Dietary medium chain fatty acid supplementation leads to reduced VLDL lipolysis and uptake rates in comparison to linoleic acid supplementation.

Dietary medium chain fatty acids (MCFA) and linoleic acid follow different metabolic routes, and linoleic acid activates PPAR receptors. Both these mechanisms may modify lipoprotein and fatty acid metabolism after dietary intervention. Our objective was to investigate how dietary MCFA and linoleic acid supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. In a randomized double blind cross-over trial, 12 male subjects (age 51±7 years; BMI 28.5±0.8 kg/m2), were divided into 2 groups according to waist-hip ratio. They were supplemented with 60 grams/day MCFA (mainly C8:0, C10:0) or linoleic acid for three weeks, with a wash-out period of six weeks in between. Lipoprotein subclasses were measured using HPLC. Lipoprotein and fatty acid metabolism were studied using a combination of several stable isotope tracers. Lipoprotein and tracer data were analyzed using computational modeling. Lipoprotein subclass concentrations in the VLDL and LDL range were significantly higher after MCFA than after linoleic acid intervention. In addition, LDL subclass concentrations were higher in lower body obese individuals. Differences in VLDL metabolism were found to occur in lipoprotein lipolysis and uptake, not production; MCFAs were elongated intensively, in contrast to linoleic acid. Dietary MCFA supplementation led to a less favorable lipoprotein profile than linoleic acid supplementation. These differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates.

Diagnostic markers based on a computational model of lipoprotein metabolism.

BACKGROUND: Dyslipidemia is an important risk factor for cardiovascular disease and type II diabetes. Lipoprotein diagnostics, such as LDL cholesterol and HDL cholesterol, help to diagnose these diseases. Lipoprotein profile measurements could improve lipoprotein diagnostics, but interpretational complexity has limited their clinical application to date. We have previously developed a computational model called Particle Profiler to interpret lipoprotein profiles. In the current study we further developed and calibrated Particle Profiler using subjects with specific genetic conditions. We subsequently performed technical validation and worked at an initial indication of clinical usefulness starting from available data on lipoprotein concentrations and metabolic fluxes. Since the model outcomes cannot be measured directly, the only available technical validation was corroboration. For an initial indication of clinical usefulness, pooled lipoprotein metabolic flux data was available from subjects with various types of dyslipidemia. Therefore we investigated how well lipoprotein metabolic ratios derived from Particle Profiler distinguished reported dyslipidemic from normolipidemic subjects. RESULTS: We found that the model could fit a range of normolipidemic and dyslipidemic subjects from fifteen out of sixteen studies equally well, with an average 8.8% ± 5.0% fit error; only one study showed a larger fit error. As initial indication of clinical usefulness, we showed that one diagnostic marker based on VLDL metabolic ratios better distinguished dyslipidemic from normolipidemic subjects than triglycerides, HDL cholesterol, or LDL cholesterol. The VLDL metabolic ratios outperformed each of the classical diagnostics separately; they also added power of distinction when included in a multivariate logistic regression model on top of the classical diagnostics. CONCLUSIONS: In this study we further developed, calibrated, and corroborated the Particle Profiler computational model using pooled lipoprotein metabolic flux data. From pooled lipoprotein metabolic flux data on dyslipidemic patients, we derived VLDL metabolic ratios that better distinguished normolipidemic from dyslipidemic subjects than standard diagnostics, including HDL cholesterol, triglycerides and LDL cholesterol. Since dyslipidemias are closely linked to cardiovascular disease and diabetes type II development, lipoprotein metabolic ratios are candidate risk markers for these diseases. These ratios can in principle be obtained by applying Particle Profiler to a single lipoprotein profile measurement, which makes clinical application feasible.

Review of the clinical development of alipogene tiparvovec gene therapy for lipoprotein lipase deficiency.

