AD/ADRD are highly complex diseases characterized by distinct molecular pathways and neuropathological phenotypes. Unfortunately, the treatment remains at best modestly effective and no new drugs have been approved since 2003. Combinatorial drug therapy for AD/ADRD treatment has not been intensively studied but it is highly promising. We hypothesize that finding repositioned drug combinations through innovative exploration of big data may uncover effective AD/ADRD treatments, with implicit advantages in overcoming drug resistance and targeting multiple biomarkers. We will combine big biomedical data from complementary sources, novel and advanced informatics models, clinical domain expertise, as well as biology knowledge and validation into a coherent framework to tackle AD/ADRD with potential combinatorial drug therapies. In an exponentially larger and more challenging space of combinatorial drug therapy, opportunities are also exponentially larger when compared with traditional single-drug models but many computational challenges need to be carefully handled. We will develop multiple computational models under two philosophical umbrellas, with focuses on quantifiable screening and biological understanding. Our findings will be validated with biological experiments from cell to mouse. If successful, we will significantly advance AD/ADRD research and benefit patients with safe and effective treatment.

Public Health Relevance

The project aims at identifying novel drug combination therapies for AD/ADRD prevention and treatment using advanced informatics methods. We propose multiple strategies focusing on quantifiable screening and biological understanding using large healthcare databases and biology/pharmacogenomics knowledgebases. The project highlights on high-throughput drug combination screening mechanisms, integrative knowledge analysis models, and biological experiments covering different targets.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
3R01AG066749-01S1
Application #
10155044
Study Section
Special Emphasis Panel (ZAG1)
Program Officer
Yuan, Jean
Project Start
2020-04-01
Project End
2025-03-31
Budget Start
2020-08-12
Budget End
2021-03-31
Support Year
1
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Type
Sch Allied Health Professions
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77030