AI linking drug interactions and how your genes affect medicines
Machine learning drives translational research from drug interactions to pharmacogenetics
This project uses artificial intelligence to find links between drug–drug interactions and patients' genes to help reduce harmful medication effects, including for people treated for breast cancer.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ohio State University NIH-funded |
| Lab location | 1 site (Columbus, UNITED STATES) |
| Project ID | NIH-11289366 on NIH RePORTER |
What this research studies
Researchers are using machine learning and natural language processing to read millions of medical papers and find patterns where drugs interact and where genetic differences change drug effects. They will combine automated extraction with human-guided (active learning) review and logical knowledge rules to create a searchable knowledge base of gene–drug and drug–drug relationships. Part of the work will look specifically at whether genetic differences in CYP3A and CYP2C19 are linked to muscle problems after taking omeprazole. The goal is to generate practical gene–drug hypotheses that can guide safer prescribing and future clinical studies.
Who could benefit from this research
Good fit: People taking multiple medications—especially breast cancer patients on complex treatment regimens—or anyone who has experienced side effects from drug combinations (including possible muscle symptoms after omeprazole) are the kinds of patients this work aims to inform.
Not a fit: Patients who are on single, uncomplicated medications, who lack relevant genetic variants in CYP enzymes, or who need immediate treatment changes are unlikely to get direct, immediate benefit from this research.
Why it matters
Potential benefit: If successful, this work could help doctors predict and avoid harmful drug combinations by accounting for both drug interactions and patients' genetic differences, improving medication safety.
How similar studies have performed: Prior machine-learning analyses by the team identified many drug–drug interaction pairs and generated new pharmacogenetic hypotheses, but translating those findings into clinical practice remains an emerging area.
Where this research is happening
Columbus, UNITED STATES
- Ohio State University — Columbus, United States (Active)
Researchers
- Principal investigator: Li, Lang — Ohio State University
- Study coordinator: Li, Lang
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.