Multiscale Interactome Analysis Prioritizes Candidate Herbs and Compounds for Diabetic Retinopathy

Diabetic retinopathy (DR), a leading cause of preventable vision loss among working-age adults, arises from chronic hyperglycemic injury to the retinal microvasculature; current interventions such as anti-vascular endothelial growth factor (VEGF) injection are largely reactive, and mechanism-anchored herbal options remain undefined. We constructed a multiscale interactome and applied a biased random-walk diffusion model to prioritize herbal candidates, candidate compounds, and mechanisms for DR. Herb–ingredient records were drawn from OASIS; ingredient targets from DrugBank, TTD, and STITCH; and disease genes from DisGeNET. For each herb and compound, we computed diffusion profiles, correlated them with the DR profile, and assessed target-overlap significance by hypergeometric test. Eleven herbs combining high correlation (top 5%) with significant overlap (p < 0.05) were retained, led by Pini Koraiensis Semen (0.0263), Benincasae Exocarpium (0.0183), and Veratri Rhizoma Et Radix (0.0162); none had been previously investigated for DR. CASP3 emerged as a target shared by all eleven herbs, and enrichment analysis highlighted the advanced glycation end product–receptor for AGE (AGE–RAGE), TNF, IL-17, and NF-κB pathways alongside apoptosis and reactive-oxygen-species responses. Compound-level analysis recovered evidence-supported quercetin, resveratrol, oleic acid, luteolin, and diosgenin and nominated tectorigenin and cucurbitacins as novel candidates. This framework recapitulates known DR biology and elevates testable multi-target leads that warrant experimental validation for diabetic retinopathy.

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Journal
International Journal of Molecular Sciences
Published
2026-10-13
DOI
https://doi.org/10.3390/ijms27209066
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Multiscale Interactome Analysis Prioritizes Candidate Herbs and Compounds for Diabetic Retinopathy

Ji-In Chae, 박치현, Sung-Kook Han, Won-Yung Lee et al.
International Journal of Molecular Sciences
Computational Drug Discovery Methods
article

Multiscale Interactome Analysis Prioritizes Candidate Herbs and Compounds for Diabetic Retinopathy

Ji-In Chae, 박치현, Sung-Kook Han, Won-Yung Lee, Yeong-Seon Ahn
article en

Abstract

Diabetic retinopathy (DR), a leading cause of preventable vision loss among working-age adults, arises from chronic hyperglycemic injury to the retinal microvasculature; current interventions such as anti-vascular endothelial growth factor (VEGF) injection are largely reactive, and mechanism-anchored herbal options remain undefined. We constructed a multiscale interactome and applied a biased random-walk diffusion model to prioritize herbal candidates, candidate compounds, and mechanisms for DR. Herb–ingredient records were drawn from OASIS; ingredient targets from DrugBank, TTD, and STITCH; and disease genes from DisGeNET. For each herb and compound, we computed diffusion profiles, correlated them with the DR profile, and assessed target-overlap significance by hypergeometric test. Eleven herbs combining high correlation (top 5%) with significant overlap (p < 0.05) were retained, led by Pini Koraiensis Semen (0.0263), Benincasae Exocarpium (0.0183), and Veratri Rhizoma Et Radix (0.0162); none had been previously investigated for DR. CASP3 emerged as a target shared by all eleven herbs, and enrichment analysis highlighted the advanced glycation end product–receptor for AGE (AGE–RAGE), TNF, IL-17, and NF-κB pathways alongside apoptosis and reactive-oxygen-species responses. Compound-level analysis recovered evidence-supported quercetin, resveratrol, oleic acid, luteolin, and diosgenin and nominated tectorigenin and cucurbitacins as novel candidates. This framework recapitulates known DR biology and elevates testable multi-target leads that warrant experimental validation for diabetic retinopathy.

International Journal of Molecular SciencesVol. 27(20)
National Forensic Institute (KR), Wonkwang University (KR)
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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