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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., Competing Interests: Declaration of interests J.J.C. is a scientific co-founder and scientific advisory board chair of EnBiotix, an antibiotic drug discovery company, and PhareBio, a non-profit social venture for antibiotic drug discovery. J.M.S. is a scientific co-founder and scientific director at PhareBio., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions., Competing Interests: Declaration of interests J.J.C. is scientific co-founder and scientific advisory board chair of EnBiotix, an antibiotic drug discovery company, and PhareBio, a non-profit venture focused on antibiotic drug development. J.M.S. is scientific co-founder and scientific director of PhareBio., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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., Competing Interests: Declaration of Interests J.J.C. is scientific co-founder and scientific advisory board chair of EnBiotix, an antibiotic drug discovery company., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules., Competing Interests: Declaration of Interests J.J.C. is scientific co-founder and SAB chair of EnBiotix, an antibiotic drug discovery company., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Antibiotics target energy-consuming processes. As such, perturbations to bacterial metabolic homeostasis are significant consequences of treatment. Here, we describe three postulates that collectively define antibiotic efficacy in the context of bacterial metabolism: (1) antibiotics alter the metabolic state of bacteria, which contributes to the resulting death or stasis; (2) the metabolic state of bacteria influences their susceptibility to antibiotics; and (3) antibiotic efficacy can be enhanced by altering the metabolic state of bacteria. Altogether, we aim to emphasize the close relationship between bacterial metabolism and antibiotic efficacy as well as propose areas of exploration to develop novel antibiotics that optimally exploit bacterial metabolic networks., (Copyright © 2019 Elsevier Inc. All rights reserved.)

A poor understanding of the mechanisms by which antibiotics traverse the outer membrane remains a considerable obstacle to the development of novel Gram-negative antibiotics. Herein, we demonstrate that the Gram-negative bacterium Escherichia coli becomes susceptible to the narrow-spectrum antibiotic vancomycin during growth at low temperatures. Heterologous expression of an Enterococcus vanHBX vancomycin resistance cluster in E. coli confirmed that the mechanism of action was through inhibition of peptidoglycan biosynthesis. To understand the nature of vancomycin permeability, we screened for strains of E. coli that displayed resistance to vancomycin at low temperature. Surprisingly, we observed that mutations in outer membrane biosynthesis suppressed vancomycin activity. Subsequent chemical analysis of lipopolysaccharide from vancomycin-sensitive and -resistant strains confirmed that suppression was correlated with truncations in the core oligosaccharide of lipopolysaccharide. These unexpected observations challenge the current understanding of outer membrane permeability, and provide new chemical insights into the susceptibility of E. coli to glycopeptide antibiotics., (Copyright © 2016 Elsevier Ltd. All rights reserved.)