New research reveals that antibiotic resistant bacteria may have an Achilles' heel, a time window when the bacteria are sensitive to antibiotic treatment. Duke University researchers Hannah Meredith, Allison Lopatkin, Deverick Anderson, and Lingchong You identify a new metric that may guide the regimen design for existing antibiotics.
In a study publishing this week in PLOS Computational Biology, the authors use modeling to guide the design of effective antibiotic protocols for moderately resistant bacteria. The researcher's new approach may help reintroduce antibiotics that might be disregarded out of concern for treatment failure for certain bacterial pathogens.
The authors find that, after a dose of antibiotics, a population of moderately resistant bacteria will be sensitive to further treatment before reaching a resistant state. Regardless of the dose concentration or duration, if subsequent doses of antibiotic are delivered while the population is "sensitive," the population declines for each additional dose. However, if subsequent doses of antibiotic are delivered after the "sensitive" period had passed, the population will recover despite the antibiotic treatment. Once this Achilles' heel has been identified for a given bacteria-antibiotic pair, a dosing regimen can be selected that will ensure the population will be reduced.
Ultimately, the authors intend to establish a standardized database that would guide the optimization of antibiotic prescriptions such that patient outcomes would improve while extending the shelf life of antibiotics.
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All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004201
Contact: Hannah Meredith
Address: Duke University
Biomedical Engineering
136 Hudson Hall
Durham, NC 27708
UNITED STATES
Phone: 8045439647
Email: hannah.meredith@duke.edu
Citation: Meredith HR, Lopatkin AJ, Anderson DJ, You L (2015) Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment. PLoS Comput Biol 11(4): e1004201. doi:10.1371/journal.pcbi.1004201
Funding: This work was partially supported by the National Science Foundation (LY, CBET-0953202), the National Institutes of Health (LY, 1R01GM098642; 1RO1GM110494), a DuPont Young Professorship (LY), a David and Lucile Packard Fellowship (LY), the National Science Foundation Graduate Research Fellowship (HRM), and the Howard G. Clark Fellowship (AJL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
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