title: Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections.
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.
authors.affiliation: Department of Biophysics, University of Michigan, Ann Arbor, MI.
authors.forename: Jason
authors.identifier: http://orcid.org/0000-0002-1596-6866
authors.initials: J
authors.lastname: Karslake
authors.affiliation: Department of Biophysics, University of Michigan, Ann Arbor, MI.
authors.forename: Jeff
authors.initials: J
authors.lastname: Maltas
authors.affiliation: Department of Biophysics, University of Michigan, Ann Arbor, MI.
authors.forename: Peter
authors.initials: P
authors.lastname: Brumm
authors.affiliation: Department of Biophysics, University of Michigan, Ann Arbor, MI.|Department of Physics, University of Michigan, Ann Arbor, MI.
authors.forename: Kevin B
authors.initials: KB
authors.lastname: Wood
mesh_terms: D000900:Anti-Bacterial Agents / Q000008:administration & dosage*; D058491:Bacterial Load / Q000187:drug effects / Q000502:physiology*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D024881:Drug Resistance, Bacterial / Q000187:drug effects / Q000502:physiology*; D013293:Enterococcus faecalis / Q000187:drug effects / Q000502:physiology*; D016908:Gram-Positive Bacterial Infections / Q000188:drug therapy* / Q000382:microbiology; D006801:Humans; D008826:Microbial Sensitivity Tests / Q000379:methods; D008954:Models, Biological
chemical_list: D000900:Anti-Bacterial Agents