Sage Journals: Discover world-class research

Abstract

Seven metabolites (1–7) were isolated from the Vietnamese Scutellaria indica, guided by anti-inflammatory activity. The identified compounds comprise scrocaffeside A (1), naringenin-7-O-glucoside (2), darendoside B (3), decaffeoylverbascoside (4), plantainoside C (5), acteoside (6), and isoacteoside (7). This is the first time the compounds 1–3 and 5 were isolated from the entire Scutellaria indica. Compounds 1–7 tested for their anti-inflammatory potential by inhibiting nitric oxide (NO) production. Compound 1 showed the most activity (IC₅₀ = 31.4 μM), followed by compounds 2, 4, 6, and 7 exhibited IC₅₀ values of 41.2, 87.4, 52.2, and 44.6 μM, respectively. However, compounds 3 and 5 were inactive (IC₅₀ > 100 μM). Molecular docking was applied to study the affinity and interactions between compound 1 and inflammatory-caused proteins based on the in vitro results. Compound 1 generated the lowest binding free energy with the cyclooxygenase-2 (COX-2) target, which was −9.8 kcal/mol, followed by inducible nitric oxide synthase (iNOS, –8.4 kcal/mol), and interleukin-8 (IL-8, –7.6 kcal/mol). The results indicate that compound 1 derived from S. indica has the potential to be further explored and developed as an inflammatory inhibitor.

Keywords

anti-inflammation COX-2 IL-8 iNOS Lipopolysaccharide (LPS)NO production RAW264.7 cells

Introduction

The Scutellaria genus, commonly known as Skullcap, belongs to the Lamiaceae family and includes numerous species found in temperate and tropical regions worldwide. These regions encompass North China, Japan, Russia, Pakistan, Taiwan, the Philippines, Thailand, Vietnam, and New Guinea.^1,2 Within this diverse genus, some species are cultivated for ornamental purposes, while others have recognized medicinal properties. All of them are categorized as herbaceous plants.^1,3 Skullcap species have contributed to a wide array of natural compounds, including steroids, flavonoids, phenolic acids, iridoids, alkaloids, phenylpropanoids, clerodane diterpenoids, triterpenes, lignans, phytosterols, tannins, polysaccharides, and essential oils.^4,5 These plants have been utilized in traditional medicine practices for millennia.¹ For example, in traditional Siberian medicine, the aerial parts of the plant are used to address various ailments such as nausea, dyspepsia, ascites, high blood pressure, gastrointestinal issues, bleeding, malaria, and acute respiratory infections.⁶ Similarly, Tibetan traditional medicine incorporates skullcap to treat conditions like tachycardia, myocarditis, pneumonia, gastrointestinal diseases, polyarthritis, and kidney and liver pain.⁷ In addition, skullcap-based remedies are known to alleviate headaches and insomnia, dilate blood vessels, and reduce arterial pressure, potentially reducing the risk of heart disease.^8,9

The Scutellaria genus encompasses 300 to 350 species, among which Scutellaria indica stands out due to its rich phytochemical profile, with approximately 61 compounds isolated from the plant.⁶ Research has highlighted various health benefits of S. indica, including its potential anti-cancer,¹⁰ anti-arginase,¹¹ anti-inflammatory,¹² antitumor,¹³ and antiviral properties.¹⁴ However, within the existing literature, only one study has investigated the inhibitory effects of flavonoids from this plant on nitric oxide (NO) production.¹² There is a noticeable research gap in the study of phenolic acid glycosides, particularly phenylethanoid glycosides isolated from S. indica, and their NO production inhibitory activity. Based on the existing research void, the chemical constituents of S. indica and their inhibition of NO production were investigated.

Results and discussion

Chemical structure elucidation

The S. indica methanolic extract was partitioned into n-hexane, ethyl acetate, and butanol-soluble fractions. Chromatographic purification of the ethyl acetate and butanol-soluble fraction led to the isolation of seven compounds (1–7) (Figure 1).

Figure 1.

Structure of the isolates (1–7) from S. indica.

