Predictive Networks-based in-silico approach for Precision Medicine-repurposing for Alzheimer's Disease

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Project Summary Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide. This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. Recently, NIA organized large-scale efforts, through AMP-/M2OVE-AD consortia, has generated the richest genotype, genomic and clinical data, which enabled an unprecedented opportunity to explore the enormous complexity of AD pathogenesis. On the other hand, through all failed clinic trials, we learned that an efficacious treatment would need to target multiple aspects of the disease and be directed towards several pathogenic processes in AD. Moreover, patients with different sex and risk factor will respond differently to the same treatment due to distinct pathological mechanisms, therefore, it became extremely critical to develop patient-specific therapeutic targets and precision medicine for each patient sub-group. However, despite tremendous interests in advancing therapy and drug development for AD, there is a paucity of advanced bioinformatics approaches available to guide the effective and efficient development of drugs and de-risk investment in these expensive therapeutic approaches. We respond to the PAR (PAR-17-032) with the goals 1) to apply novel computational systems biology approach, i.e. top-down and bottom-up predictive network for short), to analyze the existing rich genetics, genomics, proteomics, metabolomics, and clinical datasets in AMP-AD and other datasets in AD and 2) to build network models and to predict therapeutic targets of single-cell type and multi-cell cross-talk pathways contributing to the onset and progression of AD pathology; 3) to stratify patients into sub-groups according to Sex, APOE and disease-stage (whenever clinical data available) and to predict therapeutic targets for each sub-group of patients towards precision medicine (drug repurposing) in AD; 4) to use novel in- silico prediction pipeline to prioritize therapeutic targets; 5) to repurpose FDA-approved, investigational, and experimental drugs binding to prioritized therapeutic targets through (known) on-targets and/or (predicted by docking) off-targets; 6) to in-silico evaluate repurposed drugs: efficacy, toxicity, mechanism, transability through BBB; 7) to evaluate prioritized drug/combination using in-vitro and in-vivo AD models.