Discovery of bacterial biofilm inhibitors against catheter-associated urinary tract infections using computational...

One of the most frequent device-associated infections obtained in a hospital setting is catheter-associated urinary tract infection (CAUTI). The rise of CAUTIs caused by biofilm-forming bacteria, including Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, and Staphylococcus saprophyticus, has become a significant challenge due to their drug resistance phenomenon in healthcare settings. Biofilms confer resistance to both host immune responses and antimicrobial drugs, making infections difficult to treat. This review aims to explore biofilm inhibitors targeting proteins that have not yet been fully explored computationally against CAUTI-causing bacteria, and to examine the molecular mechanisms of biofilm formation in the six major bacteria. A list of potential inhibitors from both natural and synthetic sources, identified through computational approaches by various groups, has been mentioned for further research in in vitro and in vivo models. In addition, there is a considerable scope of artificial intelligence and machine learning, which has been highlighted to overcome the challenges and limitations of current states of promising inhibitor development, as well as a description of future directions for computational methods against CAUTI-associated bacterial biofilm-mediated drug resistance has been included to sort out the current limitations.