【作者】 王彦斌；

【导师】 于立新；

【作者基本信息】 南方医科大学 ， 泌尿外科， 2009， 博士

【摘要】 目的:钙调神经蛋白抑制剂（CNI）的应用使肾移植临床疗效发生了根本改善,但其较窄的治疗指数和不同个体间高度差异一直困扰着临床医师。国内外学者做了大量研究,认为与细胞色素酶P450-3A4（CYP3A4）、细胞色素酶P450-3A5（CYP3A5）和多药耐药基因1（MDR1）基因多态性关系密切,CNI所需剂量突变型纯合子＜杂合子＜野生型纯合子,其中MDR1通过编码药物转运体P-糖蛋白（P-gp）发挥作用,而P-gp又与细胞色素酶P450-3A（CYP3A）具有协同和相互调节作用。明确肾移植受者CYP3A和MDR1的基因多态性进而了解影响肾移植术后受者CNI剂量和浓度个体差异的原因,指导肾移植术后CNI用药具有重要的临床意义。研究表明中国人中CYP3A4基因变异率非常低（0.5%～2%）,对其进行分析意义有限,故我们未将CYP3A4不同基因型纳入试验研究。本研究结合中国汉族人群的基因特点全面考虑了影响肾移植术后受者CNI吸收和代谢的药物基因组学及非药物基因组学因素,通过自行设计引物采用PCR法一次性扩增目的DNA,变性高效液相色谱技术检测来自中国大陆12个省份的274例肾移植受者CYP3A5和MDR1 3435、2677、1236基因多态性,并对其分布情况进行研究,然后紧密结合肾移植术后不同时期的临床特点,采用多元线性回归分析方法研究肾移植术后不同时期他克莫司（TAC）及环孢素A（CsA）剂量及浓度个体差异影响因素及其变化规律,以探讨肾移植术后不同时期CNI剂量及浓度个体差异的原因。方法:1.自行设计引物,采用PCR法一次性扩增目的DNA,变性高效液相色谱技术检测分型。2.对在我中心行肾移植手术及复诊的274例来自中国大陆12个省份的汉族肾移植受者CYP3A5和MDR1 3435、2677、1236基因多态性及分布情况进行分析,并观察是否存在省份差异,统计学方法采用多个独立样本比较的χ²检验。3.对118例肾移植术后早期采用TAC、霉酚酸酯（MMF）及强的松（Pred）三联免疫抑制抗排斥的受者,按术后3、7、14、30天分别记录性别、年龄、身高、体质量、TAC剂量、Pred剂量、腹泻、血脂、肝功、肾功、白蛋白、血细胞比容,测定每个受者CYP3A5和MDR1 3435、2677、1236位点基因多态性及不同时期TAC全血谷浓度,然后以TAC谷浓度/（剂量×体表面积）为因变量分别进行多元线性回归分析,探讨肾移植术后早期受者TAC剂量及浓度个体差异的原因。4.对137例肾移植术后稳定期（≥1年）常规口服TAC、MMF及Pred三联免疫抑制抗排斥的受者,记录性别、年龄、身高、体质量、TAC剂量、Pred剂量、血脂、肝功、肾功、白蛋白、血细胞比容,测定每个受者CYP3A5和MDR13435、2677、1236位点基因多态性及TAC全血谷浓度,然后以TAC谷浓度/（剂量×体表面积）为因变量进行多元线性回归分析,探讨肾移植术后稳定期受者TAC剂量及浓度个体差异的原因。5.对118例早期（3天、7天、14天、30天）、103例3月、75例6月、137例稳定期（≥1年）口服TAC、MMF及Pred三联免疫抑制抗排斥的受者,记录性别、年龄、身高、体质量、TAC剂量、Pred剂量、血脂、肝功、肾功、白蛋白、血细胞比容,测定每个受者CYP3A5和MDR1 3435、2677、1236位点基因多态性及不同时期TAC全血谷浓度,然后以TAC谷浓度/（剂量×体表面积）为因变量分别进行多元线性回归分析,探讨肾移植受者TAC剂量及浓度个体差异影响因素及其变化规律。6.对117例肾移植术后稳定期（≥1年）常规口服新山地明（CsA）、MMF及Pred三联免疫抑制抗排斥的受者,记录性别、年龄、身高、体质量、CsA剂量、Pred剂量、血脂、肝功、肾功、白蛋白、血细胞比容、白细胞、中性粒细胞、淋巴细胞,测定每个受者CYP3A5和MDR1 3435、2677、1236位点基因多态性及CsA全血谷浓度,CYP3A5和MDR1 3435、2677、1236位点基因多态性,然后以CsA谷浓度/（剂量×体表面积）为因变量进行多元线性回归分析,探讨肾移植术后稳定期受者CsA剂量及浓度个体差异原因。结果:1.中国大陆汉族肾移植受者存在CYP3A5^*3、MDR1 3435C＞T、MDR12677G＞T/A、MDR1 1236C＞T基因变异,其变异频率分别为74.1%、49.5%、39.9%/13.7%、72.1%,不同省份间无统计学差异。2.肾移植术后早期服用TAC受者多元线性回归模型的拟合度偏低,Adjusted R²值在术后3、7、14、30天分别为0.284、0.267、0.417、0.324。药物基因组学因素有MDR1 2677、MDR1 1236、MDR1 3435且变化剧烈,不同个体达到相同TAC浓度所需剂量突变型纯合子＜杂合子＜野生型纯合子。非药物基因组学因素在术后3天有年龄、白蛋白和高密度脂蛋白胆固醇,7天有白蛋白、年龄和血清肌酐,14天有Pred剂量和谷草转氨酶,上述影响因素均与TAC谷浓度/（剂量×体表面积）呈正相关。3.肾移植术后稳定期服用TAC受者多元线性回归模型的拟合度好,Adjusted R²值=0.739。6个自变量对因变量的影响从大到小依次为MDR1 3435_（1）、MDR1 1236_（1）、MDR1 2677_（1）、MDR1 1236_（2）、Age和AST。对方程内各自变量单独检验MDR1 3435_（1）、MDR1 1236_（1）、年龄、MDR1 1236_（2）、MDR12677_（1）对因变量有显著性影响,P值分别为0.000、0.000、0.022、0.028和0.029。4.肾移植受者TAC剂量及浓度个体差异影响因素及其变化规律:1)多元线性回归模型拟合度肾移植术后早期偏低,术后3月明显增高,术后6月进一步增高并逐渐趋于稳定;影响TAC剂量及浓度的因素在术后早期较多且变化剧烈,术后3月以后逐渐趋于稳定。2)从药物基因组学角度来看,影响TAC剂量及浓度的主要因素是MDR1 3435、MDR1 2677、MDR1 1236且术后早期变化剧烈,CYP3A5作用相对较弱且仅在稳定期入选,稳定期影响TAC代谢的主要因素是MDR1 3435。3)除药物基因组学因素外,年龄、肝功值、白蛋白和血细胞比容与TAC浓度/（剂量×体表面积）呈正相关,是个体差异的重要原因。5.肾移植术后稳定期服用CsA受者多元线性回归模型的拟合度好,AdjustedR²值=0.824。自变量对因变量的影响从大到小依次为MDR1 3435_（1）、MDR11236_（1）、CYP3A5_（2）、低密度脂蛋白胆固醇、直接胆红素、年龄、总胆固醇、高密度脂蛋白胆固醇、淋巴细胞。单独检验有统计学意义的自变量有MDR1 3435_（1）、MDR1 1236_（1）、CYP3A5_（2）、直接胆红素、年龄、低密度脂蛋白胆固醇、高密度脂蛋白胆固醇,P值分别为0.000、0.000、0.000、0.000、0.002、0.014和0.035。结论:1.中国大陆汉族肾移植受者普遍存在CYP3A5*3、MDR1 3435C>T、MDR12677G>T/A、MDR1 1236C>T基因变异,不同省份间无明显差异。2.肾移植术后早期受者TAC剂量及浓度个体差异的原因主要是术后用药和受者剧烈的内环境变化,还与受者MDR1基因多态性、年龄、白蛋白、移植肾功能恢复情况、血脂和肝功能有关,术前应当充分透析尽可能改善患者一般情况,术后用药应当尽量避免对TAC吸收和代谢干扰大的药物。3.影响肾移植术后稳定期受者TAC剂量及浓度的主要因素有MDR1 3435、MDR1 1236、年龄、MDR1 2677和肝功,且其影响程度依次递减。4.肾移植术后TAC剂量及浓度影响因素的变化规律与肾移植临床特点相吻合,不同时期TAC剂量及浓度个体差异的影响因素不同;药物基因组学是影响TAC剂量及浓度个体差异的重要因素,临床用药应尽量避免对TAC代谢干扰大的药物;除药物基因组学因素外,年龄、肝功、白蛋白、血细胞比容和Pred剂量也是TAC剂量及浓度个体差异的重要原因。5.影响肾移植术后稳定期受者CsA剂量及浓度个体差异的主要因素有MDR1 3435、MDR1 1236、CYP3A5、直接胆红素、年龄和血脂,且其影响程度依次递减。更多还原

