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Modeling of Transfer Kinetics at the Serum-Cerebrospinal Fluid Barrier in Rabbits with Experimental Meningitis: Application to Grepafloxacin

Authors
  • Marc Pfister
  • Liping Zhang
  • Margareta Hammarlund-Udenaes
  • Lewis B. Sheiner
  • Cynthia M. Gerber
  • Martin G. Täuber
  • Philippe Cottagnoud
Publisher
American Society for Microbiology
Publication Date
Jan 01, 2003
Source
PMC
Keywords
Disciplines
  • Design
  • Pharmacology
License
Unknown

Abstract

The goals of the present study were to model the population kinetics of in vivo influx and efflux processes of grepafloxacin at the serum-cerebrospinal fluid (CSF) barrier and to propose a simulation-based approach to optimize the design of dose-finding trials in the meningitis rabbit model. Twenty-nine rabbits with pneumococcal meningitis receiving grepafloxacin at 15 mg/kg of body weight (intravenous administration at 0 h), 30 mg/kg (at 0 h), or 50 mg/kg twice (at 0 and 4 h) were studied. A three-compartment population pharmacokinetic model was fit to the data with the program NONMEM (Nonlinear Mixed Effects Modeling). Passive diffusion clearance (CLdiff) and active efflux clearance (CLactive) are transfer kinetic modeling parameters. Influx clearance is assumed to be equal to CLdiff, and efflux clearance is the sum of CLdiff, CLactive, and bulk flow clearance (CLbulk). The average influx clearance for the population was 0.0055 ml/min (interindividual variability, 17%). Passive diffusion clearance was greater in rabbits receiving grepafloxacin at 15 mg/kg than in those treated with higher doses (0.0088 versus 0.0034 ml/min). Assuming a CLbulk of 0.01 ml/min, CLactive was estimated to be 0.017 ml/min (11%), and clearance by total efflux was estimated to be 0.032 ml/min. The population kinetic model allows not only to quantify in vivo efflux and influx mechanisms at the serum-CSF barrier but also to analyze the effects of different dose regimens on transfer kinetic parameters in the rabbit meningitis model. The modeling-based approach also provides a tool for the simulation and prediction of various outcomes in which researchers might be interested, which is of great potential in designing dose-finding trials.

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