Developing Novel Beta‐Secretase Inhibitors in a Computer Model as a Possible Treatment for Alzheimer’s Disease
Alzheimer's disease (AD) is a neurological condition that causes neurons and axons in the brain to deteriorate over time and in a specific pattern. The enzyme beta-secretase-1 (BACE-1) plays a crucial role in the onset and progression of AD. In silico approaches, or computer-aided drug design, have become useful tools for reducing the number of therapeutic candidates that need to be evaluated in human clinical trials. Finding chemicals that bind to BACE-1's active site and inhibit its activity is key for preventing AD. A pharmacophore model was developed in this study based on potent BACE-1 inhibitors previously identified, and subsequently employed to screen a commercially available compound database for similar compounds. ZINC35883784 was identified with high binding affinities and hydrogen bonding interactions. Moreover, similar properties to donepezil were found in a compound made by altering the structure of ZINC35883784 called (4R,5R)-2-[1-(2-ethylcyclohexyl)ethyl]-4-hydroxy-5-(4-hydroxybutyl)cyclohexanolate (M4). Compounds were tested for interactions with BACE-1 and favorable properties. Binding scores were confirmed after molecular docking. The assessment of drug-likeness was conducted utilizing Swiss ADME analysis. Molecular dynamics simulations assessed the stability of compound interactions with BACE-1. MMPBSA calculated binding free energy and contribution energy. Results showed that M4 had strong and steady interactions with BACE-1. M4 was also analyzed by predicted NMR and retrosynthesis. However, further experiments are needed to evaluate M4's potential as a BACE-1 inhibitor.
Authors
- Tassanee Ongtanasup (ORCID: https://orcid.org/0000-0001-5541-2844)
- Komgrit Eawsakul (ORCID: https://orcid.org/0000-0002-0577-6225)
Institutions
- Walailak University (TH)
Publication Details
- Journal
- Advances in Pharmacological and Pharmaceutical Sciences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1155/adpp/5528793
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50