Elsevier

Food Chemistry

Volume 245, 15 April 2018, Pages 353-363
Food Chemistry

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

https://doi.org/10.1016/j.foodchem.2017.10.102Get rights and content

Highlights

  • After ingestion, phytochemicals (PCs) require time to reach plasma peak (Tmax).

  • Statistical modelling can predict Tmax from molecular mass and lipophilicity (log P).

  • LC-MS analysis of plant extracts yields molecular mass and log P of the PCs present.

  • Combined, functional fingerprints of plant extracts predict Tmax profiles in human.

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.

Introduction

Phytochemicals, also referred to as secondary metabolites, are the non-nutrient compounds in fruits, vegetables and other dietary plants which have been associated with reductions in the risk of major chronic diseases including cancer (Key, 2011), cardiovascular (Dauchet, Amouyel, & Dallongeville, 2009) and neurodegenerative diseases (D’Onofrio et al., 2016). More than 200,000 phytochemical structures have been identified but only a small percentage have been investigated with regard to their application in medicine, making them interesting candidates as pharmaceutically active agents (Hartmann, 2007). The health benefits of phytochemicals have been linked with their capacity to regulate oxidative stress and inflammation (OSI), which occurs as part of normal metabolism, but is also involved in the aetiology of most chronic diseases (Calder et al., 2009). The regulation of OSI by phytochemicals may occur by direct antioxidant activity or by an indirect mechanism via induction of antioxidant stress defence (Selby-Pham et al., 2017). Cells in the human body are continuously exposed to oxidising agents from the environment, foods or those produced by metabolic activities within cells. Maintaining the balance between oxidants and anti-oxidants is crucial for optimal physiological conditions in the body (Calder et al., 2009). Over-production of oxidants can cause OSI and unregulated OSI can damage large biomolecules such as proteins, DNA and lipids, which in turn results in an increased risk of chronic diseases (Kryston, Georgiev, Pissis, & Georgakilas, 2011). Therefore, regulating transient and cumulative OSI associated with daily activities and chronic diseases is important to lower OSI-related mortality.

The bio-efficacy of phytochemicals to protect human health is dependent on their absorption into circulation and delivery to the target cells (D’Archivio, Filesi, Varì, Scazzocchio, & Masella, 2010). However, phytochemicals are only transiently present in circulation after consumption because they are recognised as xenobiotics by the human body (Holst & Williamson, 2008). After consumption of dietary plants, some phytochemicals are absorbed in the small intestine and enter the circulatory system (D’Archivio et al., 2010). These phytochemicals may be modified by the liver and their hepatic metabolites re-enter the circulatory system (D’Archivio et al., 2010). The unabsorbed phytochemicals reach the large intestine and are subjected to structural transformation by the colonic microbiota. These microbial metabolites can also be absorbed via the colon (Holst & Williamson, 2008). The time required for phytochemicals or their metabolites to reach their maximal concentrations in the circulatory system (Tmax) can be an important factor to understand and optimise the health benefits of plant foods.

The importance of Tmax was demonstrated in a recent study where healthy volunteers consumed a strawberry drink two hours before, during, or two hours after a high fat meal (Huang, Park, Edirisinghe, & Burton-Freeman, 2016). The strawberry drink was observed to reduce the OSI associated with the high fat meal only when being consumed at two hours before the meal (Huang et al., 2016). Considering that Tmax of the measured phytochemicals in strawberry was approximately 1–2 h (Sandhu et al., 2016), consumption of the drink two hours before the high fat meal ensured that maximal concentration in the circulatory system coincided with the post-prandial OSI to minimise the OSI damage (measured by plasma concentration of the biomarker interleukin-6) triggered by the meal (Burton-Freeman, 2010).

