# Development of novel radioligand for PET imaging targeting the orexin 1 receptor

> **NIH NIH R03** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $193,923

## Abstract

Project Summary (Abstract)
Addiction is a chronic relapsing disorder characterized by compulsive drug-seeking which persists despite
adverse consequences [WHO 1992; American Psychiatric Association 2013]. in the past few years, misuse of
opioids (both prescribed and illegal) increased considerably. However, there are very few effective and approved
pharmacotherapies available [Pierce et al. 2012]. Recent research activities in the field of orexin biology have
demonstrated the significance of these receptors in the domain of addiction. The development of a PET imaging
tracer would permit detection and quantification of orexin receptors in vivo thereby accelerating research and
increasing our understanding of orexin functions in the domain of drug addiction.
The specific aims of this project, will focus on the synthesis, evaluation and characterization of potential OX1R
antagonists (Aim 1). Perform In vivo quantification of the binding potential (BP) of the candidate OX1R radiotracer
in baboons (Aim 2).
If successful, this project will provide “proof of concept” imaging data and a lead PET imaging tracer that can be
further examined in human as part of a future R01 application.

## Key facts

- **NIH application ID:** 10784351
- **Project number:** 1R03DA059714-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Isabelle Kovacic
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $193,923
- **Award type:** 1
- **Project period:** 2024-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10784351

## Citation

> US National Institutes of Health, RePORTER application 10784351, Development of novel radioligand for PET imaging targeting the orexin 1 receptor (1R03DA059714-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10784351. Licensed CC0.

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