Chemical constituents of Scrophulariae Radix and their antitumor activities in vitro / 中国中药杂志

The present study investigated the chemical constituents of Scrophulariae Radix and their antitumor activities in vitro. The compounds in the ethyl acetate extract were separated and purified by conventional column chromatographies(such as silica gel, Sephadex LH-20, and ODS column) and semi-preparative high-performance liquid chromatography(HPLC), and their structures were identified by various spectral techniques such as nuclear magnetic resonance(NMR) and mass spectrometry(MS). Twenty-three compounds were isolated and identified as benzyl-β-D-(3',6'-di-O-acetyl) glucoside(1), 5-O-p-methoxybenzoyl kojic acid(2), 5-O-methoxybenzoyl kojic acid(3), 7-O-methylbenzoyl kojic acid(4), 5-O-benzoyl kojic acid(5), methyl ferulate ethyl ether(6), trans-ferulic acid(7), trans-isoferulic acid(8), trans-caffeic acid(9), trans-caffeic acid methyl ester(10), caffeic acid ethyl ester(11), trans-cinnamic acid(12), trans-p-methoxycinnamic acid(13), trans-p-hydroxycinnamic acid(14), trans-p-hydroxycinnamic acid methyl ester(15), 2-(3,4-dihydroxyphenethyl) alcohol(16),(p-hydroxyphenyl) propanoic acid(17), coniferaldehyde(18), sinapaldehyde(19), benzyl β-primeveroside(20), 5-(hydroxymethyl) furfural(21), furan-2-carboxylic acid(22), and decanedioic acid(23). Among them, compound 1 is a new benzyl glucoside, compounds 2-4 are new pyranone compounds, compound 5 is a new natural product of pyranone. The NMR data of compounds 5 and 6 are reported for the first time. Compounds 6 and 20 were isolated from the Scrophularia plant for the first time. Compounds 8, 11, 14, 16, 18, 19, 22, and 23 were isolated from this plant for the first time. The in vitro cytotoxic activities of these compounds against three tumor cell lines(HepG2, A549, and 4 T1) were evaluated. The results showed that compounds 10 and 15 showed cytotoxic activities against HepG2 cells with IC_(50) values of(19.46±0.48) μmol·L~(-1) and(46.10±1.21) μmol·L~(-1).

Prediction of Disintegration Time of Traditional Chinese Medicine Tablets Based on Generalized Regression Neural Network / 中国实验方剂学杂志

Objective:This paper constructs a generalized regression neural network （GRNN） model to predict the disintegration time of traditional Chinese medicine （TCM） tablets. Method:Taking Astragali Radix as a model drug， the mixed Astragali Radix powders with different powder properties were prepared by mixing Astragali Radix extract powders with microcrystalline cellulose and lactose， which were made to Astragali Radix tablets by direct compression method. The powder properties of mixed Astragali Radix powders and the disintegration time of Astragali Radix tablets were determined， respectively. The correlation between the original data was eliminated by principal component analysis （PCA）. The principal component factors were used as the input layer of the GRNN model， and the disintegration time was used as the output layer for network training. Finally， the verification group data was used to predict the disintegration time， and the network prediction accuracy was calculated by comparing with the actual value. Result:Three principal component factors were obtained through PCA by analyzing the original nine variables that were correlated with each other （Hausner ratio， true density， tap density， compression degree， angle of repose， bulk density， porosity， water content and total dissolved solids）， which reduced the complexity of the network. The prediction value of the disintegration time based on this prediction method was in good agreement with the actual value， the error of disintegration time was 0.01-1.34 min and the average relative error was 3.16%. Conclusion:Based on the GRNN mathematical model， the physical properties of Astragali Radix extract powders can be used to accurately predict the disintegration time of Astragali Radix tablets， which provides a reference for studying the disintegration time of TCM tablets.

Construction of "2R network pharmacology" research method for effective components of traditional Chinese medicine based on "prediction of dominant components-screening of potential targets" / 中国中药杂志

Chinese traditional medicine compound is the main form of Chinese medicine clinical application. The elucidation of the effective components of traditional Chinese medicine is one of the key scientific issues to promote the modernization of traditional Chinese medicine. At present, there are many research ideas on the effective components of traditional Chinese medicine compounds. By analyzing the current status and existing problems of existing research ideas, the author proposes a "double reduction network pharmacology"(2 R network pharmacology) research method based on "prediction of dominant components-potential target selection". Chemical components with good properties were selected by ADMET property prediction technology, and compared with the blood components and target organ components to determine the dominant components with potential therapeutic effect, that is "reducing constituents"; the potential core regulatory pathway of traditional Chinese medicine compound was enriched by RNA-Seq technology combined with network database, and then the target of traditional Chinese medicine compound was mined based on the signal pathway, that is "reducing targets". To improve the efficiency and accuracy of effective component screening, the network relationship of "component target" was established by the related technology of network pharmacology. The purpose of this study is to provide practical research ideas and methods for clarifying the effective components of traditional Chinese medicine, revealing the law of compatibility of traditional Chinese medicine and clarifying the target of drug action.

