Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining

Abstract

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.

Fig 1. Flow-chart of the drug repositioning strategy for AD based on ‘omics’ data mining.

We searched the GWAS Catalogue, PubMed, and HMDB database, and extracted 244 genetic variations, 14 epigenetic modifications, 98 proteins and 86 metabolites associated with AD. We also extracted 1179 protein-metabolite interactions based on the HMDB database and found 200 proteins linked to ≥2 AD associated metabolites. In total, we shortlisted 524 AD-related proteins, 8 of which were revealed by 2 ‘omics’ approaches. By using the TTD and DrugBank database, we extracted information on drugs, targets and the drugs’ mode of action. Considering AD pathogenesis together with the drugs’ mode of action, we found 19 targets of 92 drugs with anti-AD indication that may be repurposed. We then scored these targets and found CD33 and MIF to be the two highest ranked targets. A protein-protein interaction analysis of 524 AD-related proteins detected a novel network of 11 proteins with CAD as a hub protein (functional enrichment analysis revealed that 5 of these 11 proteins are involved in the “Alanine, Aspartate and Glutamate Metabolism” pathway presented in Fig 3).

Fig 2. AD related protein-metabolite network.

1179 protein-metabolite interactions were indicated from the HMDB database. The zoomed-in inset shows that acetylcholinesterase (P22303), a known anti-AD target, interacts with 2 AD-related metabolites (Choline and Acetylcholine). The nodes with yellow color represent metabolites that were altered in AD patients, the nodes with purple color represent proteins that linked to AD associated metabolites, and the nodes with green color represent proteins that linked to ≥2 AD associated metabolites.

Fig 3

A) Protein-protein interaction analysis of 524 AD-related proteins revealed two large protein clusters: the APP network (14 yellow nodes) and the CAD network (11 red nodes). B) The functional enrichment analysis found that five proteins (labeled with red stars) of the CAD network are involved in the “Alanine, Aspartate and Glutamate Metabolism” pathway (CAD, GAD1, GAD2, GFPT1, GFPT2). The figure was generated based on the results obtained by the David online tool and the KEGG database.

Fig 4. The top two anti-AD targets, MIF and CD33, affect microglial activation.

Both CD33 and the MIF receptor (CD74) are expressed on the microglial cell surface. Antibodies/inhibitors of MIF and CD33 may be assessed for their effects in modulating AD-related neuroinflammation.