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Optimization of Murrayafoline A ethanol extraction process from the roots of Glycosmis stenocarpa, and evaluation of its Tumorigenesis inhibition activity on Hep-G2 cells

  • Quoc Toan Tran EMAIL logo , The Dan Pham , Thanh Duong Nguyen , Van Huyen Luu , Huu Nghi Do , Xuan Duy Le , Phi Hung Nguyen , Manh Cuong Nguyen , Van Chinh Luu , Minh Quan Pham , Thi Huyen Vu , Tri Nhut Pham and Dung Thuy Nguyen Pham EMAIL logo
Published/Copyright: July 16, 2021
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Open Chemistry
From the journal Open Chemistry Volume 19 Issue 1

Abstract

Glycosmis stenocarpa is a species of shrub found in the Northern provinces of Vietnam. Its roots contain different carbazolic derivatives, mainly Murrayafoline A (Mu-A), which exhibits valuable biological activities. In this study, we performed an extraction of Mu-A from the roots of G. stenocarpa and optimized this process using response surface methodology (RSM) according to a central composite design, with three independent parameters including extraction time (min), extraction temperature (°C), and solvent/material ratio (mL/g). Two dependent variables were the Mu-A content (mg/g raw materials) and extraction efficiency (%). The optimal conditions to extract Mu-A were found to be as follows: extraction temperature, 67°C; extraction time, 165 min; and solvent/material ratio, 5:1. Under these conditions, the Mu-A content and extraction efficiency were 38.94 ± 1.31 mg/g raw materials and 34.98 ± 1.18%, respectively. Mu-A exhibited antiproliferation and antitumor-promoting activity against the HepG-2 cell line. The present optimization work of Mu-A extraction from G. stenocarpa roots contributed to the attempt of designing a large-scale extraction process for the compound and further exploitation of its potential in vivo applications.

Keywords: Mu-A; G. stenocarpa ; antitumor-promoting activity; Hep-G2; optimization

1 Introduction

Carbazolic alkaloids are aromatic heterocyclic compounds composed of three condensed rings, in which two benzene rings are situated on both sides of the central nitrogen-containing pyrole ring. They are isolated from vascular plant species in the Clausena, Glycosmis, Murraya of Rutaceae family as well as non-vascular plants and several different Streptomyces species [1]. Particularly, in the genus Glycosmis, carbazolic alkaloids have been extracted and isolated from G. pentaphylla seeds and roots, G. rupestris and G. mauritiana bark, and G. arborea roots [2,3,4]. In addition to its wide availability, carbazolic alkaloids have also exhibited diverse biological activities such as antitumor, antiplatelet, antimalarial and cytotoxicity, antibacterial, antifungal, antiinflammation and antioxidation [5,6].

Murrayafoline A (Mu-A) is a carbazolic alkaloid that has one of its benzene rings containing methoxy and methyl substituents at positions 1 and 3, respectively (Figure 1). The compound is primarily isolated from plants belonging to the genus Murraya, with some species that are commonly used in folkloric medicine to treat headache, toothache, stomach ache, the common flu, arthritis, malaria, meningitis, insects, and snake bites [1,5,7]. Besides, Mu-A was also discovered in several species in the genus Glycosmis. In Vietnam, Mu-A is usually isolated from G. stenocarpa, which is also known as “cơm rượu trái hẹp,” which is one of the 125 species in Glycosmis that distributes mainly in the Northern provinces [2,3]. In 2008, Cuong et al. have synthesized three oxygen-containing derivatives of Mu-A and evaluated their antifungal activities. They realized that Mu-A and one of its synthesized derivatives possessed the ability to resist the fungus species Cladosporium cucumerinum at a dosage of 12.5 μg [5].

Figure 1 chemical structure of Mu-A.
Figure 1

chemical structure of Mu-A.

Prior to the aforementioned study, Mu-A, murrayanine, and a new alkaloid, bisisomahaniene, were isolated from G. stenocarpa roots [2]. Toan (2016) have determined the average Mu-A content in the roots of G. stenocarpa at 0.38% (w/w) [8]. This compound displayed strong inhibition against the growth of colon cancer cell lines such as SW480, HCT116, and LS174T [9]. Twelve different compounds, including Mu-A, were identified in extracts obtained from the aerial parts of G. stenocarpa and found to exhibit anti-sEH (soluble epoxide hydrolase) activity [10]. In another study, Mu-A obtained from MeOH extracts of G. stenocarpa leaves and stem exhibited anti-PMMoV (pepper mild mottle virus) activity [11]. In addition, Mu-A and its derivatives possess other valuable biological activities, such as positive influence on pericardial constriction [12], antiproliferation of colon cancer cells [13], assisting in the prevention of the development of vascular symptoms such as recurrence after subcutaneous coronary artery dilation and atherosclerosis [14], cytotoxic activity against MoLT-4 (acute lymphoblastic leukemia) and HOP-18 (lung cancer), and antiinflammatory activity [6,11,15].

In this study, the research on the optimal technology for extracting Mu-A from the roots of G. stenocarpa plants in Vietnam was carried out for the first time. In addition, we continue to explore and evaluate the liver cancer inhibitory activity Hep-G2 of the compound Mu-A. We used the surface response method (RSM) with central composite design (CCD) to optimize the three parameters of the Mu-A extraction process including the extraction temperature, extraction time and rate, and the solvent/material ratio. The research results are the premise for the exploitation and use of Mu-A compounds in the future.

2 Materials and methods

2.1 Material preparation

G. stenocarpa roots were harvested at Hoang Hoa Tham commune, Chi Linh district, Hai Duong province, Vietnam, in March 2019. The botanical sample was verified to be G. stenocarpa Tan. by Dr. Nguyen Quoc Binh, Vietnam Natural Museum – Vietnam Academy of Science and Technology (Cau Giay, Hanoi, Vietnam). The obtained roots were sorted, cleaned, chopped into 3–5 cm segments, and dried. The dried samples were then grounded using a grinder with a mesh diameter of 0.3 cm. The material was obtained in powder form from this process.

