Reverse dosimetry: interpreting trihalomethanes biomonitoring data using physiologically based pharmacokinetic modeling

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

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

Explore the Dosimetric Relationship between the Intake of Chemical Contaminants and Their Occurrence in Blood and Urine

The dosimetric relationship between the human intake dose of a chemical contaminant (an "external dose") and its concentrations in bodily fluids such as blood and urine (related to an "internal dose"), often characterized by a dose-to-concentration ratio, has critical applications in exposure science, toxicology, and risk assessment, especially in the "new approach methods" era. However, there is a lack of a mechanistic, systematic understanding of how such a dosimetric relationship depends on fundamental chemical properties, such as partition coefficients and biotransformation half-lives. Here, we investigate this issue using a well-evaluated toxicokinetic model, which links external and internal doses by quantifying the absorption and elimination of chemicals. Results are visualized in a series of chemical partitioning space plots, whereby a chemical's dose-to-concentration ratio can be approximately predicted based on its partitioning between air, water, and octanol phases. Our results indicate that when taken in equal doses, chemicals with low volatility and moderate to high hydrophobicity exhibit the highest concentrations in the blood, and chemicals undergoing significant biotransformation tend to exhibit lower concentrations in comparison to their counterparts undergoing negligible biotransformation but possessing similar partitioning properties. Chemicals with high hydrophilicity have the highest concentrations in urine. Such revealed property dependence is similar for both adults and children and for individuals with normal body weights and with obesity. Overall, insights gained from this study are important in predicting blood and urinary concentrations from exposure information and in determining the exposure rate that produces the blood or urinary concentrations observed in biomonitoring studies.

Comparing risk of disinfection byproducts in drinking water under variable scenarios of seawater intrusion

The higher levels of halides in seawater increase bromide and iodide in the coastal aquifers, leading to higher concentrations of halogenated disinfection byproducts (DBPs). The populations in the coastal areas are susceptible to increased concentrations of DBPs while many DBPs are cyto- and genotoxic to mammalian cells, and are possible/probable human carcinogens. The implications of seawater intrusion on the concentrations of trihalomethanes (THMs) and haloacetic acids (HAAs), and the risks were analyzed by adding 0.0-2.0 % seawater (SW) (by volume) and chlorine to groundwater. Bromide and iodide concentrations in groundwater (0.0 %SW) were observed as 42.5 and non-detected (ND) μg/L respectively. With 2.0 %SW, these were spiked up to 1100 and 2.1 μg/L respectively. The most common THMs (THM4), iodinated THMs (I-THMs) and HAAs were 30.4, 0.13 and 27.9 μg/L for 0.0 % SW respectively. With 2.0 %SW, these values were 106.3, 1.6 and 72.9 μg/L, respectively. At 0.0 %SW, averages of chronic daily intakes (CDI) for THM4, HAAs and I-THMs were 2.61 × 10^-4, 2.26 × 10^-4 and 7.69 × 10^-7 mg/kg/day respectively, which were increased to 9.97 × 10^-4, 4.70 × 10^-4 and 9.47 × 10^-6 mg/kg/day, respectively for 2.0 %SW. For 0.0 %SW, overall cancer risks from few DBPs was 3.09 × 10^-5 (6.46 × 10^-6 - 7.23 × 10^-5) while at 1.0 % and 2.0 %SW, risks were 4.88 × 10^-5 (1.26 × 10^-5-1.08 × 10^-4) and 4.11 × 10^-5 (1.21 × 10^-5-9.28 × 10^-5) respectively. The reduction of risks for 2.0 %SW was due to the increase of bromoform (TBM), and decrease in bromodichloromethane (BDCM) and dibromochloromethane (DBCM) at 2.0 %SW. The disability-adjusted life years (DALY) loss showed an increasing trend from 0.0 %SW (DALY: 77.30) to 1.0 %SW (DALY: 122.0) while an increase to 2.0 %SW showed a decrease in DALY (DALY: 102.8). Future study on toxicity of other regulated and emerging DBPs is warranted to better predict cancer risks.

