Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data

Estimation of volatile organic compound exposure concentrations and time to reach a specific dermal absorption using physiologically based pharmacokinetic modeling

A procedure was proposed to estimate dermal exposures based on a physiologically based pharmacokinetic (PBPK) model developed in rats. The study examined vapor concentrations ranging from 500 to 10,000 ppm for dibromomethane and 2,500 to 40,000 ppm for bromochloromethane. These concentrations were reconstructed based on chemical blood levels measured in 4 hr, with errors varying from 0.0% to 52.0%. The PBPK approach adequately predicted the blood concentrations and helped simulate contaminant transport through the stratum corneum and distribution in the body compartments. The proposed technique made it possible to estimate the skin absorption time (SAT) obtained from acute inhalation toxicity data. An inverse relationship exists between the SAT and exposure concentration. The method can be helpful in toxicology and risk assessment of hazardous volatile organic compounds.

Occupational Exposure and Health Impact Assessment of Diisocyanates in Finland

Diisocyanates are a group of chemicals widely used in different industrial applications. The critical health effects related to diisocyanate exposure are isocyanate sensitisation, occupational asthma and bronchial hyperresponsiveness (BHR). Industrial air measurements and human biomonitoring (HBM) samples were gathered in specific occupational sectors to examine MDI, TDI, HDI and IPDI and the respective metabolites from Finnish screening studies. HBM data can give a more accurate picture of diisocyanate exposure, especially if workers have been exposed dermally or used respiratory protection. The HBM data were used for conducting a health impact assessment (HIA) in specific Finnish occupational sectors. For this purpose, exposure reconstruction was performed on the basis of HBM measurements of TDI and MDI exposures using a PBPK model, and a correlation equation was made for HDI exposure. Subsequently, the exposure estimates were compared to a previously published dose-response curve for excess BHR risk. The results showed that the mean and median diisocyanate exposure levels and HBM concentrations were low for all diisocyanates. In HIA, the excess risk of BHR from MDI exposure over a working life period was highest in the construction and motor and vehicle industries and repair sectors, resulting in estimated excess risks of BHR of 2.0% and 2.6%, and 113 and 244 extra BHR cases in Finland, respectively. Occupational exposure to diisocyanates must be monitored because a clear threshold for DI sensitisation cannot be established.

Bayesian inference of chemical exposures from NHANES urine biomonitoring data

BACKGROUND

Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data.

OBJECTIVE

Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty.

METHODS

Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts.

RESULTS

Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015-2016 NHANES cohort.

SIGNIFICANCE

The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.

Health Risk Assessment of Ortho-Toluidine Utilising Human Biomonitoring Data of Workers and the General Population

The aim of this work was to demonstrate how human biomonitoring (HBM) data can be used to assess cancer risks for workers and the general population. Ortho-toluidine, OT (CAS 95-53-4) is an aniline derivative which is an animal and human carcinogen and may cause methemoglobinemia. OT is used as a curing agent in epoxy resins and as intermediate in producing herbicides, dyes, and rubber chemicals. A risk assessment was performed for OT by using existing HBM studies. The urinary mass-balance methodology and generic exposure reconstruction PBPK modelling were both used for the estimation of the external intake levels corresponding to observed urinary levels. The external exposures were subsequently compared to cancer risk levels obtained from the evaluation by the Scientific Committee on Occupational Exposure Limits (SCOEL). It was estimated that workers exposed to OT have a cancer risk of 60 to 90:106 in the worst-case scenario (0.9 mg/L in urine). The exposure levels and cancer risk of OT in the general population were orders of magnitude lower when compared to workers. The difference between the output of urinary mass-balance method and the general PBPK model was approximately 30%. The external exposure levels calculated based on HBM data were below the binding occupational exposure level (0.5 mg/m3) set under the EU Carcinogens and Mutagens Directive.

Estimation of toluene exposure in air from BMA (S-benzylmercapturic acid) urinary measures using a reverse dosimetry approach based on physiologically pharmacokinetic modeling

This study aimed to use a reverse dosimetry PBPK modeling approach to estimate toluene atmospheric exposure from urinary measurements of S-benzylmercapturic acid (BMA) in a small group of individuals and to evaluate the uncertainty associated to urinary spot-sampling compared to 24-h collected urine samples. Each exposure assessment technique was developed namely to estimate toluene air exposure from BMA measurements in 24-h urine samples (24-h-BMA) and from distributions of daily urinary BMA spot measurements (DUBSM). Model physiological parameters were described based upon age, weight, size and sex. Monte Carlo simulations with the PBPK model allowed converting DUBSM distribution (and 24-h-BMA) into toluene air levels. For the approach relying on DUBSM distribution, the ratio between the 95% probability of predicted toluene concentration and its 50% probability in each individual varied between 1.2 and 1.4, while that based on 24-h-BMA varied between 1.0 and 1.1. This suggests more variability in estimated exposure from spot measurements. Thus, estimating toluene exposure based on DUBSM distribution generated about 20% more uncertainty. Toluene levels estimated (0.0078-0.0138 ppm) are well below Health Canada's maximum chronic air guidelines. PBPK modeling and reverse dosimetry may be combined to interpret urinary metabolites data of VOCs and assess related uncertainties.

Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach

BACKGROUND

Environmental exposure to perfluorooctane sulfonate (PFOS) is associated with various adverse outcomes in humans. However, risk assessment for PFOS with the traditional risk estimation method is faced with multiple challenges because there are high variabilities and uncertainties in its toxicokinetics and toxicity between species and among different types of studies.

OBJECTIVES

This study aimed to develop a robust probabilistic risk assessment framework accounting for interspecies and inter-experiment variabilities and uncertainties to derive the human equivalent dose (HED) and reference dose for PFOS.

METHODS

A Bayesian dose-response model was developed to analyze selected 34 critical studies, including human epidemiological, animal in vivo, and ToxCast in vitro toxicity datasets. The dose-response results were incorporated into a multi-species physiologically based pharmacokinetic (PBPK) model to reduce the toxicokinetic/toxicodynamic variabilities. In addition, a population-based probabilistic risk assessment of PFOS was performed for Asian, Australian, European, and North American populations, respectively, based on reported environmental exposure levels.

RESULTS

The 5th percentile of HEDs derived from selected studies was estimated to be 21.5 (95% CI: 10.6-36.3) ng/kg/day. After exposure to environmental levels of PFOS, around 50% of the population in all studied populations would likely have >20% of increase in serum cholesterol, but the effects on other endpoints were estimated to be minimal (<10% changes). There was a small population (~10% of the population) that was highly sensitive to endocrine disruption and cellular response by environmental PFOS exposure.

CONCLUSION

Our results provide insights into a complete risk characterization of PFOS and may help regulatory agencies in the reevaluation of PFOS risk. Our new probabilistic approach can conduct dose-response analysis of different types of toxicity studies simultaneously and this method could be used to improve risk assessment for other perfluoroalkyl substances (PFAS).

PBTK model for assessment of operator exposure to haloxyfop using human biomonitoring and toxicokinetic data

Physiologically-based toxicokinetic (PBTK) models are mathematical representations of chemical absorption, distribution, metabolism and excretion (ADME) in animals. Each parameter in a PBTK model describes a physiological, physicochemical or biochemical process that affects ADME. Distributions can be assigned to the model parameters to describe population variability and uncertainty. In this study to assess potential crop sprayer operator exposure to the herbicide haloxyfop, a permeability-limited PBTK model was constructed with parameter uncertainty and variability, and calibrated using Bayesian analysis via Markov chain Monte Carlo methods. A hierarchical statistical model was developed to reconstruct operator exposure using available measurement data: experimentally determined octanol/water partition coefficient, mouse and human toxicokinetic data as well as human biomonitoring data from seven operators who participated in a field study. A chemical risk assessment was performed by comparing the estimated systemic exposure to the acceptable operator exposure level (AOEL). The analysis suggested that in one of the seven operators, the model estimates systemic exposure to haloxyfop of 49.04 ± 10.19 SD μg/kg bw in relation to an AOEL of 5.0 μg/kg bw/day. This does not represent a safety concern as this predicted exposure is well within the 100-fold uncertainty factor applied to the No Observed Adverse Effect Level (NOAEL) in animals. In addition, given the availability of human toxicokinetic data, the 10x uncertainty factor for interspecies differences in ADME could be reduced (EFSA, 2006). Thus the AOEL could potentially be raised tenfold from 5.0 to 50.0 μg/kg bw/day.

Reverse dosimetry modeling of toluene exposure concentrations based on biomonitoring levels from the Canadian health measures survey

Biomonitoring might provide useful estimates of population exposure to environmental chemicals. However, data uncertainties stemming from interindividual variability are common in large population biomonitoring surveys. Physiologically based pharmacokinetic (PBPK) models might be used to account for age- and gender-related variability in internal dose. The objective of this study was to reconstruct air concentrations consistent with blood toluene measures reported in the third Canadian Health Measures Survey using reverse dosimetry PBPK modeling techniques. Population distributions of model's physiological parameters were described based upon age, weight, and size for four subpopulations (12-19, 20-39, 40-59, and 60-79 years old). Monte Carlo simulations applied to PBPK modeling allowed converting the distributions of venous blood measures of toluene obtained from CHMS into related air levels. Based upon blood levels observed at the 50^th, 90^th and 95^th percentiles, corresponding air toluene concentrations were estimated for teenagers aged 12-19 years as being, respectively, 0.009, 0.04 and 0.06 ppm. Similarly, values were computed for adults aged 20-39 years (0.007, 0.036, and 0.06 ppm), 40-59 years (0.007, 0.036 and 0.06 ppm) and 60-79 years (0.006, 0.022 and 0.04 ppm). These estimations are well below Health Canada's maximum recommended chronic air guidelines for toluene. In conclusion, PBPK modeling and reverse dosimetry may be combined to help interpret biomonitoring data for chemical exposure in large population surveys and estimate the associated toxicological health risk.

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures

BACKGROUND

A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals.

RESULTS

We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals. PSO reveals a relationship between chemical uptake and decomposition parameter values that predicts chemical-specific BCF values with moderate statistical agreement to a limited yet diverse chemical dataset, and all without a need to retrain the model to new data.

CONCLUSIONS

The presented model requires only the octanol-water partitioning ratio to predict BCFs to a fidelity consistent with existing QSAR models. This success begs re-evaluation of the modeling assumptions; specifically, it suggests that chemical uptake into arterial blood may be limited by transport across gill membranes (diffusion) rather than by counter-current flow between gill lamellae (convection). Therefore, more detailed molecular modeling of aquatic respiration may further improve predictive accuracy of the rTK approach.