The ¹H-NMR spectrum of compound 1 showed the characteristic signals indicative of two (trans)-caffeoyl units [δ_H 7.61 (H-7′), 7.57 (H-7), 7.04 (H-2/H-2′), 6.76 (H-5/H-5′), 6.94 (H-6/H-6′), 6.31 (H-8′), 6.30 (H-8)], as well as two β-glucose units [δ_H 5.31 (1H, d, J = 7.6 Hz, H-1′′) and 4.67 (1H, d, J = 8.0 Hz, H-1′′′)], while the ¹³C-NMR spectrum showed 30 carbons (Figure 1 and Supplemental material). Notably, resonances at [δ_C 146.9 (C-7/C-7′), 127.9 (C-1/C-1′), 115.3 (C-5/C-5′), and 116.6 (C-8/C-8′)] confirmed the presence of two quaternary and two methine carbons. Comparison with the ¹³C-NMR data of 4-O-β-D-glucopyranosyl caffeic acid indicated the existence of two glucopyranosyl and two (trans)-caffeoyl moieties.¹⁵ The deshielded shifts of H-1′′ (δ_H 5.31) and H-6′′ (δ_H 4.75 and 4.64) indicated the attachment of caffeoyl moieties to C-1′′ and C-6′′, respectively (Figure 1 and Supplemental material).^15,16 The configuration of the glucopyranosyl moiety was determined to be β-D based on the coupling constant of the anomeric proton. Therefore, based on this evidence, compound 1 was identified as scrocaffeside A.¹⁵ This represents the first reported identification of scrocaffeside A from S. indica.

Compound 2 was obtained as a white powder. The optical rotation value of 2 was −105.4. Its ¹H-NMR spectrum showed the signals of protons in benzene rings. The ¹H- and ¹³C-NMR spectra showed the presence of a methine group at C-2 (δ_H 5.24/δ_C 80.2), a methylene group at C-3 (δ_H 2.64 and 2.93/δ_C 46.3), and a ketone group at C-4 (δ_C 193.2) indicated that 2 was a flavanone (Figure 1 and Supplemental material).¹⁷ Detailed analysis of the spectra revealed the presence of aromatic sugar moiety carbons. Furthermore, the ¹H-NMR spectrum showed an anomeric proton at δ_H 4.73 (1H, d, J = 7.2 Hz, H-1′′). This proton correlated with the quaternary carbon at δ_C 167.1 (C-7), confirming the position of the sugar unit (Figure 1 and Supplemental material). The sugar was identified as glucopyranoside based on Rf value and compared with authentic glucose. The absolute configuration of glucose was determined as D using gas chromatography. The circular dichroism spectrum of compound 2 exhibited a negative cotton effect at ∆ε₂₈₂ –42.18 and a positive cotton effect Δε₃₁₀ +16.14, suggesting 2S-configuration.¹² Therefore, compound 2 was identified as naringenin-7-O-glucoside,¹⁸ marking the first reported identification of naringenin-7-O-glucoside from S. indica.

Compounds 3–7 were isolated in the form of a powder. Their ¹H-NMR spectra exhibited an AA′BB′ pattern of an ethylene group [δ_H 2.74–2.81 (2H, t, H-7)/δ_C 36.6–36.8 (C-7), and 72.4–72.5 (C-8)], indicating that compounds 3–7 were phenylethanoid derivatives (Figure 1 and Supplemental material).¹⁹ In the ¹H-NMR spectrum of compounds 3–7, characteristic signals of a benzene ring were observed, along with coupling patterns corresponding to 1,3,4-trisubstituted protons (H-2, H-5, and H-6). In addition, signals from two anomeric protons [δ_H 5.12–5.21 (H-1′′) and 4.26–4.39 (H-1′′′)] suggested the presence of rhamnose and glucose moieties, respectively. Their ¹³C-NMR spectrum showed a quaternary carbon (C-1′) and 12 carbon signals of two sugar moieties (Figure 1 and Supplemental material). Analysis of the NMR spectra in detail, it was found that compounds 3–7 possessed three aromatic protons and two oxygenated carbons at C-3 and C-4. Compound 3 contained a methoxy group at C-4 [δ_H 3.96 (3H, s, 4-OCH₃)/δ_C 55.3 (4-OCH₃)]. Compound 5 had a feruloyl moiety located at C-6′, compound 6 had a caffeoyl moiety located at C-4′, and compound 7 had a caffeoyl moiety located at C-6′ (Figure 1 and Supplemental material). Comparing the ¹H- and ¹³C-NMR data of these compounds with literature data, compounds 3–7 were identified as darendoside B (3),²⁰ decaffeoylverbascoside (4),²¹ plantainoside C (5),²² acteoside (6),²¹ and isoacteoside (7).²¹ This marks the first reported identification of darendoside B (3) and plantainoside C (5) from S. indica.

