ArticleMolecular biology and evolution2017
Antibiotic Cycling and Antibiotic Mixing: Which One Best Mitigates Antibiotic Resistance?
Article in Molecular biology and evolution, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Effect of Antibiotic-Cycling Strategy on Antibiotic-Resistant Bacterial Infections or Colonization in Intensive Care Units: A Systematic Review and Meta-Analysis.Worldviews on evidence-based nursing · 2020Pooled it
- Linezolid and levofloxacin: an uncommon pairing that suppresses evolution of resistance inFEMS microbes · 2026Article
- Invariant set theory for predicting potential failure of antibiotic cycling.Infectious Disease Modelling · 2025Article
- Bacterial pathogens causing skin and soft tissue infections and antibiotic susceptibility in South Asia: a scoping review protocol.Systematic reviews · 2025Article
- The Actual Clinical Situation Ruthlessly Exposes the Challenge of Rational Care for Nosocomial and Community-Acquired Infections and Requires Even More Efforts for Satisfactory Antibiotic Stewardship.Antibiotics (Basel, Switzerland) · 2025Article
- Assessing fitness costs in malaria parasites: a comprehensive review and implications for drug resistance management.Malaria journal · 2025Review
- Mesoporous Oxidized Mn-Ca Nanoparticles as Potential Antimicrobial Agents for Wound Healing.Molecules (Basel, Switzerland) · 2024Article
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.PLoS computational biology · 2024Article
- Modeling spatial evolution of multi-drug resistance under drug environmental gradients.bioRxiv : the preprint server for biology · 2023Article
- Linking within- and between-host scales for understanding the evolutionary dynamics of quantitative antimicrobial resistance.Journal of mathematical biology · 2023Article
- Advances in the Discovery of Efflux Pump Inhibitors as Novel Potentiators to Control Antimicrobial-Resistant Pathogens.Antibiotics (Basel, Switzerland) · 2023Review
- Bacterial defences: mechanisms, evolution and antimicrobial resistance.Nature reviews. Microbiology · 2023Review
- Correlation between Antimicrobial Resistance and the Hospital-Wide Diverse Use of Broad-Spectrum Antibiotics by the Antimicrobial Stewardship Program in Japan.Pharmaceutics · 2023Article
- Evaluation of a Sequential Antibiotic Treatment Regimen of Ampicillin, Ciprofloxacin and Fosfomycin againstAntibiotics (Basel, Switzerland) · 2022Article
- Evolutionary History and Strength of Selection Determine the Rate of Antibiotic Resistance Adaptation.Molecular biology and evolution · 2022Article
- Comparing optimization criteria in antibiotic allocation protocols.Royal Society open science · 2022Article
- Wound Infection with Multi-Drug ResistantArchives of Razi Institute · 2021Article
- Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results.The Lancet. Microbe · 2021Article
- Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling.Journal of the Royal Society, Interface · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.