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Mol Biol Evol. 2017 Apr 1;34(4):802-817. doi: 10.1093/molbev/msw292.

Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?

Author information

1
Biosciences University of Exeter, Devon, United Kingdom.
2
Center for Genomic Sciences, Universidad Nacional Autonóma de México, Cuernavaca, Mexico.
3
Westmead Clinical School, Westmead Hospital, The University of Sydney, Australia.

Abstract

Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as "antibiotic cycling" and "antibiotic mixing." However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to "cycle" between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic.

Key words:

: antibiotic cycling, antibiotic mixing, optimal control, stochastic models.

PMID:
28096304
PMCID:
PMC5400377
DOI:
10.1093/molbev/msw292
[Indexed for MEDLINE]
Free PMC Article

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