Design and Prediction of ADME/Tox Properties of Novel Magnolol Derivatives
as Anticancer Agents for NSCLC Using 3D-QSAR, Molecular Docking,
MOLCAD and MM-GBSA Studies

Ossama      Daoui; Souad      Elkhattabi; Samir      Chtita
Abstract

Introduction: In this work, we used several molecular modeling techniques to design new molecules for the treatment of non-small cell lung cancer (NSCLC).
Methods: For this purpose, we applied 3D-QSAR, molecular docking, MOLCAD, ADMET, and MMGBSA studies to a series of 51 natural derivatives of magnolol.
Results: The developed models showed excellent statistical results (R² = 0.90; Q² = 0.672; R²pred = 0.86) for CoMFA and (R² = 0.82; Q² = 0.58; R² _pred = 0.78) CoMSIA. The design of eleven new molecules was based on predictions derived from the 3D-QSAR model contour maps, molecular docking and MolCAD analyses. In silico drug-like and ADMET properties studies led to the selection of four new molecules designed as potential agents for NSCLC therapy. Molecular docking and MM-GBSA simulations of proposed structures with EGFR-TKD (PDB code: 1M17) showed that ligands X10 and 30 attained better stability in the 1M17 protein pocket compared to the Erlotinib ligand used as a reference.
Conclusion: Incorporating all the molecular modelling techniques used in this work is conducive to the design of new molecules derived from the 3-(4-aminobipyridin-1-yl)methyl structure of magnolol, a candidate for drug design for the treatment of non-small cell lung cancer. Therefore, the molecular structures (X10 and 30) can be proposed as a key to designing new drugs against NSCLC.
Keywords: Magnolol, 3D-QSAR, NSCLC, molecular docking, MOLCAD, MM-GBSA.
Graphical Abstract

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Cite As
Letters in Drug Design & Discovery

Design and Prediction of ADME/Tox Properties of Novel Magnolol Derivatives as Anticancer Agents for NSCLC Using 3D-QSAR, Molecular Docking, MOLCAD and MM-GBSA Studies

Abstract

Graphical Abstract
