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A New Pharmacogenetic Algorithm to Predict the Most Appropriate Dosage of Acenocoumarol for Stable Anticoagulation in a Mixed Spanish Population

机译:一种新的药理学算法可预测混合西班牙人群中稳定稳定抗凝用乙酰氨基香豆素的最合适剂量

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摘要

There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11–21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.
机译:遗传多态性与乙酰香豆酚剂量要求之间有很强的联系。在开始抗凝治疗之前,对乙酰诺香酚的药代动力学和药效学中涉及的多态性进行基因分型将导致生活质量提高和医疗资源的更有效利用。这项研究的目的是开发一种新的算法,包括临床和遗传变量,以预测最合适的乙酰香豆酚剂量,以使广泛的患者稳定抗凝。我们从2家西班牙医院和1家主要医疗中心招募了685名患者。考虑到基因型的公平分布以形成世代队列,我们​​随机选择了80%的患者(n = 556)。剩下的20%(n = 129)构成了验证队列。使用乙酰香豆酚稳定剂量作为因变量,临床和基因型变量作为自变量,使用多元线性回归生成算法。算法中包括的变量是年龄,体重,胺碘酮使用,酶诱导剂状态,国际标准化比例目标范围以及CYP2C9 * 2(rs1799853),CYP2C9 * 3(rs1057910),VKORC1(rs9923231)和CYP4F2(rs2108622)的存在。该算法解释的确定系数(R 2 )在世代队列中为52.8%,在验证队列中为64%。通过病理学评估以下R 2 值:房颤57.4%;房颤57.4%。阀门更换,56.3%;静脉血栓栓塞性疾病占51.5%。当根据稳定剂量(<11 mg /周,11–21 mg /周,> 21 mg /周)将患者分为3个剂量组时,正确分类的患者百分比在中间组中较高,而差异在极端剂量组中,药物遗传学和临床算法之间的差异增加。对于将开始使用该药物治疗的患者,尤其是在极端剂量的患者中,我们的算法可以改善乙酰香豆酚的剂量选择。药物遗传算法的可预测性在疾病之间没有显着差异。

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