Content uploaded by Nurlaili Susanti
Author content
All content in this area was uploaded by Nurlaili Susanti on Mar 06, 2025
Content may be subject to copyright.
View
Online
Export
Citation
RESEARCH ARTICLE | JA NU ARY 30 2025
Potential of Clinacanthus nutans in the treatment of diabetes
mellitus and molecular mechanisms prediction based on
network pharmacology
Nurlaili Susanti; Arifa Mustika ; Junaidi Khotib
AIP Conf. Proc. 3186, 020045 (2025)
https://doi.org/10.1063/5.0234941
Articles You May Be Interested In
Effect of processing parameters on the phenolic content of Clinacanthus nutans leaves extract using
response surface methodology (RSM)
AIP Conf. Proc. (June 2019)
Study on mechanism of Shufeng Jiedu granules in treating novel coronavirus pneumonia based on
network pharmacology
AIP Conf. Proc. (August 2020)
Patchouli (Pogostemon cablin Benth.): Progress on its in vitro cultivation and pharmacological
perspectives
AIP Conf. Proc. (January 2025)
31 January 2025 05:14:58
Potential of Clinacanthus nutans in the Treatment of
Diabetes Mellitus and Molecular Mechanisms Prediction
based on Network Pharmacology
Nurlaili Susanti
1,2,a)
, Arifa Mustika
3,b)
, and Junaidi Khotib
4,c)
1
Doctoral Program of Medical Science, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
2
Faculty of Medicine and Health Science, Maulana Malik Ibrahim State Islamic University, Malang, Indonesia
3
Department of Anatomy, Histology, and Pharmacology, Faculty of Medicine, Universitas Airlangga, Surabaya,
Indonesia
4
Department of Pharmacy Practice, Faculty of Pharmacy, Universitas Airlangga, Surabaya, Indonesia.
a)
nurlaili.susanti-2020@fk.unair.ac.id
b)
Corresponding author: arifa-m@fk.unair.ac.id
c)
junaidi-k@ff.unair.ac.id
Abstract. Clinacanthus nutans is rich in phytoconstituents and is considered a promising medicinal plant. This study aims
to review the potential of C. nutans as an anti-diabetic and to explore its molecular mechanism based on network
pharmacology. A literature review was conducted on articles, obtained from Scopus and Google Scholar databases, showed
that C. nutans has an anti-diabetic effect. For network pharmacology analysis, the bioactive compounds of C. nutans were
acquired from previous related research and SwissADME was used to predict its Drug-likeness based on Lipinski rule and
oral bioavailability score. The overlapping between diabetes-associated genes, retrieved by DisGeNET database, and the
predicted C. nutans target genes were visualized by Venn Diagram. Protein-protein interaction networks of these
overlapping genes were identified using String-db. These interactions form a PPI network, which is subsequently evaluated
by Cytoscape 3.9.1 software to analyze potential genes and then used to construct a compound-target network. Finally, the
signaling pathways involved in these genes were analyzed based on the KEGG database. These results imply that C. nutans
has potential as anti-diabetic through several signaling pathways associated with diabetes mellitus. This provides a
scientific basis for further research on the anti-diabetic mechanism of C. nutans.
INTRODUCTION
Diabetes Mellitus (DM) is a chronic metabolic disorder marked by a lack of insulin production, action, or both,
resulting in hyperglycemia [1]. Chronic hyperglycemia leads to multiple abnormalities, including macrovascular and
microvascular complications [2][3]. DM has become the highest prevalence in the world. The global prevalence in
2019 was around 463 million (9.3% of the population), with a mortality rate of 4.2 million [4]. Although insulin and
currently available hypoglycemic drugs can control hyperglycemia, it does not entirely prevent long-term vascular
complications in diabetic patients [5]. In addition, the patient cannot tolerate some of the side effects of this drug, such
as weight gain, hypoglycemia, and abdominal discomfort [6]. Therefore, investigation for new anti-diabetic agents
has continued. Medicinal plants have gained attention as anti-diabetic agents and have been frequently utilized
empirically in traditional medicine. Apart from being a safer alternative medicine, it has multitasking abilities to target
various aspects treating of diabetes, including lowering blood glucose, increasing insulin biosynthesis, improving
insulin resistance, enhancing the antioxidant system, and preventing long-term complications due to hyperglycemia
[7].
Clinacanthus nutans, a member of the Acanthaceae family, is used as a traditional medicine in Southeast Asia. C.
nutans leaf extract contains a wide variety of chemical constituents including flavonoids, glycosides, phytosterols,
The 5th International Conference on Life Science and Technology (ICoLiST)
AIP Conf. Proc. 3186, 020045-1–020045-12; https://doi.org/10.1063/5.0234941
Published under an exclusive license by AIP Publishing. 978-0-7354-5102-5/$30.00
020045-1
31 January 2025 05:14:58
triterpenoids, and alkaloids [8]. It is called Sabah Snake Grass in Malaysia, Phaya yo in Thailand), or Dandang gendis
in Indonesia) [9]. This herb is commonly consumed as raw vegetables, juices, and herbal teas for various medicinal
purposes [10]. Many studies have reported the pharmacological activities of C. nutans, including antiviral [11],
antibacterial [12], antivenom [13], anti-inflammatory [14], anticancer [15], antioxidant [16], neuroprotective [17], and
analgetic [18]. Regarding the anti-diabetic effect, previous studies have shown that C. nutans can reduce blood glucose
levels in diabetic rats [19], but there has not been a comprehensive review on this.
