In Silico Exploration of APOE4 Inhibitors: Molecular Docking and ADMET Profiling for Alzheimer's Therapy
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Abstract
Background: Alzheimer's disease (AD), a prevalent neurodegenerative disorder, poses significant challenges to healthcare systems worldwide. The aggregation of Amyloid-β peptide and the presence of apolipoprotein E4 (apoE4) are recognized as central factors in AD pathogenesis. Despite the availability of FDA-approved drugs, there remains a dire need for more effective and targeted treatments to combat this disease. The advent of in silico methodologies offers a promising avenue for accelerating the drug discovery process, providing a cost-effective and efficient means to screen for potential therapeutic agents.
Objective: This study aimed to identify and evaluate potential inhibitors of apoE4 using in silico drug discovery methods. By targeting the interaction domain of apoE4, the study sought to discover compounds capable of reducing beta-amyloid aggregation, thus offering a novel approach to AD therapy.
Methods: Computational techniques, including the Lipinski Rule of Five and ADMET filtering, were employed to screen a dataset of 80 chemical inhibitors retrieved from the NCBI database. Molecular docking was performed to assess the binding affinities and orientations of the compounds to apoE4, utilizing Phyre2 for 3D structure prediction and AutoDock Vina for docking simulations. The selection criteria for lead compounds were based on their binding energy, hydrogen bonding interactions, and compliance with drug-like properties.
Results: Out of the initial 80 compounds screened, 15 were excluded based on the Lipinski Rule of Five, and an additional 43 were deemed unsuitable following ADMET analysis. Molecular docking identified two compounds, taxifolin and luteolin, exhibiting superior binding affinities to apoE4, with taxifolin showing the lowest binding energy of -7.4 kcal/mol and the highest number of hydrogen bonds in one of its conformations.
Conclusion: The study successfully leveraged in silico methods to identify taxifolin and luteolin as potent inhibitors of apoE4, highlighting the potential of computational drug discovery in developing novel AD therapies. These findings pave the way for future experimental validation and clinical trials to assess the therapeutic efficacy and safety of these compounds in treating Alzheimer's disease.
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