NO production inhibition

In the first experiment, to determine the non-toxicity of the isolates (1–7), the cell viability (RAW264.7 cells) was performed. The results showed that these compounds (1–7) were not toxic to the RAW 264.7 cells (at concentrations below 100 μM) (Figure 2), as determined by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay.²³ Therefore, concentrations of 1, 3, 10, and 30 μM were selected for further assays.

Figure 2.

Compounds 1–7 affect the cell viability when stimulated by Lipopolysaccharide (LPS). The data are expressed as the mean ± standard deviation of three replicates (SD, n = 3).

In the next experiment, the anti-inflammatory activity of the isolates (1–7) on the RAW 264.7 cells was evaluated using the Griess reaction.^12,23 Compound 1 showed the strongest inhibitory activity with an IC₅₀ value of 31.4 μM. Compounds 2, 4, 6, and 7 demonstrated inhibitory activity, with IC₅₀ values of 41.2, 87.4, 52.2, and 44.6 μM, respectively (Table 1). In contrast, compounds 3 and 5 showed no significant activity (IC₅₀ > 100 μM). These results suggest that compound 1 from S. indica has strong anti-inflammatory potential in inhibiting NO production, making it a promising candidate for further in-depth studies.

Table 1.

In vitro inhibitory activity of the isolates (1–7) on NO production.

Compound	IC₅₀ values (μM)^a
1	31.4 ± 1.5^*
2	41.2 ± 3.8^*
3	> 100
4	87.4 ± 5.3
5	> 100
6	52.2 ± 1.8
7	44.6 ± 4.2
L-NMMA^b	22.1 ± 0.1

L-NMMA: N(G)-monomethyl L-arginine.

Results are represented as IC₅₀ value (µM).

Positive control; The data are expressed as the mean ± SD of three replicates (n = 3).

P < 0.05 compared with the control group.

It is important to note that the control group in this experiment was not exposed to either Lipopolysaccharide (LPS) or the samples. Therefore, any observed inhibitory effects of the isolated compounds on NO production were not influenced by cytotoxic impact. When the control group was stimulated with LPS (1 µg/mL), NO production increased approximately 11-fold after 24 hours. In contrast, compound 1, at concentrations of 1, 3, 10, and 30 μM, showed a dose-dependent reduction in NO production after 24 h upon LPS stimulation (Figure 3). This data underscores the anti-inflammatory potential of compound 1 in reducing NO production even in the presence of LPS stimulation, further supporting their candidacy for the development of anti-inflammatory agents.

Figure 3.

Inhibition of LPS-induced NO production in RAW 264.7 cells by compound 1. The RAW 264.7 cells were pre-treated with compound 1 at concentrations of 1, 3, 10, and 30 μM for 1 h, followed by treatment with LPS (1 μg/mL) and incubation for 24 h. Control values were obtained in the absence of both LPS and the compound. Results are expressed as the mean ± SD of three replicates (n = 3).

