Data Mining and Pathway Analysis of Drugs Using Different Tools in the Context of Rare Diseases
Posted: 7 Feb 2020
Date Written: February 6, 2020
Rare diseases are diseases which affect a very small number of the population and in most cases, are neglected by the pharmaceutical companies (orphan diseases). This is because drug development is complicated, time-consuming and expensive with extremely low success rates thus leading to the low rate of therapeutics available for rare and orphan diseases. This has led to wide scale in silico approaches to drug repurposing, thus saving a lot of time and money in the process. In our study, few drugs for a group of rare diseases known as myeloproliferative neoplasms were investigated to get a detailed idea about their mechanisms of action (MOAs) and clinical functions by statistical analysis based on P – values. Drug information, gene signatures (up regulated and down regulated genes) and associated pathways were obtained by data-mining of public databases including the DrugBank, PubChem Compound and BioAssay, and LINCS database. Pathways pertinent to clinical uses or MOAs were obtained for the drugs. The pathway enrichment analysis based on drug target information from public databases provide a novel approach for elucidating drug MOAs and repositioning, which was done using Enrichr and FunRich.
Keywords: Rare disease, myeloproliferative neoplasms, Drug recycling
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