title: Assessment of different antibacterial effect measures used in in vitro models of infection and subsequent use in pharmacodynamic correlations for moxifloxacin.
abstract: A dilutional culture in vitro pharmacodynamic model of infection was used to assess the best measure of antibacterial effect for moxifloxacin at simulated human doses of 400 mg 24 hourly for 48 h. This was then related to two pharmacodynamic parameters, the drug area under curve: MIC ratio (AUC/MIC) and the length of time that the drug concentration remained above the MIC of the bacterium (T > MIC). Twenty-one bacterial strains (Streptococcus pneumoniae n = 6; Haemophilus influenzae n = 6; Moraxella catarrhalis n = 3; beta-haemolytic streptococci n = 3; Staphylococcus aureus n = 3; MIC range 0.06-3.6 mg/L) were tested in 69 individual simulations. The measures or parameters of antibacterial effect considered were log change in viable count in the initial inoculum at 12 h (triangle up12), 24 h (triangle up24), 36 h (triangle up36), 48 h (triangle up48), maximum reduction in count (triangle up(max)); time for bacterial counts to reduce by 100-fold from the initial density (T99) or 1,000-fold (T99.9); and area under the bacterial kill curve from 0 to 24 h (AUBKC(24)) or from 0 to 48 h (AUBKC(48)). triangle up12, triangle up24, triangle up36, triangle up48, triangle up(max), T99, T99.9 did not vary over the complete range of MICs; at high MICs, especially with Gram-positive bacteria the T99 and T99.9 values were >48 h while at low MICs, especially with Gram-negative bacteria, bacterial counts were reduced below the limit of detection with triangle up12, triangle up24, triangle up36, triangle up48 and triangle up(max) exceeding >6.5 log reduction. AUBKC(24) and AUBKC(48) varied more completely over the range of MICs and more importantly had the best within-strain reproducibility (median percentage coefficient of variation <15%). The relationship between the transformed AUBKC(24) and AUC/MIC could be described by a sigmoid Emax model but the relationship with T > MIC could not. Use of weighted least squares regression to examine the combined effect of AUC/MIC and T > MIC on AUBKC(24) indicated that AUC/MIC provided a good fit to the data (r(2) = 0.94) and adding T > MIC did not improve the model fit. Cox proportional hazards regression indicated that AUC/MIC was predictive of T99 and in a multivariate model although AUC/MIC predicted outcome after fitting AUC/MIC, T > MIC was not significant. AUBKC was thus shown to be the optimum measure of antibacterial effect to use in pharmacodynamic studies of moxifloxacin and AUC/MIC the best predictor of antibacterial effect as measured by AUBKC(24) or T99. These results are in good agreement with animal data on moxifloxacin pharmacodynamics and human data for some other fluoroquinolones.
authors.affiliation: Bristol Centre for Antimicrobial Research and Evaluation, North Bristol NHS Trust and University of Bristol, Department of Medical Microbiology, Southmead Hospital, Westbury-on-Trym, UK. MACGOWAN_A@southmead.swest.nhs.uk
authors.forename: A
authors.initials: A
authors.lastname: MacGowan
authors.forename: C
authors.initials: C
authors.lastname: Rogers
authors.forename: H A
authors.initials: HA
authors.lastname: Holt
authors.forename: M
authors.initials: M
authors.lastname: Wootton
authors.forename: K
authors.initials: K
authors.lastname: Bowker
mesh_terms: D000818:Animals; D000890:Anti-Infective Agents / Q000494:pharmacology*; D019540:Area Under Curve; D001372:Aza Compounds; D001419:Bacteria / Q000187:drug effects*; D024841:Fluoroquinolones; D008826:Microbial Sensitivity Tests; D000077266:Moxifloxacin; D011804:Quinolines
chemical_list: D000890:Anti-Infective Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D011804:Quinolines; D000077266:Moxifloxacin