# Omics analysis of HIV during synthetic opioid exposure

> **NIH NIH R61** · UNIVERSITY OF CINCINNATI · 2021 · $741,485

## Abstract

Abstract
The US is in the midst of a major opioid epidemic largely attributed to synthetic opioids. For example,
fentanyl is 50-100 times more potent than heroin and is involved in >60% of overdoses nationwide and >90%
of overdoses in Ohio, although this is almost certainly an underestimate of recreational use. Individuals with
opioid use disorder are at significant risk for transmission of HIV, and new cases of HIV are on the rise in the
Midwest and at our institution. Opioid receptors are expressed in a variety of cell types that are susceptible
to HIV infection. Commonly abused opioids promote HIV replication and virus-mediated pathology. Thus,
translational research on virus-opioid interactions is essential for optimized treatment and limiting viral
reactivation. Important knowledge regarding how synthetic opioids influence HIV latency and reactivation is
absent from the available literature. To fill this critical gap and institute a major shift forward in our
understanding of this epidemic, we propose a series of complementary in vivo studies to directly evaluate the
impact of synthetic opioids on markers of HIV latency/reactivation, viral diversity, transcription factor
expression, microRNA expression, and cell signaling pathways.

## Key facts

- **NIH application ID:** 10073492
- **Project number:** 5R61DA048439-03
- **Recipient organization:** UNIVERSITY OF CINCINNATI
- **Principal Investigator:** JASON T BLACKARD
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $741,485
- **Award type:** 5
- **Project period:** 2019-03-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10073492

## Citation

> US National Institutes of Health, RePORTER application 10073492, Omics analysis of HIV during synthetic opioid exposure (5R61DA048439-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10073492. Licensed CC0.

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