Harnessing Diverse BioInformatic Approaches to Repurpose Drugs for Alzheimer's Disease

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The exploration of genomes, transcriptomes, and proteomes derived from brains with Alzheimer's disease (AD) – including those provided by the Accelerating Medicines Partnership-AD (AMP-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 validate the importance of those pathways – distinguishing primary disease drivers from secondary events – by finding drugs that impact those pathways. 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. This bioinformatics campaign, parallel to a traditional drug campaign, uses AMP-AD data as the “laboratory” and electronic heath records(EHR) as our “clinical trial infrastructure”. Specifically, we will apply classical and network aware (prior-loaded) machine learning approaches (which have demonstrated utility in cancer-related omics datasets) to identify pathways and targets altered in AD brains at different stages of disease progression using AMP-AD data (Aim 1); and 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). Aims 1 and 2 each 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. In Aim 3, we will develop new informatics strategies to conduct in silico drug trials to validate the clinical relevance of drugs by analyzing EHR, taking advantage of the UK 20 year CPRD longitudinal records of 15M people. A second independent EHR data set, the RPDR Database (based at Mass General Hospital) with 6 M individuals followed for over 20 years, will further validate hypotheses based on the omics data sets and extant literature. This coordinated informatics program compensates for the weaknesses of each individual informatics approach to promote discovery and critical evaluation of “lead compounds” for at least some AD pathways. To execute this strategy, we have assembled a 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 DataLens 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 larger biomedical community involved in new clinical trials.