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. 2024 Jun 13;9(25):27321–27328. doi: 10.1021/acsomega.4c01878

Quality Evaluation of Tripterygium Glycoside Tablets Based on Quantitative Band-Selective 2D 1H–13C HSQC and 1H NMR Fingerprinting

Youwen Chen †,‡, Yadan Wang ‡, Yiwen Xu ‡, Shuangcheng Ma ‡,*, Huiying Yang ‡,*, Yuanyan Liu †,*, Xianfu Wu ‡,*
PMCID: PMC11209881  PMID: 38947815

Abstract

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Tripterygium glycoside tablets (TGTs) are preparations extracted and purified from Tripterygium wilfordii Hook. F and are extensively utilized in the treatment of autoimmune diseases, such as rheumatoid arthritis (RA). However, variations in production processes among manufacturers can lead to challenges in quality control and clinical utilization of TGTs. A band-selective 2D 1H–13C HSQC quantification method was applied for the determination of 13 active ingredients in TGTs. This method was validated following the guidelines of USP-NF 2022. The results demonstrated that the quantitative method exhibited excellent signal resolution, as well as sufficient accuracy, sensitivity, and stability. In addition, the 1H NMR spectra of TGTs from three manufacturers underwent analysis using principal component analysis and orthogonal partial least-squares discriminant analysis. The results revealed significant differences among the TGTs from the three manufacturers, with manufacturer 2 and manufacturer 3 demonstrating superior product consistency compared to manufacturer 1. A quality evaluation system for TGTs was developed based on band-selective 2D 1H–13C HSQC and 1H NMR, encompassing both quality markers and fingerprinting. This system offers reliable approaches and insights for enhancing the quality control of natural products.

1. Introducation

Tripterygium wilfordii Hook. F (TwHF) has long been used as a traditional herbal medicine to treat various autoimmune and inflammatory diseases in China.1 Recent pharmacological and phytochemical studies have shown that TwHF has significant anti-inflammatory and immunosuppressive activity studies, and diterpenes, triterpenes, and sesquiterpene alkaloids are the main active components in TwHF.2−5Tripterygium Glycoside Tablets (TGTs) is a preparation that is specifically enriched with diterpenes, triterpenes, and sesquiterpene alkaloids from the root of TwHF and has become the first-line therapy for RA in China.6−8 Triptolide and wilforlide A are defined as quality markers in the current quality standards set by the National Medical Products Administration of China, in which the content of triptolide should be less than 10 μg/tablet and the content of wilforlide A should be more than 10 μg/tablet.9 The TGTs contain hundreds of compounds, with terpenoids, triterpenes, and alkaloids comprising the majority. In addition to triptolide and wilforlide A, compounds such as tripdiolide, triptoquinone B, wilforgine, wilforine, celastrol, demethylzeylasteral have also been reported to exhibit significant anti-inflammatory activity.1 Therefore, it is necessary to quantitate these active ingredients in the TGTs. Due to the structural similarity among these compounds (Figures 1–3), it is challenging to separate and quantify them using conventional chromatographic methods. Hence, it is imperative to establish a method with a high resolution for the quantitative analysis of multiple active ingredients in TGTs. Furthermore, TGTs taken by RA patients may come from multiple manufacturers. As an herbal extract, the chemical compositions of TGTs from different manufacturers vary, likely attributed to their different production processes. Even different batches of TGTs from the same manufacturer may also have poor product consistency.7 Maintaining product consistency is crucial for the clinical efficacy of the medications. Therefore, it is necessary to establish a rapid and reliable method for evaluating the consistency of the TGTs from various manufacturers.

Figure 1.

Figure 1

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Structures of four diterpenes. The quantitative signals are marked in red.

Figure 3.

Figure 3

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Structures of three triterpenes. The quantitative signals are marked in red.

Figure 2.

Figure 2

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Structures of six alkaloids. The quantitative signals are marked in red.

