Identifying Potent 9-Anilinoacridine-Based HER2 Breast Cancer Inhibitors Using in Silico Computational Approaches

Potlapati Varakumar, Kalirajan Rajagopal, Fahadul Islam, Kannan Raman, Gowramma Byran, Manikandan Gurunathan, Suseela Prema, Thenmozhi Murugesan, Prashanti Chitrapu, Rashu Barua, Sheikh F. Ahmad, Sabry M. Attia, Talha Bin Emran

Article ID: 7672
Vol 37, Issue 12, 2023
DOI: https://doi.org/10.23812/j.biol.regul.homeost.agents.20233712.616
Received: 8 January 2024; Accepted: 8 January 2024; Available online: 8 January 2024; Issue release: 8 January 2024

Abstract

Background: Because of their anti-proliferative effects, 9-anilinoacridines are important as antitumor agents with DNA-intercalating properties. In this study, anticancer drugs with 9-anilinoacridines, such as amascrine and nitracrine, were developed. Pharmacophore modelling, molecular docking, Molecular Mechanics Generalized Born Surface Area (MM-GBSA), induced fit docking and a molecular dynamics (MD) study were performed to investigate the binding affinity of 9-anilinoacridines with heterocyclic substitutes as selective human epidermal growth factor receptor 2 (HER2) inhibitors for breast carcinoma. Methods: The pharmacophore model was developed using the Schrödinger suite 2019-2 phase module. 3D structures of dataset compounds were generated using Maestro version 9.6 and optimised using the LigPrep design of the Schrödinger suite 2019-2. To predict the binding free energy of the ligands in the complex with Protein Data Bank (pdb), we performed post-docked energy minimisation using the Prime MM-GBSA module. Induced fit docking studies were performed to determine the ligand-modulated dynamic behaviour shown in the protein MD study. Using the Desmond module in Schrödinger 2019-2, the complex in the optimized potentials for liquid simulations 3 (OPLS3) force fields explicit solvent system was investigated. Using the pharmacophore hypothesis, a statistically substantial 3 Dimensional Quantitative Structure Activity Relationship (3D-QSAR) design was created. Results: We obtained the top five hypotheses, and according to the scoring parameters, the best model was identified as the 3D-QSAR model A-Acceptor, D-Donar, R-Ring (model AADRRR)-21. From the contour map analysis, we found that the presence of a hydrogen-bond donor and electron-withdrawing and hydrophobic features are crucial for inhibiting the HER2 enzyme. The docking study showed that the ligands have significant G-score (Glide Score) values from –4.18 to –9.96 kcal/mol. A 15-ns MD simulation was also run to determine the molecular details involving the affinity of 3m in active site 3PP0.pdb. The binding free energy was determined using the Prime MM-GBSA module, and dG binding values were observed from –35.11 to –106.87 kcal/mol. Conclusions: We designed and developed a pharmacophore hypothesis and 3D-QSAR model and then elucidated the structural features and spatial arrangement of atoms responsible for the HER2 inhibitory activity of 9-anilinoacridines. The predicted 3D-QSAR model significantly correlated with experimentally reported in vitro antitumor activity. The findings indicated that additional pharmacophore feature modifications might enhance the HER2 inhibitory activity of 9-anilinoacridines.


Keywords

pharmacophore model;HER2 inhibitors;breast cancer;3D-QSAR model;induced fit docking;molecular dynamics


References

Supporting Agencies



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