Antibiotic Cycling and Antibiotic Mixing: which one best mitigates antibiotic resistance?
Molecular Biology and Evolution
Oxford University Press
© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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 conicting behavioural 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 prioritisation of di_erent antibiotics at di_erent 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 behavioural 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.
REB was funded during this work by an MRC Discipline Hopping Fellowship G0802611, RPM was funded by a Conacyt PhD award, all authors were supported by EPSRC grant EP/I00503X/1 (grant holder REB).
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