4.8 Article

Classic reaction kinetics can explain complex patterns of antibiotic action

Journal

SCIENCE TRANSLATIONAL MEDICINE
Volume 7, Issue 287, Pages -

Publisher

AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/scitranslmed.aaa8760

Keywords

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Funding

  1. Swiss National Science Foundation [PBEZP3_140163]
  2. German Academic Exchange Service [D/11/45747]
  3. NIH from the National Institute of General Medical Sciences [U54GM088558]
  4. NIH from the Office of the Director [DP2OD006663]
  5. Bill & Melinda Gates Foundation [OPP1111658]
  6. Swiss Foundation for Grants in Biology and Medicine [PASMP3_142724/1]
  7. NIH from the National Institute of Allergy and Infectious Diseases [R37 AI-042347]
  8. Howard Hughes Medical Institute
  9. Deutsche Forschungsgemeinschaft [MA 5320/1-1]
  10. Swedish Research Council junior investigator grant [621-2012-3564]
  11. Swiss National Science Foundation (SNF) [PASMP3_142724, PBEZP3_140163] Funding Source: Swiss National Science Foundation (SNF)
  12. Bill and Melinda Gates Foundation [OPP1111658] Funding Source: Bill and Melinda Gates Foundation

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Finding optimal dosing strategies for treating bacterial infections is extremely difficult, and improving therapy requires costly and time-intensive experiments. To date, an incomplete mechanistic understanding of drug effects has limited our ability to make accurate quantitative predictions of drug-mediated bacterial killing and impeded the rational design of antibiotic treatment strategies. Three poorly understood phenomena complicate predictions of antibiotic activity: post-antibiotic growth suppression, density-dependent antibiotic effects, and persister cell formation. We show that chemical binding kinetics alone are sufficient to explain these three phenomena, using single-cell data and time-kill curves of Escherichia coli and Vibrio cholerae exposed to a variety of antibiotics in combination with a theoretical model that links chemical reaction kinetics to bacterial population biology. Our model reproduces existing observations, has a high predictive power across different experimental setups (R-2 = 0.86), and makes several testable predictions, which we verified in new experiments and by analyzing published data from a clinical trial on tuberculosis therapy. Although a variety of biological mechanisms have previously been invoked to explain post-antibiotic growth suppression, density-dependent antibiotic effects, and especially persister cell formation, our findings reveal that a simple model that considers only binding kinetics provides a parsimonious and unifying explanation for these three complex, phenotypically distinct behaviours. Current antibiotic and other chemotherapeutic regimens are often based on trial and error or expert opinion. Our chemical reaction kinetics-based approach may inform new strategies, which are based on rational design.

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