Prediction of Novel TRPV1 Antagonist: A Combination of 3D-QSAR, Molecular Docking, MD Simulations and ADMET Prediction

Document Type : Regular Article

Authors

1 LIMOME Laboratory. Faculty of Sciences Dhar El Mahraz. Sidi Mohamed Ben Abdellah University. Fez. Morocco

2 Laboratory of Processes. Materials and Environment (LPME). Faculty of Science and Technology. Sidi Mohamed Ben Abdellah University. Fez. Morocco

3 Laboratory of Separation Technology. Lappeenranta University of Technology. Lappeenranta. Finland

4 MCNS Laboratory. Faculty of Sciences. Moulay Ismail University. Meknes. Morocco

10.22036/pcr.2022.334832.2059

Abstract

TRPV1 are ion channels capable of sensing different stimuli, integrating and translating them into signal language. TRPV1 antagonists have attracted much attention for the treatment of various diseases related to the management of pain physiology and neurogenic inflammation, such as anti-inflammatory, antineoplastic, and anti-nociceptive effects. Here, we performed a 3D-QSAR, molecular docking, and MD simulations on a novel series of indole triazole derivatives as antagonists of TRPV1. The aim was to design novel potent TRPV1 antagonists with strong inhibitory activity. The significant 3D-QSAR models showed a good correlation between experimental and predicted activity. CoMSIA was used to construct the best 3D QSAR model using the PLS method showing correlative and predictive ability (R2=0.985. Q2=0.788. SEE=0.105). Electrostatic, steric, and hydrophobic fields play an important role in the variation of biological activity. Molecular Docking analysis was used to validate the 3D-QSAR methods and to explain the binding site and interactions between the ligands and the receptor. Based on these results, a novel series of compounds were predicted. The pharmacokinetic properties of predicted compounds were analyzed by drug-likeness and ADMET prediction. The best-docked compounds were subjected to MD simulation to affirm the final candidate molecules' conformational to confirm their dynamic behavior and stability.

Graphical Abstract

Prediction of Novel TRPV1 Antagonist: A Combination of 3D-QSAR, Molecular Docking, MD Simulations and ADMET Prediction

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History

  • Receive Date: 20 March 2022
  • Revise Date: 02 June 2022
  • Accept Date: 29 June 2022

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