Brain cancer represents a highly aggressive malignant tumor with a challenging prognosis and limited treatment options. Employing advanced analytical methods, including Kinase Enrichment Analysis and Disease-Gene Network integration, the research identifies EGFR as a crucial therapeutic target for brain cancer. EGFR, a key player in cellular functions and elevated in various cancers, particularly brain cancer, is targeted using small molecule inhibitors like erlotinib and gefitinib. Despite promising results, challenges such as drug resistance and adverse effects necessitate exploration of alternative therapies. Natural compounds show significant potential for cancer with minimal associated toxicity. Thus, the natural compounds database was explored for EGFR kinase inhibitors. Utilizing molecular docking and dynamic simulation, our study identified five natural compounds—citicoline, silodosin, picroside I, canertinib, and tauroursodeoxycholic acid—as potential EGFR kinase inhibitors. Detailed exploration of their binding attributes, including pose, interacting residues, molecular interactions, dynamic behavior, and predicted binding energy, along with comparisons to the native inhibitor, underscored their potential. Notably, among the five natural compounds screened, canertinib is a known covalent inhibitor of EGFR kinase. However, its specific binding pose remains unexplored. Thus, to uncover the precise binding orientation, covalent docking simulation for canertinib was conducted. Additionally, it is noteworthy that all the five proposed compounds predicted to penetrate the blood-brain barrier, meeting the essential criteria for reaching brain. We anticipate that this study will provide valuable leads for experimental testing in the laboratory, advancing the prospects of brain cancer management.

Brain cancer represents a highly aggressive malignant tumor with a challenging prognosis and limited treatment options. Employing advanced analytical methods, including Kinase Enrichment Analysis and Disease-Gene Network integration, the research identifies EGFR as a crucial therapeutic target for brain cancer. EGFR, a key player in cellular functions and elevated in various cancers, particularly brain cancer, is targeted using small molecule inhibitors like erlotinib and gefitinib. Despite promising results, challenges such as drug resistance and adverse effects necessitate exploration of alternative therapies. Natural compounds show significant potential for cancer with minimal associated toxicity. Thus, the natural compounds database was explored for EGFR kinase inhibitors. Utilizing molecular docking and dynamic simulation, our study identified five natural compounds—citicoline, silodosin, picroside I, canertinib, and tauroursodeoxycholic acid—as potential EGFR kinase inhibitors. Detailed exploration of their binding attributes, including pose, interacting residues, molecular interactions, dynamic behavior, and predicted binding energy, along with comparisons to the native inhibitor, underscored their potential. Notably, among the five natural compounds screened, canertinib is a known covalent inhibitor of EGFR kinase. However, its specific binding pose remains unexplored. Thus, to uncover the precise binding orientation, covalent docking simulation for canertinib was conducted. Additionally, it is noteworthy that all the five proposed compounds predicted to penetrate the blood-brain barrier, meeting the essential criteria for reaching brain. We anticipate that this study will provide valuable leads for experimental testing in the laboratory, advancing the prospects of brain cancer management.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.