Nutrikinetics: Concept, technologies, applications, perspectives

Trends in Food Science & Technology 26 (2012) 4e13

Review

Nutrikinetics: Concept, technologies, applications, perspectives J.P.M. van Duynhovena,*,c,d, E.J.J. van Velzena,d, J.A. Westerhuisb,d, M. Foltza,d, D.M. Jacobsa,d and A.K. Smildeb,d a

Unilever Research and Development, Spectroscopy, Olivier van Noortlaan 120, 3133 AT Vlaardingen, The Netherlands (Tel.: D31 317482044; fax: D31 317482725; e-mail: [email protected]) b Biosystems Data Analysis, Swammerdam Institute for Life Sciences, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands c Wageningen University, Laboratory of Biophysics, Dreijenlaan 3, 6703HA Wageningen, The Netherlands d Netherlands Metabolomics Centre, Einsteinweg 55, 2333 CC, Leiden, Leiden, The Netherlands

Exposure studies are the first step in predicting bioactivity of phytochemicals in humans. Due to the interaction between phytochemicals, their food matrix, the gut microbiome and the host, the resulting exogenous metabolites in systemic circulation vary largely between individuals. Nutrikinetics is an extension of the classical pharmacokinetic concept with explicit model adaptations. The concept relies on integrated

* Corresponding author. 0924-2244/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tifs.2012.01.004

deployment of metabolic profiling, multi-level data analysis and population-based single compartment modelling. Nutrikinetics is expected to make critical contributions in understanding how phenotypes and the food matrix modulate bioactivity of dietary phytochemicals, in particular when gut microbial bioconversions are involved.

Introduction Although several epidemiological studies have shown associations between intake of selected phytochemical classes and reduced disease risk there are still only a few randomized controlled human intervention studies which successfully showed causal exposureeeffect relationships (Van Ommen et al., 2010). A number of scientific challenges are considered as limiting in establishing exposureeeffect relationships in intervention studies. Firstly, food intake does in many cases not reflect systemic exposure as bioavailability and metabolic fate upon consumption are not taken into account (Spencer, Mohsen, Minihane, & Mathers, 2008). Secondly, in intervention studies phytochemicals are often not used in their original food matrix but extracts thereof and subsequently formulated in various dosage forms. Therefore, assessment of the absorption, distribution, metabolism and excretion (ADME) of phytochemicals is a critical first step in understanding their causality in exerting health effects in humans. ADME studies are generally focussed on the relative and in some cases on the absolute bioavailability and kinetic assessment of isolated nutritive and non-nutritive compounds, mainly secondary plant metabolites. Metabolism studies in particular aim at the discovery and identification of metabolite patterns after exposure to complex plant extracts (Manach, Hubert, Llorach, & Scalbert, 2009; Scalbert et al., 2009). However, in studies aimed at assessment of complex phytochemical ingredients, their interaction with the gut microbiome (Box 1) and the host can result in a wide range of circulating exogenous metabolites (van Duynhoven et al., 2011; Gross et al., 2010; Wikoff et al., 2009), together constituting the food metabolome (Box 1) (van Duynhoven et al., 2011; Holmes et al., 2008a; van Ommen et al., 2008). One can now witness a drive towards quantitative and comprehensive description of the food metabolome (Rezzi, Ramadan, Fay, & Kochhar, 2007; Wishart, 2008a), with the aim to describe total systemic exposure to phytochemicals. For selected cases one has been able to show that the food metabolome indeed reflects dietary

