The use of Markov chain Monte Carlo uncertainty analysis to support a Public Health Goal for perchloroethylene

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

In vitro-in silico-based prediction of inter-individual and inter-ethnic variations in the dose-dependent cardiotoxicity of R- and S-methadone in humans

New approach methodologies predicting human cardiotoxicity are of interest to support or even replace in vivo-based drug safety testing. The present study presents an in vitro–in silico approach to predict the effect of inter-individual and inter-ethnic kinetic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either individual human liver microsomes or recombinant cytochrome P450 enzymes (rCYPs), were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations to predict inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. Chemical specific adjustment factors were defined and used to derive dose–response curves for the sensitive individuals. Our simulations indicated that Chinese are more sensitive towards methadone-induced cardiotoxicity with Margin of Safety values being generally two-fold lower than those for Caucasians for both methadone enantiomers. Individual PBK models using microsomes and PBK models using rCYPs combined with Monte Carlo simulations predicted similar inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. The present study illustrates how inter-individual and inter-ethnic variations in cardiotoxicity can be predicted by combining in vitro toxicity and metabolic data, PBK modelling and Monte Carlo simulations. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.

Inter-individual variation in chlorpyrifos toxicokinetics characterized by physiologically based kinetic (PBK) and Monte Carlo simulation comparing human liver microsome and Supersome™ cytochromes P450 (CYP)-specific kinetic data as model input

The present study compares two approaches to evaluate the effects of inter-individual differences in the biotransformation of chlorpyrifos (CPF) on the sensitivity towards in vivo red blood cell (RBC) acetylcholinesterase (AChE) inhibition and to calculate a chemical-specific adjustment factor (CSAF) to account for inter-individual differences in kinetics (HK_AF). These approaches included use of a Supersome^™ cytochromes P450 (CYP)-based and a human liver microsome (HLM)-based physiologically based kinetic (PBK) model, both combined with Monte Carlo simulations. The results revealed that bioactivation of CPF exhibits biphasic kinetics caused by distinct differences in the Km of CYPs involved, which was elucidated by Supersome^™ CYP rather than by HLM. Use of Supersome^™ CYP-derived kinetic data was influenced by the accuracy of the intersystem extrapolation factors (ISEFs) required to scale CYP isoform activity of Supersome^™ to HLMs. The predicted dose–response curves for average, 99th percentile and 1st percentile sensitive individuals were found to be similar in the two approaches when biphasic kinetics was included in the HLM-based approach, resulting in similar benchmark dose lower confidence limits for 10% inhibition (BMDL₁₀) and HK_AF values. The variation in metabolism-related kinetic parameters resulted in HK_AF values at the 99th percentile that were slightly higher than the default uncertainty factor of 3.16. While HK_AF values up to 6.9 were obtained when including also the variability in other influential PBK model parameters. It is concluded that the Supersome^™ CYP-based approach appeared most adequate for identifying inter-individual variation in biotransformation of CPF and its resulting RBC AChE inhibition.

Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice

Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.

High-throughput determination of high-quality interdiffusion coefficients in metallic solids: a review

Accurate interdiffusion coefficients of composition and temperature dependence are significantly important for understanding different materials processes. However, the high-throughput determination of high-quality interdiffusion coefficients, especially in multicomponent systems, has been sustaining as a challenge in materials community. This review dealt with a comprehensive summarization of the recent progress in this field, aiming at advancing a scientific routine for realizing the high-throughput determination of high-quality interdiffusion coefficients in metallic solids. First, an introduction of traditional Matano-based approaches and their recent development was given. Second, the numerical inverse methods were described, with a focus on the recently developed pragmatic numerical inverse method and related public toolkits. Potential strategies for resolving the problems about accuracy and uniqueness of the solutions to the numerical inverse methods were highlighted. The combination of numerical inverse method and diffusion multiple technique was highly proposed for high-throughput determination of interdiffusion coefficients in metallic solids with any number of components. After that, the case studies on the high-throughput determination of interdiffusivity matrices in the real Ni-based, high-entropy/high-entropy superalloys were demonstrated. Discussion on the substitution of Re in Ni-based single-crystal superalloys and the sluggish diffusion in high-entropy/high-entropy superalloys was also carried out. Fourth, the general idea for the uncertainty quantification was proposed in order to obtain high-quality interdiffusion coefficients, followed by the introduction of recent progress on the uncertainty quantification in both Matano-based methods and numerical inverse methods. Finally, the conclusions were drawn, and the future trends in diffusion community were also pointed out.

Bayesian evaluation of a physiologically based pharmacokinetic (PBPK) model for perfluorooctane sulfonate (PFOS) to characterize the interspecies uncertainty between mice, rats, monkeys, and humans: Development and performance verification

A challenge in the risk assessment of perfluorooctane sulfonate (PFOS) is the large interspecies differences in its toxicokinetics that results in substantial uncertainty in the dosimetry and toxicity extrapolation from animals to humans. To address this challenge, the objective of this study was to develop an open-source physiologically based pharmacokinetic (PBPK) model accounting for species-specific toxicokinetic parameters of PFOS. Considering available knowledge about the toxicokinetic properties of PFOS, a PBPK model for PFOS in mice, rats, monkeys, and humans after intravenous and oral administrations was created. Available species-specific toxicokinetic data were used for model calibration and optimization, and independent datasets were used for model evaluation. Bayesian statistical analysis using Markov chain Monte Carlo (MCMC) simulation was performed to optimize the model and to characterize the uncertainty and interspecies variability of chemical-specific parameters. The model predictions well correlated with the majority of datasets for all four species, and the model was validated with independent data in rats, monkeys, and humans. The model was applied to predict human equivalent doses (HEDs) based on reported points of departure in selected critical toxicity studies in rats and monkeys following U.S. EPA's guidelines. The lower bounds of the model-derived HEDs were overall lower than the HEDs estimated by U.S. EPA (e.g., 0.2 vs. 1.3 μg/kg/day based on the rat plasma data). This integrated and comparative analysis provides an important step towards improving interspecies extrapolation and quantitative risk assessment of PFOS, and this open-source model provides a foundation for developing models for other perfluoroalkyl substances.

