# The Ohio Valley Node of the Clinical Trials Network

> **NIH NIH UG1** · UNIVERSITY OF CINCINNATI · 2020 · $411,680

## Abstract

Drug repurposing for cocaine use disorder (CUD) using a combined strategy of
 artificial intelligence (AI)-based prediction and retrospective clinical
corroboration
PROJECT SUMMARY/ABSTRACT
Aim 1: Identify repurposed anti-CUD drug candidates using an AI-powered drug discovery approach
Leveraging the unique and large-scale drug and disease phenotypic relationship knowledge bases that we
have built and vast amounts of publicly available genetics and genomics data, we propose to develop an AI-
powered drug repurposing system to identify anti-CUD drug candidates from all approved drugs. The output
from Aim 1 is a list of promising repurposed anti-CUD candidates with interpretable mechanisms of action.
Aim 2: Fine tune repurposed candidates by predicting their blood-brain barrier (BBB) permeability
We will determine the BBB permeability of repurposed anti-CUD candidates identified in Aim 1 using a novel
machine learning predictive model that we built, which applies to both small and macro-molecules that
penetrate the human BBB through various biological mechanisms. The output from Aim 2 is a refined list of
promising repurposed anti-CUD candidates with interpretable mechanisms of action and high BBB permeability
in humans.
Aim 3: Evaluate repurposed candidates using patient electronic health records (EHRs)
We will evaluate repurposed anti-CUD candidates for their efficacy in ‘real world’ patients using patient
electronic health record (EHR) data. Currently we have access to EHR data of 73.9 million unique patients
including 223,460 patients diagnosed with CUD and 66,050 patients with a cocaine-positive urine drug screen.
We will perform large-scale case-control studies to evaluate the efficacy of repurposed candidates in reducing
risk, mortality, relapse, ER visits or other adverse effects of CUD patients. The output from Aim 3 is a further
refined list of promising repurposed anti-CUD candidates with interpretable mechanisms of action, high BBB
permeability in humans, and potential clinical efficacy in ‘real-world’ population. We will closely work with CTN
and delineate the most expedient pathway to FDA approval for the identified candidates. We anticipate that
these findings can be expeditiously translated into clinical trials in the CTN to benefit CUD patients.

## Key facts

- **NIH application ID:** 10231877
- **Project number:** 3UG1DA013732-21S2
- **Recipient organization:** UNIVERSITY OF CINCINNATI
- **Principal Investigator:** T John WINHUSEN
- **Activity code:** UG1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $411,680
- **Award type:** 3
- **Project period:** 2020-09-01 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10231877

## Citation

> US National Institutes of Health, RePORTER application 10231877, The Ohio Valley Node of the Clinical Trials Network (3UG1DA013732-21S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10231877. Licensed CC0.

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