An In-silico Approach to Identify Potential Drug Molecules for Alzheimer’s Disease: A Case Involving Four Therapeutic Targets

Abstract

Background: Computational methods in the ‘omics’ era have proved to be a boon in the drug discovery field. Bioinformatics and cheminformatics databases and tools complement the successful discovery of promising lead compounds in the treatment of several disease conditions, including neurodegenerative diseases, such as Alzheimer’s Disease (AD). However, commercially available drugs in the market to alleviate the disease progression in AD patients are sparse. The current research aims to apply an in-silico approach involving multi-therapeutic agents against multi-therapeutic targets through docking studies to explore potential lead compounds for AD clinical trials. Method: In the proposed research, virtual screening was performed on four US FDA-approved control drugs (donepezil (DON), galantamine (GAL), rivastigmine (RIV), and tacrine (TAC)) in order to be used for mild-moderate-severe stages of AD treatment. The panel of compounds identified through virtual screening was assessed for chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) and Pharmacokinetics (PK). The compound with good ADMET and PK score was investigated further with molecular docking against the four therapeutic targets involved in AD. Ligands showing the highest binding affinity against cholinesterase inhibitors (AChE, BuChE), receptor antagonists (NMDA), and β-amyloid peptide (Aβ), were computed. Result: The compounds quinazolidinone analogue, 2b, isoquinoline-pyridine, 1, benzylmorphine and coelenteramide, were found to be the lead candidates having least side effects and better efficacy. Conclusion: The predicted lead candidates are suitable for further investigation in the drug discovery pipeline.