Key result
A novel warfarin-dosing algorithm based on a population PK/PD model and Bayesian forecasting accurately predicted maintenance doses (r2 = 0.944) and outperformed other dosing methods.
Why the study?
Does a novel warfarin-dosing algorithm based on a population PK/PD model combined with Bayesian forecasting improve the prediction of warfarin maintenance doses in Japanese patients?
Observational (n=45)
Does a novel warfarin-dosing algorithm based on a population PK/PD model combined with Bayesian forecasting improve the prediction of warfarin maintenance doses in Japanese patients?
A novel warfarin-dosing algorithm incorporating PK/PD modeling and Bayesian forecasting accurately predicts maintenance doses, potentially facilitating individualized warfarin therapy.
AIMS: To develop a novel warfarin-dosing algorithm based on a previous population pharmacokinetic/pharmacodynamic (PK/PD) model with Bayesian forecasting to facilitate warfarin therapy. MATERIALS & METHODS: Using information on CYP2C9 and VKORC1 genotypes, S-warfarin level, dose and international normalized ratio (INR) of prothrombin time, individual PK (apparent clearance of S-warfarin [CLs]) and PD (concentration resulting in 50% of E(max) [EC(50)]) parameters were determined by Bayesian forecasting for 45 Japanese patients. Maintenance doses were described by multiple linear regression using individually estimated PK/PD parameters and INR values. The validity of the model and a comparison with other dosing methods were evaluated by bootstrap resampling and a cross-validation method. RESULTS: The plasma concentration of S-warfarin and INR were accurately predicted from individual PK/PD parameters. The following final regression model for maintenance dose was obtained; maintenance dose = 11.2 x CLs + 0.91 x EC(50) + 2.36 x INR - 9.67, giving a strong correlation between actual and predicted maintenance doses (r(2) = 0.944). Bootstrap resampling and cross-validation showed robustness and a superior predictive performance compared with other dosing methods. On the other hand, the predictability without actual measurements (S-warfarin and INR values) and Bayesian inference was comparable to other dosing methods. CONCLUSION: A novel algorithm, based on the population PK/PD model combined with Bayesian forecasting, gave precise predictions of maintenance dose, leading to individualized warfarin therapy.
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Tabuchi et al. (2009) conducted an observational in Warfarin therapy (n=45). Warfarin-dosing algorithm based on a population PK/PD model with Bayesian forecasting vs. Other dosing methods was evaluated on Correlation between actual and predicted maintenance doses (r2 = 0.944). A novel warfarin-dosing algorithm based on a population PK/PD model and Bayesian forecasting accurately predicted maintenance doses (r2 = 0.944) and outperformed other dosing methods.
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