2.2 Mu-A extraction and isolation process

The raw material in a powdered form was weighed at exactly 100 g and then transported to a 1,000 mL extraction vessel. Ethanol was added to different solvent/material ratios (varied from 2:1 to 6:1). The extraction vessel was then fitted to a condenser and heated to initiate reflux extraction. The extraction time and extraction temperature were changed according to the experimental design (30–180 min) and (40–80°C), respectively. For each studied condition, experiments were performed three times, and results were averaged. At the end of the extraction process, the extract was vacuum-filtered with a Buchner funnel and subjected to rotary evaporation to remove the solvent, yielding a total dried extract containing Mu-A.

2.3 Determination of Mu-A content using high-performance liquid chromatography (HPLC)

The phrase in 30 min was composed of an isocratic solvent of acetonitrile (ACN, A) in H2O + 0.1% formic acid (B) (A/B = 65:35, v/v). After preparation, the mobile phase was filtered through a 0.45 µm nylon membrane and subsequently ultrasonically degassed before use. The chromatographic separation was conducted on an Eclipse XDB-C18 (4.6 × 150 mm I.D., 5 μm particle size) column. The column temperature was kept steady at 30°C. Isocratic elution was carried out at a flow rate of 0.5 mL/min, along with an injection volume of 5 µL.

In a second condition, the mobile phase was composed of an isocratic solvent system of ACN (A) in H2O + 0.1% formic acid (B) (A:B = 60:40, v/v) in 30 min. The other parameters were set as in the first condition.

Mu-A solution at a concentration of 1.0 mg/mL in the diluent was scanned by a UV-Visible spectrophotometer in the range of 190–400 nm. From the UV spectra, a suitable wavelength considered for monitoring the drug was 240 nm on the basis of the higher response.

2.3.1 Construction of the calibration curve

A total of 1.2 mg of the Mu-A sample was accurately weighed and diluted in 1.2 mL of ACN to prepare a solution of 1,000 µg/mL, which was then diluted to a range of different concentrations to establish a quantitative calibration curve. Standard solutions were filtered through a 0.22 µm filter before being injected into the HPLC system.

The Mu-A standard solution (1,000 µg/mL) was completely dissolved in methanol and diluted to give six different solutions with Mu-A concentrations of 10, 50, 100, 150, 250, and 500 μg/mL, respectively. The injection volume (5 µL) was made for a standard solution to determine the reproducibility of the detector response at each concentration level. The peak area of Mu-A was plotted against the concentration to obtain the calibration graph. The concentrations of the compound were subjected to regression analysis to calculate the calibration equation and correlation coefficients (r 2). The regression equation was calculated in the form of y = ax + b, where y and x correspond to the peak ratio (compound area) and the compound concentration, respectively.

The HPLC spectrum of the analyte was prepared in the first mobile phase system to determine the optimal wavelengths. Mu-A was detected by the diode array detector at 240 nm wavelengths, which were selected through scanning the wavelength range of Mu-A. The peak signal indicates the presence of Mu-A at 18.65 min, as in Figure 2.

Figure 2 (a) UV-spectrum of Mu-A showing maximal absorption at 240 nm and (b) HPLC chromatogram of Mu-A showing a retention time of 18.65 min.
Figure 2

(a) UV-spectrum of Mu-A showing maximal absorption at 240 nm and (b) HPLC chromatogram of Mu-A showing a retention time of 18.65 min.

The linearity of the method was evaluated by analyzing a series of standard Mu-A solutions. Five microliters of each of the six working standard solutions containing 10–500 ppm of standard Mu-A were injected into the HPLC column. The elution was carried out as the above-described method, and the standard calibration curve was obtained by plotting the concentration of standard Mu-A versus the peak area (Figure 3). A good linear relationship of Mu-A was obtained within the concentration range of 10–500 ppm, and the regressive coefficient (r 2 ) and the slope of the calibration curve were determined to be 0.99977 and 120.336, respectively (Table 1).

Figure 3 Calibration curve of the Mu-A standard solution.
Figure 3

Calibration curve of the Mu-A standard solution.

Table 1

Linearity and regression characteristics of the standard Mu-A

Parameters Linearity range (µg/mL) Regression equation Correlation coefficient (r 2)
Linearity range (µg/mL) 10–500 y = 120.3336x + 356.4499 r 2 = 0.99977

According to ISO 8466-1:1990, the coefficient of variation of the method (V xo) is the ratio of the standard deviation (SD) of the method (S xo) to the appertaining mean, where S xo is the ratio of the residual SD (S y ) to the sensitivity of the calibration function (b). It is a figure of merit for the performance of the analytical method and is valid within the working range.

Thus, V xo (%) is calculated by the following equation:

(1) V xo = S xo x ¯

with S xo = S y b where S y is the residual SD (the SD of the calibration procedure) describing the scatter of the information values about the calculated regression line. It is a figure of merit describing the precision of the calibration, and b is the sensitivity of the standard curve equation.

From the data obtained in Table 2, the variational coefficient of the method (V xo) of the concentration and the peak area were 0.335 and 0.331 (<2%), indicating the precision of the calibration curve.

Table 2

Concentration versus the peak area for calculating the variational coefficient of the method (V xo)

No. Concentration (µg/mL) Peak area Sensitivity (b)
1 10 1352.1 356.450
2 50 6271.5
3 100 13372.0
4 150 18206.0
5 250 30550.0
6 500 60352.0
7 1,000 120,873
Average 294.286 35853.800
S y 351.514 42306.249
S xo 0.986 118.688
V xo 0.335 0.331

2.3.2 Specificity

The specificity of the method was assessed by comparing the chromatogram obtained from standard Mu-A with the extraction sample.

Figure 4 shows that the retention times of the standard Mu-A and Mu-A in extraction were very identical, almost the same at t R = 18.62–18.65 min. On the chromatogram of the blank sample, no peak signal appears at this retention time, confirming the specificity of the method.

Figure 4 HPLC chromatogram of blank (a), Mu-A standard (t R = 18.65 min) (b), extraction sample (t R = 18.62 min) (c), and Mu-A in extraction (t R = 18.62 min) (d).
Figure 4

HPLC chromatogram of blank (a), Mu-A standard (t R = 18.65 min) (b), extraction sample (t R = 18.62 min) (c), and Mu-A in extraction (t R = 18.62 min) (d).