The present advancement of bioinformatics technology helps scientists to determine the mechanism of herbal
plants for diverse medicinal actions. Network pharmacology has been extensively explored in the investigation of
multi-component chemical compounds of herbal plants against multi-targets treating DM [20]. Network
pharmacology is an innovative technology that uses computer-aided algorithms and virtual model construction to
visualize the network between drugs and diseases based on interactions between chemical compounds, target proteins,
and functional pathways. This gives researchers a first look at the pharmacological mechanisms underlying various
diseases and supports the development of new drugs [21]. C. nutans leaves contain many active compounds that have
the potential as anti-diabetic but have yet to be systematically analyzed for the exact mechanism. Therefore, this study
aimed to review the beneficial effects of C. nutans on diabetes and to explore the mechanism of action of its active
compounds against appropriate target genes in DM through a network pharmacology approach.
EXPERIMENTAL DETAILS
Review on Antidiabetic Potential of C. nutans
The review is carried out by collecting articles on the Google Scholar online database. Search conducted with the
keywords "Clinacanthus nutans" and "anti-diabetic" or "hypoglycemic". The inclusion criteria are research articles
on the anti-diabetic effect of C. nutans using invitro and invivo models, while review articles, editorial materials, and
book chapters are excluded. Articles are selected by title and abstract. All relevant articles were comprehensively
studied by reading the full text.
Constructing a Database of C. nutans Chemical Compounds
Information about the chemical compounds of C. nutans was discovered from previous related studies. SMILES
(Simplified Molecular Input Line Entry System) of compounds was obtained from PubChem
(https://pubchem.ncbi.nlm.nih.gov/) [22]. Chemical compounds were predicted by SwissADME (http://www.
swissadme.ch/) to identify “Drug-likeness” based on Lipinski’s rule and oral bioavailability score [23].
Constructing a Database of Target Genes Related to C. nutans Compound or Diabetes
Target genes associated with C. nutans compounds were identified using Swiss Target Prediction (STP)
(http://www.swisstargetprediction.ch/) in "Homo Sapiens” mode [24]. Protein ID was aligned using UniProt ID to
eliminate duplicate protein [25]. DM-related genes were retrieved by DisGeNET (https://www.disgenet.org/search)
databases with the keyword "diabetes mellitus" (CUI: C0011849) [26]. The overlapping genes between C. nutans
compounds and DM-related genes were shown using Venn Diagram (http://jvenn.toulouse.inra.fr/app/example.html)
[27].
Systematic Prediction of Therapeutic Pathways of C. nutans on Diabetes
Protein-protein interaction (PPI) networks of overlapping genes between C. nutans and DM-related genes were
identified using STRING database (https://string-db.org/) [28]. Protein was input using multiple proteins option,
organism was set as homo sapiens, network type was set as full string network, and the score was placed at the highest
confidence (0.900). The PPI network was then exported in TSV format. Furthermore, this network was analyzed by
Cytoscape 3.9.1 software to determine the relevance of gene based on its degree of connectivity score. The potential
target genes obtained were then used to build a compound target network that reflects the mechanism of action of the
active compound of C. nutans in diabetes [29]. Finally, gene-associated signaling pathways were analyzed and
summarized based on the KEGG database (https://www.kegg.jp/) [30].
020045-2
31 January 2025 05:14:58
RESULTS AND DISCUSSION
Antidiabetic Potential of C. nutans
A total of 8 articles were selected for the study, consisting of 4 in-vitro studies and 4 in-vivo studies. All in-vitro
studies evaluated the inhibitory activity of C. nutans against α-glucosidase and α-amylase (Tab. 1). In the small
intestine, α-glucosidase hydrolyzes carbohydrates into glucose, in contrast α-amylase hydrolyzes α-1,4-glucan
linkages in starch, maltodextrins, and maltooligosaccharides into simple sugars (dextrin, maltotriose, maltose and
glucose) [31]. Although this enzyme is not directly involved in the pathogenesis of diabetes, inhibiting α-glucosidase
and α-amylase can significantly lower post-prandial glucose levels. Therefore, it becomes an effective strategy in
treating metabolic diseases such as type 2 diabetes mellitus [32]. C. nutans showed high α-glucosidase and α-amylase
inhibitory activity with the IC50 value lower than 100 µg/mL [33],[34],[35],[36]. In addition, there was a significant
correlation of the α-glucosidase inhibitory with the antioxidant capacity and the total flavonoid contents of the
fractions [34].
TABLE 1. Summary of Invitro Studies on C. nutans Anti-diabetic Activity
Reference
Methods
Study model
Treatment
Results
Wong et al. (2014) in vitro α-glucosidase
inhibition test
aqueous
extract
IC50 30 µg/mL
Alam, MA et al. (2017) in vitro α-glucosidase
inhibition test
methanol
extract and its
fractions
IC50 methanol extract 61.39
µg/ml, hexane fraction 44.57
µg/ml, ethyl acetate fraction 53.69
µg/ml, butanol fraction 37.47
µg/ml, aqueous fraction 63.50
µg/mL
Abdullah & Kasim
(2017)
in vitro α-amylase
inhibition test
ethanol
extract
IC50 leaves 4.28 µg/mL, stem 8.43
µg/mL, whole µg/mL
Murugesu et al. (2018) in vitro α-glucosidase
inhibition test
various
solvents
extract
IC50 hexane 3.05 µg/ml, hexane-
ethyl acetate 5.54 µg/ml, ethyl
acetate 8.42 µg/ml, ethyl acetate-
methanol 37.45 µg/ml
In in-vivo studies (Tab. 2), the benefit of C. nutans was found in aqueous and methanol extracts with various
dosages ranging from 100 to 500 mg/kg and treatment durations ranging from 4 to 7 weeks. The Glucose lowering
effect has been reported in all studies. [19] and [37] found that C. nutans significantly increased blood insulin
concentration, but no evidence that higher insulin secretion results from improved pancreatic cell dysfunction. The
upregulation of the genes encoding insulin receptor substrate (IRS), phosphatidylinositol-3-phosphate (PI3K),
adiponectin, and leptin receptors has been shown to reduce insulin resistance in rats treated with C. nutans [19],[37].
