Abstract
Epidermal growth factor receptor (EGFR) promotes tumorigenic characteristics and activates cancer-associated signaling pathways such as Wnt/-catenin, transforming growth factor (TGF-β), and phosphoinositide-3-kinase (PI3K). Several inhibitors have been reported to suppress the activity of EGFR and are being used in cancer treatment. However, patients in the malignant stage of cancer show resistance to those inhibitors, opening a wide space for research to discover novel inhibitors. Therefore, we carried out machine learning and virtual screening to discover novel inhibitors with high affinity against EGFR-TK. Initially, a library of 2640 chalcones were screened out using a machine-learning model developed based on the random forest algorithm, exhibiting high sensitivity and a Receiver Operating Characteristic curve (ROC area) of 0.99. Furthermore, out of the initial 2640 screened compounds, 412 compounds exhibiting potential activity are subjected to evaluation for drug-likeness properties through different filters: Blood–brain barrier penetration, Lipinski’s rule, CMC-50 like rule, Veber rule, and Ghose filter, alongside Cell Line Cytotoxicity Prediction. A total of 30 compounds that successfully pass through all these filters are selected for molecular docking. Of these, 6 compounds display substantial binding affinity and closer interaction with the conserved catalytic residues of the target EGFR-TK compared to the reference molecule (erlotinib). Furthermore, molecular dynamics simulation studies were conducted on four compounds (CID-375861, CID-375862, CID-23636403, and CID-259166) to confirm the stability of the docked complexes over a 100 ns simulation trajectory. Additionally, the binding free energy calculations by MMPBSA reveal that these four chalcone compounds exhibit strong affinity towards the EGFR-TK enzyme, with binding free energies of − 65.421 kJ/mol, − 94.266 kJ/mol, − 80.044 kJ/mol, and − 79.734 kJ/mol, respectively. The findings from this investigation highlight a set of promising chalcone compounds that have the potential to be developed into effective drugs for the treatment of various cancers. Further research and development on these compounds could pave the way for novel therapeutic interventions.
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Acknowledgements
The authors would like to thank the Head of the Botany Department Soban Singh Jeena University, S.S.J. Campus, Almora, India, for providing the requisite facilities to conduct this research work. Department of biotechnology, Bhimtal campus, (Kumaun University), is also acknowledged by the authors for providing high-speed Internet facilities. We also extend our appreciation to Rashtriya Uchchattar Shiksha Abhiyan(RUSA), Ministry of Human Resource Development, Government of India, for the deployment of computer infrastructure to establish Bioinformatics Centre in Kumaun University, S.S.J. Campus, Almora.
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SM wrote the manuscript. TJ and PS has done all experiments parts. PM and MN analyzed and interpreted data. SC conceptualized and designed the project. VP and SC supervised the study. The whole manuscript was approved by all authors.
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Mathpal, S., Joshi, T., Sharma, P. et al. In silico screening of chalcone derivatives as promising EGFR-TK inhibitors for the clinical treatment of cancer. 3 Biotech 14, 18 (2024). https://doi.org/10.1007/s13205-023-03858-8
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DOI: https://doi.org/10.1007/s13205-023-03858-8