In silico prioritiziation of drug repositioning candidates for Alzheimer’s disease using signature search meta‐analysis

Abstract

Abstract Background Alzheimer’s disease (AD), the most common form of dementia, is a progressive neurodegenerative disorder with no effective treatment. Due to the genetic, pathological, and clinical heterogeneity among patients with AD, traditional drug development has struggled to target AD‐related pathways. Drug repositioning is a promising alternative to de novo drug development that investigates the potential of existing drugs to be effective in AD. Transcriptome signature matching is one repositioning approach that relies on the signature reversion principle to identify candidate drugs. In this study, we report on several potential candidate drugs for AD obtained by meta‐analyzing transcriptome‐based signature search methods on AMP‐AD nominated target genes for AD that are differentially expressed across seven brain regions. Method As search input, we used genes that are nominated as AD targets by the AMP‐AD initiative and significantly differentially expressed in at least one brain region. We compared their expression signatures against the CMAP and LINCS reference databases via four signature search methods (CMAP, LINCS, gCMAP, and correlation‐based methods). Significant candidate drugs were ranked based on their score for each search method and then meta‐analyzed using an ensemble rank aggregation approach employing a modified version of the Dowdall rule. Top‐scoring candidate drugs for AD were annotated using literature search and functional enrichment analysis results. Result Highest‐scoring drugs in the meta‐analysis included psycholeptics and psychoanaleptics (including acetylcholinesterase inhibitors), antibiotics, antivirals, histone deacetylase (HDAC) inhibitors, and anti‐inflammatory agents. Among those were drugs that are already used for symptomatic treatment of AD or are currently investigated in clinical trials. Novel candidate drugs reported here include antipsychotics, corticosteroids, and calcium channel blockers. Functional enrichment analysis of known targets of these candidate drugs showed shared targeting of biological processes related to dopaminergic synaptic transmission, calcium ion transport regulation, and alkaloid metabolic process Conclusion Gene expression signature matching is a promising approach for in silico drug repositioning. Our results describe several novel potential candidate drugs for AD obtained through the signature reversion principle, demonstrating the utility of meta‐analyzing data across brain regions and signature search strategies in prioritizing drug repositioning candidates for AD.