Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach

Low-dose inhalation exposure to trichloroethylene induces dopaminergic neurodegeneration in rodents

Trichloroethylene (TCE) is one of the most pervasive environmental contaminants in the world and is associated with Parkinson disease (PD) risk. Experimental models in rodents show that TCE is selectively toxic to dopaminergic neurons at high doses of ingestion, however, TCE is a highly volatile toxicant, and the primary pathway of human exposure is inhalation. As TCE is a highly lipophilic, volatile organic compound (VOC), inhalation exposure results in rapid diffusion throughout the brain, avoiding first-pass hepatic metabolism that necessitated high doses to recapitulate exposure conditions observed in human populations. We hypothesized that inhalation of TCE would induce significantly more potent neurodegeneration than ingestion and better recapitulate environmental conditions of vapor intrusion or off gassing from liquid TCE. To this end, we developed a novel, whole-body passive exposure inhalation chamber in which we exposed 10-month-old male and female Lewis rats to 50 ppm TCE (time weighted average, TWA) or filtered room air (control) over 8 weeks. In addition, we exposed 12-month-old male and female C57Bl/6 mice to 100 ppm TCE (TWA) or control over 12 weeks. Both rats and mice exposed to chronic TCE inhalation showed significant degeneration of nigrostriatal dopaminergic neurons as well as motor and gait impairments. TCE exposure also induced accumulation of pSer129-αSyn in dopaminergic neurons as well as microglial activation within the substantia nigra of rats. Collectively, these data indicate that TCE inhalation causes highly potent dopaminergic neurodegeneration and recapitulates some of the observed neuropathology associated with PD, providing a future platform for insight into the mechanisms and environmental conditions that influence PD risk from TCE exposure.

Low-dose inhalation exposure to trichloroethylene induces dopaminergic neurodegeneration in rodents

Trichloroethylene (TCE) is one of the most pervasive environmental contaminants in the world and is associated with Parkinson disease (PD) risk. Experimental models in rodents show that TCE is selectively toxic to dopaminergic neurons at high doses of ingestion, however, TCE is a highly volatile toxicant, and the primary pathway of human exposure is inhalation. As TCE is a highly lipophilic, volatile organic contaminant (VOC), inhalation exposure results in rapid diffusion throughout the brain, avoiding first-pass hepatic metabolism that necessitated high doses to recapitulate exposure conditions observed in human populations. We hypothesized that inhalation of TCE would induce significantly more potent neurodegeneration than ingestion and better recapitulate environmental conditions of vapor intrusion or off gassing from liquid TCE. To this end, we developed a novel, whole-body passive exposure inhalation chamber in which we exposed 10-month-old male and female Lewis rats to 50 ppm TCE (time weighted average, TWA) or filtered room air (control) over 8 weeks. In addition, we exposed 12-month-old male and female C57Bl/6 mice to 100 ppm TCE (TWA) or control over 12 weeks. Both rats and mice exposed to chronic TCE inhalation showed significant degeneration of nigrostriatal dopaminergic neurons as well as motor and gait impairments. TCE exposure also induced accumulation of pSer129-αSyn in dopaminergic neurons as well as microglial activation within the substantia nigra of rats. Collectively, these data indicate that TCE inhalation causes highly potent dopaminergic neurodegeneration and recapitulates some of the observed neuropathology associated with PD, providing a future platform for insight into the mechanisms and environmental conditions that influence PD risk from TCE exposure.

Current practice and recommendations for advancing how human variability and susceptibility are considered in chemical risk assessment

A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors.This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs.Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors.We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.

Reanalysis of Trichloroethylene and Tetrachloroethylene Metabolism to Glutathione Conjugates Using Human, Rat, and Mouse Liver in Vitro Models to Improve Precision in Risk Characterization

BACKGROUND

Both trichloroethylene (TCE) and tetrachloroethylene (PCE) are high-priority chemicals subject to numerous human health risk evaluations by a range of agencies. Metabolism of TCE and PCE determines their ultimate toxicity; important uncertainties exist in quantitative characterization of metabolism to genotoxic moieties through glutathione (GSH) conjugation and species differences therein.

OBJECTIVES

This study aimed to address these uncertainties using novel in vitro liver models, interspecies comparison, and a sensitive assay for quantification of GSH conjugates of TCE and PCE, S-(1,2-dichlorovinyl)glutathione (DCVG) and S-(1,2,2-trichlorovinyl) glutathione (TCVG), respectively.

METHODS

Liver in vitro models used herein were suspension, 2-D culture, and micropatterned coculture (MPCC) with primary human, rat, and mouse hepatocytes, as well as human induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep).

RESULTS

We found that, although efficiency of metabolism varied among models, consistent with known differences in their metabolic capacity, formation rates of DCVG and TCVG generally followed the patterns human ≥ rat ≥ mouse , and primary hepatocytes > iHep . Data derived from MPCC were most consistent with estimates from physiologically based pharmacokinetic models calibrated to in vivo data.

DISCUSSION

For TCE, the new data provided additional empirical support for inclusion of GSH conjugation-mediated kidney effects as critical for the derivation of noncancer toxicity values. For PCE, the data reduced previous uncertainties regarding the extent of TCVG formation in humans; this information was used to update several candidate kidney-specific noncancer toxicity values. Overall, MPCC-derived data provided physiologically relevant estimates of GSH-mediated metabolism of TCE and PCE to reduce uncertainties in interspecies extrapolation that constrained previous risk evaluations, thereby increasing the precision of risk characterizations of these high-priority toxicants. https://doi.org/10.1289/EHP12006.

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.