【Abstract】 ObjectivesCalcineurin inhibitor (CNI) does much good to renal transplantation, but its narrow therapeutic windows and differences between individuals puzzled clinicians all the time. Research from home and aboard indicated that CNI’s differences between individuals had close relationships with cytochrome P3A4 (CYP3A4), cytochrome P3A5 (CYP3A5) and Multi-drug resistance gene 1 (MDR1), the dosage of CNI for the same concentration decreased as follows: mutant homozygote, heterozygote and wild-type homozygote. MDR1 encoded P-glycoprotein (P-gp), while P-gp cooperated and adjusted with cytochrome P3A (CYP3A). As a conclusion, the detections of CYP3A and MDR1 polymorphisms in renal transplant recipients play an important role. Research from home showed that the genovariation rates of CYP3A4 were very low (0.5%～2%), in view of their limited significance, we didn’t internalize CYP3A4 into our research.In light of the genic features of the Hans in China, we took pharmacogenomics and un-pharmacogenomics factors into consideration that influenced the absorption and metabolism of CNI in renal transplant recipients. We designed the primers and amplified the objective DNA by PCR in one time, then detected the gene polymorphisms of CYP3A5 and MDR1 ( 3435, 2677 and 1236 ) with DHPLC, the clinical data consisted of 274 Hans kidney recipients from 12 provinces in China, whose distributions of CYP3A5 and MDR1 ( 3435, 2677 and 1236 ) were analyzed. Combined with the different features of postoperative stages of renal transplantation, we used multiple linear regression to identify the factors responsible for the inter-individual variations in dosage and concentration of CNI (tacrolimus (TAC) and Cyclosporine A (CsA)) in renal transplant recipients.Methods1. Designed the primers and amplified the objective DNA by PCR in one time, then detected the gene polymorphisms of CYP3A5 and MDR1 ( 3435, 2677 and 1236) with DHPLC.2. Analyzed the distributions of CYP3A5 and MDR1 ( 3435,2677 and 1236 ) of 274 Hans kidney recipients from 12 provinces in China.3. In accordance with 118 recipients in morning periods after renal transplantation, whose immune suppressions were TAC, mycophenolate (MMF) and Prednisone (Pred), at 3,7,14 and 30 days after operation, we respectively recorded their gender, age, height, weight, dosage of TAC, dosage of Pred, diarrhea, blood fat, liver function, renal function, albumn and erythrocrit, and at the same time detected their concentrations of TAC and genetic polymorphisms of CYP3A5, MDR1 ( 3435, 2677 and 1236). Multiple linear regressions were used for analysis.4. In accordance with 137 recipients in stable phases (≥1year) after renal transplantation, whose immune suppressions were TAC, MMF and Pred, we respectively recorded their gender, age, height, weight, dosage of TAC, dosage of Pred, blood fat, liver function, renal function, albumn, erythrocrit, and at the same time detected their concentrations of TAC and genetic polymorphisms of CYP3A5, MDR1 (3435,2677 and 1236). Multiple linear regressions were used for analysis. 