Insulin resistance can be triggered by oxidative stress in many insulin-target organs [38]. C. nutans has shown the
capacity to modulate the expression of various antioxidant genes, including superoxide dismutase, catalase,
glutathione reductase, and glutathione peroxidase [39]. In addition, C. nutans can dramatically lower oxidative stress
markers and boost total antioxidant levels in rat model of type 2 DM [19]. High levels of free fatty acids due to
dyslipidemia can trigger oxidative stress and increase lipid peroxidation, which is involved in the pathophysiology of
diabetes [40]. Sarega et al. (2016) found that after 7 weeks of C. nutans treatment in rats given a high-fat and
cholesterol diet had improved lipid profiles [39]. The beneficial effect of C. nutans on sorbitol-related complications
was evaluated by Umar Imam et al. (2019), which showed a significant reduction in sorbitol levels in the kidney, lens
and nerves, suggesting that it might be utilized to treat diabetic neuropathy, retinopathy and nephropathy [19]. Diabetes
is also associated with the development of atherosclerosis and cardiovascular disease. Administration of C. nutans in
a study by Azemi et al. (2020) could increase endothelial vasodilation and reduce endothelial contraction through the
expression of eNOS protein in diabetic rats [41].
020045-3
31 January 2025 05:14:58
TABLE 2. Summary of Invivo Studies on C. nutans Anti-diabetic Activity
Reference Methods
Study model
Treatment
Results
Sarega,
Imam, Ooi,
et al. (2016)
in vivo rat induced by
high fat and
cholesterol diet
Leaves aqueous
and methanol
extract dose 500*,
250*, 125mg/kg
for 7 weeks
↓ dyslipidemia
↑ serum and hepatic markers of antioxidant
status (SOD, GPx)
↓ Serum markers of oxidative stress (F2-
isoprostane)
↓ Hepatic markers of oxidative stress
(MDA)
↑ Hepatic antioxidant genes mRNA levels
(SOD, CAT, GPx, and GSR)
Sarega,
Imam, Esa,
et al. (2016)
in vivo rat induced by
high fat and
cholesterol diet
Leaves aqueous
and methanol
extract dose 500*,
250*, 125mg/kg
for 7 weeks
↓ FBG
↑ Insulin
↓ HOMA-IR
↓ serum RBP4
↑ serum adiponectin
↓ serum leptin
↑ mRNA levels of insulin resistance-related
genes (IRS, PI3K, adiponectin & leptin
receptors)
Umar Imam
et al. (2019)
in vivo Rat induced by
HFD and STZ
35 mg/kg
Leaves aqueous
extract dose 100
and 200* mg/kg
for 28 days
↓ FBG
↑ insulin
↓ Total cholesterol, ↓ LDL, ↓ TG, ↑ HDL
↓ liver F2‐isoprostane
↑ liver total antioxidant status
↑ aldose reductase in kidney, lens, and
nerve
↓ sorbitol dehydrogenase in kidney, lens,
and nerve
no histologic changes in kidney and liver
Azemi et al.
(2020)
in vivo Rat induced by
HFD and STZ
40 mg/kg
Leaves methanol
extract dose 500
mg/kg for 28 days
↓ FBG
↑ endothelial-dependent vasodilatation
↓ endothelial-dependent contraction
↑ eNOS protein expression
FIGURE 1. Schematic pathways for the anti-diabetic activity of C. nutans. ↑: increase; ↓: decrease.
This review indicates that C. nutans extract has potential as an anti-diabetic, where the proposed mechanism is
shown in Fig. 1. C. nutans reduce the absorption of carbohydrates from the small intestine and prevents a post-prandial
rise in blood glucose levels. C. nutans reduces fasting hyperglycemia, possibly due to decreased endogenous glucose
C. nutans
↓ glucose absorption in small
intestine
↑ insulin secretion
↓ hepatic glucose production
↓ insulin resistance
↓ blood
glucose
↓ diabetic
complication
↓ endothelial dysfunction and
sorbitol accumulation
020045-4
31 January 2025 05:14:58
production in the liver. C. nutans increase insulin secretion from pancreatic β cells. C. nutans reduces insulin resistance
in peripheral tissues, which may be associated with improved lipid profile, reduced oxidative stress, and increased
antioxidant enzymes. C. nutans prevent diabetes complications due to sorbitol accumulation in the kidney, lens, and
nerves and restore endothelial dysfunction in diabetes.
