bioRxivSystems BiologyPreprint
Network pharmacology-based discovery and experimental validation of novel drug repurposing candidates in Alzheimer's Disease
Attila JonesiD, Tina Loeffler, Evan Wu, Vijay R. VarmaiD, Hae Kyung ImiD, Madhav ThambisettyiD
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Abstract
Despite a growing body of evidence implicating genetic variants and proteins encoded by them with risk and pathogenesis of Alzheimers disease (AD), this knowledge has not been successfully translated into effective AD treatments. We integrated current genomic, transcriptomic and proteomic profiles of AD into a network pharmacology framework that leverages comprehensive gene-gene and drug-target interactions. This approach allowed us to screen 2,413 drugs for repurposing opportunities in AD. Computational validation and drug prioritization was followed by experimental validation in 33 cell culture-based phenotypic assays combined with Bayesian hypothesis testing. Our network-based screen rediscovered drugs in clinical trials for AD, providing computational validation. Besides many cancer drugs, the screen identified three drugs previously implicated in AD-related endophenotypes: the primary bile acid chenodiol, arundine (3,3-diindolylmethane), and cysteamine. In analysis of results from culture-based phenotypic assays, large Bayes factors supported the hypothesized benefits of arundine and the chenodiol derivative, tauroursodeoxycholic acid (TUDCA), in amyloid-{beta} clearance and release and neuroinflammation. Follow-up network analyses mechanistically implicated Regulator of G protein signaling 4 (RGS4) in the plausible therapeutic actions of arundine and TUDCA. A network pharmacology approach identified TUDCA and arundine as promising repurposing candidates in AD that rescue disease-relevant molecular phenotypes by acting on AD-associated genes through regulation of G protein signaling.
- organism: human
- assay: proteomics
- assay: bulk RNA-seq
- assay: GWAS summary statistics
- research focus: drug response / drug discovery
- approach: statistical model
- approach: Bayesian inference
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