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bioRxiv · 10.1101/2023.03.06.531338

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning

Mahinthichaichan, P.·Liu, R.·Vo, Q. N.·Ellis, C. R.·Stavitskaya, L.·Shen, J.
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Abstract

The nations 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 -opioid receptor (mOR). Thus, knowledge of opioids residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times 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, Li et al, Clin. Pharmacol. Therapeut. 2022). 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 (ML) approach to analyze the kinetic impact of fentanyls 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 computeraided drug discovery. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/531338v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15e4093org.highwire.dtl.DTLVardef@e17398org.highwire.dtl.DTLVardef@16c2aaaorg.highwire.dtl.DTLVardef@6527a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Mahinthichaichan, P., Liu, R., Vo, Q. N., Ellis, C. R., Stavitskaya, L., Shen, J.. 2023-03-07. Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning. https://doi.org/10.1101/2023.03.06.531338

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