Use of computational toxicology models to predict toxicological points of departure: A case study with triazine herbicides

BACKGROUND

Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AED_Human mg/kg/day) predictions from open access computational tools to the PBPK PODs to assess concordance.

METHODS

Computational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AED_Human PODs and population-based age-refined high throughput toxicokinetics (HTTK-Pop) to compare to age-related PBPK PODs.

RESULTS

ToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AED_Human predictions were mainly concordant with the USEPA PBPK PODs quantitatively. Low fold-differences between PBPK POD and ICE AED_Human predictions indicated that the ICE models are health-protective. HTTK-Pop age-refinements were within 10-fold of the USEPA PBPK PODs.

CONCLUSIONS

CompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.

Development of physiologically based toxicokinetic models for 3-monochloropropane-1,2-diol and glycidol

3-Monochloropropane-1,2-diol (3-MCPD), glycidol, together with their fatty acid esters are commonly presented in various food and have shown carcinogenicity in various laboratory animals. Public health risk assessment of 3-MPCD and glycidol exposure relies on quantitative tools that represent their in vivo toxicokinetics. In order to better understand the absorption, distribution, metabolism, and excretion profiles of 3-MCPD and glycidol in male rats, a physiologically based pharmacokinetic (PBTK) model was developed. The model's predictive power was evaluated by comparing in silico simulations to in vivo time course data obtained from experimental studies. Results indicate that our PBTK model successfully captured the toxicokinetics of both free chemicals in key organs, and their metabolites in accessible biological fluids. With the validated PBTK model, we then gave an animal-free example on how to extrapolate the toxicological knowledge acquired from a single gavage to a realistic dietary intake scenario. Three biomarkers, free compound in serum, urinary metabolite DHPMA, and glycidol-hemoglobin adduct (diHOPrVal) were selected for in silico simulation following constant dietary intakes, and their internal levels were correlated with proposed external daily exposure via reverse dosimetry approaches. Taken together, our model provides a computational approach for extrapolating animal toxicokinetic experiments to biomonitoring measurement and risk assessment.

Bayesian inference of chemical exposures from NHANES urine biomonitoring data

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

SIGNIFICANCE

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

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Analysis of biomonitoring data to assess employer compliance with OSHA's permissible exposure limits for air contaminants

BACKGROUND

The Occupational Safety and Health Administration (OSHA) regulates exposures to hazardous chemicals in workplace air. When contemporaneous exposure measurements are unavailable, retrospective analysis of biomarkers could provide valuable information about workers' exposures.

METHODS

Single-compartment pharmacokinetic (PK) models were created to relate the concentration of a chemical in the air to the concentration of the chemical or its metabolite in workers' blood or urine. OSHA utilized the PK models in investigations of three fatal incidents in which workers were exposed to nickel carbonyl, methyl bromide, or styrene. To obtain the minimum plausible estimate of each exposure, OSHA used conservative assumptions about parameters such as workers' inhalation rates, baseline levels of biomarker, and chemicals' volumes of distribution.

RESULTS

OSHA analyzed a worker's urinary nickel concentration and concluded that his 8-h time-weighted average exposure to nickel carbonyl was at least 0.06 mg/m³ . Analysis of a worker's postexposure, premortem blood bromide level revealed that his exposure to methyl bromide was at least 181 mg/m³ . Post-mortem blood styrene measurements suggested that a third worker's exposure to styrene was at least 625 mg/m³ . These exposures exceeded OSHA's permissible exposure limits of 0.007 mg/m³ for nickel carbonyl, 80 mg/m³ for methyl bromide, and 426 mg/m³ for styrene. OSHA successfully cited the three employers for violations of chemical exposure limits.

