Rotating antibiotics selects optimally against antibiotic resistance, in theory

Department of Mathematics, Imperial College London, SW7 2AZ, London, United Kingdom.
Mathematical Biosciences and Engineering (Impact Factor: 0.84). 07/2010; 7(3):527-52. DOI: 10.3934/mbe.2010.7.527
Source: PubMed


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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    • "Mathematical models of HCAIs have primarily been set in a single ward (49%, 47 studies), with the intensive care unit (ICU) being the most frequent setting modelled (26%, 25 studies) [14,16,22,28,29,31,32,36],[40-42,45,49,52,53,55,63,65],[72,79,82,91,101,107,108] or a simplified hospital setting, lacking any further ward structure (31%, 30 studies) [12,13,24,27,33,34,38,39],[45,46,51,58,60,64,66,68],[69,74,77,78,83-88,93,94,97],[109]. More recent studies however, have incorporated the interaction between general wards and the ICU [23,43,69] or between different wards [11]. "
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    ABSTRACT: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.
    BMC Infectious Diseases 06/2013; 13(1):294. DOI:10.1186/1471-2334-13-294 · 2.61 Impact Factor
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    • "Attempts to compare the different treatment strategies and assess their relative efficiency have been made both in empirical studies [12-17] and using theoretical analysis [10,18-20]. All in all, results obtained using both approaches have been inconclusive. "
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    ABSTRACT: Background Antibiotic resistance in bacterial infections is a growing threat to public health. Recent evidence shows that when exposed to stressful conditions, some bacteria perform higher rates of horizontal gene transfer and mutation, and thus acquire antibiotic resistance more rapidly. Methods We incorporate this new notion into a mathematical model for the emergence of antibiotic multi-resistance in a hospital setting. Results We show that when stress has a considerable effect on genetic variation, the emergence of antibiotic resistance is dramatically affected. A strategy in which patients receive a combination of antibiotics (combining) is expected to facilitate the emergence of multi-resistant bacteria when genetic variation is stress-induced. The preference between a strategy in which one of two effective drugs is assigned randomly to each patient (mixing), and a strategy where only one drug is administered for a specific period of time (cycling) is determined by the resistance acquisition mechanisms. We discuss several features of the mechanisms by which stress affects variation and predict the conditions for success of different antibiotic treatment strategies. Conclusions These findings should encourage research on the mechanisms of stress-induced genetic variation and establish the importance of incorporating data about these mechanisms when considering antibiotic treatment strategies.
    BMC Medicine 08/2012; 10(1):89. DOI:10.1186/1741-7015-10-89 · 7.25 Impact Factor
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    Mathematical Biosciences and Engineering 10/2010; 7(4):919-22. DOI:10.3934/mbe.2010.7.919 · 0.84 Impact Factor
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