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Cited by 1Computational Drug Discovery MethodsTelomeres, Telomerase, and SenescenceGenetics, Aging, and Longevity in Model Organisms

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SenolyticSynergy: An Attention-Based Network for Discovering Novel Senolytic Combinations via Human Aging Genomics

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Ye, Yaowen, Su, Ting, Gao, Jiayi, Ming, Dengming·2025·International Journal of Molecular Sciences
DOI 10.3390/ijms26189004PMC PMC12469854

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Abstract

Senolytics, a category of drugs targeting aging processes, have garnered significant attention since their emergence in 2015. Unlike traditional drug development approaches that rely on randomized screening, research on aging-related pharmaceuticals has employed mechanism-based strategies, resulting in the discovery of the pioneering combination therapy of dasatinib (D) and quercetin (Q). Although preliminary studies with senolytic drug combinations have shown promising outcomes, the predictive capabilities of the research in this field remain limited by the extensive experimental data requirements. In this study, we employed differential gene expression analysis and machine learning techniques to investigate the combinatorial effects of senolytic drugs. We identified 1624 core aging-related genes and used this dataset to retrain a multimodal attention mechanism model, creating a specialized framework, SenolyticSynergy, for predicting effective senolytic drug combinations. We then utilized 63 established senolytic compounds as starting points for combination testing, developing a comprehensive dataset of 1953 potential drug combinations for aging interventions. Following rigorous filtration, we identified 190 high-confidence drug combinations and predicted their synergistic scores. Among these combinations, ten demonstrated exceptionally high synergistic scores, exceeding 8. The combination of temsirolimus and nitazoxanide ranked first and may be the most promising candidate. The analysis of the literature data and computational studies of molecular structures using 3D modeling validated the accuracy of these predictions. This framework paves the way for large-scale research into anti-aging drug combinations, advancing research capabilities in this field.

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Crossref · 10.3390/ijms26189004

