Critical Care Medicine Department, Cairo University Hospitals, Cairo, Egypt.
Int J Clin Pharm. 2012 Dec;34(6):837-44. doi: 10.1007/s11096-012-9678-3. Epub 2012 Jul 27.
Warfarin remains a difficult drug to use due to the large variability in dose response. Clear understanding of the accuracy of warfarin pharmacogenetic dosing methods might lead to appropriate control of anticoagulation.
This study aims to evaluate the accuracy of warfarin dosing table and two pharmacogenetic algorithms, namely the algorithms of Gage et al. (Clin Pharmacol Ther 84:326-331, 2008), and the International Warfarin Pharmacogenetics Consortium algorithm (IWPC) in a real Egyptian clinical setting. Additionally, three non-pharmacogenetic dosing methods (the Gage, IWPC clinical algorithms and the empiric 5 mg/day dosing) were evaluated.
Sixty-three Egyptian patients on a stable therapeutic warfarin dose were included. Patients were recruited from the outpatient clinic of the critical care medicine department.
CYP2C9 and VKORC1 polymorphisms were genotyped by real time PCR system. Predicted doses by all dosing methods were calculated and compared with the actual therapeutic warfarin doses.
The Gage algorithm (adjusted R(2) = 0.421, and mean absolute error (MAE) = 3.3), and IWPC algorithm (adjusted R(2) = 0.419, MAE = 3.2) produced better accuracy than did the warfarin dosing table (adjusted R(2) = 0.246, MAE = 3.5), the two clinical algorithms (R(2) = 0.24, MAE = 3.7) and the fixed dose approach (MAE = 3.9). However, all dosing models produced comparable clinical accuracy with respect to proportion of patients within 1 mg/day of actual dose (ideal dose). Non-pharmacogenetic methods severely over-predicted dose (defined as ≥2 mg/day more than actual dose) compared to the three pharmacogenetic models. In comparison to non-pharmacogenetic methods, the three pharmacogenetic models performed better regarding the low dose group in terms of percentage of patients within ideal dose. In the high dose group, none of the dosing models predicted warfarin doses within ideal dose.
Our study showed that genotype-based dosing improved prediction of warfarin therapeutic dose beyond that available with the fixed-dose approach or the clinical algorithms, especially in the low-dose group. However, the two pharmacogenetic algorithms were the most accurate.
华法林的剂量反应个体差异较大，因此仍然是一种难以使用的药物。清楚了解华法林药物遗传学剂量方法的准确性可能有助于对抗凝作用进行适当控制。
本研究旨在评估华法林剂量表和两种药物遗传学算法，即 Gage 等人的算法（Clin Pharmacol Ther 84:326-331, 2008）和国际华法林药物遗传学联合会算法（IWPC）在真实的埃及临床环境中的准确性。此外，还评估了三种非药物遗传学剂量方法（Gage、IWPC 临床算法和经验性 5mg/天剂量）。
纳入 63 例在稳定治疗剂量华法林治疗的埃及患者。患者从重症监护医学科门诊招募。
通过实时 PCR 系统对 CYP2C9 和 VKORC1 多态性进行基因分型。通过所有剂量方法计算预测剂量，并与实际治疗华法林剂量进行比较。
Gage 算法（调整 R²=0.421，平均绝对误差（MAE）=3.3）和 IWPC 算法（调整 R²=0.419，MAE=3.2）比华法林剂量表（调整 R²=0.246，MAE=3.5）、两种临床算法（R²=0.24，MAE=3.7）和固定剂量方法（MAE=3.9）产生更好的准确性。然而，所有剂量模型在实际剂量（理想剂量）的 1mg/天以内的患者比例方面都具有相当的临床准确性。与三种药物遗传学模型相比，非药物遗传学方法严重高估了剂量（定义为比实际剂量高出 2mg/天以上）。与非药物遗传学方法相比，三种药物遗传学模型在低剂量组中更能预测理想剂量内的患者比例。在高剂量组中，没有一种剂量模型能预测出理想剂量内的华法林剂量。
我们的研究表明，基于基因型的剂量方法可改善华法林治疗剂量的预测，优于固定剂量方法或临床算法，尤其是在低剂量组。然而，两种药物遗传学算法是最准确的。
