Rotating antibiotics selects optimally against antibiotic resistance, in theory.
ABSTRACT The purpose of this paper is to use mathematical models to investigate the claim made in the medical literature over a decade ago that the routine rotation of antibiotics in an intensive care unit (ICU) will select against the evolution and spread of antibiotic-resistant pathogens. In contrast, previous theoretical studies addressing this question have demonstrated that routinely changing the drug of choice for a given pathogenic infection may in fact lead to a greater incidence of drug resistance in comparison to the random deployment of different drugs. Using mathematical models that do not explicitly incorporate the spatial dynamics of pathogen transmission within the ICU or hospital and assuming the antibiotics are from distinct functional groups, we use a control theoretic-approach to prove that one can relax the medical notion of what constitutes an antibiotic rotation and so obtain protocols that are arbitrarily close to the optimum. Finally, we show that theoretical feedback control measures that rotate between different antibiotics motivated directly by the outcome of clinical studies can be deployed to good effect to reduce the prevalence of antibiotic resistance below what can be achieved with random antibiotic use.
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ABSTRACT: Bacterial infections are a global health concern with high levels of mortality and morbidity associated. The resistance of pathogens to drugs is one leading cause of this problem, being common the administration of multiple drugs to improve the therapeutic effects. This review critically explores diverse aspects involved in the treatment of bacterial infections through multi-drug therapies, from a mathematical and within-host perspectives. Five recent models were selected and are reviewed. These models fall into the following question: which drugs to select, the respective dose, the administration period to effectively eradicate the infection in the shortest period of time and with reduced side effects? In this analysis, three groups of variables were considered: pharmacokinetics, pharmacodynamics and disturbance variables. To date, there is no model that fully answers to this issue for a living organism and it is questionable whether this would be possible for any case of infection.Drug Resistance Updates 07/2014; · 9.11 Impact Factor
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ABSTRACT: Intensive care units (ICU) are epicenters for the emergence of antibiotic-resistant Gram-negative bacteria (ARGNB) because of high rates of antibiotic usage, rapid patient turnover, immunological susceptibility of acutely ill patients, and frequent contact between healthcare workers and patients, facilitating cross-transmission.Antibiotic stewardship programs are considered important to reduce antibiotic resistance, but the effectiveness of strategies such as, for instance, antibiotic rotation, have not been determined rigorously. Interpretation of available studies on antibiotic rotation is hampered by heterogeneity in implemented strategies and suboptimal study designs. In this cluster-randomized, crossover trial the effects of two antibiotic rotation strategies, antibiotic mixing and cycling, on the prevalence of ARGNB in ICUs are determined. Antibiotic mixing aims to create maximum antibiotic heterogeneity, and cycling aims to create maximum antibiotic homogeneity during consecutive periods.Trials. 07/2014; 15(1):277.
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ABSTRACT: New therapeutic strategies are needed to treat infections caused by drug-resistant bacteria, which constitute a major growing threat to human health. Here, we use a high-throughput technology to identify combinatorial genetic perturbations that can enhance the killing of drug-resistant bacteria with antibiotic treatment. This strategy, Combinatorial Genetics En Masse (CombiGEM), enables the rapid generation of high-order barcoded combinations of genetic elements for high-throughput multiplexed characterization based on next-generation sequencing. We created ∼34,000 pairwise combinations of Escherichia coli transcription factor (TF) overexpression constructs. Using Illumina sequencing, we identified diverse perturbations in antibiotic-resistance phenotypes against carbapenem-resistant Enterobacteriaceae. Specifically, we found multiple TF combinations that potentiated antibiotic killing by up to 10(6)-fold and delivered these combinations via phagemids to increase the killing of highly drug-resistant E. coli harboring New Delhi metallo-beta-lactamase-1. Moreover, we constructed libraries of three-wise combinations of transcription factors with >4 million unique members and demonstrated that these could be tracked via next-generation sequencing. We envision that CombiGEM could be extended to other model organisms, disease models, and phenotypes, where it could accelerate massively parallel combinatorial genetics studies for a broad range of biomedical and biotechnology applications, including the treatment of antibiotic-resistant infections.Proceedings of the National Academy of Sciences 08/2014; · 9.81 Impact Factor