Application of FMEA and Bayesian Network in Risk Assessment of Fluidized Bed Granulation of Traditional Chinese Medicine / 中国实验方剂学杂志

Objective:To carry out the risk assessment on the factors in the process of granulation fluidized bed of traditional Chinese medicine(TCM) by using failure model and effect analysis(FMEA) and Bayesian network(BN), in order to effectively control risk factors and improve product quality. Method:The risk analysis of the fluidized bed granulation process was carried out by FMEA and the selected medium risk and high risk factors were taken as the main control points, the corresponding BN was established. The sensitivity analysis was used to screen out the main risk factors affecting particle fluidity, particle size uniformity, solubility and product cleanliness, the occurrence probability of each risk factor was determined by the evidence of unqualified particle quality, finally, taking fluidized bed granulation process of Sanye tablets as an example, the FMEA and BN were combined into the risk assessment process to verify the effectiveness and reliability of the method. Result:Based on the middle and high risk points of fluidized bed process, particle size of raw materials, moisture content and hygroscopicity of raw materials, dosage, concentration and addition amount of binder, cleaning degree and integrity of collection bag, and nozzle position, which were selected by FMEA, a fluidized bed granulation risk network with causality was constructed. Among them, hygroscopicity of raw materials, concentration and addition amount of binder, inlet temperature and atomization pressure were high probability risk factors, and the probability of occurrence were 55%, 63%, 59%and 58%, respectively. According to the Bayesian risk relationship network which controlled Sanye tablets fluidized bed granulation analysis results showed that the P values of inlet temperature, atomization pressure and concentration of binder were 0.003 4, 0.032 6 and 0.041 8, respectively in the regression model of influencing factors and particle size uniformity, indicating that there was a significant correlation between the three factors and the particle quality, which was basically consistent with the conclusion obtained by FMEA-BN method. Conclusion:The combination of FMEA and BN for visualized risk assessment of fluidized bed granulation helps to effectively control the risk factors in the granulation process, reduce product quality risks and provide strong support for the improvement of granulation process of TCM.

Investigation of Compatible Stability of Xingnaojing Injection Based on HPLC Fingerprint and Principal Component Analysis / 中国实验方剂学杂志

Objective:To investigate the compatible stability of Xingnaojing injection in combination with 9 common medicines, and to provide a reference for clinical application of this injection. Method:According to the clinical application, Xingnaojing injection was mixed with 9 common medicines and placed in the room under dark and light conditions for 6 h. The appearance of compatible solutions was observed, and the HPLC fingerprint was analyzed by similarity evaluation and principal component analysis(PCA). Result:There were no significant changes in the appearance of compatibility of Xingnaojing injection and 9 common medicines, including piracetam and sodium chloride injection, sodium chloride injection and others. The similarities of fingerprint among compatibility of Xingnaojing injection and 9 common medicines were >0.98 at 0 h of compatibility, 6 h of placement and 6 h of illumination. The results of PCA showed that 9 groups of compatible solutions were clustered into 2 categories, the compatibility of Xingnaojing injection and 8 groups including piracetam and sodium chloride injection clustered into one category, and the relative peak areas of the characteristic components of Xingnaojing injection did not change significantly after compatibility, the compatibility of Xingnaojing injection and Danshen Chuanxiongqin injection clustered into another category, the relative peak areas of some characteristic components of Xingnaojing injection increased after compatibility of 0 h and 6 h,and it was more obvious after 6 h of illumination. Conclusion:The compatibility of Xingnaojing injection and 8 common medicines including piracetam and sodium chloride injection has good stability, while the compatibility has stability problems after Xingnaojing injection mixed with Danshen Chuanxiongqin injection. It is suggested that clinical attention should be paid to their compatibility and rational combination of medicines.

Material properties and tensile strength prediction model of traditional Chinese medicine tablets based on PCA-RBF neural network / 中国中药杂志

This paper constructs a prediction model of material attribute-tensile strength based on principal component analysis-radial basis neural network( PCA-RBF),in order to predict the formability of traditional Chinese medicine tablets. Firstly,design Expert8. 0 software was used to design the dosage of different types of extracts,the mixture of traditional Chinese medicine with different physical properties was obtained,the powder properties of each extract and the tensile strength of tablets were determined,the correlation of the original input layer data was eliminated by PCA,the new variables unrelated to each other were trained as the input data of RBF neural network,and the tensile strength of the tablets was predicted. The experimental results showed that the PCA-RBF model had a good predictive effect on the tensile strength of the tablet,the minimum relative error was 0. 25%,the maximum relative error was2. 21%,and the average error was 1. 35%,which had a high fitting degree and better network prediction accuracy. This study initially constructed a prediction model of material properties-tensile strength of Chinese herbal tablets based on PCA-RBF,which provided a reference for the establishment of effective quality control methods for traditional Chinese medicine preparations.