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Figures

Figure 1 Aging Genome Analysis plots (a,b) illustrating the differentially expressed genes in the GSE141595 and GSE72815 datasets. The horizontal axis represents the log2 fold change, and the vertical axis represents the −log10 p-value. Each point represents a gene, with color coding and labels denoting the gene’s significance based on thresholds of fold change and p-value. In GSE141595, the significantly upregulated genes included SLC6A17, CRLF1, PLAG2A, and MMP3. Similarly, in GSE72815, the significantly upregulated genes included MB, CKM, IGFN1, and ACTA1. (c) displays the overlap of upregulated genes between the two datasets, revealing 78 genes commonly upregulated in the intersection of GSE141595 and GSE72815. This significant overlap mutually validates the reliability of the differential analysis data for the two gene sets. (d,e) present the total number of upregulated genes in the two datasets, amounting to 409 after excluding duplicate entries.
Figure 1 Aging Genome Analysis plots (a,b) illustrating the differentially expressed genes in the GSE141595 and GSE72815 datasets. The horizontal axis represents the log2 fold change, and the vertical axis represents the −log10 p-value. Each point represents a gene, with color coding and labels denoting the gene’s significance based on thresholds of fold change and p-value. In GSE141595, the significantly upregulated genes included SLC6A17, CRLF1, PLAG2A, and MMP3. Similarly, in GSE72815, the significantly upregulated genes included MB, CKM, IGFN1, and ACTA1. (c) displays the overlap of upregulated genes between the two datasets, revealing 78 genes commonly upregulated in the intersection of GSE141595 and GSE72815. This significant overlap mutually validates the reliability of the differential analysis data for the two gene sets. (d,e) present the total number of upregulated genes in the two datasets, amounting to 409 after excluding duplicate entries.CC BY · PubMed Central
Figure 2 Senescence-related Differential Gene Enrichment Analysis. BP: The enrichment analysis of biological processes of aging-related core DEGs demonstrated their predominant involvement in extracellular matrix organization and extracellular structure organization. CC: The enrichment analysis of cellular components of aging-related core DEGs revealed a concentration of these genes in the Z disk, I band, and sarcomere, confirming their origin from bone tissue cells. MF: The enrichment analysis of molecular functions emphasized protein phosphatase inhibitor activity, metalloendopeptidase activity, phosphatase inhibitor activity, and CCR chemokine receptor binding, all closely associated with the aging process.
Figure 2 Senescence-related Differential Gene Enrichment Analysis. BP: The enrichment analysis of biological processes of aging-related core DEGs demonstrated their predominant involvement in extracellular matrix organization and extracellular structure organization. CC: The enrichment analysis of cellular components of aging-related core DEGs revealed a concentration of these genes in the Z disk, I band, and sarcomere, confirming their origin from bone tissue cells. MF: The enrichment analysis of molecular functions emphasized protein phosphatase inhibitor activity, metalloendopeptidase activity, phosphatase inhibitor activity, and CCR chemokine receptor binding, all closely associated with the aging process.CC BY · PubMed Central
Figure 3 Molecular docking simulation verification of Temsirolimus and Nitazoxanide with the mTOR protein. (a) shows the mTOR protein’s original docking site with the Temsirolimus’s active component, (b) shows an enlarged view of the binding site between the active component of Temsirolimus and the mTOR protein, and (c) displays the binding site after docking nitazoxanide to the mTOR protein.
Figure 3 Molecular docking simulation verification of Temsirolimus and Nitazoxanide with the mTOR protein. (a) shows the mTOR protein’s original docking site with the Temsirolimus’s active component, (b) shows an enlarged view of the binding site between the active component of Temsirolimus and the mTOR protein, and (c) displays the binding site after docking nitazoxanide to the mTOR protein.CC BY · PubMed Central
Figure 4 Database Components. (a) This pie chart depicts the distribution of different databases in the entire dataset. The most significant proportion is occupied by “CellAge” at 37.4%, followed by “Aging Atlas” at 20.1%, “GSE147591” at 13.4%, “Longevity Map” at 10.9%, “HAGR” at 12.3%, and “GSE72815” at 6.1%. After preprocessing, a database of 1624 unique gene entries was obtained by removing duplicates from the remaining data. (b) The left column presents the drugs used in individual experiments, such as Cyclosporin A and Digoxin. The drugs were combined pairwise to create a new database, resulting in 1953 unique drug combinations.
Figure 4 Database Components. (a) This pie chart depicts the distribution of different databases in the entire dataset. The most significant proportion is occupied by “CellAge” at 37.4%, followed by “Aging Atlas” at 20.1%, “GSE147591” at 13.4%, “Longevity Map” at 10.9%, “HAGR” at 12.3%, and “GSE72815” at 6.1%. After preprocessing, a database of 1624 unique gene entries was obtained by removing duplicates from the remaining data. (b) The left column presents the drugs used in individual experiments, such as Cyclosporin A and Digoxin. The drugs were combined pairwise to create a new database, resulting in 1953 unique drug combinations.CC BY · PubMed Central
Figure 5 The overview of SenolyticSynergy.
Figure 5 The overview of SenolyticSynergy.CC BY · PubMed Central

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  1. Frezzato, 2020, Strategies to Fight Ibrutinib-Resistance in Chronic Lymphocytic Leukemia, Haematologica, 105, S97

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Metadata

Authors
Ye, Y., Su, T., Gao, J., & Ming, D.
Affiliations
Nanjing Tech University (CN)
Corresponding author
Ming, Dengming
ORCID
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2025-09-16
Volume
26
Issue
18
Pages
9004
ISSN
1422-0067 (ISSN-L)
DOI
10.3390/ijms26189004
PMC
PMC12469854
OpenAlex
W4414244012
Cited by
0
References
71
FWCI
0.00
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Molecular Sciences (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
Keywords
senolytics, drug combination prediction, drug synergy, aging, dasatinib, quercetin, temsirolimus, nitazoxanide, aging-related genes, differential gene expression
MeSH terms
Dasatinib, Machine Learning, Senotherapeutics, Aging, Drug Combinations, Drug Synergism, Humans, Quercetin, Genomics

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