# DEVELOPMENT OF BASELINE GENE EXPRESSION-BASED RESPONSE PREDICTOR FOR ANTI-TNFA THERAPY IN PATIENTS WITH INFLAMMATORY BOWEL DISEASES

> **NIH NIH R43** · INFLAMMATIX, INC. · 2021 · $251,371

## Abstract

ABSTRACT
Anti-TNFα therapies are widely used to treat autoimmune diseases, including inflammatory bowel disease (IBD),
a chronic inflammation of the digestive system which presents both an economic burden as well as prominent
disability and morbidity. These therapies, however, are largely ineffective in IBD patients, with primary non-
response rates varying from 10-30%, and with 23-46% of patients losing response over time. They also lead to
adverse health outcomes including increased risk of infections (most notably reactivation of tuberculosis), liver
problems, arthritis, and lymphoma. Due to biologic switching where multiple drugs are tried in sequence until an
effect is observed, they often lead to overall increased healthcare costs.
There is no clear predictive factor of response or loss of response to anti-TNFα therapies, with current research
focusing on efficacious dosage and inhibitor selection. While numerous predictors of response have been
identified in studies, none are robust enough to impact clinical practice. Overcoming this gap in the administration
of anti-TNFα therapies will be an important next step in improving their utility and reducing overall healthcare
costs, morbidity, and mortality. This project proposes a multicohort meta-analysis and machine learning
approach to discover and validate a prognostic gene signature that can differentiate anti-TNFα responders
from non-responders in IBD patients, allowing for improved decision-making and positive health outcomes.
Despite the broad biological, clinical, and technical heterogeneity inherent in such a task, the Inflammatix
analytical framework was able to identify significant differentially expressed genes that discriminate anti-TNFα
responders from non-responders (mean discovery AUC of ~0.83 and mean validation AUC of ~0.81). Supported
by these preliminary results, this project will (1) discover a robust, clinically translatable multi-gene prognostic
signature using publicly available datasets, (2) generate independent gene expression data from retrospective
cohorts of mucosal biopsy samples (n=150) to validate the signature (target AUC > ~0.8), and (3) use our
proprietary Inflammatix machine learning (IML) platform to train and validate a plethora of machine learning
algorithms to develop a robust, generalizable classifier (target AUC ~ 0.8 - 0.9) that is ready for clinical validation
via prospective studies.
Our novel prognostic signature will transform the clinical paradigm in the use and administration of anti-TNFα
therapies, maximizing treatment benefit while minimizing patient exposure to potentially harmful side effects and
adverse health outcomes, consequently reducing financial burden for the healthcare system and patient.

## Key facts

- **NIH application ID:** 10138841
- **Project number:** 1R43DK127578-01
- **Recipient organization:** INFLAMMATIX, INC.
- **Principal Investigator:** Timothy E Sweeney
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $251,371
- **Award type:** 1
- **Project period:** 2021-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10138841

## Citation

> US National Institutes of Health, RePORTER application 10138841, DEVELOPMENT OF BASELINE GENE EXPRESSION-BASED RESPONSE PREDICTOR FOR ANTI-TNFA THERAPY IN PATIENTS WITH INFLAMMATORY BOWEL DISEASES (1R43DK127578-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10138841. Licensed CC0.

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