Project Summary Late-onset Alzheimer's Disease (AD) is a slowly progressing, untreatable neurodegenerative disorder that affects a substantial fraction of the aging population today. Hundreds of clinical trials and massive investments into drug development efforts have so far not resulted in a single disease-modifying therapy that showed a significant beneficial effect on the disease. Drug repositioning, the application of approved drugs in a novel disease context, has gained increasing attention as a promising alternative to identify treatment options for AD. For successful pharmaceutical intervention in AD, a drug or drug combination needs to target the complex molecular changes observed in AD in a specific manner. To identify drugs exerting these desired effects a detailed understanding of the molecular networks across regulatory layers that underly the biological system is required. However, these networks are not readily available and are scattered across hundreds of studies and complex databases. To address this challenge, we propose TargetAD, a network-based framework that builds this molecular network from genetic associations, co-expression/correlation networks, metabolic pathways, gene regulation data, protein-protein interactions, and tissue-specific gene and protein expression data augmented with AD multi-omics associations, as well as drug-drug target data and molecular drug signatures. We will achieve this by leveraging the power of large-scale, multi-omics association results generated within NIH's large “Accelerating Medicines Partnership - Alzheimer's Disease” initiative and other large-scale population-based studies. The collective evidence will be stored in a publicly accessible graph database, which we then use for the identification of candidate drugs or drug combinations (“candidates”). Through the development of a novel network-based machine-learning method, we will rank candidates in the database by their probability to affect AD networks in a beneficial way. High-ranking candidates will be subjected to a comprehensive prioritization pipeline. To this end, we will retrospectively investigate whether longitudinal AD-related biomarker profiles of individuals who took a repositioning candidate show evidence for healthier aging in large studies of AD. These analyses will be complemented by examining whether the post- mortem neuropathological burden supports a beneficial effect of the candidate. To increase power and coverage of candidates, we will further analyze electronic health records from the UK Biobank for additional evidence. The three most promising candidates will be selected in discussion with a panel of experts. These will be evaluated by preclinical validation studies in animal models of AD. In summary, the unique combination of multidisciplinary expertise, access to high-profile datasets and advanced computational integration pipelines will allow us to identify molecular pathways disturbed in AD that are targetabl...