Department of Pathology, The Methodist Hospital, The Methodist Hospital Research Institute, Houston, TX, USA.
Pharmacogenomics. 2010 Oct;11(10):1389-402. doi: 10.2217/pgs.10.105.
Tacrolimus has a narrow therapeutic window and shows significant interindividual difference in dose requirement. In this study we aim to first identify genetic factors that impact tacrolimus dose using a candidate gene association approach, and then generate a personalized algorithm combining identified genetic and clinical factors to predict individualized tacrolimus dose.
MATERIALS & METHODS: We screened 768 SNPs in 15 candidate genes in metabolism, transport and calcineurin inhibition pathways of tacrolimus, for association with tacrolimus dose in a discovery cohort of 96 patients.
Four polymorphisms in CYP3A5 and one polymorphism in CYP3A4 were identified to be significantly associated with tacrolimus stable dose (p < 8.46 × 10(-5)). The same SNPs were identified when dose-normalized trough tacrolimus concentration was analyzed. The CYP3A5*1 allele was associated with significantly higher stable dose, bigger dose increase, higher risk of being underdosed and lower incidence of post-transplant hyperlipidemia. ABCB1 polymorphisms were not associated with stable dose. No significant difference was found between CYP3A5 expressers and nonexpressers in incidence of acute rejection and time to first rejection. Age, ethnicity and CYP3A inhibitor use could predict 30% of tacrolimus dosing variability. Adding the identified genetic polymorphisms to the algorithm increased the predictability to 58%. In two validation cohorts of 77 and 64 patients, the algorithm containing both genetic and clinical factors produced correlation coefficients of 0.63 and 0.42, respectively. This algorithm gave a prediction of the stable doses closer to the actual doses when compared with another algorithm based only on the CYP3A5 genotype.
CYP3A5 genotype is the most significant genetic factor that impacts tacrolimus dose among the genes studied. This study generated the first pharmacogenomics model that predicts tacrolimus stable dose based on age, ethnicity, genotype and comedication use. Our results highlight the importance of incorporating both genetic and clinical, demographic factors into dose prediction.
他克莫司的治疗窗较窄，其剂量需求存在显著的个体间差异。本研究旨在首先采用候选基因关联分析的方法，确定影响他克莫司剂量的遗传因素，然后结合已确定的遗传和临床因素生成个体化算法，以预测个体化他克莫司剂量。
我们在一个包含 96 例患者的发现队列中，筛选了他克莫司代谢、转运和钙调磷酸酶抑制途径中 15 个候选基因的 768 个单核苷酸多态性（SNP），以确定与他克莫司剂量相关的遗传因素。
发现 CYP3A5 中的 4 个多态性和 CYP3A4 中的 1 个多态性与他克莫司稳定剂量显著相关（p < 8.46×10⁻⁵）。当分析剂量归一化后的他克莫司谷浓度时，也发现了相同的 SNP。CYP3A5*1 等位基因与稳定剂量显著升高、剂量增加幅度更大、剂量不足风险更高以及移植后高脂血症发生率较低相关。ABCB1 多态性与稳定剂量无关。CYP3A5 表达者与不表达者在急性排斥反应的发生率和首次排斥反应的时间上无显著差异。年龄、种族和 CYP3A 抑制剂的使用可预测他克莫司剂量变化的 30%。将已确定的遗传多态性添加到算法中，可将预测性提高到 58%。在两个包含 77 例和 64 例患者的验证队列中，包含遗传和临床因素的算法分别产生了 0.63 和 0.42 的相关系数。与仅基于 CYP3A5 基因型的另一种算法相比，该算法更接近实际剂量，能更准确地预测稳定剂量。
在研究的基因中，CYP3A5 基因型是影响他克莫司剂量的最重要的遗传因素。本研究首次建立了基于年龄、种族、基因型和合并用药的预测他克莫司稳定剂量的药物基因组学模型。我们的研究结果强调了将遗传和临床、人口统计学因素纳入剂量预测的重要性。
