MIC-Based Interspecies Prediction of the Antimicrobial Effects of Ciprofloxacin on Bacteria of Different Susceptibilities in an In Vitro Dynamic Model

Abstract

Multiple predictors of fluoroquinolone antimicrobial effects (AMEs) are not usually examined simultaneously in most studies. To compare the predictive potentials of the area under the concentration-time curve (AUC)-to-MIC ratio (AUC/MIC), the AUC above MIC (AUCeff), and the time above MIC (T eff), the kinetics of killing and regrowth of four bacterial strains exposed to monoexponentially decreasing concentrations of ciprofloxacin were studied in an in vitro dynamic model. The MICs of ciprofloxacin for clinical isolates ofStaphylococcus aureus, Escherichia coli11775 (I) and 204 (II), and Pseudomonas aeruginosa were 0.6, 0.013, 0.08, and 0.15 μg/ml, respectively. The simulated values of AUC were designed to provide similar 1,000-fold (S. aureus, E. coli I, and P. aeruginosa) or 2,000-fold (E. coli II) ranges of the AUC/MIC. In each case except for the highest AUC/MIC ratio, the observation periods included complete regrowth in the time-kill curve studies. The AME was expressed by its intensity,I E (the area between the control growth and time-kill and regrowth curves up to the point where the viable counts of regrowing bacteria are close to the maximum values observed without drug). For most AUC ranges the I E-AUC curves were fitted by an E max (maximal effect) model, whereas the effects observed at very high AUCs were greater than those predicted by the model. The AUCs that produced 50% of maximal AME were proportional to the MICs for the strains studied, but maximal AMEs (I E max ) and the extent of sigmoidicity (s) were not related to the MIC. BothT eff and log AUC/MIC correlated well withI E (r 2 = 0.98 in both cases) in a species-independent fashion. UnlikeT eff or log AUC/MIC, a specific relationship between I E and log AUCeff was inherent in each strain. Although each I E and log AUCeff plot was fitted by linear regression (r 2 = 0.97 to 0.99), these plots were not superimposed and therefore are bacterial species dependent. Thus, AUC/MIC and T eff were better predictors of ciprofloxacin’s AME than AUCeff. This study suggests that optimal predictors of the AME produced by a given quinolone (intraquinolone predictors) may be established by examining its AMEs against bacteria of different susceptibilities.T eff was shown previously also to be the best interquinolone predictor, but unlike AUC/MIC, it cannot be used to compare different quinolones. AUC/MIC might be the best predictor of the AME in comparisons of different quinolones.

Use of modeling techniques to aid in antibiotic selection

“…Both AUC/MIC and T eff , but not AUC eff , were shown to be equally good intraquinolone predictors [47],and T eff was also shown to be the best interquinolone predictor [36••]. This does not mean that T eff is superior to AUC/ MIC as a predictor.…”

Pharmacokinetic/Pharmacodynamic (PK/PD) Evaluation of a Once-Daily Treatment Using Ciprofloxacin in an Extended-Release Dosage Form*

“…In an in vitro system Escherichia coli 11775 (clinical isolate, MIC = 0.013 mg/l) was exposed to changing concentrations of CIP, simulating a half-life of 4 h [27,28]. Concentrations were changed by means of a pump providing a continuous flow of broth to dilute the CIP concentrations.…”

Development of Liposomal Ciprofloxacin to Treat Lung Infections

“…Fluoroquinolones, like aminoglycosides, are generally thought to exhibit concentration-dependent antimicrobial effect instead of time-dependent killing for which effectiveness is related to the time the drug concentration remains above the MIC [ 3 ]. However, ciprofloxacin appears to operate by a blending of both the time-dependent and concentration-dependent elements [ 7 ]. For ciprofloxacin, the ratio of the area under the concentration time curve (AUC) divided by the MIC appears to be the best predictor of antimicrobial effect with values of AUC/MIC > 125 correlating with better cures [ 7 , 8 ].…”