Biomedical subjects

Cheng Yiyu

Publications and source records attributed to Cheng Yiyu.

4 recordsLinked to original sources

PubMed2006

Predicting Caco-2 permeability using support vector machine and chemistry development kit.

PURPOSE: To predict Caco-2 permeability is a valuable target for pharmaceutical research. Most of the Caco-2 prediction models are based on commercial or special software which limited their practical value. This study represents the relationship between Caco-2 permeability and molecular descriptors totally based on open source software. METHODS: The Caco-2 prediction model was constructed based on descriptors generated by open source software Chemistry Development Kit (CDK) and a support vector machine (SVM) method. Number of H-bond donors and three molecular surface area descriptors constructed the prediction model. RESULTS: The correlation coefficients (r) of the experimental and predicted Caco-2 apparent permeability for the training set and the test set were 0.88 and 0.85, respectively. CONCLUSION: The results suggest that the SVM method is effective for predicting Caco-2 permeability. Membrane permeability of compounds is determined by number of H-bond donors and molecular surface area properties.

Caco-2 Cells↗

PubMed2005

LC/MS fingerprinting of Shenmai injection: a novel approach to quality control of herbal medicines.

Chromatographic fingerprinting has been recommended as a potential and reliable strategy for the quality control of herbal medicines. Although varieties of chromatographic techniques, particularly HPLC, have been widely employed, hyphenated chromatographic approach has not been sufficiently exploited in chromatographic fingerprinting. In this work, LC/MS fingerprinting of Shenmai injection was developed. Thirty ginsenosides as well as seven ophioponins were selected to construct the LC/MS fingerprint using selective ion monitoring (SIM) mode, while previous HPLC fingerprint [H.J. Zhang, Y.J. Wu, Y.Y. Cheng, J. Pharm. Biomed. Anal. 31 (2003) 175-183] only represents the ginsenosides. Subsequently, the proposed LC/MS fingerprints were applied to identifying the product manufacturers. All the samples were accurately classified based on their LC/MS fingerprints in conjunction with principal components analysis (PCA). This study would be potentially helpful to improve the quality control ability of fingerprinting-based strategy for complex herbal medicines.

Central Nervous System Agents↗

PubMed2003

Analysis of 'SHENMAI' injection by HPLC/MS/MS.

An HPLC/MS/MS method was developed for the analysis of 'SHENMAI' injection, composed of red ginseng and ophiopogon. The constituents of 'SHENMAI' were found to be similar with those of ginseng and 39 ginsenosides were detected. By the studies of MS and MS/MS spectra and the comparison with literature data, most of these ginsenosides were identified. Based on this study, suggestions were put forward to improve the quality control system of 'SHENMAI' injection.

Chromatography, High Pressure Liquid↗

PubMedDate not supplied

Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs.

Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.

Algorithms↗