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Predicting new drug indications from network analysis

Author

Listed:
  • Yousoff Effendy Mohd Ali

    (Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Kiam Heong Kwa

    (Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Kurunathan Ratnavelu

    (Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:09:n:s0129183117501182
    DOI: 10.1142/S0129183117501182
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    Cited by:

    1. 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).

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