A Physiologically-Based Pharmacokinetic Modeling Approach Using Biomonitoring Data in Order to Assess the Contribution of Drinking Water for the Achievement of an Optimal Fluoride Dose for Dental Health in Children

Due to an optimal fluoride concentration in drinking water advised for caries prevention purposes, the population is now exposed to multiple sources of fluoride. The availability of population biomonitoring data currently allow us to evaluate the magnitude of this exposure. The objective of this work was, therefore, to use such data in order to estimate whether community water fluoridation still represents a significant contribution toward achieving a suggested daily optimal fluoride (external) intake of 0.05 mg/kg/day. Therefore, a physiologically-based pharmacokinetic model for fluoride published in the literature was used and adapted in Excel for a typical 4-year-old and 8-year-old child. Biomonitoring data from the Canadian Health Measures Survey among people living in provinces with very different drinking water fluoridation coverage (Quebec, 2.5%; Ontario, 70% of the population) were analyzed using this adapted model. Absorbed doses for the 4-year-old and 8-year-old children were, respectively, 0.03 mg/kg/day and 0.02 mg/kg/day in Quebec and of 0.06 mg/kg/day and 0.05 mg/kg/day in Ontario. These results show that community water fluoridation contributes to increased fluoride intake among children, which leads to reaching, and in some cases even exceeding, the suggested optimal absorbed dose of 0.04 mg/kg/day, which corresponds to the suggested optimal fluoride intake mentioned above. In conclusion, this study constitutes an incentive to further explore the multiple sources of fluoride intake and suggests that a new balance between them including drinking water should be examined in accordance with the age-related physiological differences that influence fluoride metabolism.

A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

A computational workflow was developed to facilitate the process of quantitative in vitro to in vivo extrapolation (QIVIVE), specifically the translation of in vitro concentration-response to in vivo dose-response relationships and subsequent derivation of a benchmark dose value (BMD). The workflow integrates physiologically based pharmacokinetic (PBPK) modeling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation. For a given set of in vitro concentration and response data the algorithm returns the posterior distribution of the corresponding in vivo, population-based dose-response values, for a given route of exposure. The novel aspect of the workflow is a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model (both parameter uncertainty and population variability) and in the structure of the PBPK model itself recognizing that the model is an approximation to reality. Both these sources of uncertainty propagate through the workflow and are quantified within the posterior distribution of in vivo dose for a fixed representative in vitro concentration. To demonstrate this process and for comparative purposes a similar exercise to previously published work describing the kinetics of ethylene glycol monoethyl ether (EGME) and its embryotoxic metabolite methoxyacetic acid (MAA) in rats was undertaken. The computational algorithm can be used to extrapolate from in vitro data to any organism, including human. Ultimately, this process will be incorporated into a user-friendly, freely available modeling platform, currently under development, that will simplify the process of QIVIVE.

Physiologically-based pharmacokinetic and toxicokinetic models for estimating human exposure to five toxic elements through oral ingestion

Biological monitoring and physiologically-based pharmacokinetic (PBPK) modelling are useful complementary tools in quantifying human exposure to elements in the environment. In this work, we used PBPK models to determine the optimal time for collecting biological samples in a longitudinal study to determine if participants who consumed allotment produce had been exposed to arsenic, cadmium, chromium, nickel or lead. There are a number of PBPK models for these elements published in the literature, which vary in size, complexity and application, given the differences in physiochemical properties of the elements, organs involved in metabolism and exposure pathways affected. We selected PBPK models from the literature to simulate the oral ingestion pathway from consumption of allotment produce. Some models required modification by reducing or removing selected compartments whilst still maintaining their original predictability. The performance of the modified models was evaluated by comparing the predicted urinary and blood elemental levels with experimental data and other model simulations published in the literature. Overall, the model predictions were consistent with literature data (r > 0.7, p < 0.05), and were influential in predicting when samples should be collected. Our results demonstrate the use of mathematical modelling in informing and optimising the design of longitudinal studies.

Indicative and complementary effects of human biological indicators for heavy metal exposure assessment

Although human biological indicators have been widely utilized for biomonitoring environmental pollutants in health exposure assessment, the relationship between internal and external exposure has not yet been adequately established. In this study, we collected and analyzed 61 rice, 56 pepper, and 58 soil samples, together with 107 hair, 107 blood, and 107 urine samples from residents living in selected intensive mining areas in China. Concentrations of most of the four elements considered (Pb, Cd, Hg, and Se) exceeded national standards, implying high exposure risk in the study areas. Regression analysis also revealed a correlation (0.33, P < 0.001) between the concentration of Pb in soil and that in human hair (as well as in human blood); to some extent, Pb content in hair and blood could therefore be used to characterize external Pb exposure. The correlation between Hg in rice and in human hair (up to 0.5, P < 0.001) further confirmed a significant indicative effect of human hair for Hg exposure. A significant correlation was also noted between concentrations of some elements in different human samples, for example, between Hg in hair and blood (0.641, P < 0.01) and between Cd in urine and blood (0.339, P < 0.01). To some extent, there could thus be mutual reflectance of the same heavy metal in different samples, with the possibility for complementary use in assessing heavy metal exposure.