Bayesian population physiologically-based pharmacokinetic model for robustness evaluation of withdrawal time in tilapia aquaculture administrated to florfenicol

The antimicrobial residues of aquacultural production is a growing public concern, leading to reexamine the method for establishing robust withdrawal time and ensuring food safety. Our study aims to develop the optimizing population physiologically-based pharmacokinetic (PBPK) model for assessing florfenicol residues in the tilapia tissues, and for evaluating the robustness of the withdrawal time (WT). Fitting with published pharmacokinetic profiles that experimented under temperatures of 22 and 28 °C, a PBPK model was constructed by applying with the Bayesian Markov chain Monte Carol (MCMC) algorithm to estimate WTs under different physiological, environmental and dosing scenarios. Results show that the MCMC algorithm improves the estimates of uncertainty and variability of PBPK-related parameters, and optimizes the simulation of the PBPK model. It is noteworthy that posterior sets generated from temperature-associated datasets to be respectively used for simulating residues under corresponding temperature conditions. Simulating the residues under regulated regimen and overdosing scenarios for Taiwan, the estimated WTs were 12-16 days at 22 °C and 9-12 days at 28 °C, while for the USA, the estimated WTs were 14-18 and 11-14 days, respectively. Comparison with the regulated WT of 15 days, results indicate that the current WT has well robustness and resilience in the environment of higher temperatures. The optimal Bayesian population PBPK model provides effective analysis for determining WTs under scenario-specific conditions. It is a new insight into the increasing body of literature on developing the Bayesian-PBPK model and has practical implications for improving the regulation of food safety.

PBPK modeling of impact of nonalcoholic fatty liver disease on toxicokinetics of perchloroethylene in mice

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease in the Western countries with increasing prevalence worldwide, may substantially affect chemical toxicokinetics and thereby modulate chemical toxicity.

OBJECTIVES

This study aims to use physiologically-based pharmacokinetic (PBPK) modeling to characterize the impact of NAFLD on toxicokinetics of perchloroethylene (perc).

METHODS

Quantitative measures of physiological and biochemical changes associated with the presence of NAFLD induced by high-fat or methionine/choline-deficient diets in C57B1/6 J mice are incorporated into a previously developed PBPK model for perc and its oxidative and conjugative metabolites. Impacts on liver fat and volume, as well as blood:air and liver:air partition coefficients, are incorporated into the model. Hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation is conducted to characterize uncertainty, as well as disease-induced variability in toxicokinetics.

RESULTS

NAFLD has a major effect on toxicokinetics of perc, with greater oxidative and lower conjugative metabolism as compared to healthy mice. The NAFLD-updated PBPK model accurately predicts in vivo metabolism of perc through oxidative and conjugative pathways in all tissues across disease states and strains, but underestimated parent compound concentrations in blood and liver of NAFLD mice.

CONCLUSIONS

We demonstrate the application of PBPK modeling to predict the effects of pre-existing disease conditions as a variability factor in perc metabolism. These results suggest that non-genetic factors such as diet and pre-existing disease can be as influential as genetic factors in altering toxicokinetics of perc, and thus are likely contribute substantially to population variation in its adverse effects.

Trichloroethylene, a ubiquitous environmental contaminant in the risk for Parkinson's disease

Organic solvents are common chemicals used in industry throughout the world, however, there is evidence for adverse health effects from exposure to these compounds. Trichloroethylene (TCE) is a halogenated solvent that has been used as a degreasing agent since the early 20th century. Due to its widespread use, TCE remains one of the most significant environmental contaminants in the US, and extensive research suggests TCE is a causative factor in a number of diseases, including cancer, fetal cardiac development, and neurotoxicity. TCE has also been implicated as a possible risk factor in the development of the most common neurodegenerative movement disorder, Parkinson's disease (PD). However, there is variable concordance across multiple occupational epidemiological studies assessing TCE (or solvent) exposure and risk for PD. In addition, there remains a degree of uncertainty about how TCE elicits toxicity to the dopaminergic system. To this end, we review the specific neurotoxic mechanisms of TCE in the context of selective vulnerability of dopaminergic neurons. In addition, we consider the complexity of combined risk factors that ultimately contribute to neurodegeneration and discuss the limitations of single-factor exposure assessments.

Modulation of Tetrachloroethylene-Associated Kidney Effects by Nonalcoholic Fatty Liver or Steatohepatitis in Male C57BL/6J Mice

Accounting for genetic and other (eg, underlying disease states) factors that may lead to inter-individual variability in susceptibility to xenobiotic-induced injury is a challenge in human health assessments. A previous study demonstrated that nonalcoholic fatty liver disease (NAFLD), one of the common underlying disease states, enhances tetrachloroethylene (PERC)-associated hepatotoxicity in mice. Interestingly, NAFLD resulted in a decrease in metabolism of PERC to nephrotoxic glutathione conjugates; we therefore hypothesized that NAFLD would protect against PERC-associated nephrotoxicity. Male C57BL/6J mice were fed a low-fat (LFD), high-fat (31% fat, HFD), or high-fat methionine/choline/folate-deficient (31% fat, MCD) diets. After 8 weeks mice were administered either a single dose of PERC (300 mg/kg i.g.) and euthanized at 1-36 h post dose, or five daily doses of PERC (300 mg/kg/d i.g.) and euthanized 4 h after last dose. Relative to LFD-fed mice, HFD- or MCD-fed mice exhibited decreased PERC concentrations and increased trichloroacetate (TCA) in kidneys. S-(1,2,2-trichlorovinyl)glutathione (TCVG), S-(1,2,2-trichlorovinyl)-l-cysteine (TCVC), and N-acetyl-S-(1,2,2,-trichlorovinyl)-l-cysteine (NAcTCVC) were also significantly lower in kidney and urine of HFD- or MCD-fed mice compared with LFD-fed mice. Despite differences in levels of nephrotoxic PERC metabolites in kidney, LFD- and MCD-fed mice demonstrated similar degree of nephrotoxicity. However, HFD-fed mice were less sensitive to PERC-induced nephrotoxicity. Thus, whereas both MCD- and HFD-induced fatty liver reduced the delivered dose of nephrotoxic PERC metabolites to the kidney, only HFD was protective against PERC-induced nephrotoxicity, possibly due to greater toxicodynamic sensitivity induced by methyl and choline deficiency. These results therefore demonstrate that pre-existing disease conditions can lead to a complex interplay of toxicokinetic and toxicodynamic changes that modulate susceptibility to the toxicity of xenobiotics.

Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

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.

Metabolism and Toxicity of Trichloroethylene and Tetrachloroethylene in Cytochrome P450 2E1 Knockout and Humanized Transgenic Mice

Trichloroethylene (TCE) and tetrachloroethylene (PCE) are structurally similar olefins that can cause liver and kidney toxicity. Adverse effects of these chemicals are associated with metabolism to oxidative and glutathione conjugation moieties. It is thought that CYP2E1 is crucial to the oxidative metabolism of TCE and PCE, and may also play a role in formation of nephrotoxic metabolites; however, inter-species and inter-individual differences in contribution of CYP2E1 to metabolism and toxicity are not well understood. Therefore, the role of CYP2E1 in metabolism and toxic effects of TCE and PCE was investigated using male and female wild-type [129S1/SvlmJ], Cyp2e1(-/-), and humanized Cyp2e1 [hCYP2E1] mice. To fill in existing gaps in our knowledge, we conducted a toxicokinetic study of TCE (600 mg/kg, single dose, i.g.) and a subacute study of PCE (500 mg/kg/day, 5 days, i.g.) in 3 strains. Liver and kidney tissues were subject to profiling of oxidative and glutathione conjugation metabolites of TCE and PCE, as well as toxicity endpoints. The amounts of trichloroacetic acid formed in the liver was hCYP2E1≈ 129S1/SvlmJ > Cyp2e1(-/-) for both TCE and PCE; levels in males were about 2-fold higher than in females. Interestingly, 2- to 3-fold higher levels of conjugation metabolites were observed in TCE-treated Cyp2e1(-/-) mice. PCE induced lipid accumulation only in liver of 129S1/SvlmJ mice. In the kidney, PCE exposure resulted in acute proximal tubule injury in both sexes in all strains (hCYP2E1 ≈ 129S1/SvlmJ > Cyp2e1(-/-)). In conclusion, our results demonstrate that CYP2E1 is an important, but not exclusive actor in the oxidative metabolism and toxicity of TCE and PCE.

Differential toxicity of water versus gavage exposure to trichloroethylene in rats

Trichloroethylene (TCE) is a persistent environmental contaminant that causes male reproductive toxicity. We investigated whether transient increases in TCE exposure modulated male reproductive toxicity by exposing rats via daily oral to repeated gavage exposures (1000 mg/kg/day) and through drinking water (0.6% TCE) for 14 weeks. The gavage route resulted in reversible reduction of epididymis weight, and reduced body weight that persisted for up to 12-weeks after cessation of exposure. Physiologically-based pharmacokinetic modeling predicted that the gavage route results in higher C_max and AUC exposure of TCE compared to drinking water exposure, explaining the observed differences in toxicity between dosing regimens.

Allium cepa Bio Assay to Assess the Water and Sediment Cytogenotoxicity in a Tropical Stream Subjected to Multiple Point and Nonpoint Source Pollutants

The present study was conducted to assess the cytotoxicity of water and sediments of an industrial effluent receiving water body in the western province of Sri Lanka using Allium cepa bioassay. Six sampling sites (Site A: Urban; B: Industrial; C: Water intake for public water supply; D: Industrial; E: Agricultural; F: Reference) were selected from the study area. Ten replicate water and sediment samples were collected from each site, and physical and chemical parameters were measured using standard analytical methods. Cytotoxicity of water and sediment elutriates were measured using Allium cepa bioassay. Despite the significant spatial variations, the overall water and sediment quality parameters of the study sites were in accordance with the standard ambient environment parameters to sustain a healthy aquatic life. In the A. cepa bulbs exposed to water samples, significant root growth variations were not observed within 48 hours of exposure. However, significant root length variations were observed in A. cepa bulbs exposed to sediment elutriates within the 48-hour exposure and the percentage root growth inhibition increased with increase of exposure time. Similar trend was observed in mitotic activity indicating significantly lower mitotic indices (compared to that of the reference site) in A. cepa root tip cells exposed to sediment elutriates than those exposed to water samples. Further, the highest number of nuclear abnormalities was recorded from root tip cells of A. cepa exposed to water and sediment samples from sites B, C, and D. Therefore, it is of extreme importance to identify the composition and speciation of these cytogenotoxic compounds in the tropical climatic conditions and to propose possible clean-up or treatment solutions to overcome this environmental and public health risk associated problem.

Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making

The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has thus far occurred for only a few environmental chemicals. In order to encourage decision-makers to embrace the critical role of PBPK modeling in risk assessment, several important challenges require immediate attention from the modeling community. The objective of this contemporary review is to highlight 3 of these challenges, including: (1) difficulties in recruiting peer reviewers with appropriate modeling expertise and experience; (2) lack of confidence in PBPK models for which no tissue/plasma concentration data exist for model evaluation; and (3) lack of transferability across modeling platforms. Several recommendations for addressing these 3 issues are provided to initiate dialog among members of the PBPK modeling community, as these issues must be overcome for the field of PBPK modeling to advance and for PBPK models to be more routinely applied in support of public health decision-making.

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.