Integration of Food Animal Residue Avoidance Databank (FARAD) empirical methods for drug withdrawal interval determination with a mechanistic population-based interactive physiologically based pharmacokinetic (iPBPK) modeling platform: example for flunixin meglumine administration

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

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.

Incorporation of the glutathione conjugation pathway in an updated physiologically-based pharmacokinetic model for perchloroethylene in mice

BACKGROUND

Perchloroethylene (perc) induced target organ toxicity has been associated with tissue-specific metabolic pathways. Previous physiologically-based pharmacokinetic (PBPK) modeling of perc accurately predicted oxidative metabolites but suggested the need to better characterize glutathione (GSH) conjugation as well as toxicokinetic uncertainty and variability.

OBJECTIVES

We updated the previously published "harmonized" perc PBPK model in mice to better characterize GSH conjugation metabolism as well as the uncertainty and variability of perc toxicokinetics.

METHODS

The updated PBPK model includes expanded models for perc and its oxidative metabolite trichloroacetic acid (TCA), and physiologically-based sub-models for conjugative metabolites. Previously compiled mouse kinetic data in B6C3F1 and Swiss-Webster mice were augmented to include data from a recent study in male C57BL/6J mice that measured perc and metabolites in serum and multiple tissues. Hierarchical Bayesian population analysis using Markov chain Monte Carlo was conducted to characterize uncertainty and inter-strain variability in perc metabolism.

RESULTS

The updated model fit the data as well or better than the previously published "harmonized" PBPK model. Tissue dosimetry for both oxidative and conjugative metabolites was successfully predicted across the three strains of mice, with estimated residuals errors of 2-fold for majority of data. Inter-strain variability across three strains was evident for oxidative metabolism; GSH conjugation data were only available for one strain.

CONCLUSIONS

This updated PBPK model fills a critical data gap in quantitative risk assessment by predicting the internal dosimetry of perc and its oxidative and GSH conjugation metabolites and lays the groundwork for future studies to better characterize toxicokinetic variability.

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.

Monte-Carlo and multi-exposure assessment for the derivation of criteria for disinfection byproducts and volatile organic compounds in drinking water: Allocation factors and liter-equivalents per day

The probability distributions of total potential doses of disinfection byproducts and volatile organic compounds via ingestion, inhalation, and dermal exposure were estimated with Monte Carlo simulations, after conducting physiologically based pharmacokinetic model simulations to takes into account the differences in availability between the three exposures. If the criterion that the 95th percentile estimate equals the TDI (tolerable daily intake) is regarded as protecting the majority of a population, the drinking water criteria would be 140 (trichloromethane), 66 (bromodichloromethane), 157 (dibromochloromethane), 203 (tribromomethane), 140 (dichloroacetic acid), 78 (trichloroacetic acid), 6.55 (trichloroethylene, TCE), and 22 μg/L (perchloroethylene). The TCE criterion was lower than the Japanese Drinking Water Quality Standard (10 μg/L). The latter would allow the intake of 20% of the population to exceed the TDI. Indirect inhalation via evaporation from water, especially in bathrooms, was the major route of exposure to compounds other than haloacetic acids (HAAs) and accounted for 1.2-9 liter-equivalents/day for the median-exposure subpopulation. The ingestion of food was a major indirect route of exposure to HAAs. Contributions of direct water intake were not very different for trihalomethanes (30-45% of TDIs) and HAAs (45-52% of TDIs).

Development of a Combined In Vitro Physiologically Based Kinetic (PBK) and Monte Carlo Modelling Approach to Predict Interindividual Human Variation in Phenol-Induced Developmental Toxicity

With our recently developed in vitro physiologically based kinetic (PBK) modelling approach, we could extrapolate in vitro toxicity data to human toxicity values applying PBK-based reverse dosimetry. Ideally information on kinetic differences among human individuals within a population should be considered. In the present study, we demonstrated a modelling approach that integrated in vitro toxicity data, PBK modelling and Monte Carlo simulations to obtain insight in interindividual human kinetic variation and derive chemical specific adjustment factors (CSAFs) for phenol-induced developmental toxicity. The present study revealed that UGT1A6 is the primary enzyme responsible for the glucuronidation of phenol in humans followed by UGT1A9. Monte Carlo simulations were performed taking into account interindividual variation in glucuronidation by these specific UGTs and in the oral absorption coefficient. Linking Monte Carlo simulations with PBK modelling, population variability in the maximum plasma concentration of phenol for the human population could be predicted. This approach provided a CSAF for interindividual variation of 2.0 which covers the 99th percentile of the population, which is lower than the default safety factor of 3.16 for interindividual human kinetic differences. Dividing the dose-response curve data obtained with in vitro PBK-based reverse dosimetry, with the CSAF provided a dose-response curve that reflects the consequences of the interindividual variability in phenol kinetics for the developmental toxicity of phenol. The strength of the presented approach is that it provides insight in the effect of interindividual variation in kinetics for phenol-induced developmental toxicity, based on only in vitro and in silico testing.

In vitro-in silico-based analysis of the dose-dependent in vivo oestrogenicity of the soy phytoestrogen genistein in humans

BACKGROUND AND PURPOSE

The in vivo oestrogenicity of genistein and its glycoside genistin is still under debate. The present study aimed to develop a physiologically based kinetic (PBK) model that provides insight in dose-dependent plasma concentrations of genistein aglycone and its metabolites and enables prediction of in vivo oestrogenic effective dose levels of genistein and genistin in humans.

EXPERIMENTAL APPROACH

A PBK model for genistein and genistin in humans was developed based on in vitro metabolic parameters. The model obtained was used to translate in vitro oestrogenic concentration-response curves of genistein to in vivo oestrogenic dose-response curves for intake of genistein and genistin.

