Network Pharmacology and Molecular Docking Based Prediction of Mechanism of Pharmacological Attributes of Glutinol

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Network Pharmacology and Molecular Docking Based Prediction of Mechanism of Pharmacological Attributes of Glutinol
by Sami I. Alzarea 1ORCID,Sumera Qasim 1,*ORCID,Ambreen Malik Uttra 2,Yusra Habib Khan 3ORCID,Fakhria A. Aljoufi 1ORCID,Shaimaa Rashad Ahmed 4,5ORCID,Madhawi Alanazi 6 andTauqeer Hussain Malhi 3ORCID
1
Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia
2
College of Pharmacy, University of Sargodha, Sargodha 40100, Pakistan
3
Department of Clinical Pharmacy, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia
4
Department of Pharmacognosy, Faculty of Pharmacy, Cairo University, Kasr El-Aini Street, Cairo 11562, Egypt
5
Department of Pharmacognosy, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia
6
Tumair General Hospital, Riyadh Second Health Cluster, Ministry of Health, Riyadh 12211, Saudi Arabia
*
Author to whom correspondence should be addressed.
Processes 2022, 10(8), 1492; https://doi.org/10.3390/pr10081492
Submission received: 4 June 2022 / Revised: 14 July 2022 / Accepted: 22 July 2022 / Published: 28 July 2022
(This article belongs to the Special Issue Network Pharmacology Modelling for Drug Discovery)
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Abstract
Glutinol, a triterpenoid compound, has no documented systematic investigation into its mechanism. Hence, we used network pharmacology to investigate glutinol’s mechanism. The chemical formula of glutinol was searched in the PubChem database for our investigation. The BindingDB Database was utilized to discover probable glutinol target genes after ADMET analysis with the pkCSM software. DAVID tools were also used to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes. We also uploaded the targets to the STRING database to obtain the protein interaction network at the same time. Then, we performed some molecular docking using glutinol and targets. Finally, we used Cytoscape to visualize and evaluate a protein–protein interaction network and a drug-target-pathway network. Glutinol has good biological activity and drug utilization, according to our findings. A total of 32 target genes were discovered. Bioinformatics and network analysis were used, allowing the discovery that these target genes are linked to carcinogenesis, diabetes, inflammatory response, and other biological processes. These findings showed that glutinol can operate on a wide range of proteins and pathways to establish a pharmacological network that can be useful in drug development and use.