1. Introduction
Plants are important natural resources for both traditional and modern therapeutic systems all throughout the world. Plants and plant derivatives have been used for medicinal purposes for thousands of years [1]. The existence of a large group of active components in medicinal plants, such as alkaloids, triterpenoids, essential oils, and phenolic compounds, contributes to their curative qualities. Plants are utilized as phytomedicine to treat a variety of ailments due to the presence of bioactive constituents [2].
Triterpenoids are one of the most important classes of phytochemicals, with more than 20,000 members identified and recognized thus far [3]. Numerous triterpenoids have been demonstrated to be helpful in experimental tests in recent years and are thought to contribute to the health-promoting qualities of food plants such as fruits, vegetables, and spices [4]. Despite the fact that triterpenoids were originally thought to be biologically inactive, new information on their various types of pharmacological and biological activities, as well as their low toxicity profiles, has reignited interest in human health and disease [5]. Triterpenoids are used for antipyretic, analgesic, hepatoprotective, anti-inflammatory, sedative, tonic, and cardio tonic properties in a variety of Asian countries [6].
Glutinol, a triterpenoid compound, first isolated from the leaves of Scoparia dulcis, possesses pharmacological attributes including anti-diabetic [7], anti-inflammatory [8] and anti-cancer [9] properties. To evaluate the anti-diabetic potential of glutinol, insulin secretory activity on isolated mice islets and MIN-6 pancreatic β-cell line was assessed. Glutinol displayed a moderate insulin secretagogue attribute with percentage inhibition of 137.25 ± 7.63% compared to insulin secretion by glucose of 100 ± 8.33% [10]. Anti-inflammatory potential of glutinol was assessed via its inhibitory potential against cyclooxygenase and nitric oxide. Glutinol displayed significant anti-inflammatory potential with an IC50 value of 1.22 µg/mL. Glutinol displayed significant anti-cancer effect against human ovarian cancer cells by altering the P13AKT signaling pathway. The IC50 value against human OVACAR3 cells was found to be 6 µM. These diverse pharmacological attributes of glutinol make it a valuable and interesting compound. However, the molecular mechanism responsible for its biological potentials has not been systematically evaluated. Therefore, in the current research, an attempt was made to explore the molecular mechanism of action of glutinol by using a network pharmacology approach. This strategy not only accelerates drug discovery but also saves time, energy, and above all, money. The procedure for glutinol’s gene prediction and analysis is shown in Figure 1.
2. Methodology
2.1. PubChem Database-Based Screening of Chemical Structure and ADMET Analysis
The PubChem database is a free and open database that houses essential information about drug development and chemical biology research [11]. The chemical formula, SMILES and CAS number of glutinol were found using the term “glutinol” in the search box. Then, using the online application pkCSM, ADMET analysis was performed for glutinol.
2.2. Target Gene Screening by Using Binding DB Database
Binding DB (
2.3. Protein–Protein Interaction Network Construction and Analysis
STRING 11.0 is an online database that can collect, assess, and integrate information regarding protein–protein interactions from all publicly available sources (
2.4. Analysis of Gene Function and Pathway Enrichment
The Gene Ontology (GO) function and KEGG pathway enrichment of proteins included in the PPI network were analyzed using the Database for Annotation, Visualization, and Integrated Discovery (
2.5. Construction of Glutinol-Target-Pathway Network
We used Cytoscape 3.7.2 to create a visual network to help us better comprehend the complicated relationships between glutinol and its targets and pathways.
2.6. Molecular Docking
Chemical Computing Group Inc.’s MOE-Dock (
3. Results
3.1. Molecular Formula and ADMET Attributes of Glutinol
The chemical formula of glutinol was retrieved from PubChem database, as shown in Figure 1. The ADMET analysis of glutinol, conducted using the pkCSM online tool, fell in the “Accepted” category. These findings indicate that glutinol possesses all drug likeness properties confirmed through ADMET analysis as shown in Table 1.
