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. 2017 Mar;38(3):434-442.
doi: 10.1038/aps.2016.163. Epub 2017 Feb 20.

Development of a novel individualized warfarin dose algorithm based on a population pharmacokinetic model with improved prediction accuracy for Chinese patients after heart valve replacement

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Development of a novel individualized warfarin dose algorithm based on a population pharmacokinetic model with improved prediction accuracy for Chinese patients after heart valve replacement

Yu-Bin Zhu et al. Acta Pharmacol Sin. 2017 Mar.

Abstract

The gene-guided dosing strategy of warfarin generally leads to over-dose in patients at doses lower than 2 mg/kg, and only 50% of individual variability in daily stable doses can be explained. In this study, we developed a novel population pharmacokinetic (PK) model based on a warfarin dose algorithm for Han Chinese patients with valve replacement for improving the dose prediction accuracy, especially in patients with low doses. The individual pharmacokinetic (PK) parameter - apparent clearance of S- and R-warfarin (CLs) was obtained after establishing and validating the population PK model from 296 recruited patients with valve replacement. Then, the individual estimation of CLs, VKORC1 genotypes, the steady-state international normalized ratio (INR) values and age were used to describe the maintenance doses by multiple linear regression for 144 steady-state patients. The newly established dosing algorithm was then validated in an independent group of 42 patients and was compared with other dosing algorithms for the accuracy and precision of prediction. The final regression model developed was as follows: Dose=-0.023×AGE+1.834×VKORC1+0.952×INR+2.156×CLs (the target INR value ranges from 1.8 to 2.5). The validation of the algorithm in another group of 42 patients showed that the individual variation rate (71.6%) was higher than in the gene-guided dosing models. The over-estimation rate in patients with low doses (<2 mg/kg) was lower than the other dosing methods. This novel dosing algorithm based on a population PK model improves the predictive performance of the maintenance dose of warfarin, especially for low dose (<2 mg/d) patients.

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Figures

Figure 1
Figure 1
Weighted residuals - Population predicted plasma concentration and time (WRES-PRED and WRES-TIME). (A, B) for R-warfarin and (C, D) for S-warfarin.
Figure 2
Figure 2
Observed plasma concentration vs Population predicted plasma concentration or individual predicted plasma concentration (DV-PRED or DV-IPRED). DV stands for the observed plasma concentration of warfarin. The dotted line stand for line y=x. PRED stands for the population predicted plasma concentration of warfarin based on the PK models. IPRED stands for the individual predicted plasma concentration of warfarin based on the PK models. (A, B) for S-warfarin and (C, D) for R-warfarin.
Figure 3
Figure 3
The comparison of the DV-PRED and the DV-IPRED scatter-plot of cohort 1 and cohort 2. (A and C) Scatter plot of observed vs population predicted plasma concentration of S- and R-warfarin in 54 patients from the validation group (cohort 2). (B and D) Scatter plot of observed vs individual predicted plasma concentration of S- and R-warfarin in 54 patients from the validation group (cohort 2).
Figure 4
Figure 4
Scatterplot of the observed dose versus the predicted dose of the 42 patients for external validation.

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