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Box 1. Definitions Human food metabolome: Fraction of exogenous metabolites in human biofluids that are derived from food digestion (Fardet et al., 2008). Nutritional Phenotype: integrated set of genetic, proteomic, metabolomic, functional and behavioural factors that form the basis for assessment of human nutritional status (Van Ommen et al., 2010). Microbiome: The ecological community of commensal, symbiotic, and pathogenic micro-organisms that literally share our body space (Lederberg & Mccray, 2001). Multi-level (ML) Data Analysis: Statistical analysis of hierarchically structured multi-level data. The method can be considered as a multivariate extension of the paired t-test and generates different multivariate submodels for the different sources of variation at the different levels in the data (Van Velzen et al., 2008; Westerhuis, van Velzen, Hoefsloot, & Smilde, 2010). Population-based Nonlinear Mixed Effect Model (NLMEM or mixed model): Statistical model for analyzing repeated (longitudinal) measurements with a nonlinear model. The model considers random individual effects on top of the fixed effect for each model parameter to optimally describe the response variable (e.g. metabolite concentration) for each individual as a nonlinear function of time. Nutridynamics (ND): The study of how food acts on a living organism, including dose-effect responses of dietary interventions. We note that this definition focuses on the in vivo dynamic component of a concept that was originally introduced with a much wider scope (de Vos et al., 2006). Nutrikinetics (NK): Application area of pharmacokinetics that studies the absorption, distribution, metabolism and excretion (ADME) of food compounds or dietary supplements within the human superorganism, including the interactions between the host metabolome and the gut microbiome. It considers the compositional complexity of dietary ingredients, background diet and inter-individual variation and integrates study design, metabolic profiling, variable selection and NLMEM. Nutrikinetic signature: A concise set of quantitative, individualized nutrikinetic parameters that summarizes the ADME characteristics of food compounds (and their metabolites) in human biofluids. Pharmacokinetics (PK): The study of the time course of drug and metabolite levels in different fluids, tissues, and excreta of the body, and of the mathematical relationships to develop models to interpret such data (Gibaldi & Perrier, 2007). Pharmacodynamics (PD): The study of how a drug acts on a living organism, including the pharmacologic response and the duration and magnitude of response observed relative to the concentration of the drug at an active site in the organism (Mosby’s Medical Dictionary, 2009). Pharmacometabonomics: Prediction of the quantitative outcome of a drug intervention in an individual based on a pre-dose mathematical model of their metabolic state (Holmes, Wilson, & Nicholson, 2008b; Nicholson, Wilson, & Lindon, 2011).

intake (Heinzmann et al., 2010; O’Sullivan, Gibney, & Brennan, 2011; Xu et al., 2010). The food metabolome is typically described by metabolic profiles derived from samples obtained at specific time-points (urine, plasma) or timeaveraged (24 h urine) mode. Sofar this has not brought the expected progress in recognizing nutritional phenotypes (Box 1) that differ in systemic exposure of complex phytochemical food ingredients (Scalbert et al., 2009; Walsh, Brennan, Malthouse, Roche, & Gibney, 2006; Winnike, Busby, Watkins, & O’Connell, 2009). Also progress in assessment of food matrix effects on systemic exposure of phytochemicals has been limited. For both areas it has been recognized that one needs to consider the kinetic nature of ADME characteristics of phytochemicals (de Vos, Castenmiller, Hamer, & Brummer, 2006). Pharmacokinetic (PK) approaches for modelling ADME of phytochemicals have been adequate in specific cases (Chen, Yin, Zuo, & Chow, 2005; Seeram et al., 2006) where important differences between nutritional vs. pharmaceutical interventions could be neglected (Gibney et al., 2005). Whereas drugs typically consist of single compounds with known chemical structures, phytochemicals are formulated as mixtures with high Compositional complexity. Discerning the metabolites of phytochemical interventions from those produced by the Dietary background can be challenging, in particular for gut microbial bioconversion products. This can be aggravated by the large Inter-individual variation in metabolic

fate of phytochemicals when gut microbial bioconversions are involved (Gibney et al., 2005; Gross et al., 2010; de Vos et al., 2006). These bottlenecks have been presented in Fig. 1, where it is also shown how these can be overcome by integrating Study design, Metabolic profiling, Variable selection and Population-based modelling. This overall approach has been coined as nutrikinetics (NK, Box 1) (van Duynhoven et al., 2011; van Velzen et al., 2009). In the current review the technological requirements for NK will be explained, as well as its scope and application areas. Future perspectives will be outlined for considering multicompartmental models, and combining NK with nutridynamic (ND) approaches as a means to link dietary exposure to Biological effect (Van Ommen et al., 2010), by accounting for Matrix effects and Nutritional Phenotypes. The nutrikinetics concept Gut metabolism and the concept of the human superorganism Phytochemicals such as polyphenols (Jacobs, Gaudier, van, & Vaughan, 2009; Selma, Espin, & Tomas-Barberan, 2009) can to a certain extent persist to the colon, where they are exposed to the gut microbial community (Crozier, Del Rio, & Clifford, 2010). The resident colonic microbiota can be regarded as a separate compartment within the human system (Fig. 2) that performs functions of which the human host is incapable. These strong and symbiotic