Comparative analysis of metabolism of trichloroethylene and tetrachloroethylene among mouse tissues and strains

Trichloroethylene (TCE) and tetrachloroethylene (PCE) are structurally similar chemicals that are metabolized through oxidation and glutathione conjugation pathways. Both chemicals have been shown to elicit liver and kidney toxicity in rodents and humans; however, TCE has been studied much more extensively in terms of both metabolism and toxicity. Despite their qualitative similarities, quantitative comparison of tissue- and strain-specific metabolism of TCE and PCE has not been performed. To fill this gap, we conducted a comparative toxicokinetic study where equimolar single oral doses of TCE (800 mg/kg) or PCE (1000 mg/kg) were administered to male mice of C57BL/6J, B6C3F1/J, and NZW/LacJ strains. Samples of liver, kidney, serum, brain, and lung were obtained for up to 36 h after dosing. For each tissue, concentrations of parent compounds, as well as their oxidative and glutathione conjugation metabolites were measured and concentration-time profiles constructed. A multi-compartment toxicokinetic model was developed to quantitatively compare TCE and PCE metabolism. As expected, the flux through oxidation metabolism pathway predominated over that through conjugation across all mouse strains examined, it is 1,200-3,800 fold higher for TCE and 26-34 fold higher for PCE. However, the flux through glutathione conjugation, albeit a minor metabolic pathway, was 21-fold higher for PCE as compared to TCE. The degree of inter-strain variability was greatest for oxidative metabolites in TCE-treated and for glutathione conjugation metabolites in PCE-treated mice. This study provides critical data for quantitative comparisons of TCE and PCE metabolism, and may explain the differences in organ-specific toxicity between these structurally similar chemicals.

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.

Modeling and Simulation of Pretomanid Pharmacokinetics in Pulmonary Tuberculosis Patients

Pretomanid is a nitroimidazole antibiotic in late-phase clinical testing as a component of several novel antituberculosis (anti-TB) regimens. A population pharmacokinetic model for pretomanid was constructed using a Bayesian analysis of data from two phase 2 studies, PA-824-CL-007 and PA-824-CL-010, conducted with adult (median age, 27 years) patients in Cape Town, South Africa, with newly diagnosed pulmonary TB. Combined, these studies included 63 males and 59 females administered once-daily oral pretomanid doses of 50, 100, 150, 200, 600, 1,000, or 1,200 mg for 14 days. The observed pretomanid plasma concentration-time profiles for all tested doses were described by a one-compartment model with first-order absorption and elimination and a sigmoidal bioavailability dependent on dose, time, and the predose fed state. Allometric scaling with body weight (normalized to 70 kg) was used for volume of distribution and clearance, with the scaling exponents equal to 1 and 3/4, respectively. The posterior population geometric means for the clearance and volume of distribution allometric constants were 4.8 ± 0.2 liters/h and 130 ± 5 liters, respectively, and the posterior population geometric mean for the half-maximum-effect dose for the reduction of bioavailability was 450 ± 50 mg. Interindividual variability, described by the percent coefficient of variation, was 32% ± 3% for clearance, 17% ± 4% for the volume of distribution, and 74% ± 9% for the half-maximum-effect dose. This model provides a dose-exposure relationship for pretomanid in adult TB patients with potential applications to dose selection in individuals and to further clinical testing of novel pretomanid-containing anti-TB regimens.

Applying a Global Sensitivity Analysis Workflow to Improve the Computational Efficiencies in Physiologically-Based Pharmacokinetic Modeling

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

Population-based dose-response analysis of liver transcriptional response to trichloroethylene in mouse

Studies of gene expression are common in toxicology and provide important clues to mechanistic understanding of adverse effects of chemicals. Most prior studies have been performed in a single strain or cell line; however, gene expression is heavily influenced by the genetic background, and these genotype-expression differences may be key drivers of inter-individual variation in response to chemical toxicity. In this study, we hypothesized that the genetically diverse Collaborative Cross mouse population can be used to gain insight and suggest mechanistic hypotheses for the dose- and genetic background-dependent effects of chemical exposure. This hypothesis was tested using a model liver toxicant trichloroethylene (TCE). Liver transcriptional responses to TCE exposure were evaluated 24 h after dosing. Transcriptomic dose-responses were examined for both TCE and its major oxidative metabolite trichloroacetic acid (TCA). As expected, peroxisome- and fatty acid metabolism-related pathways were among the most dose-responsive enriched pathways in all strains. However, nearly half of the TCE-induced liver transcriptional perturbation was strain-dependent, with abundant evidence of strain/dose interaction, including in the peroxisomal signaling-associated pathways. These effects were highly concordant between the administered TCE dose and liver levels of TCA. Dose-response analysis of gene expression at the pathway level yielded points of departure similar to those derived from the traditional toxicology studies for both non-cancer and cancer effects. Mapping of expression-genotype-dose relationships revealed some significant associations; however, the effects of TCE on gene expression in liver appear to be highly polygenic traits that are challenging to positionally map. This study highlights the usefulness of mouse population-based studies in assessing inter-individual variation in toxicological responses, but cautions that genetic mapping may be challenging because of the complexity in gene exposure-dose relationships.

