# Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $407,317

## Abstract

ABSTRACT
The parent R01 project, funded in 2016, aims to address the unmet medical need of providing individually
tailored therapy for multiple sclerosis (MS), given the growing expansion of the approved MS disease-
modifying treatments (DMTs). Specifically, the parent project aims to produce an 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 DMT. Late-onset
Alzheimer’s disease (AD) is the most common cause of dementia and neurological disability in the aging
population. People with AD experience variable trajectories of cognitive and functional decline, resembling the
variable trajectories of neurological decline in people with MS. While the choices of DMTs for AD are few and
while more promising options are only beginning to emerge, real-world data such as EHR data are gaining
importance in the drug approval process. For the supplemental project, we propose to deploy the analytical
approaches that we developed for MS to ascertain individualized disease trajectory and treatment response to
existing drugs in people with late-onset AD, using EHR data and linked registry data. We will build on existing
collaboration with Dr. Tianxi Cai (Harvard, co-I on the parent R01) who is a leading expert on EHR data
analysis and new collaboration with Dr. Howard Aizenstein (co-I, University of Pittsburgh) who provide domain
expertise in AD and cognitive aging as well as Dr. Jonathan Silverstein (co-I, University of Pittsburgh) who will
provide critical support for the EHR data generation. For data source, we will leverage the growing EHR data
warehouse at the University of Pittsburgh Medical Center, which is linked to a well-established AD cohort
research registry. Extending the parent R01 project, we will test the hypothesis that meaningful phenotypes of
AD disease trajectory can be extracted from EHR data to inform treatment response in the supplemental
project. We will accomplish two supplemental aims: (1) leverage EHR data to ascertain the individualized
trajectory of neurological impairment in AD; (2) predict response to existing AD drugs using EHR data. The
objective of the supplemental project is to identify individualized AD disease trajectory in relation to treatment
history using EHR data and enable future assessment of AD drug efficacy using real-world data and
pharmacogenomics.

## Key facts

- **NIH application ID:** 10123647
- **Project number:** 3R01NS098023-05S1
- **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:** $407,317
- **Award type:** 3
- **Project period:** 2016-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10123647

## Citation

> US National Institutes of Health, RePORTER application 10123647, Integrating EHR and Genomics to Predict Multiple Sclerosis Drug Response (3R01NS098023-05S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10123647. Licensed CC0.

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