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Fig. 1. Schematic depiction of current bottlenecks (Dietary background, Compositional complexity and Inter-individual variation) in assessment of nutrikinetic signatures for modelling Matrix effects, Nutritional Phenotypes and ultimate Biological effects. The scheme depicts the technological integration of Metabolic profiling, Study design and Variable selection, Study design and Population-based modelling (see corresponding sections in text).

microbiotaehost interactions have led to the recognition of humans as superorganisms, in which the colon operates as a bioreactor with a virtually unlimited metabolic potential (van Duynhoven et al., 2011; Martin et al., 2009). Gut microbiota composition can however be different between humans (Arumugam et al., 2011; Nicholson, 2006) and this may result in strong metabolic variations in response to food intake (Blaut & Clavel, 2007; Li et al., 2008). One-compartmental nutrikinetic models The NK concept was introduced in a human nutritional intervention study to assess in vivo the urinary excretion of gut-mediated polyphenol metabolites after a black tea intervention (van Velzen et al., 2009). In this study, the human superorganism (Goodacre, 2007; Li et al., 2008), was

considered as one compartment with a single inflow (black tea dose), and outflow (urinary excretion). In this representation (Brochot, Toth, & Bois, 2005), the interconnected system of central and peripheral compartments of the gastrointestinal tract was reduced to only one single unit. As is depicted in Fig. 3 the single compartment model lumps several underlying kinetic processes such as gut transit, on-set of microbial conversion (vertical axis, lag), gut microbial conversions and liver and intestinal phase I and II metabolism (horizontal, k). Note that specific kinetic processes such as deconjugation in the upper tract, phase III metabolism and enterohepatic cycling have not been included in this scheme for the sake of simplicity. Fig. 4A illustrates that model adaptations are required to account for dietary background baseline levels assessed in the placebo

Fig. 2. Schematic representation of the metabolic fate of polyphenols in the human superorganism. Within the host, dietary polyphenols and their microbial bioconversion products successively undergo intestinal and liver phase I and II metabolism, biliary secretion, absorption in the systemic circulation, interaction with organs, and excretion in the urine. Reproduced from van Duynhoven et al. (2011).

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model and its first-order disposition function are the basis of NK models that summarize ensembles of kinetic processes within the human superorganism. These disposing processes are usually described by exponential equations whereby the on-set, rate and extent of absorption, elimination and/or excretion across the (lumped) compartment is expressed in the model parameters (van Duynhoven et al., 2011).

Fig. 3. Schematic representation of a lumped, one-compartmental open model (dark grey box) with a single (oral) inflow and (urinary) outflow. This model is a simplified description of kinetic processes occurring within the human superorganism: transit to the colon, gut microbial bioconversions and liver phase I and II metabolism (I/II). The length of the arrows indicates approximate time scales.

group (van Velzen et al., 2009). From the net curve (Fig. 4B) kinetic and excretion parameters can be derived that lump the above kinetic processes. It has been shown that for metabolites formed in the colon these parameters are governed by microbial conversions (van Velzen et al., 2009). For plasma kinetics similar lumped models (Fig. 3) can be build as for urinary excretion (van Duynhoven et al., 2011). The lumped, one-compartmental

Fig. 4. (A) Example of urinary excretion curves of hippuric acid after placebo (blue) and black tea intervention (red). The green line represents the approximated base level of hippuric acid in the tea intervention period. (B) Net treatment function from which rate constant (k), lag time (lag) and total output can be determined. Reprinted in part with permission from van Velzen et al. (2009). Copyright 2009 American Chemical Society. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