5. In accordance with 118 recipients in morning periods, 103 recipients 3 months, 75 recipients 6 months, 137 recipients in stable phases (≥1year) after renal transplantation, whose immune suppressions were TAC, MMF and Pred, we respectively recorded their gender, age, height, weight, dosage of TAC, dosage of Pred, blood fat, liver function, renal function, albumn, erythrocrit, and at the same time detected their concentrations of TAC and genetic polymorphisms of CYP3A5, MDR1 ( 3435,2677 and 1236). Multiple linear regressions were used for analysis.6. In accordance with 117 recipients in stable phases (≥1year) after renal transplantation, whose immune suppressions were CsA, MMF and Pred, we respectively recorded their gender, age, height, weight, dosage of CsA, dosage of Pred, blood fat, liver function, renal function, albumn, erythrocrit, leucocyte, neutrophilic leukocyte and lympholeukocyte, and at the same time detected their concentrations of CsA and genetic polymorphisms of CYP3A5, MDR1 ( 3435, 2677 and 1236). Multiple linear regressions were used for analysis.Results1. The genic mutations of CYP3A5*3, MDR14 3435C>T, MDR1 2677G>T/A and MDR1 1236C>T existed in Hans kidney recipients in China, whose frequencies were as follows 74.1%, 49.5%, 39.9% / 13.7% and 72.1% , and their distributions between different provinces had no statistical distinctions.2. The fitting degrees of stepwise regression equations in recipients with TAC in morning periods after renal transplantation were low, at 3, 7, 14 and 30 days after operation, the adjusted R~2 was 0.284, 0.267, 0.417 and 0.324 respectively. From the aspect of pharmacogenomics, the main factors included MDR1 2677, MDR1 1236 and MDR1 3435, which varied intensively. Age, albumn, renal function, blood fat and liver function were important factors too.3. The fitting degrees of stepwise regression equations in recipients with TAC in stable phases after renal transplantation were high, adjusted R~2 = 0.739. The influence of the 6 independent variables on the dependent variable decreased as follows: MDR1 3435 (1), MDR1 1236 (1), MDR1 2677 (1), MDR1 1236 (2), age and AST. In the solitude analysis, MDR1 3435 (1), MDR1 1236 (1), age, MDR1 1236 (2) and MDR1 2677 (1) showed statistical significance.4. Factors responsible for inter-individual variations in dosage and concentration of TAC in renal transplant recipients: 1) Patients in early stage following renal transplantation showed rather poor fitting of the stepwise regression model, which increased obviously 3 months after the operation and further increased till reaching a stable level at 6 months. Multiple factors were found to affect TAC dosage and concentration in the early postoperative stage, during which period these factors underwent drastic variations and became stable 3 months later. 