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Topology-driven trend analysis for drug discovery

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  • Lv, Yanhua
  • Ding, Ying
  • Song, Min
  • Duan, Zhiguang

Abstract

The primary goal of the present study is to discover new drug treatments by topology analysis of drug associations and their therapeutic group network. To this end, we collected 19,869 papers dated from 1946 to 2015 that are related to autism treatment from PubMed. We extracted 145 drugs based on MeSH terms and their synonyms (the total number is 6624) within the same ATC classification hierarchy and used them to find drug associations in the collected datasets. We introduced a new topology-driven method that incorporates various network analyses including co-word network, clique percolation, weak component, pathfinding-based analysis of therapeutic groups, and detection of important drug interaction within a clique. The present study showed that the in-depth analysis of the drug relationships extracted from the literature-based network sheds new light on drug discovery research. The results also suggested that certain drugs could be repurposed for autism treatment in the future. In particular, the results indicated that the discovered four drugs such as Tocilizumab, Tacrolimus, Prednisone, and Sulfisoxazole are worthy of further study in laboratory experiments with formal assessment of possible effects on symptoms, which may provide psychologists, physicians, and researchers with data-based scientific hypotheses in autism-drug discovery.

Suggested Citation

  • Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:3:p:893-905
    DOI: 10.1016/j.joi.2018.07.007
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    3. Houqiang Yu & Yian Liang & Yinghua Xie, 2024. "Predicting Scientific Breakthroughs Based on Structural Dynamic of Citation Cascades," Mathematics, MDPI, vol. 12(11), pages 1-18, June.

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