Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network

Authors

Ko-Hong Lin
Kang-Lin Hsieh
Xiaoqian Jiang
Yejin Kim

Publication Date

1-1-2023

Journal

AMIA Summits on Translational Science Proceedings

Abstract

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, accompanying symptoms, or cotreating AD pathology and its common comorbidities. Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

PMID

37350918

PMCID

PMC10283123

PubMedCentral® Posted Date

6-16-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes