Journal of Pharmaceutical and Biomedical Analysis 95 (2014) 220–228

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Chemical comparison of Tripterygium wilfordii and Tripterygium hypoglaucum based on quantitative analysis and chemometrics methods Long Guo, Li Duan, Ke Liu, E-Hu Liu ∗ , Ping Li ∗ State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing 210009, China

a r t i c l e

i n f o

Article history: Received 18 December 2013 Received in revised form 3 March 2014 Accepted 7 March 2014 Available online 15 March 2014 Keywords: Tripterygium wilfordii Tripterygium hypoglaucum RRLC-ESI-MSn Chemometrics Quantitative analysis

a b s t r a c t Tripterygium wilfordii (T. wilfordii) and Tripterygium hypoglaucum (T. hypoglaucum), two commonly used Chinese herbal medicines derived from Tripterygium genus, have been widely used for the treatment of rheumatoid arthritis and other related inflammatory diseases in clinical therapy. In the present study, a rapid resolution liquid chromatography/electrospray ionization tandem mass spectrometry (RRLCESI-MSn ) method has been developed and validated for simultaneous determination of 19 bioactive compounds including four catechins, three sesquiterpene alkaloids, four diterpenoids, and eight triterpenoids in these two similar herbs. The method validation results indicated that the developed method had desirable specificity, linearity, precision and accuracy. Quantitative analysis results showed that there were significant differences in the content of different types of compounds in T. wilfordii and T. hypoglaucum. Moreover, chemometrics methods such as one-way ANOVA, principal component analysis (PCA) and hierarchical clustering analysis (HCA) were performed to compare and discriminate the two Tripterygium herbs based on the quantitative data of analytes, and it was proven straightforward and reliable to differentiate T. wilfordii and T. hypoglaucum samples from different origins. In conclusion, simultaneous quantification of multiple-active component by RRLC-ESI-MSn coupled with chemometrics analysis could be a well-acceptable strategy to compare and evaluate the quality of T. wilfordii and T. hypoglaucum. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Plants of the genus Tripterygium have been used in traditional Chinese medicine for the treatment of inflammation and rheumatoid arthritis for hundreds of years [1]. Tripterygium wilfordii (T. wilfordii, Leigongteng in Chinese) and Tripterygium hypoglaucum (T. hypoglaucum, Kunmingshanhaitang in Chinese), two morphologically similar species from Tripterygium genus, are widely distributed and used popularly in China and East Asia [2,3]. The preparations of the two herbs, such as “Leigongteng tablet” and “Kunmingshanhaitang tablet”, play a significant role in the clinical treatment of rheumatoid arthritis in China [4,5]. Chemical and pharmacological investigations disclosed that four types of secondary metabolites, including catechins, diterpenes, triterpenes and sesquiterpene alkaloids, were found to be

∗ Corresponding authors. Tel.: +86 25 83271379; fax: +86 25 83271379. E-mail addresses: [email protected] (E.-H. Liu), [email protected], [email protected] (P. Li). http://dx.doi.org/10.1016/j.jpba.2014.03.009 0731-7085/© 2014 Elsevier B.V. All rights reserved.

responsible for the overall curative effects of T. hypoglaucum and T. wilfordii [6]. It was reported that the sesquiterpenoid alkaloids from T. hypoglaucum and T. wilfordii displayed cytotoxic and insecticidal activities [1]. The diterpenoids and triterpenoids in genus Tripterygium were active anti-inflammatory/immunomodulating natural products and some compounds were reported to possess anticarcinogen activities [7–10]. The health benefits of catechins were also extensively documented through both in vitro and in vivo screening. The curative effects of traditional Chinese medicines are integrative action derived from a group of bioactive components, and Chinese herbs have typical characteristic of synergistic effect of multiple components on multiple target sites [11,12]. Therefore, an effective method should be established to quantify bioactive components for their quality control. Currently, some analytical methods have been established to evaluate the quality of T. wilfordii, but most of them mainly determined only a few constitutes in extracts of T. wilfordii with a long time run [13–17]. Recently, an HPLC–MS method had been developed to quantify bioactive terpenoids in T. wilfordii and commercial preparations [18]. However,

