Int J App Pharm, Vol 16, Issue 6, 2024, 345-351Original Article
SYNERGISTIC POTENTIAL OF NIGELLA SATIVA L. AND TRIGONELLA FOENUM-GRAECUM: INTEGRATED NETWORK PHARMACOLOGY FOR DIABETIC WOUND HEALING
MAHARANI RETNA DUHITA1*, RETNO SUSILOWATI1, SITI QURROTUL AINI1, RAHMI ANNISA2
1Department Biology Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, Indonesia. 2Department of Pharmacy Faculty of Medicine and Health Science Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, Indonesia
*Corresponding author: Maharani Retna Duhita; *Email: maharaniretna.duhita@uin-malang.ac.id
Received: 07 Jun 2024, Revised and Accepted: 21 Aug 2024
ABSTRACT
Objective: Diabetes Mellitus (DM) is a metabolic disorder marked by elevated blood glucose levels, and one of the issues linked to DM involves the development of Diabetic Wounds (DW). DW is susceptible to infection and develops into chronic wounds if not treated properly. This study aimed to investigate the network pharmacology of N. sativa L. and T. foenum-graecum, emphasizing on their potential as DW treatment candidates.
Methods: Various databases were used in this study, including PubChem, Dr. Duke's phytochemistry and Ethnobotany, and KNApSAcK Family. Swiss Target Prediction and Way2Drug PASS Online were utilized for biological activity and protein target prediction. The DW pathway's protein-protein interactions were examined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Cards, and STRING databases. STRING was used to predict the metabolite's action. The relationship between metabolites and target proteins was predicted using STITCH, and Cytoscape was used to visualize the network.
Result: The results showed that ten active ingredients (five active ingredients in N. sativa L. and five active ingredients in T. foenoem-graecum) contributed to DW healing by affecting Tumor Necrosis Factor (TNF), Interleukin-1beta (IL1B), JUN, Caspase 3 (CASP3), Interleukin-6 (IL-6), Alpha Kinase Threonine-1 (AKT1), Vascular Endothelial Growth Factor-A (VEGFA), and Mitogen-Activated Protein Kinase 3 (MAPK3) genes. Furthermore, the ten active ingredients correlated with twenty-eight intracellular proteins, resulting in a mechanism involving eight DW signalling pathways.
Conclusion: Based on network pharmacology analysis, we determine that N. sativa L. and T. foenoem-graecum combination can potentially treat DW.
Keywords: DW, Network pharmacology, N. sativa L., T. foenum-graecum
© 2024 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
DOI: https://dx.doi.org/10.22159/ijap.2024v16i6.51718 Journal homepage: https://innovareacademics.in/journals/index.php/ijap
INTRODUCTION
A metabolic disease called Diabetes Mellitus (DM) is characterized by increased blood glucose levels and disturbances in carbohydrate, lipid, and protein metabolism that persist for an extended period. DM can develop due to the body's incapacity to process and control blood glucose because of either an excess of insulin secreted by the pancreas or an incapacity of insulin to control blood glucose levels (Shailaja et al., 2020).
Indonesia is ranked 7th with the highest number of DM sufferers. One of the most severe complications of DM is Diabetic Wounds (DW). Domestic workers are susceptible to infection and will experience impaired wound healing if the wound is not treated correctly from the start [2]; 85% of wounds will develop into chronic wounds and even lead to amputation and death [3, 4]. Impaired DM wound healing is often caused by poor perfusion due to peripheral arterial disease [5]. In addition, impairment can also be caused by a decreased immune system, high oxidation stress, and bacterial resistance to antibiotics [6].
So far, the antiseptic agent often used to treat DW is the bioplacenton. Bioplacenton contains 10% placenta extract, 0.5% neomycin sulfate, and a gel base [7]. However, if prolonged exposure occurs, bioplacenton can cause skin irritation characterized by red spots [8].
Therefore, it is anticipated that one option for treating DW with fewer adverse effects would be the creation of medications from natural materials. Two natural substances that may be used to treat DW are N. sativa L. and T. foenoem-graecum. N. sativa L. has thymoquinone with many pharmacological effects. In diabetic rats, it was demonstrated that applying 20-40% N. sativa L. extract will speed up wound healing within 15-18 days [9]. At the same time, T. foenum-graecum extract has activity as an antibacterial, such as Propionibacterium-acne and Staphylococcus aureus, which are often found in DW [10]. Furthermore, commencing on the third day of therapy, a study that combined N. sativa L. and T. foenum-graecum extracts showed a drop in neutrophil and macrophage counts but an increase in fibroblast and collagen density counts [11].
The one-drug-one-target-one-disease approach is considered less effective in treating DW. Therefore, developing multi-target drugs and network pharmacology has become increasingly popular [12]. Network pharmacology explores how traditional medicines work using in silico models using complex networks composed of "protein-compound/disease-gene". Using the network pharmacology approach, we may look into possible connections between target genes and the active ingredients in the combination of N. sativa L. and T. foenum-graecum. The network pharmacology approach provides a deeper understanding of drug-target interactions, thus opening new opportunities for exploring more efficient and targeted medicine [13]. This study aimed to investigate the network pharmacology of N. sativa L. and T. foenum-graecum, focusing on their potential as therapy options for DW, given the background information mentioned above. Hopefully, that this study can serve as a reference for developing safe and effective DW treatments based on target genes.
