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Fujimura Y, Kawano C, Maeda-Murayama A, et al. A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations. Sci Rep. 2017;7(1):2257doi: 10.1038/s41598-017-02499-1.
Fujimura, Y., Kawano, C., Maeda-Murayama, A., Nakamura, A., Koike-Miki, A., Yukihira, D., Hayakawa, E., Ishii, T., Tachibana, H., Wariishi, H., & Miura, D. (2017). A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations. Scientific reports, 7(1), 2257. https://doi.org/10.1038/s41598-017-02499-1
Fujimura, Yoshinori, et al. "A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations." Scientific reports vol. 7,1 (2017): 2257. doi: https://doi.org/10.1038/s41598-017-02499-1
Fujimura Y, Kawano C, Maeda-Murayama A, Nakamura A, Koike-Miki A, Yukihira D, Hayakawa E, Ishii T, Tachibana H, Wariishi H, Miura D. A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations. Sci Rep. 2017 May 23;7(1):2257. doi: 10.1038/s41598-017-02499-1. PMID: 28536476; PMCID: PMC5442154.
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Sci Rep. 2017 May 23;7(1):2257. doi: 10.1038/s41598-017-02499-1.

A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.

Scientific reports

Yoshinori Fujimura, Chihiro Kawano, Ayaka Maeda-Murayama, Asako Nakamura, Akiko Koike-Miki, Daichi Yukihira, Eisuke Hayakawa, Takanori Ishii, Hirofumi Tachibana, Hiroyuki Wariishi, Daisuke Miura

Affiliations

  1. Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. [email protected].
  2. Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
  3. Okinawa Institute of Science, Technology Graduate University, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa, 904-0495, Japan.
  4. Faculty of Agriculture, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan.
  5. Faculty of Arts and Science, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581, Japan.
  6. Innovation Center for Medical Redox Navigation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. [email protected].

PMID: 28536476 PMCID: PMC5442154 DOI: 10.1038/s41598-017-02499-1

Abstract

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.

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