A New Methodology for Uncovering the Bioactive Fractions in Herbal Medicine Using the Approach of Quantitative Pattern-Activity Relationship

• Published in: Data Analytics for Traditional Chinese Medicine Research pp. 155 - 172
• Main Authors: Chau, Foo-tim, Xu, Qing-song, Sze, Daniel Man-yuen, Chan, Hoi-yan, Lau, Tsui-yan, Yuan, Da-lin, Ng, Michelle Chun-har, Fan, Kei, Mok, Daniel Kam-wah, Liang, Yi-zeng
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 05.01.2014
• Subjects: Herbal Medicine; Partial Little Square; Partial Little Square Model; Total Antioxidant Capacity; Ursolic Acid
• ISBN: 9783319038001; 3319038001
• DOI: 10.1007/978-3-319-03801-8_9

The Quantitative Pattern-Activity Relationship (QPAR) approach has been proposed recently by us and applied to the herbal medicine Radix Puerariae Lobatae and a related synthetic mixture system. Two different types of data from the chromatographic fingerprint and related bioactivity capacities of the samples were correlated quantitatively. The method thus developed provided a model for predicting total bioactivity from the chromatographic fingerprints and features in the chromatographic profiles responsible for the bioactivity. In this work, we propose a new methodology called QPAR-F here, to provide another piece of information: recommending the bioactive regions to facilitate bioassay-guided fractionation and related studies. QPAR-F makes use of chromatographic profiles instead of individual data points utilized in our previous work. The chromatograms of the system concerned are firstly divided into different regions or related fractions representing different groups of constituents. Then different combinations of these regions using the exhaustive searching strategy are processed by the partial least squares (PLS) methods to build models. The optimal models give smaller errors between the predicted and measured total bioactivity capacities. The performance of the proposed QPAR-F methodology is first evaluated by a known mixture system with combinations with active ingredients. The results confirmed that QPAR-F works very well in predicting the total antioxidant bioactivity capacities and the active regions could be correctly identified. These findings are very helpful in planning the bioassay-guided fractionation. For this data-mining process, only limited chemical and bioactivity information of the original samples or crude extracts are required. No prior knowledge of activities of the fractions under study is needed. The QPAR-F methodology was also applied to the herbal medicine, Radix Puerariae Lobatae and similar predicted models give smaller errors between the predicted and measured total antioxidant bioactivity capacities could be successfully built.