MATERIALS AND METHODS
This study employed a research method similar to Firzannida et al., (2022). The KNApSAcK family database (http://www.knapsackfamily.com/KNApSAcK/) and Dr. Duke's Phytochemical and Ethnobotanical database (https://phytochem.nal.usda.gov/) provided the metabolites used in this work. Metabolite chemical structures were verified by the PubChem database (https://pubchem.ncbi.nlm.nih.gov/).
Swiss Target Prediction platform (http://www.swisstargetprediction.ch/) was used to predict protein targets of the combination metabolites in DW, while the Way2Drug PASS Online platform (https://www.way2drug.com/passonline/) was used to predict active compounds activity. All targets were restricted to humans.
The KEGG pathway (https://www.genome.jp/kegg/pathway.html), GeneCards (https://www.genecards.org/), and STRING database (https://string-db.org/) were used to evaluate the interaction between proteins that involved in the pathway of the DW. To analyze protein-metabolite interactions related to the DW pathway, the STITCH database (http://stitch.embl.de/) was used.
Using Cytoscape v3.10.1, the pharmacology network of the combination was displayed.
RESULTS AND DISCUSSION
Data regarding the connections between metabolites, biological activities, and species are available in the extensive database KNApSAcK [15]. The phytochemical and ethnobotanical databases maintained by Dr. Duke the freely accessible databases that are highly significant for ethnomedicinal data [16]. The seed oil of N. sativa L. contained thirteen metabolites, while the seed components contained eleven. T. foenum-graecum reveals twenty-three metabolites in the seed parts, eight metabolites in the plant parts, one metabolite in the fruit, and one in the stem (table 2).
Table 1: Prediction of secondary metabolites in N. sativa L
| CAS ID | Metabolite | Molecular formula | Plant part |
| 60-33-3 | Linoleic acid | C18H32O2 | Seed Oil |
| 112-80-1 | Oleic acid | C18H34O2 | Seed oil |
| 57-88-5 | Cholesterol | C27H46O | Seed oil |
| 469-38-5 | Cycloartenol | C30H50O | Seed oil |
| 481-25-4 | Lophenol | C28H48O | Seed oil |
| 559-70-6 | beta-Amyrin | C30H50O | Seed oil |
| 127-22-0 | Taraxerol | C30H50O | Seed oil |
| 117-39-5 | Quercetin | C15H10O7 | Seed |
| 469-39-6 | Cycloeucalenol | C30H50O | Seed oil |
| 16910-32-0 | Obtusifoliol | C30H50O | Seed oil |
| 490-91-5 | Thymoquinone | C10H12O2 | Seed oil |
| 472-28-6 | (-)-Butyrospermol | C30H50O | Seed oil |
| 197250-98-9 | Kaempferol 3-glucosyl-(1->2)-galactosyl-(1->2)-glucoside | C33H40O21 | Seed |
| 197250-97-8 | Quercetin 3-glucosyl-(1->2)-galactosyl-(1->2)-glucoside | C33H40O22 | Seed |
| 197294-29-4 | Quercetin 3-(6''''-feruloylglucosyl)-(1->2)-galactosyl-(1->2)-glucoside | C43H48O25 | Seed |
| 98063-20-8 | Nigellicine | C13H14N2O3 | Seed |
| 120993-86-4 | Nigellidine | C18H18N2O2 | Seed |
| 2/9/4594 | Nigellimine | C12H13NO2 | Seed |
| 99-49-0 | Carvone | C10H14O2 | Seed |
| 117652-9 | Gramisterol | C29H48O | Seed oil |
| 96562-85-5 | Nigellimine N-oxide | C12H13NO3 | Seed |
| 39461-20-6 | Nigellone | C18H22O4 | Seed |
| 514-46-5 | Tirucallol | C30H50O | Seed oil |
Notes: CAS ID (Chemical Abstracts Service Registry Number) is a unique identification number assigned to each chemical substance recognized globally.
