# Data-driven drug discovery: investigating the molecular mechanisms of safety and efficacy

> **NIH NIH R35** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $13,271

## Abstract

Summary
Up to 30% of adverse drug events can be attributed to drug-drug interactions in metabolism or mechanism of
action. The study of drug-drug-protein interactions that drive these adverse effects, however, remains an
underexplored area of research. This research project aims to offer one of the first comprehensive insights into
the effect of genetic variants on adverse drug events, with a particular focus on drug-drug interactions. Through
the utilization of large datasets and computational methods, we aim to develop a data-driven approach to identify
genetic variations of target protein and metabolizing proteins associated with the development of adverse drug
events. Expanding from our current work, we will identify genetic variants affecting the proteins of interest,
participants in the study who have experienced the adverse drug events following use of the drugs and conduct
association studies to identify any significant variants.

## Key facts

- **NIH application ID:** 10393864
- **Project number:** 3R35GM131905-03S1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Nicholas P Tatonetti
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $13,271
- **Award type:** 3
- **Project period:** 2019-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10393864

## Citation

> US National Institutes of Health, RePORTER application 10393864, Data-driven drug discovery: investigating the molecular mechanisms of safety and efficacy (3R35GM131905-03S1). Retrieved via AI Analytics 2026-06-05 from https://api.ai-analytics.org/grant/nih/10393864. Licensed CC0.

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