In our study, scrocaffeside A (1), a phenolic acid glycoside, exhibited strong inhibitory activity on NO production with an IC₅₀ value of 31.4 ± 1.5 μM (Table 1). This potent inhibitory effect may be attributed to the presence of 3,4-hydroxyl groups or a conjugated double bond (C-2,3 double bond) with a ketone (Figure 1), both of which are known to enhance inhibitory activity.²³ Compounds 4, 6, and 7, with IC₅₀ values of 87.4, 52.2, and 44.6 μM, respectively, showed weaker inhibitory effects on NO production compared to compound 1 (Table 1). This reduction in activity may be due to the presence of rhamnose and glucose moieties in these compounds, despite the presence of 3,4-hydroxyl groups or a conjugated double bond with a ketone in the caffeoyl unit (Figure 1). Naringenin-7-O-glucoside (2), a flavanone, exhibited even lower inhibitory activity with an IC₅₀ value of 41.2 ± 3.8 μM, likely due to the absence of 3-hydroxylation or a conjugated double bond with a ketone (Figure 1).^24–26 Finally, compounds 3 and 5 were found to be inactive (IC₅₀ > 100 µM). This inactivity may be attributed to the presence of rhamnose and glucose moieties or methoxy groups in compounds 3 and 5, respectively (Figure 1).^24–26 Overall, these findings provide valuable insights into the structure-activity relationships of these compounds and their potential as anti-inflammatory agents through the modulation of NO production.

Molecular docking

Scrocaffeside A (1), identified as the most active component and naringenin-7-O-glucoside (2) were chosen for their in-depth molecular docking simulations to elucidate its primary mechanism of action as an anti-inflammatory agent. Molecular docking studies were conducted, focusing on the interactions between these compounds and three specific targets iNOS, COX-2, and IL-8.

Interaction and binding affinity between compounds 1 and 2 and the allosteric site of the iNOS enzyme were displayed in Figure 4, pointing out their interactions with the allosteric site of the protein. AutoDock Vina was used to calculate the free binding energies of the ligands. Table 2 indicated that scrocaffeside A (1) showed the potential of a strong inhibitor since this ligand forms hydrogen bonds with seven residues and hydrophobic interactions with four residues in the allosteric site of the iNOS enzyme. The free binding energy of scrocaffeside A (1) toward iNOS enzyme was −10.6 kcal/mol, which was almost the same as the most impressive free binding energy of naringenin-7-O-glucoside (2) (–10.5 kcal/mol). Noticeably, all two studied compounds were predicted to form hydrophobic interactions with Trp194, and Phe369 (Table 2).

Figure 4.

Compounds and iNOS interactions.

Table 2.

Molecular docking results of the iNOS.

Compound	Docking score (kcal/mol)	Hydrogen bond	Hydrophobic interaction
Scrocaffeside A (1)	−10.6	Gln263, Tyr347, Pro350, Gly371, Trp372, Glu377, Asp382	Trp194, Val352, Phe369, Glu377
Naringenin-7-O-glucoside (2)	−10.5	Cys200, Trp463	Trp194, Cys200, Phe369

The binding affinity and interactions of compounds 1 and 2 with the allosteric site of the COX-2 enzyme was detailed in Figure 5 and Table 3. Docking simulations of the COX-2 protein with these ligands revealed strong interactions with residues in the receptor’s active site, highlighting their potential as COX-2 inhibitors. Figure 5 illustrates the optimal binding poses of the ligands within the COX-2 receptor, emphasizing the significant binding interactions observed. AutoDock Vina was used to calculate the free binding energies of the ligands. Table 3 indicated a similarity in the value of free binding energy of scrocaffeside A (1), and naringenin-7-O-glucoside (2) toward the COX-2 receptor, which was −9.4 kcal/mol. Scrocaffeside A (1) was also predicted to form seven hydrogen bonds with some residues in the allosteric site of COX-2 protein, namely Ala199, His207, Asn222, Gln289, Glu290, Asn382, and His386 (Table 3).

Figure 5.

Compounds and COX-2 interactions.

Table 3.

Molecular docking results of the COX-2.