Table 2: Prediction of secondary metabolites in T. foenum-graecum
| CAS ID | Metabolite | Molecular formula | Plant part |
| 59-43-8 | Thiamine | C12H17N4OS | Seed |
| 578-74-5 | Apigenin 7-O-beta-D-glucopyranoside | C21H20O10 | Plant |
| 4261-42-1 | Isoorientin | C21H20O11 | Seed |
| 38953-85-4 | Isovitexin | C21H20O10 | Seed |
| 28608-75-5 | Orientin | C21H20O11 | Seed |
| 3681-93-4 | Vitexin | C21H20O10 | Seed |
| 71-00-1 | L-Histidine | C6H9N3O2 | Seed |
| 535-83-1 | Trigonelline | C7H7NO2 | Seed |
| 485-72-3 | Formononetin | C16H12O4 | Plant |
| 511-96-6 | Gitogenin | C27H44O4 | Seed |
| 126-19-2 | Sarsasapogenin | C27H44O3 | Seed |
| 126-18-1 | Smilagenin | C27H44O3 | Seed |
| 77-60-1 | Tigogenin | C27H44O3 | Seed |
| 512-06-1 | Yamogenin | C27H42O3 | Seed |
| 2150-11-0 | 3',4',7-Trihydroxyflavone | C15H10O5 | Plant |
| 25615-14-9 | Kaempferol 3,7-di-O-glucoside | C27H30O16 | Plant |
| 35927-38-9 | Vicenin 1 | C26H28O14 | Plant |
| 23666-13-9 | Vicenin | C27H30O15 | Seed |
| 59282-55-2 | Vitexin 2''-O-p-coumarate | C30H26O12 | Seed |
| 382141-37-9 | Kaempferol 3-glucosyl-(1->2)-(6''-acetylgalactoside)-7-glucoside | C35H42O22 | Plant |
| 382143-42-2 | Quercetin 3-glucosyl-(1->2)-galactoside-7-glucoside | C33H40O22 | Plant |
| 14144-06-0 | Diosgenin 3-O-beta-D-glucopyranoside | C33H52O8 | Seed |
| 470-01-9 | Neotigogenin | C27H44O3 | Seed |
| 728881-06-9 | Saponin SA-III | C38H60O12 | Fruit |
| 14002-93-8 | 3,4,7-Trimethylcoumarin | C12H12O2 | Stem |
| 99664-39-8 | Trigofoenoside D | C51H84O23 | Seed |
| 99705-66-5 | Trigofoenoside A | C45H74O18 | Seed |
| 99753-11-4 | Trigofoenoside B | C45H76O19 | Seed |
| 99753-12-5 | Trigofoenoside C | C51H86O23 | Seed |
| 511-97-7 | Yuccagenin | C27H42O4 | Seed |
| 6811-13-8 | Neogitogenin | C27H44O4 | Plant |
| 94714-56-4 | Trigofoenoside F | C51H84O23 | Seed |
| 94714-57-5 | Trigofoenoside G | C56H92O27 | Seed |
| 959774-34-6 | Apigenin 6-C-beta-arabinopyranosyl-8-C-beta-galactopyranoside | C26H28O14 | Plant |
In traditional medical practices, N. sativa L. has a long history, particularly in Asian nations like Indonesia. It has been shown that N. sativa L.’s anti-inflammatory, antibacterial, tissue growth-stimulating and antioxidant properties are therapeutically beneficial for skin wound healing [17]. T. foenum-graecum, popularly known as fenugreek, is a small perennial plant in the Fabaceae family used as a medication for several diseases, and an antioxidant [18], and an anti-inflammatory [19]. When applied to a wound, its anti-inflammatory properties are released, aiding healing process and reducing inflammation. Furthermore, fatty acids found in the seeds help to form collagen, which promotes faster wound healing and preserves skin suppleness. Prediction of biological activity and protein targets. A metabolite's biological potential or bioactivity of a metabolite can be predicted through Way2Drug PASS Online, which is accompanied by a probability of activity (Pa) value. Based on the structure of both new and existing organic compounds, Way2Drug PASS Online provides more than 4000 categories of biological activity with an average accuracy of 95%. Using Way2Drug PASS Online in the early stages of the study makes it possible to select unpromising compounds [20]. The combination of active chemicals' predicted bioactivity is shown in (table 3).
Table 3: The prediction of bioactivity of active compounds in the combination of N. sativa L. and T. foenum-graecum
| Main compound | Bioactivity prediction | Pa* (Probability of Activity) | Pi (Probability of Inactive) |
Thymoquinone (CID 10281) |
Antiinflammatory Antibacterial |
0.601 0.353 |
0.031 0.042 |
Quercetin (CID 5280343) |
Antiinflammatory Antibacterial |
0.689 0.387 |
0.017 0.033 |
| Beta-amyrin (CID 73145) |
Antiinflammatory Antibacterial |
0.843 0.206 |
0.005 0.112 |
Oleic Acid (CID 445639) |
Antiinflammatory Antibacterial |
0.614 0.332 |
0.029 0.048 |
Linoleic Acid (CID 5280450) |
Antiinflammatory Antibacterial |
0.730 0.335 |
0.012 0.047 |
Diosgenin (CID 99474) |
Antiinflammatory Antibacterial |
0.766 0.396 |
0.009 0.031 |
Tigogenin (CID 99516) |
Antiinflammatory Antibacterial |
0.714 0.460 |
0.014 0.020 |
Yuccagenin (CID 3083608) |
Antiinflammatory Antibacterial |
0.820 0.393 |
0.005 0.032 |
Smilagenin (CID 91439) |
Antiinflammatory Antibacterial |
0.714 0.460 |
0.014 0.020 |
Gitogenin (CID 441887) |
Antiinflammatory Antibacterial |
0.790 0.456 |
0.008 0.021 |
Notes: Pa (Probability of Activity) and Pi (Probability of Inactive) PASS prediction results are interpreted: (i) only activities with Pa>Pi are possible for a given compound. (ii) If Pa>0.7, there is a high probability of finding high activity experimentally.
All metabolites in N. sativa L. have biological potential associated with DW through anti-inflammatory and antibacterial activities. The inflammatory activity of N. sativa L. is predicted due to the content of thymoquinone, quercetin, and oleic acid compounds. The three active compounds have biological potential related to DW and similarities with medicinal compounds, with Pa values<0.7. In antibacterial activity, all test compounds have a Pa value<0.5, which means that these compounds have low biological activity. The predicted bioactivity of active compounds from T. foenum-graecum indicates that all compounds exhibit anti-inflammatory activity and are similar to known drug compounds (Pa>0.7). However, regarding antibacterial bioactivity, all test compounds show a Pa value<0.5, signifying low biological activity in both in silico and laboratory-scale tests. Despite this, there is a promising potential for discovering new chemical entities [21].
