A model of the complex response of Staphylococcus aureus to methicillin

Pienaar, Elsje; Whitney, Scott E.; Viljoen, Hendrik J.; Rensburg, N.F.J. van

doi:10.1016/j.jtbi.2008.12.003

Cited by 5 publications

(4 citation statements)

References 34 publications

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“…A paradoxical effect resulting in the decreased killing of bacteria with increasing concentrations of penicillin was first observed by Eagle and Musselman in 1948 (20, 21). Since then, the in vitro "Eagle effect" has been reported in the literature for many antimicrobial agents, including ceftobiprole (2,3,17,54). Interestingly, in our studies, at the higher inocula tested (10 7 and 10 8 CFU/ml) for both MIC and MBC, ceftobiprole displayed the paradoxical, or "Eagle effect" with growth of either strain occurring at concentrations Ն4-fold above the MIC (data not shown).…”

Section: Discussionsupporting

confidence: 54%

Synergistic Activity of Ceftobiprole and Vancomycin in a Rat Model of Infective Endocarditis Caused by Methicillin-Resistant and Glycopeptide-Intermediate Staphylococcus aureus

Fernandez

Abbanat

Shang

et al. 2012

Antimicrob Agents Chemother

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The therapeutic activity of ceftobiprole medocaril, the prodrug of ceftobiprole, was compared to that of vancomycin, daptomycin, and the combination of a subtherapeutic dose of ceftobiprole and vancomycin in a rat model of infective endocarditis due to methicillin-resistant Staphylococcus aureus (MRSA) (ATCC 43300) or glycopeptide-intermediate Staphylococcus aureus (GISA) (NRS4 and HIP 5836) strains. The minimum bactericidal concentrations of ceftobiprole, vancomycin, and daptomycin at bacterial cell densities similar to those encountered in the cardiac vegetation in the rat endocarditis model were 2, >64, and 8 g/ml, respectively, for MRSA ATCC 43300 and 4, >64, and 8 g/ml, respectively, for the GISA strain. Ceftobiprole medocaril administered in doses of 100 mg/kg of body weight given intravenously (i.v.) twice a day (BID) every 8 h (q8h) (equivalent to a human therapeutic dose of ceftobiprole [500 mg given three times a day [TID]) was the most effective monotherapy, eradicating nearly 5 log 10 CFU/g MRSA or 6 log 10 CFU/g GISA organisms from the cardiac vegetation and had the highest incidence of sterile vegetation compared to the other monotherapies in the endocarditis model. In in vitro time-kill studies, synergistic effects were observed with ceftobiprole and vancomycin on MRSA and GISA strains, and in vivo synergy was noted with combinations of subtherapeutic doses of these agents for the same strains. Additionally, sterile vegetations were achieved in 33 and 60%, respectively, of the animals infected with MRSA ATCC 43300 or GISA NRS4 receiving ceftobiprole-vancomycin combination therapy. In summary, ceftobiprole was efficacious both as monotherapy and in combination with vancomycin in treating MRSA and GISA infections in a rat infective endocarditis model and warrants further evaluation.

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Section: Discussionsupporting

confidence: 54%

Synergistic Activity of Ceftobiprole and Vancomycin in a Rat Model of Infective Endocarditis Caused by Methicillin-Resistant and Glycopeptide-Intermediate Staphylococcus aureus

Fernandez

Abbanat

Shang

et al. 2012

Antimicrob Agents Chemother

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“…We reviewed only models where AB consumption was introduced as an independent parameter, because we consider the process of developing resistance as a function of time and antibiotic consumption. That is why some papers were excluded at the stage of full text analysis (Bergstrom et al, 2004 ; Miller et al, 2004 ; Boni and Feldman, 2005 ; Huovinen, 2005 ; Mera, 2005 ; Reluga, 2005 ; Webb et al, 2005 ; Levin and Rozen, 2006 ; Schinazi, 2006 ; D'Agata et al, 2007 ; Zalounina et al, 2007 ; Drovandi and Pettitt, 2008 ; Pienaar et al, 2009 ; Artalejo et al, 2010 ; Haber et al, 2010 ; Kardas-Sloma et al, 2011 ; Kouyos et al, 2011 ; Opatowski et al, 2011 ; Wang et al, 2011 ; Banks et al, 2012 ; Chamchod and Ruan, 2012a , b ; Deng et al, 2012 ; Hurford et al, 2012 ; Joyner et al, 2012 ; Sotto and Lavigne, 2012 ; Yahdi et al, 2012 ; Horsburgh et al, 2013 ; Lee et al, 2013a , b ; Worby et al, 2013 ). The seven remaining models were classified according to the type of link between resistance and AB consumption.…”

Section: Methodsmentioning

confidence: 99%

What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

et al. 2015

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Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

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“…Plasma concentrations of anti-TB drugs are not necessarily a good indicator of the concentrations in bacilli. Pienaar et al (2009) have developed a mathematical model to describe the complex response of S. aureus to methicillin, including the 'Eagle effect'. They modeled the cell population as a probability density distribution of cell wall thickness.…”

Section: Introductionmentioning

confidence: 99%