Identification of phytochemicals from North African plants for treating Alzheimer's diseases and of their molecular targets by in silico network pharmacology approach

10.1016/j.jtcme.2020.08.002

Karim Raafat

Phytochemical constituents ； North-african plants ； Alzheimer's disease ； Network pharmacology

Journal of Traditional and Complementary Medicine

11卷3期（2021 / 05 / 01）

268 - 278

英文

Background: The global social expenses of Alzheimer's disease (AD) have been increased to US$1 trillion due to high cost, side-effects, and low efficiency of the current AD-therapies. Another reason is the lack of preventive drugs and the low-income situation of Asian and African countries. Accordingly, patients rather prefer traditional herbal remedies. Network-pharmacology has been a well-established method for the visualization and the construction of disorder target protein-drug framework. This could aid in the identification of drugs molecular-mechanisms. Aim: The aim of this study is to investigate the phytochemical constituents that could target Alzheimer's disease from the North African plants. This could be done by exploring their possible mechanisms of action through molecular network pharmacology-based approach. Experimental procedure: The Phytochemical-compounds of North-African plants (NAP) have been accessed from open-databank. ADME-screening has been conducted for filtering of the NAP phytochemical-constituents utilizing Qikprop-software. The open STITCH databank has been utilized for the prediction of the phytochemical-constituents target-proteins; UniProt and TDD-DB databanks have been utilized for distinguishing AD-related proteins. Phytochemical constituent-target protein (C-T) and plant-phytochemical constituent-target protein (P-C-T) frameworks have been assembled utilizing Cytoscape to interpret the anti-Alzheimer's disease mechanism of action of the targeted phytochemical constituents. Results: The NAP 6842 phytochemical-constituents (from more than 1000 plants) have been exposed to ADME and CNS modulating filtration, generating 94 phytochemical-constituents which have been subjected to target-prediction investigation. The 94 phytochemical-constituents and the 4 AD-identified targets have been associated through 155 edges which formed the main pathways related to AD. Cuparene, alpha-selinene, beta-sesquiphellandrene, calamenene, 2-4-dimethylheptane, undecane, n-tetradecane, hexadecane, nonadecane, n-eicosane, and heneicosane have had C-T network highest combined-score, whilst the proteins MAO-B, HMG-CoA, BACE1, and GCR have been the most enriched ones by comprising the uppermost combined-scores of C-T. Hypericum perforatum, Piper nigrum, Juniperus communis, Levisticum officinale, Origanum vulgare acquired the uppermost number of P-C-Target interactions. Conclusion: The phytochemical-targets prediction of NAP utilizing molecular-network pharmacology-based investigation has paved the way for networking multi-target, multi-constituent, and multi-pathway mechanisms. This may introduce potential future targets for the regulation and the management of Alzheimer's disease. Taxonomy (classification by EVISE): Alzheimer's disease, Network pharmacology, In-silico computer based approach.

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