# Machine learning drives translational research from drug interactions to pharmacogenetics

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2024 · $619,022

## Abstract

Summary
Drug-drug interactions (DDIs) and pharmacogenetics (PG) are leading causes of adverse drug events (ADEs),
with one in four patients experiencing ADEs attributable to DDIs or PG. However, despite the intrinsic
connection of their pharmacological mechanisms, DDI and PG are often studied separately. There is a
significant need for more efficient and effective translational from DDI to PG research, and newly developed
machine-learning (ML) and artificial-intelligence (AI) methods have made such research feasible. In our recent
DDI knowledge-discovery study of 25 million PubMed abstracts, we used ML and natural-language-processing
analyses for the first time to identify 986 DDI pairs with overlapping pharmacokinetic mechanisms and clinical
evidence, from which we generated 137 new PG hypotheses regarding CYP2D6 and CYP3A. In this grant
proposal, we will develop novel ML methods, including active learning that will allow human annotator
involvement and knowledge base reasoning that relies on logical rules to represent pharmacological
mechanisms. This proposal has three aims: (1) to develop an active-learning approach to perform DDI and PG
information retrieval analysis from the literature; (2) to develop a joint information-extraction and knowledge-
base-reasoning approach to perform DDI and PG information extraction analysis from the literature; and (3) (a)
to examine whether CYP3A/CYP2C19 genetic polymorphisms are associated with omeprazole-induced
myopathy, and (b) to develop a prioritization scheme to examine new PG hypotheses generated from the
literature-based discovery analyses from Aims 1 and 2 using Vanderbilt University’s BioVU biobank. These PG
findings will provide a valuable resource for the wider scientific community for potential prospective studies and
contribute significantly to the improvement of precision medicine and clinical care.

## Key facts

- **NIH application ID:** 10834150
- **Project number:** 5R01LM014199-02
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** You Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $619,022
- **Award type:** 5
- **Project period:** 2023-05-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10834150

## Citation

> US National Institutes of Health, RePORTER application 10834150, Machine learning drives translational research from drug interactions to pharmacogenetics (5R01LM014199-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10834150. Licensed CC0.

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