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Original Research | Open Access

Integrative multi-omics approach for drug repositioning in Alzheimer's disease

Jinna Yanga,bKaimin Guoa,bXiaxia Rena,cXiaolian ZhangdShuang Zhaoa,bJiansong FangdYu Weia,bPengcheng Yanga,bWenjia Wanga,bHui Wange( )Yunhui Hua,b( )
Tianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd, Tianjin 300410, China
State Key Laboratory of Chinese Medicine Modernization, Tianjin 300193, China
Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
Key Laboratory of Molecular Biophysics, Hebei Province, Institute of Biophysics, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300401, China
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Abstract

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an insidious onset, and effective therapeutic agents are urgently needed.

Objective

This study employed a multi-omics integration strategy for drug repurposing against AD.

Methods

Firstly, transcriptomic and proteomic data from AD patients were utilized to identify differentially expressed genes. Potential anti-AD small-molecule compounds were screened by integrating the Reverse Gene Expression Score (RGES) and Connectivity Map (C-Map) approaches with drug-perturbed gene expression profiles from the Library of Integrated Network-Based Cellular Signatures (LINCS), followed by blood-brain barrier (BBB) permeability prediction and structural similarity analysis. Secondly, a drug-disease network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The therapeutic potential of candidate drugs was further evaluated via network proximity analysis. Finally, in vitro validation was conducted using Okadaic acid (OA) induced SH-SY5Y and Lipopolysaccharide (LPS) induced BV2 cell models to assess cell viability and nitric oxide (NO) levels. This integrated approach provides a novel framework for identifying repurposed drugs with potential efficacy against AD.

Results

Following the collection of omics data, 227 overlapping candidate compounds were identified through two computational approaches. After BBB prediction screening, 104 drugs were selected for subsequent structural similarity analysis and literature/patent review, ultimately leading to the selection of TNP-470 and Terreic acid for validation. Network pharmacology analysis revealed that potential targets of TNP-470 for AD treatment were significantly enriched in neuroactive ligand-receptor interaction, TNF signaling, and AD-related pathways, while anti-AD targets of Terreic acid primarily involved calcium signaling, AD pathway, and cAMP signaling. Network proximity analysis demonstrated significant associations between both candidates and AD. In vitro assays demonstrated that TNP-470 significantly enhanced the viability of OA-induced SH-SY5Y cells at concentrations of 10 μM and 50 μM (p < 0.01 and p < 0.05, respectively). Additionally, within the concentration range of 0.016–10 μM, TNP-470 markedly inhibited NO production in the LPS-induced BV2 microglial cell model. Terreic acid also promoted the survival of OA-treated SH-SY5Y cells at concentrations ranging from 2 to 50 μM, and significantly reduced nitric oxide (NO) levels at a concentration of 10 μM.

Conclusion

This drug repositioning strategy based on multi-omics integration provides a novel approach for AD therapeutic development, with both TNP-470 and Terreic acid demonstrating anti-AD potential.

References

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Precision Medication

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Cite this article:
Yang J, Guo K, Ren X, et al. Integrative multi-omics approach for drug repositioning in Alzheimer's disease. Precision Medication, 2025, 2(3). https://doi.org/10.1016/j.prmedi.2025.100050

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Received: 10 June 2025
Revised: 13 July 2025
Accepted: 16 July 2025
Published: 17 September 2025
© 2025 Chinese General Practice Publishing House Co., Ltd.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).