Prediction of Radix Astragali Immunomodulatory Effect of CD80 Expression from Chromatograms by Quantitative Pattern-Activity Relationship.

Qing-Song Xu,Mary K Lam,Kei Fan,Daniel Man-Yuen Sze,Tsui-Yan Lau,Michelle Chun-Har Ng,Simon K Poon,Foo-Tim Chau,Josiah Poon

doi:10.1155/2017/3923865

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 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.

Highlights

A large pool of medicinal plants from Chinese herbal medicines (CHM) has a long historical clinical practice for more than 2000 years ago
Partial Least Square (PLS) based on the Elastic Net variable selection method increased the biological predictive capability with lower value of Root Mean Squared Errors of Prediction (RMSEP) (11.66) and higher values of Rp2 (0.55) when compared to the models developed by the standard PLS
In future more bioactivity platforms of these key mechanisms of action of Radix Astragali (RA) are required to have a more complete understanding of important compounds that are related to the overall immunomodulatory effects of RA. In this CD80-QPAR study on a commonly used herb RA, we successfully explored and exploited the relationship between the chemical and biological fingerprints to establish a chemometric predictive model

Summary

Introduction

A large pool of medicinal plants from Chinese herbal medicines (CHM) has a long historical clinical practice for more than 2000 years ago. The underlying mechanisms of action of the CHM remain largely unknown except the few examples of taxol [1] for anticancer, artesunate [2] for malaria treatment, and arsenic trioxide [3] for leukemia treatment While these three herbal derived single compounds are responsible for the effective therapies, for most of the other clinically useful CHM, the mechanisms of action have been considered as that of “multicompound multitarget.”. There exists a wide range of possible chemical compounds in each single herb or complex formula that may contribute to the clinical efficacy, but this crucial information is basically unknown at the moment. This lack of understanding of the active compounds and their targets in turn makes the quality control aspect of ensuring the batchto-batch consistency of CHM difficult if not impossible

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