KEY RESULTS

The model predicted that genistein-7-O-glucuronide was the major circulating metabolite and that levels of the free aglycone were generally low [0.5-17% of total plasma genistein at oral doses from 0.01 to 50 mg (kg·bw)^-1 ]. The predicted in vivo benchmark dose for 5% response values for oestrogenicity varied between 0.06 and 4.39 mg kg^-1 genistein. For genistin, these values were 1.3-fold higher. These values are in line with reported human data and show that oestrogenic responses can be expected at an Asian dietary and a supplementary intake, while intake resulting from a Western diet may not be effective.

CONCLUSIONS AND IMPLICATIONS

The present study shows how plasma concentrations of genistein and its metabolites and oestrogenic dose levels of genistein in humans can be predicted by combining in vitro oestrogenicity with PBK model-based reverse dosimetry, eliminating the need for human intervention studies.

Characterization of Variability in Toxicokinetics and Toxicodynamics of Tetrachloroethylene Using the Collaborative Cross Mouse Population

BACKGROUND

Evaluation of interindividual variability is a challenging step in risk assessment. For most environmental pollutants, including perchloroethylene (PERC), experimental data are lacking, resulting in default assumptions being used to account for variability in toxicokinetics and toxicodynamics.

OBJECTIVE

We quantitatively examined the relationship between PERC toxicokinetics and toxicodynamics at the population level to test whether individuals with increased oxidative metabolism are be more sensitive to hepatotoxicity following PERC exposure.

METHODS

Male mice from 45 strains of the Collaborative Cross (CC) were orally administered a single dose of PERC (1,000 mg/kg) or vehicle (Alkamuls-EL620) and euthanized at various time points (n = 1/strain/time). Concentration–time profiles were generated for PERC and its primary oxidative metabolite trichloroacetate (TCA) in multiple tissues. Toxicodynamic phenotyping was also performed.

RESULTS

Significant variability among strains was observed in toxicokinetics of PERC and TCA in every tissue examined. Based on area under the curve (AUC), the range of liver TCA levels spanned nearly an order of magnitude (~8-fold). Expression of liver cytochrome P4502E1 did not correlate with TCA levels. Toxicodynamic phenotyping revealed an effect of PERC on bodyweight loss, induction of peroxisome proliferator activated receptor-alpha (PPARα)-regulated genes, and dysregulation of hepatic lipid homeostasis. Clustering was observed among a) liver levels of PERC, TCA, and triglycerides; b) TCA levels in liver and kidney; and c) TCA levels in serum, brain, fat, and lung.

CONCLUSIONS

Using the CC mouse population model, we have demonstrated a complex and highly variable relationship between PERC and TCA toxicokinetics and toxicodynamics at the population level. https://doi.org/10.1289/EHP788.

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.

Application of Physiologically Based Absorption Modeling to Characterize the Pharmacokinetic Profiles of Oral Extended Release Methylphenidate Products in Adults

A previously presented physiologically-based pharmacokinetic model for immediate release (IR) methylphenidate (MPH) was extended to characterize the pharmacokinetic behaviors of oral extended release (ER) MPH formulations in adults for the first time. Information on the anatomy and physiology of the gastrointestinal (GI) tract, together with the biopharmaceutical properties of MPH, was integrated into the original model, with model parameters representing hepatic metabolism and intestinal non-specific loss recalibrated against in vitro and in vivo kinetic data sets with IR MPH. A Weibull function was implemented to describe the dissolution of different ER formulations. A variety of mathematical functions can be utilized to account for the engineered release/dissolution technologies to achieve better model performance. The physiological absorption model tracked well the plasma concentration profiles in adults receiving a multilayer-release MPH formulation or Metadate CD, while some degree of discrepancy was observed between predicted and observed plasma concentration profiles for Ritalin LA and Medikinet Retard. A local sensitivity analysis demonstrated that model parameters associated with the GI tract significantly influenced model predicted plasma MPH concentrations, albeit to varying degrees, suggesting the importance of better understanding the GI tract physiology, along with the intestinal non-specific loss of MPH. The model provides a quantitative tool to predict the biphasic plasma time course data for ER MPH, helping elucidate factors responsible for the diverse plasma MPH concentration profiles following oral dosing of different ER formulations.

Evaluation of Interindividual Human Variation in Bioactivation and DNA Adduct Formation of Estragole in Liver Predicted by Physiologically Based Kinetic/Dynamic and Monte Carlo Modeling

Estragole is a known hepatocarcinogen in rodents at high doses following metabolic conversion to the DNA-reactive metabolite 1'-sulfooxyestragole. The aim of the present study was to model possible levels of DNA adduct formation in (individual) humans upon exposure to estragole. This was done by extending a previously defined PBK model for estragole in humans to include (i) new data on interindividual variation in the kinetics for the major PBK model parameters influencing the formation of 1'-sulfooxyestragole, (ii) an equation describing the relationship between 1'-sulfooxyestragole and DNA adduct formation, (iii) Monte Carlo modeling to simulate interindividual human variation in DNA adduct formation in the population, and (iv) a comparison of the predictions made to human data on DNA adduct formation for the related alkenylbenzene methyleugenol. Adequate model predictions could be made, with the predicted DNA adduct levels at the estimated daily intake of estragole of 0.01 mg/kg bw ranging between 1.6 and 8.8 adducts in 10(8) nucleotides (nts) (50th and 99th percentiles, respectively). This is somewhat lower than values reported in the literature for the related alkenylbenzene methyleugenol in surgical human liver samples. The predicted levels seem to be below DNA adduct levels that are linked with tumor formation by alkenylbenzenes in rodents, which were estimated to amount to 188-500 adducts per 10(8) nts at the BMD10 values of estragole and methyleugenol. Although this does not seem to point to a significant health concern for human dietary exposure, drawing firm conclusions may have to await further validation of the model's predictions.

