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 journalArticleResearchpeer-review

6 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 (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 ‘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 languageEnglish
Pages (from-to)353-363
Number of pages11
JournalFood Chemistry
Volume245
DOIs
Publication statusPublished - 15 Apr 2018
Externally publishedYes

Keywords

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

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