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JAC Advance Access published online on February 4, 2008
Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkm536
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Original research

Antimicrobial breakpoints for Gram-negative aerobic bacteria based on pharmacokinetic–pharmacodynamic models with Monte Carlo simulation

Christopher R. Frei1,2,*, Nathan P. Wiederhold1,2 and David S. Burgess1,2

1 Center for Advancement of Research and Education in Infectious Diseases, The University of Texas at Austin, Austin, TX, USA 2 Pharmacotherapy Education and Research Center, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Received 23 July 2007; returned 10 October 2007; revised 12 December 2007; accepted 12 December 2007

* Corresponding author. Tel: +1-210-5678371; Fax: +1-210-5678328; E-mail: freic{at}uthscsa.edu

Objectives: This study describes a comprehensive programme designed to develop pharmacokinetic–pharmacodynamic (PK–PD) breakpoints for numerous antimicrobial classes against key Gram-negative aerobic bacteria.

Methods: A 10 000 subject Monte Carlo simulation was constructed for 13 antimicrobials (21 dosing regimens). Published pharmacokinetic data and protein binding were varied according to log-normal and uniform distributions. MICs were fixed at single values from 0.03 to 64 mg/L. The PK–PD susceptible breakpoint was defined as the MIC at which the probability of target attainment was ≥90%. PK–PD, CLSI and European Committee on Antimicrobial Susceptibility Testing breakpoints were applied to MICs from the 2005 worldwide Meropenem Yearly Susceptibility Test Information Collection database to evaluate the impact of breakpoint discrepancies.

Results: PK–PD breakpoints were within one dilution of the CLSI and European breakpoints for all antimicrobials tested—with a few exceptions. When discrepancies were noted, the PK–PD breakpoint was lower than the CLSI breakpoint [ceftriaxone (0.5 versus 8 mg/L), ertapenem (0.25 versus 2 mg/L), ciprofloxacin (0.125 versus 1 mg/L) and levofloxacin (0.25–0.5 versus 2 mg/L)] and higher than the European breakpoint [ceftazidime (4–8 versus 1 mg/L), aztreonam (4–8 versus 1 mg/L), although ciprofloxacin was an exception to this pattern (0.125 versus 0.5–1 mg/L)]. For Enterobacteriaceae, breakpoint discrepancies resulted in modest (≤10%) differences in the percentages susceptible. In contrast, large (>15%) discrepancies were noted for Pseudomonas aeruginosa and Acinetobacter baumannii.

Conclusions: Breakpoint agreement exists for imipenem, meropenem and the aminoglycosides. In contrast, discrepancies exist for piperacillin/tazobactam, cephalosporins, ertapenem, aztreonam and the fluoroquinolones. These discrepancies are most pronounced for P. aeruginosa and A. baumannii.

Key Words: Pseudomonas , Acinetobacter , β-lactam antibiotics , stochastic , computer modelling


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