Physiological fidelity or model parsimony? The relative performance of reverse-toxicokinetic modeling approaches

Background

Physiologically-based toxicokinetic (PBTK) models are often developed to facilitate in vitro to in vivo extrapolation (IVIVE) using a top-down, compartmental approach, favoring architectural simplicity over physiological fidelity despite the lack of general guidelines relating model design to dynamical predictions. Here we explore the impact of design choice (high vs. low fidelity) on chemical distribution throughout an animal’s organ system.

Results

We contrast transient dynamics and steady states of three previously proposed PBTK models of varying complexity in response to chemical exposure. The steady states for each model were determined analytically to predict exposure conditions from tissue measurements. Steady state whole-body concentrations differ between models, despite identical environmental conditions, which originates from varying levels of physiological fidelity captured by the models. These differences affect the relative predictive accuracy of the inverted models used in exposure reconstruction to link effects-based exposure data with whole-organism response thresholds obtained from in vitro assay measurements.

Conclusions

Our results demonstrate how disregarding physiological fideltiy in favor of simpler models affects the internal dynamics and steady state estimates for chemical accumulation within tissues, which, in turn, poses significant challenges for the exposure reconstruction efforts that underlie many IVIVE methods. Developing standardized systems-level models for ecological organisms would not only ensure predictive consistency among future modeling studies, but also ensure pragmatic extrapolation of in vivo effects from in vitro data or modeling exposure-response relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0407-3) contains supplementary material, which is available to authorized users.

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict in Vivo Toxicity from in Vitro Data

The development of reliable nonanimal based testing strategies, such as in vitro bioassays, is the holy grail in current human safety testing of chemicals. However, the use of in vitro toxicity data in risk assessment is not straightforward. One of the main issues is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves. These dose-response curves are needed in toxicological risk assessment to obtain a point of departure to determine safe exposure levels for humans. Recent scientific developments enable this translation of in vitro concentration-response curves to in vivo dose-response curves using physiologically based kinetic (PBK) modeling-based reverse dosimetry. The present review provides an overview of the examples available in the literature on the prediction of in vivo toxicity using PBK modeling-based reverse dosimetry of in vitro toxicity data, showing that proofs-of-principle are available for toxicity end points ranging from developmental toxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity to DNA adduct formation. This review also discusses the promises and pitfalls, and the future perspectives of the approach. Since proofs-of-principle available so far have been provided for the prediction of toxicity in experimental animals, future research should focus on the use of in vitro toxicity data obtained in human models to predict the human situation using human PBK models. This would facilitate human- instead of experimental animal-based approaches in risk assessment.

Animal-Free Chemical Safety Assessment

The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.

Using publicly available data, a physiologically-based pharmacokinetic model and Bayesian simulation to improve arsenic non-cancer dose-response

Publicly available data can potentially examine the relationship between environmental exposure and public health, however, it has not yet been widely applied. Arsenic is of environmental concern, and previous studies mathematically parameterized exposure duration to create a link between duration of exposure and increase in risk. However, since the dose metric emerging from exposure duration is not a linear or explicit variable, it is difficult to address the effects of exposure duration simply by using mathematical functions. To relate cumulative dose metric to public health requires a lifetime physiologically-based pharmacokinetic (PBPK) model, yet this model is not available at a population level. In this study, the data from the U.S. total diet study (TDS, 2006-2011) was employed to assess exposure: daily dietary intakes for total arsenic (tAs) and inorganic arsenic (iAs) were estimated to be 0.15 and 0.028μg/kg/day, respectively. Meanwhile, using National Health and Nutrition Examination Survey (NHANES, 2011-2012) data, the fraction of urinary As(III) levels (geometric mean: 0.31μg/L) in tAs (geometric mean: 7.75μg/L) was firstly reported to be approximately 4%. Together with Bayesian technique, the assessed exposure and urinary As(III) concentration were input to successfully optimize a lifetime population PBPK model. Finally, this optimized PBPK model was used to derive an oral reference dose (Rfd) of 0.8μg/kg/day for iAs exposure. Our study also suggests the previous approach (by using mathematical functions to account for exposure duration) may result in a conservative Rfd estimation.

Contribution of inorganic arsenic sources to population exposure risk on a regional scale

Chronic exposure to inorganic arsenic (iAs) in the human population is associated with various internal cancers and other adverse outcomes. The purpose of this study was to estimate a population-scale exposure risk attributable to iAs consumptions by linking a stochastic physiological-based pharmacokinetic (PBPK) model and biomonitoring data of iAs in urine. The urinary As concentrations were obtained from a total of 1,043 subjects living in an industrial area of Taiwan. The results showed that the study subjects had an iAs exposure risk of 27 % (the daily iAs intake for 27 % study subjects exceeded the WHO-recommended value, 2.1 μg iAs day(-1) kg(-1) body weight). Moreover, drinking water and cooked rice contributed to the iAs exposure risk by 10 and 41 %, respectively. The predicted risks in the current study were 4.82, 27.21, 34.69, and 64.17 %, respectively, among the mid-range of Mexico, Taiwan (this study), Korea, and Bangladesh reported in the literature. In conclusion, we developed a population-scale-based risk model that covered the broad range of iAS exposure by integrating stochastic PBPK modeling and reverse dosimetry to generate probabilistic distribution of As intake corresponding to urinary As measured from the cohort study. The model can also be updated as new urinary As information becomes available.

