title: Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model.
abstract: We have previously described a general semimechanistic pharmacokinetic-pharmacodynamic (PKPD) model that successfully characterized the time course of antibacterial effects seen in bacterial cultures when exposed to static concentrations of five antibacterial agents of different classes. In this PKPD model, the total bacterial population was divided into two subpopulations, one growing drug-susceptible population and one resting drug-insensitive population. The drug effect was included as an increase in the killing rate of the drug-susceptible bacteria with a maximum-effect (E(max)) model. The aim of the present study was to evaluate the ability of this PKPD model to describe and predict data from in vitro experiments with dynamic concentration-time profiles. Dynamic time-kill curve experiments were performed by using an in vitro kinetic system, where cultures of Streptococcus pyogenes were exposed to benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, or vancomycin using different starting concentrations (2 and 16 times the MIC) and elimination conditions (human half-life, reduced half-life, and constant concentrations). The PKPD model was applied, and the observations for the static as well as dynamic experiments were compared to model predictions based on parameter estimation using (i) static data, (ii) dynamic data, and (iii) combined static and dynamic data. Differences in experimental settings between static and dynamic experiments did not affect the growth kinetics of the bacteria significantly. With parameter reestimation, the structure of our previously proposed PKPD model could well characterize the bacterial growth and killing kinetics when exposed to dynamic concentrations with different elimination rates of all five investigated antibiotics. Furthermore, the model with parameter estimates based on data from only the static time-kill curve experiments could predict the majority of the time-kill curves from the dynamic experiments reasonably well. Adding data from dynamic experiments in the estimation improved the model fit for cefuroxime and vancomycin, indicating some differences in sensitivity to experimental conditions among the antibiotics studied.
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 / Q000493:pharmacokinetics* / Q000494:pharmacology*; D001372:Aza Compounds / Q000493:pharmacokinetics / Q000494:pharmacology; D002444:Cefuroxime / Q000493:pharmacokinetics / Q000494:pharmacology; D004917:Erythromycin / Q000493:pharmacokinetics / Q000494:pharmacology; D024841:Fluoroquinolones; 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 / Q000378:metabolism; D014640:Vancomycin / Q000493:pharmacokinetics / Q000494:pharmacology
chemical_list: D000900:Anti-Bacterial Agents; D001372:Aza Compounds; D024841:Fluoroquinolones; D011804:Quinolines; D004917:Erythromycin; D014640:Vancomycin; D002444:Cefuroxime; D010400:Penicillin G; D000077266:Moxifloxacin