Network Pharmacology of C. nutans Compounds with Targets Related to Diabetes
The review of previous studies above shows that C. nutans leaves have potential as anti-diabetic, but the underlying
molecular mechanism is still unclear. This study further explores the molecular mechanisms underlying the anti-
diabetic activity of C. nutans using a network pharmacology approach. A total of 22 compounds contained in C. nutans
were found in previous related research [42],[43],[44],[45],[46]. All compounds were identified Drug-likeness
properties according to Lipinski's rule (Molecular Weight <500g/mol; Hydrogen Bond Acceptor (HBA) <10;
Hydrogen Bond Donor (HBD) ≤5), Moriguchi octanol-water partition coefficient (MLogP) ≤4.15; Lipinski's
Violations ≤1) and Bioavailability Score (> 0.1) (Table 3) [[47]]. The molecular weight ranges from 118.14 to 570.68
with Indazole as the lightest molecule and Nigramide B as the heaviest molecule. All compounds met these criteria,
except 3 compounds had a Lipinski violation more than 1, that is Isoorientin, Orientin, and Shaftoside. Drug likeness
determines the possibility of a compound being an oral drug, while bioavailability is the amount of active compound
that is absorbed and reaches circulation [23].
TABLE 3. Drug-likeness Properties of the C. nutans Compounds
No Compound Lipinski Rules Lipinski's
Violations
Bioavailability
Score
MW
HBA
HBD
MLogP
1
isovitexin
432.38
10
7
-2.02
1
0.55
2
vitexin
432.38
10
7
-2.02
1
0.55
3
Isoorientin
448.38
11
8
-2.51
2
0.17
4
orientin
448.38
11
8
-2.51
2
0.17
5
shaftoside
564.49
14
10
-3.97
3
0.17
6 3,3-di-O-
methylellagic acid
330.25 8 2 0.65 0 0.55
7
5-oxoprolinate
128.11
3
1
-0.93
0
0.85
8
apigenin
254.24
4
2
1.08
0
0.55
9
Clinacoside A
298.31
8
4
-2.57
0
0.55
10
Clinacoside B
282.31
7
4
-2.5
0
0.55
11
Clinacoside C
339.36
8
5
-3.18
0
0.55
12
Stigmasterol
412.69
1
1
6.62
1
0.55
13
beta-sitosterol
414.71
1
1
6.73
1
0.55
14
Betulin
442.72
2
2
6
1
0.55
15
lupeol
426.72
1
1
6.92
1
0.55
16
Indazole
118.14
1
1
1.12
0
0.55
17
Aurantiamide
402.49
3
3
3.07
0
0.55
18
aurantiamide acetate
444.52
4
2
3.41
0
0.55
19
piperine
285.34
3
0
2.39
0
0.55
20 1-[7-(3,4-
Methylenedioxypheny
l)heptatrienoyl]piperi
dine
311.37 3 0 2.78 0 0.55
21 1-[(2E,4E)-7-(3,4-
methylenedioxypheny
l)-2,4-
heptadienoyl]pyrrolid
ine
299.36 3 0 2.62 0 0.55
22
nigramide B
570.68
6
0
3.71
1
0.55
020045-5
31 January 2025 05:14:58
FIGURE 2. Overlapping genes between target genes of C. nutans compounds (A) and DM-related genes (B)
Initially, 494 target genes linked to the C. nutans compounds were retrieved from the Swiss Target Prediction
database (Supp. Table 1) and 2.568 DM-related genes were retrieved from DisGeNET databases (Supp. Table 2).
Subsequently, overlapping genes were predicted by Venn diagrams (Fig. 2) and it found that 232 genes are common
targets of both C. nutans and DM-related genes (Supp. Table 3). C. nutans compounds have potential as anti-diabetic
by acting on multiple target genes involved in diabetes mellitus. This is following the principles of herbal in traditional
Chinese medicine which the therapeutic approach of multifactorial disease uses the concept of multi-component and
multi-target action that can be understood through a pharmacology network [48],[49].
FIGURE 3. PPI network of overlapping genes between C. nutans and Diabetes Mellitus
A
B
020045-6
31 January 2025 05:14:58
The Protein-Protein Interaction (PPI) network of overlapping genes between C. nutans and DM showed 232 nodes
(Fig. 3), representing the number of genes involved, and 482 edges, representing the number of interactions. According
to the degree value obtained by the topological analysis, the top 10 genes were further selected (Tab. 4). The higher
degree value indicates that the protein has a greater role in the pathogenesis of diabetes mellitus. 10 Genes obtained
from the highest to the lowest degree values are PIK3CA, PIK3R1, JAK2, JAK1, EGFR, TYK2, MAPK1, PTPN1,
PTPN6, and NFKBIA. These genes then constructed their PPI network and are shown in Fig. 4. These genes interact
with each other to form 10 nodes and 35 edges.
TABLE 4. Top 10 genes ranked by degree of connectivity.
Gen Degree
PIK3CA
14
PIK3R1
13
JAK2
12
JAK1
11
EGFR
11
TYK2
10
MAPK1
9
PTPN1
9
PTPN6
8
NFKBIA
6
FIGURE 4. PPI network of the core target for C. nutans against Diabetes Mellitus
We also constructed a compound–target network using Cytoscape software to further clarify the mechanism of C.
nutans active compound in treating DM (Fig. 5). It was known that 12 components are reserved as the main active
compounds of C. nutans which have potential in treating DM. These 12 compounds belong to the group of flavonoid
(3,3-di-O-methylellagic acid and apigenin), glucosides (Clinacoside A), phytosterols (Stigmasterol and Beta-
sitosterol), terpenoids (Betulin and Lupeol), and alkaloids (Aurantiamide, Aurantiamide acetate, 1-[(2E,4E)-7-(3,4-
methylenedioxyphenyl)-2,4-heptadienoyl pyrrolidine, and Nigramide B).
