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<p>Type 2 Diabetes Mellitus (T2DM) is a multifactorial metabolic disorder embedded in impaired adipose tissue function and underlying elements of altered lipid metabolism. Adipokines and lipid-processing enzymes play a critical role in the regulatory mechanisms of metabolic homeostasis. To report the possible modulators of lipid metabolism from <i>Hygrophila auriculata</i> via the integrated in silico approaches of network pharmacology, molecular docking, and dynamic simulations. The dedicated approach involved identifying phytochemicals from <i>H. auriculata</i> via GC-MS, followed by obtaining their physicochemical structures from PubChem. The ADMET profiling and drug-likeness of phytochemicals were screened using SWISS ADME and pkCSM. To retain biological relevance, putative protein targets were initially identified through network pharmacology analysis and hub-gene analysis, followed by molecular docking for the top-ranked diabetes-related targets to validate compound–protein binding interactions. Network pharmacology, utilising STRING and Cytoscape (with the CytoHubba plugin), was employed to identify hub genes. Molecular docking and MM-GBSA calculations were performed for T2DM-related targets (PPAR-γ, LIPE, ADIPOQ, LPL, APOB), and WaterMap were used to assess complex stability and binding interactions. Out of 73 phytocompounds, 13-docosenamide had the best combined ADMET data and showed strong binding affinities (−10.2 to −9.1 kcal/mol) to the primary targets. The docking studies showed that hydrophobic and hydrogen bonding interactions were favourable. Although the MD simulations of the complex showed excellent stability over 100 ns, the studies support its possible function as a modulator of adipokine function and lipid metabolism. 13-Docosenamide from <i>H. auriculata</i> shows promise as a multitargeted lipid metabolism regulator for the management of T2DM. The current study expands upon previous reports of crude <i>H. auriculata</i> extracts by identifying specific bioactive phytochemicals and confirming their multiple target interactions with adipokine-related proteins through network pharmacology, docking, and MD simulations, thus clearly delineating the precise mechanistic role of each compound in the regulation of diabetes. This bioinformatics approach is economical and provides a valuable pipeline for identifying new drug compounds with antidiabetic properties, while facilitating future in vitro and in vivo investigations.</p>

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Networking Pharmacology and Integrated Bioinformatic Analysis of Hygrophila Auriculata for Lipid Metabolism Modulation in Type 2 Diabetes Mellitus

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Snega Saravanan,
  • Edward Arockiasamy

摘要

Type 2 Diabetes Mellitus (T2DM) is a multifactorial metabolic disorder embedded in impaired adipose tissue function and underlying elements of altered lipid metabolism. Adipokines and lipid-processing enzymes play a critical role in the regulatory mechanisms of metabolic homeostasis. To report the possible modulators of lipid metabolism from Hygrophila auriculata via the integrated in silico approaches of network pharmacology, molecular docking, and dynamic simulations. The dedicated approach involved identifying phytochemicals from H. auriculata via GC-MS, followed by obtaining their physicochemical structures from PubChem. The ADMET profiling and drug-likeness of phytochemicals were screened using SWISS ADME and pkCSM. To retain biological relevance, putative protein targets were initially identified through network pharmacology analysis and hub-gene analysis, followed by molecular docking for the top-ranked diabetes-related targets to validate compound–protein binding interactions. Network pharmacology, utilising STRING and Cytoscape (with the CytoHubba plugin), was employed to identify hub genes. Molecular docking and MM-GBSA calculations were performed for T2DM-related targets (PPAR-γ, LIPE, ADIPOQ, LPL, APOB), and WaterMap were used to assess complex stability and binding interactions. Out of 73 phytocompounds, 13-docosenamide had the best combined ADMET data and showed strong binding affinities (−10.2 to −9.1 kcal/mol) to the primary targets. The docking studies showed that hydrophobic and hydrogen bonding interactions were favourable. Although the MD simulations of the complex showed excellent stability over 100 ns, the studies support its possible function as a modulator of adipokine function and lipid metabolism. 13-Docosenamide from H. auriculata shows promise as a multitargeted lipid metabolism regulator for the management of T2DM. The current study expands upon previous reports of crude H. auriculata extracts by identifying specific bioactive phytochemicals and confirming their multiple target interactions with adipokine-related proteins through network pharmacology, docking, and MD simulations, thus clearly delineating the precise mechanistic role of each compound in the regulation of diabetes. This bioinformatics approach is economical and provides a valuable pipeline for identifying new drug compounds with antidiabetic properties, while facilitating future in vitro and in vivo investigations.