3.2. Prediction of Glutinol’s Target Genes
Potential genes targeted by glutinol were retrieved from BindingDB database. The results revealed 32 target genes linked to glutinol, as shown in Table 2. These target genes were then utilized for further investigations.
3.3. Protein–Protein Interaction Network
Target genes of glutinol were imported into the STRING database with the filter of Homo sapiens as species to achieve a protein interaction network. The results were then imported to Cytoscape for results visualization (Figure 2). Circle size and color were different according to the degree value. There were 42 nodes and 81 edges in the PPI network. Network Analyzer in Cytoscape reported an average node degree value of 3.85, a betweenness centrality value of 0.05, and a closeness centrality value of 0.33. A total of eight nodes had degree values, betweenness centrality, and closeness centrality that were above the average. These could be the key glutinol targets that contribute to its pharmacological effect. These nodes with their names and details are listed in Table 3.
3.4. GO Enrichment Analysis
We used the DAVID tool to perform GO enrichment analysis on the 32 identified genes in order to further investigate them. The Benjamini–Hochberg procedure was used to correct p-values, and the top 10 significantly enriched items in the BP, MF, and CC categories were picked based on P < 0.05, as shown in Figure 3. BP (42 records), MF (32 records), and CC (15 recordings) accounted for 71.74 percent, 16.67 percent, and 11.59 percent, respectively. Target proteins in the BP category were mostly implicated in steroid metabolic process, intracellular receptor signaling pathway, regulation of inflammatory response, steroid biosynthetic process and organic hydroxyl compound biosynthetic process. The target proteins in the MF category were mostly engaged in steroid binding, nuclear receptor activity ligand activated transcription factor activity, transcription coactivator binding and transcription cofactor binding. The target proteins in the CC category were engaged in synaptic cleft, external side of plasma membrane, intrinsic component of external side of plasma membrane, cytoplasmic side of membrane and transcription regulator complex.
3.5. KEGG Enrichment Analysis
We also ran KEGG enrichment analyses on these candidate genes using the DAVID program. Twenty-nine potential target genes from 32 target genes were found to be enriched in the KEGG pathway enrichment study, and 10 signal pathways were strongly linked to the target genes (P < 0.05). The 10 pathways are depicted in Figure 4, along with their enrichment ratios. The pathways that were highly enriched included chemical carcinogenesis-receptor activation (hsa05207), insulin resistance (has04931), prolactin signaling pathway (has04917), and complement and coagulation cascade (hsa04610).
3.6. Network Analysis
We created a drug-target-pathway network diagram using Cytoscape 3.7.2 to highlight the interaction between substance (glutinol), targets, and pathway in greater detail. Figure 5 depicts a network with 40 nodes and 59 edges. The compound was represented by a green circle, targets were represented by red inverted triangles, and pathways by yellow triangles. According to this network, RELA can be considered as the hub gene, as it was enriched in almost all enriched pathways.
3.7. Molecular Docking
It is assumed that ligand–receptor complexes with lower binding energy exhibit strong interactions with receptors. There is currently no universal standard for active molecule target screening. As a starting point for screening, active components with a binding energy of less than −5.0 kJ/mol were chosen. Molecular docking revealed that 5 out of 8 identified target proteins had a glutinol affinity of less than −5.0 kJ/mol. Figure 6 displays the top five docking outcomes with the lowest binding energy. Table 4 and Figure 6 illustrate the results. Molecular docking results also showed that as compared to other selected genes, CYP19A1 showed significant interaction with glutinol, with a binding energy of −10.1795 kJ/mol. Data about the top ten docked poses of glutinol with selected targets are provided in the supplemental table.
4. Discussion
Currently, network pharmacology is receiving more and more attention during the medication development and utilization process [16]. This technique can, first and foremost, evaluate, screen, and optimize several key properties of medications in order to speed up or simplify the drug development process. This study not only identified some important biological features and genes potentially associated with glutinol using this network analysis method, but also included GO and KEGG enrichment analysis.
