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<p>Heparanase (HPSE) plays a critical role in tumor progression by degrading heparan sulfate chains in the extracellular matrix and modulating the tumor microenvironment. As a result, it has emerged as a promising therapeutic target. Aminoglycoside-derived sulfated glycan mimetics have shown potential as HPSE inhibitors, but rational optimization is challenged by complex steric and electrostatic interactions. Here, we used three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling to identify key structural features governing HPSE inhibition, providing new predictive structure–activity insights for sulfated aminoglycoside-based HPSE inhibitors. Guided by these insights, we evaluated the lead compound <b>L17</b> in HPSE-dependent cancer cell lines and observed concentration-dependent inhibition of proliferation, suppression of invasion, and reduction of extracellular HPSE levels. In silico ADMET predictions flagged CYP3A4 as a potential liability, but experimental assays confirmed minimal inhibition, indicating a favorable metabolic profile. This integrated approach provides mechanistic insight into aminoglycoside-based HPSE inhibitors and supports their rational optimization as anticancer therapeutics.</p><p></p>

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Integrated 3D-QSAR and cellular profiling of sulfated and hydrophobic aminoglycoside glycans to modulate heparanase activity

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Junzhe Wang,
  • Livia Philip,
  • Tharuka Beragama Vithanage,
  • Andrew Gulewicz,
  • Hawau Abdulsalam,
  • Hien M. Nguyen

摘要

Heparanase (HPSE) plays a critical role in tumor progression by degrading heparan sulfate chains in the extracellular matrix and modulating the tumor microenvironment. As a result, it has emerged as a promising therapeutic target. Aminoglycoside-derived sulfated glycan mimetics have shown potential as HPSE inhibitors, but rational optimization is challenged by complex steric and electrostatic interactions. Here, we used three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling to identify key structural features governing HPSE inhibition, providing new predictive structure–activity insights for sulfated aminoglycoside-based HPSE inhibitors. Guided by these insights, we evaluated the lead compound L17 in HPSE-dependent cancer cell lines and observed concentration-dependent inhibition of proliferation, suppression of invasion, and reduction of extracellular HPSE levels. In silico ADMET predictions flagged CYP3A4 as a potential liability, but experimental assays confirmed minimal inhibition, indicating a favorable metabolic profile. This integrated approach provides mechanistic insight into aminoglycoside-based HPSE inhibitors and supports their rational optimization as anticancer therapeutics.