Biofilm formation is considered a major cause of therapeutic failure because bacteria in biofilms have higher protection against antimicrobials. Thus, biofilm-related infections are extremely challenging to treat and pose major concerns for public health, along with huge economic impacts. Pseudomonas aeruginosa, in particular, is a “critical priority” pathogen, responsible for severe infections, especially in cystic fibrosis patients because of its capacity to form resistant biofilms. Therefore, new therapeutic approaches are needed to complete the pipeline of molecules offering new targets and modes of action. Biofilm formation is mainly controlled by Quorum Sensing (QS), a communication system based on signaling molecules. In the present study, we employed a molecular docking approach (Autodock Vina) to assess two series of chromones-based compounds as possible ligands for PqsR, a LuxR-type receptor. Most compounds showed good predicted affinities for PqsR, higher than the PQS native ligand. Encouraged by these docking results, we synthesized a library of 34 direct and 25 retro chromone carboxamides using two optimized routes from 2-chromone carboxylic acid as starting material for both series. We evaluated the synthesized carboxamides for their ability to inhibit the biofilm formation of P. aeruginosa in vitro. Overall, results showed several chromone 2-carboxamides of the retro series are potent inhibitors of the formation of P. aeruginosa biofilms (16/25 compound with % inhibition ≥ 50% at 50 μM), without cytotoxicity on Vero cells (IC50 > 1.0 mM). The 2,4-dinitro-N-(4-oxo-4H-chromen-2-yl) benzamide (6n) was the most promising antibiofilm compound, with potential for hit to lead optimization.
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http://dx.doi.org/10.3390/ph15040417 | DOI Listing |
Biofilms are resistant microbial cell aggregates that pose risks to health and food industries and produce environmental contamination. Accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy.
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