The analysis by HPLC using an isocratic solvent system of ACN–H2O + 0.1% formic acid (60:40 (v/v)), resulted in the identification of Mu-A at retention times of 21.51, 21.33, and 21.55 min for the standard, extract, and extract plus standard samples, respectively (Figure 5).

Figure 5 HPLC chromatogram of the Mu-A standard sample (t R = 21.51 min) (left), extract sample (t R = 21.33 min) (middle), and Mu-A plus extract sample (t R = 21.55 min) (right).
Figure 5

HPLC chromatogram of the Mu-A standard sample (t R = 21.51 min) (left), extract sample (t R = 21.33 min) (middle), and Mu-A plus extract sample (t R = 21.55 min) (right).

2.3.3 Limit of detection (LOD) and limit of quantitation (LOQ) of the method

By using the signal-to-noise method, the peak-to-peak noise around the analyte retention time is measured, and subsequently, the concentration of the analyte that would yield a signal equal to a certain value of signal-to-noise (S/N) ratio is estimated. The noise magnitude is measured by the autointegrator of the instrument.

According to Shrivastava and Gupta (2011), an S/N ratio of 3 is generally accepted for estimating LOD and an S/N ratio of 10 is used for estimating LOQ [16].

Thus, the S/N ratio is calculated by equation (2) as

(2) S N = 2 H h

where H is the height of the peak and h is the peak-to-peak background noise.

The results of LOD and LOQ are illustrated in Table 3. According to the data obtained, the LOD and LOQ of Mu-A were determined to be 0.09 and 0.55 µg/mL, respectively. These values indicated that the method was sensitive.

Table 3

Limit of detection (LOD) and limit of quantification (LOQ)

Concentration of Mu-A (µg/mL) Retention time (t R) Peak height (H) h S/N
1.00 18.65 5.3 0.6 17.66
0.55 18.29 2.6 0.5 10.41
0.10 18.57 0.92 0.5 3.68
0.09 18.34 0.84 0.5 3.36
0.08 18.53 0.69 0.6 2.30

2.3.4 Repeatability of the method

The repeatability of the method was evaluated by assaying three replicate injections of Mu-A at three different concentrations (100, 120, and 150 μg/mL), on the same day and under the same experimental conditions:

(3) RSD (%) = SD x ¯ × 100 %

where SD = ∑ ( x i − x ¯ ) 2 n − 1 ; x ̅ , the average result, is calculated by summing the individual results and dividing this sum by the number (n) of individual values.

The SD value was calculated by using the STDEV function in excel. The results presented in Table 4 revealed that this method has good precision with RSD values of the retention time, area, and height of the Mu-A peak as 0.100, 0.539, and 0.997%, respectively. The percentage relative SD (RSD%) was found to be less than 2, which proved that the developed method was precise.

Table 4

Repeatability data of the determination of Mu-A

Concentration (µg/mL) No. Retention time (t R) Peak area Peak height
100 1 18.525 12007.1 314.3
2 18.541 12144.3 320.2
3 18.538 12295.1 324.8
Average 18.535 12148.83 319.77
% RSD 0.046 1.186 1.646
120 1 18.585 14412.4 381.5
2 18.535 14503.8 383.8
3 18.591 14448.8 383.7
Average 18.570 14455.00 383.00
% RSD 0.166 0.318 0.339
150 1 18.585 18050.1 479.9
2 18.618 18009.9 471.0
3 18.598 18035.6 472.5
Average 18.600 18031.87 474.47
% RSD 0.089 0.113 1.004
Average % RSD 0.100 0.539 0.997

2.3.5 Accuracy

The accuracy of the method was confirmed by studying the recovery of all samples at three different concentrations by replicated analysis (n = 3). Samples of known concentration (reference standard solutions) were analyzed, and the measured values from the respective area counts were compared with the true values.

The recovery is calculated by equation (4) as

(4) Recovery ( % ) = Amount found Amount added × 100 %

The results obtained from the determination of accuracy, expressed as percentage recovery, are summarized in Table 5.

Table 5

Determination of accuracy expressed as recovery percentage

Blank no. Concentration in the blank (µg/mL) Concentration added (µg/mL) Concentration found (µg/mL) Recovery (%) Average recovery (%)
1 0 100 96.81985 96.820 97.998
2 97.95997 97.960
3 99.21335 99.213
1 0 120 116.80853 97.340 97.635
2 117.56739 97.973
3 117.11055 97.592
1 0 150 147.03817 98.025 97.940
2 146.70448 97.803
3 146.98521 97.990

From these results, the method enables accurate quantitative estimation of Mu-A because all the results were within an acceptable limit.

2.4 Determination of the Mu-A content and extraction yield

The Mu-A content (Y 1), expressed as mg/g of dried material, was determined according to the following formula:

Y 1 mg g = w 2 w 1

where w 1 (g) is the mass of the dried powdered material and w 2 (mg) is the mass of the Mu-A obtained.

The extraction yield (Y 2) (%) was determined by the following formula:

Y 2 ( % ) = w 3 w 1 × 100

where w 3 (g) is the mass of the dried total extract and w 1 (g) is the mass of the dried, powdered material.

2.5 Experiment design for RSM optimization

Based on our initial experiments, we found that the three main technological factors that greatly influence the Mu-A extraction process are extraction temperature, extraction time, and the ratio of solvent to raw material. Therefore, the effects of these parameters on the extraction yield and the Mu-A total output were estimated by using RSM combining with the Box–Wilson central composite design [17]. The optimal conditions were selected from 17 sets of experimental ones with a coefficient of α = 1.215 using Design-Expert 7.0.0 software (Stat-Ease, Minneapolis, MN, United States). The ranges of the selected parameters, along with the central point, were identified based on results obtained from preliminary experiments (Tables 6 and 7). The link between independent and dependent variables was expressed in the form of the second-order polynomial equation, and the two-way analysis of variance (ANOVA) was carried out to confirm the validity of the model [18].

Table 6

Independent variables and their corresponding levels

Independent variables Codes A variable range (Δ) Levels
−α −1 0 1 +α
Z 1: Temperature (°C) X 1 20 35 40 60 80 85
Z 2: Extraction time (min) X 2 60 47 60 120 180 193
Z 3: Solvent/material ratio (v/w) X 3 2 1.6 2 4 6 6.4

Coefficient α = 1.215.