Estimation of residue depletion of cyadox and its marker residue in edible tissues of pigs using physiologically based pharmacokinetic modelling

Physiologically based pharmacokinetic (PBPK) models are powerful tools to predict tissue distribution and depletion of veterinary drugs in food animals. However, most models only simulate the pharmacokinetics of the parent drug without considering their metabolites. In this study, a PBPK model was developed to simultaneously describe the depletion in pigs of the food animal antimicrobial agent cyadox (CYA), and its marker residue 1,4-bisdesoxycyadox (BDCYA). The CYA and BDCYA sub-models included blood, liver, kidney, gastrointestinal tract, muscle, fat and other organ compartments. Extent of plasma-protein binding, renal clearance and tissue-plasma partition coefficients of BDCYA were measured experimentally. The model was calibrated with the reported pharmacokinetic and residue depletion data from pigs dosed by oral gavage with CYA for five consecutive days, and then extrapolated to exposure in feed for two months. The model was validated with 14 consecutive day feed administration data. This PBPK model accurately simulated CYA and BDCYA in four edible tissues at 24-120 h after both oral exposure and 2-month feed administration. There was only slight overestimation of CYA in muscle and BDCYA in kidney at earlier time points (6-12 h) when dosed in feed. Monte Carlo analysis revealed excellent agreement between the estimated concentration distributions and observed data. The present model could be used for tissue residue monitoring of CYA and BDCYA in food animals, and provides a foundation for developing PBPK models to predict residue depletion of both parent drugs and their metabolites in food animals.

Development of a physiologically based pharmacokinetic model for assessment of human exposure to bisphenol A

A previously developed physiologically based pharmacokinetic (PBPK) model for bisphenol A (BPA) in adult rhesus monkeys was modified to characterize the pharmacokinetics of BPA and its phase II conjugates in adult humans following oral ingestion. Coupled with in vitro studies on BPA metabolism in the liver and the small intestine, the PBPK model was parameterized using oral pharmacokinetic data with deuterated-BPA (d6-BPA) delivered in cookies to adult humans after overnight fasting. The availability of the serum concentration time course of unconjugated d6-BPA offered direct empirical evidence for the calibration of BPA model parameters. The recalibrated PBPK adult human model for BPA was then evaluated against published human pharmacokinetic studies with BPA. A hypothesis of decreased oral uptake was needed to account for the reduced peak levels observed in adult humans, where d6-BPA was delivered in soup and food was provided prior to BPA ingestion, suggesting the potential impact of dosing vehicles and/or fasting on BPA disposition. With the incorporation of Monte Carlo analysis, the recalibrated adult human model was used to address the inter-individual variability in the internal dose metrics of BPA for the U.S. general population. Model-predicted peak BPA serum levels were in the range of pM, with 95% of human variability falling within an order of magnitude. This recalibrated PBPK model for BPA in adult humans provides a scientific basis for assessing human exposure to BPA that can serve to minimize uncertainties incurred during extrapolations across doses and species.

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.

Evaluation of the interindividual human variation in bioactivation of methyleugenol using physiologically based kinetic modeling and Monte Carlo simulations

The present study aims at predicting the level of formation of the ultimate carcinogenic metabolite of methyleugenol, 1'-sulfooxymethyleugenol, in the human population by taking variability in key bioactivation and detoxification reactions into account using Monte Carlo simulations. Depending on the metabolic route, variation was simulated based on kinetic constants obtained from incubations with a range of individual human liver fractions or by combining kinetic constants obtained for specific isoenzymes with literature reported human variation in the activity of these enzymes. The results of the study indicate that formation of 1'-sulfooxymethyleugenol is predominantly affected by variation in i) P450 1A2-catalyzed bioactivation of methyleugenol to 1'-hydroxymethyleugenol, ii) P450 2B6-catalyzed epoxidation of methyleugenol, iii) the apparent kinetic constants for oxidation of 1'-hydroxymethyleugenol, and iv) the apparent kinetic constants for sulfation of 1'-hydroxymethyleugenol. Based on the Monte Carlo simulations a so-called chemical-specific adjustment factor (CSAF) for intraspecies variation could be derived by dividing different percentiles by the 50th percentile of the predicted population distribution for 1'-sulfooxymethyleugenol formation. The obtained CSAF value at the 90th percentile was 3.2, indicating that the default uncertainty factor of 3.16 for human variability in kinetics may adequately cover the variation within 90% of the population. Covering 99% of the population requires a larger uncertainty factor of 6.4. In conclusion, the results showed that adequate predictions on interindividual human variation can be made with Monte Carlo-based PBK modeling. For methyleugenol this variation was observed to be in line with the default variation generally assumed in risk assessment.

Development of a physiologically based pharmacokinetic model for flunixin in cattle (Bos taurus)

Frequent violation of flunixin residues in tissues from cattle has been attributed to non-compliance with the USFDA-approved route of administration and withdrawal time. However, the effect of administration route and physiological differences among animals on tissue depletion has not been determined. The objective of this work was to develop a physiologically based pharmacokinetic (PBPK) model to predict plasma, liver and milk concentrations of flunixin in cattle following intravenous (i.v.), intramuscular (i.m.) or subcutaneous (s.c.) administration for use as a tool to determine factors that may affect the withdrawal time. The PBPK model included blood flow-limited distribution in all tissues and elimination in the liver, kidney and milk. Regeneration of parent flunixin due to enterohepatic recirculation and hydrolysis of conjugated metabolites was incorporated in the liver compartment. Values for physiological parameters were obtained from the literature, and partition coefficients for all tissues but liver and kidney were derived empirically. Liver and kidney partition coefficients and elimination parameters were estimated for 14 pharmacokinetic studies (including five crossover studies) from the literature or government sources in which flunixin was administered i.v., i.m. or s.c. Model simulations compared well with data for the matrices following all routes of administration. Influential model parameters included those that may be age or disease-dependent, such as clearance and rate of milk production. Based on the model, route of administration would not affect the estimated days to reach the tolerance concentration (0.125 mg kg(-1)) in the liver of treated cattle. The majority of USDA-reported violative residues in liver were below the upper uncertainty predictions based on estimated parameters, which suggests the need to consider variability due to disease and age in establishing withdrawal intervals for drugs used in food animals. The model predicted that extravascular routes of administration prolonged flunixin concentrations in milk, which could result in violative milk residues in treated cattle.

