The most prominent condition affecting adults over the age of 65 today is Alzheimer's disease (AD), with almost 11% of the elderly population affected. The pathophysiology of AD includes the build-up of amyloid-beta (A?) plaques and neurofibrillary tangles of the protein tau. Currently, no disease modifying therapies exist, and recent high-profile failures of clinical trials targeting A? pathways underscore the critical need for new therapeutic approaches to this disease. A single drug can take as long as 15 years and over 2 billion dollars to develop from bench to clinic. Recent developments in systems-level drug repositioning techniques include the Connectivity Map database, whereby an FDA approved compound is screened for potential uses in other diseases. This method has been validated in several diseases as a cost and time-effective way to approach drug discovery. However, drug repositioning has yet to be validated in AD. Apolipoprotein (apo) E4 as the greatest genetic risk factor for AD with a semi-dominant inheritance pattern. The allele is present in approximately 25% of people in the US, and 60?70% of homozygotes will develop AD in their lifetime. We have used precision medicine to leverage the complex genetics behind AD pathology to specifically interrogate apoE4-specific signature of AD in large publicly available transcriptomic data of the brain. We applied a computational drug repositioning pipeline that measures anti-correlation of a compound's transcriptomic affect in cells with the apoE4/E4 signature of AD to predict compounds that will reverse this disease fingerprint. We then validated one of the top predicted compounds, bumetanide, as effective in ameliorating the behavioral deficits in apoE4 knock-in mice. I propose to continue my research through the following three specific aims: 1) Develop and apply a classification method to identify drugs with predicted efficacy in apoE4-mediated AD via their transcriptomic similarity to bumetanide. 2) Apply a prediction method to identify drug combinations with predicted efficacy in apoE4-mediated AD using the Connectivity Map database and in vivo RNA-seq validation, and 3) Validate the identified drugs and drug combination therapy for rescuing behavioral and neuronal deficits in apoE4-KI mice. Through these aims, I will identify and validate three repurposed therapeutics with efficacy in treating apoE4-mediated AD in animal models.
The research proposed in this fellowship application will use precision medicine and computational drug repositioning techniques applied to publicly available transcriptomic data predict and validate three repurposed drug therapies with efficacy for treating apoE4-mediated Alzheimer's disease in animal models.
