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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Abstract |
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Introduction |
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Materials and methods |
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The model and strains employed have been described previously.1618 Briefly, six strains of S. pneumoniae, moxifloxacin MIC range 0.083.6 mg/L, six strains of H. influenzae MICs 0.061.0 mg/L, three strains of M. catarrhalis MICs 0.061.0 mg/L, three strains of S. aureus MICs 0.061.0 mg/L and three ß-haemolytic streptococcal strains MICs 0.161.8 mg/L were used. Moxifloxacin was obtained from Bayer AG (Wuppertal, Germany) and MICs were determined by BSAC guidelines. The concentrations were to simulate serum concentration in man after an oral dose every 24 h. The area-under-the-curve (AUC) was 24.4 mg/L.
For all experiments samples were taken from the central chamber throughout the 48 h period, that is at time 0, 1, 2, 3, 4, 5, 6, 7, 10, 12, 22, 24, 25, 26, 27, 28, 29, 30, 31, 34, 36, 46 and 48 h for assessment of viable bacterial count.
The antibacterial activity of moxifloxacin was assessed by calculating the log change in viable count compared with time 0 at 12 h (12), 24 h (
24), 36 h (
36) and 48 h (
48). In addition, the maximum reduction in viable count was recorded (
max). The AUBKC (log cfu/mLh) was calculated for time periods 024 h (AUBKC24) and 48 h (AUBKC48). The time to kill either 99% of the initial inoculum and 99.9% (T99 and T99.9, respectively) were also calculated.
The percentage coefficient of variation was calculated as %CV = [s.d. mean] x 100. To explore the relationship between AUBKC24 and AUC/MIC and between AUBKC24 and the time the concentration exceeds the MIC (T > MIC) a series of non-linear models were fitted to the means of the replicate experiments. A number of transformations of the pharmacodynamic parameter, AUBKC24, were considered and a variety of non-linear Emax models were fitted. Akaike Information Criterion was used to compare models and plots of residuals against fitted values were used to assess the adequacy of the model fit (Win Nonlin, SCI, CA, USA). To assess the combined influence of AUC/MIC and T > MIC on the untransformed AUBKC24 weighted least squares regression was used. Again, means of the replicated experiments were analysed. Weights (proportional to the universe of the variance) were used to stabilize the variance. The sensitivity of the results to variation in the choice of weights was assessed.
Cox proportional hazards regression was used to assess whether AUC/MIC or T > MIC was predictive of T99.
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Results |
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Both AUC/MIC and T > MIC were predictive of T99 (P = 0.001 and P = 0.01 respectively). Cox proportional hazards regression was used to assess whether AUC/MIC and/or T > MIC was related to T99. When considered together in a multivariate model, while AUC/MIC continued to be predictive of the outcome after fitting AUC/MIC, T > MIC was no longer significant (change in model fit when adding T > MIC to a model containing AUC/MIC, 2 log L = 0.5, P = 0.48). The predictor AUC/MIC was fitted as a continuous variable. The assumption of a linear relationship between ln (hazard ratio) and AUC/MIC was assessed and there was no evidence to suggest a non-linear relationship. The proportional hazards assumption was also assessed and the statistical tests indicated that the assumption was tenable.
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Discussion |
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The present data confirm these broad conclusions, as non-area parameters do not vary once certain MICs are reached; that is, with high MICs in Gram-positive bacteria T99.9 or T99 are >48 h while at low MICs, especially with Gram-negative bacteria, reductions in viable count are all >6.5 log. Therefore these measures become non-discriminatory once certain MIC values are reached. In addition, and more importantly, we were able to study the within-strain reproducibility of each measure which indicated that AUBKC had the lowest %CV. Previous data using conventional timekill curves also support this conclusion.20 IE was selected by Firsov et al.,5 in preference to AABKC or AUBKC as it showed a broadly linear relationship to drug AUC over the range 0.1100 mg/Lh, which is equivalent to an AUC/MIC ratio of 7.77700. The use of IE as the primary antibacterial measure may be associated with some potential problems. First, the relationship between AUC/MIC ratio and bacteriological or clinical outcome probably follows a sigmoid Emax shape over a range 101000 not a linear one as for IE,1,2,8 and secondly IE critically depends on bacterial regrowth; while clinically rapid early killing may be more important. Also IE varies with the T > MIC, such that for a similar AUC/MIC ratio, two small doses of ciprofloxacin will produce a larger IE than one large one.17 Data from other in vitro models indicate that large infrequent doses have a greater or equivalent antibacterial effect to several smaller doses.10,12,14,21
Using AUBKC24 as the main measure of antibacterial effect, we were able to relate it to AUC/MIC using a sigmoid Emax model and when we modelled the combined effect of AUC/MIC and T > MIC on AUBKC24, T > MIC was not found to be predictive of AUBKC24. In addition, but unlike others,14,15 we were able to show that AUC/MIC was predictive of T99 and that in a multivariate model after fitting of AUC/MIC, T > MIC was not significant. This implies that for moxifloxacin in our model system, AUC/ MIC is the main pharmacodynamic factor. This is in agreement with animal data with moxifloxacin22 and clinical data with ciprofloxacin and grepafloxacin.1,2
In conclusion, AUBKC24 is the optimal measure of antibacterial effect in this in vitro pharmacodynamic model of infection, and the antibacterial effect of moxifloxacin is determined by the AUC/MIC ratio. AUC/MIC is also predictive of T99.
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Notes |
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References |
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Received 14 June 1999; returned 13 October 1999; revised 10 January 2000; accepted 1 February 2000