{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng EJ"],"funding":["Swiss National Science Foundation","NIAID NIH HHS","NCI NIH HHS"],"pagination":["712-728.e9"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11031330"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["31(4)"],"pubmed_abstract":["There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts."],"journal":["Cell chemical biology"],"pubmed_title":["Discovery of antibiotics that selectively kill metabolically dormant bacteria."],"pmcid":["PMC11031330"],"funding_grant_id":["R01 AI144369","T32 CA009216","R01 AI146194","203071","K25 AI168451"],"pubmed_authors":["Linnehan B","Collins JJ","Andrews IW","Herneisen A","Lourido S","Renner LD","Wong F","Anahtar MN","Valeri JA","Krishnan A","Bandyopadhyay P","Zheng EJ","Schulte F","Stokes JM"],"additional_accession":[]},"is_claimable":false,"name":"Discovery of antibiotics that selectively kill metabolically dormant bacteria.","description":"There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Apr","modification":"2026-06-01T07:53:07.4Z","creation":"2026-04-08T10:43:59.591Z"},"accession":"S-EPMC11031330","cross_references":{"pubmed":["38029756"],"doi":["10.1016/j.chembiol.2023.10.026"]}}