Compound	Docking score (kcal/mol)	Hydrogen bond	Hydrophobic interaction
Scrocaffeside A (1)	−9.4	Ala199, His207, Asn222, Gln289, Glu290, Asn382, His386	Ala202, Lys211, Val291
Naringenin-7-O-glucoside (2)	−9.3	Lys211, Thr212, Asn382, Tyr385	His207, Val295, His386, His388, Leu391

The binding affinity and interactions between studied compounds (1 and 2) and the allosteric site of the IL-8 enzyme are shown in Figure 6 and Table 4. The highlighted binding interactions between the ligands and the IL-8 receptor indicate the best-docked poses (Figure 6). AutoDock Vina was used to calculate the free binding energies of the ligands.

Figure 6.

Compounds and IL-8 interactions.

Table 4.

Molecular docking results of the IL-8.

Compound	Docking score (kcal/mol)	Hydrogen bond	Hydrophobic interaction
Scrocaffeside A (1)	−7.6	Lys9, Tyr11, Pro14, Glu46	Phe19, Leu41, Arg45
Naringenin-7-O-glucoside (2)	−6.9	Lys9, Thr10, Tyr11, Arg45, Cys48	Tyr11, Phe15, Leu41

Scrocaffeside A (1) demonstrated strong affinity with the allosteric site of IL-8 protein as the value of free binding energy was −7.6 kcal/mol. This ligand formed hydrogen bonds with Lys9, Tyr11, Pro14, and Glu46. In the case of naringenin-7-O-glucoside (2), the free binding energy was −6.9 kcal/mol (Table 4).

In this experiment, Lipinski’s²⁷ rule of five was employed to evaluate a compound’s druggability. According to this rule, a compound with good drug-likeness should have a molecular weight (MW) below 500 Daltons, fewer than 5 hydrogen bond donors (HBD), fewer than 10 hydrogen bond acceptors (HBA), and a log P value less than 5 (Table 5).

Table 5.

Analysis results of physicochemical property.

Compound	MW (g/mol)	Log P	nHBD	nHBA	TPSA	MR	Lipinski violation	Log S	nRotB
Scrocaffeside A (1)	666.58	−1.41	10	17	282.59	154.65	3	−2.71	12
Naringenin-7-O-glucoside (2)	434.39	0.23	6	10	166.14	103.69	1	−2.97	4

MW: molecular weight; log P: log of octanol/water partition coefficient; nHBD: number of hydrogen bond donor(s); nHBA: number of hydrogen bond acceptor(s); TPSA: total polar surface area; MR: molar refractivity; log S: log of solubility; nRotB: number of rotatable bond(s).

In addition, aqueous solubility (log S), the number of rotatable bonds, molar refractivity (MR), and total polar surface area (TPSA) were also assessed. For optimal oral bioavailability and intestinal absorption, the TPSA value should not exceed 140 Å, the MR value should range between 40 to 130, and the number of rotatable bonds should be below 10 (Table 5).²⁷ Scrocaffeside A (1) does not adhere to Lipinski’s rule of five criteria for molecular weight (MW), number of hydrogen bond donors (HBD), and hydrogen bond acceptors (HBA). In addition, it exceeds the recommended values for good oral bioavailability and intestinal absorption. Naringenin-7-O-glucoside (2) also fails to meet Lipinski’s rule of five due to having six HBD. Furthermore, it exhibits a TPSA value of 166.14 Å, slightly surpassing the 140 Å threshold. Both compounds (1) and (2) display adequate aqueous solubility with log S values of −2.71 and −2.91, respectively.

Table 6 presents the in silico predictions of the absorption, distribution, metabolism, and excretion (ADME) properties of the studied compounds. Both scrocaffeside A (1) and naringenin-7-O-glucoside (2) were predicted not to permeate the blood-brain barrier (BBB per) and not to inhibit CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4. Moreover, they were found to have no significant interaction with P-glycoprotein (P-gp) affecting gastrointestinal absorption.

Table 6.

Absorption, distribution, metabolism, and excretion (ADME) predictions results.