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little is known about the contents of catechins in Tripterygium herbs. Moreover, compared with tremendous amount of research data regarding T. wilfordii, reports on T. hypoglaucum are relatively fewer. To the best of our knowledge, no research has been conducted to compare the contents of bioactive components in T. hypoglaucum and T. wilfordii and no chemometric approach has been employed to distinguish these two herbal drugs. The triple quadrupole tandem mass spectrometry technique has advantages of abundant mass fragmentations and many scan modes afforded by tandem mass spectrometry can provide the required specificity and sensitivity. Multiple reaction monitoring (MRM), a tandem MS scan mode unique to triple quadrupole MS instrumentation, has been used for decades for quantitation of analytes in highly complex sample matrices since the targeted approach can ensure higher sensitivity and selectivity by selectively detecting characteristic ions of the analytes [19–22]. Such a method has been demonstrated to be useful for simultaneous determination of multiple components in herbal medicines and their comprehensive quality evaluation. To make a comprehensive comparison of T. hypoglaucum and T. wilfordii and evaluate their quality, in this paper, we developed and validated an accurate and reliable rapid resolution liquid chromatography/electrospray ionization triple quadrupole mass spectrometry (RRLC-ESI-MSn ) method for simultaneous determination of 19 bioactive constituents including four catechins, three sesquiterpene alkaloids, four diterpenoids and eight triterpenoids in these two herbal drugs. The MRM mode was employed for the excellent sensitivity and selectivity. The quantitative results were further analyzed by multivariate statistical analysis to provide more information about the chemical difference of T. hypoglaucum and T. wilfordii. 2. Experimental 2.1. Chemicals, materials and reagents Acetonitrile, methanol and formic acid of HPLC grade were purchased from Merck (Darmstadt, Germany), HPLC grade water was prepared using a Milli-Q water purification system (Millipore, MA, USA). Other reagents and chemicals were of analytical grade. The reference compounds of gallocatechin (1), epigallocatechin (2), catechin (3), epicatechin (4) and reserpine (internal standard, IS.) were purchased from Chengdu Must Bio-technology Co., Ltd. (Chengdu, China). Triptolide (6), triptonide (7), triptophenolide (9) and celastrol (17) were obtained from Nanjing Zelang medical Technology Co., Ltd. (Nanjing, China). Wilforidine (5), wilforgine (8), wilforine (10), triptotriterpenic acid C (11), triptoquinone A (12), demethylregelin (13), demethylzeylasteral (14), orthosphenic acid (15), regelindiol B (16), wilforlide A (18) and wilforlide B (19) were isolated from the root of T. hypoglaucum in our laboratory. The structures (Fig. 1) of these compounds were identified by ESIMS, 1 H and 13 C NMR techniques in comparison with the literature data. The purity of all constituents were determined to be >98% by high performance liquid chromatography–diode array detection analysis. 15 batches of T. hypoglaucum and 13 batches of T. wilfordii from different provinces in China were collected for the experiment. The voucher specimens, identified by Prof. Ping Li from Department of Pharmacognosy in China Pharmaceutical University, have been deposited in the State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China. 2.2. Preparation of standard solutions A stock solution containing 19 standards was weighed accurately, dissolved in methanol, and diluted to provide a series of standard solutions with gradient concentrations. To each standard

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solution, an aliquot of stock solution of reserpine (IS.) was added to makeup a final concentration of 70 ng/ml. These solutions were stored in a refrigerator at 4 ◦ C for further analysis.

2.3. Preparation of sample solutions 28 batches of samples were cut into smaller pieces, further ground into powder and passed through a 50-mesh sieve. 0.3 g of sample powder was accurately weighed and transferred to a 50-ml glass-stoppered conical flask. An appropriate amount of internal standard solution and 25 ml methanol were added, and weighed the filled flask with a precision of ±0.01 g. The sample solution was refluxed for 2 h in a 75 ◦ C water bath, cooled to room temperature, and then adjusted to the initial weight by adding methanol as needed. After centrifuged at 13,000 rpm/min for 10 min, two microliter of the supernatant was injected into the HPLC instrument for analysis.

2.4. Instrument and chromatographic conditions Chromatographic analysis was performed on an Agilent series 1290 HPLC system equipped with a quaternary pump, a degasser, an autosampler, a thermostated column compartment (Agilent Technologies, Palo Alto, CA, USA). Chromatographic separation was carried out at 25 ◦ C on an Agilent ZorBax Extend-C18 column (4.6 mm × 50 mm, 1.8 ␮m). The mobile phase consisted of 0.3% formic acid solution (A) and acetonitrile (B) using a gradient elution of 10–40% B at 0–5 min, 40–65% B at 5–20 min, 65–80% B at 20–25 min, 80–95% B at 25–30 min. The flow rate was kept at 0.5 ml/min. All MS experiments were conducted on an Agilent 6460 triple quadrupole mass spectrometer equipped with electrospray ionization source (Agilent Corporation, MA, USA). Quantification was performed in the positive ionization by multiple reaction monitoring (MRM) mode. The MS conditions were as follows: drying gas temperature, 350 ◦ C; drying gas flow, 10 L/min; nebulizer pressure, 35 psi; capillary voltage, 4000 V. Data acquisition was performed with MassHunter Workstation (Agilent Technologies, USA).