Alipogene tiparvovec (AAV1-LPL(S447X)) gene therapy is developed to prevent complications and decrease the clinical morbidity of lipoprotein lipase deficiency (LPLD). LPLD is an autosomal recessive disease associated with severe hypertriglyceridemia (hyperTG), severe chylomicronaemia, and low HDL. Acute pancreatitis, the most frequent serious clinical LPLD complication, is a complex and heterogeneous inflammatory condition having many causes including hyperTG and chylomicronaemia. In many patients, low fat diet and currently available lipid lowering drugs are ineffective to prevent hyperTG or pancreatitis in LPLD. The clinical development program of alipogene tiparvovec includes observational studies as well as phase I/II and II/III clinical trials. Pooled data are collected on safety and efficacy issues, including the incidence of pancreatitis.

New in vitro dermal absorption database and the prediction of dermal absorption under finite conditions for risk assessment purposes.

Most QSARs for dermal absorption predict the permeability coefficient, K(p), of a molecule, which is valid for infinite dose conditions. In practice, dermal exposure mostly occurs under finite dose conditions. Therefore, a simple model to predict finite dose dermal absorption from infinite dose data (K(p) and lag time) and the stratum corneum/water partition coefficient (K(SC,W)) was developed. To test the model, a series of in vitro dermal absorption experiments was performed under both infinite and finite dose conditions using acetic acid, benzoic acid, bis(2-ethylhexyl)phthalate, butoxyethanol, cortisone, decanol, diazinone, 2,4-dichlorophenol, ethacrynic acid, linolenic acid, octylparaben, oleic acid, propylparaben, salicylic acid and testosterone. For six substances, the predicted relative dermal absorption was not statistically different from the measured value. For all other substances, measured absorption was overpredicted by the model, but most of the overpredictions were still below the European default absorption value. In conclusion, our finite dose prediction model provides a useful and cost-effective estimate of dermal absorption, to be used in risk assessment for non-volatile substances dissolved in water at non-irritating concentrations.

Systematic construction of a conceptual minimal model of plasma cholesterol levels based on knockout mouse phenotypes.

Elevated plasma cholesterol, a well-known risk factor for cardiovascular diseases, is the result of the activity of many genes and their encoded proteins in a complex physiological network. We aim to develop a minimal kinetic computational model for predicting plasma cholesterol levels. To define the scope of this model, it is essential to discriminate between important and less important processes influencing plasma cholesterol levels. To this end, we performed a systematic review of mouse knockout strains and used the resulting dataset, named KOMDIP, for the identification of key genes that determine plasma cholesterol levels. Based on the described phenotype of mouse knockout models, 36 of the 120 evaluated genes were marked as key genes that have a pronounced effect on the plasma cholesterol concentration. The key genes include well-known genes, e.g., Apoe and Ldlr, as well as genes hardly linked to cholesterol metabolism so far, e.g., Plagl2 and Slc37a4. Based on the catalytic function of the genes, a minimal conceptual model was defined. A comparison with nine conceptual models from literature revealed that each of the individual published models is less complete than our model. Concluding, we have developed a conceptual model that can be used to develop a physiologically based kinetic model to quantitatively predict plasma cholesterol levels.

Developing computational model-based diagnostics to analyse clinical chemistry data.

This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data. These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large amounts of data that are difficult to interpret at first sight, but have high diagnostic potential. Computational models aid interpretation and make the data accessible for clinical diagnosis. We discuss the issues that a modeller has to take into account during the design, construction and evaluation phases of model development. We use the example of Particle Profiler development, a model-based diagnostic tool for lipoprotein disorders, as a case study, to illustrate our considerations. The case study also offers techniques for efficient model formulation, model calculation, workflow structuring and quality control.

Nutritional systems biology modeling: from molecular mechanisms to physiology.

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

Improved cholesterol phenotype analysis by a model relating lipoprotein life cycle processes to particle size.