CONCLUSIONS

Analysis of biomarkers via PK modeling enables retrospective evaluations of workers' acute exposures to hazardous chemicals. These techniques are useful to occupational regulators who assess employer compliance with mandatory exposure limits.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

High-throughput PBTK models for in vitro to in vivo extrapolation

INTRODUCTION

Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals.

AREAS COVERED

This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data.

EXPERT OPINION

HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.

Using quantitative modeling tools to assess pharmacokinetic bias in epidemiological studies showing associations between biomarkers and health outcomes at low exposures

Biomarkers of exposure can be measured at lower and lower levels due to advances in analytical chemistry. Using these sensitive methods, some epidemiology studies report associations between biomarkers and health outcomes at biomarker levels much below those associated with effects in animal studies. While some of these low exposure associations may arise from increased sensitivity of humans compared with animals or from species-specific responses, toxicology studies with drugs, commodity chemicals and consumer products have not generally indicated significantly greater sensitivity of humans compared with test animals for most health outcomes. In some cases, these associations may be indicative of pharmacokinetic (PK) bias, i.e., a situation where a confounding factor or the health outcome itself alters pharmacokinetic processes affecting biomarker levels. Quantitative assessment of PK bias combines PK modeling and statistical methods describing outcomes across large numbers of individuals in simulated populations. Here, we first provide background on the types of PK models that can be used for assessing biomarker levels in human population and then outline a process for considering PK bias in studies intended to assess associations between biomarkers and health outcomes at low levels of exposure. After providing this background, we work through published examples where these PK methods have been applied with several chemicals/chemical classes - polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated biphenyl ethers (PBDE) and phthalates - to assess the possibility of PK bias. Studies of the health effects of low levels of exposure will be improved by developing some confidence that PK bias did not play significant roles in the observed associations.

Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors

The intrinsic metabolic clearance rate (Cl_int) and the fraction of the chemical unbound in plasma (f_up) serve as important parameters for high-throughput toxicokinetic (TK) models, but experimental data are limited for many chemicals. Open-source quantitative structure-activity relationship (QSAR) models for both parameters were developed to offer reliable in silico predictions for a diverse set of chemicals regulated under the U.S. law, including pharmaceuticals, pesticides, and industrial chemicals. As a case study to demonstrate their utility, model predictions served as inputs to the TK component of a risk-based prioritization approach based on bioactivity/exposure ratios (BERs), in which a BER < 1 indicates that exposures are predicted to exceed a biological activity threshold. When applied to a subset of the Tox21 screening library (6484 chemicals), we found that the proportion of chemicals with BER <1 was similar using either in silico (1133/6484; 17.5%) or in vitro (148/848; 17.5%) parameters. Further, when considering only the chemicals in the Tox21 set with in vitro data, there was a high concordance of chemicals classified with either BER <1 or >1 using either in silico or in vitro parameters (767/848, 90.4%). Thus, the presented QSARs may be suitable for prioritizing the risk posed by many chemicals for which measured in vitro TK data are lacking.

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

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

Integration of biomonitoring data and reverse dosimetry modeling to assess population risks of arsenic-induced chronic kidney disease and urinary cancer

Chronic exposure to inorganic arsenic (iAs) is associated with chronic kidney disease (CKD) and urinary cancer, but the risks are poorly understood. Human biomonitoring can serve as a tool to better quantify human exposure and to conduct risk assessment. We aimed to assess the population risks of CKD and urinary cancer due to iAs intake based on the blood arsenic concentrations of 601 participants in Taiwan. A physiologically based pharmacokinetic modeling-based reverse dosimetry was conducted to estimate the daily intakes of iAs (DI_iAs). We performed the benchmark dose (BMD) modeling for CKD using participants' estimated glomerular filtration rate (eGFR) and the estimated DIiAs to derive a point of departure (POD). Margin of exposure (MOE) was used to characterize the risks. The population with eGFR values of <60 mL/min/1.73 m² had significantly higher DI_iAs (median: 3.20 μg/kg/day, 2.5th-97.5th percentiles: 2.35-4.67 μg/kg/day) than those with normal renal function (1.99, 1.22-3.42 μg/kg/day). The POD for CKD was 1.557 μg/kg/day, which could serve as a possible reference value for CKD risk assessment. The MOEs indicated that the CKD risk due to iAs intake may potentially be a cause for high concern for the population with reduced renal function. The iAs-induced urinary cancer risk may be a cause for moderate-to-high concern.

