Bristol Centre for Antimicrobial Research and Evaluation, North Bristol NHS Trust and University of Bristol, Department of Medical Microbiology, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK
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Introduction |
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The simplest type of experiment using in vitro pharmacodynamic models tests the simulated serum pharmacokinetic profile of a drug at a defined dose against a range of pathogens likely to be encountered in clinical trials and subsequent clinical use. Efficacy is judged by bacterial killing over the time of study.13 For example, many studies have recently been published on the effects of ciprofloxacin, levofloxacin, moxifloxacin, ofloxacin, sparfloxacin and trovafloxacin against Streptococcus pneumoniae.49 Variations on this basic design include the simulation of increasing doses at the same frequency (dose escalation studies); studying the emergence of antibacterial resistance during drug exposure; and using resistant isogenic or other mutants with increased drug MICs.1012 Aside from these descriptive studies, in vitro models are also used to examine which pharmacodynamic factors are associated with maximizing antibacterial effects or minimizing the emergence of resistance. This process is inherently more complex than simply describing antibacterial effects. The following factors may affect the conclusions of these experiments:
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There have been very few systematic studies of the reproducibility and predictive value of the various measures of antibacterial effect used in in vitro models. The simplest parameters depend on a single bacterial count or change in bacterial count from the inoculum, measured in cfu/mL. The time after exposure for these observations is often related to the dosing interval, 24 h or 48 h of the dosing simulation, or the time to reach the minimum bacterial count. While these approaches are widely used3,14,17,3032 there is little consistency as to the times during the simulation at which changes in viable counts are calculated. Another measure of killing commonly used is the time taken for the drug to kill 99.9% of the initial inoculum,16,1820,33 but this measure of killing is arbitrary: times to 90%, 99% or 99.99% kill, are all equally valid. In addition, it is unclear if the time taken to kill 99.9% of the initial inoculum (99.9) is a reliable measure of drug activity. For example, using
99.9 to measure antibacterial effect, it was not possible to relate the activity of trovafloxacin, clinafloxacin, ciprofloxacin, sparfloxacin or levofloxacin against S. aureus to AUC/MIC, Cmax/MIC or t > MIC.18 Similarly, the antibacterial effects of ciprofloxacin, levofloxacin and trovafloxacin against S. pneumoniae were assessed using
99.9 and no relationship between AUC/ MIC, Cmax/MIC or t > MIC was found.19 In contrast, a descriptive analysis of
99.9 at different AUC/MIC ratios of trovafloxacin, ofloxacin and ciprofloxacin against S. pneumoniae seemed to indicate a relationship between AUC/MIC and
99.9.34 The failure of some workers to detect a relationship between
99.9 and pharmacodynamic parameters may be related to the undetected emergence of resistance in these simulations, which will obscure the true
99.9 value.
Areas related to the timekill curve have also been used to describe killing, but again there is no agreement as to which area measure is most appropriate. Rustige & Wiedemann35 used a measure termed the area above the curve (AAC), measured in cfu (log difference from inoculum)/ mLh. A negative AAC value is associated with bacterial growth and a positive one with killing; increasing values of AAC are related to increasing kill (Figure 2).36 The area under the bacterial timekill curve (AUBKC) has also been used, sometimes excluding bacterial regrowth, but usually including it. AUBKC is usually standardized to take account of variation in initial inoculum.25,33,37 The AUBKC is inversely related to killing: as killing increases, AUBKC decreases. AUBKC has been shown to be a reproducible and robust measure of bacterial killing in systems using fixed antibiotic concentrations and in in vitro models.38,39 It also varies across a wide range of MICs, unlike the log change in viable count (which has a maximum response once bacterial counts are reduced below the minimum level of detection) and time taken to kill 99.9% of the inoculum (which may never occur when testing strains with high MICs as killing is of insufficient magnitude).40
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Methodologies used for the detection of resistance are less variable, with many using direct culture from the in vitro model on to a range of plates with increasing quinolone concentrations.11,16,17 Less commonly, MICs are determined before and after exposure.14,25
In our view, the three main pharmacodynamic parameters (t > MIC, Cmax/MIC and AUC/MIC) should all be related to measures of antibacterial effect and/or emergence of resistance. The value of other parameters, such as weighted AUC or AUC > MIC, awaits further evaluation.15,21 However, in many studies only one or two pharmacodynamic parameters are related to outcome (Table). For example, Madaras-Kelly et al.33 and MacGowan et al.8 explored only the relationship between AUC/MIC and the antibacterial effect while, in an otherwise comprehensive analysis, t > MIC appeared to be omitted.25,45 Others have omitted Cmax/MIC from their calculations.23 The number of occasions on which t > MIC, Cmax/MIC and AUC/MIC have all been included in analysis is relatively small and even then the ability of the model to describe adequately the relationship between a measure of antibacterial effect and pharmacodynamic parameters has been variable.1820,26,31,44