Table 7

Experimental design and response values

Run X 1 X 2 X 3 Y 1 (mg/g) Y 2 (%)
1 −1 −1 −1 18.2 ± 0.17 15.4 ± 0.11
2 +1 −1 −1 20.2 ± 0.15 17.1 ± 0.12
3 −1 +1 −1 21.5 ± 0.18 18.6 ± 0.15
4 +1 +1 −1 26.8 ± 0.21 24.1 ± 0.17
5 −1 −1 +1 19.9 ± 0.11 17.5 ± 0.17
6 +1 −1 +1 26.4 ± 0.16 27.1 ± 0.14
7 −1 +1 +1 27.8 ± 0.15 24.3 ± 0.18
8 +1 +1 +1 35.2 ± 0.22 32.8 ± 0.22
9 −1.215 0 0 24.1 ± 0.19 20.7 ± 0.13
10 +1.215 0 0 32.6 ± 0.13 28.8 ± 0.18
11 0 −1.215 0 25.7 ± 0.14 22.4 ± 0.12
12 0 +1.215 0 37.8 ± 0.25 33.5 ± 0.23
13 0 0 −1.215 28.4 ± 0.14 25.1 ± 0.19
14 0 0 +1.215 34.9 ± 0.22 31.3 ± 0.23
15 0 0 0 33.4 ± 0.20 30.2 ± 0.21
16 0 0 0 33.9 ± 0.23 30.5 ± 0.19
17 0 0 0 34.3 ± 0.22 31.0 ± 0.24

2.6 Preparation of purified Mu-A

About 30 g of the dried total extract was completely dissolved in 180 mL of EtOH/H2O (v/v, 5/1) and partitioned with dichloromethane (CH2Cl2), yielding 11.6 g of extract. This partition was then subjected to column chromatography with n-hexane/EtOAc (90/1, v/v) as the mobile phase. Thin-layer chromatography was used to identify and combine Mu-A fractions together, and the solvent was removed under reduced pressure, yielding a 0.48 g mixture containing Mu-A >80%. This mixture was recrystallized with n-hexane/EtOAC (20/1, v/v) for 10 h at 4°C. The crystals were filtered and washed, yielding 0.36 g of purified Mu-A.

2.7 Cell line and culture

The cultures of human hepatocellular carcinoma (Hep-G2 ATCC®-HB-8065TM) cell line were obtained from ATCC (Manassas, VA, USA) and maintained at 37°C in 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM; Sigma Aldrich Inc., Saint. Louis, MO, USA) supplemented with penicillin (100 UI/mL), 10% heat-inactivated fetal bovine serum (FBS), streptomycin (100 mg/mL), and L-glutamine (2 mM).

2.8 Cell proliferation assay

Cell viability was determined using the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay. In a 96-well microplate, cells were diluted to a density of 5 × 104 cells. The samples at different concentrations (0.63–5 µg/mL) were added to the cells and incubated at 37°C and 5% CO2 for 48 h.

At the end of incubation, 20 µL of MTT (Sigma-Aldrich, St. Louis, MO, USA) was added to the wells and incubated at 37°C for 4 h. Absorbance was recorded at 540/720 nm by using a Spark multimode reader (Tecan, Männedorf, Switzerland). DMSO and ellipticine were used as controls. All the experiments were repeated three times independently. The growth inhibition was assessed using the following formula:

Inhibition rate (%) = (1 − ODsampl/ODcon) × 100%

where ODsampl and ODcon are the optical densities of the experimental sample groups and control, respectively.

Data were expressed as mean ± SD and analyzed by two-way ANOVA at a 95% confidence level. The half-maximal inhibitory concentration (IC50) was calculated using the prism dose-response curve constructed from the inhibition percentage versus the sample concentrations.

2.9 Antitumor-promoting experiments in vitro

The antitumor-promoting activity was estimated by the inhibitory ability against the soft agar colony of the Hep-G2 cell line, as previously described by Gao et al. [19]. Briefly, cells growing exponentially in the monolayer culture were harvested and suspended in 0.33% agar medium containing 10% FBS and the tumor promoter, 12-O-tetradecanoylphorbol 13-acetate (TPA 1.6 nM), with or without 5 μg/mL Mu-A. The suspension (1.0 mL) containing 1 × 104 cells was plated into Petri dishes over a bottom 0.5% agar layer containing the same concentration of TPA and/or samples. Soft agar colonies were recorded on the 10th day of incubation. The inhibitory activities were expressed as % of DMSO control with an average of three independent experiments using the Hep-G2 cell line.

  1. Ethical approval: The conducted research is not related to either human or animal use.

3 Results

3.1 Optimization of the technical parameters in the Mu-A extraction process

3.1.1 Effect of individual technical factors on the extraction process of Mu-A

Based on the preliminary results, technical factors to be studied were selected at a basic scale, which were as follows: 60°C of extraction temperature, 120 min of extraction time, solvent/material ratio of 4:1 (v/w), and ethanol as a solvent. The effect of these factors on the Mu-A content and extraction efficiency is shown in Figure 5.

In the first set of experiments, the extraction temperature ranging from 40 to 80°C was studied. Other parameters were fixed as follows: 120 min of extraction time and solvent/material ratio of 4:1 (v/w). Figure 6a shows that as the temperature increases from 40 to 60°C, the Mu-A content increased from 24.5 to 34.1 mg/g and the extraction efficiency increased from 23.9 to 32.6%. When the temperature reached 70–80°C, both the Mu-A content and extraction efficiency were proportionally decreased. A possible explanation could be that a higher temperature is able to accelerate the diffusion process of compounds [20]. However, when the temperature extraction is too high (70–80°C), the solvent evaporates very quickly in the extraction process because the boiling temperature of ethanol is 78°C, reducing the extraction ability. In addition, when the extraction temperature surpasses the boiling temperature of the solvent, there are many air bubbles in the extraction solution, which also reduce the extraction capacity of the solvent. In addition, high temperatures could also increase costs due to higher energy consumption [21]. Therefore, 60°C was selected as the extraction temperature for subsequent experiments.