Nutrikinetic signatures Inter-individual differences in gut microbial bioconversion capacity within a human population (Cerda, TomasBarberan, & Espin, 2005; Seeram et al., 2006) may partially explain the large variation in the ultimate bioactivity of food compounds (Williamson & Manach, 2005). It has already been shown that inter-individual variations in gut microbial function are partially reflected in the metabolic profiles or metabotypes (Holmes et al., 2008b; Stella et al., 2006). Such metabotypes provide a static readout of the metabolic state of an individual and allow segregation of metabolic phenotypes or subpopulations within a test cohort (Nicholson, 2006; Waldram et al., 2009). Longitudinal data may however be more useful to understand and quantify gut microbial bioconversion capacity (Smilde et al., 2010). We have defined a nutrikinetic signature (Box 1) as a quantitative summary of the disposition of food compounds (and their metabolites) within the human superorganism to provide a functional measure for bioconversion capacity on the individual level (van Velzen et al., 2009). Required technologies Metabolic profiling Our phytochemical intake consists of thousands of compounds with widely varying concentration levels (Wishart, 2008a). When (part of) these phytochemicals are absorbed, secreted and/or digested within the human superorganism, analysis of the human food metabolome (Fardet et al., 2008; Manach et al., 2009; Wishart, 2008a) becomes a major challenge. Metabolic profiling provides an unbiased approach for discovering and identifying nutritional biomarkers (Fave, Draper, & Mathers, 2009; Manach et al., 2009; Rezzi et al., 2007; Wishart, 2008a). Advances in analytical instrumentation now allow for comprehensive and simultaneous metabolic profiling in biological samples (Lindon & Nicholson, 2008). Global metabolic profiling, which non-selectively captures metabolites is typically performed by high-field (>600 MHz) NMR, and has successfully been applied to describe both the food and endogenous metabolome in urine, plasma (Wishart, 2008b), feces (Jacobs et al., 2008) as well as in vitro models for colonic fermentation (Gross et al., 2010). Although NMR-based metabolic profiling is considered as relatively insensitive, it still captures a significant part of the microbially-mediated part of the food metabolome in urine. Thus it has played a key role in developing the concept of

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the human superorganism concept encompassing the symbiotic gut microbiota-host crosstalk (Martin et al., 2009; Nicholson & Lindon, 2008). NMR-based profiling was also instrumental in the first demonstration of the NK concept (van Velzen et al., 2009). Meanwhile, more sensitive MS-based profiling approaches focussing on specific compound classes have been developed to capture less abundant exogenous food metabolites. Sensitivity enhancement is typically achieved by defining sub-metabolomes in a wide-angle hypothesis-driven manner. An example is the focused profiling of phenolic acids by GC-MS (Grun et al., 2008) in plasma, urine, feces and in vitro models, hence capturing a major part of the microbial bioconversion products of polyphenols. The disadvantage of these focused GCeMS profiling methods is the laborious sample pretreatment requiring deconjugation and subsequent derivatisation to increase volatility. Thus most information on conjugative host metabolism is discarded and only relatively small bioconversion products are observed. In comparison, untargeted LC-MS profiling platforms (Sang, Yang, Buckley, & Yang, 2008) are less biased in their sample pre-treatment, capture more and larger conjugated species but encounter bottlenecks in identification of relevant metabolites (Fardet et al., 2008). An intrinsic limitation of current MS-based profiling approaches is that they are mostly semi-quantitative (Fave et al., 2009; Scalbert et al., 2009) and not suitable for making mass-balances between dietary input and metabolic output in urine, plasma and feces. For the latter purpose one needs to take recourse to quantitative targeted LC-MS approaches, tailored to assess a broad range of phytochemicals in plasma and urine (Fave et al., 2009). The requirement of pure exometabolite standards can mostly not be met for conjugated phytochemicals or microbial bioconversion products thereof occurring in urine and plasma. Hence, one often needs to rely on assumptions for metabolite quantification (Garrido, Monagas, Gomez-Cordoves, Martin-Alvarez, & Llorach, 2010). The joint deployment of MS-based metabolic profiling with PK modelling has already been proposed as an approach to assess phytochemical exposure (Lan & Jia, 2010), in the next sections we will describe further integration of study design and data analysis approaches. Study design and variable selection When using untargeted metabolic profiling approaches one will first need to select relevant markers of intake before one can build nutrikinetic models. Phytochemical dosages are however mostly small compared to the interindividual biological variation. These confounding sources of variation can be separated before the actual data analysis according to an analysis of variance (ANOVA). Separation of inter- (phenotypic) and intra-individual variation is at best achieved with cross-over designs where all subjects undergo all treatments and serve as their own control. In this way, the dietary treatment effect is assessed only with regard to the background of the smaller intra-

