Research progress of continuous manufacturing and process knowledge system of traditional Chinese medicine / 中国中药杂志

In the past few years, continuous manufacturing(CM) has been put forward by the FDA. Pharmaceutical enterprises are encouraged to promote the implementation of CM, which has become a hot research direction of pharmaceutical technology. In February 2019, the FDA issued a draft guideline for the implementation of CM, which greatly promoted the development of CM and provided reference for continuous manufacturing of traditional Chinese medicine(TCM). The production process of TCM is a complex system. With the innovation of production equipment and the promotion of automation and informatization of TCM production, the exis-ting policies, regulations and traditional production control capacity are difficult to meet the market demand for high-quality TCM pro-ducts. In this paper, we reviewed the new technologies and methods of quality control in accordance with the characteristics of TCM production by referring to modern manufacturing technology, information technology and quality control technology. Based on the "QbD" theory and "PAT" technology, process knowledge system(PKS), an advanced control strategy, was proposed to provide a reference for the implementation of CM in TCM production.

Rapid determination of active components in Ginkgo biloba leaves by near infrared spectroscopy combined with genetic algorithm joint extreme learning machine / 中国中药杂志

Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.

Simultaneous determination of five saponins in Yaobitong capsule by HPLC-CAD / 药学学报

A high performance liquid chromatography charged aerosol detector (HPLC-CAD) method was established for the simultaneous determination of five saponins (notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, Ginsenoside Rd) in Yaobitong capsule, providing a method for quality control. The sample was extracted with methanol and chromatographic separation was performed on a Waters Xbridge Phenol column (150 mm×4.6 mm, 3.5 μm) using acetonitrile-water as the mobile phase with gradient elution at a flow rate of 1.0 mL·min-1. The column temperature was 30 ℃ and the injection volume was 10 μL. The nebulizer temperature of CAD was 35 ℃ and the air pressure was 60.2 psi, the filtration was 3.6 s, and the collection frequency was 5 Hz. Notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1 and ginsenoside Rd showed a good linear relationship in the range of 16.96-203.5 μg·mL-1 (R2 = 0.999 3), 54.46-653.5 μg·mL-1 (R2 = 0.999 3), 10.96-131.5 μg·mL-1 (R2 = 0.999 6), 51.50-618.0 μg·mL-1 (R2 = 0.999 0), 15.94-191.3 μg·mL-1 (R2 = 0.999 4), respectively. The average recoveries were 98.96%, 100.8%, 94.76%, 100.1%, 103.1%, and RSDs were 0.87%, 1.46%, 1.85%, 2.06%, 0.96% (n = 6), respectively. The proposed method is accurate, simple and reliable, and can be used for the determination of five saponins in Yaobitong capsule.