Indexing metadata

In silico studies on olive oil polyphenolic natural products to identify neuroprotective lead compounds beneficial in the treatment of Alzheimer's disease


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document In silico studies on olive oil polyphenolic natural products to identify neuroprotective lead compounds beneficial in the treatment of Alzheimer's disease
 
2. Creator Author's name, affiliation, country Khouloud Hachani; National University of Science and Technology
 
2. Creator Author's name, affiliation, country Fahd Othmani; National University of Science and Technology; Oman
 
2. Creator Author's name, affiliation, country Mohamed Essam; National University of Science and Technology; Oman
 
2. Creator Author's name, affiliation, country Md Jawaid Akhtar; College of Pharmacy, National University of Science and Technology, Muscat, Oman; Oman
 
2. Creator Author's name, affiliation, country SHAH ALAM KHAN; National University of Science and Technology; Oman
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) Alzheimer’s disease;Neuroprotection; Phenolic compounds; Antioxidant; AChE; BACE-1
 
4. Description Abstract

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders. Nearly 44 million people across the globe are living with it. In spite of tremendous progress in understanding the pathophysiology of AD, only a few drugs have been approved by FDA to date that too provides only symptomatic relief. We have contemplated historical and religious backgrounds in addition to the literature review and concluded that polyphenolic natural products from olive oil can be used for the treatment of AD. The current computational study was designed to investigate the potential of phenolic metabolites present in olive oil to identify lead molecule(s) that could help in fighting against AD. A total of 21 phenolic compounds from olive oil were selected, and their SMILES notations were generated using Chemsketch. Cheminformatics software such as, Molinspiration to predict the bioactivity scores and physicochemical properties, PASS to predict the acetylcholinesterase (AChE) inhibition, neuroprotective, antioxidants, and anti-inflammatory activities; OSIRIS for pharmacokinetic profile and toxicity and Autodock Vina for molecular docking were used for in silico studies. The results were compared with four clinically used AD drugs. All the tested compounds were predicted to possess anti-inflammatory activity (0.357-0.831 Pa score) and antioxidant activity (0.320-0.903 Pa values), but none of the compounds was found to be a butyrylcholinesterase inhibitor. Out of 21 initial polyphenolic compounds, we selected the best two bioactive compounds, luteolin and elenolic acid, based on their bioactivity and toxicity profile. Luteolin showed the most stable binding to both beta-secretase- 1 (BACE) and AChE enzymes followed by elenolic acid. It is concluded that luteolin and elenolic acid are the most potent polyphenolic compounds of the olive, which act at multiple targets in AD pathogenesis. These compounds hold promise for the development of anti-Alzheimer's therapy.

 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 20-05-2023
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://revues.imist.ma/index.php/AJMAP/article/view/33347
 
10. Identifier Digital Object Identifier (DOI) https://doi.org/10.48347/IMIST.PRSM/ajmap-v9i1.33347
 
11. Source Title; vol., no. (year) Arabian Journal of Medicinal and Aromatic Plants; Vol 9, No 1 (2023)
 
12. Language English=en en
 
13. Relation Supp. Files Untitled (42KB)
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions Copyright (c) 2023 Arabian Journal of Medicinal and Aromatic Plants
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.