title: Pharmacokinetic/pharmacodynamic (PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a step toward model-based dose optimization.
abstract: A pharmacokinetic-pharmacodynamic (PKPD) model that characterizes the full time course of in vitro time-kill curve experiments of antibacterial drugs was here evaluated in its capacity to predict the previously determined PK/PD indices. Six drugs (benzylpenicillin, cefuroxime, erythromycin, gentamicin, moxifloxacin, and vancomycin), representing a broad selection of mechanisms of action and PK and PD characteristics, were investigated. For each drug, a dose fractionation study was simulated, using a wide range of total daily doses given as intermittent doses (dosing intervals of 4, 8, 12, or 24 h) or as a constant drug exposure. The time course of the drug concentration (PK model) as well as the bacterial response to drug exposure (in vitro PKPD model) was predicted. Nonlinear least-squares regression analyses determined the PK/PD index (the maximal unbound drug concentration [fC(max)]/MIC, the area under the unbound drug concentration-time curve [fAUC]/MIC, or the percentage of a 24-h time period that the unbound drug concentration exceeds the MIC [fT(>MIC)]) that was most predictive of the effect. The in silico predictions based on the in vitro PKPD model identified the previously determined PK/PD indices, with fT(>MIC) being the best predictor of the effect for β-lactams and fAUC/MIC being the best predictor for the four remaining evaluated drugs. The selection and magnitude of the PK/PD index were, however, shown to be sensitive to differences in PK in subpopulations, uncertainty in MICs, and investigated dosing intervals. In comparison with the use of the PK/PD indices, a model-based approach, where the full time course of effect can be predicted, has a lower sensitivity to study design and allows for PK differences in subpopulations to be considered directly. This study supports the use of PKPD models built from in vitro time-kill curves in the development of optimal dosing regimens for antibacterial drugs.
authors.affiliation: Department of Pharmaceutical Biosciences, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden. elisabet.nielsen@farmbio.uu.se
authors.forename: Elisabet I
authors.initials: EI
authors.lastname: Nielsen
authors.forename: Otto
authors.initials: O
authors.lastname: Cars
authors.forename: Lena E
authors.initials: LE
authors.lastname: Friberg
mesh_terms: D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000493:pharmacokinetics* / Q000494:pharmacology* / Q000627:therapeutic use; D019540:Area Under Curve; D001372:Aza Compounds / Q000493:pharmacokinetics / Q000494:pharmacology; D002444:Cefuroxime / Q000493:pharmacokinetics / Q000494:pharmacology; D003198:Computer Simulation; D004917:Erythromycin / Q000493:pharmacokinetics / Q000494:pharmacology; D004926:Escherichia coli / Q000187:drug effects*; D024841:Fluoroquinolones; D005839:Gentamicins / Q000493:pharmacokinetics / Q000494:pharmacology; D006801:Humans; D008826:Microbial Sensitivity Tests; D000077266:Moxifloxacin; D010400:Penicillin G / Q000493:pharmacokinetics / Q000494:pharmacology; D011804:Quinolines / Q000493:pharmacokinetics / Q000494:pharmacology; D013297:Streptococcus pyogenes / Q000187:drug effects*; D014640:Vancomycin / Q000493:pharmacokinetics / Q000494:pharmacology
chemical_list: D000900:Anti-Bacterial Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D005839:Gentamicins; D011804:Quinolines; D004917:Erythromycin; D014640:Vancomycin; D002444:Cefuroxime; D010400:Penicillin G; D000077266:Moxifloxacin