Tong Hoi Y, Dávila-Fajardo Cristina Lucía, Borobia Alberto M, Martínez-González Luis Javier, Lubomirov Rubin, Perea León Laura María, Blanco Bañares María J, Díaz-Villamarín Xando, Fernández-Capitán Carmen, Cabeza Barrera José, Carcas Antonio J
Department of Clinical Pharmacology, La Paz University Hospital, IdiPAZ, Madrid, Spain.
Department of Clinical Pharmacy, San Cecilio University Hospital, Institute for Biomedical Research, Ibs, Granada, Spain.
PLoS One. 2016 Mar 15;11(3):e0150456. doi: 10.1371/journal.pone.0150456. eCollection 2016.
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 CYP2C92 (rs1799853), CYP2C93 (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.
基因多态性与醋硝香豆素的剂量需求之间存在密切关联。在开始抗凝治疗前,对参与醋硝香豆素药代动力学和药效动力学的多态性进行基因分型,将改善生活质量并更有效地利用医疗资源。本研究的目的是开发一种新算法,该算法纳入临床和基因变量,以预测广泛患者群体中稳定抗凝所需的最合适醋硝香豆素剂量。我们从2家西班牙医院和1个初级医疗保健中心招募了685名患者。我们随机选择80%的患者(n = 556),考虑到基因型的公平分布以形成生成队列。其余20%(n = 129)组成验证队列。使用多元线性回归生成算法,将醋硝香豆素稳定剂量作为因变量,临床和基因变量作为自变量。该算法纳入的变量包括年龄、体重、胺碘酮使用情况、酶诱导剂状态、国际标准化比值目标范围以及CYP2C92(rs1799853)、CYP2C93(rs1057910)、VKORC1(rs9923231)和CYP4F2(rs2108622)的存在情况。该算法在生成队列中解释的决定系数(R2)为52.8%,在验证队列中为64%。通过病理学评估的以下R2值为:心房颤动,57.4%;瓣膜置换,56.3%;静脉血栓栓塞性疾病,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,中间组中正确分类患者的百分比更高,而在极端剂量组中,药物遗传学算法与临床算法之间的差异增大。我们的算法可以改善将开始使用该药物治疗的患者的醋硝香豆素剂量选择,特别是在极端剂量的患者中。药物遗传学算法的可预测性在不同疾病之间没有显著差异。