Increased plasma cholesterol is a known risk factor for cardiovascular disease. Lipoprotein particles transport both cholesterol and triglycerides through the blood. It is thought that the size distribution of these particles codetermines cardiovascular disease risk. New types of measurements can determine the concentration of many lipoprotein size-classes but exactly how each small class relates to disease risk is difficult to clear up. Because relating physiological process status to disease risk seems promising, we propose investigating how lipoprotein production, lipolysis, and uptake processes depend on particle size. To do this, we introduced a novel model framework (Particle Profiler) and evaluated its feasibility. The framework was tested using existing stable isotope flux data. The model framework implementation we present here reproduced the flux data and derived lipoprotein size pattern changes that corresponded to measured changes. It also sensitively indicated changes in lipoprotein metabolism between patient groups that are biologically plausible. Finally, the model was able to reproduce the cholesterol and triglyceride phenotype of known genetic diseases like familial hypercholesterolemia and familial hyperchylomicronemia. In the future, Particle Profiler can be applied for analyzing detailed lipoprotein size profile data and deriving rates of various lipolysis and uptake processes if an independent production estimate is given.

Use of physiologically based biokinetic (PBBK) modeling to study estragole bioactivation and detoxification in humans as compared with male rats.

The extent of bioactivation of the herbal constituent estragole to its ultimate carcinogenic metabolite 1'-sulfooxyestragole depends on the relative levels of bioactivation and detoxification pathways. The present study investigated the kinetics of the metabolic reactions of both estragole and its proximate carcinogenic metabolite 1'-hydroxyestragole in humans in incubations with relevant tissue fractions. Based on the kinetic data obtained a physiologically based biokinetic (PBBK) model for estragole in human was defined to predict the relative extent of bioactivation and detoxification at different dose levels of estragole. The outcomes of the model were subsequently compared with those previously predicted by a PBBK model for estragole in male rat to evaluate the occurrence of species differences in metabolic activation. The results obtained reveal that formation of 1'-oxoestragole, which represents a minor metabolic route for 1'-hydroxyestragole in rat, is the main detoxification pathway of 1'-hydroxyestragole in humans. Due to a high level of this 1'-hydroxyestragole oxidation pathway in human liver, the predicted species differences in formation of 1'-sulfooxyestragole remain relatively low, with the predicted formation of 1'-sulfooxyestragole being twofold higher in human compared with male rat, even though the formation of its precursor 1'-hydroxyestragole was predicted to be fourfold higher in human. Overall, it is concluded that in spite of significant differences in the relative extent of different metabolic pathways between human and male rat there is a minor influence of species differences on the ultimate overall bioactivation of estragole to 1'-sulfooxyestragole.

Quercetin increases the bioavailability of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) in rats.

This study investigates whether the previous observation that quercetin increases the transport of PhIP through Caco-2 monolayers in vitro could be confirmed in an in vivo rat model. Co-administration of 1.45 micromol PhIP/kg bw and 30 micromol quercetin/kg bw significantly increased the blood AUC(0-8h) of PhIP in rats to 131+/-14% of the AUC(0-8h) for rats dosed with PhIP alone. Significantly increased blood PhIP levels were detected at 15, 30, 45 and 180 min. At 4 and 8h post-dosing a difference in the PhIP levels in the blood between the two treatment groups was no longer observed. In vitro and in silico modeling of PhIP transport using Caco-2 cells and a previously described kinetic model for PhIP transport revealed that the relative increase in PhIP transport caused by quercetin is dependent on the concentration of the two compounds. When substituting the PhIP and quercetin concentrations used in the in vivo experiment in the kinetic model, an effect of quercetin on PhIP transport was predicted that matches the actual effect of 131% observed in vivo. It is concluded that quercetin increases the bioavailability of the pro-carcinogen PhIP in rats pointing at a potential adverse effect of this supposed beneficial food ingredient.

A network biology model of micronutrient related health.