2.5. Method validation 2.5.1. Calibration curve, limit of detection (LOD) and limit of quantification (LOQ) For the calibration curves, each concentration of standard solutions was analyzed in triplicate. All calibration curves were constructed from the peak area ratio of the tested reference peak to that of the internal standard versus their concentrations. The LODs and LOQs for each analyte were defined by the concentrations that generated peaks with signal-to-noise values (S/Ns) of 3 and 10, respectively.

2.5.2. Precision, stability and repeatability The precision of the developed method was determined by the intra- and inter-day variations. For intra-day test, the samples were analyzed for six times within the same day, while for inter-day test, the samples were examined twice per day for three consecutive days. The concentration of each solution was determined by a calibration curve formed at the same day. For the stability test, the same sample was stored at room temperature and analyzed by replicate injection at 0, 2, 4, 8, 16 and 24 h. To confirm the repeatability, six replicates of the same samples were extracted and analyzed. The relative standard deviation (RSD) was chosen for measure of precision, stability and repeatability.

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Fig. 1. Chemical structures of 19 reference substances.

2.5.3. Recovery Recovery was used to further evaluate the accuracy of the method. The recovery test was done by adding accurate amounts of 19 standard solutions to 0.15 g powder of the same sample in sextuplicate. Then the samples were extracted and analyzed by the above-established method, the average recoveries were estimated by the formula: Recovery (%) =

(detected − original amount) × 100 spiked

2.6. Date analysis One-way ANOVA was carried out based on the contents of each compound between the 13 batches of T. wilfordii and 15 batches of T. hypoglaucum (Table 3). Hierarchical clustering analysis (HCA) was performed by SPSS 18.0. A method named as between-groups linkage was applied, and squared euclidean distance was selected as measurement for analysis. Principal component analysis (PCA) was carried out by SIMCA-P+ 13.0 Software. When the contents of investigated compounds were below the quantitation limit or not detected in the samples, the values of such elements were considered to be 0. 3. Results and discussion 3.1. Optimization of extraction conditions In order to make a comprehensive comparison on the chemical constituents of the two herbs, the extraction conditions were optimized. The extraction methods, extraction solvents and extraction

time were investigated. The results showed that refluxing extraction was more effective than ultrasonic extraction. It was also found that 100% methanol was the most efficient extraction solvent among the tested different concentrations of ethanol and methanol. In addition, the efficiencies of refluxing extraction were measured in different time (1, 1.5, 2, 2.5 and 3 h). It was demonstrated that the target components could be extracted completely within 2 h. Finally, the sample solutions were prepared by refluxing extraction with methanol for 2 h. 3.2. Optimization of the chromatographic and mass spectrometric conditions In order to achieve a rapid and efficient analysis, a short chromatographic column packed with 1.8 ␮m porous particles was employed in HPLC system. Different mobile phase systems (methanol–water, acetonitrile–water, methanol–acid aqueous solution, and acetonitrile–acid aqueous solution) were examined and compared in order to obtain good chromatographic behavior and appropriate ionization. It was showed that acetonitrile–acid aqueous solution was better than others. Furthermore, formic acid (0.1%, 0.2%, 0.3%, and 0.4%, v/v) was added into the mobile phase to improve the peak shape and restrain the peak tailing. Finally, acetonitrile – 0.3% aqueous formic acid was chosen as the eluting solvent system to give the acceptable separation and ionization within a run time of 30 min. MS spectra were studied in both positive and negative modes. The positive ion mode was selected as the ionization efficiency of all compounds was higher in that mode. Typical total ion chromatograms of T. hypoglaucum and T. wilfordii samples were showed in Supplement Fig. S1. The MRM mode was eventually adopted

L. Guo et al. / Journal of Pharmaceutical and Biomedical Analysis 95 (2014) 220–228

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Fig. 2. Total ion MRM chromatograms of the sample obtained in positive mode for the investigated compounds. The peak numbers are in accordance with the compound numbers in Fig. 1.

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in this work as it had great advantages in reducing interference and enhancing sensitivity over the selected ion monitoring (SIM). The chemical structures of 19 components were characterized based on their retention behavior and MS fragmentation information compared to the data from standards and literatures. For the optimization of MRM conditions, the fragmentor voltage (FV) and collision energy (CE) were chosen as the major optimal factors because they played important roles in parent and product ion responses. Each investigated analyte was directly infused into the mass spectrometer, and the precursor ions and product ions were preliminarily selected. Then the FV and CE for product ions were optimized to achieve the most abundant, specific, and stable transition for each compound. Retention time (RT) and MS information for each analyte including [M+H]+ , precursor and product ions, FV and CE are shown in Table 1. The results indicated that the MRM mode has more advantages in separation of overlapped constituents and quantification of low-abundance analytes in complicated mixtures.

