Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting

Abstract

The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquid-handling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community.