Alzheimer disease (AD) is the most common cause of dementia and one of the leading sources of morbidity and mortality in the aging population. Despite enormous social and economic costs associated with AD, current drugs are directed towards symptomatic relief and none are curative. In this project titled ?An integrated reverse engineering approach toward rapid drug repositioning for Alzheimer?s disease,? we propose to develop an innovative integrated drug repositioning strategy that combines computation-based drug prediction, computation-based human brain-blood-barrier (BBB) permeability prediction, retrospective large-scale clinical corroboration, and prospective experimental testing to rapidly identify anti-AD drug candidates. First, we will develop novel computational approaches to identify repositioning anti-AD candidates from all (>2,600) FDA-approved drugs. Second, we will develop novel multifaceted biology-based computational methods to predict which repositioned drug candidates can cross BBB in humans. Third, we will perform large-scale retrospective case-control studies to corroborate the clinical efficacy of repositioned drug candidates using patient electronic health record (EHR) data of >50 million patients. Finally, we will evaluate the therapeutic potential of promising repositioned candidates in experimental models. Our study will generate a large amount of data/knowledge/hypotheses that could serve as a starting point for us and others to conduct hypothesis-driven drug repositioning studies in other animal models of AD and in AD patients. We will build a comprehensive Alzheimer Drug Repositioning Knowledge Base (ADRKB) and develop interactive web applications to make ADRKB publicly available. The unique and powerful strength of our project is our ability to seamlessly combine novel computational predictions, retrospective clinical corroboration using patient EHRs, and experimental testing in animal models of AD to rapidly identify innovative drug candidates that may work in real-world AD patients. The repositioned drug candidates will have interpretable mechanisms of action, are highly likely to cross BBB in humans, have clinical effectiveness evidence gathered from ?real-world? AD patients, and have demonstrated efficacy in mouse models of AD. We anticipate that these findings can be expeditiously translated into clinical trials and benefit 5.4 million AD patients in United States and 47 million AD patients worldwide.
! Alzheimer?s disease (AD) is a chronic and progressive neurodegenerative disorder that causes enormous personal and societal burdens. Currently there exist no cures for AD. In this project titled ?An integrated reverse engineering approach toward rapid drug repositioning for Alzheimer?s disease,? we propose to develop an innovative integrated drug repositioning strategy that combines novel computational drug prediction, novel computational brain-blood-barrier (BBB) permeability prediction, retrospective large-scale clinical corroboration, and prospective experimental testing to rapidly identify repositioned anti-AD drug candidates. The output of our project will be a list of ranked repositioned drug candidates with interpretable mechanisms of action, high BBB permeability in humans, supporting clinical efficacy evidence gathered from ?real-world? AD patients, and demonstrated efficacy in mouse models of AD. We anticipate that these findings can be expeditiously translated into clinical trials and benefit AD patients. Our study will generate a large amount of data/knowledge/hypotheses that could serve as a starting point for others to conduct hypothesis- driven biomedical and clinical drug repositioning studies in different animal models of AD and in patients.
| Belur Nagaraj, Anil; Joseph, Peronne; Kovalenko, Olga et al. (2018) Evaluating class III antiarrhythmic agents as novel MYC targeting drugs in ovarian cancer. Gynecol Oncol 151:525-532 |
| Zheng, Chunlei; Xu, Rong (2018) Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data. BMC Bioinformatics 19:500 |