3.3. Method validation The calibration curves exhibited good linearity (r2 > 0.9989) within the test range. The LODs and LOQs were less than 5.3 ng/ml and 14.1 ng/ml, respectively. The intra- and inter-day variations (RSDs) of peak area for 19 analytes were less than 3.48 and 4.65%, respectively. The repeatability presented as RSDs were in the range from 1.78 to 4.20%. The stability solutions presented as RSDs were less than 4.19%. The recoveries varied between 93.66% and 105.32% with RSDs less than 4.78%. The data were showed in Table 2, which prove the validated method is precise, accurate and sensitive for the simultaneous quantification of all the samples.

3.4. Quantification of 19 bioactive compounds in T. wilfordii and T. hypoglaucum The developed analytical method was subsequently applied to quantitatively analyze 28 samples collected from different regions in China, including 15 batches of T. hypoglaucum and 13 batches of T. wilfordii. The contents of 19 analytes were calculated with internal standard methods based on the respective calibration curves. The typical MRM chromatograms of analytes are shown in Fig. 2 and the quantitative results are presented in Table 3. Compared with the reported analytical methods, RRLC-ESI-MSn is a powerful

technique for quantitative analysis of multi-component in terms of time savings and sensitivity. Among the constitutes examined, catechins (including gallocatechin, epigallocatechin, catechin and epicatechin) were abundantly present in both T. wilfordii and T. hypoglaucum with the total contents in the range of 245.38–3828.24 ␮g/g, although the contents of total catechins in T. wilfordii (the mean value was 2387.57 ␮g/g) were higher than that in T. hypoglaucum (the mean value was 1011.22 ␮g/g). In contrast, the contents of sesquiterpene alkaloids (including wilforidine, wilforgine and wilforine) were higher in T. hypoglaucum compared to T. wilfordii. The total sesquiterpene alkaloids in the test samples varied from 38.80 to 1040.90 ␮g/g with a mean value of 354.86 ␮g/g. It should be noted that the content of diterpenoids were very low in T. wilfordii and T. hypoglaucum, even some active compounds (triptolide and triptonide) could not be quantified or detected in several samples. The concentrations of the eight triterpenoids were in the range from 41.91 to 1148.76 ␮g/g, and the mean value was 327.18 ␮g/g. Celastrol, a potent anti-inflammatory and anti-carcinogen triterpenoid compound, was relatively abundant and its mean concentration was 245.46 ␮g/g in T. hypoglaucum and 36.72 ␮g/g in T. wilfordii, respectively. The contents of diterpenoids and triterpenoids were unstable and there was no significant regularity in both T. hypoglaucum and T. wilfordii samples. It was also found that there were significant differences among different batches of T. hypoglaucum samples in terms of the contents of the investigated bioactive components. The content distributions of each type of compounds in each sample were displayed in Supplement Fig. S2–S6. 3.5. Comparison of T. wilfordii and T. hypoglaucum by chemometrics analysis Although the bioactive constituents of the two medicinal herbs were similar, the content of each compound varied greatly in these two similar herbal medicines. To compare and evaluate the quality of T. wilfordii and T. hypoglaucum, multivariate analysis approaches were performed based on the characteristics of the contents of nineteen investigated compounds. In order to find the maker compounds and reduce the number of investigated compounds for chemical comparison of T. wilfordii and T. hypoglaucum, one-way ANOVA was firstly employed for analysis the contents of each compound between the 13 batches of T. wilfordii and 15 batches of T. hypoglaucum. The p-value of 0.05 was set as the filtering standard to maintain the contents of the

Table 1 Retention time, related MS data of the target compounds. No.

Compound

RT (min)

[M+H]+ (m/z)

Precusor ion

Product ion

FV (V)

CE (V)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 I.S.