Based on KEGG analysis (Fig. 6), the core target genes of the C. nutans active compounds in DM play a role in
several signaling pathways including PI3K/Akt, Jak-Stat, MAPK, and NFKB signaling pathways. PI3K is an
important protein in insulin signal transduction [50]. PI3K is a heterodimer consisting of the p110α catalytic subunit
encoded by the PIK3CA gene and the p85α regulatory subunit encoded by the PIK3R1 gene [51]. PI3K can be
activated by several ligands, including growth factors, cytokines, and insulin. PI3K activates signaling Akt, primarily
expressed in insulin-responsive tissues, to regulate glucose and lipid metabolism, promoting the translation of GLUT4,
and triggering insulin secretion from pancreatic β cells [52]. The Jak-Stat signaling pathway, activated by the cytokines
IL-6 and IFN-γ, has been shown to co-immunoprecipitate with insulin receptors and induce IRS-1 phosphorylation
that triggers PI3K activation [53].
020045-7
31 January 2025 05:14:58
FIGURE 5. Compound–target network of the core target for C. nutans against DM
FIGURE 6. Prediction of the therapeutic pathway of C. nutans in Diabetes Mellitus ( Trigger Prevent)
The MAPK pathway mediates signal integration and induces a variety of cellular responses including proliferation,
differentiation, inflammation, and apoptosis. Oxidative stress conditions induced by glucolipotoxicity and
inflammatory cytokines activate the MAPK pathway that plays a role in developing insulin resistance and pancreatic
020045-8
31 January 2025 05:14:58
β cell dysfunction [54]. The MAPK pathway is also involved in activating signaling pathways that trigger uncontrolled
remodeling of the heart and blood vessels and lead to the development of cardiovascular complications of diabetes
[55]. The NF-KB signaling pathway plays an important role in regulating inflammation by releasing proinflammatory
cytokines such as IL-1β and IL-6 that trigger insulin resistance and islet inflammation [56]. Therefore, inhibition of
these pathways can be a target for treating diabetes mellitus.
CONCLUSION
Previous studies showed that C. nutans extract has potential as an anti-diabetic by inhibiting α-glucosidase,
decreasing fasting glucose production, increasing insulin secretion, and improving insulin resistance. Furthermore,
network pharmacology predicted that its anti-diabetic properties could be attributed to 12 compounds corresponding
to 10 target genes. These genes were associated with multiple signaling pathways, including PI3K/AKT, Jak-Stat,
MAPK, and NFKB signaling pathways and involved in regulating glucose homeostasis, anti-inflammation, β cell
proliferation, and antiapoptotic. Despite its limitations, this study provided the basis for further pharmacological and
clinical research on the molecular mechanisms of C. nutans in treating diabetes.
ABBREVIATIONS
Akt: Protein kinase B (PKB); BAD: Bcl-2-antagonist of cell death; Bcl2: Apoptosis regulator Bcl-2; Bclxl:
Apoptosis regulator Bcl-Xl; CDK: Cyclin-dependent kinase 2; CREB: Cyclic AMP-responsive element-binding
protein; Cyt-R: Cytokine receptor; EGFR: Epidermal growth factor receptor; FOXO: Forkhead box protein; GF:
Growth factor; GLUT4: Glucose transporter 4; GRB2: Growth factor receptor-bound protein 2; GSK3b: Glycogen
synthase kinase 3 beta; IKKa: Inhibitor of nuclear factor kappa-B kinase; INSR: Insulin receptor; IRS: Insulin receptor
substrate; Jak: Janus kinase 1; JNK: c-Jun N-terminal kinase; MAPK: Mitogen-activated protein kinase
MEKK1: Mitogen-activated protein kinase kinase kinase 1; NFKB: Nuclear factor kappa beta; PTPN: Tyrosine-
protein phosphatase non-receptor; PI3K: Phosphoinositide 3-kinase; TNFa: Tumor necrosis factor; TNFR: Tumor
necrosis factor receptor; TYK2: Tyrosine-protein kinase 2.
ACKNOWLEDGMENTS
The first author gratefully acknowledgments for the scholarship given under scheme The Development of Maulana
Malik Ibrahim State Islamic University of Malang Phase II, East Java Project supported by the Indonesian Ministry
of Religion and the Saudi Fund for Development.
APPENDIX
Supplementary table for this article can be found at
https://drive.google.com/file/d/1bLBTvXGkKAdV0EURt46XHXDKh0mCTsZD/view?usp=sharing
REFERENCES
1. ADA, Classification and diagnosis of diabetes: Standards of medical care in Diabetes-2019. Diabetes Care, 42
(2019) S13–S28. https://doi.org/10.2337/dc18-S002.
2. Y. Zheng, S. H. Ley, & F. B. Hu, Global aetiology and epidemiology of type 2 diabetes mellitus and its
complications. Nature Reviews Endocrinology, 14 (2018) 88–98. https://doi.org/10.1038/nrendo.2017.151.
3. J. L. Harding, M. E. Pavkov, D. J. Magliano, J. E. Shaw, & E. W. Gregg, Global trends in diabetes complications:
a review of current evidence. Diabetologia, 62 (2019) 3–16. https://doi.org/10.1007/s00125-018-4711-2.
4. IDF, IDF Diabetes Atlas 9th Edition. (2019) 19–21. https://diabetesatlas.org/data/en/world/.
5. J. Thrasher, Pharmacologic Management of Type 2 Diabetes Mellitus: Available Therapies. The American
Journal of Medicine, 130 (2017) S4–S17. https://doi.org/10.1016/j.amjmed.2017.04.004.
6. S. A. Stein, E. M. Lamos, & S. N. Davis, A review of the efficacy and safety of oral antidiabetic drugs. Expert
Opin Drug Saf, 12 (2013) 153–175. https://doi.org/10.1517/14740338.2013.752813.A.
