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|Title:||Prediction of radix astragali immunomodulatory effect of CD80 expression from chromatograms by quantitative pattern-activity relationship||Authors:||Ng, MCH
|Issue Date:||2017||Publisher:||Hindawi Publishing Corporation||Source:||BioMed research international, 2017, 3923865 How to cite?||Journal:||BioMed research international||Abstract:||The current use of a single chemical component as the representative quality control marker of herbal food supplement is inadequate. In this CD80-Quantitative-Pattern-Activity-Relationship (QPAR) study, we built a bioactivity predictive model that can be applicable for complex mixtures. Through integrating the chemical fingerprinting profiles of the immunomodulating herb Radix Astragali (RA) extracts, and their related biological data of immunological marker CD80 expression on dendritic cells, a chemometric model using the Elastic Net Partial Least Square (EN-PLS) algorithm was established. The EN-PLS algorithm increased the biological predictive capability with lower value of RMSEP (11.66) and higher values of R-p(2) (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RAextract's bioactivities using the chemical fingerprint inputs. Furthermore, this bioactivity prediction platform facilitates identification of key bioactivity-related chemical components within complex mixtures for future drug discovery and understanding of the batch-to-batch consistency for quality clinical trials.||URI:||http://hdl.handle.net/10397/76591||ISSN:||2314-6133||EISSN:||2314-6141||DOI:||10.1155/2017/3923865||Rights:||Copyright © 2017 Michelle Chun-har Ng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following article: Michelle Chun-har Ng, Tsui-yan Lau, Kei Fan, et al., “Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship,” BioMed Research International, vol. 2017, Article ID 3923865, 11 pages, 2017 is available at https://doi.org/10.1155/2017/3923865.
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