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

IF 2.4 Q3 BIOCHEMICAL RESEARCH METHODS
Bioinformatics and Biology Insights Pub Date : 2025-09-24 eCollection Date: 2025-01-01 DOI:10.1177/11779322251379037
Vaishali Pankaj, Inderjeet Bhogal, Sudeep Roy
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引用次数: 0

Abstract

Histone deacetylases (HDACs) are essential epigenetic regulators, with HDAC6 overexpression linked to estrogen receptor (ER) activity and breast cancer progression. While several HDAC6 inhibitors have been investigated, their clinical success remains limited due to toxicity and off-target effects, necessitating the discovery of novel, selective inhibitors. This study employs a multi-stage computational approach to identify potent HDAC6 inhibitors for breast cancer therapy. A large-scale virtual screening of 264 834 compounds was conducted, followed by molecular docking, molecular dynamics (MD) simulations (100 ns), molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. The HDI-3 emerged as the most promising candidate among replicate simulations, exhibiting a substantially favorable MM/GBSA binding free energy of -130.67 kcal/mol-indicative of strong thermodynamic stability and stronger binding affinity compared to reference inhibitors Trichostatin A and Ricolinostat. Molecular dynamics simulations revealed that HDI-3 maintained structural stability, persistent key interactions with active site residues (ASP649, HIS651, ASP742), and low conformational fluctuations. The ADMET evaluation confirmed HDI-3's favorable pharmacokinetic properties, including optimal bioavailability, non-mutagenicity, and low hepatotoxicity. Essential dynamics and principal component analysis further validated its stable binding profile. While these findings highlight HDI-3 as a selective and pharmacologically viable HDAC6 inhibitor, it is important to acknowledge that the results are entirely computational. Therefore, experimental validation is essential to confirm the compound's efficacy and safety. This integrated computational pipeline provides an efficient strategy to accelerate targeted drug discovery, laying the groundwork for future experimental investigations.

通过多阶段计算机模拟鉴定乳腺癌有效的HDAC6抑制剂。
组蛋白去乙酰化酶(hdac)是必不可少的表观遗传调节因子,HDAC6过表达与雌激素受体(ER)活性和乳腺癌进展有关。虽然已经研究了几种HDAC6抑制剂,但由于毒性和脱靶效应,它们的临床成功仍然有限,因此需要发现新的选择性抑制剂。本研究采用多阶段计算方法来确定有效的HDAC6抑制剂用于乳腺癌治疗。对264 834个化合物进行了大规模的虚拟筛选,随后进行了分子对接、分子动力学(MD)模拟(100 ns)、分子力学/广义出生表面积(MM/GBSA)结合自由能计算和吸收、分布、代谢、排泄和毒性(ADMET)预测。在重复模拟中,HDI-3是最有希望的候选者,显示出非常有利的MM/GBSA结合自由能-130.67 kcal/mol,这表明与参考抑制剂Trichostatin a和Ricolinostat相比,HDI-3具有较强的热力学稳定性和更强的结合亲和力。分子动力学模拟表明,HDI-3保持结构稳定,与活性位点残基(ASP649, HIS651, ASP742)的关键相互作用持续存在,构象波动较小。ADMET评估证实HDI-3具有良好的药代动力学特性,包括最佳的生物利用度、非突变性和低肝毒性。本质动力学和主成分分析进一步验证了其稳定的结合轮廓。虽然这些发现强调HDI-3是一种选择性和药理学上可行的hdac - 6抑制剂,但重要的是要承认这些结果完全是计算性的。因此,实验验证对于确认该化合物的有效性和安全性至关重要。这种集成的计算管道为加速靶向药物发现提供了一种有效的策略,为未来的实验研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics and Biology Insights
Bioinformatics and Biology Insights BIOCHEMICAL RESEARCH METHODS-
CiteScore
6.80
自引率
1.70%
发文量
36
审稿时长
8 weeks
期刊介绍: Bioinformatics and Biology Insights is an open access, peer-reviewed journal that considers articles on bioinformatics methods and their applications which must pertain to biological insights. All papers should be easily amenable to biologists and as such help bridge the gap between theories and applications.
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