Micronutrients are involved in specific biochemical pathways and have dedicated functions in the body, but they are also interconnected in complex metabolic networks, such as oxidative-reductive and inflammatory pathways and hormonal regulation, in which the overarching function is to optimise health. Post-genomic technologies, in particular metabolomics and proteomics, both of which are appropriate for plasma samples, provide a new opportunity to study the metabolic effects of micronutrients in relation to optimal health. The study of micronutrient-related health status requires a combination of data on markers of dietary exposure, markers of target function and biological response, health status metabolites, and disease parameters. When these nutrient-centred and physiology/health-centred parameters are combined and studied using a systems biology approach with bioinformatics and multivariate statistical tools, it should be possible to generate a micronutrient phenotype database. From this we can explore external factors that define the phenotype, such as lifestage and lifestyle, and the impact of genotype, and the results can also be used to define micronutrient requirements and provide dietary advice. New mechanistic insights have already been developed using biological network models, for example genes and protein-protein interactions in the aetiology of type 2 diabetes mellitus. It is hoped that the challenge of applying this approach to micronutrients will, in time, result in a change from micronutrient oriented to a health oriented views and provide a more holistic understanding of the role played by multiple micronutrients in the maintenance of homeostasis and prevention of chronic disease, for example through their involvement in oxidation and inflammation.

A physiologically based biokinetic (PBBK) model for estragole bioactivation and detoxification in rat.

The present study defines a physiologically based biokinetic (PBBK) model for the alkenylbenzene estragole in rat based on in vitro metabolic parameters determined using relevant tissue fractions, in silico derived partition coefficients, and physiological parameters derived from the literature. The model consists of eight compartments including liver, lung and kidney as metabolizing compartments, and additional compartments for fat, arterial blood, venous blood, rapidly perfused tissue and slowly perfused tissue. Evaluation of the model was performed by comparing the PBBK predicted dose-dependent formation of the estragole metabolites 4-allylphenol and 1'-hydroxyestragole glucuronide to literature reported levels of these metabolites, which were demonstrated to be in the same order of magnitude. With the model obtained the relative extent of bioactivation and detoxification of estragole at different oral doses was examined. At low doses formation of 4-allylphenol, leading to detoxification, is observed to be the major metabolic pathway, occurring mainly in the lung and kidney due to formation of this metabolite with high affinity in these organs. Saturation of this metabolic pathway in the lung and kidney leads to a relative increase in formation of the proximate carcinogenic metabolite 1'-hydroxyestragole, occurring mainly in the liver. This relative increase in formation of 1'-hydroxyestragole leads to a relative increase in formation of 1'-hydroxyestragole glucuronide and 1'-sulfooxyestragole the latter being the ultimate carcinogenic metabolite of estragole. These results indicate that the relative importance of different metabolic pathways of estragole may vary in a dose-dependent way, leading to a relative increase in bioactiviation of estragole at higher doses.

Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS: compound class targeting in a metabolomics workflow.

We report a sensitive, generic method for quantitative profiling of bile acids and other endogenous metabolites in small quantities of various biological fluids and tissues. The method is based on a straightforward sample preparation, separation by reversed-phase high performance liquid-chromatography mass spectrometry (HPLC-MS) and electrospray ionisation in the negative ionisation mode (ESI-). Detection is performed in full scan using the linear ion trap Fourier transform mass spectrometer (LTQ-FTMS) generating data for many (endogenous) metabolites, not only bile acids. A validation of the method in urine, plasma and liver was performed for 17 bile acids including their taurine, sulfate and glycine conjugates. The method is linear in the 0.01-1 microM range. The accuracy in human plasma ranges from 74 to 113%, in human urine 77 to 104% and in mouse liver 79 to 140%. The precision ranges from 2 to 20% for pooled samples even in studies with large number of samples (n>250). The method was successfully applied to a multi-compartmental APOE*3-Leiden mouse study, the main goal of which was to analyze the effect of increasing dietary cholesterol concentrations on hepatic cholesterol homeostasis and bile acid synthesis. Serum and liver samples from different treatment groups were profiled with the new method. Statistically significant differences between the diet groups were observed regarding total as well as individual bile acid concentrations.

QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish.

Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of Chemicals (REACH). The objective of the present study was to generate in vitro acute toxicity data that could be used to develop a QSAR model to describe acute in vivo toxicity of chlorinated alkanes. Cytotoxicity of a series of chlorinated alkanes to Chinese hamster ovary (CHO) cells was observed at concentrations similar to those that have been shown previously to be toxic to fish. Strong correlations exist between the acute in vitro toxicity of the chlorinated alkanes and (i) hydrophobicity [modeled by the calculated log K ow (octanol-water partition coefficient); r (2) = 0.883 and r int (2) = 0.854] and (ii) in vivo acute toxicity to fish ( r (2) = 0.758). A QSAR model has been developed to predict in vivo acute toxicity to fish, based on the in vitro data and even on in silico log K ow data only. The developed QSAR model is applicable to chlorinated alkanes with up to 10 carbon atoms, up to eight chlorine atoms, and log K ow values lying within the range from 1.71 to 5.70. Out of the 100204 compounds on the European Inventory of Existing Chemicals (EINECS), our QSAR model covers 77 (0.1%) of them. Our findings demonstrate that in vitro experiments and even in silico calculations can replace animal experiments in the prediction of the acute toxicity of chlorinated alkanes.

Transcriptomics analysis of interactive effects of benzene, trichloroethylene and methyl mercury within binary and ternary mixtures on the liver and kidney following subchronic exposure in the rat.

The present research aimed to study the interaction of three chemicals, methyl mercury, benzene and trichloroethylene, on mRNA expression alterations in rat liver and kidney measured by microarray analysis. These compounds were selected based on presumed different modes of action. The chemicals were administered daily for 14 days at the Lowest-Observed-Adverse-Effect-Level (LOAEL) or at a two- or threefold lower concentration individually or in binary or ternary mixtures. The compounds had strong antagonistic effects on each other's gene expression changes, which included several genes encoding Phase I and II metabolizing enzymes. On the other hand, the mixtures affected the expression of "novel" genes that were not or little affected by the individual compounds. The three compounds exhibited a synergistic interaction on gene expression changes at the LOAEL in the liver and both at the sub-LOAEL and LOAEL in the kidney. Many of the genes induced by mixtures but not by single compounds, such as Id2, Nr2f6, Tnfrsf1a, Ccng1, Mdm2 and Nfkb1 in the liver, are known to affect cellular proliferation, apoptosis and tissue-specific function. This indicates a shift from compound specific response on exposure to individual compounds to a more generic stress response to mixtures. Most of the effects on cell viability as concluded from transcriptomics were not detected by classical toxicological endpoints illustrating the benefit of increased sensitivity of assessing gene expression profiling. These results emphasize the benefit of applying toxicogenomics in mixture interaction studies, which yields biomarkers for joint toxicity and eventually can result in an interaction model for most known toxicants.

Predicted serum folate concentrations based on in vitro studies and kinetic modeling are consistent with measured folate concentrations in humans.

The nutritional quality of new functional or fortified food products depends on the bioavailability of the nutrient(s) in the human body. Bioavailability is often determined in human intervention studies by measurements of plasma or serum profiles over a certain time period. These studies are time and cost consuming and often appear to lack an optimal study design, leading to follow-up intervention trials. Therefore, an alternative approach is needed that will optimize the development of new products. This study describes an approach to predict human serum concentrations after the consumption of (fortified) food products. The concept is based on the integration of in vitro results with kinetic modeling. As a case study, human serum folate concentrations were predicted after the consumption of folate-fortified milk products for 4 wk. Oral bioavailability was investigated using a step-wise approach in which luminal bioaccessibility and intestinal absorption were independently evaluated. Subsequently, these in vitro data were integrated in a kinetic mathematical (in silico) model to predict serum folate concentrations after the intake of a single dose and during long-term consumption. This approach was evaluated in comparison to a human intervention study in which folic acid-fortified milk products were tested for their effect on serum folate concentrations. A high predictive quality of this alternative in vitro/in silico approach was demonstrated. Finally, this methodology was applied to predict serum folate concentrations after intake of different fortified milk products for 4 wk, showing its benefits for the development of new nutritional products.

Quantum chemistry based quantitative structure-activity relationships for modeling the (sub)acute toxicity of substituted mononitrobenzenes in aquatic systems.

