Identification of novel hits as highly prospective dual agonists for mu and kappa opioid receptors: an integrated in silico approach

Abstract

Opioid agonists are used clinically for the treatment of acute and chronic pain, however, their clinical use is limited due to the presence of undesired side effects. Dual agonists, simultaneously targeting mu and kappa opioid receptors, show fewer side effects than that of selective agonists. In the present work, 2D- and 3D- Quantitative Structure Activity Relationship studies were performed on a series of aminomorphinan derivatives as dual agonists, using a wide range of descriptors. The aim of the study was to identify the structural requirements for the activity of these compounds towards mu and kappa opioid receptors and using the models, with best external predictability, for predicting the activities of new hits obtained from shape based virtual screening of drug like compounds from ZINC database. Genetic algorithm-based GFA and G/PLS techniques were used to derive the 2D-QSAR models. Common feature-based pharmacophore was used for aligning the compounds for 3D-QSAR. All the models were validated both internally and externally using statistical metrics. The coverage estimation of the models was carried out with applicability domain calculation. Six enriched hits were identified as novel prospective dual agonist based on good Blood Brain Barrier permeability and their activities towards mu and kappa opioid receptors, predicted by the best QSAR models. The known potent dual agonist, cyclorphan, and two highly prospective dual agonists were docked to both the receptors and binding free energies were calculated using MMGBSA. Molecular dynamics studies were performed on the docked complexes with both the receptors to establish stability of the complexes.

Keywords:

• opioids
• dual agonists
• quantitative structure–activity relationship
• virtual screening
• docking
• MD simulations

Acknowledgement

Authors thank Council for Scientific and Industrial Research (CSIR), New Delhi, for providing financial grant. NG (Emeritus Scientist, CSIR) thanks CSIR for ES project grant. IB thanks CSIR for project fellowship from CSIR network project GENESIS. PV thanks CSIR for fellowship.

Disclosure statement

No potential conflict of interest was reported by the authors.