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Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov

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  • Jeong, Yoo Kyung
  • Heo, Go Eun
  • Kang, Keun Young
  • Yoon, Dong Sup
  • Song, Min

Abstract

Increasing interest in developing treatments for pancreatic cancer has led to a surge in publications in the field. Analyses of drug-research trends are needed to minimize risk in anti-cancer drug development. Here, we analyzed publications on anti-cancer drugs extracted from PubMed records and ClinicalTrials datasets. We conducted a drug cluster analysis by proposing the entity Dirichlet Multinomial Regression (eDMR) technique and in-depth network analysis of drug cluster and target proteins. The results show two distinct research clusters in both the ClinicalTrials dataset and the PubMed records. Specifically, various targets associated with anti-cancer drugs are investigated in new drug testing while the diverse chemicals are studied together with a standard therapeutic agent in the academic literature. In addition, our study confirms that drug research published in PubMed is preceded by clinical trials. Although we only evaluate drugs for pancreatic cancer in the present study, our method can be applied to drug-research trends of other diseases.

Suggested Citation

  • Jeong, Yoo Kyung & Heo, Go Eun & Kang, Keun Young & Yoon, Dong Sup & Song, Min, 2016. "Trajectory analysis of drug-research trends in pancreatic cancer on PubMed and ClinicalTrials.gov," Journal of Informetrics, Elsevier, vol. 10(1), pages 273-285.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:1:p:273-285
    DOI: 10.1016/j.joi.2016.01.003
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    Cited by:

    1. 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.
    2. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).

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