Predicting risk and loss of disability-adjusted life years (DALY) from selected disinfection byproducts in multiple water supply sources in Saudi Arabia

Disinfection byproducts (DBPs) in drinking water is an issue in many countries. Many DBPs are possible or probable human carcinogens while few DBPs pose cyto- and genotoxic effects to the mammalian cells. The populations are likely to consume DBPs with drinking water throughout their lifetimes. A number of DBPs are regulated in many countries to protect humans. In this study, human exposure, risk and disability-adjusted life years (DALY) were predicted from DBPs in multiple water supply systems, including groundwater (GW), desalinated water (DW) and blend water (BW). The averages of lifetime excess cancer risks from GW, DW and BW were 4.15 × 10^-6, 1.75 × 10^-5 and 2.59 × 10^-5 respectively. The populations in age groups of 0 - <2, 2-16 and >16 years contributed 25.4-25.7%, 28.6-29.6% and 45.0-45.7% to the total risks respectively. The DALY from GW, DW and BW were estimated to be 5.8, 27.0 and 39.9 years, respectively while the corresponding financial burdens were US$ 0.63, 2.93 and 4.34 million respectively. The findings are likely to assist in selecting the supply water sources to better control human exposure and risk from DBPs.

Comparison of in vivo derived and scaled in vitro metabolic rate constants for several volatile organic compounds (VOCs)

Metabolic rate parameters estimation using in vitro data is necessary due to numbers of chemicals for which data are needed, trend towards minimizing laboratory animal use, and limited opportunity to collect data in human subjects. We evaluated how well metabolic rate parameters derived from in vitro data predict overall in vivo metabolism for a set of environmental chemicals for which well validated and established methods exist. We compared values of VmaxC derived from in vivo vapor uptake studies with estimates of VmaxC scaled up from in vitro hepatic microsomal metabolism studies for VOCs for which data were available in male F344 rats. For 6 of 7 VOCs, differences between the in vivo and scaled up in vitro VmaxC estimates were less than 2.6-fold. For bromodichloromethane (BDCM), the in vivo derived VmaxC was approximately 4.4-fold higher than the in vitro derived and scaled up VmaxC. The more rapid rate of BDCM metabolism estimated based in vivo studies suggests other factors such as extrahepatic metabolism, binding or other non-specific losses making a significant contribution to overall clearance. Systematic and reliable utilization of scaled up in vitro biotransformation rate parameters in PBPK models will require development of methods to predict cases in which extrahepatic metabolism and binding as well as other factors are likely to be significant contributors.

Development and evaluation of a high throughput inhalation model for organic chemicals

Currently it is difficult to prospectively estimate human toxicokinetics (particularly for novel chemicals) in a high-throughput manner. The R software package httk has been developed, in part, to address this deficiency, and the aim of this investigation was to develop a generalized inhalation model for httk. The structure of the inhalation model was developed from two previously published physiologically based models from Jongeneelen and Berge (Ann Occup Hyg 55:841-864, 2011) and Clewell et al. (Toxicol Sci 63:160-172, 2001), while calculated physicochemical data was obtained from EPA's CompTox Chemicals Dashboard. In total, 142 exposure scenarios across 41 volatile organic chemicals were modeled and compared to published data. The slope of the regression line of best fit between log-transformed simulated and observed blood and exhaled breath concentrations was 0.46 with an r2 = 0.45 and a root mean square error (RMSE) of direct comparison between the log-transformed simulated and observed values of 1.11. Approximately 5.1% (n = 108) of the data points analyzed were >2 orders of magnitude different than expected. The volatile organic chemicals examined in this investigation represent small, generally lipophilic molecules. Ultimately this paper details a generalized inhalation component that integrates with the httk physiologically based toxicokinetic model to provide high-throughput estimates of inhalation chemical exposures.

