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

• 批准号: 10301407
• 负责人: TIANXI CAI
• 金额: $ 25.15万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10301407
• 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/antagonist; 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

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.

项目总结/文摘