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.

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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