Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder caused by interactions among multiple genetic and environmental factors. Apolipoprotein (apo) E4 has been identified as the major genetic risk factor for AD. It increases the risk and lowers the age of onset of AD in a gene dose?dependent manner. The genetic complexity and multifactorial nature of AD pose unique challenges for developing effective therapies and suggest the need for a precision medicine approach that takes into account individual variability. For the past several decades, new drug development efforts to target specific AD-related pathways have shown promise in animal studies, only to fail during human trials. Since the process of developing new drugs for AD is complicated and takes a long time and the related costs are extremely high, there is a pressing need to consider unconventional drug development strategies, such as repositioning drugs currently used for other conditions. The approach of drug repositioning has a number of advantages over the development of new drugs and has been applied successfully to various disease conditions. However, attempts at drug repositioning for AD treatment usually target specific AD-related pathways or mechanisms and have been largely unsuccessful or still under development. As we learn about the complexity of AD genetics and pathogenesis and the associated co-morbid conditions, it is becoming clear that efficacious treatments of AD will likely need to target multiple aspects of the disease and be directed towards several pathogenic processes, and as a result will likely require precision medicine and combination therapy of the repurposed drugs. The recent convergence of two factors presents an unprecedented opportunity to advance rational drug repositioning and data-driven development of drug combinations. First is the availability of public datasets from large-scale genomic, transcriptomic, and other molecular profiling databases. Second is the development of computational approaches and the network concept of drug targets and the power of phenotypic screening, which allows us to investigate the ability of one or more therapeutic agents to perturb entire molecular networks away from disease states. This proposal aims to capitalize on this promise by accomplishing three aims: (1) to analyze publically available, large-scale transcriptomic datasets of AD patients and age-matched controls to identify apoE genotype-specific gene expression signatures of AD, (2) to pursue drug repositioning based on apoE genotype-specific gene expression signatures of AD and validate the top drug candidates in apoE genotype-specific mouse models of AD, and (3) to explore combination therapy using drug repositioning based on apoE genotype-specific signatures of AD and validate the predicted combination candidates in apoE genotype-specific mouse models of AD. The outcomes of the proposed studies will shed light on the pathogenesis of AD and potentially identify existing drugs for treating or preventing AD.
The goal of this project is to analyze publically available, large-scale transcriptomic datasets of AD patients and age-matched controls to identify apoE genotype-specific gene expression signatures of AD and to pursue drug repositioning based on apoE genotype-specific gene expression signatures of AD and validate the top drug candidates in mouse models of AD. The outcomes of the proposed studies will shed light on the pathogenesis of AD and potentially identify existing drugs for treating or preventing AD.