Characterization of inter-tissue and inter-strain variability of TCE glutathione conjugation metabolites DCVG, DCVC, and NAcDCVC in the mouse

Trichloroethylene (TCE) is a ubiquitous environmental toxicant that is a liver and kidney carcinogen. Conjugation of TCE with glutathione (GSH) leads to formation of nepthrotoxic and mutagenic metabolites postulated to be critical for kidney cancerdevelopment; however, relatively little is known regarding their tissue levels as previous analytical methods for their detection lacked sensitivity. Here, an LC-MS/MS-based method for simultaneous detection of S-(1,2-dichlorovinyl)-glutathione (DCVG), S-(1,2-dichlorovinyl)-L-cysteine (DCVC), and N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (NAcDCVC) in multiple mouse tissues was developed. This analytical method is rapid, sensitive (limits of detection (LOD) 3-30 fmol across metabolites and tissues), and robust to quantify all three metabolites in liver, kidneys, and serum. The method was used to characterize inter-tissue and inter-strain variability in formation of conjugative metabolites of TCE. Single oral dose of TCE (24, 240 or 800 mg/kg) was administered to male mice from 20 inbred strains of Collaborative Cross. Inter-strain variability in the levels of DCVG, DCVC, and NAcDCVC (GSD = 1.6-2.9) was observed. Whereas NAcDCVC was distributed equally among analyzed tissues, highest levels of DCVG were detected in liver and DCVC in kidneys. Evidence indicated that inter-strain variability in conjugative metabolite formation of TCE might affect susceptibility to adverse health effects and that this method might aid in filling data gaps in human health assessment of TCE.

Editor's Highlight: Comparative Dose-Response Analysis of Liver and Kidney Transcriptomic Effects of Trichloroethylene and Tetrachloroethylene in B6C3F1 Mouse

Trichloroethylene (TCE) and tetrachloroethylene (PCE) are ubiquitous environmental contaminants and occupational health hazards. Recent health assessments of these agents identified several critical data gaps, including lack of comparative analysis of their effects. This study examined liver and kidney effects of TCE and PCE in a dose-response study design. Equimolar doses of TCE (24, 80, 240, and 800 mg/kg) or PCE (30, 100, 300, and 1000 mg/kg) were administered by gavage in aqueous vehicle to male B6C3F1/J mice. Tissues were collected 24 h after exposure. Trichloroacetic acid (TCA), a major oxidative metabolite of both compounds, was measured and RNA sequencing was performed. PCE had a stronger effect on liver and kidney transcriptomes, as well as greater concentrations of TCA. Most dose-responsive pathways were common among chemicals/tissues, with the strongest effect on peroxisomal β-oxidation. Effects on liver and kidney mitochondria-related pathways were notably unique to PCE. We performed dose-response modeling of the transcriptomic data and compared the resulting points of departure (PODs) to those for apical endpoints derived from long-term studies with these chemicals in rats, mice, and humans, converting to human equivalent doses using tissue-specific dosimetry models. Tissue-specific acute transcriptional effects of TCE and PCE occurred at human equivalent doses comparable to those for apical effects. These data are relevant for human health assessments of TCE and PCE as they provide data for dose-response analysis of the toxicity mechanisms. Additionally, they provide further evidence that transcriptomic data can be useful surrogates for in vivo PODs, especially when toxicokinetic differences are taken into account.

A framework and case studies for evaluation of enzyme ontogeny in children's health risk evaluation

Knowledge of the ontogeny of Phase I and Phase II metabolizing enzymes may be used to inform children's vulnerability based upon likely differences in internal dose from xenobiotic exposure. This might provide a qualitative assessment of toxicokinetic (TK) variability and uncertainty pertinent to early lifestages and help scope a more quantitative physiologically based toxicokinetic (PBTK) assessment. Although much is known regarding the ontogeny of metabolizing systems, this is not commonly utilized in scoping and problem formulation stage of human health risk evaluation. A framework is proposed for introducing this information into problem formulation which combines data on enzyme ontogeny and chemical-specific TK to explore potential child/adult differences in internal dose and whether such metabolic differences may be important factors in risk evaluation. The framework is illustrated with five case study chemicals, including some which are data rich and provide proof of concept, while others are data poor. Case studies for toluene and chlorpyrifos indicate potentially important child/adult TK differences while scoping for acetaminophen suggests enzyme ontogeny is unlikely to increase early-life risks. Scoping for trichloroethylene and aromatic amines indicates numerous ways that enzyme ontogeny may affect internal dose which necessitates further evaluation. PBTK modeling is a critical and feasible next step to further evaluate child-adult differences in internal dose for a number of these chemicals.

New Rodent Population Models May Inform Human Health Risk Assessment and Identification of Genetic Susceptibility to Environmental Exposures

BACKGROUND

This paper provides an introduction for environmental health scientists to emerging population-based rodent resources. Mouse reference populations provide an opportunity to model environmental exposures and gene-environment interactions in human disease and to inform human health risk assessment.

OBJECTIVES

This review will describe several mouse populations for toxicity assessment, including older models such as the Mouse Diversity Panel (MDP), and newer models that include the Collaborative Cross (CC) and Diversity Outbred (DO) models.

METHODS

This review will outline the features of the MDP, CC, and DO mouse models and will discuss published case studies investigating the use of these mouse population resources in each step of the risk assessment paradigm.

DISCUSSION

These unique resources have the potential to be powerful tools for generating hypotheses related to gene-environment interplay in human disease, performing controlled exposure studies to understand the differential responses in humans for susceptibility or resistance to environmental exposures, and identifying gene variants that influence sensitivity to toxicity and disease states.

CONCLUSIONS

These new resources offer substantial advances to classical toxicity testing paradigms by including genetically sensitive individuals that may inform toxicity risks for sensitive subpopulations. Both in vivo and complementary in vitro resources provide platforms with which to reduce uncertainty by providing population-level data around biological variability. https://doi.org/10.1289/EHP1274.