Gallocatechin Epigallocatechin Catechin Epicatechin Wilforidine Triptolide Triptonide Wilforgine Triptophenolide Wilforine Triptotriterpenic acid C Triptoquinone A Demethylregelin Demethylzeylasteral Orthosphenic acid Regelindiol B Celastrol Wilforlide A Wilforlide B Reserpine

1.79 2.15 2.51 2.90 5.41 6.04 8.88 11.56 13.10 13.51 13.83 15.50 17.44 19.31 20.30 21.86 24.54 27.07 29.11 6.67

307.1 307.1 291.1 291.1 780.3 361.2 359.2 858.3 313.2 868.3 473.3 329.2 471.3 481.2 489.3 487.3 451.2 455.3 453.3 609.2

307.1 307.1 291.1 291.1 780.3 361.2 359.2 858.3 313.2 868.3 437.2 329.2 453.2 481.2 471.2 469.2 451.2 437.2 453.3 609.2

139.1 139.1 139.1 139.1 176.1 105.2 128.1 178.1 225.2 178.1 189.2 163.1 119.1 231.1 189.2 145.1 201.1 191.2 407.2 195.1

96 96 96 96 164 148 118 220 138 220 122 112 112 152 102 112 112 122 132 190

10 10 10 10 54 42 62 66 18 66 14 14 46 26 50 54 26 14 14 38

Table 2 Regression equation, LOD, LOQ, precision, repeatability, stability and recovery of 19 investigated compounds.

Detected (␮g)

Recovery (%)

RSD (%) 30.39 50.37 51.15 102.67 3.62 2.36 0.30 52.33 0.92 12.55 4.66 20.17 4.59 2.72 6.47 0.35 15.06 0.41 0.78

32.0 51.0 58.0 106.0 4.0 2.38 0.32 50.0 0.90 11.5 4.6 20.0 4.3 2.5 6.0 0.33 14.0 0.4 0.75

62.95 104.08 111.94 210.94 7.59 4.83 0.62 103.58 1.79 23.62 9.29 39.64 8.62 5.36 12.20 0.68 28.26 0.83 1.56

101.74 105.32 104.80 102.14 99.33 103.59 99.87 102.49 96.32 96.23 100.64 97.37 93.66 105.24 95.42 100.66 94.25 103.94 104.32

1.93 1.81 3.41 3.33 3.28 2.92 1.45 1.00 3.78 2.66 3.02 4.78 1.34 1.21 0.88 3.99 4.05 0.51 4.13 2.72 2.22 2.19 3.50 3.84 2.63 3.79 3.85 2.25 3.45 2.94 2.98 1.78 3.42 2.47 3.39 4.20 3.62 3.02 1.58 1.66 1.96 1.59 1.49 1.07 1.62 0.94 0.57 2.30 2.03 1.88 1.95 1.67 1.87 2.02 2.34 2.68 3.48

3.64 3.15 4.55 4.32 1.86 1.92 3.46 1.85 1.03 2.05 3.43 3.50 3.58 3.66 3.37 3.49 3.39 4.32 4.65 y = 1.9712x − 0.1062 y = 2.4811x − 0.1339 y = 1.5129x + 0.079 y = 1.9577x + 0.1823 y = 5.7671x + 0.0348 y = 0.1227x + 0.0039 y = 0.1888x − 0.0011 y = 8.6866x − 0.0241 y = 3.0037x + 0.1904 y = 11.96x − 0.3922 y = 0.5125x − 0.0153 y = 0.4953x − 0.0065 y = 0.2412x − 0.005 y = 4.4292x − 0.047 y = 0.0485x + 0.0052 y = 0.5951x + 0.0006 y = 27.919x + 0.5211 y = 1.6998x + 0.0216 y = 1.4026x − 0.0232

0.9993 0.9997 0.9996 0.9995 0.9994 0.9999 0.9998 0.9998 0.9992 0.9993 0.9993 0.9996 0.9993 0.9996 0.9989 0.9993 0.9999 0.9996 0.9996

9.2–9200 14.0–9160 13.0–8415 18.0–11500 9.0–6160 15.4–9660 10.0–5660 10.0–6410 9.2–5750 5.2–6500 7.6–9500 9.0–11300 10.0–6500 6.7–8330 30.0–15830 9.3–5830 17.3–10830 5.7–3580 13.0–8160

1.1 0.91 0.32 0.29 0.03 5.3 2.3 0.029 0.13 0.017 0.38 2.3 2.4 2.2 3.1 1.9 2.9 0.12 1.6

3.9 3.7 0.67 0.46 0.081 14.1 9.0 0.086 0.46 0.043 1.9 7.9 5.2 6.5 6.6 4.7 8.7 0.6 6.5

Inter-day (n = 3) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

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3.5.2. Principal component analysis (PCA) PCA is an unsupervised pattern recognition method used for analysing, classifying and reducing the dimensionality of numerical datasets in a multivariate problem [23], and it has been widely used to assess and compare the quality of herbal medicines [24–27]. In the present study, PCA was carried out to provide more information about the chemical difference of T. wilfordii and T. hypoglaucum samples. Contents of 13 selected compounds were simultaneously calculated and the score scatter plot and the loading scatter plot are displayed in Fig. 4. The first and second principal components (PC1 and PC2) described 52.7% and 13.9% of the variability in the original