Use of computational toxicology (CompTox) tools to predict in vivo toxicity for risk assessment

Computational Toxicology tools were used to predict toxicity for three pesticides: propyzamide (PZ), carbaryl (CB) and chlorpyrifos (CPF). The tools used included: a) ToxCast/Tox21 assays (AC₅₀ s μM: concentration 50% maximum activity); b) in vitro-to-in vivo extrapolation (IVIVE) using ToxCast/Tox21 AC₅₀s to predict administered equivalent doses (AED: mg/kg/d) to compare to known in vivo Lowest-Observed-Effect-Level (LOEL)/Benchmark Dose (BMD); c) high throughput toxicokinetics population based (HTTK-Pop) using AC₅₀s for endpoints associated with the mode of action (MOA) to predict age-adjusted AED for comparison with in vivo LOEL/BMDs. ToxCast/Tox21 active-hit-calls for each chemical were predictive of targets associated with each MOA, however, assays directly relevant to the MOAs for each chemical were limited. IVIVE AEDs were predictive of in vivo LOEL/BMD₁₀s for all three pesticides. HTTK-Pop was predictive of in vivo LOEL/BMD₁₀s for PZ and CPF but not for CB after human age adjustments 11-15 (PZ) and 6-10 (CB) or 6-10 and 11-20 (CPF) corresponding to treated rat ages (in vivo endpoints). The predictions of computational tools are useful for risk assessment to identify targets in chemical MOAs and to support in vivo endpoints. Data can also aid is decisions about the need for further studies.

High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA's Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (µM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Reduction of DBPs in synthetic water by indoor techniques and its implications on exposure and health risk

Disinfection byproducts (DBPs) in municipal supply water have been a concern. Many DBPs have been characterized as possible and probable human carcinogens, which can pose elevated cancer risks through lifetime exposure to municipal supply water. Few DBPs are regulated in many countries to control human exposure and risk from DBPs. In risk assessment studies, concentration of DBPs in water distribution systems is often used, whereas populations are typically exposed to indoor tap water. Through employing several techniques, DBPs can be reduced prior to water consumption, which is likely to reduce human exposure and risk of DBPs. This study investigated six indoor techniques in reducing trihalomethanes (THMs) and haloacetic acids (HAAs) in synthetic water and the effects of these techniques on exposure and risk. The techniques are: S1, S2: storing water in a refrigerator with and without lids respectively; S3, S4: boiling water for 1 min followed by storing in a refrigerator with and without lids respectively; S5, S6: filtering water using new and used granular activated carbon (GAC) filters and storing in a refrigerator without lids. Storing of water (S1, S2) reduced THMs in the range of 14.8-47.2% while boiling (S3, S4) and filtration (S5, S6) reduced THMs in the range of 77.3-92.8%. In S1-S4 techniques, HAAs were not reduced significantly while in S5 - S6 techniques, HAAs were reduced in the range of 64.7-69.8%. In S3-S6 techniques, overall cancer and non-cancer risks were reduced by 45.5-82.6% and 26.3-80.0% respectively. The findings might prove useful in understanding DBPs exposure, associated risks, strategies to minimize exposure to these contaminants and updating regulatory guidelines for better protection of health risks from DBPs.

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions

Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of high-throughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (f_up) and intrinsic hepatic clearance (Cl_int) were measured for rats (for 67 and 77 chemicals, respectively), combined with f_up and Cl_int literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC₅₀ and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets–restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration–outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans.