individual variation. This was illustrated in recent cross-over intervention studies with polyphenol-rich test products, where the observed effects on the food metabolome were small, subtle and highly variable. Nevertheless, using multi-level analysis the major sources of variation were identified at the levels of treatment and subjects (intraand inter-individual variation, respectively) (Van Dorsten et al., 2010; Van Velzen et al., 2008). Hence, variation splitting resulted in an improved identification and significance of the selected dietary exposure markers compared to the traditionally used approaches like PLSDA or OPLSDA (Westerhuis et al., 2010). As in any other statistical approach, it is essential that the multivariate model is properly validated (Westerhuis, Hoefsloot, et al., 2008; Westerhuis, van Velzen, Hoefsloot, & Smilde, 2008). Double-cross validation (2CV), cross-model validation (CMV) and permutation testing have extensively been explored and used for that purpose. The use of negative controls (i.e. known metabolites that are not related or affected by the nutritional treatment) may contribute to further biological validation (Westerhuis, Hoefsloot, et al., 2008; Westerhuis, van Velzen, et al., 2008). Study design and population-based modelling Baseline concentrations of urinary metabolites of phytochemicals can be substantial, but this has been overcome by integrating cross-over design and simultaneous modelling of concentration-time curves in placebo and intervention periods (van Velzen et al., 2009). In this approach, baseline concentrations were simultaneously estimated with other nutrikinetic model parameters. Further model improvement was achieved by introducing a population-based approach where all individual concentration-time curves obtained from a study cohort were fitted simultaneously, typically by means of nonlinear mixed effects modelling (MertensTalcott et al., 2008). Population-based models (Lavielle & Mentre, 2007) estimate population parameters and the variation across individuals. After fitting the model, estimates of the parameters per individual can be obtained, thereby obtaining a model per individual. Especially in situations of sparse sampling and unbalanced data, population-based modelling has a marked advantage over building a separate model for each individual. In such sparse and unbalanced sampling cases, the latter approach will result in unstable and unreliable (individual) parameter estimates. Population-based modelling has been proven useful for assessment of inter-individual variation of nutrikinetic model parameters. Examples reported in the literature showed that the plasma concentration-time curves of drug compounds in rats (Geldof et al., 2007) and humans (Feng et al., 2008) could be well parameterized by nonlinear mixed effects modelling (NLMEM). Even with a sparse sampling design the developed population models showed adequate predictive ability and model stability, as was shown on a dietary polyphenol intervention study (van Velzen, 2010). Despite low plasma concentrations, the sparsity problem, and the

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inter-individual variation, the population models provided adequate descriptions of the concentration-time curves. Hence, population-based modelling offers the ability to generate quantitative nutrikinetic signatures of food components in human test populations both on the individual level as well as on the population level. Moreover, it facilitates the analysis of sparse time course data from unbalanced data or incomplete study designs, which may be important in view of cost and ethical considerations. Applications of nutrikinetics Influence of the food matrix on systemic exposure The food matrix may have a significant effect on the rate, on-set, and extent of absorption of (bioactive) food compounds after oral administration. Firstly, absorption of phytochemicals can be influenced by physico-chemical interactions, which can influence both gastrointestinal transit and absorption rates (Duchateau & Klaffke, 2008). Such interactions have been proposed between dairy and flavonoids (Roowi, Mullen, Edwards, & Crozier, 2009; Urpi-Sarda, Khan, Monagas, Rotches-Ribalta, & Lamuela-Raventos, 2010). Secondly, the food matrix can modulate the activity of the microbiota, and consequentially impact on

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bioavailability, bioconversion, host metabolism and NK of other (functional) food compounds (Clavel et al., 2005; Jacobs et al., 2009). Such a mechanism has been proposed to explain modulation of gut microbial bioconversion of flavonoids by bran (Gu, Rooney, & Prior, 2007). Thirdly, since most phytochemical sources have a rich chemical complexity, mutual interactions between phytochemicals can take place at the level of transporters and metabolising enzymes. This was recently evaluated by nutrikinetic assessment in plasma of polyphenol metabolites originating from black tea and wine/grape extracts (van Velzen, 2010). Phenotyping by means of gut microbial nutrikinetic signatures The nutrikinetic response upon a single oral polyphenol dose typically results in strong inter-individual variation of urinary excretion of gut microbial polyphenol endmetabolites (Daykin et al., 2005; Del Rio et al., 2011; van Duynhoven et al., 2011; van Velzen et al., 2009). Fig. 5 illustrates the variation in cumulative output (A), excretion rate (B) and lag time (C), as derived from lumped single compartment modelling of urinary excretion upon a black tea intervention. In the same intervention study,

Fig. 5. Estimated nutrikinetic quantities obtained from hippuric acid, 4-hydroxyhippuric acid and 1,3-dihydroxyphenyl-2-O-sulphate excretion in urine of 20 volunteers (horizontal axis) upon a single bolus dose intervention of black tea. (A) normalized cumulative (48 h) output, (B) normalized first-order rate constants (ke) and (C) normalized lag times. Detailed explanation of symbols can be found in van Velzen et al. (2009). Copyright 2009 American Chemical Society.