NMR can provide structural information such as chemical shifts, J-coupling coefficients, and number of peaks for compound identification, and also enables accurate quantification by peak area.10,111H NMR can rapidly characterize the protons of all compounds in complex mixtures, and the spectra are as characteristic as fingerprints.12 Based on multivariate statistical analysis, 1H NMR fingerprinting plays a great role in the classification of herbal plants by species, geographic origin, and seasonal differences, as well as in the identification of authenticity, production, and batches of herbal preparations.13−17 Quantitative NMR is highly reproducible and versatile.18,19 However, for complex natural products, 1H NMR spectra sometimes show severe peak overlap, which makes it difficult to achieve signal separation and accurate integration. 2D 1H–13C HSQC obtains high resolution while maintaining acceptable sensitivity by extending the signal to the 13C dimension.19 Therefore, 2D 1H–13C HSQC experiments are increasingly used for the quantitative analysis of complex mixtures, involving a range of natural products, body fluids, and food products.20−25 Band-selective 2D 1H–13C HSQC is a 2D NMR spectrum with high resolution due to the excitation of a specified region resulting in a much higher sampling density of that region.26,27 Even for botanical extracts containing structural analogs, band-selective 2D 1H–13C HSQC can significantly separate the signals of multiple analogs. For example, Xiao et al. completed the identification and quantification of 12 lignans in Sambucus williamsii Hance by band-selective 2D 1H–13C HSQC.27

In recent years, multicomponent quantification by LC-MS and bioactivity determination have gradually become the primary strategies for quality evaluation of TGTs,6,28 but NMR is still not mentioned even as a stable and rapid method. In this study, we determined 13 active ingredients in TGTs from 3 manufacturers by band-selective 2D 1H–13C HSQC spectra. The linearity, accuracy, precision, and quantitation limit of the method were validated by USP-NF 2022. In addition, we acquired 1H NMR spectra of 65 batches of TGTs from 3 manufacturers. Combined with PCA and OPLS-DA, consistency evaluation and classification of TGTs from three manufacturers were achieved using 1H NMR spectra. This work combines quantitative band-selective 2D 1H–13C HSQC, 1H NMR, and multivariate statistical analysis to comprehensively evaluate TGTs from 3 manufacturers in terms of both quality markers and product consistency.

2. Results and Discussion

2.1. Band-Selective 2D 1H–13C HSQC Spectra of TGTs

The regular 2D 1H–13C HSQC spectra of TGTs exhibit severe signal overlap (Figure 4). To minimize signal interference between terpenoids, triterpenes, and alkaloids, C–H pairs with similar chemical environments and distinguishable from the other classes were selected as quantitative signals for each class of compounds (Figures 1–3). Band-selective 2D 1H–13C HSQC spectra with high resolution can be obtained by exciting the region in which the quantitative signals of terpenoids, triterpenes, and alkaloids are located. It provided excellent signal separation and thus contributed to signal identification. Based on spectra of standards (Figures S1–S13), 13 compounds were identified from band-selective 2D 1H–13C HSQC spectra of TGTs (Figure 4), respectively. The content of the two compounds in the TGTs was estimated by an external standard curve.

Figure 4.

Figure 4

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2D 1H–13C HSQC spectra of TGTs. (A, B, C) Band-selective 2D 1H–13C HSQC spectra acquired based on the quantitative signals of diterpenes, alkaloids, and triterpenes, respectively. (D, E,F) Zoomed-out corresponding regions of the regular 2D 1H–13C HSQC spectra.

2.2. Method Validation

2.2.1. Lineraty and Range

To verify the linearity, a series of standard solutions of 13 compounds were prepared, respectively. The standard curves were constructed by plotting the peak volumes of quantitative signals versus the concentrations (Figures S14–S26). The regression equations and coefficients of determination (R2) for these compounds are given in Table 1. All standard curves exhibited good linearity with R2 greater than 0.99. Based on the equations, the contents of 13 compounds in TGTs from three manufacturers were calculated (Table 2).