The Impact of Scaling Factor Variability on Risk-Relevant Pharmacokinetic Outcomes in Children: A Case Study Using Bromodichloromethane (BDCM)

Biotransformation rates extrapolated from in vitro data are used increasingly in human physiologically based pharmacokinetic (PBPK) models. This practice requires use of scaling factors, including microsomal content (mg of microsomal protein/g liver, MPPGL), enzyme specific content, and liver mass as a fraction of body weight (FVL). Previous analyses indicated that scaling factor variability impacts pharmacokinetic (PK) outcomes used in adult population dose-response studies. This analysis was extended to pediatric populations because large inter-individual differences in enzyme ontogeny likely would further contribute to scaling factor variability. An adult bromodichloromethane (BDCM) model (Kenyon, E. M., Eklund, C., Leavens, T. L., and Pegram, R. A. (2016a). Development and application of a human PBPK model for bromodichloromethane (BDCM) to investigate impacts of multi-route exposure. J. Appl. Toxicol. 36, 1095-1111) was re-parameterized for neonates, infants, and toddlers. Monte Carlo analysis was used to assess the impact of pediatric scaling factor variation on model-derived PK outcomes compared with adult findings. BDCM dose metrics were estimated following a single 0.05-liter drink of water or a 20-min bath, under typical (5 µg/l) and plausible higher (20 µg/l) BDCM concentrations. MPPGL, CYP2E1, and FVL values reflected the distribution of reported pediatric population values. The impact of scaling factor variability on PK outcome variation was different for each exposure scenario, but similar for each BDCM water concentration. The higher CYP2E1 expression variability during early childhood was reflected in greater variability in predicted PK outcomes in younger age groups, particularly for the oral exposure route. Sensitivity analysis confirmed the most influential parameter for this variability was CYP2E1, particularly in neonates. These findings demonstrate the importance of age-dependent scaling factor variation used for in vitro to in vivo extrapolation of biotransformation rates.

Health risk assessment of trihalomethanes mixtures from daily water-related activities via multi-pathway exposure based on PBPK model

In this study, the concentrations of trihalomethanes (THMs) in tap water and direct drinking water were analyzed, and based on the human behavior patterns and building parameters, the concentrations of THMs in indoor air were simulated with the water-air concentration conversion model. In addition, concentrations of THMs in human tissues were predicted based on physiologically based pharmacokinetic (PBPK) model, and the health risk of THMs for participants were estimated. Furthermore, the carcinogenic risk of mixtures according to the method proposed by USEPA and PBPK model based method were calculated and compared. The concentrations of chloroform, bromodichloromethane, dibromochloromethane and bromoform in tap water were 11.28-16.21, 4.83-6.28, 0.81-1.32 and 0.08-0.21 μg/L, and those in direct drinking water were 3.29-6.88, 0.35-0.47, 0.03-0.08 and 0.04-0.08 μg/L, respectively. The results of water-air concentration conversion model demonstrated that pollutants in air had a strong correlation with water-related activities. Multi-pathway PBPK model showed that THMs concentrations in liver, kidney and richly perfused tissue were higher than those in other tissues. The results of risk assessment showed that the mean risk levels of mixtures were 1.69 × 10^-4 and 1.72 × 10^-4 calculated by the USEPA recommended method and PBPK based method, which seriously exceeded the acceptable level. TCM and BDCM were the major risk factors, and inhalation was the main exposure route of THMs.

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

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

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.

Health risk assessment of haloacetonitriles in drinking water based on internal dose

