Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing, current medical treatments for dementia are purely symptomatic and hardly effective, regardless of the advances in the molecular characterization of the disease. In the present study we developed a novel multi-relational association mining method to predict targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieved a high performance and resulted in numerous predicted drug targets including several serine threonine kinase and a G protein coupled receptor. The predicted drug targets were mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis and long-term memory. Among the highly represented kinase family, DLG4 (PSD-95) had the highest degree centrality while among the highly druggable GPCRs, the bradikynin receptor 2 was highlighted. These putative targets have been shown in previous studies to be related to memory and cognition and hold promises for the development of novel therapeutic approaches for dementia.
Figure 1. The systematic workflow of our methodological approach. Drug targets (DTs) were obtained by collecting information from different pharmaceutical company websites in the different phases of the drug discovery process (in red, yellow and orange). The interaction network of DTs then was then constructed by extracting the direct 1-step neighbors of the DT based on the i2d database (the blue nodes in the network). Following the integration of multiple and heterogeneous data types by using the MRAM method, the rules were induced to predict the potential DTs. We characterized the functionality of the potential DTs by testing over-represented Gene Ontology biological process terms and pathways.
Summary of statistically significant Gene Ontology biological processes functional annotation corresponding to the putative DT list as obtained from REVIGO. Nodes are GO terms and edges represent the strongest GO terms pairwise similarity. Colors represent the p-values (low values in green, high in red). Only significant GO terms are shown (P < 0.001).
Interaction network of drug targets including DTs and first neighbors as extracted from the i2d database. The DTs are highlighted in red.
Nguyen TP, Priami C & Caberlotto L. Novel drug target identification for the treatment of dementia using multi-relational association mining. Sci Rep. 2015 Jul 8;5:11104.