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Exploring the multi-gene regulatory molecular mechanism of Saudi Arabian flora against epilepsy based on data mining, network pharmacology and docking analysis

Epilepsy is a chronic neurological disorder marked by recurrent seizures, significantly affecting the population in Saudi Arabia across all age demographics. The global prevalence of active epilepsy is around 6.38/1,000 persons and in the Arabian region, the median prevalence of active epilepsy is 4...

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Detalles Bibliográficos
Autores principales: Falah Alshehri, Faez, Alzahrani, Fuad M, Alkhoshaiban, Abdulaziz, Saad Al Shehri, Zafer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448170/
https://www.ncbi.nlm.nih.gov/pubmed/37638220
http://dx.doi.org/10.1016/j.jsps.2023.101732
Descripción
Sumario:Epilepsy is a chronic neurological disorder marked by recurrent seizures, significantly affecting the population in Saudi Arabia across all age demographics. The global prevalence of active epilepsy is around 6.38/1,000 persons and in the Arabian region, the median prevalence of active epilepsy is 4.4/1,000 persons. However, over 75% of individuals are untreated. Consequently, the development of therapeutic strategies with increased efficacy and safety profiles is essential to improve the survival rate among epilepsy patients. The current study integrates network pharmacology along with Bioinformatics approaches to explore the potential molecular mechanisms of local flora of Saudi Arabia including Solanum incanum, Abrus precatorius, Withania somnifera, and Azadirachta indica in epilepsy treatment. In the preliminary phase, data related to the bioactive components of the local plants and the associated target genes of both these plants and epilepsy were gathered from scientific literature and open-source databases. This data was then analyzed to identify common targets between the plants and ovarian cancer. Based on these common targets, a protein–protein interaction (PPI) network was constructed utilizing the STRING database, which was subsequently incorporated into the Cytoscape software for identification of hub genes based on their degree of connectivity. Lastly, an interplay network depicting the associations between the compounds and the overlapping genes was formulated via Cytoscape, to study the potential network pharmacology implications of these active compounds in relation to ovarian cancer. Following that, a compound-target protein-pathway network was constructed which uncovered that namely abrectorin, genistin, (+)-catechin, precatorine, (+)-ascorbic acid, licoflavanone, skrofulein, stigmasterone, 5,7-Dihydroxy-4′-methoxy-8,3′-di-C-prenylflavanone could potentially be used as antagonists for the therapeutic management of epilepsy by targeting TNF and TP53 proteins. Furthermore, the implementation of molecular docking reinforces the binding affinity of the compound, indicating a robust stability of the forecasted compounds at the docked site. This research lays both a theoretical and experimental groundwork for more profound investigations and establishes a practical method for the strategic employment of active compounds in the development of anti-epileptic therapeutics.