Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship
| Autor: | Mary K. Lam, Simon K. Poon, Michelle Chun-har Ng, Daniel Man-Yuen Sze, Josiah Poon, Foo-Tim Chau, Kei Fan, Tsui-Yan Lau, Qing-Song Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Elastic net regularization Quantitative structure–activity relationship Article Subject Quantitative Structure-Activity Relationship lcsh:Medicine Computational biology Biology General Biochemistry Genetics and Molecular Biology Cell Line 03 medical and health sciences Fingerprint Antigens CD80 Drug Discovery Humans Immunologic Factors Radix Biological data General Immunology and Microbiology Traditional medicine Drug discovery Plant Extracts lcsh:R General Medicine Dendritic Cells Expression (computer science) Astragalus propinquus 030104 developmental biology Gene Expression Regulation B7-1 Antigen Chemical fingerprinting Research Article Drugs Chinese Herbal |
| Zdroj: | BioMed Research International, Vol 2017 (2017) BioMed Research International |
| ISSN: | 2314-6133 |
| DOI: | 10.1155/2017/3923865 |
| Popis: | 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 Rp2 (0.55) when compared to the standard PLS model. This CD80-QPAR platform provides a useful predictive model for unknown RA extract’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. |
| Databáze: | OpenAIRE |
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