Literature DB >> 16940088

Mechanism-based pharmacodynamic models of fluoroquinolone resistance in Staphylococcus aureus.

Philip Chung1, Patrick J McNamara, Jeffrey J Campion, Martin E Evans.   

Abstract

Pharmacodynamic modeling from earlier experiments in which two ciprofloxacin-susceptible Staphylococcus aureus strains and their corresponding resistant grlA mutants were exposed to a series of ciprofloxacin (J. J. Campion, P. J. McNamara, and M. E. Evans, Antimicrob. Agents Chemother. 49:209-219, 2005) and levofloxacin (J. J. Campion et al., Antimicrob. Agents Chemother. 49:2189-2199, 2005) pharmacokinetic profiles in an in vitro system indicated that the subpopulation-specific estimated maximal killing rate constants were similar for both agents, suggesting a common mechanism of action. We propose two novel pharmacodynamic models that assign mechanisms of action to fluoroquinolones (growth inhibition or death stimulation) and compare the abilities of these models and two other maximum effect models (net effect and MIC based) to describe and predict the changes in the population dynamics observed during our previous in vitro system experiments with ciprofloxacin. A high correlation between predicted and observed viable counts was observed for all models, but the best fits, as assessed by diagnostic tests, and the most precise parameter estimates were obtained with the growth inhibition and net effect models. All models, except the death stimulation model, correctly predicted that resistant subpopulations would not emerge when a high-density culture was exposed to a high initial concentration designed to rapidly eradicate low-level-resistant grlA mutants. Additional experiments are necessary to elucidate which of the proposed mechanistic models best characterizes the antibacterial effects of fluoroquinolone antimicrobial agents.

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Year:  2006        PMID: 16940088      PMCID: PMC1563538          DOI: 10.1128/AAC.00736-05

Source DB:  PubMed          Journal:  Antimicrob Agents Chemother        ISSN: 0066-4804            Impact factor:   5.191


  21 in total

1.  Comparative killing rates of fluoroquinolones and cell wall-active agents.

Authors:  J C Fung-Tomc; E Gradelski; L Valera; B Kolek; D P Bonner
Journal:  Antimicrob Agents Chemother       Date:  2000-05       Impact factor: 5.191

2.  Application of a mathematical model to prevent in vivo amplification of antibiotic-resistant bacterial populations during therapy.

Authors:  Nelson Jumbe; Arnold Louie; Robert Leary; Weiguo Liu; Mark R Deziel; Vincent H Tam; Reetu Bachhawat; Christopher Freeman; James B Kahn; Karen Bush; Michael N Dudley; Michael H Miller; George L Drusano
Journal:  J Clin Invest       Date:  2003-07       Impact factor: 14.808

3.  Novel pharmacokinetic-pharmacodynamic model for prediction of outcomes with an extended-release formulation of ciprofloxacin.

Authors:  Alison K Meagher; Alan Forrest; Axel Dalhoff; Heino Stass; Jerome J Schentag
Journal:  Antimicrob Agents Chemother       Date:  2004-06       Impact factor: 5.191

4.  Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens.

Authors:  Roland R Regoes; Camilla Wiuff; Renata M Zappala; Kim N Garner; Fernando Baquero; Bruce R Levin
Journal:  Antimicrob Agents Chemother       Date:  2004-10       Impact factor: 5.191

5.  Bactericidal mechanism of gatifloxacin compared with other quinolones.

Authors:  Elizabeth Gradelski; Benjamin Kolek; Daniel Bonner; Joan Fung-Tomc
Journal:  J Antimicrob Chemother       Date:  2002-01       Impact factor: 5.790

6.  Comparison of four basic models of indirect pharmacodynamic responses.

Authors:  N L Dayneka; V Garg; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1993-08

7.  Pharmacodynamic modeling of the evolution of levofloxacin resistance in Staphylococcus aureus.

Authors:  Jeffrey J Campion; Philip Chung; Patrick J McNamara; William B Titlow; Martin E Evans
Journal:  Antimicrob Agents Chemother       Date:  2005-06       Impact factor: 5.191

8.  Selection of a moxifloxacin dose that suppresses drug resistance in Mycobacterium tuberculosis, by use of an in vitro pharmacodynamic infection model and mathematical modeling.