Human health effects of tetrachloroethylene: key findings and scientific issues

BACKGROUND

The U.S. Environmental Protection Agency (EPA) completed a toxicological review of tetrachloroethylene (perchloroethylene, PCE) in February 2012 in support of the Integrated Risk Information System (IRIS).

OBJECTIVES

We reviewed key findings and scientific issues regarding the human health effects of PCE described in the U.S. EPA's Toxicological Review of Tetrachloroethylene (Perchloroethylene).

METHODS

The updated assessment of PCE synthesized and characterized a substantial database of epidemiological, experimental animal, and mechanistic studies. Key scientific issues were addressed through modeling of PCE toxicokinetics, synthesis of evidence from neurological studies, and analyses of toxicokinetic, mechanistic, and other factors (tumor latency, severity, and background rate) in interpreting experimental animal cancer findings. Considerations in evaluating epidemiological studies included the quality (e.g., specificity) of the exposure assessment methods and other essential design features, and the potential for alternative explanations for observed associations (e.g., bias or confounding).

DISCUSSION

Toxicokinetic modeling aided in characterizing the complex metabolism and multiple metabolites that contribute to PCE toxicity. The exposure assessment approach-a key evaluation factor for epidemiological studies of bladder cancer, non-Hodgkin lymphoma, and multiple myeloma-provided suggestive evidence of carcinogenicity. Bioassay data provided conclusive evidence of carcinogenicity in experimental animals. Neurotoxicity was identified as a sensitive noncancer health effect, occurring at low exposures: a conclusion supported by multiple studies. Evidence was integrated from human, experimental animal, and mechanistic data sets in assessing adverse health effects of PCE.

CONCLUSIONS

PCE is likely to be carcinogenic to humans. Neurotoxicity is a sensitive adverse health effect of PCE.

Characterization of the human kinetic adjustment factor for the health risk assessment of environmental contaminants

A default uncertainty factor of 3.16 (√10) is applied to account for interindividual variability in toxicokinetics when performing non-cancer risk assessments. Using relevant human data for specific chemicals, as WHO/IPCS suggests, it is possible to evaluate, and replace when appropriate, this default factor by quantifying chemical-specific adjustment factors for interindividual variability in toxicokinetics (also referred to as the human kinetic adjustment factor, HKAF). The HKAF has been determined based on the distributions of pharmacokinetic parameters (e.g., half-life, area under the curve, maximum blood concentration) in relevant populations. This article focuses on the current state of knowledge of the use of physiologically based algorithms and models in characterizing the HKAF for environmental contaminants. The recent modeling efforts on the computation of HKAF as a function of the characteristics of the population, chemical and its mode of action (dose metrics), as well as exposure scenario of relevance to the assessment are reviewed here. The results of these studies, taken together, suggest the HKAF varies as a function of the sensitive subpopulation and dose metrics of interest, exposure conditions considered (route, duration, and intensity), metabolic pathways involved and theoretical model underlying its computation. The HKAF seldom exceeded the default value of 3.16, except in very young children (i.e., <≈ 3 months) and when the parent compound is the toxic moiety. Overall, from a public health perspective, the current state of knowledge generally suggest that the default uncertainty factor is sufficient to account for human variability in non-cancer risk assessments of environmental contaminants.

A physiologically based in silico model for trans-2-hexenal detoxification and DNA adduct formation in human including interindividual variation indicates efficient detoxification and a negligible genotoxicity risk

A number of α,β-unsaturated aldehydes are present in food both as natural constituents and as flavouring agents. Their reaction with DNA due to their electrophilic α,β-unsaturated aldehyde moiety may result in genotoxicity as observed in some in vitro models, thereby raising a safety concern. A question that remains is whether in vivo detoxification would be efficient enough to prevent DNA adduct formation and genotoxicity. In this study, a human physiologically based kinetic/dynamic (PBK/D) model of trans-2-hexenal (2-hexenal), a selected model α,β-unsaturated aldehyde, was developed to examine dose-dependent detoxification and DNA adduct formation in humans upon dietary exposure. The kinetic model parameters for detoxification were quantified using relevant pooled human tissue fractions as well as tissue fractions from 11 different individual subjects. In addition, a Monte Carlo simulation was performed so that the impact of interindividual variation in 2-hexenal detoxification on the DNA adduct formation in the population as a whole could be examined. The PBK/D model revealed that DNA adduct formation due to 2-hexenal exposure was 0.039 adducts/10⁸ nucleotides (nt) at the estimated average 2-hexenal dietary intake (0.04 mg 2-hexenal/kg bw) and 0.18 adducts/10⁸ nt at the 95th percentile of the dietary intake (0.178 mg 2-hexenal/kg bw) in the most sensitive people. These levels are three orders of magnitude lower than natural background DNA adduct levels that have been reported in disease-free humans (6.8-110 adducts/10⁸ nt), suggesting that the genotoxicity risk for the human population at realistic dietary daily intakes of 2-hexenal may be negligible.

Weighted Markov chains for forecasting and analysis in Incidence of infectious diseases in jiangsu Province, China

This paper first applies the sequential cluster method to set up the classification standard of infectious disease incidence state based on the fact that there are many uncertainty characteristics in the incidence course. Then the paper presents a weighted Markov chain, a method which is used to predict the future incidence state. This method assumes the standardized self-coefficients as weights based on the special characteristics of infectious disease incidence being a dependent stochastic variable. It also analyzes the characteristics of infectious diseases incidence via the Markov chain Monte Carlo method to make the long-term benefit of decision optimal. Our method is successfully validated using existing incidents data of infectious diseases in Jiangsu Province. In summation, this paper proposes ways to improve the accuracy of the weighted Markov chain, specifically in the field of infection epidemiology.