Compound	Log K_p (cm/s)	Inhibitor Interaction
Compound	Log K_p (cm/s)	GI Abs	BBB per	P-gp substrate	CYP1A2 inhibitor	CYP2C19 inhibitor	CYP2C9 inhibitor	CYP2D6 inhibitor	CYP3A4 inhibitor
Scrocaffeside A (1)	−11.11	Low	No	No	No	No	No	No	No
Naringenin-7-O-glucoside (2)	−8.49	Low	No	Yes	No	No	No	No	No

GI Abs: gastrointestinal absorption; BBB per: blood-brain barrier permeability; P-gp: P-glycoprotein; CYP: cytochrome P.

Table 7 results indicate that the LD₅₀ values of (1) and (2) were found to be 3.84 and 2.92, respectively, using the DL-AOT prediction server. Based on these predicted results, naringenin-7-O-glucoside (2) falls into the “Warning” group, while scrocaffeside A (1) is classified as “Caution” one.

Table 7.

Toxicity predicted by DL-AOT Prediction Server.²⁸

Compound	LD₅₀ (mg/kg)	Toxicity
Scrocaffeside A	3.84	Caution
Naringenin-7-O-glucoside	2.92	Warning

Limitations of the study

Limitations of the study include the exclusive focus on isolating compounds (1–7) and assessing their inhibitory effects on NO production. The study did not evaluate other inflammatory agents such as interferon-gamma (IFN-γ), prostaglandin E2 (PGE2), tumor necrosis factor-alpha (TNF-α), and interleukins (ILs). Moreover, the assessment of iNOS and COX-2 in Western blot was also not investigated. However, this study also obtained positive results, including compounds 1–3, and 5 for the first isolation from Vietnamese S. indica, the evaluation of NO inhibition of isolated compounds, and docking studies. The results obtained will serve as the groundwork for more complete research and will be published in the near future.

Conclusion

In conclusion, the phytochemical investigation of the entire S. indica from Vietnam has resulted in the isolation of seven metabolites, namely scrocaffeside A (1), naringenin-7-O-glucoside (2), darendoside B (3), decaffeoylverbascoside (4), plantainoside C (5), acteoside (6), and isoacteoside (7). Notably, this study showed the first isolation and identification of compounds 1–3 and plantainoside C (5) from S. indica. Their anti-inflammatory activity was evaluated based on inhibiting the NO production. Scrocaffeside A (1) exhibited strong inhibitory activity on NO production (IC₅₀ = 31.4 μM). Compounds 2, 4, 6, and 7 showed weak inhibitory activity with IC₅₀ values ranging from 41.2 to 87.4 μM. However, compounds 3 and 5 were inactive (IC₅₀ > 100 μM). Scrocaffeside A (1) was revealed as an inhibitor of IL-8, COX-2, and iNOS receptors with binding free energy of −7.6, –9.4, and −10.6 kcal/mol, respectively. The results showed that the compounds isolated from S. indica can potentially treat inflammation by inhibiting NO production.

Experimental

Chemistry

General experimental procedure, plant material, extraction and isolation, and physiochemical and NMR data of isolated compounds (1–7), please see Supplemental materials file.

Biology

Cell culture, cell viability assay, and determination of NO production, please see Supplemental materials file.

Molecular docking study

Preparation of proteins and ligands

The crystal structures of iNOS, COX-2, and IL-8 are available in the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB ID: 3E7G, 5IKQ, and 5D14, respectively) and were downloaded. UCSF Chimera 1.17.3 was used to prepare the proteins and ligands. The “Dock Prep” tool removed all non-standard residues, added polar hydrogen atoms, and assigned Gasteiger charges to the proteins. The prepared proteins and ligands were saved in PDB format.

Molecular docking

AutoDock Vina 1.2.5, integrated into UCSF Chimera, conducted the molecular docking. The CASTp 3.0 server identified the active sites of the proteins. Grids were centered to encompass all residues identified by CASTp.²⁹ The docking process used a maximum of 10 conformers based on the Broyden-Fletcher-Goldfarb-Shanno algorithm. Default parameters of AutoDock Vina were employed, and conformers were ranked by binding free energy. Conformers with the lowest binding free energy were selected. AutoDock Vina was executed on a Windows 10 Pro operating system with a 2.53 GHz Intel Core i5 processor.

