Unlocking the Therapeutic Potential of Alisertib in Breast Cancer: An In-Depth Exploration of Molecular Targets Using Network Pharmacology and Gene Expression Network

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Abstract

Purpose: Breast cancer is a prevalent and serious disease marked by uncontrolled cell growth. Alisertib, a small-molecule inhibitor, shows potential in cancer treatment by blocking cell proliferation. This study uses a network pharmacology approach to identify Alisertib's potential targets in breast cancer. Methods: We used network pharmacology, molecular dynamic simulation and binding free energies approaches to identify the potential molecular target of Alisertib in Breast Cancer. Results: SwissTarget, SuperPred, and CLCpred identified 100, 721, and 327 potential targets for Alisertib, respectively. From DisGeNet, 6,941 markers associated with breast cancer were identified, among which 29 proteins overlapped as both disease-associated genes and drug targets. Furthermore, the CytoHubba identified 10 hub genes from the PPI network of 29 common targets, with FGFR2, FGFR4, and MAPK7 ranked best based on their degree score. Moreover, the docking analysis revealed a docking scores of -8.854 kcal/mol, -7.373 kcal/mol and -7.262 kcal/mol for FGFR2, FGFR4, and MAPK7-Alisertib complexes respectively. The stable interaction of identified targets and Alisertib was further validated by the 200 ns molecular dynamics simulation. Binding free energy calculations using MM/GBSA yielded values of -61.0977 kcal/mol for the FGFR2-alisertib complex, -52.0032 kcal/mol for FGFR4-alisertib, and -47.9903 kcal/mol for MAPK7-alisertib. These results suggest that Alisertib exhibits stronger binding affinity for FGFR2, FGFR4 and MAPK7 compared to the control. Conclusion: These findings suggest that Alisertib has a strong binding affinity and favorable pharmacological interactions with FGFR4, FGFR2, and MAPK7, highlighting its potential as a targeted therapeutic for breast cancer. Consequently, Alisertib warrants further investigation in preclinical and clinical settings to evaluate its efficacy in treating malignant neoplasm of the breast.

Original languageEnglish
Article number108
JournalJournal of Pharmaceutical Innovation
Volume20
Issue number3
DOIs
StatePublished - Jun 2025

Keywords

  • Binding free energy
  • Breast cancer
  • Molecular dynamic simulation
  • Network pharmacology
  • PPI

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