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Multiscale Interactome-Guided Discovery Candidate Herbs and Active Ingredients Against Hyperthyroidism by Biased

Seok-Hoon Han1, Ji-Hwan Kim2, Yewon Han3

  • 1College of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea.

International Journal of Molecular Sciences
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies potential herbal treatments for hyperthyroidism by analyzing herb-compound-target networks. It highlights Geranii Herba and Gastrodiae Rhizoma as promising candidates for developing new therapies.

Keywords:
active ingredientsherbshyperthyroidismmultiscale network

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Area of Science:

  • Computational biology
  • Systems pharmacology
  • Traditional Chinese Medicine

Background:

  • Hyperthyroidism involves excess thyroid hormone and a hypermetabolic state.
  • Current treatments exist, but mechanism-based options are needed.
  • Herbal medicine offers potential for novel therapeutic discovery.

Purpose of the Study:

  • To identify potential herbal candidates, active compounds, and mechanisms for hyperthyroidism treatment.
  • To develop and apply a computational framework for prioritizing natural products.
  • To discover novel therapeutic leads for hyperthyroidism.

Main Methods:

  • Constructed a multiscale interactome network.
  • Applied a biased random-walk diffusion model to prioritize herbal candidates.
  • Integrated data from OASIS, DrugBank, TTD, STITCH, and DisGeNET.
  • Assessed herb-disease relationships using correlation and target overlap.

Main Results:

  • Prioritized herbal candidates including Geranii Herba, Gastrodiae Rhizoma, and Veratri Rhizoma Et Radix.
  • Identified key signaling pathways: MAPK, PI3K-AKT, p53, HIF-1, and thyroid hormone signaling.
  • Proposed novel compounds like resveratrol and cardamomin, and confirmed known agents like ellagic acid.

Conclusions:

  • The computational framework successfully identified known and novel herbal therapeutics for hyperthyroidism.
  • This approach supports the discovery of multi-target natural product-based therapies.
  • Highlights the potential of integrating systems biology and network pharmacology for drug discovery.