Pharmacometric Characterization of Dabigatran Hemodialysis

Abstract

BackgroundHemodialysis has been shown to be a useful method of decreasing dabigatran plasma levels in situations that require rapid elimination of this thrombin inhibitor. However, there is currently no clinical recommendation for the accelerated/optimized elimination of dabigatran via hemodialysis (e.g., flow rates, filter type, duration of dialysis).ObjectivesThe primary objective of the present work was to characterize, via pharmacometric methods, the effects of different blood flow rates in hemodialysis on the pharmacokinetics of dabigatran, using data from a dedicated phase I dialysis study of end-stage renal disease (ESRD) patients. In addition, the effects of various clinically relevant hemodialysis settings were evaluated by simulation to assess their potential use in non-ESRD situations.MethodsSeven patients with ESRD were investigated in an open-label, fixed-sequence, two-period comparison trial. A population pharmacokinetic model was developed to fit the data and then used for various simulations. Data analyses were performed using NONMEM®, Berkeley Madonna, or SAS.ResultsThe pharmacokinetics of dabigatran were best described by a two-compartment model with first-order absorption and a lag time. In addition to total body clearance in ESRD subjects, a first-order dialysis clearance was implemented which was greater than zero during hemodialysis and zero during the interdialytic periods. The relationship between the dialysis clearance and the blood flow rate was best described by the Michaels function. Simulations showed that varying clinically relevant dialysis settings such as filter properties or flow rates had only minor effects. Dialysis duration had the strongest impact on dabigatran plasma concentration. The observed geometric mean redistribution effect after hemodialysis was low (<16 %). The final model was successfully evaluated through the prediction of plasma concentrations from a case report undergoing dialysis.ConclusionsThis analysis allowed the influences of various hemodialysis parameters on the dabigatran plasma concentration to be predicted in detail for the first time. Dialysis duration was identified as having the strongest impact on the reduction in dabigatran plasma concentration. The model developed here can potentially serve as a tool to provide guidance when considering the use of hemodialysis in patients who have received dabigatran.

Enhanced Elimination of Dabigatran Through Extracorporeal Methods

“…The pharmacokinetics of dabigatran have been evaluated in several studies [23,30,39,[57][58][59][60][61][62][63]. In an open-label, parallel group study [39], subjects given one dose of dabigatran (150 mg) were stratified by severity of renal impairment (mild, creatinine clearance (CrCl) >50 to ≤80 mL/min; moderate, CrCl >30 to ≤50 mL/min; severe, CrCl ≤30 mL/min).…”

“…Redistribution of dabigatran was insignificant (<16 %) after the end of the hemodialysis session. A pharmacometric approach was applied to evaluate the influence of various blood flow rates on the pharmacokinetics of dabigatran [58]. The study concluded that altering filter properties or flow rates had only a minor impact on the amount of dabigatran removed; the most important variable associated with dabigatran removal was the duration of hemodialysis.…”

2015 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation

“…w przypadku upośledzonej funkcji nerek, należy utrzymać odpowiednią diurezę i można rozważyć dializę, jakkolwiek określenie znaczenia dializy w tym wskazaniu klinicznym pozostaje wyzwaniem i doświadczenia są ograniczone [266]. Krwotok domózgowy lub krwawienie obejmujące narząd krytyczny, taki jak np.…”

Pharmacokinetic Assessment in Patients Receiving Continuous RRT

“…While standard noncompartmental modeling may be used to calculate key pharmacokinetic parameters, the time course for drug concentrations in the plasma, effluent, and urine (when available) can also be subjected to compartmental modeling using a population-based approach (36)(37)(38). Based on the model that best describes the data, covariate relationships can be evaluated to describe the effect of covariates such as treatment and patient variables (e.g., Q E , body weight) on the pharmacokinetic model parameters (such as clearance and V D ).…”