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Discriminatory components retracing strategy for monitoring the preparation procedure of Chinese patent medicines by fingerprint and chemometric analysis.

Yao S, Zhang J, Wang D, Hou J, Yang W, Da J, Cai L, Yang M, Jiang B, Liu X, Guo DA, Wu W - PLoS ONE (2015)

Bottom Line: As a result, the holistic inconsistencies of ninety-three batches of SKIs were identified and five discriminatory components including emodic acid, gallic acid, caffeic acid, chrysophanol-O-glucoside, and p-coumaroyl-O-galloyl-glucose were labeled as the representative targets to explain the retracing strategy.It was suggested that the production process should be standardized by taking the concentration of the discriminatory components as the diagnostic marker to ensure the stable and consistent quality for multi-batches of products.It is believed that the effective and practical strategy would play a critical role in the guidance of manufacturing and help improve the safety of the final products.

View Article: PubMed Central - PubMed

Affiliation: National Engineering Laboratory for TCM Standardization Technology, Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.

ABSTRACT
Chinese patent medicines (CPM), generally prepared from several traditional Chinese medicines (TCMs) in accordance with specific process, are the typical delivery form of TCMs in Asia. To date, quality control of CPMs has typically focused on the evaluation of the final products using fingerprint technique and multi-components quantification, but rarely on monitoring the whole preparation process, which was considered to be more important to ensure the quality of CPMs. In this study, a novel and effective strategy labeling "retracing" way based on HPLC fingerprint and chemometric analysis was proposed with Shenkang injection (SKI) serving as an example to achieve the quality control of the whole preparation process. The chemical fingerprints were established initially and then analyzed by similarity, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to evaluate the quality and to explore discriminatory components. As a result, the holistic inconsistencies of ninety-three batches of SKIs were identified and five discriminatory components including emodic acid, gallic acid, caffeic acid, chrysophanol-O-glucoside, and p-coumaroyl-O-galloyl-glucose were labeled as the representative targets to explain the retracing strategy. Through analysis of the targets variation in the corresponding semi-products (ninety-three batches), intermediates (thirty-three batches), and the raw materials, successively, the origins of the discriminatory components were determined and some crucial influencing factors were proposed including the raw materials, the coextraction temperature, the sterilizing conditions, and so on. Meanwhile, a reference fingerprint was established and subsequently applied to the guidance of manufacturing. It was suggested that the production process should be standardized by taking the concentration of the discriminatory components as the diagnostic marker to ensure the stable and consistent quality for multi-batches of products. It is believed that the effective and practical strategy would play a critical role in the guidance of manufacturing and help improve the safety of the final products.

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Chemometric analysis of ninety-three batches of SKIs. A) PCA score plot, B) PCA loading plot, C) PLS-DA biplot, D) VIP plot.
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pone.0121366.g003: Chemometric analysis of ninety-three batches of SKIs. A) PCA score plot, B) PCA loading plot, C) PLS-DA biplot, D) VIP plot.

Mentions: PCA was performed allowing visualization of holistic distribution of the SKI products and further evaluation of the quality consistency for those samples. A two-component PCA model was obtained which cumulatively accounted for 68.1% of the variation; the total variance explained for the first principal component is 53.1% and that for the second principal component is 15.0%. Through a visual analysis of Fig. 3A, the samples are mainly separated into two groups. Forty-three batches got tightly clustered in Circle I including S32–S34, S47–S85, S93, and thirty-nine batches in Circle II including S1–S46 except S11, S15–S17, and S32–S34. The other samples involving S11 and S15–S17 in Circle III, and S86–S92 in Circle IV as the outliers diverged significantly. Consistently, those samples were just the corresponding ones with poor similarity (Table 2). The loading plot (Fig. 3B) displays the contribution of each variable to the discrimination. Theoretically, the further the variable departs from the zero of the X-axis and the Y-axis, the more the variable contributes to the clustering [12, 23]. Based on that rule, five major representative discriminatory variables were identified preliminarily, corresponding to the peaks at the retention times of 61.0, 7.95, 24.3, 53.1 and 41.0 min.


