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Published on: December 26, 2016
Computational profiling and virtual screening of MEK inhibitors for triple-negative breast cancer therapy
Ya-Kun Zhang1, Jian-Bo Tong1, Rong Wang1
1College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China.
Abstract:
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.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s40203-025-00438-x.
Insights
Researchers identified a novel compound targeting MEK signaling for triple-negative breast cancer (TNBC). This computational drug design approach offers a promising new therapeutic avenue for TNBC, a challenging malignancy.
Area of Science:
- Oncology
- Pharmacology
- Computational Chemistry
Background:
- Triple-negative breast cancer (TNBC) presents significant therapeutic challenges due to its aggressive nature and limited treatment options.
- Aberrant activation of Mitogen-Activated Protein Kinase Kinase (MEK) in the MAPK pathway is implicated in TNBC progression, making MEK a key therapeutic target.
Purpose of the Study:
- To identify novel therapeutic agents for TNBC by targeting MEK signaling.
- To leverage computational strategies for rational drug design and discovery.
Main Methods:
- Development of a ligand-based pharmacophore model using Selumetinib.
- Virtual screening of chemical databases (ChemSpider, ChEBI, TCMDB).
- In silico evaluation including Lipinski's rule, ADMET prediction, molecular docking, molecular dynamics, and binding free energy calculations.
Main Results:
- Identification of 33 potential MEK inhibitor candidates.
- Selection of a single compound exhibiting favorable pharmacokinetic and bioactivity profiles.
- Computational validation confirmed stable binding and high affinity of the selected compound to MEK.
Conclusions:
- The identified compound shows potential as a novel therapeutic agent for TNBC.
- This study highlights the efficacy of integrated computational approaches in accelerating drug discovery for challenging diseases.
- Further experimental validation and structure optimization are warranted.
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