Abstract
Alzheimer’s disease (AD) is a triple health threat – with soaring prevalence, enormous costs and lack of
effective treatment. However, efforts in drug discovery and repurposing for the treatment of AD have had
limited success. The failure is largely attributed to the adoption of a reductionist model of “one-drug-one-gene-
one-disease”. As AD is a multi-facet complex disease, a new treatment approach is urgently needed to
simultaneously target multiple pathological processes responsible for the onset and progress of AD, some of
which are also common to other diseases that cause dementia. In this application, we will develop an
innovative translational bioinformatics approach to addressing challenges in AD drug discovery. Our approach
is based on a new paradigm of systems pharmacology, which focuses on defining multiple targets to a single
drug or a drug combination, and studying the effect of the drug(s) on perturbing disease-causing networks.
Over the last ten years, we have developed a novel structural systems pharmacology (SSP) platform that can
predict genome-wide high-resolution protein-chemical interactions and correlate molecular interactions with
phenotype responses. The SSP platform synergistically combines novel methods from machine learning,
bioinformatics, biophysics, and systems biology. We have successfully applied the SSP platform to drug
repurposing, polypharmacology, side effect prediction, precision medicine, and Genome-Wide Association
Studies. Building on our successful proof-of-concept studies, and in close collaborations with experimental
laboratories, we will develop, and rigorously test a novel SSP approach to AD drug repurposing and
polypharmacology. Firstly, we will develop a drug-gene-disease multi-layered network model (MULAN) that
links FDA-approved drugs with neurodegenerative diseases through protein-chemical interactions, gene-
disease associations, and chemical-disease associations through integrating multiple omics data. Secondly,
we will improve and apply our proven successful SSP platform, which can accurately infer novel relations from
sparse and noisy MULAN, to identify safe FDA-approved drugs that can be repurposed for AD treatment.
Finally, we will experimentally test FDA-approved drugs identified for their binding activity of drug targets and
anti-AD potency in cell and animal models. The successful completion of this project will provide an integrated
computational modeling framework for AD drug repurposing and polypharmacology as well as identify novel
targeted anti-AD therapeutics toward pre-clinical trials.