2) In terms of pharmacogenomics, the major factors affecting TAC dosage and concentration included MDR1 3435, MDR1 2677 and MDR1 1236 polymorphisms, which vastly varied between the patients early after the operation. Of these polymorphic sites, CYP3A5 produced only minor effects on TAC dosage and concentration, and was not included as an active factor until the stable phases ( over 1 year ) following the transplantation; MDR1 3435 was found to be the predominant factor affecting TAC metabolism in the stable phase. 3) Age, liver function, albumin and hematocrit were found to be positively correlated to the independent variable TAC concentration / (dosage * body surface area), and identified as important factors responsible for the intra-individual variation of TAC dosage and concentration.5. The fitting degrees of stepwise regression equations in recipients with CsA in stable phases after renal transplantation were high, adjusted R~2 = 0.824. The influence of the independent variables on the dependent variable decreased as follows: MDR1 3435 (1), MDR1 1236 (1) , CYP3A5 (2), LDL, DBIL, Age, TC, HDL and L. In the solitude analysis, MDR1 3435 (1), MDR1 1236(1), CYP3A5 (2), DBIL, Age, LDL and HDL showed statistical significance.Conclusions1. The genic mutations of CYP3A5*3, MDR1 3435C>T, MDR1 2677G>T/A and MDR1 1236OT generally exist in Hans kidney recipients in China, and their distributions have no differences between different provinces.2. The main reasons for the differences of TAC dosage and concentration between individuals in morning periods after renal transplantation are medicines and changes of internal environment after operation. The genetic polymorphisms of MDR1, age, albumn, renal function, blood fat and liver function are important factors too.3. The main reasons for the differences of TAC dosage and concentration between individuals in stable phases after renal transplantation are MDR1 3435, MDR1 1236, age, MDR1 2677 and liver function, and the extents of their influences decrease in order.4. The variations in the factors affecting TAC dosage and concentration after renal transplantation are consistent with the clinical features of the renal transplantation, and these factors vary with the postoperative stages. Pharmacogenomic factors produce the most conspicuous effect on TAC dosage and concentration differences, and agent that may interfere with TAC absorption and metabolism should be avoided after the operation. Age, liver function, albumin and hematocrit are also important factors responsible for the inter-individual variations in dosage and concentration of TAC in renal transplant recipients.5. The main reasons for the differences of CsA dosage and concentration between individuals in stable phases after renal transplantation are MDR1 3435, MDR1 1236, CYP3A5, DBIL, age and blood fat, and their influences decrease in order.更多还原