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Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections

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  • Jason Karslake
  • Jeff Maltas
  • Peter Brumm
  • Kevin B Wood

Abstract

The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.Author Summary: The pace of antibiotic discovery has rapidly slowed in the last few decades, creating an urgent need to reevaluate and optimize therapies based on current drugs. In this work, we combine quantitative laboratory experiments on bacterial populations with mathematical models of antimicrobial therapies to demonstrate that bacterial populations of different sizes (densities) may respond very differently to the same antibiotic treatment. As a result, otherwise successful antibiotic treatments may fail to eradicate bacterial infections that have reached a critical population density. Our findings indicate that it is important to consider the size of a bacterial infection when designing effective antimicrobial therapies.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1005098
    DOI: 10.1371/journal.pcbi.1005098
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    References listed on IDEAS

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    1. Joseph Peter Torella & Remy Chait & Roy Kishony, 2010. "Optimal Drug Synergy in Antimicrobial Treatments," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-9, June.
    2. 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.
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    1. Jeff Maltas & Kevin B Wood, 2019. "Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-34, October.
    2. 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.
    3. Andy Hoyle & David Cairns & Iona Paterson & Stuart McMillan & Gabriela Ochoa & Andrew P Desbois, 2020. "Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-20, August.
    4. Neythen J Treloar & Alex J H Fedorec & Brian Ingalls & Chris P Barnes, 2020. "Deep reinforcement learning for the control of microbial co-cultures in bioreactors," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-18, April.

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