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A Method to Study the C924T Polymorphism of the Thromboxane A2 Receptor Gene
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A Method to Study the C924T Polymorphism of the Thromboxane A2 Receptor Gene

Published on: April 1, 2019

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VKORC1 and CYP2C9 genotypic data-based dose prediction alone does not accurately predict warfarin dose requirements

Yung An Chua, Wan Zaidah Abdullah, Zurkurnai Yusof

    Turkish Journal of Medical Sciences
    |October 2, 2015
    PubMed

    Insights

    Warfarin dosing algorithms based solely on VKORC1 and CYP2C9 genes may be inaccurate. Incorporating clinical factors like INR and drug interactions is crucial for precise warfarin dosing.

    Area of Science:

    • Pharmacogenomics
    • Clinical Pharmacology

    Background:

    • VKORC1 and CYP2C9 genetic variations are used to predict warfarin dose.
    • Existing warfarin dosing algorithms may not be universally accurate.

    Purpose of the Study:

    • To evaluate a warfarin dosing algorithm based on VKORC1 and CYP2C9 genes in Malaysian patients.
    • To assess the accuracy of genotype-based warfarin dosing predictions.

    Main Methods:

    • Investigated CYP2C9*2, CYP2C9*3, and VKORC1-1639G>A genotypes in five Malay patients.
    • Collected data on daily warfarin doses, international normalized ratio (INR), and vitamin K-dependent (VKD) clotting factor activities.
    • Compared actual mean warfarin doses with predicted doses from an Asian-developed genotypic model.

    Main Results:

    • A VKORC1-1639 GA genotype patient required a lower warfarin dose than predicted, possibly due to celecoxib coadministration.
    • Predicted warfarin doses from the algorithm differed significantly from actual doses in some patients.
    • The worst prediction exceeded the required warfarin dose by 54.34%.

    Conclusions:

    • Genetic polymorphisms alone are insufficient for accurate warfarin dose prediction.
    • Clinical factors such as INR, VKD clotting factors, and concomitant drug use significantly impact warfarin requirements.
    • Integrating dynamic clinical variables into dosing models is essential for improved accuracy.
    Abstract

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