Graph theory enables drug repurposing. How a mathematical model can drive the discovery of hidden Mechanisms of Action

TL;DR

This paper presents a novel graph-based methodology leveraging natural language biomedical knowledge and stochastic analysis to identify hidden mechanisms of drug action, aiding drug repurposing especially for rare diseases.

Contribution

The authors introduce a new graph-theoretic approach combined with computational linguistics to automatically discover and rank potential drug-disease relations and mechanisms of action.

Findings

Successfully retrieves known mechanisms of action.

Identifies new potential modes of action.

Effective in drug repurposing for rare diseases.

Abstract

We introduced 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. These paths are ranked according to their relevance, exploiting a measure induced by a stochastic process defined on the graph. Here we show, providing real-world examples, how the method successfully retrieves known pathophysiological Mode of Actions 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.