Abstract
Alzheimer's disease affects 6.9 million Americans aged 65 and older, a number expected to double by 2060. Eight FDA-approved drugs target Alzheimer's, but no cure is available, and most treatments are symptomatic. Drug repurposing, the use of FDA-approved drugs for new indications, is a promising strategy to address this lack of effective therapies. However, despite prior safety approval, repurposable drugs may still trigger unexpected side-effects in new contexts. This study introduces a network-based approach to minimize side-effect risk in drug repositioning, focusing on QT interval prolongation, a cardiac side-effect observed in Alzheimer's patients treated with acetylcholinesterase inhibitors. The method integrates Mode-of-Action and Random Walk with Restart analyses to identify repositioning candidates while assessing QT-related risk. This strategy identified promising compounds including acamprosate, tolcapone, sitagliptin, and diazoxide, with potential to mitigate disease pathology. Gene set enrichment analysis was used to computationally assess the compounds' ability to reverse disease-related gene expression signatures.