The bioavailability of phytochemicals is dependent on their chemical structures and dietary intake forms (D’Archivio et al., 2010). We have developed a statistical model, the Phytochemical Absorption Prediction (PCAP) model, to predict Tmax of dietary phytochemicals absorbed in human upper intestine based on their molecular mass, lipophilicity (expressed as log P, the logarithm of the partition coefficient between water and 1-octanol) and dietary intake forms (Selby-Pham, Miller, Howell, Dunshea, & Bennett, 2017). Application of this model allows direct calculation of values of Tmax of phytochemicals using molecular mass and log P. However, the PCAP model can only be applied to individual (known) phytochemicals. In order to expand the practical usefulness of the model, it is necessary that molecular mass and log P of complex mixtures of phytochemicals, reflecting their typical mode of consumption, can be identified. Accordingly, further development of methods to predict the Tmax range arising from uncharacterised phytochemicals mixtures is required. Chemical identification, which presents additional challenges such as the need for a previously purified, synthesised or characterised chemical standard of specific phytochemical, is not required for this purpose.

The retention of compounds on C18 reverse phase columns during liquid chromatography (LC) is controlled by lipophilicity and therefore correlated with log P (Valko, 2004). This feature of reverse phase LC allows for the development of multiple methods to estimate the log P of drug compounds (Valko, 2004) and natural products (Camp, Campitelli, Carroll, Davis, & Quinn, 2013). Further, LC may be coupled with mass spectrometry (MS) so that, in addition to allowing the determination of log P from retention time, MS identifies the accurate molecular mass of the compound, referred to as a ‘molecular feature’, until the chemical identity of the compound is confirmed (Flamini et al., 2013, Tsao and Li, 2013).

The aim of this research was to apply LC-MS methodology to simultaneously determine values of log P and molecular mass of individual phytochemicals present in extracts of selected dietary plants. A further aim was to develop a data processing workflow to convert the LC-MS data output to individual Tmax values of phytochemicals absorbed in the human upper intestine, using the PCAP model. The Tmax values were then used to generate a characteristic ‘functional fingerprint’ which represents the human upper intestinal absorption kinetic profile of the tested plant extract. Finally, validation of predicted functional fingerprints was investigated by comparison with published clinical evidence of plasma Tmax and regulation of OSI, for similar extracts.

Section snippets

Chemicals and reagents

Chemicals and reagents including chloroform, methanol, Na2CO3, gallic acid, formic acid, acetonitrile, l-histidine, (S)-dihydroorotate, shikimate, 4-pyridoxate, 3-hydroxybenzyl alcohol, 2,5-dihydroxybenzoate, 3-hydroxybenzaldehyde, trans-cinnamate, estradiol-17α, deoxycholate, retinoate, oleic acid and heptadecanoate were from Sigma-Aldrich (St Louis, MO, USA). Folin-Ciocalteu reagent was from Merck (Darmstadt, Germany).

Preparation of plant extracts

Fresh forms of dietary plant materials were purchased from local retailers

Proximate analyses of plant extracts

Proximate analyses of eight project extracts and four reference extracts included total solids, total nitrogen, total lipids and total ash (Supplementary Table S1) and mineral profiling (Supplementary Table S2). The total solids of the project extracts varied between 83.35 and 99.65% total weight whilst the total solids of the reference extracts ranged from 98.78 to 99.43% total weight (Supplementary Table S1). The nitrogen contents ranged from 0.9 to 5.49% total weight for the project extracts

Functional fingerprint profiling by LC-MS using log P and molecular mass

This study reported a novel and high-throughput method to obtain a prediction of human upper intestinal absorption of plant extracts, referred to as a ‘functional fingerprint’. Firstly, plant extracts were analysed by LC-MS to identify the log P, molecular mass and relative abundance of the phytochemicals present in the plant mixture. Each combination of log P and molecular mass represents a phytochemical within the plant mixture. The PCAP model (Selby-Pham et al., 2017) was then applied to

Conclusion

Human upper intestinal absorption of plant extracts can be predicted using LC-MS analysis in combination with the statistical PCAP model. The data processing method utilises an untargeted approach that does not rely on compound identification and therefore is not limited by the availability of standards. The predicted absorption profile obtained by this method provides a unique functional fingerprint for each individual plant extract showing the time required for the phytochemical mixtures to

Acknowledgments

This project has been funded by Horticulture Innovation Australia Limited using the Vegetable levy and funds from the Australian Government. Metabolite analysis was conducted at Metabolomics Australia (The University of Melbourne, Australia), a NCRIS initiative under Bioplatforms Australia Pty Ltd.

Conflict of interest

The authors declare no conflict of interests.

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