A Network Medicine framework for prioritizing repurposable drug candidates for Alzheimer’s disease while assessing the Long QT Syndrome risk as a side-effect

Alzheimer’s disease affects 6.9 million Americans aged 65 and older, a number projected to double by 2060. Although eight FDA-approved drugs are available, no cure exists, and most treatments remain symptomatic. Drug repurposing, the use of approved drugs for new indications, represents a promising strategy to address this therapeutic gap. However, despite prior safety approval, repurposable drugs can still trigger unexpected side-effects in new contexts. The landscape of side-effect prediction tools is heterogeneous, but Network Medicine stands out for its interpretability, scalability, and performance. As a holistic paradigm that analyzes system-wide molecular interactions, it enables the evaluation of therapeutic efficacy and safety through the study of the relationships between drug targets and proteins implicated in diseases and side-effects. In this thesis, we introduce a Network Medicine framework to minimize side-effect risk in drug repositioning, with a focus on QT interval prolongation, a cardiac side-effect reported in Alzheimer’s patients treated with acetylcholinesterase inhibitors. This framework combines a Mode-of-Action and Random Walk with Restart analyses to identify repositioning candidates while simultaneously assessing QT-related risk. The first analysis is based on the proximity between drug targets, disease-associated genes, and those related to the side-effect, generating a “drug action map” that places drugs according to the position of their targets within the human protein-protein interaction network. This approach makes it possible to identify the most promising candidates, proximal to the disease but distal from the side-effect. The second analysis adopts a diffusion-based strategy on the network, simulating the propagation of drug effects through protein–protein interactions. By focusing on the diffusion profiles of drugs known to be associated with QT interval prolongation, it is possible to identify not only their direct targets but also secondary ones potentially involved in the adverse effect, thereby enabling a more comprehensive assessment of QT prolongation among the candidate drugs. Using this strategy, we identified promising compounds, including acamprosate, tolcapone, sitagliptin, and diazoxide, with the potential to mitigate disease pathology. Finally, gene set enrichment analysis was employed to computationally assess the compounds’ ability to reverse disease-related gene expression signatures.