[Chemical constituents of Scrophulariae Radix and their antitumor activities in vitro].

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-ß-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 ß-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).

[Construction of "2R network pharmacology" research method for effective components of traditional Chinese medicine based on "prediction of dominant components-screening of potential targets"].

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.

[Material properties and tensile strength prediction model of traditional Chinese medicine tablets based on PCA-RBF neural network].

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.