A physiologically based pharmacokinetic model for the oxime TMB-4: simulation of rodent and human data

Multiple oximes have been synthesized and evaluated for use as countermeasures against chemical warfare nerve agents. The current U.S. military and civilian oxime countermeasure, 2-[(hydroxyimino)methyl]-1-methylpyridin-1-ium chloride (2-PAM), is under consideration for replacement with a more effective acetylcholinesterase reactivator, 1,1'-methylenebis{4-hydroxyiminomethyl}pyridinium dimethanesulfonate (MMB-4). Kinetic data in the scientific literature for MMB-4 are limited; therefore, a physiologically based pharmacokinetic (PBPK) model was developed for a structurally related oxime, 1,1'-trimethylenebis{4-hydroximinomethyl}pyridinium dibromide. Based on a previous model structure for the organophosphate diisopropylfluorophosphate, the model includes key sites of acetylcholinesterase inhibition (brain and diaphragm), as well as fat, kidney, liver, rapidly perfused tissues and slowly perfused tissues. All tissue compartments are diffusion limited. Model parameters were collected from the literature, predicted using quantitative structure-property relationships or, when necessary, fit to available pharmacokinetic data from the literature. The model was parameterized using rat plasma, tissue and urine time course data from intramuscular administration, as well as human blood and urine data from intravenous and intramuscular administration; sensitivity analyses were performed. The PBPK model successfully simulates rat and human data sets and has been evaluated by predicting intravenous mouse and intramuscular human data not used in the development of the model. Monte Carlo analyses were performed to quantify human population kinetic variability in the human evaluation data set. The model identifies potential pharmacokinetic differences between rodents and humans, indicated by differences in model parameters between species. The PBPK model can be used to optimize the dosing regimen to improve oxime therapeutic efficacy in a human population.

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.

Translational research to develop a human PBPK models tool kit-volatile organic compounds (VOCs)

Toxicity and exposure evaluations remain the two of the key components of human health assessment. While improvement in exposure assessment relies on a better understanding of human behavior patterns, toxicity assessment still relies to a great extent on animal toxicity testing and human epidemiological studies. Recent advances in computer modeling of the dose-response relationship and distribution of xenobiotics in humans to important target tissues have advanced our abilities to assess toxicity. In particular, physiologically based pharmacokinetic (PBPK) models are among the tools than can enhance toxicity assessment accuracy. Many PBPK models are available to the health assessor, but most are so difficult to use that health assessors rarely use them. To encourage their use these models need to have transparent and user-friendly formats. To this end the Agency for Toxic Substances and Disease Registry (ATSDR) is using translational research to increase PBPK model accessibility, understandability, and use in the site-specific health assessment arena. The agency has initiated development of a human PBPK tool-kit for certain high priority pollutants. The tool kit comprises a series of suitable models. The models are recoded in a single computer simulation language and evaluated for use by health assessors. While not necessarily being state-of-the-art code for each chemical, the models will be sufficiently accurate to use for screening purposes. This article presents a generic, seven-compartment PBPK model for six priority volatile organic compounds (VOCs): benzene (BEN), carbon tetrachloride (CCl(4)), dichloromethane (DCM), perchloroethylene (PCE), trichloroethylene (TCE), and vinyl chloride (VC). Limited comparisons of the generic and original model predictions to published kinetic data were conducted. A goodness of fit was determined by calculating the means of the sum of the squared differences (MSSDs) for simulation vs. experimental kinetic data using the generic and original models. Using simplified solvent exposure assumptions for oral ingestion and inhalation, steady-state blood concentrations of each solvent were simulated for exposures equivalent to the ATSDR Minimal Risk Levels (MRLs). The predicted blood levels were then compared to those reported in the National Health and Nutrition Examination Survey (NHANES). With the notable exception of BEN, simulations of combined oral and inhalation MRLs using our generic VOC model yielded blood concentrations well above those reported for the 95th percentile blood concentrations for the U.S. populations, suggesting no health concerns. When the PBPK tool kit is fully developed, risk assessors will have a readily accessible tool for evaluating human exposure to a variety of environmental pollutants.

Physiologically based pharmacokinetic model use in risk assessment--Why being published is not enough

A panel of experts in physiologically based pharmacokinetic (PBPK) modeling and relevant quantitative methods was convened to describe and discuss model evaluation criteria, issues, and choices that arise in model application and computational tools for improving model quality for use in human health risk assessments (HHRAs). Although publication of a PBPK model in a peer-reviewed journal is a mark of good science, subsequent evaluation of published models and the supporting computer code is necessary for their consideration for use in HHRAs. Standardized model evaluation criteria and a thorough and efficient review process can reduce the number of review and revision iterations and hence the time needed to prepare a model for application. Efficient and consistent review also allows for rapid identification of needed model modifications to address HHRA-specific issues. This manuscript reports on the workshop where a process and criteria that were created for PBPK model review were discussed along with other issues related to model review and application in HHRA. Other issues include (1) model code availability, portability, and validity; (2) probabilistic (e.g., population-based) PBPK models and critical choices in parameter values to fully characterize population variability; and (3) approaches to integrating PBPK model outputs with other HHRA tools, including benchmark dose modeling. Two specific case study examples are provided to illustrate challenges that were encountered during the review and application process. By considering the frequent challenges encountered in the review and application of PBPK models during the model development phase, scientists may be better able to prepare their models for use in HHRAs.

Development of a human Physiologically Based Pharmacokinetic (PBPK) Toolkit for environmental pollutants

Physiologically Based Pharmacokinetic (PBPK) models can be used to determine the internal dose and strengthen exposure assessment. Many PBPK models are available, but they are not easily accessible for field use. The Agency for Toxic Substances and Disease Registry (ATSDR) has conducted translational research to develop a human PBPK model toolkit by recoding published PBPK models. This toolkit, when fully developed, will provide a platform that consists of a series of priority PBPK models of environmental pollutants. Presented here is work on recoded PBPK models for volatile organic compounds (VOCs) and metals. Good agreement was generally obtained between the original and the recoded models. This toolkit will be available for ATSDR scientists and public health assessors to perform simulations of exposures from contaminated environmental media at sites of concern and to help interpret biomonitoring data. It can be used as screening tools that can provide useful information for the protection of the public.

