Deep Learning-Driven Anticancer Drug Discovery: Emodepside as a Potential Therapeutic Candidate for Triple-Negative Breast Cancer
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Le résumé fourni par la source
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with a poor prognosis. The absence of effective targeted therapies and endocrine treatment options leads to limited therapeutic options, which remains one of the major clinical challenges in TNBC management. Drug discovery is typically a lengthy and costly process that could be significantly improved through drug repurposing. However, the biological complexity and insufficient repurposing strategies hinder the reuse. This study aims to develop a deep learning-based framework to accelerate drug discovery for TNBC, identify novel therapeutic candidates, and uncover potential drug targets. We developed a deep neural network framework to predict the anticancer efficacy, toxicity profiles, and structural similarities of compounds. By applying this platform to screen over 6,000 compounds from the Drug Repurposing Hub, we identified promising candidates with potential therapeutic efficacy and safety profiles against TNBC. The top-predicted compounds were subsequently validated through comprehensive in vitro and in vivo functional assays. Furthermore, we employed transcriptomic sequencing and mass spectrometry-based proteomics to elucidate the molecular mechanisms underlying the anti-TNBC activity. We identified emodepside, a structurally unique molecule diverging from conventional anticancer agents that exhibited potent antitumor efficacy across multiple TNBC cell lines. Significantly, emodepside administration (5 mg/kg) inhibited tumor growth in xenograft models. Integrated multiomics analyses (RNA-seq/CETSA-MS) identified NAMPT as the primary target. This study demonstrates the viability of our deep learning models to discover structurally novel anticancer agents that are distinct from conventional drugs, thereby expanding the therapeutic arsenal for TNBC patients. Emodepside emerges as a promising TNBC therapeutic candidate, with a possible mechanism of promoting TNBC cell apoptosis via NAMPT inhibition.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep Learning-Driven Anticancer Drug Discovery: Emodepside as a Potential Therapeutic Candidate for Triple-Negative Breast Cancer
- Date Crossref
- 22/07/2026
- Éditeur
- American Chemical Society (ACS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shandong First Medical University pays non établi dans la noticeUniversité ou école supérieure
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Qilu Hospital of Shandong University pays non établi dans la noticeÉtablissement de santé
Shandong First Medical University et Qilu Hospital of Shandong University.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.