020045-9
31 January 2025 05:14:58
7. D. K. Patel, R. Kumar, D. Laloo, & S. Hemalatha, Diabetes mellitus: An overview on its pharmacological aspects
and reported medicinal plants having antidiabetic activity. Asian Pacific Journal of Tropical Biomedicine, 2
(2012) 411–420. https://doi.org/10.1016/S2221-1691(12)60067-7.
8. L. T.-H. Tan, K. Y. Khaw, Y. S. Ong, T. M. Khan, L.-H. Lee, Wai-Leng Lee, & B.-H. Goh, An Overview of
Clinacanthus nutans (Burm. f.) Lindau as a Medicinal Plant with Diverse Pharmacological Values. In M.K.
Swamy,ed., Plant-Deriv. Bioact. Prod. Prop. Ther. Appl. (Singapore: Springer, 2020), pp. 461–492.
https://doi.org/10.1007/978-981-15-1761-7.
9. A. Alam, S. Ferdosh, K. Ghafoor, A. Hakim, A. S. Juraimi, A. Khatib, & Z. I. Sarker, Clinacanthus nutans: A
review of the medicinal uses, pharmacology and phytochemistry. Asian Pacific journal of Tropical Medicine,
(2016) 1–8.
10. M. N. A. Kamarudin, M. R. Sarker, H. A. Kadir, & L. C. Ming, Ethnopharmacological uses, phytochemistry,
biological activities, and therapeutic applications of Clinacanthus nutans (Burm. f.) Lindau: A comprehensive
review. Journal of Ethnopharmacology, 206 (2017) 245–266. https://doi.org/10.1016/j.jep.2017.05.007.
11. P. Kunsorn, N. Ruangrungsi, V. Lipipun, A. Khanboon, & K. Rungsihirunrat, The identities and anti-herpes
simplex virus activity of Clinacanthus nutans and Clinacanthus siamensis. Asian Pacific Journal of Tropical
Biomedicine, 3 (2013) 284–290. https://doi.org/10.1016/S2221-1691(13)60064-7.
12. S. Arullappan, P. Rajamanickam, N. Thevar, & C. C. Kodimani, In vitro screening of cytotoxic, antimicrobial
and antioxidant activities of Clinacanthus nutans (Acanthaceae) leaf extracts. Tropical Journal of Pharmaceutical
Research, 13 (2014) 1455–1461. https://doi.org/10.4314/tjpr.v13i9.11.
13. S. Daduang, N. Sattayasai, J. Sattayasai, P. Tophrom, A. Thammathaworn, A. Chaveerach, & M.
Konkchaiyaphum, Screening of plants containing Naja naja siamensis cobra venom inhibitory activity using
modified ELISA technique. Analytical Biochemistry, 341 (2005) 316–325.
https://doi.org/10.1016/j.ab.2005.03.037.
14. C. W. Mai, K. S. I. Yap, M. T. Kho, N. H. Ismail, K. Yusoff, K. Shaari, S. Y. Chin, & E. S. H. Lim, Mechanisms
underlying the anti-inflammatory effects of Clinacanthus nutans lindau extracts: Inhibition of cytokine production
and toll-like receptor-4 activation. Frontiers in Pharmacology, 7 (2016) 1–11.
https://doi.org/10.3389/fphar.2016.00007.
15. K. S. Wang, C. K. Chan, A. F. A. Hidayat, Y. H. Wong, & H. A. Kadir, Clinacanthus nutans Induced Reactive
Oxygen Species-dependent Apoptosis and Autophagy in HCT116 Human Colorectal Cancer Cells.
Pharmacognosy Magazine, 15 (2019) 87–97. https://doi.org/10.4103/pm.pm.
16. H. S. Kong, K. H. Musa, & N. Abdullah Sani, Clinacanthus nutans (Belalai Gajah / Sabah Snake Grass):
Antioxidant optimization on leaves and stems. AIP Conference Proceedings, 1784 (2016) 1–6.
https://doi.org/10.1063/1.4966768.
17. A. A. Azam, I. S. Ismail, M. F. Shaikh, K. Shaari, & F. Abas, Effects of Clinacanthus nutans leaf extract on
lipopolysaccharide -induced neuroinflammation in rats: A behavioral and 1H NMR-based metabolomics study.
Avicenna journal of phytomedicine, 9 (2019) 164–186. https://doi.org/10.22038/ajp.2018.11787.
18. Z. A. Zakaria, M. H. Abdul Rahim, M. H. Mohd Sani, M. H. Omar, S. M. Ching, A. Abdul Kadir, & Q. U.
Ahmed, Antinociceptive activity of petroleum ether fraction obtained from methanolic extract of Clinacanthus
nutans leaves involves the activation of opioid receptors and NO-mediated/cGMP-independent pathway. BMC
Complementary and Alternative Medicine, 19 (2019) 1–14. https://doi.org/10.1186/s12906-019-2486-8.
19. M. Umar Imam, M. Ismail, A. George, S. M. Chinnappan, & A. Yusof, Aqueous leaf extract of Clinacanthus
nutans improved metabolic indices and sorbitol-related complications in type II diabetic rats (T2D). Food Science
and Nutrition, 7 (2019) 1482–1493. https://doi.org/10.1002/fsn3.988.
20. W. Li, G. Yuan, Y. Pan, C. Wang, & H. Chen, Network pharmacology studies on the bioactive compounds and
action mechanisms of natural products for the treatment of diabetes mellitus: A review. Frontiers in
Pharmacology, 8 (2017) 1–10. https://doi.org/10.3389/fphar.2017.00074.
