Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning Analysis.

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Opioid overdose deaths, particularly from fentanyl, have reached unprecedented levels.
• Naloxone, a μ-opioid receptor (mOR) antagonist, is a critical reversal agent.
• Understanding opioid binding kinetics, specifically residence time, is crucial for evaluating naloxone's effectiveness.

Purpose of the Study:

• To estimate the residence times (τ) of fentanyl and morphine analogs at the mOR.
• To compare simulated residence times with experimental kinetic and binding data.
• To develop a machine learning model for predicting the kinetic impact of fentanyl substituents on mOR interactions.

Main Methods:

• Metadynamics simulations were employed to calculate the residence times of 19 opioid analogs.
• Simulated residence times were compared against experimentally determined kinetic, dissociation, and naloxone inhibitory constants.
• A machine learning approach was developed to analyze structure-activity relationships based on mOR residue interactions.

Main Results:

• Residence times for 15 fentanyl and 4 morphine analogs were successfully estimated.
• Microscopic simulations revealed common binding mechanisms and determinants of dissociation kinetics for fentanyl analogs.
• A proof-of-concept machine learning model demonstrated the potential to analyze substituent effects on opioid kinetics.

Conclusions:

• Accurate estimation of opioid residence times is vital for assessing naloxone efficacy.
• Computational methods provide insights into opioid-mOR binding mechanisms and kinetics.
• Machine learning offers a generalizable approach for optimizing ligand residence times in drug discovery.