Prediction of phenprocoumon maintenance dose and phenprocoumon plasma concentration by genetic and non-genetic parameters
Prediction of phenprocoumon maintenance dose and phenprocoumon plasma concentration by genetic and non-genetic parameters
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DOI：
10.1007/s00228-010-0950-y
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发表时间：
2011-04-01
影响因子：
2.9
通讯作者：
Oldenburg, Johannes
中科院分区：
文献类型：
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作者：
Geisen, Christof;Luxembourg, Beate;Oldenburg, Johannes
对维生素K拮抗剂的抗凝反应具有高度的个体差异性。参与维生素K循环的酶的几个基因的单核苷酸多态性（snp）对phenprocoumon剂量变异性和phenprocoumon血浆浓度的影响仍在研究中。我们评估了75例患者VKORC1 c.- 1639g > A、CYP2C9*2、CYP2C9*3、CYP4F2 c. 1297g > A、CALU c.*4A > G、EPHX1 c. 337t > c、GGCX c.214+597G > A、F7 c.- 402g > A、F7 c.- 401g > T、PROC c.- 228c > T和PROC c.- 215g > A以及临床和人口学参数对稳态phenpropromon治疗的影响。建立了phenprocoumon总血浆浓度和治疗性抗凝所需的每日phenprocoumon剂量的预测模型。VKORC1 c -1639基因型是phenprocoumon日剂量（校正R-2 = 37.6%）和phenprocoumon总浓度（校正R-2 = 38.3%）的主要预测因子。CYP2C9影响phenprocoumon浓度，但不影响剂量要求。在多元线性回归模型中，维生素K周期、伴随用药、尼古丁使用和饮酒的其他基因的snp不能预测苯丙库蒙浓度和苯丙库蒙剂量需求。用VKORC1 c.-1639、CYP2C9基因型、年龄和BMI预测苯丙单抗浓度。phenprocoumon每日剂量需求的最终预测模型包括VKORC1 c -1639基因型、年龄和身高，占个体间变异的48.6%。phenprocoumon维持剂量的粗略预测可以通过一组有限的参数（VKORC1、年龄、身高）来实现。研究的CYP4F2、CALU、EPHX1、GGCX、F7和PROC的snp并没有提高基于药理学的phenprocoumon给药方程的预测值。
The anticoagulation response to vitamin K antagonists is characterised by high inter-individual variability. The impact of single nucleotide polymorphisms (SNPs) in several genes of enzymes involved in the vitamin K cycle on phenprocoumon dose variability and phenprocoumon plasma concentrations is still under investigation.We assessed the influence of VKORC1 c.-1639G > A, CYP2C9*2, CYP2C9*3, CYP4F2 c.1297G > A, CALU c.*4A > G, EPHX1 c.337T > C, GGCX c.214+597G > A, F7 c.-402G > A, F7 c.-401G > T, PROC c.-228C > T and PROC c.-215G > A along with clinical and demographic parameters on steady-state phenprocoumon therapy in 75 patients. A prediction model was developed for total phenprocoumon plasma concentrations and daily phenprocoumon doses required for therapeutic anticoagulation.The VKORC1 c.-1639 genotype was the main predictor of the phenprocoumon daily dose (adjusted R-2 = 37.6%) and the total phenprocoumon concentration (adjusted R-2 = 38.3%). CYP2C9 affected the phenprocoumon concentration, but not the dose requirements. SNPs in the other genes of the vitamin K cycle, concomitant medication, nicotine use and alcohol consumption did not predict phenprocoumon concentrations and phenprocoumon dose requirements in a multiple linear regression model. Phenprocoumon concentrations were predicted by VKORC1 c.-1639, CYP2C9 genotype, age and BMI. The final prediction model for the daily phenprocoumon dose requirements comprised VKORC1 c.-1639 genotype, age and height accounting for 48.6% of the inter-individual variability.A rough prediction of phenprocoumon maintenance doses can be achieved by a limited set of parameters (VKORC1, age, height). The investigated SNPs in CYP4F2, CALU, EPHX1, GGCX, F7, and PROC did not improve the predictive value of a pharmacogenetic-based dosing equation for phenprocoumon.