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High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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PathSynergy: a deep learning model for predicting drug synergy in liver cancer

Fengyue Zhang1, Xuqi Zhao1, Jinrui Wei2

  • 1Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, No. 100, East Daxue Road, Xixiangtang District, Nanning 530004, Guangxi, China.

Briefings in Bioinformatics
|April 24, 2025
PubMed

Insights

A new model, PathSynergy, identifies synergistic drug combinations for liver cancer, improving treatment efficacy. This approach accelerates the discovery of novel therapies to combat drug resistance and enhance patient outcomes.

Area of Science:

  • Oncology
  • Computational Biology
  • Pharmacology

Background:

  • Liver cancer is a leading cause of cancer mortality globally.
  • Advanced liver cancer has a poor 5-year survival rate (30%).
  • Current first-line targeted therapies (sorafenib, lenvatinib) face resistance issues, necessitating combination strategies.

Purpose of the Study:

  • To develop a novel predictive model, PathSynergy, for identifying synergistic drug combinations against liver cancer.
  • To overcome the limitations of traditional, time-consuming, and costly methods for discovering drug synergy.

Main Methods:

  • Integrated drug features, cell line data, drug-target interactions, and signaling pathways.
  • Employed graph neural networks and pathway map mapping.
  • Compared PathSynergy's performance against baseline models for classification, accuracy, and precision.

Main Results:

  • PathSynergy demonstrated superior performance compared to baseline models.
  • Six FDA-approved drugs (pimecrolimus, topiramate, nandrolone decanoate, fluticasone propionate, zanubrutinib, levonorgestrel) were identified as synergistic with sorafenib or lenvatinib.
  • These synergistic drug combinations against liver cancer were validated for the first time.

Conclusions:

  • The PathSynergy model offers a novel computational approach for discovering synergistic drug combinations.
  • This method has broad potential applications in drug discovery and personalized medicine for liver cancer treatment.
Keywords:
deep learningdrug synergy predictiongraph neural networkliver cancerpathway map mapping

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