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Context <p>Overexpression of Epidermal Growth Factor Receptor (EGFR) is commonly found in triple negative breast cancer. This subtype does not contain the oestrogen receptors (ER), progesterone receptors (PR), and HER2 amplification. The wide scope of biological activities and varied structural classes of natural phenolic compounds paves significant opportunities for drug discovery. The modern drug discovery process combines natural phenolic compounds and computational or AI techniques to develop novel drugs. Considering this fact, EGFR inhibitors were designed based upon the multiscale computational studies of natural phenolic compounds to identify the potent anticancer molecules for triple negative breast cancer (TNBC).</p> Method <p>Preliminary screening of all natural phenolic compounds (containing 440 phenolic compounds) was done by molecular docking studies using the glide utility of Maestro tools. The top scoring molecules were further subjected to MD simulation studies by using Desmond software. The obtained trajectory files were processed through R Studio software for data analysis employing PCA analysis, cluster analysis, and DCCM calculation. Then, binding free energies of complexes were determined by MMGBSA calculation, and drug likeliness of molecules were checked by <i>in silico</i> ADME calculation. These obtained <i>in silico</i> generated data were analysed, and it was concluded that the identified molecules bearing phenolic compounds would be a promising scaffold for EGFR inhibitors and anticancer drugs for TNBC. However, experimental results are needed to prove the obtained results.</p>

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Computational screening of natural phenolic compounds as potential EGFR inhibitors against triple negative breast cancer

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Prashant Kurkute,
  • Sakshi Wagh,
  • Komal Nandode,
  • Amit Gangwal,
  • Sumit Sonawane,
  • Vedant Shimpi,
  • Azim Ansari,
  • Yogeeta O. Agrawal,
  • Mrunali Patil,
  • Mohd Usman MohdSiddique

摘要

Context

Overexpression of Epidermal Growth Factor Receptor (EGFR) is commonly found in triple negative breast cancer. This subtype does not contain the oestrogen receptors (ER), progesterone receptors (PR), and HER2 amplification. The wide scope of biological activities and varied structural classes of natural phenolic compounds paves significant opportunities for drug discovery. The modern drug discovery process combines natural phenolic compounds and computational or AI techniques to develop novel drugs. Considering this fact, EGFR inhibitors were designed based upon the multiscale computational studies of natural phenolic compounds to identify the potent anticancer molecules for triple negative breast cancer (TNBC).

Method

Preliminary screening of all natural phenolic compounds (containing 440 phenolic compounds) was done by molecular docking studies using the glide utility of Maestro tools. The top scoring molecules were further subjected to MD simulation studies by using Desmond software. The obtained trajectory files were processed through R Studio software for data analysis employing PCA analysis, cluster analysis, and DCCM calculation. Then, binding free energies of complexes were determined by MMGBSA calculation, and drug likeliness of molecules were checked by in silico ADME calculation. These obtained in silico generated data were analysed, and it was concluded that the identified molecules bearing phenolic compounds would be a promising scaffold for EGFR inhibitors and anticancer drugs for TNBC. However, experimental results are needed to prove the obtained results.