Evidence based computational drug repositioning candidate screening pipeline design: Case Study

Abstract

Traditional drug development is time and cost consuming process, conversely, drug repositioning is an emerging approach to discover novel usages of existing drugs with a better risk-versus-reward trade-off. Computational technology is playing a key role in drug repositioning to screening the best drug repositioning candidates from a large candidate library. Recent efforts made for computer aided drug repositioning are mostly focusing on applying/developing data mining algorithms against wild type of large scale of biomedical data. In this paper, we introduce a novel computational pipeline designed for drug repositioning candidate screening based on existing phenotypical association (disease-disease association) discovery and pathway enrichment analysis by exploring systems biology data relevant to the interested phenotypical association specifically. To demonstrate usability and evaluate efficacy of this novel pipeline, we successfully conducted a case study by identifying potential drug repositioning candidates for Alzheimer's disease (AD) based on the studied phenotypical association between cancer and AD.