연구성과 홍보

Front Chem. 2025 Apr 16:13:1527008.

Title : Computational identification of potential natural terpenoid inhibitors of MDM2 for breast cancer therapy: molecular docking, molecular dynamics simulation, and ADMET analysis

Authors : Eva Azme#1, Md Mahmudul Hasan#1, Md Liakot Ali#1, Rashedul Alam#2,3, Neamul Hoque1, Fabiha Noushin1, Mohammed Fazlul Kabir2, Ashraful Islam1, Tanzina Sharmin Nipun1, S M Moazzem Hossen1*, Hea-Jong Chung4,5*

Affiliations :

1Department of Pharmacy, Faculty of Biological Sciences, University of Chittagong, Chittagong, Bangladesh.

2Department of Biotechnology, Harrisburg University of Science and Technology, Harrisburg, PA, United States.

3Department of Biochemistry and Molecular Biology, Hollings Cancer Center, Medical University of South Carolina, Charleston, SC, United States.

4Honam Regional Center, Korea Basic Science Institute (KBSI), Gwangju, Republic of Korea.

5Department of Bio-Analysis Science, University of Science and Technology, Daejeon, Republic of Korea.

DOI: 10.3389/fchem.2025.1527008.

Abstract :

Background: Breast cancer (BC) remains a leading cause of cancer-related mortality in women. The oncoprotein MDM2 negatively regulates the tumor suppressor p53, and its overexpression in BC promotes tumor progression and resistance to therapy. Targeting the MDM2-p53 interaction represents a promising therapeutic approach. However, many existing MDM2 inhibitors suffer from poor pharmacokinetics and off-target toxicity, necessitating the discovery of novel, more selective alternatives. This study aims to identify natural terpenoid compounds with potent MDM2 inhibitory potential through computational approaches.

Methods: A library of 398 natural terpenoids was sourced from the NPACT database and filtered based on Lipinski's Rule of Five. A two-stage docking strategy was applied: 1) rigid protein-flexible ligand docking to screen for high-affinity binders, followed by 2) ensemble docking using multiple MDM2 conformations derived from molecular dynamics (MD) simulations. The top candidates were further evaluated for their pharmacokinetic and toxicity profiles using ADMET analysis. Finally, 150 ns MD simulations and binding free energy (MM-PBSA) calculations were performed to assess the stability and strength of protein-ligand interactions.

Results: Three terpenoid compounds, olean-12-en-3-beta-ol, cabralealactone, and 27-deoxyactein demonstrated strong binding affinities toward MDM2 in ensemble docking studies. ADMET analysis confirmed their favorable pharmacokinetic properties. Further MD simulations indicated that these compounds formed highly stable complexes with MDM2. Notably, 27-deoxyactein exhibited the lowest binding free energy (-154.514 kJ/mol), outperforming the reference inhibitor Nutlin-3a (-133.531 kJ/mol), suggesting superior binding stability and interaction strength.

Conclusion: Our findings highlight 27-deoxyactein as a promising MDM2 inhibitor with strong binding affinity, stability, and a favorable pharmacokinetic profile. This study provides a computational foundation for further experimental validation, supporting the potential of terpenoid-based MDM2 inhibitors in BC therapy.