Targeting natural compounds against HER2 kinase domain as potential anticancer drugs applying pharmacophore based...

Area of Science:

• Oncology
• Computational Chemistry
• Pharmacology

Background:

• Human epidermal growth factor receptors (EGFRs), including HER2, are crucial in cancer signaling.
• HER2 is a key target in breast cancer treatment due to its association with aggressive disease.
• Aberrant signal transduction via EGFRs drives various cancer types.

Purpose of the Study:

• To identify novel small molecule inhibitors targeting the HER2 kinase domain using computational methods.
• To discover potential therapeutic leads for HER2-positive breast cancers from natural compounds.
• To validate potential inhibitors through in silico screening, docking, and molecular dynamics simulations.

Main Methods:

• Generated a 3D-QSAR pharmacophore model (Hypo1) for HER2 inhibition.
• Screened the Universal Natural Product Database (UNPD) against the pharmacophore model.
• Performed molecular docking (CDOCKER) and ADMET/Lipinski's Rule of Five analysis.
• Conducted molecular dynamics (MD) simulations using GROMACS for stability assessment.

Main Results:

• A validated pharmacophore model successfully identified 33 potential HER2 inhibitors from the UNPD.
• Docking and ADMET analysis narrowed down candidates, with some showing higher scores than the reference.
• MD simulations confirmed the stability of the top compounds (Hit1 and Hit2) in complex with HER2.
• Two compounds, Hit1 and Hit2, were identified as promising leads based on docking, interactions, and stability.

Conclusions:

• The study successfully identified two novel compounds (Hit1 and Hit2) with potential as HER2 inhibitors.
• These compounds represent promising leads for developing new therapies against HER2-positive breast cancers.
• The integrated computational approach (3D-QSAR, docking, MD) is effective for drug discovery against kinase targets.