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Graph Theory Enables Drug Repurposing – How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action

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  • Ruggero Gramatica
  • T Di Matteo
  • Stefano Giorgetti
  • Massimo Barbiani
  • Dorian Bevec
  • Tomaso Aste

Abstract

We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.

Suggested Citation

  • Ruggero Gramatica & T Di Matteo & Stefano Giorgetti & Massimo Barbiani & Dorian Bevec & Tomaso Aste, 2014. "Graph Theory Enables Drug Repurposing – How a Mathematical Model Can Drive the Discovery of Hidden Mechanisms of Action," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0084912
    DOI: 10.1371/journal.pone.0084912
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    3. Joaquín Goñi & Andrea Avena-Koenigsberger & Nieves Velez de Mendizabal & Martijn P van den Heuvel & Richard F Betzel & Olaf Sporns, 2013. "Exploring the Morphospace of Communication Efficiency in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
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    1. Ondřej Filip & Karel Janda & Ladislav Krištoufek, 2018. "Ceny biopaliv a souvisejících komodit: analýza s použitím metod minimální kostry grafu a hierarchických stromů [Prices of Biofuels and Related Commodities: an Analysis Using Methods of Minimum Span," Politická ekonomie, Prague University of Economics and Business, vol. 2018(2), pages 218-239.

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