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<p>Breast cancer remains one of the most prevalent malignancies in women worldwide, with triple-negative breast cancer (TNBC) posing considerable therapeutic challenges due to its poor prognosis and limited treatment options. Aberrant activation of Mitogen-Activated Protein Kinase Kinase (MEK), a pivotal kinase within the MAPK signaling pathway, has been implicated in TNBC progression, rendering it a compelling therapeutic target. In this study, the FDA-approved MEK inhibitor Selumetinib was utilized as a lead compound to generate a ligand-based pharmacophore model, which guided systematic virtual screening across ChemSpider, ChEBI, and TCMDB databases. Thirty-three potential candidates were identified, and subsequent assessment based on Lipinski’s rule, ADMET prediction, and molecular docking resulted in the selection of a single compound with favorable pharmacokinetic and bioactivity profiles. Molecular docking, molecular dynamics simulations, and binding free energy calculations further corroborated the compound’s stable binding conformation and high affinity toward MEK. Collectively, these findings substantiate the potential of the identified compound as a promising TNBC therapeutic and provide a theoretical framework for subsequent structure optimization and experimental validation, underscoring the value of integrating computational strategies in rational drug design.</p>

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Computational profiling and virtual screening of MEK inhibitors for triple-negative breast cancer therapy

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Ya-Kun Zhang,
  • Jian-Bo Tong,
  • Rong Wang,
  • Wen Tian,
  • Yan-Rong Zeng

摘要

Breast cancer remains one of the most prevalent malignancies in women worldwide, with triple-negative breast cancer (TNBC) posing considerable therapeutic challenges due to its poor prognosis and limited treatment options. Aberrant activation of Mitogen-Activated Protein Kinase Kinase (MEK), a pivotal kinase within the MAPK signaling pathway, has been implicated in TNBC progression, rendering it a compelling therapeutic target. In this study, the FDA-approved MEK inhibitor Selumetinib was utilized as a lead compound to generate a ligand-based pharmacophore model, which guided systematic virtual screening across ChemSpider, ChEBI, and TCMDB databases. Thirty-three potential candidates were identified, and subsequent assessment based on Lipinski’s rule, ADMET prediction, and molecular docking resulted in the selection of a single compound with favorable pharmacokinetic and bioactivity profiles. Molecular docking, molecular dynamics simulations, and binding free energy calculations further corroborated the compound’s stable binding conformation and high affinity toward MEK. Collectively, these findings substantiate the potential of the identified compound as a promising TNBC therapeutic and provide a theoretical framework for subsequent structure optimization and experimental validation, underscoring the value of integrating computational strategies in rational drug design.