Figure 6 Effects of extraction temperature (a), extraction time (b), and solvent/material ratio (c) on the Mu-A content and extraction efficiency of G. stenocarpa roots.
Figure 6

Effects of extraction temperature (a), extraction time (b), and solvent/material ratio (c) on the Mu-A content and extraction efficiency of G. stenocarpa roots.

In the second set of experiments, the extraction time ranging from 30 to 180 min was studied, while other parameters were held fixed. Figure 6b shows that as the extraction time increased from 30 to 120 min, both the Mu-A content and extraction efficiency increased significantly from 19.8 to 36.4 mg/g and 16.1 to 33.1%, respectively. Such a rapid increase of the two tested variables became stagnant at 36.8 mg/g and 32.7%, respectively as the extraction time was increased to 150 min. When the extraction time continued to increase to 180 min, the Mu-A content and extraction efficiency only changed slightly. This pattern could be explained as follows. Initially, an increase in the extraction time allowed more solutes to diffuse into the solvent. However, at a certain threshold, equilibrium would be established, and even a great increase in the extraction time would not produce any noticeable change in the extraction yield [22]. Besides, a longer extraction duration also costs more energy, which leads to an increase in production expenses. Therefore, we have selected an extraction time of 120 min for the next set of experiments.

Finally, the effects of solvent/material ratio ranging from 2:1 to 6:1 (v/w) on the Mu-A content and extraction efficiency were studied. Other parameters were held fixed. As shown in Figure 6c, when the ratio increased from 2:1 to 4:1, both the Mu-A content and extraction efficiency increased from 20.5 to 33.6 mg/g and from 20.7 to 32.7% respectively. Both the Mu-A content and extraction efficiency reached their maxima at a ratio of 5:1 and were recorded to be 37.9 mg/g and 33.4% respectively. When the ratio increased to 6:1, the Mu-A content experienced a slight, insignificant decrease to 37.8 mg/g and the extraction efficiency remained unchanged. This could be explained by the fact that as the amount of solvent increased, the raw materials absorbed the solvent more easily, which facilitated the diffusion of a higher amount of compounds into the solvent. When the ratio between the solvent and material was low, the extract reached its saturation point more rapidly, hindering the extraction efficiency. However, at a certain limit, the addition of the solvent was not able to dissolve more extract, thus causing a decrease in the extraction efficiency [21]. We could see that within the range of 2:1 and 6:1 v/w, the extraction process was greatly affected. Therefore, in order to fully evaluate the effects of this technical factor, we selected the levels as low, high, and base for the solvent/material ratio of 2:1, 6:1, and 4:1 (v/w) as the parameters for the experimental design matrix.

3.1.2 Statistical prediction and analysis model

Response surfaces display the results of the interaction of different technological factors on the objective function (Mu-A content and extraction yield), optimized according to a central composite design. The extraction temperature, time, and solvent/material ratio are the independent variables, while the Mu-A content (Y 1) and extraction yield (Y 2) were dependent variables (Table 7). From our single-factor investigation results, we have determined the central values and the range of the technological factors (Table 6). Once optimized conditions were obtained, real experiments were conducted and experimental values were compared to predicted values to determine the model’s validity.

The experimental design matrix was composed of 17 experiments with 17 sets of results. From the results obtained, we evaluated the model through multiple regression analysis of the experimental data using the F-value, p-value, and R 2 value. ANOVA analysis of the regression model showed that the models were fitted well and highly significant. The F-values of Y 1 and Y 2 were calculated to be 31.1 and 35.29, respectively, and also low p-value (p < 0.0001). These results suggested that all models were statistically significant (Table 8). The model’s coefficients of determination (R 2) were 97.56 and 97.084% of the response variability demonstrating the capability and accuracy of the constructed model within the limited ranges. The F-values of lack-of-fit of Y 1 and Y 2 were 14.2 and 14.77, respectively, which implied that the lack-of-fit was insignificant, as compared to the pure error. This indicated the sufficient accuracy of the polynomial model.

Table 8

Regression coefficients of the predicted second-order polynomial models for the Mu-A content and extraction yield

Source Y 1 Y 2
F-value p-value F-value p-value
Model 31.1 <0.0001a 35.29 <0.0001a
X 1 42.81 0.0003a 65.05 <0.0001a
X 2 73.46 <0.0001a 68.98 <0.0001a
X 3 40.05 0.0004a 61.01 0.0001a
X 1 X 2 1.04 0.3418NS 0.53 0.4919NS
X 1 X 3 2.57 0.1531NS 8.57 0.0221a
X 2 X 3 2.73 0.1427NS 0.38 0.5564NS
X 1 2 54.6 0.0002a 56.03 0.0001a
X 2 2 14.79 0.0063a 14.42 0.0067a
X 3 2 15.61 0.0055a 12.31 0.0099a
Lack-of-fit 14.2 0.0671NS 14.77 0.0646NS
R 2 0.9756 0.9784

a p < 0.05; NS = not significant.

Regression equations (5) and (6) (Table 9) showed that all X 1, X 2, and X 3 technical factors had a positive effect on the value of the objective equation. When X 1, X 2, and X 3 increased, the values of the objective equations also increased. More specifically, the order of positive effect on the objective equations increased from X 3 to X 1 to X 2 (X 3 < X1 < X 2 ), corresponding to the coefficients of X 3, X 1, and X 2. However, in regression equation (6), the influence level of the technical factors did not differ much, as their corresponding coefficients were not very different at 3.11, 3.21, and 3.30, corresponding to X 3, X 1, and X 2, respectively.

Table 9

Empirical second-order polynomial model of the Mu-A content and extraction yield

Response Model equations R 2 p-value
Y 1 – Mu-A content Y 1 = 34.66 + 2.88X 1 + 3.77X 2 + 2.78X 3 − 4.8X 1 2 − 2.5X 2 2 − 2.57X3 2 (5) 0.9756 <0.0001
Y 2 – extraction yield Y 2 = 30.93 + 3.21X 1 + 3.30X 2 + 3.11X 3 + 1.36X 1 X 3 − 4.4X 1 2 − 2.23X 2 2 − 2.06X 3 2 (6) 0.9784 <0.0001

3.1.3 Analysis of the response surfaces

Based on the obtained second-order polynomial equation, data were analyzed with response surfaces constructed in three-dimensional space using Design Expert software. The X and Y axes represented two changing variables while the remaining one was fixed at the center. The Z-axis represented one of the two criteria to be evaluated (i.e., Mu-A content or extraction yield). The three-dimensional response surfaces are shown in Figure 7.

