Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis

研究特殊治疗无反应者和遗传学以预测类风湿关节炎的治疗反应

基本信息

批准号：
10430273
负责人：
TIANXI CAI
金额：
$ 19.29万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10430273
关键词：
Address Algorithms Antirheumatic Agents Arthritis Autoimmune Biological Clinic Clinical Clinical Data Code Cohort Studies Data Disease Disease-Modifying Second-Line Drugs Drug Costs Drug Prescriptions Electronic Health Record Epidemiology Future Gene Cluster Genetic Genetic Models Genetic Risk Genotype Heterogeneity Inflammation Inflammatory Inflammatory Arthritis Insurance Knowledge Link Machine Learning Measurement Medical Medical Genetics Medical Records Mission Modeling National Institute of Arthritis and Musculoskeletal and Skin Diseases Natural Language Processing Patient Care Patients Pharmaceutical Preparations Pharmacotherapy Phenotype Prediction of Response to Therapy Publishing Quality of life Research Rheumatoid Arthritis Risk-Taking Spondylarthropathies Subgroup TNF gene Testing Text Time Visit arthropathies base biobank clinical care clinical predictors cohort cost demographics design electronic data electronic structure epidemiological model epidemiology study follow-up genetic predictors genetic risk factor genomic data improved infection risk inhibitor inhibitor therapy innovation joint inflammation new therapeutic target non-drug novel novel strategies optimal treatments patient screening patient subsets predicting response response risk variant small molecule success supervised learning treatment response

项目摘要

PROJECT SUMMARY/ABSTRACT A major challenge in caring for patients with rheumatoid arthritis (RA) is determining the optimal therapy. Several effective biologic disease modifying anti-rheumatic drugs (bDMARDs) are available for RA, reflecting both advances in therapy, and the heterogeneity of RA; subsets of patients respond while others do not. Prior studies focused on patients with a good response to tumor necrosis factor inhibitor (TNFi), the most common bDMARD, with limited success in finding predictors that can be used in clinical care. This proposal seeks to address that gap in knowledge by taking a different direction. The objective of this study is to focus on exceptional bDMARD non-responders, defining and characterizing patients who have been on ≥3 classes of bDMARDs for RA. We will test whether data available in clinical electronic health record data (EHR) or genomic data can identify exceptional non-responders from TNFi responders. In Aim 1, we leverage data from an EHR cohort of ~16K RA patients to determine clinical factors associated with exceptional non-response using traditional epidemiologic approaches. As well, we will apply approaches using machine learning and topic modeling that will enable us to evaluate the predictiveness of a broader range of features. Examples of features include billing codes, prescriptions, and medical concepts extracted from text notes using natural language processing. In Aim 2, we will test whether RA genetic risk factors available in a subset of patients in Aim 1, and those of other inflammatory arthritides, e.g. axial spondyloarthropathy, can predict exceptional non- response to bDMARD therapy. As part of aim 2, we will also incorporate any predictive clinical factors identified in Aim 1 through the traditional or topic modeling approach. The overarching hypothesis is that the exceptional non-responders may be less “RA-like” than patients who respond to TNFi, with fewer RA genetic risk alleles and classic RA features from the narrative notes. This definition provides a new way to sub- phenotype RA, focusing on those that will have a poor response to therapy. This study is significant because a screen will be helpful not only in the clinic but can also identify patients to target for future studies of novel drug targets. This approach is innovative because it considers contemporary data where patients now have more “opportunity” to fail 3 classes of bDMARDs, where in the past there were only a limited number available. These data will be examined both using traditional epidemiologic models and newer approaches such as topic modeling that can integrate a broader range of data types. Finally, this proposal is designed to anticipate a time when patients will come for their visit with genetic data as part of their medical record.

项目摘要/摘要照顾类风湿关节炎（RA）的主要挑战是确定最佳治疗。几种有效修饰抗毒药（BDMARD）的生物学疾病可用于RA，反映两者的治疗进展和RA的异质性；患者的子集反应，而其他人则没有。事先的研究重点是对肿瘤坏死因子抑制剂（TNFI）的反应良好的患者，这是最常见的 BDMard，在寻找可用于临床护理的预测因子方面的成功有限。该提议试图通过迈出不同的方向来解决知识中的差距。这项研究的目的是关注特殊的bdmard非反应者，定义和表征≥3类的患者 RA的BDMARDS。我们将测试临床电子健康记录数据（EHR）中是否有数据或基因组数据可以从TNFI响应者那里识别出异常的非反应者。在AIM 1中，我们利用来自〜16K RA患者的EHR队列，以确定与异常无反应相关的临床因素使用传统的流行病学方法。同样，我们将使用机器学习和主题建模将使我们能够评估更广泛的功能的预测。示例的例子功能包括使用自然的账单代码，处方和从文本注释中提取的医学概念语言处理。在AIM 2中，我们将测试RA遗传危险因素是否在一部分患者中可用 AIM 1，以及其他炎症性关节化的AIM，例如轴向赞助性关节炎，可以预测异常的非 - 对BDMARD治疗的反应。作为AIM 2的一部分，我们还将结合任何预测性临床因素通过传统或主题建模方法在AIM 1中识别。总体假设是与对TNFI做出反应的患者相比，非凡的非反应者可能“像RA”，RA通用较少风险等位基因和经典的RA功能。该定义为子提供了一种新的方法表型RA，重点是对治疗反应不佳的表型。这项研究很重要，因为筛查不仅在诊所中会有所帮助，而且还可以识别患者以对未来的新药物进行研究目标。这种方法具有创新性，因为它考虑了当代数据，患者现在有更多 “机会”失败了3类BDMARD，过去只有有限的数字。这些数据将使用传统的流行病学模型和诸如主题之类的新方法进行检查可以集成更广泛的数据类型的建模。最后，该建议旨在预期作为病历的一部分，患者将访问遗传数据的时间。