To estimate the health risk of haloacetonitriles in different kinds of drinking water, the concentrations of haloacetonitriles in tap water, boiled water and direct drinking water were detected. The physiologically based pharmacokinetic (PBPK) model was used to calculate internal dose in the human body for haloacetonitriles through ingestion, and the probability distributions of the non-carcinogenic risk of haloacetonitriles for human via drinking water were assessed. This study found that the mean concentrations of dichloroacetonitrile (DCAN) in tap water, boiled water and direct drinking water were 0.955 μg/L, 0.207 μg/L and 0.127 μg/L, and those of dibromoacetonitrile (DBAN) were 0.221 μg/L, 0.104 μg/L, 0.089 μg/L, respectively. In China, direct drinking water is used most frequently, so the concentrations of haloacetonitriles in direct drinking water were used to obtain data on the internal dose of haloacetonitriles. In addition, the simulation results for the PBPK model showed that the highest and lowest concentrations of DCAN occurred in the liver and venous blood, respectively. The peak concentrations of DBAN in each tissue were in the decreasing order liver > rapidly perfused tissue > kidney > slowly perfused tissues > fat > arterial blood (venous blood). In addition, the highest 95th percentile hazard quotients (HQ) value of haloacetonitriles via drinking water for humans was 8.89 × 10^-3, much lower than 1. The 95th percentile hazard index (HI) was 0.046, which was also lower than 1, suggesting that there was no obvious non-carcinogenic risk.

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).

Occurrences and changes of disinfection by-products in small water supply systems

The small water supply systems (WSSs) often report high concentrations of disinfection by-products (DBPs) in drinking water. In this study, occurrences of trihalomethanes (THMs) and haloacetic acids (HAAs) in Newfoundland and Labrador (NL), Canada, were investigated from 441 WSSs for a period of 18 years (1999-2016). The WSSs were divided into groundwater (GWP) and surface water (SWP) systems, which were further classified into eight sub-groups (P1-P8) based on the population served (≤ 100; 101-250; 251-500; 501-1000; 1001-3000; 3001-5000; 5001-10,000; and 10,000+, respectively). The DBPs with probable and possible carcinogenic forms were estimated. Overall, 31.1% of WSSs were GWP, in which averages of THMs and HAAs were 32.2 and 27.7 μg/L, respectively, while the SWP had averages of THMs and HAAs of 97.6 and 129.2 μg/L, respectively. The very small WSSs (P1-P3) of GWP had averages of THMs and HAAs in the ranges of 29.1-43.5 and 15.8-64.3 μg/L, respectively. The P1-P3 of SWP had averages of THMs and HAAs in the ranges of 92.6-112.8 and 108.0-154.0 μg/L, respectively, which often exceeded the Canadian guideline limits. If the samples represented the populations homogenously, the total populations exposed to THMs or HAA₅ above the guideline values would be in the range of 132.08-181.38 in thousands (30.3-41.6% of total populations). The probable and possible carcinogenic forms of THMs in GWP and SWP were in the ranges of 4.8-48.8 and 4.4-7.0% of THMs, respectively. In HAAs, carcinogenic forms were in the ranges of 82.6-98.4 and 97.6-98.7%, respectively. The findings indicated that the SWP might need further attention to better protect human health.

In vitro to in vivo extrapolation for high throughput prioritization and decision making

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability

The thousands of chemicals present in the environment (USGAO, 2013) must be triaged to identify priority chemicals for human health risk research. Most chemicals have little of the toxicokinetic (TK) data that are necessary for relating exposures to tissue concentrations that are believed to be toxic. Ongoing efforts have collected limited, in vitro TK data for a few hundred chemicals. These data have been combined with biomonitoring data to estimate an approximate margin between potential hazard and exposure. The most "at risk" 95th percentile of adults have been identified from simulated populations that are generated either using standard "average" adult human parameters or very specific cohorts such as Northern Europeans. To better reflect the modern U.S. population, we developed a population simulation using physiologies based on distributions of demographic and anthropometric quantities from the most recent U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data. This allowed incorporation of inter-individual variability, including variability across relevant demographic subgroups. Variability was analyzed with a Monte Carlo approach that accounted for the correlation structure in physiological parameters. To identify portions of the U.S. population that are more at risk for specific chemicals, physiologic variability was incorporated within an open-source high-throughput (HT) TK modeling framework. We prioritized 50 chemicals based on estimates of both potential hazard and exposure. Potential hazard was estimated from in vitro HT screening assays (i.e., the Tox21 and ToxCast programs). Bioactive in vitro concentrations were extrapolated to doses that produce equivalent concentrations in body tissues using a reverse dosimetry approach in which generic TK models are parameterized with: 1) chemical-specific parameters derived from in vitro measurements and predicted from chemical structure; and 2) with physiological parameters for a virtual population. For risk-based prioritization of chemicals, predicted bioactive equivalent doses were compared to demographic-specific inferences of exposure rates that were based on NHANES urinary analyte biomonitoring data. The inclusion of NHANES-derived inter-individual variability decreased predicted bioactive equivalent doses by 12% on average for the total population when compared to previous methods. However, for some combinations of chemical and demographic groups the margin was reduced by as much as three quarters. This TK modeling framework allows targeted risk prioritization of chemicals for demographic groups of interest, including potentially sensitive life stages and subpopulations.