The metabolite content of the combination can target a wide variety of protein classes in the body, including those relevant to DW, according to the target protein prediction database of the Swiss Target Prediction. Probability values>0 were obtained for proteins evaluated using the GeneCards database (table 4). From 0 to 1, this number shows how comparable the forecast results are. The likelihood of an accurate forecast increases with probability [22]. GeneCards is a human gene database containing known and successfully predicted genomic, proteomic, transcriptomic, genetic, and functional data. The investigation identified one hundred thirty-one target proteins involving N. sativa L. and T. foenum-graecum metabolites in DW were identified from the investigation.
Table 4: Prediction of metabolites target proteins in the combination of N. sativa L. and T. foenum-graecum
| PubChem ID | Compound name | Target genes linked to DW |
| 10281 | Thymoquinone | TLR9, NOS3, IL6, PTPN1 |
| 5280343 | Quercetin | KDR, MMP9 |
| 73145 | Beta amyrin | PPARG, MAPK3, PTPN1, NOS2, MDM2 |
| 445639 | Oleic Acid | PPARG, NOS2, MAPK3, PTPN1 |
| 5280450 | Linoleic Acid | PPARG, NOS2, IL6, MAPK14, MAPK1, MDM2, MAPK3 |
| 99474 | Diosgenin | PTPN1, MDM2, MAPK14 |
| 99516 | Tigogenin | MDM2, NOS2, MAPK14, VEGFA, FGF2, KDR |
| 3083608 | Yuccagenin | MAPK14, MDM2, PTPN1 |
| 91439 | Smilagenin | KDR, MAPK14, MDM2, VEGFA, NOS2 |
| 441887 | Gitogenin | KDR, MAPK14, MDM2, MAPK1, FGF2, VEGFA |
The KEGG pathway provided the DW signaling pathway prediction. Target protein interactions having a minimum interaction score of>0.400 (medium confidence) within a range of 0-1, as determined by STRING, were the focus of the study. Interactions are more physiologically important, and the higher the interaction score. This number denotes a biologically significant link (interaction). With 16.568 chemicals in its database, KEGG is a metabolic pathway that may be used to predict an organism's metabolic pathways [23]. Researchers frequently utilize STRING to evaluate protein-protein interactions in bioinformatics studies because of its high coverage, simplicity of usage, and reliable scoring system [24]. Eight target proteins were generated by analyzing DW pathway prediction using the KEGG pathway and STRING. These target proteins included the TNF signaling pathway, the Advanced Glycation End Products (AGE) receptor AGE (RAGE) signaling pathway in diabetic complications, the Toll-Like Receptor (TLR) signaling pathway, the NF-kappa B (NFKB) signaling pathway, the Vascular Endothelial Growth Factor (VEGF) signaling pathway, the Mitogen‑Activated Protein Kinase (MAPK) signaling pathway, and the Transforming Growth Factor Beta (TGF-β) signaling pathway (table 5).
Table 5: Predicted relationship in the combination of N. sativa L. and T. foenum-graecum target proteins with DW signaling pathways
| Proteins involved in DW | DW signaling pathway | Proteins involved in DW signaling pathways |
TNF IL1B JUN CASP3 IL-6 AKT1 VEGFA MAPK3 |
IL17 signaling pathway | IL6, CASP3, IL1B, TNF, MAPK3, JUN, VEGFA |
| TNF signaling pathway | IL6, CASP3, IL1B, TNF, MAPK3, JUN, AKT1 | |
| AGE-RAGE signaling pathway in diabetic complication | IL6, IL1B, CASP3, TNF, AKT1, MAPK3, JUN | |
| TLR signaling pathway | IL6, IL1B, TNF, AKT1, MAPK3, JUN | |
| Nf-kappa B signaling pathway | IL1B, TNF | |
| VEGF signaling pathway | AKT1, MAPK3 | |
| MAPK signaling pathway | IL1B, CASP3, TNF, AKT1, MAPK3, JUN | |
| TGF-beta signaling pathway | TNF, MAPK3, VEGFA |
STITCH's database and analytical tool, called STITCH examines how bioactive compounds interact with the target protein. Fig. 1 depicts the development of the STITCH-based interaction network between metabolite chemicals in N. sativa L. and T. foenum-graecum and their target proteins. A medium level of confidence is indicated by STITCH results, with a minimum interaction score of>0.400 (medium confidence). The stronger the link between the bioactive molecule and its target protein, the thicker the interaction line. The STITCH interaction analysis indicates that five active substances: diosgenin, linoleic acid, oleic acid, thymoquinone, and quercetin interact directly with surface receptor proteins. Additionally, as a result of integration with STITCH, a network view is available to view the binding affinity of chemical compounds in an interaction network. STITCH briefly explains how a molecule could affect its interaction partner [25]. Moreover, the quantity of intracellular proteins on that surface signifies the transition from chemical signals (extracellular) to intracellular signals.
