Twenty-four-hour area under the concentration-time curve/MIC ratio as a generic predictor of fluoroquinolone antimicrobial effect by using three strains of Pseudomonas aeruginosa and an in vitro pharmacodynamic model

Abstract

Several investigators have suggested that the 24-h area under the concentration-time curve (AUC)/MIC ratio (AUC/MIC24 or AUIC24) can be used to make comparisons of antimicrobial activity between fluoroquinolone antibiotics. Limited data exist regarding the generic predictive ability of AUC/MIC24 for the antimicrobial effects of fluoroquinolones. The purposes of the present investigation were to determine if the AUC/MIC24 can be used as a generic outcome predictor of fluoroquinolone antibacterial activity and to determine if a similar AUC/MIC24 breakpoint can be established for different fluoroquinolones. Using an in vitro pharmacodynamic model, 29 duplicate concentration time-kill curve experiments simulated AUC/MIC24s ranging from 52 to 508 SIT-1.h (inverse serum inhibitory titer integrated over time) with ciprofloxacin or ofloxacin against three strains of Pseudomonas aeruginosa. Each 24-h experiment was performed in cation-supplemented Mueller-Hinton broth with a starting inoculum of 10(6) CFU/ml. At timed intervals cation-supplemented Mueller-Hinton broth samples were collected for CFU and fluoroquinolone concentration determinations. Transformation of bacterial counts into the cumulative bacterial effect parameter of the 24-h area under the effect curve (AUEC24) was performed for each concentration time-kill curve. Multivariate regression analysis was used to compare pharmacodynamic predictors (AUC/MIC24, 24-h AUC, peak concentration [Cmax] to MIC ratios [Cmax:MIC], etc.) with ln AUEC24. To identify threshold breakpoint AUC/MIC24s, AUEC24s were stratified by the magnitude of AUC/MIC24 into subgroups, which were analyzed for differences in antibacterial effect. The Kruskal-Wallis test and subsequent Tukey's multiple comparison test were used to determine which AUC/MIC subgroups were significantly different. Multiple regression analysis revealed that only AUC/MIC24 (r2 = 0.65) and MIC (r2 = 0.03) were significantly correlated with antibacterial effect. At similar AUC/MIC24s, yet different MICs, Cmaxs, or elimination half-lives, the AUEC24s were similar for both fluoroquinolones. The relationship between AUC/MIC24 and ln AUEC24 was best described by a sigmoidal maximal antimicrobial effect (Emax) model (r2 = 0.72; Emax = 9.1; AUC/MIC50 = 119 SIT-1.h; S = 2.01 [S is an exponent that reflects the degree of sigmoidicity]). Ciprofloxacin-bacteria AUC/MIC24 values of < 100 SIT-1.h were significantly different (P < 0.05) from the AUC/MIC24 values of > 100 SIT-1.h. An ofloxacin AUC/MIC24 of > 100 SIT-1.h and an AUC/MIC24 of < 100 SIT-1.h exhibited a trend toward a significant difference (P > 0.05 but < 0.1). The inverse relationship between drug exposure and MIC increase postexposure was described by a sigmoidal fixed Emax model (AUC/MIC24, r2 = 0.40; AUC/MIC50 = 95 SIT-1.h; S = 1.97; Cmax:MIC, r2 = 0.41; Cmax:MIC50 = 7.3; S = 2.01). These data suggest that AUC/MIC24 may be the most descriptive measurement of fluoroquinolone antimicrobial activity against P. aeruginosa, that ofloxacin and ciprofloxacin have similar AUC/MIC24 threshold breakpoints at approximately 100 SIT-1.h, that the concentration-dependent selection of resistant organisms may parallel the threshold breakpoint of the antimicrobial effect, and that AUC/MIC24 generically describes the antibacterial effects of different fluoroquinolones.

Use of modeling techniques to aid in antibiotic selection

“…On the other hand, C max /MIC, AUC/MIC, and T eff were not predictive of in vitro and in vivo effects produced by seven antibiotics, including ciprofloxacin and fleroxacin [34]. Similarly, AUC/MIC determined over a 24-hour period (AUC 24 /MIC or AUC) did not predict the effects of four quinolones, in an in vitro, dynamic model [38], which contrasts with data reported in a model using two other quinolones [37]. At first glance, these and similar contradictions between reported optimal predictors may seem incompatible.…”

Pharmacodynamics of Moxifloxacin and Levofloxacin Simulating Human Serum and Lung Concentrations*

“…Among the currently available fluoroquinolones moxifloxacin is characterized by significantly improved pharmacodynamic properties against both gram-positive and gram-negative bacteria, in particular as high concentrations at the focus of infection translate into an augmented killing of the causative pathogens irrespective of their penicillin and/or macrolide susceptibilities, or even QRDR mutations. Several investigators have also studied the pharmacodynamics of various fluoroquinolones and have shown that quinolones differ in their pharmacodynamic properties, even if their antibacterial activities are almost identical [63,[68][69][70][71][72].…”

Antimicrobial Resistance: Factors and Outcomes

“…Newer dosing strategies also have been employed for concentration-dependent antimicrobials to optimize their pharmacodynamic properties and maximize efficacy. Such strategies include the use of extendedinterval dosing regimens for aminoglycosides and the use of high doses of fluoroquinolones to achieve high concentrations relative to the pathogen MICs [56][57][58].…”