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Text Mining-Based Drug Discovery in Osteoarthritis

Rong-Guo Yu1, Jia-Yu Zhang2, Zhen-Tao Liu3

  • 1Department of Orthopedics, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou 350007, Fujian, China.

Journal of Healthcare Engineering
|May 6, 2021
PubMed
Abstract

Insights

Bioinformatics identified 15 key genes and 32 potential drugs for osteoarthritis (OA) treatment. Eight genes are targeted by 53 drugs, offering new avenues for OA therapy and drug repositioning.

Area of Science:

  • Bioinformatics and computational biology
  • Genomics and molecular biology
  • Pharmacology and drug discovery

Background:

  • Osteoarthritis (OA) is a chronic joint disease causing pain and stiffness.
  • Current treatments, including NSAIDs, have limited efficacy.
  • Novel therapeutic targets and drugs are needed for OA management.

Purpose of the Study:

  • To identify potential genes and drugs associated with osteoarthritis using bioinformatics.
  • To explore new therapeutic strategies for OA through data mining and analysis.

Main Methods:

  • Text mining to identify OA and NSAID-related genes.
  • Gene ontology (GO), KEGG pathway, and protein-protein interaction (PPI) network analysis.
  • Quantitative real-time polymerase chain reaction (qRT-PCR) for gene expression validation.
  • Drug Gene Interaction Database query for drug discovery.

Main Results:

  • Fifteen genes showed significantly increased expression in OA samples.
  • Eight of these genes are targeted by 53 drugs.
  • Twenty-one existing OA drugs and 32 novel drugs (FDA-approved for other conditions) were identified.

Conclusions:

  • Identified 15 genes and 32 potential drugs for improving OA treatment.
  • Highlighted opportunities for drug repositioning and novel target pharmacology in OA.
  • The study provides a foundation for developing new therapeutic strategies for osteoarthritis.

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