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Updated: Aug 6, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Deep Learning-Driven Anticancer Drug Discovery: Emodepside as a Potential Therapeutic Candidate for Triple-Negative
Yiyue Xu1, Taotao Dong2, Butuo Li1
1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong250117, China.
Abstract:
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.
Insights
A deep learning framework identified emodepside as a novel therapeutic for triple-negative breast cancer (TNBC). This drug shows potent antitumor effects and targets NAMPT, offering a new treatment avenue for this aggressive cancer.
Area of Science:
- Oncology
- Computational Biology
- Drug Discovery
Background:
- Triple-negative breast cancer (TNBC) is aggressive with limited treatment options.
- Drug repurposing accelerates discovery but faces biological complexity.
- Novel therapeutic strategies are crucial for TNBC management.
Purpose of the Study:
- Develop a deep learning framework for accelerated TNBC drug discovery.
- Identify novel therapeutic candidates and potential drug targets for TNBC.
- Validate drug efficacy and elucidate molecular mechanisms.
Main Methods:
- Deep neural network for predicting compound efficacy and toxicity.
- Screening of over 6,000 compounds from the Drug Repurposing Hub.
- In vitro and in vivo validation, transcriptomic sequencing, and proteomics.
Main Results:
- Identified emodepside, a structurally unique compound with potent anti-TNBC efficacy.
- Emodepside inhibited tumor growth in xenograft models.
- NAMPT identified as the primary molecular target via multiomics analysis.
Conclusions:
- Deep learning effectively identifies novel, structurally distinct anticancer agents.
- Emodepside is a promising therapeutic candidate for TNBC.
- Emodepside may exert anti-TNBC effects by inhibiting NAMPT and promoting apoptosis.
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