A Personalized Medicine Approach to Improve the Prediction of Azathioprine Toxicity

改善硫唑嘌呤毒性预测的个性化医疗方法

• 批准号: 10453718
• 负责人: Cecilia Pilar Chung
• 金额: $ 16.82万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-01-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10453718
• 关键词: Adverse effects; Adverse event; Affect; Asian; Azathioprine; Bone Marrow Suppression; Candidate Disease Gene; Clinical; Clinical Data; Databases; Development; Dose; Drug Kinetics; Drug toxicity; Effectiveness; Enrollment; Enzymes; Essential Drugs; Face; Frequencies; GSTP1 gene; Gene Expression; Gene Expression Profiling; Gene Expression Regulation; Genes; Genetic; Genetic Determinism; Genetic Models; Genetic Polymorphism; Genetic Risk; Genetic Variation; Genome Scan; Genotype; Goals; HLA-DQA1; HLA-DRB1; Haplotypes; Hematologic Neoplasms; Immunologics; Immunosuppressive Agents; Inflammatory; Inflammatory Bowel Diseases; Link; Mediating; Medical; Medical Genetics; Modeling; Myelosuppression; Organ Transplantation; Other Genetics; Pancreatitis; Pathway interactions; Patient Care; Patient Rights; Patient risk; Patients; Pharmaceutical Preparations; Pharmacogenetics; Phase; Phenotype; Phospholipase C; Play; Population Sizes; Precision Medicine Initiative; Program Development; Records; Reporting; Reproducibility; Research; Resources; Rheumatism; Risk; Role; Signal Transduction; TPMT gene; Techniques; Testing; Therapeutic Index; Tissue-Specific Gene Expression; Tissues; Toxic effect; Validation; Variant; Veterans; World Health Organization; adverse drug reaction; base; biobank; clinical practice; clinically significant; cohort; genetic association; genetic predictors; genetic variant; genome wide association study; high risk; improved; inter-individual variation; novel; personalized medicine; programs; response; side effect; thiopurine; thiopurine methyltransferase

Abstract Azathioprine is an immunosuppressive drug widely used for the treatment of rheumatic and other inflammatory conditions. However, it has a narrow therapeutic index, and the frequency of clinically significant side effects associated with its use is approximately 50%. Based on clinical importance and differences in mechanisms, this project focuses on two of the most serious adverse effects of AZA: myelosuppression and pancreatitis. Currently, clinicians are limited to thiopurine methyltransferase (TPMT) testing to predict patients' risk for azathioprine toxicity. Despite their usefulness, TPMT polymorphisms explain only one in four cases of myelosuppression associated with azathioprine, and they do not predict pancreatitis. Recent evidence suggests other genetic variants have important roles in azathioprine-related side effects. For example, NUDT15 and the HLA- DQA1*02:01–HLA-DRB1*07:01 haplotype are genetic determinants of myelosuppression and pancreatitis, respectively. Nevertheless, their usefulness in routine clinical practice and their combined ability to predict side effects of AZA remains unclear. The overarching hypothesis of this proposal is that genetic risk scores can identify patients who develop azathioprine toxicity. Using state of the art and novel techniques and resources, we will conduct genetic and gene expression association analyses, leveraging two large practice- based biobanks: (1) Vanderbilt's BioVU, one of the largest practice-based biobanks in the U.S., and (2) the Million Veteran Program (MVP), currently enrolling, collecting clinical data from, and genotyping U.S. Veterans. In Aim 1, we will conduct genetic association analyses to discover novel genetic predictors of myelosuppression and pancreatitis in patients taking azathioprine. In Aim 2, we will test the hypothesis that novel genetic variants, identified by gene expression association analyses, predict AZA-related myelosuppression and pancreatitis. We will predict gene expression by utilizing the Genotype Tissue-Expression (GTEx) database. In Aim 3, we will combine all variants identified from Aims 1 and 2 to generate two genetic risk scores (i.e., myelosuppression risk and pancreatitis risk) for patients in the BioVU cohort. We will further validate the genetic risk scores in the independent MVP cohort. This project aims to further the goals of the Precision Medicine Initiative by constructing two genetic models that will predict serious and frequent side effects of azathioprine. Better prediction capacity will offer better treatment options for patients and advance personalized medicine, which seeks to deliver “the right drug, at the right dose, to the right patient.”

抽象的 硫唑嘌呤是一种被广泛用于风湿性和其他炎症治疗的免疫抑制药物 状况。但是，它具有狭窄的治疗指数，临床上显着的副作用的频率 与其使用相关的约为50％。基于临床重要性和机制差异，这 项目侧重于AZA最严重的两种不良影响：骨髓抑制和胰腺炎。现在， 临床医生仅限于硫呼丁甲基转移酶（TPMT）测试，以预测患者的硫唑嘌呤的风险 毒性。尽管它们有用，但TPMT多态性仅解释了四分之一的骨髓抑制。 与硫唑嘌呤相关，并且不能预测胰腺炎。最近的证据表明其他遗传 变体在硫唑嘌呤相关的副作用中具有重要作用。例如，nudt15和hla-- DQA1*02：01 – HLA-DRB1*07：01单倍型是骨髓抑制和胰腺炎的遗传决定剂， 分别。然而，它们在常规临床实践中的有用性及其预测一侧的综合能力 AZA的影响尚不清楚。该提议的总体假设是遗传风险评分 可以鉴定出患有硫唑嘌呤毒性的患者。使用艺术和新颖的技术， 资源，我们将进行遗传和基因表达关联分析，利用两个大型实践 - 基于生物库：（1）范德比尔特的Biovu是美国最大的基于实践的生物库之一，以及（2） 百万退伍军人计划（MVP），目前正在招募，从美国退伍军人那里收集临床数据。 在AIM 1中，我们将进行遗传关联分析，以发现骨髓抑制的新遗传预测因子 服用硫唑嘌呤的患者的胰腺炎。在AIM 2中，我们将检验以下假设：新型遗传变异 通过基因表达关联分析鉴定，可以预测与AZA相关的骨髓抑制和胰腺炎。我们 将通过使用基因型组织表达（GTEX）数据库来预测基因表达。在AIM 3中，我们将 结合AIM 1和2确定的所有变体，以产生两个遗传风险评分（即骨髓抑制风险 BIOVU队列患者的胰腺炎风险）。我们将进一步验证遗传风险评分 独立的MVP队列。 该项目的目的是通过建立两个遗传模型来实现Precision Medicion Initiative的目标 将预测硫唑嘌呤的严重且经常的副作用。更好的预测能力将提供更好的治疗 患者的选择和进步的个性化医学，该药物旨在提供“正确剂量的正确药物， 向正确的病人。”