Statistical inferences from formaldehyde DNA-protein cross-link data: improving methods for characterization of uncertainty

Physiologically based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application to pharmacological and environmental health problems. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. This paper demonstrates how improved statistical inference on estimated model parameters from both frequentist and Bayesian points of view can be routinely carried out. We work with a previously developed PBPK model for the formation and disposition of DNA-protein cross-links formed by inhaled formaldehyde in the nasal lining of rats and rhesus monkeys. We purposefully choose this model because it is based on sparse time-course data.

Development and evaluation of a harmonized physiologically based pharmacokinetic (PBPK) model for perchloroethylene toxicokinetics in mice, rats, and humans

This article reports on the development of a "harmonized" PBPK model for the toxicokinetics of perchloroethylene (tetrachloroethylene or perc) in mice, rats, and humans that includes both oxidation and glutathione (GSH) conjugation of perc, the internal kinetics of the oxidative metabolite trichloroacetic acid (TCA), and the urinary excretion kinetics of the GSH conjugation metabolites N-Acetylated trichlorovinyl cysteine and dichloroacetic acid. The model utilizes a wider range of in vitro and in vivo data than any previous analysis alone, with in vitro data used for initial, or "baseline," parameter estimates, and in vivo datasets separated into those used for "calibration" and those used for "evaluation." Parameter calibration utilizes a limited Bayesian analysis involving flat priors and making inferences only using posterior modes obtained via Markov chain Monte Carlo (MCMC). As expected, the major route of elimination of absorbed perc is predicted to be exhalation as parent compound, with metabolism accounting for less than 20% of intake except in the case of mice exposed orally, in which metabolism is predicted to be slightly over 50% at lower exposures. In all three species, the concentration of perc in blood, the extent of perc oxidation, and the amount of TCA production is well-estimated, with residual uncertainties of ~2-fold. However, the resulting range of estimates for the amount of GSH conjugation is quite wide in humans (~3000-fold) and mice (~60-fold). While even high-end estimates of GSH conjugation in mice are lower than estimates of oxidation, in humans the estimated rates range from much lower to much higher than rates for perc oxidation. It is unclear to what extent this range reflects uncertainty, variability, or a combination. Importantly, by separating total perc metabolism into separate oxidative and conjugative pathways, an approach also recommended in a recent National Research Council review, this analysis reconciles the disparity between those previously published PBPK models that concluded low perc metabolism in humans and those that predicted high perc metabolism in humans. In essence, both conclusions are consistent with the data if augmented with some additional qualifications: in humans, oxidative metabolism is low, while GSH conjugation metabolism may be high or low, with uncertainty and/or interindividual variability spanning three orders of magnitude. More direct data on the internal kinetics of perc GSH conjugation, such as trichlorovinyl glutathione or tricholorvinyl cysteine in blood and/or tissues, would be needed to better characterize the uncertainty and variability in GSH conjugation in humans.

Chemical-specific screening criteria for interpretation of biomonitoring data for volatile organic compounds (VOCs)--application of steady-state PBPK model solutions

The National Health and Nutrition Examination Survey (NHANES) generates population-representative biomonitoring data for many chemicals including volatile organic compounds (VOCs) in blood. However, no health or risk-based screening values are available to evaluate these data from a health safety perspective or to use in prioritizing among chemicals for possible risk management actions. We gathered existing risk assessment-based chronic exposure reference values such as reference doses (RfDs), reference concentrations (RfCs), tolerable daily intakes (TDIs), cancer slope factors, etc. and key pharmacokinetic model parameters for 47 VOCs. Using steady-state solutions to a generic physiologically-based pharmacokinetic (PBPK) model structure, we estimated chemical-specific steady-state venous blood concentrations across chemicals associated with unit oral and inhalation exposure rates and with chronic exposure at the identified exposure reference values. The geometric means of the slopes relating modeled steady-state blood concentrations to steady-state exposure to a unit oral dose or unit inhalation concentration among 38 compounds with available pharmacokinetic parameters were 12.0 microg/L per mg/kg-d (geometric standard deviation [GSD] of 3.2) and 3.2 microg/L per mg/m(3) (GSD=1.7), respectively. Chemical-specific blood concentration screening values based on non-cancer reference values for both oral and inhalation exposure range from 0.0005 to 100 microg/L; blood concentrations associated with cancer risk-specific doses at the 1E-05 risk level ranged from 5E-06 to 6E-02 microg/L. The distribution of modeled steady-state blood concentrations associated with unit exposure levels across VOCs may provide a basis for estimating blood concentration screening values for VOCs that lack chemical-specific pharmacokinetic data. The screening blood concentrations presented here provide a tool for risk assessment-based evaluation of population biomonitoring data for VOCs and are most appropriately applied to central tendency estimates for such datasets.

Bayesian analysis of a physiologically based pharmacokinetic model for perchloroethylene in humans

Perchloroethylene (PCE) is a widely distributed pollutant in the environment, and is the primary chemical used in dry cleaning. PCE-induced liver cancer was observed in mice, and central nervous system (CNS) effects were reported in dry-cleaning workers. To support reconstruction of human PCE exposures, including the potential for CNS effects, an existing physiologically based pharmacokinetic (PBPK) model for PCE in the human (Covington et al., 2007) was modified by adding a brain compartment. A Bayesian approach, using Markov chain Monte Carlo (MCMC) analysis, was employed to re-estimate the parameters in the modified model by combining information from prior distributions for the model parameters and experimental data. Experimental data were obtained from five different human pharmacokinetic studies of PCE inhalation exposures ranging from 150 ppm to as low as 0.495 ppm. The data include alveolar or exhaled breath concentrations of PCE, blood concentrations of PCE and trichloroacetic acid (TCA), and urinary excretion of TCA. The PBPK model was used to predict target tissue dosimetry of PCE and its key metabolite, TCA, during and after the inhalation exposures. Posterior analysis was performed to see whether convergence criteria for each parameter were satisfied and whether the model with posterior distributions may be used to make accurate predictions of human kinetic data. With posteriors, the trend of percent of PCE metabolized in the liver at low concentrations was predicted under different exposure conditions. The 95th percentile for the fraction PCE metabolized at a concentration of 1 ppb was estimated to be 1.89%.

