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

The nation's opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the mu-opioid receptor (mOR). Thus, knowledge of the opioid's residence time is important for assessing the effectiveness of naloxone. Here, we estimated the residence times (tau) of 15 fentanyl and 4 morphine analogs using metadynamics and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann et al. Clin. Pharmacol. Therapeut. 2022, 120, 1020-1232). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning approach to analyze the kinetic impact of fentanyl's substituents based on the interactions with mOR residues. This proof -of-concept approach is general; for example, it may be used to tune ligand residence times in computer-aided drug discovery.

Automated summary

In 2021, the nation witnessed a record high of opioid overdose deaths, primarily due to synthetic opioids such as fentanyl. Understanding the residence time of opioids is crucial for evaluating the effectiveness of naloxone, a FDA-approved reversal agent. This study estimated the residence times of various fentanyl and morphine analogs and compared them with recent measurements of opioid kinetic properties and naloxone inhibitory constants. Microscopic simulations provided insights into the binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs, leading to the development of a machine learning approach for analyzing the kinetic impact of fentanyl's substituents.