The number of deaths by opioid overdose has increased drastically recently. A growing number of research groups have gather efforts to discover and develop novel molecules, aiming the reduction of adverse effects of opioid analgesics, without sacrificing the therapeutic benefits. Chronic and post-operatory pain is mainly handled with μ-opioid receptor (MOR) agonists, such as morphine. However, the misuse of these substances is accompanied by undesirable overdose consequences, for instance respiratory suppression and tolerance. At molecular level, ligand binding at the orthosteric site of the receptor, may trigger two different signaling pathways: Gi-protein dependent mechanism or recruitment of β-arrestins 1 and 2. Previous experiments suggest that β-arrestins recruitment after MOR activation could be related to adverse effects. In contrast, the Gi-protein pathway is considered as the desirable signaling cascade.  Few molecules, e.g. herkinorin, promote Gi protein signaling pathway over β-arrestins recruitment, a process called biased signaling or functional selectivity. Thus, the improvement of efficient strategies for the discovery of MOR biased ligands on this and other G-protein coupled receptors are warranted. In previous work, we have generated a protein-ligand interaction fingerprint (PLIF) based on docking and molecular dynamics analysis of several biased ligands. This chemoinformatic tool is intended to detect biased ligands on large databases. Given the increase number of new biased ligands identified in the last few years, it is expected that enhanced descriptions of the functional selectivity phenomenon will emerge based on new ligand-based approaches such as pharmacophore modeling. In this work, we analyzed the main interactions of several recently reported MOR biased ligands. This allowed the generation of new pharmacophoric features which fine-tune the selection resulted from PLIF-filtered sets. Ligand interactions with the μ-opioid receptor were obtained by docking analysis for all compounds and molecular dynamics for the most potent ones. The results suggest that the new biased compounds have a similar interaction profile than the known biased ligands, according to the previous results obtained by PLIF analysis. Altogether the information obtained in this work provides a framework for future discovery and development of innovative MOR biased-ligands.

Pharmacophore generation of μ-opioid receptor biased-ligands: Uncovering structural features from molecular modeling analysis

2019 Spring National Meeting

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