Assessing drug distribution in tissues expressing P-glycoprotein using physiologically based pharmacokinetic modeling: identification of important model parameters through global sensitivity analysis

The structural complexity of a PBPK model is usually accompanied with significant uncertainty in estimating its input parameters. In the last decade, the global sensitivity analysis, which accounts for the variability of all model input parameters simultaneously as well as their correlations, has gained a wide attention as a powerful probing technique to identify and control biological model uncertainties. However, the current sensitivity analysis techniques used in PBPK modeling often neglect the correlation between these input parameters. We introduce a new strategy in the PBPK modeling field to investigate how the uncertainty and variability of correlated input parameters influence the outcomes of the drug distribution process based on a model we recently developed to explain and predict drug distribution in tissues expressing P-glycoprotein (P-gp). As direct results, we will also identify the most important input parameters having the largest contribution to the variability and uncertainty of model outcomes. We combined multivariate random sampling with a ranking procedure. Monte-Carlo simulations were performed on the PBPK model with eighteen model input parameters. Log-normal distributions were assumed for these parameters according to literature and their reported correlations were also included. A multivariate sensitivity analysis was then performed to identify the input parameters with the greatest influence on model predictions. The partial rank correlation coefficients (PRCC) were calculated to establish the input-output relationships. A moderate variability of predicted C(last) and C(max) was observed in liver, heart and brain tissues in the presence or absence of P-gp activity. The major statistical difference in model outcomes of the predicted median values has been obtained in brain tissue. PRCC calculation confirmed the importance for a better quantitative characterisation of input parameters related to the passive diffusion and active transport of the unbound drug through the blood-tissue membrane in heart and brain. This approach has also identified as important input parameters those related to the drug metabolism for the prediction of model outcomes in liver and plasma. The proposed Monte-Carlo/PRCC approach was aimed to address the effect of input parameters correlation in a PBPK model. It allowed the identification of important input parameters that require additional attention in research for strengthening the physiological knowledge of drug distribution in mammalian tissues expressing P-gp, thereby reducing the uncertainty of model predictions.

Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities

A conceptual/computational framework for exposure reconstruction from biomarker data combined with auxiliary exposure-related data is presented, evaluated with example applications, and examined in the context of future needs and opportunities. This framework employs physiologically based toxicokinetic (PBTK) modeling in conjunction with numerical "inversion" techniques. To quantify the value of different types of exposure data "accompanying" biomarker data, a study was conducted focusing on reconstructing exposures to chlorpyrifos, from measurements of its metabolite levels in urine. The study employed biomarker data as well as supporting exposure-related information from the National Human Exposure Assessment Survey (NHEXAS), Maryland, while the MENTOR-3P system (Modeling ENvironment for TOtal Risk with Physiologically based Pharmacokinetic modeling for Populations) was used for PBTK modeling. Recently proposed, simple numerical reconstruction methods were applied in this study, in conjunction with PBTK models. Two types of reconstructions were studied using (a) just the available biomarker and supporting exposure data and (b) synthetic data developed via augmenting available observations. Reconstruction using only available data resulted in a wide range of variation in estimated exposures. Reconstruction using synthetic data facilitated evaluation of numerical inversion methods and characterization of the value of additional information, such as study-specific data that can be collected in conjunction with the biomarker data. Although the NHEXAS data set provides a significant amount of supporting exposure-related information, especially when compared to national studies such as the National Health and Nutrition Examination Survey (NHANES), this information is still not adequate for detailed reconstruction of exposures under several conditions, as demonstrated here. The analysis presented here provides a starting point for introducing improved designs for future biomonitoring studies, from the perspective of exposure reconstruction; identifies specific limitations in existing exposure reconstruction methods that can be applied to population biomarker data; and suggests potential approaches for addressing exposure reconstruction from such data.

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

BACKGROUND

One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available.

OBJECTIVES

We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected.

METHODS

We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis.

RESULTS

Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures < or = 67 microg/L [corrected] in tap water and < or = 0.02 microg/L in ambient household air.

CONCLUSIONS

Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure-health evaluation-risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.

On the incorporation of chemical-specific information in risk assessment

This paper describes the evolution of chemical risk assessment from its early dependence on generic default approaches to the current situation in which mechanistic and biokinetic data are routinely incorporated to support a more chemical-specific approach. Two methodologies that have played an important role in this evolution are described: mode-of-action evaluation and physiologically based biokinetic (PBBK) modelling. When used together, these techniques greatly increase the opportunity for the incorporation of biokinetic and mechanistic data in risk assessment. The resulting risk assessment approaches are more appropriately tailored to the specific chemical and are more likely to provide an accurate assessment of the potential hazards associated with human exposures. The appropriate application of PBBK models in risk assessment demands well-formulated statements about the chemical mode of action. It is this requirement for an explicit, mechanistic hypothesis that gives biologically motivated models their power, but at the same time serves as the greatest impediment to the acceptance of a chemical-specific risk assessment approach by regulators. The chief impediment to the regulatory acceptance and application of PBBK models in risk assessment is concern about uncertainties associated with their use. To some extent such concerns can be addressed by the development of generally accepted approaches for model evaluation and quantitative uncertainty analysis. In order to assure the protection of public health while limiting the economic and social consequences of over-regulation, greater dialogue between researchers and regulators is crucially needed to foster an increased use of emerging scientific information and innovative methods in chemical risk assessments.