Literature DB >> 20578784

Rotating antibiotics selects optimally against antibiotic resistance, in theory.

Robert E Beardmore1, Rafael Peña-Miller.   

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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Year:  2010        PMID: 20578784     DOI: 10.3934/mbe.2010.7.527

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  19 in total

1.  Multidrug therapy and evolution of antibiotic resistance: when order matters.

Authors:  Gabriel G Perron; Sergey Kryazhimskiy; Daniel P Rice; Angus Buckling
Journal:  Appl Environ Microbiol       Date:  2012-06-22       Impact factor: 4.792

2.  Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics.

Authors:  Allen A Cheng; Huiming Ding; Timothy K Lu
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-11       Impact factor: 11.205

3.  Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes.

Authors:  Christiane P Goulart; Mentar Mahmudi; Kristina A Crona; Stephen D Jacobs; Marcelo Kallmann; Barry G Hall; Devin C Greene; Miriam Barlow
Journal:  PLoS One       Date:  2013-02-13       Impact factor: 3.240

4.  Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards.

Authors:  Roger D Kouyos; Pia Abel Zur Wiesch; Sebastian Bonhoeffer
Journal:  PLoS Comput Biol       Date:  2011-03-03       Impact factor: 4.475

5.  Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review.

Authors:  D E Ramsay; J Invik; S L Checkley; S P Gow; N D Osgood; C L Waldner
Journal:  Epidemiol Infect       Date:  2018-07-31       Impact factor: 4.434

6.  The effects of antibiotic cycling and mixing on acquisition of antibiotic resistant bacteria in the ICU: A post-hoc individual patient analysis of a prospective cluster-randomized crossover study.

Authors:  Pleun J van Duijn; Walter Verbrugghe; Philippe G Jorens; Fabian Spöhr; Dirk Schedler; Maria Deja; Andreas Rothbart; Djillali Annane; Christine Lawrence; Matjaz Jereb; Katja Seme; Franc Šifrer; Viktorija Tomič; Francisco Estevez; Jandira Carneiro; Stephan Harbarth; Marc J M Bonten
Journal:  PLoS One       Date:  2022-05-03       Impact factor: 3.752

7.  Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance.

Authors:  Portia M Mira; Kristina Crona; Devin Greene; Juan C Meza; Bernd Sturmfels; Miriam Barlow
Journal:  PLoS One       Date:  2015-05-06       Impact factor: 3.240

Review 8.  Modelling the transmission of healthcare associated infections: a systematic review.

Authors:  Esther van Kleef; Julie V Robotham; Mark Jit; Sarah R Deeny; William J Edmunds
Journal:  BMC Infect Dis       Date:  2013-06-28       Impact factor: 3.090

9.  Implications of stress-induced genetic variation for minimizing multidrug resistance in bacteria.

Authors:  Uri Obolski; Lilach Hadany
Journal:  BMC Med       Date:  2012-08-13       Impact factor: 8.775

Review 10.  Strategies to minimize antibiotic resistance.

Authors:  Chang-Ro Lee; Ill Hwan Cho; Byeong Chul Jeong; Sang Hee Lee
Journal:  Int J Environ Res Public Health       Date:  2013-09-12       Impact factor: 3.390

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