Antibiotic Management Strategies Fail to Curb Resistance

A new study concludes that neither of the two current antibiotic regimens employed in hospitals can effectively mitigate antibiotic resistance.

Researchers at the University of Exeter, the University of Sydney, and other institutions conducted a theoretical mathematical study to try and explain why recent clinical trials are unable to resolve the highly controversial issue of which strategy to combat antibiotic resistance is superior - antibiotic cycling or mixing. Cycling implements the restriction and prioritization of different antibiotics at different times in hospitals; in antibiotic mixing, appropriate antibiotics are allocated to patients in a random fashion.

The researchers concluded, based on almost four million patient days of treatment tracked in the clinical trials, that the literature that showed support for the theoretical optimality of mixing was misplaced, and that clinical datasets cannot choose between the two. Their analysis is consistent with an emerging pattern that shows that neither cycling nor mixing are better than the other at mitigating pathogen selection for antibiotic resistance. The study was published on January 17, 2017, in Molecular Biology and Evolution.

“Mathematically speaking, it was very clear early in this study that antibiotic mixing was not the optimal way of allocating antibiotic to patients, yet this is what some clinicians have come believe,” said lead author Professor Robert Beardmore, PhD, of Exeter University. “But communicating this was difficult, given the complexity of the mathematical ideas. In the end, the real mathematical optimum is little more than common sense: get the right drugs to the right patients as soon as possible.”

“It is clear that information-rich, personalized protocols can outperform antibiotic cycling and mixing in mathematical models, but even this conclusion can depend on nuanced model circumstances,” said study co-author Rafael Pena-Miller, PhD, of Exeter University. “In the doomsday scenario that multi-drug resistance is endemic and present in every infection before the patient begins treatment, it matters little which treatment patients are given. But before that stark situation arises, targeting appropriate treatment at as many individuals as possible outperforms both mixing and cycling.”

“Personalized medicine is rapidly becoming a reality with dramatic increases in the availability of clinical testing at the point of care,” added senior author Professor Jon Iredell, MD, of the University of Sydney. “Antibiotic use in severe infection remains one of the most powerful interventions in medicine, and intelligent use of antibiotics is essential to optimize immediate patient outcomes and to preserve long-term benefits.”

The researchers therefore recommend that individualized treatments, both pathogen-specific and patient-specific, may be a necessity to properly optimize antibiotic use. By using computer models to study different personalized medicine scenarios, they advocate the use of devices that target infections based on rapid diagnoses of the pathogen responsible for the infection from molecular signatures or blood cultures.