A novel approach for estimating ingested dose associated with paracetamol overdose

AIM

In cases of paracetamol (acetaminophen, APAP) overdose, an accurate estimate of tissue-specific paracetamol pharmacokinetics (PK) and ingested dose can offer health care providers important information for the individualized treatment and follow-up of affected patients. Here a novel methodology is presented to make such estimates using a standard serum paracetamol measurement and a computational framework.

METHODS

The core component of the computational framework was a physiologically-based pharmacokinetic (PBPK) model developed and evaluated using an extensive set of human PK data. Bayesian inference was used for parameter and dose estimation, allowing the incorporation of inter-study variability, and facilitating the calculation of uncertainty in model outputs.

RESULTS

Simulations of paracetamol time course concentrations in the blood were in close agreement with experimental data under a wide range of dosing conditions. Also, predictions of administered dose showed good agreement with a large collection of clinical and emergency setting PK data over a broad dose range. In addition to dose estimation, the platform was applied for the determination of optimal blood sampling times for dose reconstruction and quantitation of the potential role of paracetamol conjugate measurement on dose estimation.

CONCLUSIONS

Current therapies for paracetamol overdose rely on a generic methodology involving the use of a clinical nomogram. By using the computational framework developed in this study, serum sample data, and the individual patient's anthropometric and physiological information, personalized serum and liver pharmacokinetic profiles and dose estimate could be generated to help inform an individualized overdose treatment and follow-up plan.

Uncertainties in human health risk assessment of environmental contaminants: A review and perspective

Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health. A range of uncertainties exist through the source-to-outcome continuum, including exposure assessment, hazard and risk characterisation. While various strategies have been applied to characterising uncertainty, classical approaches largely rely on how to maximise the available resources. Expert judgement, defaults and tools for characterising quantitative uncertainty attempt to fill the gap between data and regulation requirements. The experiences of researching 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) illustrated uncertainty sources and how to maximise available information to determine uncertainties, and thereby provide an 'adequate' protection to contaminant exposure. As regulatory requirements and recurring issues increase, the assessment of complex scenarios involving a large number of chemicals requires more sophisticated tools. Recent advances in exposure and toxicology science provide a large data set for environmental contaminants and public health. In particular, biomonitoring information, in vitro data streams and computational toxicology are the crucial factors in the NexGen risk assessment, as well as uncertainties minimisation. Although in this review we cannot yet predict how the exposure science and modern toxicology will develop in the long-term, current techniques from emerging science can be integrated to improve decision-making.

A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE

The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico, and in chemico systems can be integrated and utilized for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus, a realistic description of the variation in the physiology of the human population being modeled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.

Uses of NHANES Biomarker Data for Chemical Risk Assessment: Trends, Challenges, and Opportunities

BACKGROUND

Each year, the U.S. NHANES measures hundreds of chemical biomarkers in samples from thousands of study participants. These biomarker measurements are used to establish population reference ranges, track exposure trends, identify population subsets with elevated exposures, and prioritize research needs. There is now interest in further utilizing the NHANES data to inform chemical risk assessments.

OBJECTIVES

This article highlights a) the extent to which U.S. NHANES chemical biomarker data have been evaluated, b) groups of chemicals that have been studied, c) data analysis approaches and challenges, and d) opportunities for using these data to inform risk assessments.

METHODS

A literature search (1999-2013) was performed to identify publications in which U.S. NHANES data were reported. Manual curation identified only the subset of publications that clearly utilized chemical biomarker data. This subset was evaluated for chemical groupings, data analysis approaches, and overall trends.

RESULTS

A small percentage of the sampled NHANES-related publications reported on chemical biomarkers (8% yearly average). Of 11 chemical groups, metals/metalloids were most frequently evaluated (49%), followed by pesticides (9%) and environmental phenols (7%). Studies of multiple chemical groups were also common (8%). Publications linking chemical biomarkers to health metrics have increased dramatically in recent years. New studies are addressing challenges related to NHANES data interpretation in health risk contexts.

CONCLUSIONS

This article demonstrates growing use of NHANES chemical biomarker data in studies that can impact risk assessments. Best practices for analysis and interpretation must be defined and adopted to allow the full potential of NHANES to be realized.

Breath analysis: technical developments and challenges in the monitoring of human exposure to volatile organic compounds

At present, there is a growing concern about human quality of life. In particular, there is an awareness of the impact of volatile organic compounds (VOCs) on the environment and human health, so the monitoring of human exposure to VOCs is an increasingly urgent need. Biomonitoring is theoretically more accurate compared with traditional ambient air monitoring, and it plays an essential role in human environmental exposure assessment. Breath analysis is a biomonitoring method with many advantages, which is applicable to assessments of human exposure to a large number of VOCs. Techniques are being developed to improve the sensitivity and precision of breath analysis based on in-direct and direct measurements which will be reviewed in this paper. This paper briefly reviews the frequently used methods in both of these categories, specifically highlighting some promising new techniques. Furthermore, this review also provides theoretical background knowledge about the use of breath analysis as a biomonitoring tool for human exposure assessment. A review of the application of breath analysis to human exposure monitoring during last two decades is also provided according to occupational/non-occupational exposure. Obstacles and potential challenges in this field are also summarized. Based on the gradual improvements in the theoretical basis and technology reviewed in this paper, breath analysis is an enormous potential approach for the monitoring of human exposure to VOCs.

