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

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.