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the systemic (plasma) exposure of the gut-mediated metabolites 5-(30 ,40 -dihydroxyphenyl)-g-valerolactone (M6) and 5-(30 -methoxy-40 -hydroxyphenyl)-g-valerolactone (M2) of black tea was also strongly dependent on the individual (van Duynhoven et al., 2011). These nutrikinetic variations in urinary excretion and plasma circulation were attributed to individual differences in gut microbial bioconversion capacity, thus hold potential for describing the gut microbial contributions to nutritional phenotypes. The association of the nutrikinetic signature of M2 and M6 with the gut microbiome was further examined in a 3-way Spearman’s correlation experiment. Modulation in systemic exposure of M6 was positively correlated with few families of the Clostridium and Actinobacteria clusters. This association was recently confirmed by characterisation of gut microbial strains with polyphenol bioconversion capacity, thus confirming the gut microbial contribution to the nutritional phenotype (Kutschera, Engst, Blaut, & Braune, 2011). However, also for polyphenol metabolites that go directly into host circulation significant inter-individual variations in exposure and absorption rate can be observed (Chen et al., 2005), which implies that inter-individual variations observed for gut-mediated metabolites may not fully be attributed to the gut microbiome. Hence, the current single compartment models need to be extended to account for multicompartment and co-metabolome interactions in the human superorganism. Perspectives Multicompartmental analysis The current NK models particularly adapt to lumped, onecompartmental kinetic studies. We envisage extended NK models for the characterization and parameterization of multicompartmental (co-metabolome) interactions (van Duynhoven et al., 2011). Examples of in vivo multicompartmental studies have been reported earlier and focused on the ADME of quercetin and its metabolites in rats (Chen et al., 2005). Other multicompartmental nutritional studies used metabolic signatures (or metabotypes) instead of kinetic readouts to assess interorganismal and co-metabolome interactions within the human superorganism (Claus et al., 2008; Martin et al., 2008; Martin et al., 2009; Montoliu, Martin, Collino, Rezzi, & Kochhar, 2009). The currently established NK models are actually reduced differential equation systems which provide aggregated descriptions of the disposition of food compounds (and their metabolites) across lumped compartments. The complexity and dimensionality of these NK models will however significantly increase when considering the numerous interconnectivities between the separate compartments of the human superorganism. These multicompartmental interactions can usually not be characterized using merely in vivo human nutritional studies. Additional knowledge to deduce viable estimations of the system properties, can be provided by in vitro nutrikinetic experiments with human microbiota (van Duynhoven et al., 2011). These in vitro models may help to gain preliminary

insights into the utilization of food ingredients by the human gut microflora (Appeldoorn, Vincken, Aura, Hollman, & Gruppen, 2009; Eeckhaut et al., 2008; Gross et al., 2010; Jacobs et al., 2009; Macfarlane & Macfarlane, 2007). A limitation of in vitro models is that events can occur at rather different time scales as for the in vivo situation, and that intermediate metabolites are not absorbed by the host. Animals studies provide more realistic data on general mechanisms and ADME underlying food metabolism (Bolling, Court, Blumberg, & Chen, 2010; Gonthier, Verny, Besson, Remesy, & Scalbert, 2003; Martin et al., 2007) but meet more experimental and ethical constraints. The integration of in vivo nutrikinetic data and knowledge obtained from in vitro experiments and animal studies requires adapted computational solutions like grey modelling (Westerhuis, Derks, Hoefsloot, & Smilde, 2007) for constructing a multicompartmental description of the systems biology model. Exposureeeffect relations: linking nutrikinetics with nutridynamics Dietary interventions generally act within highly regulated homeostatic systems of healthy human individuals. As a consequence, current approaches focussing on disease risk biomarkers are mostly insensitive for detecting the effects of dietary interventions that help to sustain health in human individuals that are already healthy (Van Ommen, Keijer, Heil, & Kaput, 2009). Hence there is a growing awareness that ‘health’ should not just be defined as “the absence of disease”, but is more accurately described as “resilience of homeostatic control”, i.e. the ability to cope with daily challenges without drifting out of the regulated homeostatic/allostatic zone. To investigate the effect of dietary intake in healthy individuals more adequately, challenge tests have been proposed to assess homeostatic regulation of inflammatory, psychological, oxidative and metabolic stress as being the overarching drivers of health (Van Ommen et al., 2009). These considerations may explain why deployment of pharmacodynamics (PD, Box 1) to assess impact of diet and health has met only limited success. Whereas PD has been proven useful in describing the dose-dependent longitudinal effects of drugs on single disease risk related targets or markers (Abdel-Rahman & Kauffman, 2004) it does not recognize the subtle effects of dietary interventions on homeostatic resilience. These intricacies are considered in nutridynamics (ND, Box 1), a concept originally defined within a wide context (de Vos et al., 2006). Besides exposureeeffect relationships of foods, the original nutridynamics definition covered all stages of food related dynamic processes including food processing, dietary intake, and biological effects. Within this broad context, we emphasize dynamic observations of homeostatic resilience as a measure for health status. Here we envisage NK/ND applications where nutrikinetic signatures may be used for characterizing dietary exposure and challenge tests to assess impact on homeostatic resilience. It has already been shown that urinary metabolic

