Literature DB >> 11978503

Breakpoints: current practice and future perspectives.

Johan W Mouton1.   

Abstract

Much of the discussion over the past decades on the value and setting of breakpoints has been due to the fact that the breakpoint was used in two ways; as an indicator to predict the probability of clinical success and also to detect resistant (sub) populations. It is apparent that these two meanings have lead to a different approach to setting, interpretation and use of breakpoints based on clinical efficacy on the one hand and breakpoints based on detection of resistance on the other. Nevertheless, several of the current guidelines make no perceptible distinction between these two meanings. A case is therefore strongly made to recognize that there is a difference between clinical and microbiological breakpoints. The microbiological breakpoint may be used to detect organisms that do not belong to the natural bacterial population, but somehow have acquired resistance and might be useful in recognizing emergence of resistant subpopulations and may lead to subsequent measures to be taken. Alternatively, the clinical breakpoint is of principal value to the clinician in that it results in a classification of S (susceptible), I (intermediate susceptible) and R (resistant) and is used in clinical practice and correlate with a measure of clinical efficacy. Methods developed during the last few years to arrive at meaningful clinical breakpoints are discussed, such as CART analysis and Monte Carlo simulation. In discussing future developments, it is suggested that current reports containing S, I, and R be at least supplemented with the MICs measured and, using current techniques available such as Monte Carlo simulation, provide the probability of successful eradication of the micro-organism and successful treatment based on population pharmacokinetics and Minimal Inhibitory Concentration (MIC) distributions.

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Year:  2002        PMID: 11978503     DOI: 10.1016/s0924-8579(02)00028-6

Source DB:  PubMed          Journal:  Int J Antimicrob Agents        ISSN: 0924-8579            Impact factor:   5.283


  20 in total

1.  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

2.  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

3.  The relative contributions of physical structure and cell density to the antibiotic susceptibility of bacteria in biofilms.

Authors:  Amy E Kirby; Kimberly Garner; Bruce R Levin
Journal:  Antimicrob Agents Chemother       Date:  2012-03-26       Impact factor: 5.191

4.  Population dynamics of antibiotic treatment: a mathematical model and hypotheses for time-kill and continuous-culture experiments.

Authors:  Bruce R Levin; Klas I Udekwu
Journal:  Antimicrob Agents Chemother       Date:  2010-06-01       Impact factor: 5.191

5.  Evaluating ciprofloxacin dosing for Pseudomonas aeruginosa infection by using clinical outcome-based Monte Carlo simulations.

Authors:  Sheryl Zelenitsky; Robert Ariano; Godfrey Harding; Alan Forrest
Journal:  Antimicrob Agents Chemother       Date:  2005-10       Impact factor: 5.191

6.  Pharmacokinetics of penicillin G in infants with a gestational age of less than 32 weeks.

Authors:  Anouk E Muller; Joost DeJongh; Ymka Bult; Wil H F Goessens; Johan W Mouton; Meindert Danhof; John N van den Anker
Journal:  Antimicrob Agents Chemother       Date:  2007-07-23       Impact factor: 5.191

7.  Pharmacokinetics of clindamycin in pregnant women in the peripartum period.

Authors:  Anouk E Muller; Johan W Mouton; Paul M Oostvogel; P Joep Dörr; Rob A Voskuyl; Joost DeJongh; Eric A P Steegers; Meindert Danhof
Journal:  Antimicrob Agents Chemother       Date:  2010-02-22       Impact factor: 5.191

8.  Novel concentration-killing curve method for estimation of bactericidal potency of antibiotics in an in vitro dynamic model.

Authors:  Y Q Liu; Y Z Zhang; P J Gao
Journal:  Antimicrob Agents Chemother       Date:  2004-10       Impact factor: 5.191

9.  Laboratory detection of Haemophilus influenzae with decreased susceptibility to nalidixic acid, ciprofloxacin, levofloxacin, and moxifloxacin due to GyrA and ParC mutations.

Authors:  María Pérez-Vázquez; Federico Román; Belén Aracil; Rafael Cantón; José Campos
Journal:  J Clin Microbiol       Date:  2004-03       Impact factor: 5.948

10.  Antimicrobial breakpoint estimation accounting for variability in pharmacokinetics.

Authors:  Goue Denis Gohore Bi; Jun Li; Fahima Nekka
Journal:  Theor Biol Med Model       Date:  2009-06-26       Impact factor: 2.432

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