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.

Applying Machine Learning Techniques to the Audit of Antimicrobial Prophylaxis

“…Fifth, we are concerned about the issue of inappropriate surgical prophylaxis or limited antimicrobial options leading to the selection of resistant bacteria. Some strategies were proposed to prevent the emergence of resistant bacteria, such as combination regimens [38,39] and adequate dosing to maintain the serum concentration of antibiotic above the minimal inhibitory concentration of the bacteria during the treatment [39,40]. Machine learning may be used to deal with this issue, which involves alternative surgical prophylactic regimens and emergence of resistant bacteria, based on the hypothesis that the new resistant bacteria may be prevented or treated with new prophylactic regimens.…”

Impact of a Novel Anticoccidial Analogue on Systemic Staphylococcus aureus Infection in a Bioluminescent Mouse Model

“…These results suggest that a combination of NCL179 with sub-inhibitory concentrations of colistin could potentially increase activity against colistin-resistant KAPE and other GNB. The combination could also reduce colistin toxicity levels, lower the likelihood of resistance development and decrease treatment time while increasing overall survival rate, as suggested by similar in vitro and in vivo models and analytical frameworks [ 53 , 54 , 55 , 56 ]. Therefore, the synergistic effect of NCL179 in combination with colistin is a promising development for a new chemical class scaffold to treat GNB infections.…”

Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling

“…Besides models and clinical studies, a third tool, which has surprisingly been understudied so far, is in vitro experimental evolution. While evolution experiments are widely used to investigate the evolution and maintenance of antibiotic resistance and are also used to study the effect of combining antibiotics, population-wide treatment strategies have to our knowledge barely been simulated in the laboratory (for a recent exception, see [30]). As an intermediate between models and clinical trials, they can help to close the gap between theoretical and clinical studies in the future.…”