3.5.1. Hierarchical clustering analysis (HCA) To compare and classify the two similar herbs, T. wilfordii and T. hypoglaucum, HCA was subsequently performed for the contents of the 13 compounds selected by one-way ANOVA. A method named as between-groups linkage was applied, and squared euclidean distance was selected as measurement for analysis. As showed in Fig. 3, T. wilfordii samples (TW1–TW13) could be included in one cluster and T. hypoglaucum samples (TH1–TH15) in the other cluster. The results indicated that T. hypoglaucum and T. wilfordii could be distinguished from each other based on the chemical profile.

compounds which differed in the respective varieties with statistical significance. Consequently, thirteen compounds including two catechins (compounds 3, 4), three sesquiterpene alkaloids (compounds 5, 8, 10), four triterpenoids (compounds 6, 7, 9, 12) and four diterpenoids (compounds 13, 14, 16, 17) were selected as the main elements (Supplement Table S1), which indicated that the contents of the 13 compounds in T. wilfordii and T. hypoglaucum samples were significantly different and might be available for quality comparison of the two similar herbs.

Fig. 3. Dendrograms of hierarchical cluster analysis for 28 samples. The sample codes were the same as in Table 3.

L. Guo et al. / Journal of Pharmaceutical and Biomedical Analysis 95 (2014) 220–228

Spiked (␮g) 3.12 3.56 2.87 3.07 2.32 2.99 3.05 1.63 2.66 1.93 3.13 3.09 2.90 4.19 2.84 3.82 3.03 3.54 3.44

Original (␮g) Intra-day (n = 6)

Recovery (n = 6) Stability RSD (%) Repeatability RSD (%) (n = 6) Precision RSD (%) LOQ (ng/ml) LOD (ng/ml) Linear range (ng/ml) R2 Regression equation No.

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Table 3 The contents of 19 compounds in Tripterygium hypoglaucum and Tripterygium wilfordii (␮g/g, n = 3). Origin

1

2

3*

4*

5*

6*

7*

8*

9*

10*

11

12*

13*

14*

15

16*

17*

18

19

TH1 TH2 TH3 TH4 TH5 TH6 TH7 TH8 TH9 TH10 TH11 TH12 TH13 TH14 TH15 TW1 TW2 TW3 TW4 TW5 TW6 TW7 TW8 TW9 TW10 TW11 TW12 TW13

Yunnan Yunnan Yunnan Yunnan Yunnan Guizhou Guizhou Guizhou Guizhou Guizhou Guizhou Sichuan Sichuan Sichuan Sichuan Jiangsu Jiangsu Jiangsu Hubei Hunan Hunan Anhui Anhui Jiangxi Jiangxi Henan Henan Sichuan

198.04 92.08 181.76 232.99 27.14 168.77 101.53 91.32 151.95 198.34 182.19 37.16 43.05 41.93 113.88 232.25 237.79 226.86 349.02 181.86 142.02 156.65 202.08 163.94 180.62 333.47 422.94 269.19

334.57 180.48 258.49 1010.11 46.23 800.15 247.20 688.82 434.90 403.70 278.75 193.27 174.16 65.46 292.86 163.37 211.46 312.37 667.01 108.68 89.11 170.90 224.68 147.59 237.23 843.47 340.56 272.86

369.17 146.57 524.39 195.54 40.95 54.24 136.19 30.26 249.03 259.76 317.77 4.66 24.00 57.12 170.04 1308.02 885.38 1281.34 1100.24 1119.13 1072.13 848.89 1284.42 1061.83 1026.42 1395.70 1461.93 1220.12

664.92 347.79 919.25 486.84 108.21 123.60 209.44 110.65 625.77 466.54 517.87 10.29 281.91 142.13 302.15 614.02 486.84 760.53 1025.65 501.69 476.96 456.96 745.95 493.81 710.54 1255.60 893.09 663.19

25.18 40.81 5.12 47.20 18.92 27.95 4.26 16.47 6.66 29.97 16.94 83.19 8.58 14.95 15.71 33.71 11.93 11.25 11.23 tr tr tr tr tr tr tr 12.33 55.22

17.64 11.48 8.97 40.32 11.20 74.29 40.67 99.78 10.65 16.55 14.25 15.53 2.61 10.42 12.07 tr – – – – – tr – – tr tr – 6.44

2.03 tra tr tr 2.13 9.39 2.48 19.26 tr tr –b 9.83 tr 2.02 2.20 tr tr tr – tr – – – tr tr tr – 2.17

