Classic reaction kinetics can explain complex patterns of antibiotic action

Pia Abel Zur Wiesch, Sören Abel, Spyridon Gkotzis, Paolo Ocampo, Jan Engelstädter, Trevor Hinkley, Carsten Magnus, Matthew K. Waldor, Klas Udekwu, Ted Cohen

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

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 singlecell 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 (R2 = 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 postantibiotic 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.

Original languageEnglish (US)
JournalScience Translational Medicine
Volume7
Issue number287
DOIs
StatePublished - May 13 2015

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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