Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data

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Project Summary: Alzheimer’s disease (AD) and AD-related dementias (ADRD) is the 6th leading cause of death affecting about 5.7 million Americans. Generally, one in five women and one in ten men are expected to develop AD/ADRD; and the number of people living with AD/ADRD is expected to grow to 14 million in the next two decades. The quality of life of AD/ADRD patients is gradually diminished and caring for AD/ADRD patients imposes tremendous emotional and financial burden on family caregivers, communities, and healthcare systems. However, up until now, there is no cure and not even effective treatment for AD/ADRD patients, probably due to the complex mechanisms involved in the pathogenesis of AD/ADRD. As drug development is becoming increasingly expensive and time-consuming (with estimated cost from $648 million8 to $2.5 billion9 and an average of 9-12 years for new drugs), drug repurposing, aiming to discover new uses of existing drugs, is one potential solution to speed up the drug development for AD/ADRD. However, previous attempts on drug repurposing for AD/ADRD based on omics data have not been successful so far, indicating that animal models may not translate to humans as readily as hoped. New methods that can speed up drug development for AD/ADRD are needed. In this study, we propose to detect drugs that can be potentially repurposed for AD/ADRD using 4 unique EHR data sets. This study will address the critical challenges of EHR-based drug repurposing including incomplete patient’s information and misclassification error associated bias. Aim 1 will focus on a drug repurposing knowledgebase for AD/ADRD, natural language processing methods to extract risk factors from clinical narratives, and phenotyping algorithms to accurately identify MCI and AD/ADRD patients to support the patient cohort construction. In Aim 2, we will develop drug repurposing methods that account for the high-dimensional of risk factors and misclassification error associated bias and apply them to detect drug repurposing signals using large collections of EHRs from (1) the OneFlorida network (2) the Cerner Health Facts database, (3) EHR from physician practice at University of Texas Health Science Center at Houston, and (4) EHR data from the University of Pennsylvania. In Aim 3, we propose to validate the top-ranked signals through a prospective cohort study. We will recruit patients and routinely collect detailed pragmatic information and genotypes to validate the efficacy of the identified drug signals. The success of our study will: (1) produce a knowledgebase with timely updated risk factors, biomarkers, genotypes, and drug signals for AD/ADRD, (2) develop an open- source drug repurposing package - RAIDER (Repurposing Alzheimer Impacting Drugs using Electronic health Records) for AD/ADRD, and (3) generate drug repurposing signals validated in a prospective cohort study, which will inform the design of future large-scale national trials for AD/ADRD.