Systematic Alzheimer's disease drug repositioning (SMART) based on bioinformatics-guided phenotype screening and image-omics

项目总结

PROJECT SUMMARY Given the complexity of Alzheimer's Disease (AD) pathogenesis and the associated co-morbid conditions, both the “depth” and the “width” of currently available drug repurposing solutions need to be improved in order to deliver effective AD therapeutic solutions. The depth of a drug-repurposing project refers to the level of understanding of disease mechanism and drug-target interactions across a wide searching space for the combination of dosage and treatment time. Achieving depth requires a reliable AD model system that comprehensively recapitulates AD pathogenesis in a human brain-like environment, and sophisticated transcriptomic profiles, which can reveal molecular-level changes underlying disease-reversing phenotypes across multiple treatment conditions. The width of a therapy search relies on the efficacy of predicting and validating effects of candidate compounds from an enormous search space. Width can be achieved from novel computational algorithms connecting –omics changes with phenotypic changes, thus guiding the search with improved knowledge on mechanisms and avoiding exhaustive testing of every available drug. Integrating the systems medicine and drug repositioning expertise of the Wong Lab at the Houston Methodist Research Institute of Houston Methodist Hospital with the Alzheimer's biology expertise of the Kim and Tanzi labs at Massachusetts General Hospital, we propose a SysteMatic Alzheimer's disease drug ReposiTioning (SMART) framework based on bioinformatics-guided phenotype screening. Reformatting a novel three- dimensional human neural stem cell culture model of AD (a.k.a. Alzheimer's in a dish) developed in the Kim and Tanzi labs for high content screening, the Wong lab screened 2,640 known drugs and bioactive compounds and obtained a panel of 38 primary hits that strongly inhibit β-amyloid-driven p-tau accumulation. We hypothesize that iteratively running relatively small screens with our novel 3D cell model and applying systematic artificial intelligence modeling to the transcriptomic profiles of the screening hits will allow us to: 1) quickly obtain a panel of robust novel drug candidates for AD, and 2) gain an in-depth understanding of disease mechanisms from those repositioned drug candidates, which will subsequently improve the success rate of predicting novel hits. Using the primary 38 hits as a starting point, the SMART computational modules will update the existing NeuriteIQ software package to quantify the image data from high content screening; it will also incorporate publicly available big data transcriptomic profiles to predict candidate compounds inducing similar pathway changes as those original compounds, effectively expanding the search width to tens of thousands of compounds while only requiring functional validation of less than 100 drug candidates. The validated predictions will, in turn, add to the panel of known hits that will launch the next round of computational predictions and experimental validations, efficiently generating candidates for novel AD therapies (Aim 1). SMART's iterative prediction-validation scheme effectively connects more transcriptomic profiles to desirable phenotypic changes. Thus, we will apply systematic image-omics modeling to uncover novel mechanisms driving such phenotypes. For all the validated hits, dose-responses for the phenotype of pTau inhibition will be obtained using the 3D culture model; while the dose-responses for individual genes and pathways will be modeled through public and in-house generated transcriptomic profiles. We will use Partial Least Square Regression models to identify gene modules with matching dose-response curves as the phenotypes, thus allowing us to go beyond the confinement of canonical pathway maps and identify novel functional modules specifically related to phenotypes of interest (Aim 2). Selected compounds derived from the previous two aims will be evaluated in human neurons directly derived from AD patients and in animal models (Aim 3). Success of this work will lead to new AD therapeutic compounds ready for translation into clinical trials, as well as a deeper understanding of the molecular mechanisms of AD pathophysiology. In addition, the SMART framework for drug repositioning will be generalizable to other big data and disease platforms.