21. T. ting Luo, Y. Lu, S. kai Yan, X. Xiao, X. lu Rong, & J. Guo, Network Pharmacology in Research of Chinese
Medicine Formula: Methodology, Application and Prospective. Chinese Journal of Integrative Medicine, 26
(2020) 72–80. https://doi.org/10.1007/s11655-019-3064-0.
22. S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, Q. Li, B. A. Shoemaker, P. A. Thiessen, B. Yu, L.
Zaslavsky, J. Zhang, & E. E. Bolton, PubChem in 2021: New data content and improved web interfaces. Nucleic
Acids Research, 49 (2021) D1388–D1395. https://doi.org/10.1093/nar/gkaa971.
23. A. Daina, O. Michielin, & V. Zoete, SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness
and medicinal chemistry friendliness of small molecules. Scientific Reports, 7 (2017) 1–13.
https://doi.org/10.1038/srep42717.
020045-10
31 January 2025 05:14:58
24. A. Daina, O. Michielin, & V. Zoete, SwissTargetPrediction: updated data and new features for efficient prediction
of protein targets of small molecules. Nucleic Acids Research, 47 (2019) W357–W3664.
https://doi.org/10.1093/nar/gkz382.
25. The UniProt Consortium, UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Research, 49
(2021) D480–D489. https://doi.org/10.1093/nar/gkaa1100.
26. J. Piñero, J. M. Ramírez-Anguita, J. Saüch-Pitarch, F. Ronzano, E. Centeno, F. Sanz, & L. I. Furlong, The
DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research, 48 (2020) D845–
D855. https://doi.org/10.1093/nar/gkz1021.
27. P. Bardou, J. Mariette, F. Escudié, C. Djemiel, & C. Klopp, jvenn: an interactive Venn diagram viewer. BMC
Bioinformatics, 15 (2014) 1–7. https://doi.org/10.1186/1471-2105-15-293.
28. D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A.
Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen, & C. Von Mering, STRING v10: Protein-protein interaction
networks, integrated over the tree of life. Nucleic Acids Research, 43 (2015) D447–D452.
https://doi.org/10.1093/nar/gku1003.
29. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, & T. Ideker,
Cytoscape: A Software Environment for Integrated Models. Genome Research, 13 (2003) 2498–2504.
https://doi.org/10.1101/gr.1239303.metabolite.
30. M. Kanehisa, Y. Sato, M. Furumichi, K. Morishima, & M. Tanabe, New approach for understanding genome
variations in KEGG. Nucleic Acids Research, 47 (2019) D590–D595. https://doi.org/10.1093/nar/gky962.
31. R. Tundis, M. R. Loizzo, & F. Menichini, Natural Products as α-Amylase and α-Glucosidase
Inhibitors and their Hypoglycaemic Potential in the Treatment of Diabetes: An Update. Mini-Reviews in
Medicinal Chemistry, 10 (2010) 315–331. https://doi.org/10.2174/138955710791331007.
32. J. Riyaphan, C. H. Jhong, S. R. Lin, C. H. Chang, M. J. Tsai, D. N. Lee, P. J. Sung, M. K. Leong, & C. F. Weng,
Hypoglycemic efficacy of docking selected natural compounds against α-glucosidase and α-amylase. Molecules,
23 (2018) 1–10. https://doi.org/10.3390/molecules23092260.
33. F. C. Wong, A. L. Yong, E. P. S. Ting, S. C. Khoo, H. C. Ong, & T.-T. Chai, Antioxidant, metal chelating, anti-
glucosidase activities and phytochemical analysis of selected tropical medicinal plants. Iranian journal of
Pharmaceutical Research, 13 (2014) 1409–1415.
34. M. A. Alam, I. S. M. Zaidul, K. Ghafoor, F. Sahena, M. Hakim, M. Y. Rafii, H. M. Abir, M. F. Bostanudin, V.
Perumal, & A. Khatib, In vitro antioxidant and, α-glucosidase inhibitory activities and comprehensive metabolite
profiling of methanol extract and its fractions from Clinacanthus nutans. BMC complementary and Alternative
Medicine, 17 (2017) 1–10.
35. N. Abdullah & K. F. Kasim, In-Vitro Antidiabetic Activity of Clinacanthus nutans Extracts. International Journal
of Pharmacognosy and Phytochemical Research, 9 (2017) 846–852.
36. S. Murugesu, Z. Ibrahim, Q.-U. Ahmed, N.-I. N. Yusoff, B.-F. Uzir, Vikneswari Perumal, F. Abas, K. Saari, H.
El-Seedi, & A. Khatib, Characterization of α-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by
Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Simulation. Molecules,
23 (2018) 1–21.
37. N. Sarega, M. U. Imam, N. M. Esa, N. Zawawi, & M. Ismail, Effects of phenolic-rich extracts of Clinacanthus
nutans on high fat and high cholesterol diet-induced insulin resistance. BMC complementary and Alternative
Medicine, 16 (2016) 1–11.
38. D. Pitocco, M. Tesauro, R. Alessandro, G. Ghirlanda, & C. Cardillo, Oxidative stress in diabetes: Implications
for vascular and other complications. International Journal of Molecular Sciences, 14 (2013) 21525–21550.
https://doi.org/10.3390/ijms141121525.
39. N. Sarega, M. U. Imam, D. J. Ooi, K. W. Chan, N. M. Esa, N. Zawawi, & M. Ismail, Phenolic rich extract from
Clinacanthus nutans attenuates hyperlipidemia-associated oxidative stress in rats. Oxidative Medicine and
Cellular Longevity, 2016 (2016) 1–16.