Reprint of PopGen: A virtual human population generator

The risk assessment of environmental chemicals and drugs is moving towards a paradigm shift in approach which seeks the full replacement animal testing with high throughput, mechanistic, in vitro systems. This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to translate in vitro concentration-response relationships to an exposure or dose, route and duration regime in people. In this report we describe PopGen a virtual human population generator which is a user friendly, open access web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in a wide range of healthy human populations. We demonstrate how PopGen can be used for QIVIVE by providing input to a PBPK model, at an appropriate level of detail, to reconstruct exposure from human biomonitoring data. We discuss how the process of exposure reconstruction from blood biomarkers, in general, is analogous to exposure or dose reconstruction from concentration-response measurements made in proposed in vitro cell based systems which are assumed to be surrogates for target organs.

PBPK and population modelling to interpret urine cadmium concentrations of the French population

As cadmium accumulates mainly in kidney, urinary concentrations are considered as relevant data to assess the risk related to cadmium. The French Nutrition and Health Survey (ENNS) recorded the concentration of cadmium in the urine of the French population. However, as with all biomonitoring data, it needs to be linked to external exposure for it to be interpreted in term of sources of exposure and for risk management purposes. The objective of this work is thus to interpret the cadmium biomonitoring data of the French population in terms of dietary and cigarette smoke exposures. Dietary and smoking habits recorded in the ENNS study were combined with contamination levels in food and cigarettes to assess individual exposures. A PBPK model was used in a Bayesian population model to link this external exposure with the measured urinary concentrations. In this model, the level of the past exposure was corrected thanks to a scaling function which account for a trend in the French dietary exposure. It resulted in a modelling which was able to explain the current urinary concentrations measured in the French population through current and past exposure levels. Risk related to cadmium exposure in the general French population was then assessed from external and internal critical values corresponding to kidney effects. The model was also applied to predict the possible urinary concentrations of the French population in 2030 assuming there will be no more changes in the exposures levels. This scenario leads to significantly lower concentrations and consequently lower related risk.

The human early-life exposome (HELIX): project rationale and design

BACKGROUND

Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The "exposome" concept encompasses the totality of exposures from conception onward, complementing the genome.

OBJECTIVES

The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the "early-life exposome." Here we describe the general design of the project.

METHODS

In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures.

CONCLUSIONS

HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.

Lifetime PCB 153 bioaccumulation and pharmacokinetics in pilot whales: Bayesian population PBPK modeling and Markov chain Monte Carlo simulations

Physiologically based pharmacokinetic (PBPK) models for wild animal populations such as marine mammals typically have a high degree of model uncertainty and variability due to the scarcity of information and the embryonic nature of this field. Parameters values used in marine mammals models are usually taken from other mammalian species (e.g. rats or mice) and might not be entirely suitable to properly explain the kinetics of pollutants in marine mammals. Therefore, several parameters for a PBPK model for the bioaccumulation and pharmacokinetics of PCB 153 in long-finned pilot whales were estimated in the present study using the Bayesian approach executed with Markov chain Monte Carlo (MCMC) simulations. This method uses 'prior' information of the parameters, either from the literature or from previous model runs. The advantage is that this method uses such 'prior' parameters to calculate probability distributions to determine 'posterior' values that best explain the field observations. Those field observations or datasets were PCB 153 concentrations in blubber of long-finned pilot whales from Sandy Cape and Stanley, Tasmania, Australia. The model predictions showed an overall decrease in PCB 153 levels in blubber over the lifetime of the pilot whales. All parameters from the Sandy Cape model were updated using the Stanley dataset, except for the concentration of PCB 153 in the milk. The model presented here is a promising and preliminary start to PBPK modeling in long-finned pilot whales that would provide a basis for non-invasive studies in these protected marine mammals.

Improving Dietary Exposure Models by Imputing Biomonitoring Data through ABC Methods

New data are available in the field of risk assessment: the biomonitoring data which is measurement of the chemical dose in a human tissue (e.g. blood or urine). These data are original because they represent direct measurements of the dose of chemical substances really taken up from the environment, whereas exposure is usually assessed from contamination levels of the different exposure media (e.g. food, air, water, etc.) and statistical models. However, considered alone, these data provide little help from the perspective of Public Health guidance. The objective of this paper is to propose a method to exploit the information provided by human biomonitoring in order to improve the modeling of exposure. This method is based on the Kinetic Dietary Exposure Model which takes into account the pharmacokinetic elimination and the accumulation phenomenon inside the human body. This model is corrected to account for any possible temporal evolution in exposure by adding a scaling function which describes this evolution. Approximate Bayesian Computation is used to fit this exposure model from the biomonitoring data available. Specific summary statistics and appropriate distances between simulated and observed statistical distributions are proposed and discussed in the light of risk assessment. The promoted method is then applied to measurements of blood concentration of dioxins in a group of French fishermen families. The outputs of the model are an estimation of the body burden distribution from observed dietary intakes and the evolution of dietary exposure to dioxins in France between 1930 and today. This model successfully fit to dioxins data can also be used with other biomonitoring data to improve the risk assessment to many other contaminants.

