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

> **NIH NIH R21** · BRIGHAM AND WOMEN'S HOSPITAL · 2022 · $192,876

## Abstract

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.

## Key facts

- **NIH application ID:** 10430273
- **Project number:** 5R21AR078339-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** TIANXI CAI
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $192,876
- **Award type:** 5
- **Project period:** 2021-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10430273

## Citation

> US National Institutes of Health, RePORTER application 10430273, Studying exceptional treatment non-responders and genetics to predict treatment response in rheumatoid arthritis (5R21AR078339-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10430273. Licensed CC0.

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