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Centrality analysis in a drug network and its application to drug repositioning

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  • Keng, Ying Ying
  • Kwa, Kiam Heong
  • Ratnavelu, Kurunathan

Abstract

Centrality measures play a vital role in network analysis by which important nodes within a network are identified from structural perspectives. In this study, we applied three fundamental centrality measures (degree, closeness, and betweenness) to analyze a drug network where drugs are connected based on their side-effect similarities. The results suggest that centralities of drugs in the network may have a significant implication in drug repositioning – a process of discovering new therapeutic uses of existing drugs. Given a particular disease, the drugs that have been approved for treating it were ranked by their centralities. It is shown that the top central ones among them are more likely to repurpose their neighboring drugs as new treatment options for the disease, as compared to their random and peripheral counterparts. Our predictions have proved to be in line with clinical interests indicated by the existing clinical studies in ClinicalTrials.gov database. The present work offers novel insights into complementing drug repositioning efforts while portraying the significance of network centrality measures in guiding systematic analysis for a successful network application.

Suggested Citation

  • Keng, Ying Ying & Kwa, Kiam Heong & Ratnavelu, Kurunathan, 2021. "Centrality analysis in a drug network and its application to drug repositioning," Applied Mathematics and Computation, Elsevier, vol. 395(C).
    • Handle: RePEc:eee:apmaco:v:395:y:2021:i:c:s0096300320308237
    DOI: 10.1016/j.amc.2020.125870
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    References listed on IDEAS

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    1. Li, Chao & Wang, Li & Sun, Shiwen & Xia, Chengyi, 2018. "Identification of influential spreaders based on classified neighbors in real-world complex networks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 512-523.
    2. Wang, Juan & Li, Chao & Xia, Chengyi, 2018. "Improved centrality indicators to characterize the nodal spreading capability in complex networks," Applied Mathematics and Computation, Elsevier, vol. 334(C), pages 388-400.
    3. Yousoff Effendy Mohd Ali & Kiam Heong Kwa & Kurunathan Ratnavelu, 2017. "Predicting new drug indications from network analysis," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(09), pages 1-19, September.
    4. DiMasi, Joseph A. & Hansen, Ronald W. & Grabowski, Henry G., 2003. "The price of innovation: new estimates of drug development costs," Journal of Health Economics, Elsevier, vol. 22(2), pages 151-185, March.
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