PopGen: A virtual human population generator

The risk assessment of environmental chemicals and drugs is moving towards a paradigm shift in approach which seeks the full replacement animal testing with high throughput, mechanistic, in vitro systems. This new vision will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated using in vitro, in silico and in chemico systems, can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited for this and are obligatory in order to translate in vitro concentration-response relationships to an exposure or dose, route and duration regime in people. In this report we describe PopGen, a virtual human population generator which is a user friendly, open access web-based application for the prediction of realistic anatomical, physiological and phase 1 metabolic variation in a wide range of healthy human populations. We demonstrate how PopGen can be used for QIVIVE by providing input to a PBPK model, at an appropriate level of detail, to reconstruct exposure from human biomonitoring data. We discuss how the process of exposure reconstruction from blood biomarkers, in general, is analogous to exposure or dose reconstruction from concentration-response measurements made in proposed in vitro cell based systems which are assumed to be surrogates for target organs.

Distribution is a major factor affecting bioaccumulation of decabrominated diphenyl ether: Chinese sturgeon (Acipenser sinensis) as an example

While decabromodiphenyl ether (BDE-209) has very low bioavailability and a rapid biotransformation rate, it exhibits high bioaccumulation in wildlife. To explore the bioaccumulation mechanism of BDE-209 in organisms, its toxicokinetic processes were investigated in Chinese sturgeons from the Yangtze River. Different from less brominated BDEs, lipids did not play an important role in the distribution of BDE-209 with relatively high concentrations detected in liver (54.5 ± 3.3 ng/g wet weight (ww)), gills (47.4 ± 2.9 ng/g ww), and intestine (41.9 ± 3.0 ng/g ww), followed by stomach (21.9 ± 9.0 ng/g ww), muscle (19.1 ± 5.6 ng/g ww), heart (7.5 ± 5.2 ng/g ww), gonad (6.8 ± 4.9 ng/g ww), adipose (4.9 ± 1.2 ng/g ww), and egg (2.8 ± 2.3 ng/g ww). In vitro metabolism of BDE-209 by microsomal fractions of Chinese sturgeon found that BDE-209 was biotransformed rapidly with the rate constant (K) of 0.039 h(-1) in liver. BDE-126, BDE-154, BDE-188, BDE-184, BDE-183, BDE-202, BDE-201, and BDE-204/197 were observed as debrominated products of BDE-209 after incubation, and their formation rates were 0.026, 0.016, and 0.006 h(-1) for BDE-126 BDE-184, and BDE-154, respectively. The concentration ratios between heart and intestine for individual PBDEs suggested slow delivery of BDE-209 among tissues after absorption. A Bayesian hierarchical model was further developed to estimate partition coefficients in a physiologically based pharmacokinetic model of BDE-209 in Chinese sturgeon. The estimated partition coefficients between tissues and blood were higher than those of less brominated BDE or PCBs in various animals, suggesting that the low partition ratios from blood to tissues would lead to high bioaccumulation of BDE-209, especially in absorbing organs.

DoseSim: a tool for pharmacokinetic/pharmacodynamic analysis and dose reconstruction

Assessing and improving the safety of chemicals and the efficacy of drugs depends on an understanding of the biodistribution, clearance and biological effects of the chemical(s) of interest. A promising methodology for the prediction of these phenomena is physiologically based pharmacokinetic/pharmacodynamic modeling, which centers on the prediction of chemical absorption, distribution, metabolism and excretion (pharmacokinetics) and the biological effects (pharmacodynamics) of the chemical on the organism. Strengths of this methodology include modeling across multiple scales of biological organization and facilitate the extrapolation of results across routes of exposure, dosing levels and species. It is also useful as the foundation for tools to (i) predict biomarker levels (concentrations of chemical species found in the body that indicate exposure to a foreign chemical), given a chemical dose or exposure; (ii) reconstruct a dose, given the levels of relevant biomarkers; and (iii) estimate population variability. Despite the importance and promise of physiologically based pharmacokinetic /pharmacodynamics-based approaches to forward and reverse dosimetry, there is currently a lack of user-friendly, freely available implementations that are accessible and useful to a broad range of users. DoseSim was developed to begin to fill this gap.

AVAILABILITY

The application is available under the GNU General Public License from http://scb.colostate.edu/dosesim.html.

Interpreting PCB levels in breast milk using a physiologically based pharmacokinetic model to reconstruct the dynamic exposure of Italian women

Polychlorinated biphenyls (PCBs) are persistent contaminants suspected to cause adverse health effects in humans. As PCBs levels in food have not been monitored frequently in the past, modeling approaches based on environmental data have been proposed to predict the human dietary intake. In this work, we propose to improve these approaches by taking into account internal levels of PCBs in humans. This methodology is based on the analysis of biomonitoring data using exposure and physiologically based pharmacokinetic (PBPK) modeling to determine the most probable scenario of exposure. Breast milk concentrations were measured in Italian women for PCB-138, PCB-153 and PCB-180. For each congener, three exposure scenarios were derived and a PBPK model was used to relate the lifetime exposure to the breast milk levels. For the three PCBs, we determined the most probable scenario of exposure. Our results support the adequacy of the exposure and the PBPK models for PCB-180 and PCB-153, whereas we observed discrepancies between the models and the biomonitoring data for PCB-138. Our intake estimates are in good agreement with previous exposure assessments based solely on food contamination demonstrating the relevance of our approach to reconstruct accurately the exposure and to fill in data gaps on exposure.