Table 1. Regression Equation of 13 Compounds.
no. compound regression equation R2
1 tripdiolide y = 8.16 × 1010x +1.02 × 108 0.9999
2 triptolide y = 8.06 × 1010x + 2.55 × 108 0.9999
3 triptriolide y = 8.14 × 1010x + 1.62 × 108 0.9997
4 triptoquinone B y = 8.13 × 1010x + 4.41 × 108 0.9998
5 wilforine y = 3.98 × 1010x + 1.86 × 109 0.9985
6 wilforgine y = 3.95 × 1010x + 2.28 × 109 0.9983
7 wilfortrine y = 3.97 × 1010x + 1.59 × 109 0.9984
8 euonine y = 3.97 × 1010x + 1.63 × 108 0.9989
9 euonymine y = 3.79 × 1010x + 6.57 × 109 0.9978
10 peritassine A y = 3.98 × 1010x – 2.02 × 109 0.9996
11 celastrol y = 4.61 × 1010x + 3.32 × 108 0.9998
12 demethylzeylasteral y = 4.60 × 1010x + 1.31 × 108 0.9999
13 wilforlide A y = 4.42 × 1010x + 8.13 × 108 0.9996
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Table 2. Contents of 13 Compounds in TGTs from Three Manufacturers.
compound content (μg/tablet)
manufacturer 1 (n = 6) manufacturer 2 (n = 6) manufacturer 3 (n = 6)
tripdiolide 1.51 ± 0.05 7.36 ± 0.03 3.84 ± 0.05
triptolide 3.80 ± 0.06 7.28 ± 0.04 4.95 ± 0.03
triptriolide 1.70 ± 0.06 3.73 ± 0.05 1.51 ± 0.04
triptoquinone B 12.97 ± 0.12 43.61 ± 0.56 35.33 ± 0.52
wilforine 80.17 ± 1.23 50.04 ± 0.87 65.06 ± 1.07
wilforgine 125.85 ± 1.52 72.39 ± 0.95 95.36 ± 2.31
wilfortrine 128.20 ± 2.21 61.25 ± 1.05 44.42 ± 0.85
euonine 41.10 ± 0.77 26.84 ± 0.39 46.93 ± 0.52
euonymine 50.96 ± 1.20 17.80 ± 0.26 38.69 ± 1.17
peritassine A 57.94 ± 1.07 34.94 ± 0.86 64.05 ± 0.96
celastrol 2.37 ± 0.07 8.23 ± 0.19 61.23 ± 0.79
demethylzeylasteral 4.09 ± 0.08 44.15 ± 0.36 37.19 ± 0.24
wilforlide A 44.89 ± 0.54 51.28 ± 0.40 25.36 ± 0.38
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2.2.2. Accuracy

The accuracy was evaluated by spiking experiments. Since we were unable to obtain a blank preparation without the 13 compounds, we assessed the recovery by adding different levels of standards to the samples to be tested. The samples prepared by manufacturer 2’s TGTs were spiked with different levels (high, medium, and low levels) of reference standards. Quantification of 13 compounds in synthetic samples was performed according to an established method. Comparing the results obtained with the theoretical values, the recoveries of 13 compounds ranged from 95.25% to 104.36% (Table 3).

Table 3. Validation Results of 13 Compounds in TGTs.
compound recovery repeatability intermediate precision LOQ (mM)
tripdiolide 103.21% 1.67% 2.31% 0.05
triptolide 103.10% 0.09% 2.15% 0.04
triptriolide 104.36% 2.50% 3.15% 0.04
triptoquinone B 98.97% 1.23% 1.98% 0.06
wilforine 97.25% 1.64% 2.42% 0.44
wilforgine 96.59% 1.65% 2.15% 0.41
wilfortrine 98.56% 1.78% 3.02% 0.46
euonine 99.02% 1.48% 2.81% 0.35
euonymine 97.62% 2.27% 2.65% 0.36
peritassine A 95.25% 1.93% 2.37% 0.41
celastrol 97.18% 2.18% 3.24% 0.08
demethylzeylasteral 101.76% 1.14% 2.05% 0.07
wilforlide A 99.05% 1.16% 2.28% 0.11
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2.2.3. Precision

Six samples were prepared in parallel to assess the repeatability. The mean RSD values of 13 compounds ranged from 0.09 to 2.50% (Table 3). In addition, three different analysts performed the analytical procedure twice at different times to assess the intermediate precision, and the RSD values of 13 compounds ranged from 1.98 to 3.15%. The precision validation criteria for compounded pharmaceutical finished products in USP-NF 2022 state that the RSD should not be more than 2.0% (repeatability) or 3.0% (intermediate precision). Considering the complex composition of TGTs, the precision of this quantitative method basically meets the pharmacopoeial requirements.