The development of a new drug is dependent not only on the drug’s pharmacological effects, but also on its pharmacokinetic properties in terms of safety and availability in the body. The availability and toxicity profile of a lead molecule were anticipated using ADMET properties for test compounds. Predicted absorption metrics such as water solubility (log mol/L), skin permeability (log Kp), and intestinal solubility (percent absorbed) can be used in ADMET assessments to determine test compound therapeutic potential. According to certain investigations, substances with absorption properties have the ability to pass through the gut barrier and reach the target molecule via passive penetration. The results of the water solubility test demonstrated that glutinol is well absorbed. Furthermore, when compared to a reference value, the estimated intestinal solubility of the test drug showed good efficacy. When compared to a standard value (>30 percent abs), glutinol exhibited excellent intestinal solubility. Skin permeability ratings were also greater than the industry norm (−2.5 log Kp), indicating their utility as lead structures and demonstrating their drug-like action. Similarly, the permeability of the blood–brain barrier (BBB) and the central nervous system (CNS) of all screened substances was equal to standard values. According to many research reports, substances with a log BB value of more than 0.3 can cross the BBB, whereas those with a log BB value larger than −1 have poor brain penetration. Aside from these factors, the metabolic properties of test substances were investigated using the cytochrome P450 isoforms CYP3A4 and CYP1A2 [4]. The drug-likeness of glutinol was further supported by results for excretion and toxicity studies using total clearance (log mL/min/kg), LD50 values, maximum tolerated dose (MTD), and Ames toxicity.
The process of discovering new drugs necessitates finding target genes. Many genes and proteins have intricate connections with an increasing number of chemicals, substances, and medications [17]. Simultaneously, an increasing number of online analysis tools have been established in these investigations. BindingDB database research discovered 32 possible target genes connected to glutinol, as shown in Table 2. The 32 putative target genes were then examined using the STRING database. An important step in the drug discovery process is the hunt for target genes. The degrees of separation, betweenness centrality, and closeness centrality were all higher than the average of 3.85 nodes, the majority of which (such as CCND1, ESR1, CYP19A1, HMGCR, PTPRC, RELA, ELANE, and ITGAV) are involved in carcinogenesis and insulin resistance. At the same time, we used molecular docking to further investigate the probable interactions between glutinol and these targets. More likely, it will be that the ligand and receptor complex with a lower conformational energy confirms ligand stability. We found that the binding energy of 5 of the 8 targets was less than −5.0 kJmol−1, indicating that glutinol may directly interact with these targets using a screening threshold of −5.0 kJmol−1. As a result, glutinol’s pharmacological activity could possibly be due to these targets.
We used the DAVID program to perform more GO and pathway analysis on these possible genes. Glutinol is linked to the steroid metabolic process, intracellular receptor signaling pathway, control of the inflammatory response, steroid biosynthesis, and organic hydroxyl compound biosynthesis, according to BP items. These findings suggest that glutinol has anti-inflammatory and anti-tumor properties. Chemical carcinogenesis-receptor activation and insulin resistance proved to be the most enriched pathways, according to KEGG pathway analysis. As a result, glutinol may have anti-cancer properties by primarily targeting AR, ITGAV, ESR1, ESR2, RELA, and VDR. Similarly, another study found that glutinol had anti-cancer properties, which is consistent with our GO and KEGG findings. The drug-target network diagram of glutinol, on the other hand, suggests that glutinol could have a broad range of pharmacological effects.
5. Conclusions
Our research revealed that glutinol has a wide range of pharmacological effects. At the same time, we looked into glutinol’s likely mechanism of action, which could be exploited to produce more effective anticancer and anti-diabetic medications. Our findings offer a new perspective on glutinol research, development, and therapeutic application.