Absorption, distribution, metabolism, excretion, and toxicity predictions

All compounds underwent screening based on Lipinski’s rule of five for drug-likeness. The SwissADME web tool calculated pharmacokinetic parameters for the compounds. In addition, acute oral toxicity predictions were performed using the DL-AOT prediction server.^27,28,30

Supplemental Material

sj-docx-1-chl-10.1177_17475198241272457 – Supplemental material for Nitric oxide production inhibitors from Vietnamese Scutellaria indica: An in vitro and in silico study

Supplemental material, sj-docx-1-chl-10.1177_17475198241272457 for Nitric oxide production inhibitors from Vietnamese Scutellaria indica: An in vitro and in silico study by Dao Cuong To, Phi-Hung Nguyen, Le Minh Hoang, Hoa Thi Nguyen, Truong Thi Viet Hoa, Truong Thi Thuy Nhung, Phuong Dai Nguyen Nguyen, Ngu Truong Nhan, Hong Khuyen Thi Pham and Phu Chi Hieu Truong in Journal of Chemical Research

Footnotes

Acknowledgements

The authors thank Dr Nguyen Quoc Binh for his help in the identification of the plant materials.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was financially supported by the Vietnam Academy of Science and Technology under the grant number: UDPTCN 03/21-23.

ORCID iDs

Dao Cuong To

Phi-Hung Nguyen

Truong Thi Thuy Nhung

Supplemental material

Supplemental material for this article is available online.

References

Shang

, et al The genus Scutellaria an ethnopharmacological and phytochemical review. J Ethnopharmacol 2010; 128: 279–313.

Sun

Cleistogamy in Scutellaria indica (Labiatae): effective mating system and population genetic structure. Mol Ecol 1999; 8: 1285–1295.

Joshee

Patrick

Mentreddy

, et al Skullcap: potential medicinal crop. In: Janick

Whipkey

(eds) Trends in New Crops and New Uses. Alexandria, VA: ASHS Press, 2002, pp. 580–586.

Zhou

Zuo

Liu

, et al Unraveling spatial metabolome of the aerial and underground parts of Scutellaria baicalensis by matrix-assisted laser desorption/ionization mass spectrometry imaging. Phytomedicine 2024; 123: 155259.

Huang

Wang

, et al Scutellaria baicalensis: a promising natural source of antiviral compounds for the treatment of viral diseases. Chin J Nat Med 2023; 21: 563–575.

Karimov

Botirov

EK.

Structural diversity and state of knowledge of flavonoids of the Scutellaria L. genus. Russ J Biooroganic Chem 2017; 43: 691–711.

Schwabl

Geistlich

McHugh

Tibetan medicine in Europe: historical, practical and regulatory aspects. Forsch Komplementmed 2006; 13: 1–6.

Luo

Liu

Zou

, et al Network pharmacology study of Seleromitrion diffusa (Willd). R. J. Wang and Scutellaria barbata D. Don in the treatment of chronic atrophic gastritis. Intell Pharm 2024; 2: 45–50.

Chanchal

Singh

Bhushan

, et al An updated review of Chinese skullcap (Scutellaria baicalensis): emphasis on phytochemical constituents and pharmacological attributes. Pharmacol Res: Mod Chin Med 2023; 9: 100326.

10.

Bae

Min

Park

, et al Cytotoxic flavonoids from Scutellaria indica. Planta Med 1994; 60: 280–281.

11.

Kim

Cuong

Hung

, et al Arginase II inhibitory activity of flavonoid compounds from Scutellaria indica. Arch Pharm Res 2013; 36: 922–926.

12.

Cuong

Hung

Lee

, et al Anti-inflammatory activity of phenolic compounds from the whole plant of Scutellaria indica. Bioorg Med Chem Lett 2015; 25: 1129–1134.

13.

Min

Chung

Bae

KH.

