 2018)

• Record Type: Journal Article
• Authors: Hodyna, Diana; Kovalishyn, Vasyl; Semenyuta, Ivan; Blagodatnyi, Volodymyr; Rogalsky, Sergiy; Metelytsia, Larisa
• Is Part Of: Computational biology and chemistry. Volume 73(2018)
• Page Start: 127
• Page End: 138
• Publication Date: 2018-04
• Subjects: Imidazolium ionic liquids -- QSAR models -- OCHEM -- Molecular docking -- Staphylococcus aureus; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.01.012
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Classification and regression QSAR models were created to predict the antibacterial activity of imidazolium ionic liquids. A series of synthesized 1, 3-dialkylimidazolium ILs with predicted activity were tested in vitro against S. aureus ATCC 25923 and its clinical isolate. The active imidazolium ILs with alkyl chain of 12 carbon atoms or with two identical alkyl chains C8 or C9 are effective anti-Staphylococcus aureus agents. The high activity of 7 ILs was analyzed by the molecular docking to prokaryotic homologue of a eukaryotic tubulin FtsZ. Аbstract: This paper describes Quantitative Structure-Activity Relationships (QSAR) studies, molecular docking and in vitro antibacterial activity of several potent imidazolium-based ionic liquids (ILs) against S. aureus ATCC 25923 and its clinical isolate. Small set of 131 ILs was collected from the literature and uploaded in the OCHEM database. QSAR methodologies used Associative Neural Networks and Random Forests (WEKA-RF) methods. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q 2 = 0.82–0.87 for regression models and overall prediction accuracies of 80–82.1% for classification models. The proposed QSAR models are freely available online on OCHEM server at https://ochem.eu/article/107364 and can be used for estimation of antibacterial activity of new imidazolium-based ILs. A series of synthesized 1, 3-dialkylimidazolium ILs with predicted activity were evaluated in vitro . The high activity of 7 ILs against S. aureus strain and its clinical isolate was measured and thereafter analyzed by the molecular docking to prokaryotic homologue of a eukaryotic tubulin FtsZ.