40. M. Matsuda & I. Shimomura, Increased oxidative stress in obesity: Implications for metabolic syndrome,
diabetes, hypertension, dyslipidemia, atherosclerosis, and cancer. Obesity Research and Clinical Practice, 7
(2013) 1–12. https://doi.org/10.1016/j.orcp.2013.05.004.
41. A. K. Azemi, S. S. Mokhtar, & A. H. G. Rasool, Clinacanthus nutans Leaves Extract Reverts Endothelial
Dysfunction in Type 2 Diabetes Rats by Improving Protein Expression of eNOS. Oxidative Medicine and
Cellular Longevity, 2020 (2020) 1–10.
42. K. Teshima, T. Kaneko, K. Ohtani, R. Kasai, S. Lhieochaiphant, C. Picheansoonthon, & K. Yamasaki, Sulfur-
containing glucosides from Clinacanthus nutans. Phytochemistry, 48 (1998) 831–835.
020045-11
31 January 2025 05:14:58
43. J. L. Chelyn, M. H. Omar, N. S. A. Mohd Yousof, R. Ranggasamy, M. I. Wasiman, & Z. Ismail, Analysis of
flavone C- glycosides in the leaves of clinacanthus nutans (Burm. f.) Lindau by HPTLC and HPLC-UV/DAD.
The Scientific World Journal, 2014 (2014) 1–6. https://doi.org/10.1155/2014/724267.
44. L. W. Khoo, A. Mediani, N. K. Z. Zolkeflee, S. W. Leong, I. S. Ismail, A. Khatib, K. Shaari, & F. Abas,
Phytochemical diversity of Clinacanthus nutans extracts and their bioactivity correlations elucidated by NMR
based metabolomics. Phytochemistry Letters, 14 (2015) 123–133. https://doi.org/10.1016/j.phytol.2015.09.015.
45. P. L. Teoh, A. Y. F. Cheng, M. Liau, F. F. Lem, G. P. Kaling, F. N. Chua, & B. E. Cheong, Chemical composition
and cytotoxic properties of Clinacanthus nutans root extracts. Pharmaceutical Biology, 55 (2017) 394–401.
https://doi.org/10.1080/13880209.2016.1242145.
46. H. Z. Diao, W. H. Chen, J. Cao, T. M. Shao, X. P. Song, & C. R. Han, Furofuran lignans and alkaloids from
Clinacanthus nutans. Natural Product Research, 33 (2019) 1317–1321.
https://doi.org/10.1080/14786419.2018.1475375.
47. K. K. Oh, M. Adnan, & D. H. Cho, Network pharmacology of bioactives from Sorghum bicolor with targets
related to diabetes mellitus. PLoS ONE, 15 (2020) 1–24. https://doi.org/10.1371/journal.pone.0240873.
48. R. Zhang, X. Zhu, H. Bai, & K. Ning, Network pharmacology databases for traditional Chinese medicine: Review
and assessment. Frontiers in Pharmacology, 10 (2019) 1–14. https://doi.org/10.3389/fphar.2019.00123.
49. Z. Zhou, B. Chen, S. Chen, M. Lin, Y. Chen, S. Jin, W. Chen, & Y. Zhang, Applications of Network
Pharmacology in Traditional Chinese Medicine Research. Evidence-based Complementary and Alternative
Medicine, 2020 (2020) 1–7.
50. S. Alsadat & H. Khorami, PI3K / AKT pathway in modulating glucose homeostasis and its alteration in Diabetes.
Annals of Medical and Biomedical Sciences, 1 (2015) 46–55.
51. J. Boucher, A. Kleinridders, & C. Ronald Kahn, Insulin receptor signaling in normal and insulin-resistant states.
Cold Spring Harbor Perspectives in Biology, 6 (2014) 1–23. https://doi.org/10.1101/cshperspect.a009191.
52. X. Huang, G. Liu, J. Guo, & Z. Su, The PI3K/AKT pathway in obesity and type 2 diabetes. International journal
of biological Sciences, 14 (2018) 1483–1496.
53. S. Galic, N. Sachithanandan, T. W. Kay, & G. R. Steinberg, Suppressor of cytokine signalling (SOCS) proteins
as guardians of inflammatory responses critical for regulating insulin sensitivity. Biochemical Journal, 461
(2014) 177–188. https://doi.org/10.1042/BJ20140143.
54. V. Sidarala & A. Kowluru, The Regulatory Roles of Mitogen-Activated Protein Kinase (MAPK) Pathways in
Health and Diabetes: Lessons Learned from the Pancreatic β-Cell. Recent Pat Endocr Metab Immune Drug
Discov, 10 (2017) 76–84. https://doi.org/10.2174/1872214810666161020154905.The.
55. F. F. Jubaidi, S. Zainalabidin, I. S. Taib, Z. Abdul Hamid, N. N. Mohamad Anuar, J. Jalil, N. A. Mohd Nor, & S.
B. Budin, The Role of PKC-MAPK Signalling Pathways in the Development of Hyperglycemia-Induced
Cardiovascular Complications. International journal of molecular sciences, 23 (2022) 1–24.
https://doi.org/10.3390/ijms23158582.
56. H. Khodabandeloo, S. Gorgani-Firuzjaee, S. Panahi, & R. Meshkani, Molecular and cellular mechanisms linking
inflammation to insulin resistance and β-cell dysfunction. Translational Research, (2015) 1–63.
https://doi.org/10.1016/j.trsl.2015.08.011.
020045-12
31 January 2025 05:14:58
