Pharmacokinetic-Pharmacodynamic Modelling of Pre-existing and Emerging Resistance of Pseudomonas aeruginosa to Colistin
A. Mohamed (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: In recent years, colistin has gained popularity as a last resort antibiotic in the battle against resistant bacteria. Pseudomonas aeruginosa is well known to develop resistance against multiple antibiotics and thus, there is a need to ensure proper dosing of colistin either as a monotherapy or in combination with other antibiotics. As colistin is administered as CMS, a prodrug, there is a delay before efficient concentrations are obtained and a loading dose may be warranted. The aim of this study was to develop a pharmacokinetic-pharmacodynamic (PKPD) model that describes the time course of the bactericidal activity of colistin against wild-type and resistant P.aeruginosa in vitro, and to investigate the bacterial kill after different dosing schedules based on PK in patients and the developed model.
Methods: In-vitro time kill curve experiments were conducted for 24 hours on two strains of Pseudomonas aeruginosa, wild-type (ATCC 27853) and a clinically isolated resistant-type (PL0603761). Colistin exposure was at initial concentrations ranging between 0.25-16 times the MIC. Actual colistin concentrations were measured at 0, 8 and 24 hours by LCMS-MS (1) as colistin is binding to material and is degrading during the experiments. Semi-mechanistic PKPD models with compartments for susceptible and resting bacteria (2) were fitted to the observed bacterial counts in NONMEM. Prediction of bacterial kill in patients was based on PK in a previous study (3).
Results: As expected, the growth rate was significantly lower for the resistant strain with the drug effect best described by a power function. The application of actual colistin concentrations in the modeling was important in the characterization of the concentration-effect relationship. The emergence of resistance in the experiments was best described by a binding function (4). VPCs showed the adequacy of the model for both wild-type and resistant bacteria. For the wild-type bacteria, it was predicted that it took 10 hours to reach a bacterial count of log10 2 following a loading dose of 6MU CMS compared to 22 h for a dose of 3MU. None of the dose levels was sufficient to reduce the resistant bacterial counts.
Conclusions: The PKPD model for colistin described both wild-type and resistant mutants and will be valuable in the exploration of potential dosing regimens. For the resistant bacteria, a combination of colistin with other antibiotics is indicated.
References:
[1] Jansson B, Karvanen M, Cars O, Plachouras D and Friberg LE. Quantitative analysis of colistin A and colistin B in plasma and culture medium using a simple precipitation step followed by LC/MS/MS. Journal of Pharmaceutical and Biomedical Analysis. 2009; 49: 760-767.
[2] 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.
[3] Plachouras D, Karvanen M, Friberg LE, Papadomichelakis E, Antoniadou A, Tsangaris I, Karaiskos I, Poulakou G, Kontopidou F, Armaganidis A, Cars O and Giamarellou H. Population Pharmacokinetic Analysis of Colistin Methanesulfonate and Colistin after Intravenous Administration in Critically Ill Patients. Antimicrob Agents Chemother. 2009; 53(8):3430-3436.
[4] Mohamed A, Nielsen EI, Cars O and Friberg LE. A Pharmacokinetic-Pharmacodynamic Model for Gentamicin and its Adaptive Resistance with Predictions of Dosing Schedules in Newborn Infants (Manuscript)