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profiles acquired before a pharmacological intervention can be predictive for its biological effect (Clayton, Baker, Lindon, Everett, & Nicholson, 2009; Nicholson et al., 2011). We foresee that this ‘pharmacometabolomics’ concept (Box 1) can be further empowered by using nutrikinetic signatures instead of static pre-dose metabolic profiles. Such approaches will bring opportunities in personalized nutrition within reach (Nicholson, 2006; van Duynhoven et al., 2011). Acknowledgements We are grateful to the European Commission for their financial support of the GutSystem project (MTKI-CT-2006042786) under the Framework 6 Marie-Curie Transfer of Knowledge Industry-Academia Strategic Partnership scheme. Part of this project was carried out within the research program of the Netherlands Metabolomics Centre (NMC) which is part of the Netherlands Genomics Initiative / Netherlands Organization for Scientific Research. Ursula Garczarek, Ferdi van Dorsten, Sonja Peters, Elaine Vaughan, Rober Kemperman and Guus Duchateau are greatly acknowledged for their many scientific contributions in constructing the NK concept. References Abdel-Rahman, S. M., & Kauffman, R. E. (2004). The integration of pharmacokinetics and pharmacodynamics: understanding doseresponse. Annual Review of Pharmacology and Toxicology, 44, 111e136. Appeldoorn, M. M., Vincken, J. P., Aura, A. M., Hollman, P. C. H., & Gruppen, H. (2009). Procyanidin dimers are metabolized by human microbiota with 2-(3,4-dihydroxyphenyl)acetic acid and 5(3,4-dihydroxyphenyl)-gamma-valerolactone as the major metabolites. Journal of Agricultural and Food Chemistry, 57, 1084e1092. Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., et al. (2011). Enterotypes of the human gut microbiome. Nature, 473, 174e180. Blaut, M., & Clavel, T. (2007). Metabolic diversity of the intestinal microbiota: implications for health and disease. Journal of Nutrition, 137, 751Se755S. Bolling, B. W., Court, M. H., Blumberg, J. B., & Chen, C. Y. O. (2010). The kinetic basis for age-associated changes in quercetin and genistein glucuronidation by rat liver microsomes. Journal of Nutritional Biochemistry, 21, 498e503. Brochot, C., Toth, J., & Bois, F. Y. (2005). Lumping in pharmacokinetics. Journal of Pharmacokinetics and Pharmacodynamics, 32, 719e736. Cerda, B., Tomas-Barberan, F. A., & Espin, J. C. (2005). Metabolism of antioxidant and chemopreventive ellagitannins from strawberries, raspberries, walnuts, and oak-aged wine in humans: identification of biomarkers and individual variability. Journal of Agricultural and Food Chemistry, 53, 227e235. Chen, X., Yin, O. Q. P., Zuo, Z., & Chow, M. S. S. (2005). Pharmacokinetics and modeling of quercetin and metabolites. Pharmaceutical Research, 22, 892e901. Claus, S. P., Tsang, T. M., Wang, Y. L., Cloarec, O., Skordi, E., Martin, F. P., et al. (2008). Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. Molecular Systems Biology, 4, 1e14.

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