Articlenpj antimicrobials and resistance2025
Predicting drug inactivation by changes in bacterial growth dynamics.
Article in npj antimicrobials and resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Two Worlds, One Battle: How Bacteria and Malignancies Converge on Drug Resistance.International journal of molecular sciences · 2026Review
- Mixing Is Dispensable for Optical Density-Based High-Throughput Growth Screening Assay in Fission Yeast.International journal of molecular sciences · 2026Article
- AssiST: convolutional neural network for analysis of antibiotic susceptibility testing.Bioinformatics advances · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Studying how antibacterials operate at subinhibitory concentrations reveals how they impede normal growth. While previous works demonstrated drugs can impact multiple aspects of growth, such as prolonging the doubling time or reducing the maximal bacterial load, a systematic understanding of this phenomenon is lacking. It remains unknown if common principles dictate how drugs interfere with growth. We monitored growth curves across thirty-eight drugs, spanning multiple mechanisms of action in Escherichia coli to deconvolve their impact on the lag, growth rate, and carrying capacity and developed a mathematical framework to quantitatively compare their effects. We discovered that drugs induced considerably different inhibition phenotypes, which were independent from the drug's target. Functional assays of drug inactivation revealed that drug inactivation is a key shared factor underlying a lag-associated phenotype. Our work reveals that likely drug inactivation can be directly inferred from growth dynamics which is instrumental for rapidly identifying drug-inactivating bacteria.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.