# An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2020 · $339,298

## Abstract

PROJECT SUMMARY
Most U.S. adults (68%) take dietary supplements and there is increasing evidence of drug-supplement
interactions (DSIs); In recent years, there has been increasing evidence supporting the role of DSs in ADRD in
preventing cognitive impairment but there is limited evidence and the sample sizes have been small. Real-
world data (RWD) especially the EHR contain detailed treatment and response information from patients and
could be used to detect the usage and effect of DSs, DSIs, which is more translational to clinical outcomes
(e.g., MCI to ADRD conversion). To the best of our knowledge, there is no investigation on DSs usage and
safety among patients in MCI and ADRD using EHR data. Our current parent award is focusing on the
development of a translational informatics framework to enable the discovery of drug-supplement interactions
(DSIs) by linking scientific evidence from the biomedical literature. To response to NOT-AG-20-008, this
administrative supplement application will complement our parent award in multiple aspects: (1) developing
novel and advanced data analytic methods for mining RWD in EHR, (2) identifying DSs usage information
among patients with ADRD, and (3) detecting safety and effect of DS among patients with ADRD from existing
EHR data. In our preliminary work, we have investigated the methods to identify DSs terms on EHR and
developed natural language processing (NLP) methods to identify use status of DSs. We will further our efforts
to collect a EHR dataset with DSs usage and AE-DSs signals from AD patients and develop innovative
informatics methods to extract such information. Our specific aims are: (1) identifying DSs usage among
patients with MCI and ADRD from existing EHR data; and (2) detecting the DSs safety signals and exploring
the effect of DSs use on the conversion from MCI to ADRD from existing EHR data. The successful completion
of this project will stimulate our further investigation on the role of DS use in patients with ADRD in a larger
scale involving EHR data from other healthcare institutions.

## Key facts

- **NIH application ID:** 10119590
- **Project number:** 3R01AT009457-04S1
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** RUI ZHANG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $339,298
- **Award type:** 3
- **Project period:** 2017-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10119590

## Citation

> US National Institutes of Health, RePORTER application 10119590, An Informatics Framework for Discovery and Ascertainment of Drug-Supplement Interactions (3R01AT009457-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10119590. Licensed CC0.

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