2010 - Berlin - Germany

PAGE 2010: Applications- Anti-infectives
Ami Fazlin Syed Mohamed

Predictions of Dosing Schedules of Gentamicin in Neonates Based on a Pharmacokinetic/Pharmacodynamic Model Considering Adaptive Resistance

A. Mohamed (1), E.I. Nielsen (1), O. Cars (2) and L.E. Friberg (1).

(1) Department of Pharmaceutical Biosciences, Uppsala University, Uppsala; (2) Department of Infectious Diseases, University Hospital, Uppsala, Sweden.

Objectives: Adaptive resistance, a pharmacodynamic process of aminoglycosides, is a reversible refractoriness to the bactericidal action. It is developing during the first dosing interval, is enhanced by higher doses, and augmented by consecutive doses if administered before the bacteria return to their susceptibility stage [1, 2]. A previous PKPD model of bacteria kill of gentamicin in vitro [3] consisted of a compartment of resistant bacteria added to a semi-mechanistic model developed for other antibiotics [4] but there was a lack of fit to some of the experimental dosing schedules. The aim of this work was to explore a different PKPD model and to conduct predictions that can be used to suggest optimized dosing schedules in neonates based on the time-course of bacteria kill and adaptive resistance, as well as previous information on risk for toxicity.

Methods: In vitro time kill curve experiments were conducted for 24-48 hours on a strain of Escherichia coli. Gentamicin exposure was either at constant concentration ranging between 0.125-16 times the MIC or in a dynamic kinetic system with different dosing regimens; 1-8 times the MIC every 12 or 24 hours with simulated two-compartment kinetics. Bacterial counts were monitored with frequent sampling throughout the experiments. All data were fit simultaneously in NONMEM. The adaptive resistance was modeled as a binding function where the degree of binding resulted in a reduction of Emax of the bacteria kill. Predictions were conducted for neonates of different weights and ages by allowing the concentrations predicted by a previously developed 3-compartment PK model to drive the bacteria kill [5].

Results: The model could describe the data that showed that gentamicin has a fast bactericidal effect with clear indication of adaptive resistance. Full development of adaptive resistance was predicted to occur after approximately 2 days of exposure and therefore 24 hour dosing intervals was predicted to be more efficacious in bacterial killing than those with a 36 or 48 hour  time interval. The predictions also suggested that because the concentrations were around the estimated Ec50 of 10 mg/L, the benefit to increase the dose from the standard 4 mg/kg to 5 mg/kg was limited.

Conclusion: The semi-mechanistic model with the binding process was superior to the previously described model with a compartment of resistant bacteria. For the sizes and ages of neonates investigated, the PKPD model predicted a 4mg/kg dose of gentamicin witha 24-hour dose interval to be more efficacious compared to a higher dose with a 36 or 48 hour dosing interval.

[1]. Barclay ML and Begg EJ. Aminoglycoside Adaptive Resistance: Importance for effective dosage regimens. Drugs 2001; 61(6): 713-721.
[2]. Barclay ML, Begg EJ, Chambers ST. Adaptive resistance following single doses of gentamicin in a dynamic in vitro model. Antimicrob Agents Chemother 1992; 36: 1951-7
[3]. Mohamed A, Nielsen EI, Cars O and Friberg LE. Pharmacokinetic/Pharmacodynamic Modeling of Adaptive Resistance of Gentamicin. PAGE. 2009.
[4]. Nielsen EI, Viberg A, Löwdin E, Cars O, Karlsson MO, Sandström M. Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments. Antimicrob Agents Chemother. 2007; 51(1):128-136.
[5]. Nielsen EI, Sandström M, Honore PH, Ewald U and Friberg LE. Developmental pharmacokinetics of gentamicin in preterm and term neonates. Population modeling of a prospective study. Clin Pharmacokinet 2009; 48(4): 253-263.

Reference: PAGE 19 (2010) Abstr 1876 [www.page-meeting.org/?abstract=1876]
Poster: Applications- Anti-infectives