Target Organ Metabolism, Toxicity, and Mechanisms of Trichloroethylene and Perchloroethylene: Key Similarities, Differences, and Data Gaps

Trichloroethylene (TCE) and perchloroethylene or tetrachloroethylene (PCE) are high-production volume chemicals with numerous industrial applications. As a consequence of their widespread use, these chemicals are ubiquitous environmental contaminants to which the general population is commonly exposed. It is widely assumed that TCE and PCE are toxicologically similar; both are simple olefins with three (TCE) or four (PCE) chlorines. Nonetheless, despite decades of research on the adverse health effects of TCE or PCE, few studies have directly compared these two toxicants. Although the metabolic pathways are qualitatively similar, quantitative differences in the flux and yield of metabolites exist. Recent human health assessments have uncovered some overlap in target organs that are affected by exposure to TCE or PCE, and divergent species- and sex-specificity with regard to cancer and noncancer hazards. The objective of this minireview is to highlight key similarities, differences, and data gaps in target organ metabolism and mechanism of toxicity. The main anticipated outcome of this review is to encourage research to 1) directly compare the responses to TCE and PCE using more sensitive biochemical techniques and robust statistical comparisons; 2) more closely examine interindividual variability in the relationship between toxicokinetics and toxicodynamics for TCE and PCE; 3) elucidate the effect of coexposure to these two toxicants; and 4) explore new mechanisms for target organ toxicity associated with TCE and/or PCE exposure.

Physiologically Based Pharmacokinetic Model of Rifapentine and 25-Desacetyl Rifapentine Disposition in Humans

Rifapentine (RPT) is a rifamycin antimycobacterial and, as part of a combination therapy, is indicated for the treatment of pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis Although the results from a number of studies indicate that rifapentine has the potential to shorten treatment duration and enhance completion rates compared to other rifamycin agents utilized in antituberculosis drug regimens (i.e., regimens 1 to 4), its optimal dose and exposure in humans are unknown. To help inform such an optimization, a physiologically based pharmacokinetic (PBPK) model was developed to predict time course, tissue-specific concentrations of RPT and its active metabolite, 25-desacetyl rifapentine (dRPT), in humans after specified administration schedules for RPT. Starting with the development and verification of a PBPK model for rats, the model was extrapolated and then tested using human pharmacokinetic data. Testing and verification of the models included comparisons of predictions to experimental data in several rat tissues and time course RPT and dRPT plasma concentrations in humans from several single- and repeated-dosing studies. Finally, the model was used to predict RPT concentrations in the lung during the intensive and continuation phases of a current recommended TB treatment regimen. Based on these results, it is anticipated that the PBPK model developed in this study will be useful in evaluating dosing regimens for RPT and for characterizing tissue-level doses that could be predictors of problems related to efficacy or safety.

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.

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.

Bayesian evaluation of a physiologically-based pharmacokinetic (PBPK) model of long-term kinetics of metal nanoparticles in rats

Biomathematical modeling quantitatively describes the disposition of metal nanoparticles in lungs and other organs of rats. In a preliminary model, adjustable parameters were calibrated to each of three data sets using a deterministic approach, with optimal values varying among the different data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo (MCMC) simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. The previously-developed model structure and some physiological parameter values were modified to improve physiological realism. The data from one of the three previously-identified studies and from two other studies were used for model calibration. The data from the one study that adequately characterized mass balance were used to generate parameter distributions. When data from a second study of the same nanomaterial (iridium) were added, the level of agreement was still acceptable. Addition of another data set (for silver nanoparticles) led to substantially lower precision in parameter estimates and large discrepancies between the model predictions and experimental data for silver nanoparticles. Additional toxicokinetic data are needed to further evaluate the model structure and performance and to reduce uncertainty in the kinetic processes governing in vivo disposition of metal nanoparticles.

Computational pharmacokinetics/pharmacodynamics of rifampin in a mouse tuberculosis infection model

One critical approach to preclinical evaluation of anti-tuberculosis (anti-TB) drugs is the study of correlations between drug exposure and efficacy in animal TB infection models. While such pharmacokinetic/pharmacodynamic (PK/PD) studies are useful for the identification of optimal clinical dosing regimens, they are resource intensive and are not routinely performed. A mathematical model capable of simulating the PK/PD properties of drug therapy for experimental TB offers a way to mitigate some of the practical obstacles to determining the PK/PD index that best correlates with efficacy. Here, we present a preliminary physiologically based PK/PD model of rifampin therapy in a mouse TB infection model. The computational framework integrates whole-body rifampin PKs, cell population dynamics for the host immune response to Mycobacterium tuberculosis infection, drug-bacteria interactions, and a Bayesian method for parameter estimation. As an initial application, we calibrated the model to a set of available rifampin PK/PD data and simulated a separate dose fractionation experiment for bacterial killing kinetics in the lungs of TB-infected mice. The simulation results qualitatively agreed with the experimentally observed PK/PD correlations, including the identification of area under the concentration-time curve as best correlating with efficacy. This single-drug framework is aimed toward extension to multiple anti-TB drugs in order to facilitate development of optimal combination regimens.

Comparative analysis of the relationship between trichloroethylene metabolism and tissue-specific toxicity among inbred mouse strains: liver effects

Trichloroethylene (TCE) is a widely used organic solvent. Although TCE is classified as carcinogenic to humans, substantial gaps remain in our understanding of interindividual variability in TCE metabolism and toxicity, especially in the liver. A hypothesis was tested that amounts of oxidative metabolites of TCE in mouse liver are associated with hepatic-specific toxicity. Oral dosing with TCE was conducted in subacute (600 mg/kg/d; 5 d; 7 inbred mouse strains) and subchronic (100 or 400 mg/kg/d; 1, 2, or 4 wk; 2 inbred mouse strains) designs. The quantitative relationship was evaluated between strain-, dose-, and time-dependent formation of TCE metabolites from cytochrome P-450-mediated oxidation (trichloroacetic acid [TCA], dichloroacetic acid [DCA], and trichloroethanol) and glutathione conjugation [S-(1,2-dichlorovinyl)-L-cysteine and S-(1,2-dichlorovinyl)glutathione] in serum and liver, and various hepatic toxicity phenotypes. In subacute study, interstrain variability in TCE metabolite amounts was observed in serum and liver. No marked induction of Cyp2e1 protein levels in liver was detected. Serum and hepatic levels of TCA and DCA were correlated with increased transcription of peroxisome proliferator-marker genes Cyp4a10 and Acox1 but not with degree of induction in hepatocellular proliferation. In subchronic study, serum and liver levels of oxidative metabolites gradually decreased over time despite continuous dosing. Hepatic protein levels of CYP2E1, ADH, and ALDH2 were unaffected by treatment with TCE. While the magnitude of induction of peroxisome proliferator-marker genes also declined, hepatocellular proliferation increased. This study offers a unique opportunity to provide a scientific data-driven rationale for some of the major assumptions in human health assessment of TCE.

