Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining
Traditional drug development for Alzheimer's disease (AD) is costly, time consuming and burdened by a very low success rate. An alternative strategy is drug repositioning, redirecting existing drugs for another disease. The large amount of biological data accumulated to date warrants a comprehensive investigation to better understand AD pathogenesis and facilitate the process of anti AD drug repositioning. Hence, we generated a list of anti AD protein targets by analyzing the most recent publically available 'omics' data, including genomics, epigenomics, proteomics and metabolomics data. The information related to AD pathogenesis was obtained from the OMIM and PubMed databases. Drug target data was extracted from the DrugBank and Therapeutic Target Database. We generated a list of 524 AD related proteins, 18 of which are targets for 75 existing drugs novel candidates for repurposing as anti AD treatments. We developed a ranking algorithm to prioritize the anti AD targets, which revealed CD33 and MIF as the strongest candidates with seven existing drugs. We also found 7 drugs inhibiting a known anti AD target (acetylcholinesterase) that may be repurposed for treating the cognitive symptoms of AD. The CAD protein and 8 proteins implicated by two 'omics' approaches (ABCA7, APOE, BIN1, PICALM, CELF1, INPP5D, SPON1, and SOD3) might also be promising targets for anti AD drug development. Our systematic 'omics' mining suggested drugs with novel anti AD indications, including drugs modulating the immune system or reducing neuroinflammation that are particularly promising for AD intervention. Furthermore, the list of 524 AD related proteins could be useful not only as potential anti AD targets but also considered for AD biomarker development.
PloS one