394.14 390.97 328.76 490.64 700.00 552.95 467.34 633.45 169.57 322.21 225.12 373.73 221.33 599.19 454.63 29.91 8.96 10.34 8.99 6.48 6.17 7.63 6.32 5.15 5.95 6.05 6.60 175.32

7.25 5.08 4.36 3.08 14.84 69.15 21.06 93.80 2.85 2.38 8.28 119.86 47.09 15.40 17.58 4.39 11.91 2.64 tr 5.60 9.50 7.20 5.60 6.40 tr tr 1.10 10.36

82.37 147.48 104.52 160.85 148.17 460.00 36.11 181.28 28.33 177.98 181.57 333.33 28.49 125.05 105.85 41.65 34.93 36.32 45.09 34.59 34.45 35.26 34.98 33.65 35.13 34.91 35.51 92.63

32.34 35.57 28.05 32.19 32.92 18.39 11.02 10.00 19.52 33.61 57.42 11.92 82.38 28.87 35.83 23.24 17.25 22.13 9.38 32.56 28.97 13.45 25.79 22.36 17.84 6.70 18.20 13.65

66.62 26.05 137.50 145.89 40.49 1.70 2.76 6.38 66.30 39.89 153.61 8.50 4.56 39.23 74.51 211.90 332.14 133.89 43.95 109.69 142.88 121.26 293.45 137.36 55.58 20.60 173.49 67.30

31.30 17.49 32.71 15.65 21.63 19.80 12.97 16.42 22.06 14.61 49.72 43.35 28.28 19.64 44.04 24.30 17.18 16.40 7.12 19.52 19.75 11.06 21.05 15.31 17.52 6.27 17.26 10.54

24.47 9.24 1.66 56.60 18.87 173.30 306.91 115.01 25.06 34.38 19.75 193.24 23.30 12.86 93.62 3.29 2.00 1.74 1.35 0.74 0.75 0.95 0.46 0.42 0.31 0.28 0.86 8.06

48.92 22.44 60.37 58.17 48.71 168.52 47.25 168.69 23.19 46.47 93.88 232.93 38.28 41.36 97.70 117.27 79.75 98.39 22.08 101.21 97.18 69.57 84.14 67.22 50.01 12.79 88.40 42.46

5.23 4.64 5.92 5.50 3.22 6.60 2.71 6.46 4.21 10.88 7.28 6.27 12.93 4.10 9.99 1.41 1.10 1.20 0.59 0.63 0.67 0.59 1.58 0.64 0.73 0.00 0.85 0.57

103.84 22.31 32.55 358.44 167.54 678.47 677.29 315.77 47.37 72.43 54.81 650.91 39.54 117.59 343.13 37.06 41.11 45.75 12.00 39.13 32.04 36.10 56.71 39.71 47.41 14.32 34.15 41.84

3.50 13.89 3.17 4.55 4.99 1.96 2.30 1.93 4.55 8.61 14.75 1.57 39.77 4.40 13.04 5.33 2.62 3.39 1.41 3.84 4.24 1.92 2.73 2.40 1.89 0.54 3.48 2.42

3.15 9.63 2.81 3.09 5.37 2.26 2.62 3.39 3.47 5.90 14.63 8.57 14.77 4.96 10.38 9.34 4.37 7.58 2.89 7.07 9.02 3.99 5.15 4.87 3.36 1.01 6.53 4.56

* a b

The content of the compound in T. wilfordii and T. hypoglaucum samples was significantly different (p < 0.05). Less than the quantifiable limit. Not detected.

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Sample

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Fig. 4. PCA score scatter plot and loading scatter plot of 28 samples. The sample codes in score scatter plot were the same as in Table 3. TH corresponding to Tripterygium hypoglaucum samples and TW corresponding to Tripterygium wilfordii samples. The dot numbers in loading scatter plot were in accordance with the compound numbers in Fig. 1.

observations, respectively. The results were similar with HCA analysis, twenty-eight sample dots were classified into group A and group B, corresponding to T. hypoglaucum and T. wilfordii, respectively. Dots present in group B were relatively nearer to each other, indicating a closer relationship among the 13 batches of T. wilfordii. Dots in group A were relatively scattered, especially TH6, TH7, TH8 and TH12, suggesting diversification of the 15 batches of samples, which indicated that the quality of T. hypoglaucum samples were less stable compared with T. wilfordii samples. From the loading scatter plot, it could be observed that different variables have different contributions in samples differentiation. The variables having similar contents of PC1, and PC2 were located in points near to each other and had the same effect on similarity and dissimilarity of different samples. The chemometrics analysis results indicated the contents of the bioactive compounds in T. wilfordii and T. hypoglaucum were

