Paweł Wiczling (1), Alicja Bartkowska-Śniatkowska (2), Oliwia Szerkus (1), Danuta Siluk (1), Jowita Rosada-Kurasińska (2), Justyna Warzybok (3), Agnieszka Borsuk (1), Roman Kaliszan (1), Edmund Grześkowiak (3), Agnieszka Bienert (3)
(1) Department of Biopharmaceutics and Pharmacokinetics, Medical University of Gdansk, Poland; (2) Department of Anaesthesiology and Intensive Therapy, Poznan University of Medical Sciences; (3) Department of Clinical Pharmacy and Biopharmacy, Poznan University of Medical Sciences
Objectives: The purpose of this study was to assess the pharmacokinetics of dexmedetomidine in the ICU settings during the long-term infusion and to compare it with the existing literature data using the Bayesian population modeling with literature-based informative priors.
Methods: 38 patients were included in the analysis with concentration measurements obtained at two occasions: first from 0 to 24 hr after infusion initiation and second from 0 to 8 hr after infusion end. The data analysis was conducted using WINBUGS software. The prior information on dexmedetomidine pharmacokinetics was elicited from the literature study pooling results from a relatively large group of children.
Results: A two compartment PK model with allometrically scaled parameters, maturation of clearance and t-student residual distribution on a log-scale was used to describe the data. The incorporation of time depended (different between the two occasions) PK parameters improved the model. It turned out that volume of distribution is 1.5-fold higher during the second occasion. There was also an evidence of increased (1.3-fold) clearance for the second occasion with posterior probability of 62%.
Conclusion: A population PK model of dexmedetomidine was developed. The application of Bayesian modeling with informative priors allowed elucidation of time-dependent changes in PK parameters during the long-term infusion using poorly informative data.
Reference: PAGE 25 () Abstr 5723 [www.page-meeting.org/?abstract=5723]
Poster: Drug/Disease modeling - Paediatrics