Estrogen receptor potentially stable conformations from molecular dynamics as a structure-based pharmacophore model...

Area of Science:

• Computational chemistry and molecular modeling
• Drug discovery and development
• Oncology and pharmacology

Background:

• Breast cancer treatment faces challenges due to drug resistance, necessitating novel therapeutic strategies.
• Existing drugs like tamoxifen can lead to resistance, highlighting the need for new drug candidates.
• Computer-aided drug design offers a promising avenue for identifying effective treatments against resistant breast cancer.

Purpose of the Study:

• To develop a novel, real-time approach for identifying potential breast cancer drug candidates.
• To utilize molecular dynamics and pharmacophore modeling to overcome drug resistance.
• To screen compounds targeting the estrogenic receptor for improved efficacy.

Main Methods:

• Employed molecular dynamics simulations to sample multi-conformational states of the estrogenic receptor.
• Generated pharmacophore models based on Gibbs free binding energy and interaction energies.
• Validated pharmacophore models and screened compounds using virtual screening techniques.
• Assessed drug-likeness of potential candidates using ADMET, TopKat®, and Lipinski's rule of five.

Main Results:

• Identified energetically stable conformations of the estrogenic receptor.
• Developed validated structure-based pharmacophore models for virtual screening.
• Screened compounds against tamoxifen and tamoxifen-resistance inhibitor frames.
• Identified Andrographidine F from *Andrographis paniculata* as a favorable compound based on drug-likeness properties.

Conclusions:

• Molecular dynamics-based computer-aided drug design is a viable strategy for identifying novel breast cancer drug candidates.
• Andrographidine F shows potential as a therapeutic agent against breast cancer, particularly in cases of drug resistance.
• Further in vitro and in vivo studies are warranted to validate the efficacy of Andrographidine F.