Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures

Sophie N.B. Selby-Pham; Kate S. Howell; Frank R. Dunshea; Joel Ludbey; Adrian Lutz; Louise Bennett

doi:10.1016/j.foodchem.2017.10.102

Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures

Sophie N.B. Selby-Pham, Kate S. Howell, Frank R. Dunshea, Joel Ludbey, Adrian Lutz, Louise Bennett

Research output: Contribution to journal › Article › Research › peer-review

12 Citations (Scopus)

Abstract

A diet rich in phytochemicals confers benefits for health by reducing the risk of chronic diseases via regulation of oxidative stress and inflammation (OSI). For optimal protective bio-efficacy, the time required for phytochemicals and their metabolites to reach maximal plasma concentrations (T_max) should be synchronised with the time of increased OSI. A statistical model has been reported to predict T_max of individual phytochemicals based on molecular mass and lipophilicity. We report the application of the model for predicting the absorption profile of an uncharacterised phytochemical mixture, herein referred to as the ‘functional fingerprint’. First, chemical profiles of phytochemical extracts were acquired using liquid chromatography mass spectrometry (LC-MS), then the molecular features for respective components were used to predict their plasma absorption maximum, based on molecular mass and lipophilicity. This method of ‘functional fingerprinting’ of plant extracts represents a novel tool for understanding and optimising the health efficacy of plant extracts.

Original language	English
Pages (from-to)	353-363
Number of pages	11
Journal	Food Chemistry
Volume	245
DOIs	https://doi.org/10.1016/j.foodchem.2017.10.102
Publication status	Published - 15 Apr 2018
Externally published	Yes

Keywords

LC-MS
Log P
Molecular mass
Phytochemical absorption prediction model
Secondary metabolites
Tmax
Untargeted profiling

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10.1016/j.foodchem.2017.10.102

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title = "Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures",

abstract = "A diet rich in phytochemicals confers benefits for health by reducing the risk of chronic diseases via regulation of oxidative stress and inflammation (OSI). For optimal protective bio-efficacy, the time required for phytochemicals and their metabolites to reach maximal plasma concentrations (Tmax) should be synchronised with the time of increased OSI. A statistical model has been reported to predict Tmax of individual phytochemicals based on molecular mass and lipophilicity. We report the application of the model for predicting the absorption profile of an uncharacterised phytochemical mixture, herein referred to as the {\textquoteleft}functional fingerprint{\textquoteright}. First, chemical profiles of phytochemical extracts were acquired using liquid chromatography mass spectrometry (LC-MS), then the molecular features for respective components were used to predict their plasma absorption maximum, based on molecular mass and lipophilicity. This method of {\textquoteleft}functional fingerprinting{\textquoteright} of plant extracts represents a novel tool for understanding and optimising the health efficacy of plant extracts.",

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AU - Howell, Kate S.

AU - Dunshea, Frank R.

AU - Ludbey, Joel

AU - Lutz, Adrian

AU - Bennett, Louise

PY - 2018/4/15

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KW - LC-MS

KW - Log P

KW - Molecular mass

KW - Phytochemical absorption prediction model

KW - Secondary metabolites

KW - Tmax

KW - Untargeted profiling

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JO - Food Chemistry

JF - Food Chemistry

ER -

Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures

Abstract

Keywords

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