Using AI and drug modeling to understand why genes and other medicines change clopidogrel response
Merging artificial intelligence (AI) and pharmacometrics to elucidate gene-drug interactions linked to clopidogrel responsiveness in genetically heterogeneous patients
This project uses AI, genetic data, and drug-response models to find why people with different genes or taking other medications respond differently to clopidogrel.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Puerto Rico Med Sciences NIH-funded |
| Lab location | 1 site (San Juan, United States) |
| Project ID | NIH-11376876 on NIH RePORTER |
What this research studies
Researchers will combine genome-wide genetic data from diverse patients with machine-learning methods to create a weighted genetic risk score linked to clopidogrel response. They will build semi-mechanistic population pharmacokinetic–pharmacodynamic (PK–PD) models to simulate how co-medication with cilostazol and other drugs alters clopidogrel exposure and effect. The team will integrate the AI-derived risk scores and PK–PD results to identify the most important factors driving low or high drug response. Findings are intended to guide more personalized dosing and safer antiplatelet therapy.
Who could benefit from this research
Good fit: Adults taking clopidogrel—especially those also on cilostazol or other interacting drugs—who can provide medication history and genetic/sample data.
Not a fit: People who are not taking clopidogrel or who are unwilling to provide genetic or medication information are unlikely to benefit directly from this project.
Why it matters
Potential benefit: If successful, this work could help tailor clopidogrel dosing to a person’s genes and other medicines, reducing treatment failure and side effects.
How similar studies have performed: Previous research has shown that CYP2C19 genetics affect clopidogrel response, but combining GWAS-derived weighted risk scores, AI, and PK–PD modeling for drug–drug interactions is a novel approach.
Where this research is happening
San Juan, United States
- University of Puerto Rico Med Sciences — San Juan, United States (Active)
Researchers
- Principal investigator: Duconge, Jorge — University of Puerto Rico Med Sciences
- Study coordinator: Duconge, Jorge
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.