Antitumor activity of 2(S)-5,2′,5′-trihydroxy-7,8-dimethoxyflavanone and its analogues. Arch Pharm Res 1997; 20: 368–371.

14.

Ooi

LSM

Wang

, et al Antiviral activities of medicinal herbs traditionally used in southern mainland China. Phytother Res 2004; 18: 718–722.

15.

Zhu

Huang

Deng

, et al Three new caffeoyl glycosides from the roots of Picrorhiza scrophulariiflora. Molecules 2008; 13: 729–735.

16.

Sang

Lao

Wang

, et al Furostanol saponins from Allium tuberosum. Phytochemistry 1999; 52: 1611–1615.

17.

Nguyen

Hoang

, et al

Nitric oxide production inhibitors from Polygonum multiflorum.

J Appl Pharm Sci 2024; 14: 130–135.

18.

Yousuf

Sudha

Murugesan

, et al Isolation of prunin from the fruit shell of Bixa orellana and the effect of β-cyclodextrin on its binding with calf thymus DNA. Carbohydr Res 2013; 365: 46–51.

19.

Narayanan

Venkatesh

Singh

, et al Synthesis of phenylethanoid glycosides from acrylic esters of glucose and aryldiazonium salts via palladium-catalyzed cross-coupling reactions and evaluation of their anti-Alzheimer activity. Carbohydr Res 2023; 532: 108920.

20.

Chu

Yao

, et al Revealing quality chemicals of Tetrastigma hemsleyanum roots in different geographical origins using untargeted metabolomics and random-forest based spectrum-effect analysis. Food Chem 2024; 449: 139207.

21.

Kanchanapoom

Kasai

Yamasaki

Phenolic glycosides from Markhamia stipulata. Phytochemistry 2002; 59: 557–563.

22.

Geng

Yang

Chou

, et al Bioguided isolation of angiotensin-converting enzyme inhibitors from the seeds of Plantago asiatica L. Phytother Res 2010; 24: 1088–1094.

23.

Hoang

Hoa

TTV

Nhung

TTT

, et al Nitric oxide inhibitors from the stem bark of Biancaea decapetala: an in vitro and in silico study. Rev Bras Farmacogn 2024; 34: 666–672.

24.

Kröncke

Fehsel

Kolb-Bachofen

Inducible nitric oxide synthase in human diseases. Clin Exp Immunol 1998; 113: 147–156.

25.

Kim

Cheon

Kim

, et al Effects of naturally occurring flavonoids on nitric oxide production in the macrophage cell line RAW 264.7 and their structure-activity relationships. Biochem Pharmacol 1999; 58: 759–765.

26.

Tanaka

Ohgo

Katayanagi

, et al Anti-inflammatory effects of green soybean extract irradiated with visible light. Sci Rep 2014; 4: 4732.

27.

Lipinski

CA.

Drug-like properties and the causes of poor solubility and poor permeability. J Pharmacol Toxicol Methods 2000; 44: 235–249.

28.

Mukherjee

Rajan

Deep learning model for identifying critical structural motifs in potential endocrine disruptors. J Chem Inf Model 2021; 61: 2187–2197.

29.

Tian

Chen

Lei

, et al CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res 2018; 46: W363–W367.

30.

Daina

Michielin

Zoete

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017; 7: 42717.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

2.85 MB

Nitric oxide production inhibitors from Vietnamese Scutellaria indica : An in vitro and in silico study

Abstract

Keywords

Introduction

Results and discussion

Chemical structure elucidation

NO production inhibition

Molecular docking

Limitations of the study

Conclusion

Experimental

Chemistry

Biology

Molecular docking study

Preparation of proteins and ligands

Molecular docking

Absorption, distribution, metabolism, excretion, and toxicity predictions

Supplemental Material

sj-docx-1-chl-10.1177_17475198241272457 – Supplemental material for Nitric oxide production inhibitors from Vietnamese Scutellaria indica: An in vitro and in silico study

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iDs

Supplemental material

References

Supplementary Material