Abstract
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.
Highlights
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results
It is likely that the molecular mechanisms involved in initiation and progression of AD are obscured in end-stage RNAseq profiles as a result of the actions of myriad signaling pathways and feedback loops, leading to widespread transcriptional changes only indirectly associated with disease mechanism30–33
In this paper, we described the development of drug repurposing in Alzheimer’s disease (DRIAD), a machine learning framework for evaluating potential relationships between a disease and any biological process that can be described by a list of genes
Summary
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. An alternative is to use repurposing as a way of testing a therapeutic concept that could subsequently be advanced, with additional medicinal chemistry and functional testing, to become a NME This is potentially valuable in the case of AD in which the underlying disease mechanisms remain poorly understood and the potential for multiple distinct disease drivers exists. Repurposing drugs for AD has received increasing attention, but approaches to date have been largely hypothesis-driven, based on overlap between an existing pharmacological mechanism of action (MOA) and a putative diseasecausing mechanism or results of a clinical trial. We sought to develop a repurposing approach that made combined use of -omic datasets on drug-induced perturbation of neuronal cells and molecular changes that occur in the brains of individuals suffering from different stages of AD, as collected by the Accelerating Medicines Partnership - Alzheimer’s Disease (AMP-AD) effort. Gene expression features prominently in previous drug repositioning efforts focused on aging and AD16,22, with several approaches making use of the Connectivity Map and related tools to identify compounds that induce similar transcriptional changes in a given disease context
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