significantly different, and the samples could be classified based on quantitative analysis. The selected 13 constituents including two catechins, three sesquiterpene alkaloids, four triterpenoids and four diterpenoids could be used as the markers for quality control of Tripterygium herbs. 4. Conclusion In the present study, an efficient strategy was proposed for quality control of T. hypoglaucum and T. wilfordii, that is, RRLC-ESI-MSn analysis was employed for quantitation of 19 bioactive compounds in the two Tripterygium species, and chemometrics such as one-way ANOVA, HCA and PCA were performed to compare and discriminate the two Tripterygium herbs based on the quantitative data. The results indicated that T. hypoglaucum samples could be distinguished from T. wilfordii. The proposed method could be utilized as

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a quality control method for Tripterygium herbs. The comparative activity of T. hypoglaucum and T. wilfordii is still under investigation in our laboratory, and will be reported in the near future. Acknowledgments The authors greatly appreciate financial support from Program for New Century Excellent Talents in University, National Natural Science Foundation of China (81202898) and Natural Science Foundation of Jiangsu Province (BK20130652). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jpba.2014.03.009. References [1] A.M. Brinker, J. Ma, P.E. Lipsky, I. Raskin, Medicinal chemistry and pharmacology of genus Tripterygium (Celastraceae), Phytochemistry 68 (2007) 732–766. [2] X. Tao, L.S. Davis, P.E. Lipsky, Effect of an extract of the Chinese herbal remedy Tripterygium wilfordii Hook. F. on human immune responsiveness, Arthritis Rheum. 34 (1991) 1274–1281. [3] X. Tao, P.E. Lipsky, The Chinese anti-inflammatory and immunosuppressive herbal remedy Tripterygium wilfordii Hook. F, Rheum. Dis. Clin. North Am. 26 (2000) 29–50. [4] J. Bao, S.M. Dai, A Chinese herb Tripterygium wilfordii Hook. F. in the treatment of rheumatoid arthritis: mechanism, efficacy, and safety, Rheumatol. Int. 31 (2011) 1123–1129. [5] X. Tao, J. Younger, F.Z. Fan, B. Wang, P.E. Lipsky, Benefit of an extract of Tripterygium wilfordii Hook. F. in patients with rheumatoid arthritis: a double-blind, placebo-controlled study, Arthritis Rheum. 46 (2002) 1735–1743. [6] J. Ma, M. Dey, H. Yang, A. Poulev, R. Pouleva, R. Dorn, P.E. Lipsky, E.J. Kennelly, I. Raskin, Anti-inflammatory and immunosuppressive compounds from Tripterygium wilfordii, Phytochemistry 68 (2007) 1172–1178. [7] Y. Gong, B. Xue, J. Jiao, L. Jing, X. Wang, Triptolide inhibits COX-2 expression and PGE2 release by suppressing the activity of NF-kappaB and JNK in LPS-treated microglia, J. Neurochem. 107 (2008) 779–788. [8] N. Lin, C. Liu, C. Xiao, H. Jia, K. Imada, H. Wu, A. Ito, Triptolide, a diterpenoid triepoxide, suppresses inflammation and cartilage destruction in collageninduced arthritis mice, Biochem. Pharmacol. 73 (2007) 136–146. [9] R. Kannaiyan, M.K. Shanmugam, G. Sethi, Molecular targets of celastrol derived from Thunder of God Vine: potential role in the treatment of inflammatory disorders and cancer, Cancer Lett. 303 (2011) 9–20. [10] N. Gay, G. Li, D. Liu, Y. Zhang, Y. Qian, H. Zhang, S. Guo, M. Sunagawa, T. Hisamitsu, Y. Liu, Celastrol inhibits lipopolysaccharide-stimulated rheumatoid fibroblast-like synoviocyte invasion through suppression of TLR4/NF-␬Bmediated matrix metalloproteinase-9 expression, PLoS ONE 8 (2013) e68905. [11] Y. Ren, P. Zhang, D. Yan, J. Wang, X. Du, X. Xiao, A strategy for the detection of quality fluctuation of a Chinese herbal injection based on chemical fingerprinting combined with biological fingerprinting, J. Pharm. Biomed. Anal. 56 (2011) 436–442.

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Chemical comparison of Tripterygium wilfordii and Tripterygium hypoglaucum based on quantitative analysis and chemometrics methods.

Tripterygium wilfordii (T. wilfordii) and Tripterygium hypoglaucum (T. hypoglaucum), two commonly used Chinese herbal medicines derived from Tripteryg...
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