High-throughput screening and molecular dynamics simulation of natural product-like compounds against alzheimer’s disease through multitarget approach

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Abstract

Alzheimer’s disease (AD) is a progressive neurological disorder that affects 50 million people. Despite this, only two classes of medication have been approved by the FDA. Therefore, we have planned to develop therapeutics by multitarget approach. We have explored the library of 2029 natural product-like compounds for their multi-targeting potential against AD by inhibiting AChE, BChE (cholinergic pathway) MAO-A, and MOA-B (oxidative stress pathway) through in silico high-throughput screening and molecular dynamics simulation. Based on the binding energy of these target enzymes, approximately 189 compounds exhibited a score of less than −10 kcal/mol against all targets. However, none of the control inhibitors exhibited a binding affinity of less than −10 kcal/mol. Among these, the top 10 hits of compounds against all four targets were selected for ADME-T analysis. As a result, only F0850-4777 exhibited an acceptable range of physicochemical properties, drug-likeness, pharmacokinetics, and suitability for BBB permeation with high GI-A and non-toxic effects. The molecular dynamics study confirmed that F0850-4777 remained inside the binding cavity of targets in a stable conformation throughout the simulation and Prime-MM/GBSA study revealed that van der Waals’ energy (∆GvdW ) and non-polar solvation or lipophilic energy (∆GSol_Lipo ) contribute favorably towards the formation of a stable protein–ligand complex. Thus, F0850-4777 could be a potential candidate against multiple targets of two pathophysiological pathways of AD and opens the doors for further confirmation through in vitro and in vivo systems.

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Iqbal, D., Rehman, M. T., Bin Dukhyil, A., Rizvi, S. M. D., Al Ajmi, M. F., Alshehri, B. M., … Alsaweed, M. (2021). High-throughput screening and molecular dynamics simulation of natural product-like compounds against alzheimer’s disease through multitarget approach. Pharmaceuticals, 14(9). https://doi.org/10.3390/ph14090937

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