Use of urinary trichloroacetic acid as an exposure biomarker of disinfection by-products in cancer studies

Urinary trichloroacetic acid (TCAA) has been proposed as a valid exposure biomarker for ingested disinfection by-products (DBP) for reproductive studies. However, it has never been used in epidemiologic studies on cancer. We investigate the performance of urinary TCAA as a biomarker of DBP exposure in the framework of an epidemiologic study on cancer. We conducted home visits to collect tap water, first morning void urine, and a 48h fluid intake diary among 120 controls from a case-control study of colorectal cancer in Barcelona, Spain. We measured urine TCAA and creatinine, and 9 haloacetic acids and 4 trihalomethanes (THM) in tap water. Lifetime THM exposure was estimated based on residential history since age 18 plus routine monitoring data. Robust linear regressions were used to estimate mean change in urinary TCAA adjusted by covariates. Among the studied group, mean age was 74 years (range 63-85) and 41 (34%) were females. Mean total tap water consumption was 2.2l/48h (standard error, 0.1l/48h). Geometric mean urine TCAA excretion rate was 17.3pmol/min [95%CI: 14.0-21.3], which increased 2% for a 10% increase in TCAA ingestion and decreased with total tap water consumption (-17%/l), water intake outside home (-32%), plasmatic volume (-64%/l), in smokers (-79%), and in users of non-steroidal anti-inflammatory drugs (-50%). Urinary TCAA levels were not associated with lifetime THM exposure. In conclusion, our findings support that urine TCAA is not a valid biomarker in case-control studies of adult cancer given that advanced age, comorbidites and medication use are prevalent and are determinants of urine TCAA levels, apart from ingested TCAA levels. In addition, low TCAA concentrations in drinking water limit the validity of urine TCAA as an exposure biomarker.

Trichloroethylene biotransformation and its role in mutagenicity, carcinogenicity and target organ toxicity

Metabolism is critical for the mutagenicity, carcinogenicity, and other adverse health effects of trichloroethylene (TCE). Despite the relatively small size and simple chemical structure of TCE, its metabolism is quite complex, yielding multiple intermediates and end-products. Experimental animal and human data indicate that TCE metabolism occurs through two major pathways: cytochrome P450 (CYP)-dependent oxidation and glutathione (GSH) conjugation catalyzed by GSH S-transferases (GSTs). Herein we review recent data characterizing TCE processing and flux through these pathways. We describe the catalytic enzymes, their regulation and tissue localization, as well as the evidence for transport and inter-organ processing of metabolites. We address the chemical reactivity of TCE metabolites, highlighting data on mutagenicity of these end-products. Identification in urine of key metabolites, particularly trichloroacetate (TCA), dichloroacetate (DCA), trichloroethanol and its glucuronide (TCOH and TCOG), and N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (NAcDCVC), in exposed humans and other species (mostly rats and mice) demonstrates function of the two metabolic pathways in vivo. The CYP pathway primarily yields chemically stable end-products. However, the GST pathway conjugate S-(1,2-dichlorovinyl)glutathione (DCVG) is further processed to multiple highly reactive species that are known to be mutagenic, especially in kidney where in situ metabolism occurs. TCE metabolism is highly variable across sexes, species, tissues and individuals. Genetic polymorphisms in several of the key enzymes metabolizing TCE and its intermediates contribute to variability in metabolic profiles and rates. In all, the evidence characterizing the complex metabolism of TCE can inform predictions of adverse responses including mutagenesis, carcinogenesis, and acute and chronic organ-specific toxicity.

Physiologically based pharmacokinetic (PBPK) modeling of interstrain variability in trichloroethylene metabolism in the mouse

BACKGROUND

Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, interindividual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data.

OBJECTIVES

We evaluated the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study.

METHODS

We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and 1 hybrid mouse strains to calibrate and extend existing physiologically based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). We used a Bayesian population analysis of interstrain variability to quantify variability in TCE metabolism.

RESULTS

Concentration-time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation were less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production were less variable (2-fold range) than DCA production (5-fold range), although the uncertainty bounds for DCA exceeded the predicted variability.

CONCLUSIONS

Population PBPK modeling of genetically diverse mouse strains can provide useful quantitative estimates of toxicokinetic population variability. When extrapolated to lower doses more relevant to environmental exposures, mouse population-derived variability estimates for TCE metabolism closely matched population variability estimates previously derived from human toxicokinetic studies with TCE, highlighting the utility of mouse interstrain metabolism studies for addressing toxicokinetic variability.

Evaluation of simple in vitro to in vivo extrapolation approaches for environmental compounds

As part of an effort to support in silico/in vitro based risk assessment, we evaluated the accuracy associated with conducting simple in vitro to in vivo extrapolation (IVIVE) for environmental compounds using available in vitro human metabolism data. The IVIVE approach was applied to a number of compounds with a wide range of properties spanning the diversity of characteristics of environmental compounds, and where possible the resulting estimates of the in vivo steady-state blood concentration were compared with estimates derived on the basis of human in vivo kinetic data. There appears to be a systematic bias in the estimation of intrinsic clearance (Clint) from in vitro versus in vivo data, with in vitro based estimates underestimating in vivo clearance for small values of Clint but with the opposite relationship at large values of Clint. Nevertheless, the resulting estimates of Css were in good agreement. The chief drawback of the simple approach used in this study, which performs the IVIVE prediction for the parent compound only, is that it is not applicable for toxicity associated with a metabolite.

