Repurposing Phytochemicals against Breast Cancer (MCF-7) using Classical Structure-Based Drug Design

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Pharmacology

Background:

• Breast cancer poses a significant global health challenge due to its prevalence and potential severity.
• Targeting the estrogen receptor alpha (ERα) is a key strategy in breast cancer therapy by suppressing tumor cell proliferation.
• MCF-7 cell line is a relevant model for studying ERα-mediated effects in breast cancer.

Purpose of the Study:

• To identify potential inhibitors of estrogen receptor alpha (ERα) using in-silico methods.
• To explore novel therapeutic strategies for ERα-associated breast cancer.

Main Methods:

• Integrated Quantitative Structure-Activity Relationship (QSAR) models with machine learning.
• Employed molecular docking to predict potential ERα binders.
• Utilized molecular dynamics simulations to assess complex stability.
• Conducted ADMET analysis to evaluate compound pharmacokinetic properties.

Main Results:

• Two compounds, 162412 and 443440, exhibited strong binding affinities to ERα, comparable to known binders.
• Molecular dynamics simulations confirmed stable ligand binding within the ERα active site.
• ADMET profiling indicated favorable drug-like properties for the identified compounds.
• Calculated binding free energies for 162412 and 443440 were -27.32 kcal/mol and -25.00 kcal/mol, respectively.

Conclusions:

• Compounds 162412 and 443440 show promise as novel anti-ERα agents for breast cancer therapy.
• Further preclinical and clinical studies are warranted to validate their therapeutic potential.
• This research could lead to new treatments for ERα-driven breast cancers.