title: Graph theory enables drug repurposing--how a mathematical model can drive the discovery of hidden mechanisms of action.
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.
authors.affiliation: Department of Mathematics, King's College London, London, United Kingdom ; Therametrics AG, Stans, Switzerland.
authors.forename: Ruggero
authors.initials: R
authors.lastname: Gramatica
authors.affiliation: Department of Mathematics, King's College London, London, United Kingdom.
authors.forename: T
authors.initials: T
authors.lastname: Di Matteo
authors.affiliation: Therametrics AG, Stans, Switzerland.
authors.forename: Stefano
authors.initials: S
authors.lastname: Giorgetti
authors.affiliation: Therametrics AG, Stans, Switzerland.
authors.forename: Massimo
authors.initials: M
authors.lastname: Barbiani
authors.affiliation: Therametrics AG, Stans, Switzerland.
authors.forename: Dorian
authors.initials: D
authors.lastname: Bevec
authors.affiliation: Department of Computer Science, University College London, London, United Kingdom.
authors.forename: Tomaso
authors.initials: T
authors.lastname: Aste
mesh_terms: D001549:Benzamides / Q000627:therapeutic use; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D007562:Creutzfeldt-Jakob Syndrome / Q000188:drug therapy; D058492:Drug Repositioning / Q000379:methods*; D006801:Humans; D000068877:Imatinib Mesylate; D008962:Models, Theoretical; D010879:Piperazines / Q000627:therapeutic use; D011743:Pyrimidines / Q000627:therapeutic use; D012507:Sarcoidosis / Q000188:drug therapy; D014660:Vasoactive Intestinal Peptide / Q000627:therapeutic use
chemical_list: D001549:Benzamides; D010879:Piperazines; D011743:Pyrimidines; D014660:Vasoactive Intestinal Peptide; D000068877:Imatinib Mesylate