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.

Trichloroethylene: Mechanistic, epidemiologic and other supporting evidence of carcinogenic hazard

The chlorinated solvent trichloroethylene (TCE) is a ubiquitous environmental pollutant. The carcinogenic hazard of TCE was the subject of a 2012 evaluation by a Working Group of the International Agency for Research on Cancer (IARC). Information on exposures, relevant data from epidemiologic studies, bioassays in experimental animals, and toxicity and mechanism of action studies was used to conclude that TCE is carcinogenic to humans (Group 1). This article summarizes the key evidence forming the scientific bases for the IARC classification. Exposure to TCE from environmental sources (including hazardous waste sites and contaminated water) is common throughout the world. While workplace use of TCE has been declining, occupational exposures remain of concern, especially in developing countries. The strongest human evidence is from studies of occupational TCE exposure and kidney cancer. Positive, although less consistent, associations were reported for liver cancer and non-Hodgkin lymphoma. TCE is carcinogenic at multiple sites in multiple species and strains of experimental animals. The mechanistic evidence includes extensive data on the toxicokinetics and genotoxicity of TCE and its metabolites. Together, available evidence provided a cohesive database supporting the human cancer hazard of TCE, particularly in the kidney. For other target sites of carcinogenicity, mechanistic and other data were found to be more limited. Important sources of susceptibility to TCE toxicity and carcinogenicity were also reviewed by the Working Group. In all, consideration of the multiple evidence streams presented herein informed the IARC conclusions regarding the carcinogenicity of TCE.

Human health effects of trichloroethylene: key findings and scientific issues

BACKGROUND

In support of the Integrated Risk Information System (IRIS), the U.S. Environmental Protection Agency (EPA) completed a toxicological review of trichloroethylene (TCE) in September 2011, which was the result of an effort spanning > 20 years.

OBJECTIVES

We summarized the key findings and scientific issues regarding the human health effects of TCE in the U.S. EPA's toxicological review.

METHODS

In this assessment we synthesized and characterized thousands of epidemiologic, experimental animal, and mechanistic studies, and addressed several key scientific issues through modeling of TCE toxicokinetics, meta-analyses of epidemiologic studies, and analyses of mechanistic data.

DISCUSSION

Toxicokinetic modeling aided in characterizing the toxicological role of the complex metabolism and multiple metabolites of TCE. Meta-analyses of the epidemiologic data strongly supported the conclusions that TCE causes kidney cancer in humans and that TCE may also cause liver cancer and non-Hodgkin lymphoma. Mechanistic analyses support a key role for mutagenicity in TCE-induced kidney carcinogenicity. Recent evidence from studies in both humans and experimental animals point to the involvement of TCE exposure in autoimmune disease and hypersensitivity. Recent avian and in vitro mechanistic studies provided biological plausibility that TCE plays a role in developmental cardiac toxicity, the subject of substantial debate due to mixed results from epidemiologic and rodent studies.

CONCLUSIONS

TCE is carcinogenic to humans by all routes of exposure and poses a potential human health hazard for noncancer toxicity to the central nervous system, kidney, liver, immune system, male reproductive system, and the developing embryo/fetus.

Application of Markov chain Monte Carlo analysis to biomathematical modeling of respirable dust in US and UK coal miners

A biomathematical model was previously developed to describe the long-term clearance and retention of particles in the lungs of coal miners. The model structure was evaluated and parameters were estimated in two data sets, one from the United States and one from the United Kingdom. The three-compartment model structure consists of deposition of inhaled particles in the alveolar region, competing processes of either clearance from the alveolar region or translocation to the lung interstitial region, and very slow, irreversible sequestration of interstitialized material in the lung-associated lymph nodes. Point estimates of model parameter values were estimated separately for the two data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. When model parameters were calibrated simultaneously to the two data sets, agreement between the derived parameters for the two groups was very good, and the central tendency values were similar to those derived from the deterministic approach. These findings are relevant to the proposed update of the ICRP human respiratory tract model with revisions to the alveolar-interstitial region based on this long-term particle clearance and retention model.

Addressing human variability in next-generation human health risk assessments of environmental chemicals

BACKGROUND

Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment.

OBJECTIVE

Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum.

METHODS

This review was informed by a National Research Council workshop titled "Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making." We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals.

DISCUSSION

One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification-and extent of characterization-of interindividual variability in the human population.

CONCLUSIONS

Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.

Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation between in vitro exposures, in vivo physiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Metabolic Pathways of Bromochloromethane in Rats

Bromochloromethane (BCM) is a volatile compound and a by-product of disinfection of water by chlorination. Physiologically based pharmacokinetic (PBPK) models are used in risk assessment applications. An updated PBPK model for BCM is generated and applied to hypotheses testing calibrated using vapor uptake data. The two different metabolic hypotheses examined are (1) a two-pathway model using both CYP2E1 and glutathione transferase enzymes and (2) a two-binding site model where metabolism can occur on one enzyme, CYP2E1. Our computer simulations show that both hypotheses describe the experimental data in a similar manner. The two pathway results were comparable to previously reported values (V_max⁡ = 3.8 mg/hour, K_m = 0.35 mg/liter, and k_GST = 4.7 /hour). The two binding site results were V_max⁡1 = 3.7 mg/hour, K_m⁡1 = 0.3 mg/hour, CL₂ = 0.047 liter/hour. In addition, we explore the sensitivity of different parameters for each model using our obtained optimized values.