Fig. 1: Confidence results through the STITCH database. Interaction of metabolites and protein intracellular with associated activity. Grey color indicates protein-protein interactions, green indicates chemical-protein interactions, and red indicates metabolite interactions.
Network pharmacology visualized with Cytoscape is shown in fig. 2. Cytoscape visualizes network molecular interactions and biological signaling pathways [26]. N. sativa L. and T. foenum-graecum metabolites interact with eight surface receptor proteins and twenty-eight intracellular outer proteins. The mechanism involves 8 DW signalling pathways: Interleukin-17 (IL-17), TNF, AGE-RAGE, TLR, NFKB, VEGF, MAPK, and TGF-β.
IL-17 signaling pathway as a proinflammatory cytokine produced by Th 17 will stimulate T cells to secrete proinflammatory mediators such as Interleukin-1 (IL-1), Tumor Necrosis Factor-α (TNF-α), chemokines, and IL-6 [27]. It is known that linoleic acid and quercetin compounds inhibit IL-17 by reducing IL-6 expression, as shown by scores of 0.839 and 0.951, respectively [28, 29]. A family member of IL-17A, commonly referred to as IL-17, has recently been successfully studied by researchers, and it was found that IL-17-deficient mice had delayed wound closure. Another aspect of wound healing is the repair of blood vessels to provide nutrients to the recovering organs, which often requires the process of angiogenesis (formation of new blood vessels). IL-17 promotes the production of Vascular Endothelial Growth Factor A (VEGFA) by epithelial and fibroblastic cells to stimulate angiogenesis [30]. As demonstrated by interaction values of 0.800 and 0.947, diosgenin and quercetin are hypothesized to transport chemical signals that the IL-17 receptor protein receives, triggering the VEGFA signaling pathway (table 6).
Fig. 2: Visualization of the pharmacological network of metabolites included in the combination of T. foenum-graecum and N. sativa L. (yellow), surface receptors (purple), and intracellular proteins (blue), and DW signaling pathways (green) with cytoscape
Table 6: Interaction analysis of target proteins and metabolites in the combination of N. sativa L. and T. foenum-graecum
| Node 1 | Node 2 | Score |
| Diosgenin | VEGFA | 0.800 |
| Linoleic acid | IL-6 | 0.839 |
| Oleic acid | CASP3 | 0.826 |
| Quercetin | AKT1 | 0.957 |
| Quercetin | CASP3 | 0.953 |
| Quercetin | IL1B | 0.738 |
| Quercetin | IL-6 | 0.951 |
Monocytes and macrophages can produce TNF-α, which is a proinflammatory cytokine. TNF-α plays a role in many processes in the body, including the pathogenesis of various diseases. TNF-α levels were found to be three times gater in diabetic rat wounds compared to normal wounds. In vitro research revealed that TNF-α boosted the expression of apoptosis-related genes, such as Akt and p53, and genes implicated in inflammation, cytokines, TLR, NF-κB, and Casp3 [31]. The compounds oleic acid, quercetin, and thymoquinone are thought to inhibit TNF-α expression through decreasing caspase-3 because they have interaction values of 0.826, 0.953, and 0.822, respectively. Decreased in caspase-3 levels reduce fibroblast apoptosis and increase ECM production [32–34]. In addition, quercetin compounds can inhibit the activation of cJun-NH2 terminal kinase (JNK), as shown by a score of 0.948. Inhibition of JNK activation can reduce the expression of inflammatory genes and protein secretion [35].
Type 2 diabetes and its consequences pathophysiology are linked to gene transcription changes triggered by the AGE-RAGE signaling pathway[36]. Low-density lipoprotein (LDL) oxidation and elevated oxidative stress are essential factors in Cardiovascular Disease (CVD) pathophysiology and are mediated by AGE-RAGE. As a ligand for RAGE, oxidized LDL stimulates the production of TGF-β, C - C-reactive protein (CRP), inflammatory cytokines, and Protein Kinase C (PKC). It activates several intracellular pathways, including NF-κB, p38, MAPK, and JNK. This contributes to the pathogenesis of CVD by promoting vascular calcification and hardening of the blood vessel's medial layer. In addition, under conditions of high glucose levels, various inflammatory factors, such as IL-6, TNF-α, and the AGE-RAGE combination, can activate IL-1β [37]. The quercetin compound is thought to inhibit the AGE-RAGE signaling pathway so inflammatory factors such as IL-1β are not activated, as indicated by an interaction score of 0.738 [38]. Thus, these findings may suggest that inhibiting AGE-RAGE signaling in DW is connected with a decrease in persistent inflammation and healing success [31].
One of the targeted Pattern Recognition Receptors (PRRs) in controlling the human immune system and inflammatory cascade is the TLR signaling pathway. As a defence mechanism, TLRs activation triggers signaling pathways; repairs injured tissue, and frequently release various inflammatory cytokines and immunological modulators. TLRs in and around wounds regulate inflammatory generation and the function of the innate immune system. Studies have indicated that elevated TLRs 2 and 4 expression in macrophages obtained from the bone marrow of non-obese diabetic rats is linked to proinflammatory cytokines and NF-κB activation [39]. The metabolism of glucose, cell differentiation, proliferation, and inflammatory responses are all linked to the NFKB signaling pathway. By modifying various NFKB pathways, the expression of cytokines and inflammatory chemicals contributes significantly to the pathogenesis of diabetes and its related microvascular and macrovascular consequences. By promoting the synthesis of proinflammatory factors, NFKB takes part in the inflammatory phase of the wound healing process. Therefore, controlling variables associated with the NFKB signaling pathway can control the inflammatory response during wound healing. Patients with diabetic foot ulcers can effectively increase their wound healing efficiency with this method [40].
