A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.
A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.
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DOI：
10.1038/s41598-017-02499-1
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发表时间：
2017-05-23
影响因子：
4.6
通讯作者：
Miura D
中科院分区：
文献类型：
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作者：
Fujimura Y;Kawano C;Maeda-Murayama A;Nakamura A;Koike-Miki A;Yukihira D;Hayakawa E;Ishii T;Tachibana H;Wariishi H;Miura D
尽管了解其化学成分对于准确预测多组分药物、营养保健品和食品的生物活性至关重要，但目前尚无分析方法能够根据多种共存因素的复杂行为轻松预测多组分系统的生物活性。在此，我们提出一种代谢轮廓分析（MP）策略，用于评估含有各种小分子的系统的生物活性。通过一种高通量、非靶向分析方法，获得了来自21个绿茶提取物（GTE）样本组的多种生物活性草药样本的成分轮廓。该方法采用基质辅助激光解吸电离 - 质谱（MALDI - MS）技术，使用1,5 - 二氨基萘（1,5 - DAN）作为光学基质来检测源自GTE的成分。多元统计分析揭示了不同GTE在抗氧化活性、氧自由基吸收能力（ORAC）方面的差异。构建了一个可靠的生物活性预测模型，以便根据其成分平衡来预测不同GTE的ORAC。这种化学计量学方法能够通过多组分而非单一组分信息来评估GTE的生物活性。通过计算对构建预测模型贡献最大的少数选定成分的总丰度，就可以轻松评估生物活性。使用多种生物活性样本组的1,5 - DAN - MALDI - MS - MP代表了一种有前景的策略，可用于筛选具有生物活性预测能力的多组分因素，并为粗多组分系统选择有效的具有生物活性预测能力的化学组合。
Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization–mass spectrometry (MALDI–MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI–MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems.