Identification of Potent HDAC6 Inhibitors for Breast Cancer Through Multi-Stage In Silico Modeling.

Area of Science:

• Epigenetics and Molecular Biology
• Computational Chemistry and Drug Discovery
• Oncology

Background:

• Histone deacetylases (HDACs) are critical epigenetic regulators.
• HDAC6 overexpression is associated with estrogen receptor (ER) activity and breast cancer progression.
• Existing HDAC6 inhibitors face challenges with toxicity and off-target effects, driving the need for novel, selective agents.

Purpose of the Study:

• To identify potent and selective HDAC6 inhibitors for breast cancer therapy using a multi-stage computational approach.
• To evaluate the binding affinity, stability, and pharmacokinetic properties of potential drug candidates.

Main Methods:

• Large-scale virtual screening of over 264,000 compounds.
• Molecular docking, 100 ns molecular dynamics (MD) simulations, and MM/GBSA binding free energy calculations.
• Absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions and essential dynamics analysis.

Main Results:

• HDI-3 emerged as the most promising candidate, demonstrating strong binding affinity (-130.67 kcal/mol MM/GBSA) and thermodynamic stability.
• MD simulations confirmed HDI-3's stable interactions with key active site residues (ASP649, HIS651, ASP742) and low conformational fluctuations.
• ADMET predictions indicated favorable pharmacokinetic properties for HDI-3, including good bioavailability and low toxicity.

Conclusions:

• HDI-3 is a computationally identified selective and pharmacologically viable HDAC6 inhibitor with potential for breast cancer treatment.
• The integrated computational pipeline offers an efficient strategy for targeted drug discovery.
• Experimental validation is essential to confirm the efficacy and safety of HDI-3.