Conceptualization, A.M.U. and S.Q.; methodology, S.R.A.; software, S.Q.; validation, Y.H.K. and F.A.A.; formal analysis, M.A.; investigation, S.I.A.; data curation, S.I.A.; writing—original draft preparation, A.M.U.; writing—review and editing, S.Q.; visualization, S.R.A. and T.H.M.; supervision, Y.H.K. and T.H.M.; project administration, S.I.A.; funding acquisition, S.I.A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The authors are also thankful to the central laboratory at Jouf University, Sakaka, Aljouf, Saudi Arabia.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figure 3. GO enrichment analysis of target genes. Biological Process (BP), Cellular Component (CC), Molecular Function (MF). Height of bar represents number of target genes.
Figure 6. 3D and 2D images of best docked results A purple dotted line depicts metal or ion interaction, and a green dotted line depicts side-chain proton acceptor/donors. Basic and acidic amino acids are represented by blue and red circles, respectively. Certain amino acids have a blue backdrop because they have been exposed to solvents. Additionally, ligand atoms with blue coloring in front of them show solvent exposure.
Figure 6. 3D and 2D images of best docked results A purple dotted line depicts metal or ion interaction, and a green dotted line depicts side-chain proton acceptor/donors. Basic and acidic amino acids are represented by blue and red circles, respectively. Certain amino acids have a blue backdrop because they have been exposed to solvents. Additionally, ligand atoms with blue coloring in front of them show solvent exposure.
ADMET analysis of glutinol.
Molecular Weight | Absorption | Distribution | Metabolism | Excretion | Toxicity | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
WS | IS | SP | BBB | CNSP | CYP3A4 Substrate | CYP2C19 inhibitor | TC | MTD | ORAT | HT | SS | AMES | |
426.72 | −6.49 | 94.41 | −2.816 | 0.665 | −1.905 | Yes | No | −0.037 | −0.603 | 2.298 | No | No | No |
BBBP = blood brain barrier permeability (logBBB), CNSP = CNS permeability (log PS), IS = intestinal solubility (%abs), ORAT = oral rat acute toxicity (LD50), SP = skin permeability (log Kp), TC = total clearance (logml/min/kg), WS = water solubility (logmol/L), MTD (Maximum tolerated dose).
Potential genes targeted by glutinol.
S. No. | Gene | UniProt ID | Description |
---|---|---|---|
1 | DHCR24 | Q15392 | Delta(24)-sterol reductase |
2 | HMGCR | P04035 | 3-hydroxy-3-methylglutaryl-coenzyme reductase |
3 | ACHE | P22303 | Acetylcholinesterase |
4 | AKR1B10 | O60218 | Aldo-keto reductase family 1 member B10 |
5 | GAA | P10253 | Lysosomal alpha-glucosidase |
6 | CRYAA | P02489 | Alpha-crystallin A chain |
7 | CRYAB | P02511 | Alpha-crystallin B chain |
8 | PRKAA2 | P54646 | 5′-AMP-activated protein kinase catalytic subunit alpha-2 |
9 | AR | P10275 | Androgen receptor |
10 | ALOX15 | P16050 | Polyunsaturated fatty acid lipoxygenase ALOX15 |
11 | F3 | P13726 | Tissue factor |
12 | F10 | P00742 | Coagulation factor X |
13 | CYP17A1 | P05093 | Steroid 17-alpha-hydroxylase/17,20 lyase |
14 | CYP19A1 | P11511 | Aromatase |
15 | LIG1 | P18858 | DNA ligase 1 |
16 | CDC25B | P30305 | M-phase inducer phosphatase 2 |
17 | ESR2 | Q92731 | Estrogen receptor beta |
18 | ESR1 | P03372 | Estrogen receptor |
19 | GRIN1 | Q05586 | Glutamate receptor ionotropic, NMDA 1 |
20 | ITGAV | P06756 | Integrin alpha-V |
21 | PTPRC | P08575 | Receptor-type tyrosine-protein phosphatase C |
22 | ELANE | P08246 | Neutrophil elastase |
23 | RELA | Q04206 | Transcription factor p65 |
24 | RORC | P51449 | Nuclear receptor ROR-gamma |
25 | OSBP2 | Q969R2 | Oxysterol-binding protein 2 |
26 | NR1H3 | Q13133 | Oxysterols receptor LXR-alpha |
27 | PTPN1 | P18031 | Tyrosine-protein phosphatase non-receptor type 1 |
28 | F2 | P00734 | Prothrombin |
39 | SREBF2 | Q12772 | Sterol regulatory element-binding protein 2 |
30 | SHBG | P04278 | Sex hormone-binding globulin |
31 | PTPN2 | P17706 | Tyrosine-protein phosphatase non-receptor type 2 |
32 | VDR | P11473 | Vitamin D3 receptor |
Major protein interaction network topological parameters.
