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Discovering the anti-cancer phytochemical rutin against breast cancer through the methodical platform based on traditional medicinal knowledge

  • Jungwhoi Lee*
  • , Jungsul Lee*
  • , Woo Gwang Sim
  • , Jae Hoon Kim
  • , Chulhee Choi*
  • , Jongwook Jeon
  • *Corresponding author for this work
  • Jeju National University
  • Korea Advanced Institute of Science and Technology
  • University of California at San Francisco
  • Ilias Biologics Inc.

Research output: Contribution to journalJournal articlepeer-review

Abstract

A number of therapeutic drugs have been developed from functional chemicals found in plants. Knowledge of plants used for medicinal purposes has historically been transmitted by word of mouth or through literature. The aim of the present study is to provide a systemic platform for the development of lead compounds against breast cancer based on a traditional medical text. To verify our systematic approach, integrating processes consisted of text mining of traditional medical texts, 3-D virtual docking screening, and in vitro and in vivo experimental validations were demonstrated. Our text analysis system identified rutin as a specific phytochemical traditionally used for cancer treatment. 3-D virtual screening predicted that rutin could block EGFR signaling. Thus, we validated significant anticancer effects of rutin against breast cancer cells through blockade of EGFR signaling pathway in vitro. We also demonstrated in vivo anti-cancer effects of rutin using the breast cancer recurrence in vivo models. In summary, our innovative approach might be proper for discovering new phytochemical lead compounds designing for blockade of malignant neoplasm including breast cancer.

Original languageEnglish
Pages (from-to)594-599
Number of pages6
JournalBMB Reports
Volume56
Issue number11
DOIs
StatePublished - 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • 3-D virtual screening
  • EGFR
  • In-silico
  • Medical classic
  • Rutin
  • Text-mining

Quacquarelli Symonds(QS) Subject Topics

  • Biological Sciences

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