Figure 7 Response surface of the Mu-A content (a) and extraction yield (b).
Figure 7

Response surface of the Mu-A content (a) and extraction yield (b).

On the response surfaces, the dark red zones are optimization regions. There, the objective function values of Y 1, Y 2 lie within their region of maximum value. Based on the response surfaces in Figure 7a and b, we could see that the response surfaces of X 1/X 2 and X 2/X 3 displayed a larger optimization region compared to X 1/X 3, and hence, it can be concluded that X 1/X 2 and X 2/X 3 had greater effects than X 1/X 3.

3.1.4 Optimization and model verification

The independent variables were optimized by calculating the second-order regression equations to maximize the Mu-A content and total extraction yield. The level of importance of the two responses was chosen as follows: Mu-A content (Y 1) at level 4 and extraction yield (Y 2) at level 3. The predicted data showed that the Mu-A content and extraction yield attain their maximal at an extraction temperature of 67.6°C in 165.6 min, and the solvent/material ratio of 5.14:1 (Table 10 and Figure 8). However, in order to meet realistic operational conformations, the optimal extraction conditions have been modified as follows: extraction temperature, 67°C; extraction time, 165 min; and solvent/material ratio, 5:1. Under optimized conditions, the predicted and experimental values of the Mu-A content were 37.9266 mg/g and 38.94 ± 1.31 mg/g; and 34.3441% and 34.98 ± 1.18% for extraction efficiency, respectively. As these two values are approximately equal to each other, our established model has high compatibility.

Table 10

Values of independent variables and real variables

Independent variables Real variables
X 1 X 2 X 3 Extraction temperature (°C) Extraction time (min) Solvent/material ratio (v/w)
0.38 0.76 0.57 67.6 165.6 5.14:1
Figure 8 Optimal conditions by the solution of ramps.
Figure 8

Optimal conditions by the solution of ramps.

3.2 Antiproliferation and antitumor-promoting activity of Mu-A

By evaluating using the MTT-assay, Mu-A was found to exhibit cell proliferation inhibition activity, with an IC50 value of 3.98 µg/mL. The results from Table 11 also show that Mu-A is a prospective inhibitor of tumor formation in vitro when tested at the highest sample concentration of 5 µg/mL.

Table 11

Antiproliferation and antitumor-promoting activity of Mu-A against Hep-G2 cells

Samples Max. concentration Cell survival (%) IC50 (μg/mL) Soft agar colony induction (% of TPA-control)
DMSO 1% 100 — 0
Mu-A 5 μg/mL 33.3 3.98 48.21

*All values are the averages of three corresponding experiments.

As shown in Figure 9, inhibition of tumor formation by Mu-A was observed as 48.21% compared to the negative control and TPA control. These prospective results open up a new research direction: investigation on the mechanism behind Mu-A’s inhibitory activity against tumor formation (namely, by affecting the cellular signaling pathway linked to kinases activity or reactive oxygen species), as well as in vivo and clinical testing.

Figure 9 Image of antiproliferation activity against cancer cell line Hep-G2 cultured on 3D soft agar (control sample (a), Mu-A sample (b)). Scale bars represent 25 mm.
Figure 9

Image of antiproliferation activity against cancer cell line Hep-G2 cultured on 3D soft agar (control sample (a), Mu-A sample (b)). Scale bars represent 25 mm.

4 Conclusion

In this study, RSM combined with the central composite experiment designs was applied to optimize the extraction process of Mu-A from the roots of G. stenocarpa, with three experimental parameters: extraction temperature (°C), extraction time (min), and solvent/material ratio (mL/g). Dependent variables studied were the Mu-A content (mg/g) and extraction efficiency (%). The optimized conditions obtained were found to be the following: extraction temperature, 67°C; extraction time, 165 min; and solvent/material ratio, 5:1. Under these conditions, the Mu-A content and extraction efficiency were determined to be 38.94 ± 1.31 mg/g and 34.98 ± 1.18%, respectively. The experimental results for antiproliferation activity on the Hep-G2 cell line showed that Mu-A is a highly prospective compound, with significant antitumor-promoting activity. Overall, the present study has contributed to the development of optimal conditions for the Mu-A extraction process, which is able to produce high extraction yield while maintaining the proliferation activity of the compound against tumor-forming cells. These findings could be employed in developing industrial-scale extraction of the high-value compound, as well as providing fundamental information for in-depth studies on the underlying mechanisms of its tumor inhibitory actions and in vivo applications.

Acknowledgement

The authors gratefully acknowledge the support from the Vietnam Academy of Science and Technology.

  1. Funding information: This research was funded by the Vietnam Academy of Science and Technology under grant number UDPTCN 08/19-21.

  2. Author contributions: Xuan Duy Le and Phi-Hung Nguyen – formal analysis; Quoc Toan Tran, Duong Thanh Nguyen, Van Huyen Luu, Manh Cuong Nguyen, Van Chinh Luu, Minh Quan Pham, Thi Huyen Vu, Tri Nhut Pham, and Dung Thuy Nguyen Pham – investigation; Huu Nghi Do; supervision, The Dan Pham – methodology; Quoc Toan Tran – writing – original draft; Dung Thuy Nguyen Pham – writing – review & editing.