Name | Degree | Betweenness Centrality | Closeness Centrality |
---|---|---|---|
CCND1 | 13 | 0.356617 | 0.507463 |
ESR1 | 13 | 0.243091 | 0.5 |
CYP19A1 | 7 | 0.253281 | 0.43038 |
HMGCR | 7 | 0.242254 | 0.343434 |
PTPRC | 7 | 0.420766 | 0.447368 |
RELA | 6 | 0.076522 | 0.404762 |
ELANE | 4 | 0.192513 | 0.34 |
ITGAV | 3 | 0.096702 | 0.336634 |
Glutinol-target molecular docking analysis.
Targets | Binding Energy (kJ/mol) | Interaction | ||
---|---|---|---|---|
CCND1 | −8.3554 | 2.59 | 43 | LysA33 |
ESR1 | −5.3991 | 2.84 |
36 |
Glu353 |
CYP19A1 | −10.1795 | 2.7 |
66 |
Arg375 |
HMGCR | −5.9682 | 2.81 | 75 | AspB690 |
PTPRC | −8.8985 | 2.02 |
38 |
GlnA485 |
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Dar, R.A.; Shahnawaz, M.; Qazi, P.H. General overview of medicinal plants: A review. J. Phytopharm.; 2017; 6, pp. 349-351. [DOI: https://dx.doi.org/10.31254/phyto.2017.6608]
2. Siddiqui, A.J.; Danciu, C.; Ashraf, S.A.; Moin, A.; Singh, R.; Alreshidi, M.; Patel, M.; Jahan, S.; Kumar, S.; Alkhinjar, M.I.M. et al. Plants-derived biomolecules as potent antiviral phytomedicines: New insights on ethnobotanical evidences against coronaviruses. Plants; 2020; 9, 1244. [DOI: https://dx.doi.org/10.3390/plants9091244] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32967179]
3. Ghosh, S. Biosynthesis of structurally diverse triterpenes in plants: The role of oxidosqualene cyclases. Proc. Indian Natl. Sci. Acad.; 2016; 82, pp. 1189-1210. [DOI: https://dx.doi.org/10.16943/ptinsa/2016/48578]
4. Hassan, M.; Azhar, M.; Abbas, Q.; Raza, H.; Moustafa, A.A.; Shahzadi, S.; Ashraf, Z.; Seo, S.Y. Finding novel anti-carcinomas compounds by targeting SFRP4 through molecular modeling, docking and dynamic simulation studies. Curr. Comput. Aided Drug Des.; 2018; 14, pp. 160-173. [DOI: https://dx.doi.org/10.2174/1573409914666180112100122] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29332600]
5. Sartori, S.B.; Singewald, N. Novel pharmacological targets in drug development for the treatment of anxiety and anxiety-related disorders. Pharmacol. Ther.; 2019; 204, 107402. [DOI: https://dx.doi.org/10.1016/j.pharmthera.2019.107402] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31470029]
6. Lim, S.H.; Jeon, E.S.; Lee, J.; Han, S.Y.; Chae, H. Pharmacognostic outlooks on medical herbs of Sasang typology. Integr. Med. Res.; 2017; 6, pp. 231-239. [DOI: https://dx.doi.org/10.1016/j.imr.2017.06.005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28951836]
7. Pamunuwa, G.; Karunaratne, D.; Waisundara, V.Y. Antidiabetic properties, bioactive constituents, and other therapeutic effects of Scoparia dulcis. Evid. Based Complement. Altern. Med.; 2016; [DOI: https://dx.doi.org/10.1155/2016/8243215] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27594892]
8. Adebayo, S.A.; Shai, L.J.; Eloff, J.N. First isolation of glutinol and a bioactive fraction with good anti-inflammatory activity from n-hexane fraction of Peltophorum africanum leaf. Asian Pac. J. Trop. Med.; 2017; 10, pp. 42-46. [DOI: https://dx.doi.org/10.1016/j.apjtm.2016.12.004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28107863]
