Literature DB >> 14711078

Impact of pharmacodynamics on breakpoint selection for susceptibility testing.

Johan W Mouton1.   

Abstract

In their report, Ericsson and Sherris stated that "adequately described sensitivity categorization schemes have been based on four main concepts" and went on to describe the advantages and inherent limitations of those four criteria. These limitations are very comparable with what is increasingly viewed as limitations to the current breakpoint systems because those four concepts have always been tried to be brought together in one breakpoint system. In line of this development, the European Committee on Antimicrobial Susceptibility Testing has recently recognized that clinical breakpoints based on PK-PD relationships confer a different meaning to resistance than early detection of microorganisms that do not belong to the natural bacterial population, but somehow have acquired resistance mechanisms and have introduced a wild-type cutoff breakpoint conveying a different meaning than clinical breakpoint. Similarly, there is increasing worry on the setting of clinical breakpoints based on frequency distributions because they do not bear an optimal relationship between dose and bacteriologic or clinical effect. This is especially apparent for older drugs because PK-PD was not used until a few years ago. Because dose-effect relationships have now been reasonably well established for most drugs, these should now be used to reappraise current clinical breakpoints.

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Year:  2003        PMID: 14711078     DOI: 10.1016/s0891-5520(03)00062-x

Source DB:  PubMed          Journal:  Infect Dis Clin North Am        ISSN: 0891-5520            Impact factor:   5.982


  11 in total

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2.  Designing fluoroquinolone breakpoints for Streptococcus pneumoniae by using genetics instead of pharmacokinetics-pharmacodynamics.

Authors:  H J Smith; A M Noreddin; C G Siemens; K N Schurek; J Greisman; C J Hoban; D J Hoban; G G Zhanel
Journal:  Antimicrob Agents Chemother       Date:  2004-09       Impact factor: 5.191

3.  Use of Monte Carlo simulations to select therapeutic doses and provisional breakpoints of BAL9141.

Authors:  Johan W Mouton; Anne Schmitt-Hoffmann; Stuart Shapiro; Norman Nashed; Nieko C Punt
Journal:  Antimicrob Agents Chemother       Date:  2004-05       Impact factor: 5.191

Review 4.  Setting and revising antibacterial susceptibility breakpoints.

Authors:  John Turnidge; David L Paterson
Journal:  Clin Microbiol Rev       Date:  2007-07       Impact factor: 26.132

5.  Functional relationship between bacterial cell density and the efficacy of antibiotics.

Authors:  Klas I Udekwu; Nicholas Parrish; Peter Ankomah; Fernando Baquero; Bruce R Levin
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Review 6.  A long journey from minimum inhibitory concentration testing to clinically predictive breakpoints: deterministic and probabilistic approaches in deriving breakpoints.

Authors:  A Dalhoff; P G Ambrose; J W Mouton
Journal:  Infection       Date:  2009-07-23       Impact factor: 3.553

7.  Optimal dose finding of garenoxacin based on population pharmacokinetics/pharmacodynamics and Monte Carlo simulation.

Authors:  Yusuke Tanigawara; Kenji Nozawa; Hisatsugu Tsuda
Journal:  Eur J Clin Pharmacol       Date:  2011-07-28       Impact factor: 2.953

8.  Pharmacokinetics of aztreonam in healthy subjects and patients with cystic fibrosis and evaluation of dose-exposure relationships using monte carlo simulation.

Authors:  Alexander A Vinks; Ronald N van Rossem; Ron A A Mathôt; Harry G M Heijerman; Johan W Mouton
Journal:  Antimicrob Agents Chemother       Date:  2007-06-18       Impact factor: 5.191

9.  Effect of differences in MIC values on clinical outcomes in patients with bloodstream infections caused by gram-negative organisms treated with levofloxacin.

Authors:  Robyn Defife; Marc H Scheetz; Joe M Feinglass; Michael J Postelnick; Kimberly K Scarsi
Journal:  Antimicrob Agents Chemother       Date:  2008-12-15       Impact factor: 5.191

10.  Antimicrobial breakpoints for gram-negative aerobic bacteria based on pharmacokinetic-pharmacodynamic models with Monte Carlo simulation.

Authors:  Christopher R Frei; Nathan P Wiederhold; David S Burgess
Journal:  J Antimicrob Chemother       Date:  2008-02-04       Impact factor: 5.790

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