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Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

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  • Troy Day
  • Andrew F Read

Abstract

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.Author Summary: The evolution of antimicrobial resistant pathogens threatens much of modern medicine. For over one hundred years, the advice has been to ‘hit hard’, in the belief that high doses of antimicrobials best contain resistance evolution. We argue that nothing in evolutionary theory supports this as a good rule of thumb in the situations that challenge medicine. We show instead that the only generality is to either use the highest tolerable drug dose or the lowest clinically effective dose; that is, one of the two edges of the therapeutic window. This approach suggests treatment options not currently considered, and simplifies the experiments required to identify the dose that best retards resistance evolution.

Suggested Citation

  • Troy Day & Andrew F Read, 2016. "Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?," PLOS Computational Biology, Public Library of Science, vol. 12(1), pages 1-20, January.
  • Handle: RePEc:plo:pcbi00:1004689
    DOI: 10.1371/journal.pcbi.1004689
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    References listed on IDEAS

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    1. Robert A. Gatenby, 2009. "A change of strategy in the war on cancer," Nature, Nature, vol. 459(7246), pages 508-509, May.
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    Cited by:

    1. Elsa Hansen & Jason Karslake & Robert J Woods & Andrew F Read & Kevin B Wood, 2020. "Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations," PLOS Biology, Public Library of Science, vol. 18(5), pages 1-20, May.
    2. Marianne Bauer & Isabella R Graf & Vudtiwat Ngampruetikorn & Greg J Stephens & Erwin Frey, 2017. "Exploiting ecology in drug pulse sequences in favour of population reduction," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-17, September.
    3. Greenspoon, Philip B. & Mideo, Nicole, 2017. "Evolutionary rescue of a parasite population by mutation rate evolution," Theoretical Population Biology, Elsevier, vol. 117(C), pages 64-75.
    4. Jason Karslake & Jeff Maltas & Peter Brumm & Kevin B Wood, 2016. "Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-21, October.

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