Structure-guided design of partial agonists at an opioid receptor

Balazs R Varga1, Sarah M Bernhard1, Amal El Daibani1

  • 1Center for Clinical Pharmacology, Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO, USA.

Nature Communications
|March 14, 2025
PubMed

Insights

Researchers developed C6-Quino, a novel partial agonist for the delta opioid receptor (δOR). This non-addictive analgesic shows promise for chronic pain relief, avoiding seizures and other opioid side effects.

Area of Science:

  • Pharmacology
  • Neuroscience
  • Medicinal Chemistry

Background:

  • Chronic pain and opioid overdose deaths necessitate non-addictive analgesics with novel mechanisms.
  • The delta opioid receptor (δOR) is a promising target due to its lack of respiratory depression compared to the mu opioid receptor (µOR).
  • Previous δOR full agonists caused seizures, limiting clinical utility; partial agonists offer controlled activation but their mechanisms are unclear.

Purpose of the Study:

  • To develop and characterize a selective δOR partial agonist with improved safety and efficacy.
  • To elucidate the molecular mechanism underlying partial agonism at the δOR.

Main Methods:

  • Structure-based design of a bitopic ligand (C6-Quino).
  • Functional assays to assess G-protein and arrestin pathway activity.
  • Cryo-electron microscopy (cryo-EM) to determine receptor-ligand interactions.
  • In vivo studies in chronic pain models.

Main Results:

  • C6-Quino selectively acts as a δOR partial agonist.
  • Differential G-protein and arrestin pathway signaling was observed.
  • Cryo-EM confirmed interaction with the sodium binding pocket.
  • C6-Quino demonstrated oral analgesic activity in chronic pain models without causing seizures or µOR-related adverse effects.

Conclusions:

  • C6-Quino represents a new strategy for developing δOR-targeted analgesics.
  • This partial agonist offers pain relief without the adverse effects of full agonists or traditional opioids.
  • Provides a framework for optimizing signaling profiles of Class A GPCRs.

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