Galvez-Llompart Maria, Hierrezuelo Jesús, Blasco Mariluz, Zanni Riccardo, Galvez Jorge, de Vicente Antonio, Pérez-García Alejandro, Romero Diego
Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, University of Valencia, Burjassot, Spain.
Department of Physical Chemistry, University of Valencia, Burjassot, Spain.
J Enzyme Inhib Med Chem. 2024 Dec;39(1):2330907. doi: 10.1080/14756366.2024.2330907. Epub 2024 Apr 23.
Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.
抗菌耐药性(AMR)是一个紧迫的全球性问题,抗生素的滥用和细菌生物膜的形成使其更加恶化,细菌生物膜导致了高达80%的人类细菌感染。本研究提出了一种计算策略来应对抗菌耐药性,即通过基于分子拓扑开发三种新型定量构效关系(QSAR)模型,以识别潜在的抗生物膜和抗菌剂。这些模型旨在确定革兰氏阳性菌抗菌、革兰氏阴性菌抗菌以及生物膜形成抑制活性的化学拓扑模式。这些模型被应用于对一个商业化学数据库的虚拟筛选,从而选出了58种化合物。随后的试验表明,其中三种化合物表现出最有前景的抗菌活性,在提高食品和医疗器械安全性方面具有潜在应用价值。