  3. Conflict of interest: The authors declare no conflict of interest.

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Received: 2021-01-19
Revised: 2021-06-05
Accepted: 2021-06-12
Published Online: 2021-07-16

© 2021 Quoc Toan Tran et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/chem-2021-0067
Keywords for this article
Mu-A; G. stenocarpa; antitumor-promoting activity; Hep-G2; optimization
Creative Commons
BY 4.0

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  23. Comparative study of antioxidant and anticancer activities and HPTLC quantification of rutin in white radish (Raphanus sativus L.) leaves and root extracts grown in Saudi Arabia
  24. Comparison of adsorption properties of commercial silica and rice husk ash (RHA) silica: A study by NIR spectroscopy
  25. Sodium borohydride (NaBH4) as a high-capacity material for next-generation sodium-ion capacitors
  26. Aroma components of tobacco powder from different producing areas based on gas chromatography ion mobility spectrometry
  27. The effects of salinity on changes in characteristics of soils collected in a saline region of the Mekong Delta, Vietnam
  28. Synthesis, properties, and activity of MoVTeNbO catalysts modified by zirconia-pillared clays in oxidative dehydrogenation of ethane
  29. Synthesis and crystal structure of N,N′-bis(4-chlorophenyl)thiourea N,N-dimethylformamide
  30. Quantitative analysis of volatile compounds of four Chinese traditional liquors by SPME-GC-MS and determination of total phenolic contents and antioxidant activities
  31. A novel separation method of the valuable components for activated clay production wastewater
  32. On ve-degree- and ev-degree-based topological properties of crystallographic structure of cuprite Cu2O
  33. Antihyperglycemic effect and phytochemical investigation of Rubia cordifolia (Indian Madder) leaves extract
  34. Microsphere molecularly imprinted solid-phase extraction for diazepam analysis using itaconic acid as a monomer in propanol
  35. A nitric oxide-releasing prodrug promotes apoptosis in human renal carcinoma cells: Involvement of reactive oxygen species
  36. Machine vision-based driving and feedback scheme for digital microfluidics system
  37. Study on the application of a steam-foam drive profile modification technology for heavy oil reservoir development
  38. Ni–Ru-containing mixed oxide-based composites as precursors for ethanol steam reforming catalysts: Effect of the synthesis methods on the structural and catalytic properties
  39. Preparation of composite soybean straw-based materials by LDHs modifying as a solid sorbent for removal of Pb(ii) from water samples
  40. Synthesis and spectral characterizations of vanadyl(ii) and chromium(iii) mixed ligand complexes containing metformin drug and glycine amino acid
  41. In vitro evaluation of lactic acid bacteria with probiotic activity isolated from local pickled leaf mustard from Wuwei in Anhui as substitutes for chemical synthetic additives
  42. Utilization and simulation of innovative new binuclear Co(ii), Ni(ii), Cu(ii), and Zn(ii) diimine Schiff base complexes in sterilization and coronavirus resistance (Covid-19)
  43. Phosphorylation of Pit-1 by cyclin-dependent kinase 5 at serine 126 is associated with cell proliferation and poor prognosis in prolactinomas
  44. Molecularly imprinted membrane for transport of urea, creatinine, and vitamin B12 as a hemodialysis candidate membrane
  45. Optimization of Murrayafoline A ethanol extraction process from the roots of Glycosmis stenocarpa, and evaluation of its Tumorigenesis inhibition activity on Hep-G2 cells
  46. Highly sensitive determination of α-lipoic acid in pharmaceuticals on a boron-doped diamond electrode
  47. Synthesis, chemo-informatics, and anticancer evaluation of fluorophenyl-isoxazole derivatives
  48. In vitro and in vivo investigation of polypharmacology of propolis extract as anticancer, antibacterial, anti-inflammatory, and chemical properties
  49. Topological indices of bipolar fuzzy incidence graph
  50. Preparation of Fe3O4@SiO2–ZnO catalyst and its catalytic synthesis of rosin glycol ester
  51. Construction of a new luminescent Cd(ii) compound for the detection of Fe3+ and treatment of Hepatitis B
  52. Investigation of bovine serum albumin aggregation upon exposure to silver(i) and copper(ii) metal ions using Zetasizer
  53. Discoloration of methylene blue at neutral pH by heterogeneous photo-Fenton-like reactions using crystalline and amorphous iron oxides
  54. Optimized extraction of polyphenols from leaves of Rosemary (Rosmarinus officinalis L.) grown in Lam Dong province, Vietnam, and evaluation of their antioxidant capacity
  55. Synthesis of novel thiourea-/urea-benzimidazole derivatives as anticancer agents
  56. Potency and selectivity indices of Myristica fragrans Houtt. mace chloroform extract against non-clinical and clinical human pathogens
  57. Simple modifications of nicotinic, isonicotinic, and 2,6-dichloroisonicotinic acids toward new weapons against plant diseases
  58. Synthesis, optical and structural characterisation of ZnS nanoparticles derived from Zn(ii) dithiocarbamate complexes
  59. Presence of short and cyclic peptides in Acacia and Ziziphus honeys may potentiate their medicinal values
  60. The role of vitamin D deficiency and elevated inflammatory biomarkers as risk factors for the progression of diabetic nephropathy in patients with type 2 diabetes mellitus
  61. Quantitative structure–activity relationship study on prolonged anticonvulsant activity of terpene derivatives in pentylenetetrazole test
  62. GADD45B induced the enhancing of cell viability and proliferation in radiotherapy and increased the radioresistance of HONE1 cells
  63. Cannabis sativa L. chemical compositions as potential plasmodium falciparum dihydrofolate reductase-thymidinesynthase enzyme inhibitors: An in silico study for drug development
  64. Dynamics of λ-cyhalothrin disappearance and expression of selected P450 genes in bees depending on the ambient temperature
  65. Identification of synthetic cannabinoid methyl 2-{[1-(cyclohexylmethyl)-1H-indol-3-yl] formamido}-3-methylbutanoate using modern mass spectrometry and nuclear magnetic resonance techniques
  66. Study on the speciation of arsenic in the genuine medicinal material honeysuckle
  67. Two Cu(ii)-based coordination polymers: Crystal structures and treatment activity on periodontitis
  68. Conversion of furfuryl alcohol to ethyl levulinate in the presence of mesoporous aluminosilicate catalyst
  69. Review Articles
  70. Hsien Wu and his major contributions to the chemical era of immunology
  71. Overview of the major classes of new psychoactive substances, psychoactive effects, analytical determination and conformational analysis of selected illegal drugs
