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Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimers Disease


Abstract The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (AD) by powerful computational tools has the potential of developing new knowledge, including the identification of pathways and targets that may be involved in the initiation and/or progression of the disease. The challenge is to find drugs that impact those pathways, and then validate the importance of those pathways ? distinguishing primary disease drivers from secondary events. Repurposing FDA-approved drugs is one approach to probe potential pathways in proof of concept, and ultimately therapeutic, clinical trials. Here, we propose to discover and validate hypotheses for drug repurposing in AD through three integrated, complementary informatics approaches. Specifically, we will apply classical and network aware (prior-loaded) machine learning approaches to identify pathways and targets altered in AD brains at different stages of disease progression using data from Accelerating Medicines Partnership-AD available through Synapse (Aim 1); we will use systems pharmacology approaches to discover the target selectivity of lead compounds in human neuronal and glial cell types using unbiased RNA-seq, proteomic and imaging studies followed by pathway analysis (Aim 2). Each of these two Aims has two approaches: data-driven, hypothesis-generating analyses to discern disease-relevant drug signals; and hypothesis-testing in which positive findings from one approach are evaluated using the other approaches to assess rigor and reproducibility. Moreover, RNA-seq and proteomic data collected in cultured human CNS cell types following exposure to potential disease drivers and/or FDA-approved drugs in Aim 2 will be fed back into Aim 1 as CNS-cell type-derived priors to refine the predictive models. In Aim 3, we will develop new informatics strategies to conduct in-silico drug trials in EHR data with ?prospective? outcomes to validate hypotheses based on the omics data sets and extant literature, using two big data sets: the UK 20 year CPRD longitudinal records of 20M National Health Service patients, and the RPDR Database (based at Partners Healthcare) with 6 M individuals followed for over 20 years. This integrated informatics program compensates for the limitations of each individual informatics approach to promote discovery and critical evaluation of ?lead compounds? for known and novel AD pathways. To execute this strategy, we have assembled a multi-site, multi-disciplinary team with expertise ranging from clinical care to computer science and systems pharmacology. Some of the team members are AD experts and others bring an outsider's perspective. Finally, as a deliverable, we will create open-source data packages to release all the supporting evidence, software, and data with provenance in accordance with FAIR (findable, accessible, interoperable and reproducible) standards through Synapse and the AlzDataLens platform developed at MGH (Aim 4). These data packages will help to prioritize follow on clinical and translational studies including collaborations with industry or members of the community at large involved in new clinical trials.

Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.