9. Chen, Y.; Li, J. Glutinol inhibits the proliferation of human ovarian cancer cells via PI3K/AKT signaling pathway. Trop. J. Pharm. Res.; 2021; 20, pp. 1331-1335. [DOI: https://dx.doi.org/10.4314/tjpr.v20i7.2]
10. Sharma, K.R.; Adhikari, A.; Hafizur, R.M.; Hameed, A.; Raza, S.A.; Kalauni, S.K.; Miyazaki, J.I.; Choudhary, M.I. Potent insulin secretagogue from Scoparia dulcis Linn of Nepalese origin. Phytother. Res.; 2015; 29, pp. 1672-16755. [DOI: https://dx.doi.org/10.1002/ptr.5412] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26178652]
11. Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res.; 2019; 47, pp. D1102-D1109. [DOI: https://dx.doi.org/10.1093/nar/gky1033] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30371825]
12. Chandran, U.; Patwardhan, B. Network ethnopharmacological evaluation of the immunomodulatory activity of Withania somnifera. J. Ethnopharmacol.; 2017; 197, pp. 250-256. [DOI: https://dx.doi.org/10.1016/j.jep.2016.07.080] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27487266]
13. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P. et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res.; 2019; 47, pp. D607-D613. [DOI: https://dx.doi.org/10.1093/nar/gky1131] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30476243]
14. Jiang, Y.; Zhong, M.; Long, F.; Yang, R.; Zhang, Y.; Liu, T. Network pharmacology-based prediction of active ingredients and mechanisms of Lamiophlomis rotata (Benth.) Kudo against rheumatoid arthritis. Front. Pharmacol.; 2019; 10, 1435. [DOI: https://dx.doi.org/10.3389/fphar.2019.01435] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31849678]
15. Parveen, S.; Kalsoom, S.; Bibi, R.; Asghar, A.; Hameed, A.; Ahmed, W.; Hassan, A. Computational and biological studies of novel thiazolyl coumarin derivatives synthesized through Suzuki coupling. Turk. J. Chem.; 2020; 44, pp. 1610-1622. [DOI: https://dx.doi.org/10.3906/kim-2005-19] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33488257]
16. Wang, W.-X.; Zhang, Y.-R.; Luo, S.-Y.; Zhang, Y.-S.; Zhang, Y.; Tang, C. Chlorogenic acid, a natural product as potential inhibitor of COVID-19: Virtual screening experiment based on network pharmacology and molecular docking. Nat. Prod. Res.; 2022; 36, pp. 2580-2584. [DOI: https://dx.doi.org/10.1080/14786419.2021.1904923] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33769143]
17. Atanasov, A.G.; Waltenberger, B.; Pferschy-Wenzig, E.M.; Linder, T.; Wawrosch, C.; Uhrin, P.; Temml, V.; Wanga, L.; Schwaigerb, S.; Heiss, E.H. et al. Discovery and resupply of pharmacologically active plant-derived natural products: A review. Biotechnol. Adv.; 2015; 33, pp. 1582-1614. [DOI: https://dx.doi.org/10.1016/j.biotechadv.2015.08.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26281720]
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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.
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Details




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 Tumair General Hospital, Riyadh Second Health Cluster, Ministry of Health, Riyadh 12211, Saudi Arabia;