Molecular docking and dynamics guided approach to identify potential anti-inflammatory molecules as NRF2 activator to protect against drug-induced liver injury (DILI): a computational study

Abstract

Inflammation and oxidative stress can contribute to the etiology of metabolic and chronic illnesses. The ability to prevent oxidative stress induced diseases such as cancer, cardiovascular disease, Alzheimer’s disease, and others has been the subject of global research. Drug-induced liver injury (DILI) pathogenesis can be either due to oxidative stress or inflammatory response elicited by the drug, its metabolite, or herbal supplements. Our present research uses computational studies to identify a molecule with anti-inflammatory properties that can operate as an NRF2 activator. Acquiring and preparing the KEAP1-NRF2 Protein (PDB: 4L7D) with Schrodinger Suite was followed by developing a ligand library (Anti-inflammatory library downloaded from ChemDiv database). Molecular docking studies were performed in HTVS, SP, and XP modes, respectively. Based on the docking score, interaction, ADMET and binding free energy, the top ten compounds were selected and subjected to induced-fit docking (IFD) analysis for further study. The top three molecules were chosen for a molecular dynamics (MD) simulation study. Using the Desmond module of the Schrodinger Suite, the stability of the protein-ligand complex and protein-ligand contact throughout 100ns were evaluated during the MD simulation study. In our study, it was observed that three compounds exhibit exceptional stability and retain the essential interaction throughout the studies, and it is anticipated that these compounds may act as effective NRF2 activators. Further in vitro and in vivo assessments can be conducted to determine its potential to prevent DILI via acting as an NRF2 activator for future drug development.

Communicated by Ramaswamy H. Sarma

Keywords:

• NRF2
• anti-inflammatory
• DILI
• molecular docking
• molecular dynamics
• ADMET

Authors’ contributions

Ajay Mili contributed to conceptualization, in silico modeling, analysis and interpretation of data, original draft preparation. Subham Das contributed to conceptualization, in silico modeling, interpretation of data, data curation, manuscript editing and supervision. Krishnadas Nandakumar contributed to interpretation of data. Richard Lobo contributed to interpretation of data, supervision.

Acknowledgments

Author Ajay Mili and Subham Das are thankful to the Manipal Academy of Higher Education (MAHE) for providing Dr. T.M.A. Pai Doctoral Fellowship. The authors also acknowledge with thanks to Manipal College of Pharmaceutical Sciences (MCOPS) and the Manipal Academy of Higher Education (MAHE) for providing facilities for this work. The authors are grateful to Manipal-Schrödinger Centre for Molecular Simulations. The authors are also thankful to BioRender.com and ChemDraw.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported by Intramural Fund by the Manipal Academy of Higher Education (MAHE).