The use of different measures of antibacterial effect and incomplete sets of pharmacodynamic parameters is further complicated by different methods of relating pharmacodynamic parameter to an antibacterial effect. These range from the entirely descriptive11,14,16,17,30 to linear regression analysis45 and use of simple or sigmoidal Emax models.25,32 Some authors have also used stepwise multiple regression analysis.25,26 Most multivariate analyses indicate that AUC/ MIC is predictive of antibacterial effect.22,23
How do the different approaches in terms of models used, doses simulated, endpoints measured, pharmacodynamic parameters included and analytical tools used affect conclusions about quinolone pharmacodynamics in terms of antibacterial efficacy or prevention of the emergence of resistance? Studies that have explored the relationship of three main pharmacodynamic predictors to anti bacterial effect have come to different conclusions. All three pharmacodynamic parameters were poor predictors of response in a model designed to mimic device-related infection31 and were unable to predict antibacterial effect against S. aureus, S. pneumoniae or B. fragilis when measured by the time to 99.9% kill.18,20 However, using AUBKC as the measure of antibacterial effect and multiple regression analysis, two groups have shown AUC/MIC to be the best predictor of AUBKC.25,27 Others, using AUBGC/AUBKC as the endpoint, showed that Cmax/MIC was best related to antibacterial effect.44 When IE is used as the endpoint, then t > MIC best predicts the outcome for data from different quinolones or from the same quinolone given in different dosing regimens.45 This is because IE is dependent on the time it takes for bacteria to start regrowing and this will be determined by t > MIC plus any post-antibiotic effect. Hence, quinolones with long half-lives will have a greater IE for the same serum AUC and dosing twice will have a greater IE than dosing once if the half-life and AUC are constant.45 In a further set of experiments, Firsov et al.45 explored the relationships of AUC/ MIC, t > MIC and AUC > MIC to IE using data generated from simulations of trovafloxacin and ciprofloxacin administration. t > MIC and AUC/MIC had a linear relationship to IE that was not species specific. However, only AUC/ MIC enabled comparisons to be made between the two quinolones. The reason for this is that, for a given t > MIC value, either quinolone produced the same IE while, for a given AUC/MIC value, trovafloxacin produced a larger IE than ciprofloxacin owing to its longer half-life. Hence, it would appear that, using this model, IE can be predicted for all quinolones on the basis of t > MIC but with AUC/MIC the IE varies with the quinolone half-life.46,47 Given the difference in factors determining IE compared with other outcome measures, some caution should be employed in interpreting these findings, at least until they are confirmed by others and related more clearly to human pharmacodynamic studies. There is more consensus (based only on descriptive analysis) that a high Cmax/MIC ratio is associated with reduced emergence of resistance.11,17,25,33,48
Some data related to bacterial killing also argue in favour of larger infrequent doses; for example, simulations of enoxacin 1 g every 24 h produced more reliable killing than 500 mg every 12 h.17 Simulations of 1200 mg ciprofloxacin as a single 24 h dose produced lower minimum bacterial counts than simulations of 600 mg given 12 hourly or 400 mg given 8 hourly, but did result in more regrowth.11 The time to 99.9% kill was shorter for some strains of S. aureus when the same daily doses of ciprofloxacin, ofloxacin or levofloxacin were simulated as a single 24 h dose rather than two 12 h doses.16,49 In contrast, using a model system dependent on stepwise declining concentrations and minimum bacterial counts as the antibacterial measure, simulations of ciprofloxacin 400 mg given 8 hourly were more bactericidal than ciprofloxacin 600 mg 12 hourly against Klebsiella pneumoniae and Pseudomonas aeruginosa.14,30 The IE model also favours two small doses of ciprofloxacin rather than one large one.45 The antibacterial measure chosen will affect the conclusion reached; for example, in a simulation of ciprofloxacin 1 g 24 hourly compared with 500 mg 12 hourly, the maximum reduction in viable count, the change in count after 12 h and the slope of initial kill all favoured 24 hourly dosing over 12 hourly dosing, but both regimens were equivalent with respect to change in count after 24 h, AUBKC and time to 99.9% kill. For none of the antibacterial effect measures could it be shown that 12 hourly dosing was superior to 24 hourly dosing.32
At present there are no direct data from in vitro models to support the suggestions based on animal and clinical data that, if Cmax/MIC is >10, then Cmax/MIC predicts outcome, while if Cmax/MIC is <10, then AUC/MIC predicts outcome.27 However, in our study simulating doses of ciprofloxacin once- and twice-daily, >50% of Cmax/MIC ratios were <10 and AUC/MIC was the best predictor of outcome.26 In contrast, work with trovafloxacin and two vancomycin-intermediate S. aureus strains indicated that Cmax/MIC was the best predictor of outcome despite Cmax/ MIC ratios being <10.44
In conclusion, there is a consensus that descriptive analysis of data from in vitro models indicates that high Cmax/MIC values are important in preventing emergence of resistance. Furthermore, dose fractionation studies (in which the AUC/MIC is the same for all doses simulated) indicate that larger, less frequent quinolone doses produce more rapid killing early in drug exposure, although regrowth often occurs. Most data suggest that AUC/MIC is the best predictor of antibacterial effect, but once the appropriate value of this ratio has been obtained, larger, less frequent doses may be more effective than smaller, more frequent ones.
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Notes |
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References |
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Received 20 August 1999; returned 13 January 2000; revised 11 February 2000; accepted 13 March 2000