The VEGF signaling pathway is a protein that stimulates vasculogenesis and angiogenesis. Decreased levels of VEGF receptor-2 are thought to cause poor wound healing [41]. Therefore, VEGF has been used as a biomarker of DW healing. VEGF stimulates several cellular signaling pathways, including the Akt 1 pathway, for various cellular functions that accelerate wound healing. An interaction value of 0.957 suggests that quercetin may transport chemical signals that the VEGF receptor protein receives and uses to activate the Akt-1 signaling pathway. Akt-1 is a vital component of the Pi3K/Akt signaling pathway and regulates many stresses, including metabolism, proliferation, migration, growth, and angiogenesis [42].
A class of serine/threonine protein kinases involved in the immunological response, inflammation, cell proliferation, apoptosis, and hormone signaling is known as the MAPK signaling pathway. It has been established that there are three main subfamilies of MAPK genes: p38 MAPKs, JNK, and Extracellular Signal-Regulated Kinases (ERK). ERK, JNK, and p38 MAPK are the three primary subfamilies of MAPK genes. According to theories, autophagy is inactivated with a downregulation of the p38/MAPK pathway, preventing keratinocyte migration in conditions with elevated glucose levels. This demonstrates the critical function of the MAPK pathway in the healing of DW [35].
TGF-β1 and TGF-β2 are key members of the TGF-β superfamily crucial in wound healing. TGF-β is present in wound fluid in several investigations. TGF-β performs various regulatory cellular tasks at the cellular regulatory level, including drawing fibroblasts and macrophages to the wound site to aid in healing. Furthermore, TGF-β plays a crucial role in wound healing in DW, although it is not significantly elevated compared to other reduced growth factors, which could postpone healing [37].
In silico approaches have various advantages, including lowering the number of compounds or molecules, accelerating the discovery of most compound-protein interactions via database searches, and reducing the need for laboratory animals in predictive studies. In silico prediction not only saves time in developing ligand-based medications and serves as a starting point for in vitro and in vivo testing but also supports N. sativa L. and T. foenum-graecum as therapeutic candidates, particularly for DW. However, the limitations of this method must be recognized, such as the enormous volume of data processed. High accuracy is essential, and the danger of bias varies according to the database utilized for predictions. Some possible active metabolites of N. sativa L. and T. foenum-graecum that are not currently included in the database may raise the risk of prediction mistakes and diminish the accuracy of study results.
CONCLUSION
Network pharmacology analysis of N. sativa L. and T. foenum-graecum revealed a correlation between their metabolites, surface targets or receptors, intracellular proteins, and DW signaling pathways. This suggests that both plants have potential as drug candidates for DW. This study can be further developed with potential clinical application for DW healing.
ACKNOWLEDGMENT
We thank Ms. Nabila Rahmadani, S. Farm., M. Biomed from the Faculty of Medicine and Health Sciences, for her collaboration and meticulous efforts in data collection.
AUTHORS CONTRIBUTIONS
The authors acknowledge their contributions to this research as follows: Research conception and design: RS, RA; Data collection: MRD, SQA Analysis and interpretation of results: MRD, SQA; Preparation of manuscript draft: MRD, SQA. All authors reviewed the results and approved the final version of the manuscript.
CONFLICTS OF INTERESTS
No potential conflict of interest relevant to this article was reported.
REFERENCES
Shailaja K, Abraham A, Bhargavi B, Devika R. Influence of pharmaceutical care activities on knowledge attitude and practice (KAP) among diabetic patients in a Tertiary Care Hospital. Int J Pharm Pharm Sci. 2020;12(5):36-40. doi: 10.22159/ijpps.2020v12i5.36984.
Arman E, AA, Dafriani P, Almasdy D. Combined effect of topical application of virgin coconut oil (vco) and black cumin oil (nigella sativa) on the upregulation of vegf gene expression and wound healing in diabetic ulcerated rats. Int J App Pharm. 2024;16(1):35-40. doi: 10.22159/ijap.2024.v16s1.07.
Marchianti AC, Prameswari MC, Sakinah EN, Ulfa EU. The enhancement of collagen synthesis process on diabetic wound by Merremia mammosa (Lour.) extracts fraction. Int J Pharm Pharm Sci. 2019 Feb;11(2):47-50. doi: 10.22159/ijpps.2019v11i2.30170.
Maslova E, Eisaiankhongi L, Sjoberg F, McCarthy RR. Burns and biofilms: priority pathogens and in vivo models. NPJ Biofilms Microbiomes. 2021;7(1):73. doi: 10.1038/s41522-021-00243-2, PMID 34504100.
Giannopoulos S, Armstrong EJ. Diabetes mellitus: an important risk factor for peripheral vascular disease. Expert Rev Cardiovasc Ther. 2020;18(3):131-7. doi: 10.1080/14779072.2020.1736562, PMID 32129693.
