Combined Ligand/Structure-Based Virtual Screening and Molecular Dynamics Simulations of Steroidal Androgen Receptor...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Oncology

Background:

• Antiandrogen therapies targeting the androgen receptor (AR) are standard for prostate cancer (PCa).
• Emerging drug resistance to antiandrogens necessitates the development of novel AR antagonists.
• A need exists for new therapeutic strategies against advanced PCa.

Purpose of the Study:

• To investigate structure-activity relationships of 7α-substituted dihydrotestosterone derivatives.
• To identify novel AR antagonists with potent antiandrogenic activity.
• To discover new drug candidates for overcoming antiandrogen resistance in PCa.

Main Methods:

• Quantitative Structure-Activity Relationship (QSAR) modeling using Multiple Linear Regression (MLR).
• Virtual screening of dihydrotestosterone derivatives from PubChem.
• Molecular docking, molecular dynamics (MD) simulations, and MM/GBSA for binding analysis.

Main Results:

• A validated QSAR model demonstrated high predictive ability for antiandrogenic activity.
• Novel compounds (CID_70128824, CID_70127147, CID_70126881) were identified with superior in silico bioactivity compared to bicalutamide.
• Analysis confirmed similar binding modes and mechanisms to known AR antagonists, with van der Waals interactions being key.

Conclusions:

• The identified dihydrotestosterone derivatives represent promising novel AR antagonists.
• These compounds offer a potential therapeutic avenue for drug-resistant prostate cancer.
• Computational methods effectively guided the discovery of potent antiandrogenic agents.