The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients.
The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients.
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DOI：
10.1097/fpc.0b013e32834e1641
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发表时间：
2012-01
影响因子：
2.6
通讯作者：
Haas DW
中科院分区：
文献类型：
--
作者：
Birdwell KA;Grady B;Choi L;Xu H;Bian A;Denny JC;Jiang M;Vranic G;Basford M;Cowan JD;Richardson DM;Robinson MP;Ikizler TA;Ritchie MD;Stein CM;Haas DW
他克莫司是一种在肾移植中广泛使用的免疫抑制药物，由于其显着的个体间药代动力学变异性和狭窄的治疗指数，需要进行治疗药物监测。先前的研究已确定 CYP3A5 rs776746 与他克莫司清除率、血液浓度和剂量需求相关。其他药物吸收、分布、代谢和消除 (ADME) 基因变异的重要性尚未得到很好的表征。我们使用新型 DNA 生物库和电子病历资源来识别与他克莫司剂量要求相关的 ADME 变异。对 446 名已服用他克莫司至稳态的肾移植受者进行了广泛的 ADME 基因分型。该队列是从范德比尔特大学的 DNA 生物库 BioVU 获得的，其中包含链接的、去识别化的电子病历数据。基因分型包括 Affymetrix DMET Plus（1936 个多态性）、定制 Sequenom MassARRAY iPLEX Gold 检测（95 个多态性）和祖先信息标记。主要结果是他克莫司的剂量需求，定义为血液浓度与剂量之比。在根据种族和其他临床因素进行调整的分析中，我们复制了他克莫司血药浓度与剂量比与 CYP3A5 rs776746 的关联 (p = 7.15 × 10−29)，并确定了与 rs776746 连锁不平衡的 9 个变异体之间的关联，其中包括 8 个 CYP3A4 变异体。没有 NR1/2 变异显着相关。年龄、体重和血红蛋白也与结果显着相关。在最终模型中，rs776746 解释了剂量需求中 39% 的变异，46% 是由包含临床协变量的模型解释的。这项研究强调了 DNA 生物库和电子病历在他克莫司药物基因组学研究中的效用。
Tacrolimus, an immunosuppressive drug widely prescribed in kidney transplantation, requires therapeutic drug monitoring due to its marked interindividual pharmacokinetic variability and narrow therapeutic index. Previous studies have established that CYP3A5 rs776746 is associated with tacrolimus clearance, blood concentration, and dose requirement. The importance of other drug absorption, distribution, metabolism, and elimination (ADME) gene variants has not been well characterized. We used novel DNA biobank and electronic medical record resources to identify ADME variants associated with tacrolimus dose requirement. Broad ADME genotyping was performed on 446 kidney transplant recipients who had been dosed to steady state with tacrolimus. The cohort was obtained from Vanderbilt's DNA biobank, BioVU, which contains linked, de-identified electronic medical record data. Genotyping included Affymetrix DMET Plus (1936 polymorphisms), custom Sequenom MassARRAY iPLEX Gold assay (95 polymorphisms), and ancestry-informative markers. The primary outcome was tacrolimus dose requirement defined as blood concentration-to-dose ratio. In analyses that adjusted for race and other clinical factors, we replicated the association of tacrolimus blood concentration-to-dose ratio with CYP3A5 rs776746 (p = 7.15 × 10−29), and identified associations with nine variants in linkage disequilibrium with rs776746, including eight CYP3A4 variants. No NR1/2 variants were significantly associated. Age, weight, and hemoglobin were also significantly associated with the outcome. In final models, rs776746 explained 39% of variability in dose requirement, and 46% was explained by the model containing clinical covariates. This study highlights the utility of DNA biobanks and electronic medical records for tacrolimus pharmacogenomic research.