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.
Highlights
The inhibitory effects of antibiotics often decrease with increasing density of the starting microbial population, a phenomenon known as the inoculum effect [1]
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
We combine quantitative laboratory experiments on bacterial populations with mathematical models of antimicrobial therapies to demonstrate that bacterial populations of different sizes may respond very differently to the same antibiotic treatment
Summary
The inhibitory effects of antibiotics often decrease with increasing density of the starting microbial population, a phenomenon known as the inoculum effect [1]. The consequences of the inoculum effect for microbial population dynamics, the design of optimal treatment strategies, and the evolution of resistance are debated, in part because antibiotic efficacy is typically measured as a function of initial population (inoculum) size, not as an explicit function of population density. The IE is commonly described as an increase in the minimum inhibitory concentration (MIC) of a drug as a function of inoculum size [1]. Because clinical therapies often involve time-dependent drug doses that span both suband super-MIC levels [7], it is difficult to systematically evaluate and optimize these dosing regimens without knowing the direct functional relationship between per capita growth rate, cell density, and drug concentration
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