Authors:  Tawanda Gumbo; Arnold Louie; Mark R Deziel; Linda M Parsons; Max Salfinger; George L Drusano
Journal:  J Infect Dis       Date:  2004-09-24       Impact factor: 5.226

9.  Pharmacoimmunodynamics of methylprednisolone: trafficking of helper T lymphocytes.

Authors:  L E Fisher; E A Ludwig; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1992-08

10.  Quinolone-DNA interaction: sequence-dependent binding to single-stranded DNA reflects the interaction within the gyrase-DNA complex.

Authors:  Christian G Noble; Faye M Barnard; Anthony Maxwell
Journal:  Antimicrob Agents Chemother       Date:  2003-03       Impact factor: 5.191

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  13 in total

1.  A Roadblock-and-Kill Mechanism of Action Model for the DNA-Targeting Antibiotic Ciprofloxacin.

Authors:  Nikola Ojkic; Elin Lilja; Susana Direito; Angela Dawson; Rosalind J Allen; Bartlomiej Waclaw
Journal:  Antimicrob Agents Chemother       Date:  2020-08-20       Impact factor: 5.191

2.  Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model.

Authors:  Elisabet I Nielsen; Otto Cars; Lena E Friberg
Journal:  Antimicrob Agents Chemother       Date:  2011-01-31       Impact factor: 5.191

3.  Semimechanistic pharmacokinetic-pharmacodynamic model with adaptation development for time-kill experiments of ciprofloxacin against Pseudomonas aeruginosa.

Authors:  Nicolas Grégoire; Sophie Raherison; Claire Grignon; Emmanuelle Comets; Manuella Marliat; Marie-Cécile Ploy; William Couet
Journal:  Antimicrob Agents Chemother       Date:  2010-04-05       Impact factor: 5.191

4.  Pharmacodynamics of vancomycin at simulated epithelial lining fluid concentrations against methicillin-resistant Staphylococcus aureus (MRSA): implications for dosing in MRSA pneumonia.

Authors:  Yoriko Harigaya; Jürgen B Bulitta; Alan Forrest; George Sakoulas; Alan J Lesse; Joseph M Mylotte; Brian T Tsuji
Journal:  Antimicrob Agents Chemother       Date:  2009-07-13       Impact factor: 5.191

5.  Development and qualification of a pharmacodynamic model for the pronounced inoculum effect of ceftazidime against Pseudomonas aeruginosa.

Authors:  Jürgen B Bulitta; Neang S Ly; Jenny C Yang; Alan Forrest; William J Jusko; Brian T Tsuji
Journal:  Antimicrob Agents Chemother       Date:  2008-10-13       Impact factor: 5.191

6.  Exploring the role of the immune response in preventing antibiotic resistance.

Authors:  Andreas Handel; Elisa Margolis; Bruce R Levin
Journal:  J Theor Biol       Date:  2008-11-08       Impact factor: 2.691

7.  Mechanism-based pharmacokinetic-pharmacodynamic models of in vitro fungistatic and fungicidal effects against Candida albicans.

Authors:  Nicolas Venisse; Nicolas Grégoire; Manuella Marliat; William Couet
Journal:  Antimicrob Agents Chemother       Date:  2008-01-07       Impact factor: 5.191

8.  Sequential Time-Kill, a Simple Experimental Trick To Discriminate between Pharmacokinetics/Pharmacodynamics Models with Distinct Heterogeneous Subpopulations versus Homogenous Population with Adaptive Resistance.

Authors:  A Chauzy; H Ih; M Jacobs; S Marchand; N Grégoire; W Couet; J M Buyck
Journal:  Antimicrob Agents Chemother       Date:  2020-07-22       Impact factor: 5.191

Review 9.  Mechanism-based pharmacokinetic-pharmacodynamic modeling of antimicrobial drug effects.

Authors:  David Czock; Frieder Keller
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-09-29       Impact factor: 2.410

10.  Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling.

Authors:  Matthieu Jacobs; Nicolas Grégoire; William Couet; Jurgen B Bulitta
Journal:  PLoS Comput Biol       Date:  2016-03-11       Impact factor: 4.475

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