  72. An overview of persistent organic pollutants along the coastal environment of Kuwait
  73. Mechanism underlying sevoflurane-induced protection in cerebral ischemia–reperfusion injury
  74. COVID-19 and SARS-CoV-2: Everything we know so far – A comprehensive review
  75. Challenge of diabetes mellitus and researchers’ contributions to its control
  76. Advances in the design and application of transition metal oxide-based supercapacitors
  77. Color and composition of beauty products formulated with lemongrass essential oil: Cosmetics formulation with lemongrass essential oil
  78. The structural chemistry of zinc(ii) and nickel(ii) dithiocarbamate complexes
  79. Bioprospecting for antituberculosis natural products – A review
  80. Recent progress in direct urea fuel cell
  81. Rapid Communications
  82. A comparative morphological study of titanium dioxide surface layer dental implants
  83. Changes in the antioxidative properties of honeys during their fermentation
  84. Erratum
  85. Erratum to “Corrosion study of copper in aqueous sulfuric acid solution in the presence of (2E,5E)-2,5-dibenzylidenecyclopentanone and (2E,5E)-bis[(4-dimethylamino)benzylidene]cyclopentanone: Experimental and theoretical study”
  86. Erratum to “Modified TDAE petroleum plasticiser”
  87. Corrigendum
  88. Corrigendum to “A nitric oxide-releasing prodrug promotes apoptosis in human renal carcinoma cells: Involvement of reactive oxygen species”
  89. Special Issue on 3rd IC3PE 2020
  90. Visible light-responsive photocatalyst of SnO2/rGO prepared using Pometia pinnata leaf extract
  91. Antihyperglycemic activity of Centella asiatica (L.) Urb. leaf ethanol extract SNEDDS in zebrafish (Danio rerio)
  92. Selection of oil extraction process from Chlorella species of microalgae by using multi-criteria decision analysis technique for biodiesel production
  93. Special Issue on the 14th Joint Conference of Chemistry (14JCC)
  94. Synthesis and in vitro cytotoxicity evaluation of isatin-pyrrole derivatives against HepG2 cell line
  95. CO2 gas separation using mixed matrix membranes based on polyethersulfone/MIL-100(Al)
  96. Effect of synthesis and activation methods on the character of CoMo/ultrastable Y-zeolite catalysts
  97. Special Issue on Electrochemical Amplified Sensors
  98. Enhancement of graphene oxide through β-cyclodextrin composite to sensitive analysis of an antidepressant: Sulpiride
  99. Investigation of the spectroelectrochemical behavior of quercetin isolated from Zanthoxylum bungeanum
  100. An electrochemical sensor for high sensitive determination of lysozyme based on the aptamer competition approach
  101. An improved non-enzymatic electrochemical sensor amplified with CuO nanostructures for sensitive determination of uric acid
  102. Special Issue on Applied Biochemistry and Biotechnology 2020
  103. Fast discrimination of avocado oil for different extracted methods using headspace-gas chromatography-ion mobility spectroscopy with PCA based on volatile organic compounds
  104. Effect of alkali bases on the synthesis of ZnO quantum dots
  105. Quality evaluation of Cabernet Sauvignon wines in different vintages by 1H nuclear magnetic resonance-based metabolomics
  106. Special Issue on the Joint Science Congress of Materials and Polymers (ISCMP 2019)
  107. Diatomaceous Earth: Characterization, thermal modification, and application
  108. Electrochemical determination of atenolol and propranolol using a carbon paste sensor modified with natural ilmenite
  109. Special Issue on the Conference of Energy, Fuels, Environment 2020
  110. Assessment of the mercury contamination of landfilled and recovered foundry waste – a case study
  111. Primary energy consumption in selected EU Countries compared to global trends
  112. Modified TDAE petroleum plasticiser
  113. Use of glycerol waste in lactic acid bacteria metabolism for the production of lactic acid: State of the art in Poland
  114. Topical Issue on Applications of Mathematics in Chemistry
  115. Theoretical study of energy, inertia and nullity of phenylene and anthracene
  116. Banhatti, revan and hyper-indices of silicon carbide Si2C3-III[n,m]
  117. Topical Issue on Agriculture
  118. Occurrence of mycotoxins in selected agricultural and commercial products available in eastern Poland
  119. Special Issue on Ethnobotanical, Phytochemical and Biological Investigation of Medicinal Plants
  120. Acute and repeated dose 60-day oral toxicity assessment of chemically characterized Berberis hispanica Boiss. and Reut in Wistar rats
  121. Phytochemical profile, in vitro antioxidant, and anti-protein denaturation activities of Curcuma longa L. rhizome and leaves
  122. Antiplasmodial potential of Eucalyptus obliqua leaf methanolic extract against Plasmodium vivax: An in vitro study
  123. Prunus padus L. bark as a functional promoting component in functional herbal infusions – cyclooxygenase-2 inhibitory, antioxidant, and antimicrobial effects
  124. Molecular and docking studies of tetramethoxy hydroxyflavone compound from Artemisia absinthium against carcinogens found in cigarette smoke
  125. Special Issue on the Joint Science Congress of Materials and Polymers (ISCMP 2020)
  126. Preparation of cypress (Cupressus sempervirens L.) essential oil loaded poly(lactic acid) nanofibers
  127. Influence of mica mineral on flame retardancy and mechanical properties of intumescent flame retardant polypropylene composites
  128. Production and characterization of thermoplastic elastomer foams based on the styrene–ethylene–butylene–styrene (SEBS) rubber and thermoplastic material
  129. Special Issue on Applied Chemistry in Agriculture and Food Science
  130. Impact of essential oils on the development of pathogens of the Fusarium genus and germination parameters of selected crops
  131. Yield, volume, quality, and reduction of biotic stress influenced by titanium application in oilseed rape, winter wheat, and maize cultivations
  132. Influence of potato variety on polyphenol profile composition and glycoalcaloid contents of potato juice
  133. Carryover effect of direct-fed microbial supplementation and early weaning on the growth performance and carcass characteristics of growing Najdi lambs
  134. Special Issue on Applied Biochemistry and Biotechnology (ABB 2021)
  135. The electrochemical redox mechanism and antioxidant activity of polyphenolic compounds based on inlaid multi-walled carbon nanotubes-modified graphite electrode
  136. Study of an adsorption method for trace mercury based on Bacillus subtilis
  137. Special Issue on The 1st Malaysia International Conference on Nanotechnology & Catalysis (MICNC2021)
  138. Mitigating membrane biofouling in biofuel cell system – A review
  139. Mechanical properties of polymeric biomaterials: Modified ePTFE using gamma irradiation
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