Computational discovery of lead compounds as potential therapeutics/diagnostics for Alzheimer Disease
||Computational discovery of lead compounds as potential therapeutics/diagnostics for Alzheimer Disease|
||SNIC Small Compute|
||Rajnish Kumar <firstname.lastname@example.org>|
||2021-05-01 – 2022-05-01|
Central cholinergic systems play a primary role in multiple autonomic, behavioral and cognitive functions and are known to be affected in neurodegenerative disorders such as Alzheimer's disease (AD), dementia with Lewy Bodies (DLB), and Parkinson's disease. The two most common dementia disorders are AD and DLB, representing 60% and 15-20% of the dementia population. At present there is no treatment available to reverse or cease the progression of disease. However some drugs are used to provide symptomatic relief from the disease but they have limited benefit. Thus, a more accurate and early diagnosis tool for AD is urgently needed, which can help to initiate the treatment before a widespread brain damage. It's still a challenge to specific and early diagnosis of AD in clinics, because of lack of understanding of definitive etiology of AD. In the past decade a number of radiolabeled imaging agents were developed and successfully used in clinical diagnosis of AD. However, as many as 30% of healthy elderly subjects with no clinical signs of dementia show retention of these imaging agents in the brain. Thereby, new more suitable in vivo PET biomarkers are highly desirable. The acetylcholine synthesizing enzyme, ChAT appears as a promising target for developing such new in vivo PET imaging probes. ChAT was discovered about a century ago and is the most specific cholinergic marker, yet currently no PET ligand aimed at this enzyme exists. With the development of novel imaging agents specifically targeting ChAT which is involved in cholinergic system may lead to early diagnosis of AD.
The main objective of this project is to identify small, neutral, and moderately lipophilic lead molecules with high binding potential to the ChAT using computer aided molecular modeling approach.
In the current work we will use structure based and ligand based drug design approaches to identify novel and potent inhibitors of ChAT. Virtual screening is more cost effective than HTS of a large library of molecules. The 3D crystal structure of ChAT will be used for virtual screening of chemical databases (ZINC, FDA approved drug, NCI, and ChEMBL etc.) to discover novel hit compounds. After identification of promising potential hit compounds for ChAT inhibition, they will subsequently filtered by modified Lipinski's Rule of Five, ADME (absorption, distribution, metabolism, and excretion) properties to screen the BBB permeable compounds. Finally a number of potent molecules will be designed on the basis of molecular dynamics studies.