2.2.4. Quantitation Limit

The quantitation limit was estimated by calculating the standard deviation of the integrated noise from six regions on band-selective 2D 1H–13C HSQC spectra and then dividing by the slope of the calibration line and multiplying by 10. The quantitation limit of 13 compounds ranged from 0.04 to 0.46 mM.

2.2.5. Robustness

The results of quantitative 2D 1H–13C HSQC are affected by parameters such as the relaxation time and temperature. With other parameters fixed, D1 was changed to 2.5 s, and the probe temperature was replaced by 303 K. The data obtained were compared with the original data (t test) and no significant differences were found. This indicates that the method has a certain degree of robustness.

2.3. Consistency Evaluation and Classification of TGTs Based on 1H NMR

To assess the consistency of TGTs from different manufacturers, PCA was performed on the data sets of 1H NMR spectra from 38 batches of TGTs (D1-D38, Figures S27–S29). Figure 5A shows the score plot for PCA, a five-component model explaining 100% of the variance. The contribution of principal component 1 (PC1) and principal component 2 (PC2) to the total variance was 66.7 and 13.1%, respectively. The TGTs from the 3 manufacturers were well separated on the PCA score plot, with samples from each manufacturer largely within the 95% confidence interval. It indicates that TGTs from different manufacturers have significant differences and TGTs from the same manufacturer have a high degree of similarity and consistency. In terms of dispersion, the points of manufacturers 2 and 3 were significantly more concentrated than those of manufacturer 1, indicating that the drug quality of manufacturers 2 and 3 was more stable.

Figure 5.

Figure 5

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Score plot of the PCA model (A) and OPLS-DA model (B). Each point in the plot represents the 1H NMR spectrum of an individual sample, and samples from different manufacturers are marked with different colors.

In addition, an OPLS-DA classification model was developed for TGTs from 3 manufacturers based on the 1H NMR spectra from 38 batches of TGTs (Figure 5B). The data were divided into 7 folds for cross-validation, R2 is 0.97, and Q2 is 0.96 when only component 1 and component 2 are considered, indicating that the OPLS-DA model for TGTs from the 3 manufacturers has good prediction.29 Additionally, we collected 1H NMR of another 27 batches of TGTs and used these spectral data sets as the prediction set to validate the OPLS-DA model.30,31 By importing the prediction set (x1–x27) into the OPLS-DA model and setting these samples to no class, the YPred value, the probability of the prediction set being classified into the specified class, can then be read from the “Classification List” module of SIMCA 14.1. A sample is considered to belong to a specified class when the YPred value is greater than 0.65, and when the YPred value is less than 0.3, the sample is considered not to belong to that class. The Classification List of the prediction set showed the prediction results for samples x1–x27 (Table S1), which is consistent with the actual classification. It meant that the sensitivity, specificity, and accuracy of the developed model were 100%.30

3. Conclusions

In this study, a quantitative band-selective 2D 1H–13C HSQC method was applied for the determination of 13 active ingredients in TGTs from 3 manufacturers. The established quantitative method was validated according to USP-NF 2022, and the results showed that the method was sufficiently accurate, precise, and sensitive. The results showed the great potential of band-selective 2D 1H–13C HSQC for the quantification of multiple components of herbs. In addition, 1H NMR of 65 batches of TGTs from the three manufacturers were collected, and the consistency of TGTS from each manufacturer was evaluated and a classification model of TGTs was developed based on PCA and OPLS-DA. The 1H NMR fingerprinting of TGTs from the same manufacturer all showed a high degree of similarity, while products from manufacturers 2 and 3 had better consistency than those from manufacturer 1. Compared with the original method for quality evaluation of TGTs, the established system achieves quality control of TGTs in two dimensions: quality markers and overall chemical profile, which is of significance for the quality evaluation of natural products and their related preparations.

