We have recently used computational models to identify the protease inhibitor indinavir (used as an antiviral for HIV) as a positive allosteric modulator at the ?7-nicotinic acetylcholine receptor. We now propose to build on this discovery as well as other publications describing compounds that are positive allosteric modulators of the ?7- nicotinic acetylcholine receptor. We propose identifying further compounds that possess activity against this or one of 9 other targets implicated in Alzheimer?s disease using a combination of Bayesian machine learning and in vitro assays. Generating such data will enable us to potentially provide more specific compounds as well as building datasets that can be used to build predictive models to identify additional compounds with activity at these Alzheimer?s targets. These combined efforts should in the first instance provide commercially viable treatments which will be used to experimentally validate our computational models that can be shared with the Alzheimer?s disease scientific community. We are proposing to build and validate models based on public databases, select compounds for testing and use the data generated as a starting point for further optimization.

Public Health Relevance

Alzheimer?s disease (AD) is one of the most common neurodegenerative disorders that causes dementia and it is characterized by amyloid deposition of a 39-42 AA peptide (A?) processed from the amyloid precursor protein (APP) and neurofibrillary tangles (NFT). Many palliative drugs are available for the disease but there is still an urgent need for curative drugs with greater efficacy. We need to understand the key factors involved in disease progression and their suitability as drug targets for discovering new drugs against Alzheimer's disease. We hence propose to study several of these targets for Alzheimer?s disease involving all the key steps in the pathway, including several old and new targets. We will use our ?Assay Central? software to compile structure-activity data for building computational models, that can be used to selected compounds to test activity in vitro as a starting point for further optimization.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
3R44GM122196-03S1
Application #
9881110
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Ravichandran, Veerasamy
Project Start
2017-01-01
Project End
2020-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Collaborations Pharmaceuticals, Inc.
Department
Type
DUNS #
079704473
City
Fuquay Varina
State
NC
Country
United States
Zip Code
27526