Fifteen experimental literature data sets on the acute toxicity of substituted nitrobenzenes to algae (Scenedesmus obliquus, Chlorella pyrenoidosa, C. vulgaris), daphnids (Daphnia magna, D. carinata), fish (Cyprinus carpio, Poecilia reticulata), protozoa (Tetrahymena pyriformis), bacteria (Phosphobacterium phosphoreum), and yeast (Saccharomyces cerevisiae) were used to establish quantum chemistry based quantitative structure-activity relationships (QSARs). The logarithm of the octanol/water partition coefficient, log Kow, and the energy of the lowest unoccupied molecular orbital, Elumo, were used as descriptors. Suitable QSAR models (0.65 < r2 < 0.98) to predict acute toxicity of substituted mononitrobenzenes to protozoa, fish, daphnids, yeast, and algae have been derived. The log Kow was a sufficient descriptor for all cases, with the additional Elumo descriptor being required only for algae. The QSARs were found to be valid for neutral substituted mononitrobenzenes with no -OH, -COOH, or -CN substituents attached directly to the ring. From the 100,196 European Inventory of Existing Commercial Substances (EINECS), 497 chemicals were identified that fit the selection criteria for the established QSARs. Based on these results, an advisory tool has been developed that directs users to the appropriate QSAR model to apply for various types of organisms within specified log Kow ranges. Using this tool, it is possible to obtain a good indication of the toxicity of a large set of EINECS chemicals and newly developed substituted mononitrobenzenes to five different organisms without the need for additional experimental testing.

An in vitro and in silico study on the flavonoid-mediated modulation of the transport of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) through Caco-2 monolayers.

The present study describes the effect of different flavonoids on the absorption of the pro-carcinogen PhIP through Caco-2 monolayers and the development of an in silico model describing this process taking into account passive diffusion and active transport of PhIP. Various flavonoids stimulated the apical to basolateral PhIP transport. Using the in silico model for flavone, kaempferol and chrysoeriol, the apparent Ki value for inhibition of the active transport to the apical side was estimated to be below 53 muM and for morin, robinetin and taxifolin between 164 and 268 microM. For myricetin, luteolin, naringenin and quercetin, the apparent Ki values were determined more accurately and amounted to 37.3, 12.2, 11.7 and 5.6 microM respectively. Additional experiments revealed that the apical to basolateral PhIP transport was also increased in the presence of a typical BCRP or MRP inhibitor with apparent Ki values in the same range as those of the flavonoids. This observation together with the fact that flavonoids are known to be inhibitors of MRPs and BCRP, corroborates that inhibition of these apical membrane transporters is involved in the flavonoid-mediated increased apical to basolateral PhIP transport. Based on the apparent Ki values obtained, it is concluded that the flavonols, at the levels present in the regular Western diet, are capable of stimulating the transport of PhIP through Caco-2 monolayers from the apical to the basolateral compartment. This points to flavonoid-mediated stimulation of the bioavailability of PhIP and, thus, a possible adverse effect of these supposed beneficial food ingredients.

Association between consumption of cruciferous vegetables and condiments and excretion in urine of isothiocyanate mercapturic acids.

A high intake of cruciferous vegetables is associated with a reduced risk of cancer and cardiovascular diseases. This protective effect has been linked to isothiocyanates, enzymatic hydrolysis products of glucosinolates. In this study, the metabolic fate of glucosinolates and isothiocyanates after ingestion of 19 different cruciferous vegetables was studied in three male subjects. After the consumption of 13 cruciferous vegetables (glucosinolate content, 0.01-0.94 mmol/kg) and six condiments (isothiocyanate content, 0.06-49.3 mmol/kg), eight different isothiocyanate mercapturic acids were determined in urine samples. Excretion levels after the consumption of raw vegetables and condiments were higher (bioavailability, 8.2-113%) as compared to cooked vegetables (bioavailability, 1.8-43%), but the excretion rate was similar (t1/2=2.1-3.9 h). Isothiocyanates in urine remain longer at a nonzero level after the consumption of glucosinolates from cooked vegetables, as compared to raw vegetables and condiments, and maximal levels in urine were reached about 4 h later. Isothiocyanate mercapturic acids can be used as a biomarker to reflect the active dose of isothiocyanates absorbed.