4. Materials and Methods

4.1. Sample Preparation

A randomly selected batch of TGT from each manufacturer was ground separately into a powder. The powder equal to 20 tablets was dispersed in 30 mL of methanol and extracted by ultrasonication for 30 min. For the determination of diterpenes and alkaloid compounds in TGTs, the methanol extract was evaporated and redissolved with a small amount of ethyl acetate and then purified by Sep-Pak Alumina N cartridges (Waters, Part No. WAT054630), using 30 mL of ethyl acetate as eluent. The eluate was evaporated and redissolved with an appropriate amount of CDCl3 to fix the final volume of 1 mL. For the determination of triterpenes in TGTs, the methanol extract was evaporated and redissolved with a small amount of ethyl acetate and then purified by Sep-Pak Silica cartridges (Waters, Part NO. WAT036920). The eluate was evaporated and redissolved with an appropriate amount of CDCl3 to fix the final volume of 1 mL. All samples to be tested were prepared six times in parallel. The samples with a concentration of 20 tablets·mL–1 were used separately for band-selective 2D 1H–13C HSQC experiments.

On the other hand, 65 batches of TGT powder (equivalent to five tablets) from three manufacturers were individually weighed and extracted by ultrasonication with 20 mL of methanol for 30 min. The extract was filtered, evaporated, and redissolved with 1 mL of CDCl3. Finally, 500 μL of the solutions were taken for 1H NMR experiments. A schematic diagram of the sampling method is provided in the supplementary data (Figure S30).

4.2. Band-Selective 2D 1H–13C HSQC Experiments and Data Processing

All spectra were recorded on a Bruker Ascend 600 nuclear magnetic resonance spectrometer (Bruker Corp., Switzerland). The T1 values of the quantitative signals of the 13 compounds were determined by the standard experiment “PROTONT1″ and the T1 values ranged from 0.71 to 1.20 s. The band-selective 2D 1H–13C HSQC experiments were performed with the Bruker shsqcetgpsisp2.2 pulse program, which uses a band-selective shaped 13C refocusing pulse to avoid spectral folding. For diterpenes, the excitation region covered 0.8–1.4 ppm in F2 (1H channel) and 10–25 ppm in F1 (13C channel); the region of alkaloids was ranged of 4.5–5.3 ppm in F2 and 74.2–79 ppm in F1; the region of triterpenes was ranged of 5–7 ppm in F2 and 115–135 ppm in F1. Other acquisition parameters for both experiments were consistent as follows: temperature at 298 K; 16 dummy scans; 32 scans; a 6 s relaxation delay; a 101 receiver gain; 1024 points collected in F2 and 256 increments in F1. Zero filling was applied to 4 K in F2 and 1 K in F1. A 1H 90° hard pulse was automatically calibrated before spectra acquisition. All the spectra data were processed using the Topspin 4.1.3 software for manual phase correction and automatic baseline correction. The automatic peak detection routine (“peak picking”) in the 2D mode was employed in the spectral region of interest, with the following parameters: mi 0.01; maxi 1; ppdiag 1; ppresol 5; ppiptyp parabolic; psign all. The peaks were integrated using the automatic peak integration of the topspin software and setting the threshold to 0.001.

4.3. 1H NMR Fingerprinting and Multivariate Statistical Analysis

1H NMR spectra of the samples were recorded by using a standard Bruker zg30 pulse sequence. A total of 63,536 data points were collected with a spectral width of 20 ppm, a relaxation delay of 1 s, 128 repetitions, and a receiver gain of 16. Phase and baseline corrections were performed manually for all spectra by Topspin 4.1.3 software. To compare the 1H NMR fingerprinting of TGTs from three manufacturers, the 1H NMR spectra were divided into buckets of 0.04 ppm on the Bruker AssureNMR 2.2 software. The solvent peak (7.26–7.30 ppm) was removed from the data set. The remaining data were imported into SIMCA 14.1 (Umetrics, Sweden) for Pareto scaling before PCA and OPLS-DA.

Acknowledgments

The authors kindly acknowledge the financial support from National Institutes for Food and Drug Control of China.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.4c01878.

  • 1H NMR and 2D 1H–13C HSQC spectra of 13 compounds; 1H NMR spectra of TGTs from three manufacturers; classification list of prediction set; and schematic diagram of the sampling method (DOCX)

Author Contributions

Y.C.: conceptualization, investigation, experiment, writing—review and editing. Y.W.: formal analysis, material collection, experiment, writing—review and editing. Y.X.: supervision, investigation, software. Y.L.: design, formal analysis, project administration. X.W.: methodology, literature research, experimental validation. S.M.: material collection, supervision, investigation.

The authors declare no competing financial interest.

Supplementary Material

ao4c01878_si_001.docx (2.8MB, docx)

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