Discriminatory components retracing strategy for monitoring the preparation procedure of Chinese patent medicines by fingerprint and chemometric analysis.

Yao S, Zhang J, Wang D, Hou J, Yang W, Da J, Cai L, Yang M, Jiang B, Liu X, Guo DA, Wu W - PLoS ONE (2015)

Chemometric analysis of ninety-three batches of SKIs. A) PCA score plot, B) PCA loading plot, C) PLS-DA biplot, D) VIP plot.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359105&req=5

pone.0121366.g003: Chemometric analysis of ninety-three batches of SKIs. A) PCA score plot, B) PCA loading plot, C) PLS-DA biplot, D) VIP plot.
Mentions: PCA was performed allowing visualization of holistic distribution of the SKI products and further evaluation of the quality consistency for those samples. A two-component PCA model was obtained which cumulatively accounted for 68.1% of the variation; the total variance explained for the first principal component is 53.1% and that for the second principal component is 15.0%. Through a visual analysis of Fig. 3A, the samples are mainly separated into two groups. Forty-three batches got tightly clustered in Circle I including S32–S34, S47–S85, S93, and thirty-nine batches in Circle II including S1–S46 except S11, S15–S17, and S32–S34. The other samples involving S11 and S15–S17 in Circle III, and S86–S92 in Circle IV as the outliers diverged significantly. Consistently, those samples were just the corresponding ones with poor similarity (Table 2). The loading plot (Fig. 3B) displays the contribution of each variable to the discrimination. Theoretically, the further the variable departs from the zero of the X-axis and the Y-axis, the more the variable contributes to the clustering [12, 23]. Based on that rule, five major representative discriminatory variables were identified preliminarily, corresponding to the peaks at the retention times of 61.0, 7.95, 24.3, 53.1 and 41.0 min.

Bottom Line: As a result, the holistic inconsistencies of ninety-three batches of SKIs were identified and five discriminatory components including emodic acid, gallic acid, caffeic acid, chrysophanol-O-glucoside, and p-coumaroyl-O-galloyl-glucose were labeled as the representative targets to explain the retracing strategy.It was suggested that the production process should be standardized by taking the concentration of the discriminatory components as the diagnostic marker to ensure the stable and consistent quality for multi-batches of products.It is believed that the effective and practical strategy would play a critical role in the guidance of manufacturing and help improve the safety of the final products.

View Article: PubMed Central - PubMed

Affiliation: National Engineering Laboratory for TCM Standardization Technology, Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.

ABSTRACT
Chinese patent medicines (CPM), generally prepared from several traditional Chinese medicines (TCMs) in accordance with specific process, are the typical delivery form of TCMs in Asia. To date, quality control of CPMs has typically focused on the evaluation of the final products using fingerprint technique and multi-components quantification, but rarely on monitoring the whole preparation process, which was considered to be more important to ensure the quality of CPMs. In this study, a novel and effective strategy labeling "retracing" way based on HPLC fingerprint and chemometric analysis was proposed with Shenkang injection (SKI) serving as an example to achieve the quality control of the whole preparation process. The chemical fingerprints were established initially and then analyzed by similarity, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to evaluate the quality and to explore discriminatory components. As a result, the holistic inconsistencies of ninety-three batches of SKIs were identified and five discriminatory components including emodic acid, gallic acid, caffeic acid, chrysophanol-O-glucoside, and p-coumaroyl-O-galloyl-glucose were labeled as the representative targets to explain the retracing strategy. Through analysis of the targets variation in the corresponding semi-products (ninety-three batches), intermediates (thirty-three batches), and the raw materials, successively, the origins of the discriminatory components were determined and some crucial influencing factors were proposed including the raw materials, the coextraction temperature, the sterilizing conditions, and so on. Meanwhile, a reference fingerprint was established and subsequently applied to the guidance of manufacturing. It was suggested that the production process should be standardized by taking the concentration of the discriminatory components as the diagnostic marker to ensure the stable and consistent quality for multi-batches of products. It is believed that the effective and practical strategy would play a critical role in the guidance of manufacturing and help improve the safety of the final products.

Show MeSH