# Examination of fentanyl-induced insensitivity to risk of punishment during decision making and the potential use of methadone and buprenorphine in attenuating risk-taking deficits

> **NIH NIH R21** · UNIVERSITY OF TEXAS AT AUSTIN · 2022 · $235,139

## Abstract

Project Summary:
Poor decision making and elevated risk taking can significantly contribute to continued drug use and/or
promote relapse after chronic drug exposure. These behavioral impairments are particularly evident in
individuals with opioid use disorder (OUD), who exhibit pronounced elevations in risk taking both in the
laboratory and in real-world settings. The majority of preclinical research to date has focused on the
mechanisms by which hypersensitivity to reward promotes poor decision making and continued opioid use;
however, we have only a rudimentary understanding of the mechanisms by which opioid-induced changes in
sensitivity to risk of punishment contribute to such aberrant and maladaptive behavior. The goal of this R21 is
to elucidate the causal relationship between OUD and increased risk taking and to identify mechanisms by
which opioid-induced elevations in risk taking can be reduced. This information will provide critical
preliminary data for a R01 application designed to understand the neural mechanisms underlying opioid’s
impact on risk taking. To achieve this goal, we will use a rat model of risk taking (the “Risky Decision-Making
Task”) that recapitulates real-life decision making in that it incorporates both reward and risk of punishment.
Prior work using this model showed chronic exposure to cocaine causes lasting increases in punished risk
taking in male and female rats. More recent preliminary data demonstrate a similar causal relationship between
self-administration of the synthetic opioid fentanyl and elevated risk taking in males. The proposed experiments
will build on these findings and test the central hypothesis that insensitivity to risk of punishment during
decision making develops early in opioid use and persists into long-term abstinence. Our secondary
hypothesis is that, due to their distinct pharmacological properties, chronic administration of long-acting mu-
opioid receptor agonists will reduce fentanyl-induced elevations in risk taking via restoration of sensitivity to risk
of punishment. These hypotheses will be tested using a behavioral pharmacological approach. Aim 1 will
determine the trajectory of fentanyl-induced elevations in risk taking by monitoring changes in risk taking during
fentanyl use, withdrawal and protracted abstinence. This Aim will also allow us to determine whether, like
males, fentanyl causes elevations in risk taking in females. Aim 2 will determine whether chronic administration
of methadone and buprenorphine, long-acting mu-opioid receptor agonists whose pharmacological properties
not only differ from illicit opioids but also differ from each other, reduce fentanyl-induced increases in risk taking
via restoration of sensitivity to risk of punishment. Collectively, these findings will provide insight into the impact
of OUD on sensitivity to risk of punishment during decision making and reveal mechanisms that could be
leveraged to ameliorate risk-taking deficits and promote long-l...

## Key facts

- **NIH application ID:** 10373348
- **Project number:** 1R21DA053462-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Caitlin Anne Orsini
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $235,139
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10373348

## Citation

> US National Institutes of Health, RePORTER application 10373348, Examination of fentanyl-induced insensitivity to risk of punishment during decision making and the potential use of methadone and buprenorphine in attenuating risk-taking deficits (1R21DA053462-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10373348. Licensed CC0.

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