Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists.

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Opioids are vital analgesics but cause dangerous side effects like addiction and tolerance.
• Targeting the mu-opioid receptor (MOR) in inflamed tissues specifically could mitigate these risks.

Purpose of the Study:

• To present a multi-objective optimal affinity approach for discovering pH-specific MOR ligands.
• To develop and implement a virtual drug discovery pipeline for identifying safer opioid candidates.

Main Methods:

• Utilized a virtual drug discovery pipeline combining protonation state-dependent preparation and high-throughput virtual screening.
• Employed a differential docking pipeline for multi-objective affinity optimization.
• Screened a library of over 50,000 ligands.

Main Results:

• Identified a morphine-like opioid derivative with enhanced binding affinity to MOR at acidic pH versus neutral pH.
• Confirmed increased MOR specificity at acidic pH for NFEPP and novel fluorinated fentanyl/morphine derivatives compared to parent compounds.
• Discovered novel molecules with predicted pH-specific MOR binding affinities.

Conclusions:

• The presented differential docking pipeline enables large-scale identification of safer, more specific drug candidates.
• This approach holds promise for developing targeted opioid therapeutics with reduced adverse effects.