Search

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. [Display omitted] • Growth inhibition is not synonymous with lethality against stationary-phase E. coli • Experimental screening and machine learning identify lethal compounds • Toxicity filtering prioritizes antibacterial drugs for further characterization • Semapimod selectively disrupts the Gram-negative outer membrane Zheng, Valeri, Andrews et al. employ a dilution-regrowth assay and a machine learning (ML) model to identify compounds with activity against stationary-phase bacteria. Semapimod, an anti-inflammatory small molecule with a favorable toxicity profile, demonstrates antibacterial activity against stationary-phase E. coli and A. baumannii by disrupting the outer membrane. [ABSTRACT FROM AUTHOR]

The vast majority of bactericidal antibiotics display poor efficacy against bacterial persisters, cells that are in a metabolically repressed state. Molecules that retain their bactericidal functions against such bacteria often display toxicity to human cells, which limits treatment options for infections caused by persisters. Here, we leverage insight into metabolism-dependent bactericidal antibiotic efficacy to design antibiotic combinations that sterilize both metabolically active and persister cells, while minimizing the antibiotic concentrations required. These rationally designed antibiotic combinations have the potential to improve treatments for chronic and recurrent infections. • Antibiotics are strongly or weakly dependent on metabolism (SDM or WDM) • Combinations of SDM and WDM antibiotics sterilize bacteria, while dose-sparing • SDM and WDM drug interactions are undetectable in growth-inhibition assays Zheng et al. rationally combine antibiotics using insights on bacterial metabolism to identify drug-drug combinations that sterilize bacterial cultures of both metabolically active and persister cells, while dose-sparing toxic antibiotics. [ABSTRACT FROM AUTHOR]