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<p>Insulin resistance is a core pathological hallmark of metabolic syndromes like type 2 diabetes mellitus. Peroxisome proliferator-activated receptor gamma (PPAR-γ) is a classical therapeutic target for improving insulin sensitivity, yet the clinical utility of its synthetic agonists faces diverse adverse reactions. We previously showed that phillyrin, an important component of <i>Forsythia suspensa</i>, could improve insulin resistance in obesity. However, its direct molecular targets remain not fully understood. Herein, we adopted a combined in silico and experimental approach to determine whether phillyrin could function as a ligand of PPAR-γ. We implemented a multi-scale strategy integrating network pharmacology, molecular modeling, with experimental verification. Network pharmacology analysis was employed to predict system-level targets and pathways associated with phillyrin. Subsequently, molecular docking and molecular dynamics simulations were conducted using computational chemistry methods to characterize the binding mode, dynamic stability, and binding free energy (MM-GBSA) of phillyrin within the PPAR-γ ligand-binding domain. Finally, key computational predictions were experimentally validated in insulin-resistant 3T3-L1 adipocytes and in a high-fat diet-induced murine model of insulin resistance.</p>

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Identification of phillyrin as a PPAR-γ ligand through network pharmacology and molecular modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Ling Huang,
  • Zhizheng Fang,
  • Rongchun Han,
  • Xiaohui Tong

摘要

Insulin resistance is a core pathological hallmark of metabolic syndromes like type 2 diabetes mellitus. Peroxisome proliferator-activated receptor gamma (PPAR-γ) is a classical therapeutic target for improving insulin sensitivity, yet the clinical utility of its synthetic agonists faces diverse adverse reactions. We previously showed that phillyrin, an important component of Forsythia suspensa, could improve insulin resistance in obesity. However, its direct molecular targets remain not fully understood. Herein, we adopted a combined in silico and experimental approach to determine whether phillyrin could function as a ligand of PPAR-γ. We implemented a multi-scale strategy integrating network pharmacology, molecular modeling, with experimental verification. Network pharmacology analysis was employed to predict system-level targets and pathways associated with phillyrin. Subsequently, molecular docking and molecular dynamics simulations were conducted using computational chemistry methods to characterize the binding mode, dynamic stability, and binding free energy (MM-GBSA) of phillyrin within the PPAR-γ ligand-binding domain. Finally, key computational predictions were experimentally validated in insulin-resistant 3T3-L1 adipocytes and in a high-fat diet-induced murine model of insulin resistance.