Lipsky BA, Senneville E, Abbas ZG, Aragon Sanchez J, Diggle M, Embil JM. Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update). Diabetes Metab Res Rev. 2020;36 Suppl 1:e3280. doi: 10.1002/dmrr.3280, PMID 32176444.
Sukmawan YP, Alifiar I, Nurdianti L, Ningsih WR. Wound healing effectivity of the ethanolic extracts of Ageratum conyzoides L. leaf (white and purple flower type) and Centella asiatica and astaxanthin combination gel preparation in an animal model. Turk J Pharm Sci. 2021;18(5):609-15. doi: 10.4274/tjps.galenos.2021.34676, PMID 34719189.
Ivanalee AS, Yudaniayanti IS, Yunita MN, Triakoso N, Hamid IS, Saputro AL. Efektivitas sugar dressing (100% Gula) dalam meningkatkan kepadatan kolagen pada proses penyembuhan luka bakar buatan pada kulit tikus putih (Rattus norvegicus) jantan. J Med Vet. 2018;1(3):134. doi:10.20473/jmv.vol1.iss3.2018.134-141.
Nourbar E, Mirazi N, Yari S, Rafieian Kopaei M, Nasri H. Effect of hydroethanolic extract of Nigella sativa L. on skin wound healing process in diabetic male rats. Int J Prev Med. 2019;10:18. doi: 10.4103/ijpvm.IJPVM_276_18, PMID 30820305.
Bahar M, Yusmaini H. Efek antimikroba ekstrak lidah buaya (aloe vera) terhadap isolat bakteri penyebab acne vulgaris secara in vitro. Jurnal Kedokteran Dan Kesehatan. 2017;11(2). doi: https://doi.org/10.33533/jpm.v11i2.222.
Susilowati R, Rohmanningrum UM. Effective combination of Nigella sativa and Trigonella foenum graecum seed extract on wound healing in diabetic mice. J Biodjati. 2023 May;8(1):106-16. doi: 10.15575/biodjati.v8i1.19968.
Tan X, Pei W, Xie C, Wang Z, Mao T, Zhao X. Network pharmacology identifies the mechanisms of action of tongxie anchang decoction in the treatment of irritable bowel syndrome with diarrhea predominant. Evid Based Complement Alternat Med. 2020 Nov 17;2020:2723705. doi: 10.1155/2020/2723705, PMID 33281910.
Chandran U, Mehendale N, Patil S, Chaguturu R, Patwardhan B. Network pharmacology. Innovative Approaches in Drug Discovery. 2017;127-64. doi: 10.1016/B978-0-12-801814-9.00005-2.
Firzannida F, Bagaskara S, Savira SS, Fadnurrahim A, Rofida S. Network pharmacology of black cumin (Nigella sativa L.) as a candidate of OMAI in colorectal cancer: in silico study. Indones J Biotechnol. 2022;27(2):87-98. doi: 10.22146/ijbiotech.70699.
Corso M, Perreau F, Mouille G, Lepiniec L. Specialized phenolic compounds in seeds: structures functions and regulations. Plant Sci. 2020 Jul;296:110471. doi: 10.1016/j.plantsci.2020.110471.
Savithramma N, Yugandhar P, Prasad KS, Ankanna S, Chetty KM. Ethnomedicinal studies on plants used by yanadi tribe of Chandragiri reserve forest area Chittoor district Andhra Pradesh India. J Intercult Ethnopharmacol. 2016;5(1):49-56. doi: 10.5455/jice.20160122065531, PMID 27069725.
Sallehuddin N, Nordin A, Idrus BT HJ Ruszymah, Fauzi MB. Nigella sativa and its active compound thymoquinone accelerate wound healing in an in vivo animal model: a comprehensive review. Int J Environ Res Public Health. 2020 Jun;17(11):4160. doi: 10.3390/ijerph17114160, PMID 32545210.
Szabo K, Gesztelyi R, Lampe N, Kiss R, Remenyik J, Pesti Asboth G. Fenugreek (Trigonella foenum-Graecum) seed flour and diosgenin preserve endothelium-dependent arterial relaxation in a rat model of early-stage metabolic syndrome. Int J Mol Sci. 2018 Mar;19(3):798. doi: 10.3390/ijms19030798, PMID 29534453.
VS, PS, AR. Antimicrobial activity of Trigonella foenum-Graecum L. (Fenugreek). Eur J Exp Bio. 2017 Jan;7(1):1-4. doi: 10.21767/2248-9215.100004.
Filimonov DA, Lagunin AA, Gloriozova TA, Rudik AV, Druzhilovskii DS, Pogodin PV. Prediction of the biological activity spectra of organic compounds using the pass online web resource. Chem Heterocycl Comp. 2014 Jun;50(3):444-57. doi: 10.1007/s10593-014-1496-1.
Ramadhan DS, Fakih TM, Arfan A. Activity prediction of bioactive compounds contained in Etlingera elatior against the SARS-CoV-2 main protease: an in silico approach. Borneo J Pharm. 2020;3(4):235-42. doi: 10.33084/bjop.v3i4.1634.
Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. Swiss target prediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2024 Jul 1;42:32-8. doi: 10.1093/nar/gku293, PMID 24892161.
