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

> **NIH NIH R35** · CEDARS-SINAI MEDICAL CENTER · 2022 · $195,510

## Abstract

Project Summary
I am proposing a precision pharmacology and pharmacovigilance research program that couples observational
data analysis with prospective laboratory experiments to advance drug safety and efficacy. Our ability to collect
and store massive amounts of molecular, clinical, and behavioral data has the potential to fundamentally
transform translational medicine. It is not difficult to imagine a world where our devices and doctors work
together seamlessly to provide personalized guidance and treatment to maximize our health and longevity.
And that, in turn, the data generated by these encounters be collected, organized, and analyzed by biomedical
researchers to invent the next generation of interventions. However, there are significant challenges prohibiting
meaningful progress toward this vision. I have identified four that I plan to address:
 (1) There is a dearth of pharmacological knowledge for many subpopulations, most notably minorities
 (non-Whites), women, and children;
 (2) Observational data, from what is captured by devices to what is collected in medical records, is of
 dubious validity and value;
 (3) There is a limited understanding of the molecular mechanisms of drug reactions and drug-drug
interactions;
 (4) There is no clear method of meaningfully sharing patient data while preserving privacy.
There is no single solution that will solve all of these challenges. Each will require a unique combination of data
science, informatics, and experiments. In the previously funded project, we made significant advancements in
the characterization of adverse drug reactions and drug-drug interactions, the molecular modeling of
pharmacological pathways, and the application of statistical data mining to electronic health records. I
accomplished this by leveraging distinct data sources against each other to focus attention on only those
hypotheses that repeatedly replicate under a variety of conditions. I then validated those hypotheses
experimentally using animal and cellular models. Challenges 2 and 3 are natural extensions of this previous
work, where I will address how to use data for purposes other than what it was collected for (secondary use)
and develop new systems models to explain the physiological effects of drug-gene and drug-drug interactions.
Challenges 1 and 4 represent new avenues of research where I will address the challenges of pharmacological
studies in diverse populations and the increasingly important issue of balancing openness and transparency in
science with patients' rights to privacy. The challenges laid out above are significant and, likely, will not be
solved in within five years. However, the pursuit of these challenges will generate new knowledge that has the
potential to significantly improve drug design, advance precision medicine, and guide drug safety governance.

## Key facts

- **NIH application ID:** 10833947
- **Project number:** 7R35GM131905-05
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Nicholas P Tatonetti
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $195,510
- **Award type:** 7
- **Project period:** 2019-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833947

## Citation

> US National Institutes of Health, RePORTER application 10833947, Data-driven drug discovery: investigating the molecular mechanisms of safety and efficacy (7R35GM131905-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10833947. Licensed CC0.

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