Reconstruction of Exposure to m-Xylene from Human Biomonitoring Data Using PBPK Modelling, Bayesian Inference, and Markov Chain Monte Carlo Simulation

There are numerous biomonitoring programs, both recent and ongoing, to evaluate environmental exposure of humans to chemicals. Due to the lack of exposure and kinetic data, the correlation of biomarker levels with exposure concentrations leads to difficulty in utilizing biomonitoring data for biological guidance values. Exposure reconstruction or reverse dosimetry is the retrospective interpretation of external exposure consistent with biomonitoring data. We investigated the integration of physiologically based pharmacokinetic modelling, global sensitivity analysis, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of inhalation exposure to m-xylene. We used exhaled breath and venous blood m-xylene and urinary 3-methylhippuric acid measurements from a controlled human volunteer study in order to evaluate the ability of our computational framework to predict known inhalation exposures. We also investigated the importance of model structure and dimensionality with respect to its ability to reconstruct exposure.

Application of physiologically based pharmacokinetic models in chemical risk assessment

Post-exposure risk assessment of chemical and environmental stressors is a public health challenge. Linking exposure to health outcomes is a 4-step process: exposure assessment, hazard identification, dose response assessment, and risk characterization. This process is increasingly adopting "in silico" tools such as physiologically based pharmacokinetic (PBPK) models to fine-tune exposure assessments and determine internal doses in target organs/tissues. Many excellent PBPK models have been developed. But most, because of their scientific sophistication, have found limited field application-health assessors rarely use them. Over the years, government agencies, stakeholders/partners, and the scientific community have attempted to use these models or their underlying principles in combination with other practical procedures. During the past two decades, through cooperative agreements and contracts at several research and higher education institutions, ATSDR funded translational research has encouraged the use of various types of models. Such collaborative efforts have led to the development and use of transparent and user-friendly models. The "human PBPK model toolkit" is one such project. While not necessarily state of the art, this toolkit is sufficiently accurate for screening purposes. Highlighted in this paper are some selected examples of environmental and occupational exposure assessments of chemicals and their mixtures.

Development of lead source-specific exposure standards based on aggregate exposure assessment: Bayesian inversion from biomonitoring information to multipathway exposure

Exposure of children to lead is of great concern, and the exposure standards for different media are important for protecting public safety. However, these media-specific standards often fail to ensure the safety of children even when environmental lead levels are lower than the quality standards since humans are often exposed to lead via multiple pathways. To establish exposure standards to protect children from hazards associated with exposure to lead, an analytical tool for assessing aggregate exposure to lead based on Bayesian hierarchical model was developed, and then was used to update the external lead exposure of diet, paint, soil, air and drinking water using the blood lead levels in Chinese children aged 1-6 years. On the basis of updated external exposure, the source allocations for diet, paint, soil, air, and drinking water in China were 65.80 ± 7.92%, 16.98 ± 7.88%, 13.65 ± 5.05%, 3.36 ± 1.75%, and 0.20 ± 0.14%, respectively. Based on the estimated source allocations, the exposure standards were evaluated to be 0.2 μg/m(3), 24.25 mg/kg, 0.027 μg/L, 0.051 μg/mg, 0.042 μg/mg, 38.02 μg/mg for air, soil, water, grains, vegetables, and paint, respectively. Since the standards setting procedure was based on the multipathway aggregate exposure assessment of lead, the newly proposed exposure standards should ensure the safety of children.

A physiologically based pharmacokinetic model for capreomycin

The emergence of multidrug-resistant tuberculosis (MDR-TB) has led to a renewed interest in the use of second-line antibiotic agents. Unfortunately, there are currently dearths of information, data, and computational models that can be used to help design rational regimens for administration of these drugs. To help fill this knowledge gap, an exploratory physiologically based pharmacokinetic (PBPK) model, supported by targeted experimental data, was developed to predict the absorption, distribution, metabolism, and excretion (ADME) of the second-line agent capreomycin, a cyclic peptide antibiotic often grouped with the aminoglycoside antibiotics. To account for interindividual variability, Bayesian inference and Monte Carlo methods were used for model calibration, validation, and testing. Along with the predictive PBPK model, the first for an antituberculosis agent, this study provides estimates of various key pharmacokinetic parameter distributions and supports a hypothesized mechanism for capreomycin transport into the kidney.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Evaluation of biomarkers of exposure and potential harm in smokers, former smokers and never-smokers

: The objective of this study was to obtain baseline data on biomarkers of exposure (BoE) and biomarkers of potential harm (BoPH) in smokers, former smokers and never-smokers.

: This was a cross-sectional study of 80 healthy male and female volunteers over 21 years old, self-selected for smoking status. Subjects were pre-screened by medical staff at an independent clinical research unit, within 1 week prior to a single overnight residential visit and sample collection.

: All BoE were able to differentiate between the two smoking groups and smokers from all non-smokers. There was a strong correlation between cigarettes smoked per day and total urinary nicotine equivalents (TNE; r=0.85). TNE correlated better with 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol levels than cigarettes smoked per day (r=0.75 and r=0.56, respectively). Of the BoPH included in this study, seven (11-dehydro-thromboxane B2, 2, 3-dinorthromboxane B2, 8-epi prostaglandin F

: While BoE clearly differentiate between groups based on self-declared smoking status, most BoPH examined could not do so in a consistent manner. The dynamics of BoPH levels are not well understood. Future studies of BoPH should eliminate potential confounding factors and increase the number of subjects to allow the investigation of genetic polymorphism in metabolic pathways.

Clin Chem Lab Med 2009;47:311–20.