Altman T, Travers M, Kothari A, Caspi R, Karp PD. A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinformatics. 2013;14:112. doi: 10.1186/1471-2105-14-112, PMID 23530693.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta Cepas J. String v11: protein-protein association networks with increased coverage supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-13. doi: 10.1093/nar/gky1131, PMID 30476243.
Szklarczyk D, Santos A, Von Mering C, Jensen LJ, Bork P, Kuhn M. Stitch 5: augmenting protein chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(D1):D380-4. doi: 10.1093/nar/gkv1277, PMID 26590256.
Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape stringapp: network analysis and visualization of proteomics data. J proteome Res. 2019 Feb;18(2):623-32. doi: 10.1021/acs.jproteome.8b00702.
Hadian Y, Bagood MD, Dahle SE, Sood A, Isseroff RR. Interleukin-17: a potential target for chronic wounds. Mediators Inflamm. 2019 Nov;2019:1297675. doi: 10.1155/2019/1297675, PMID 31827374.
FU J, Huang J, Lin M, Xie T, You T. Quercetin promotes diabetic wound healing via switching macrophages from M1 to M2 polarization. J Surg Res. 2020;246:213-23. doi: 10.1016/j.jss.2019.09.011, PMID 31606511.
Rodrigues HG, Vinolo MA, Sato FT, Magdalon J, Kuhl CM, Yamagata AS. Oral administration of linoleic acid induces new vessel formation and improves skin wound healing in diabetic rats. PLoS One. 2016 Oct;11(10):e0165115. doi: 10.1371/journal.pone.0165115, PMID 27764229.
Majumder S, McGeachy MJ. IL-17 in the pathogenesis of disease: good intentions gone awry. Annu Rev Immunol. 2021;39:537-56. doi: 10.1146/annurev-immunol-101819-092536, PMID 33577346.
Stachura A, Khanna I, Krysiak P, Paskal W, Wlodarski P. Wound healing impairment in type 2 diabetes model of leptin-deficient mice a mechanistic systematic review. Int J Mol Sci. 2022;23(15):8621. doi: 10.3390/ijms23158621, PMID 35955751.
Cooper PO, Haas MR, Noonepalle SK, Shook BA. Dermal drivers of injury-induced inflammation: contribution of adipocytes and fibroblasts. Int J Mol Sci. 2021;22(4):1933. doi: 10.3390/ijms22041933, PMID 33669239.
Hohmann MS, Habiel DM, Coelho AL, Verri WA, Hogaboam CM. Quercetin enhances ligand-induced apoptosis in senescent idiopathic pulmonary fibrosis fibroblasts and reduces lung fibrosis in vivo. Am J Respir Cell Mol Biol. 2019;60(1):28-40. doi: 10.1165/rcmb.2017-0289OC, PMID 30109946.
Kmail A, Said O, Saad B. How thymoquinone from Nigella sativa accelerates wound healing through multiple mechanisms and targets. Curr Issues Mol Biol. 2023;45(11):9039-59. doi: 10.3390/cimb45110567, PMID 37998744.
Wang X, LI W, LU S, MA Z. Modulation of the wound healing through noncoding RNA interplay and GSK-3β/NF-κB signaling interaction. Int J Genomics. 2021 Aug;2021:9709290. doi: 10.1155/2021/9709290, PMID 34485505.
Khalid M, Petroianu G, Adem A. Advanced glycation end products and diabetes mellitus: mechanisms and perspectives. Biomolecules. 2022 Apr;12(4):542. doi: 10.3390/biom12040542, PMID 35454131.
Zheng SY, Wan XX, Kambey PA, Luo Y, HU XM, Liu YF. Therapeutic role of growth factors in treating diabetic wound. World J Diabetes. 2023;14(4):364-95. doi: 10.4239/wjd.v14.i4.364, PMID 37122434.
Jiang M, Wang X, Wang P, Peng W, Zhang B, Guo L. Inhibitor of RAGE and glucose-induced inflammation in bone marrow mesenchymal stem cells: effect and mechanism of action. Mol Med Rep. 2020;22(4):3255-62. doi: 10.3892/mmr.2020.11422, PMID 32945430.
Yehualashet AS. Toll-like receptors as a potential drug target for diabetes mellitus and diabetes-associated complications. Diabetes Metab Syndr Obes. 2020 Dec;13:4763–77. doi: 10.2147/DMSO. S274844.
Suryavanshi SV, Kulkarni YA. NF-κβ: a potential target in the management of vascular complications of diabetes. Front Pharmacol. 2017 Nov 7;8:798. doi: 10.3389/fphar.2017.00798, PMID 29163178.
QI M, Zhou Q, Zeng W, WU L, Zhao S, Chen W. Growth factors in the pathogenesis of diabetic foot ulcers. Front Biosci (Landmark Ed). 2018;23(2):310-7. doi: 10.2741/4593, PMID 28930549.
Zulkefli N, Che Zahari CN, Sayuti NH, Kamarudin AA, Saad N, Hamezah HS. Flavonoids as potential wound healing molecules: emphasis on pathways perspective. Int J Mol Sci. 2023 Feb;24(5):4607. doi: 10.3390/ijms24054607, PMID 36902038.