# Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $340,616

## Abstract

PROJECT SUMMARY
The growing expansion of the approved multiple sclerosis (MS) disease-modifying treatments (DMTs) and the
variable responses to MS treatment have created an unmet medical need to provide individually tailored
therapy. Efforts to bring precision medicine to provide individualized MS treatment selection have been
impeded by our limited understanding of the factors that determine treatment response. While genomics hold
the promise for closing this knowledge gap, the insufficient number of patients with detailed treatment
response data and the modest effect size of genetic variants that influence treatment responses are the main
limiting factors in pharmacogenomics studies. As electronic health records (EHR) become widely adopted and
increasingly standardized and as we implement sophisticated computational and statistical methods to harness
the EHR data, EHR systems can become cost-effective platforms to perform large-scale treatment response
studies in real-life settings. Our team with a history of productive collaborations and diverse expertise (led by PI
Dr. Xia) previously developed robust algorithms to identify 5,495 MS patients from the Partners HealthCare
EHR systems and then model MS disease activity in these patients using EHR data. The Partners EHR system
contains longitudinal clinical information on thousands of MS patients from two large academic medical centers
and is linked to a well-characterized MS patient research registry and biobanks with existing genomics data.
For the proposed study, we will test the hypothesis that meaningful phenotypes of MS disease activity can be
extracted from EHR data to inform treatment response, and that additional common genetic variants exist in
the population and can predict therapeutic response in MS when combined with clinical features derived from
EHR data. The proposed study has three aims with the overall goal to produce a computational and analytic
approach capable of identifying MS disease activity in relation to treatment history using EHR data and
integrate with genomics profile to develop a predictive model of therapeutic response to commonly prescribed
DMTs in this cohort of 5,495 MS patients, including injectable (interferon-β, glatiramer acetate) and oral
(fingolimod, dimethyl fumarate) options. Specifically, we will (1) leverage narrative electronic health records
data (e.g., clinical notes, radiology reports) and natural language processing (NLP) to ascertain individualized
response to DMTs (n=600 for each DMT); (2) Identify clinical features from electronic health record data (e.g.,
diagnoses, exposures) that predict response to DMTs using a systematic phenome-wide approach; (3)
Develop and test a comprehensive predictive model of individualized response to DMTs that incorporates
clinical and genetic predictors. This research has the potential impact to be transformative by contributing to a
major knowledge gap regarding the factors that influence treatment response and ...

## Key facts

- **NIH application ID:** 9963407
- **Project number:** 5R01NS098023-05
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Zongqi Xia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $340,616
- **Award type:** 5
- **Project period:** 2016-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963407

## Citation

> US National Institutes of Health, RePORTER application 9963407, Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response (5R01NS098023-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9963407. Licensed CC0.

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