A Method for Identifying Prevalent Chemical Combinations in the U.S. Population

BACKGROUND

Through the food and water they ingest, the air they breathe, and the consumer products with which they interact at home and at work, humans are exposed to tens of thousands of chemicals, many of which have not been evaluated to determine their potential toxicities. Furthermore, while current chemical testing tends to focus on individual chemicals, the exposures that people actually experience involve mixtures of chemicals. Unfortunately, the number of mixtures that can be formed from the thousands of environmental chemicals is enormous, and testing all of them would be impossible.

OBJECTIVES

We seek to develop and demonstrate a method for identifying those mixtures that are most prevalent in humans.

METHODS

We applied frequent itemset mining, a technique traditionally used for market basket analysis, to biomonitoring data from the 2009-2010 cycle of the continuous National Health and Nutrition Examination Survey (NHANES) to identify combinations of chemicals that frequently co-occur in people.

RESULTS

We identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the U.S. population, as well as three supercombinations consisting of relatively many chemicals that occur in a small but nonnegligible proportion of the U.S. population.

CONCLUSIONS

We demonstrated how FIM can be used in conjunction with biomonitoring data to narrow a large number of possible chemical combinations down to a smaller set of prevalent chemical combinations. https://doi.org/10.1289/EHP1265.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening.

A decision tree approach to screen drinking water contaminants for multiroute exposure potential in developing guideline values

The consideration of inhalation and dermal routes of exposures in developing guideline values for drinking water contaminants is important. However, there is no guidance for determining the eligibility of a drinking water contaminant for its multiroute exposure potential. The objective of the present study was to develop a 4-step framework to screen chemicals for their dermal and inhalation exposure potential in the process of developing guideline values. The proposed framework emphasizes the importance of considering basic physicochemical properties prior to detailed assessment of dermal and inhalation routes of exposure to drinking water contaminants in setting guideline values.

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

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

Estimation of iodine nutrition and thyroid function status in late-gestation pregnant women in the United States: Development and application of a population-based pregnancy model

Previously, a deterministic biologically-based dose-response (BBDR) pregnancy model was developed to evaluate moderate thyroid axis disturbances with and without thyroid-active chemical exposure in a near-term pregnant woman and fetus. In the current study, the existing BBDR model was adapted to include a wider functional range of iodine nutrition, including more severe iodine deficiency conditions, and to incorporate empirically the effects of homeostatic mechanisms. The extended model was further developed into a population-based model and was constructed using a Monte Carlo-based probabilistic framework. In order to characterize total (T4) and free (fT4) thyroxine levels for a given iodine status at the population-level, the distribution of iodine intake for late-gestation pregnant women in the U.S was reconstructed using various reverse dosimetry methods and available biomonitoring data. The range of median (mean) iodine intake values resulting from three different methods of reverse dosimetry tested was 196.5-219.9μg of iodine/day (228.2-392.9μg of iodine/day). There was minimal variation in model-predicted maternal serum T4 and ft4 thyroxine levels from use of the three reconstructed distributions of iodine intake; the range of geometric mean for T4 and fT4, was 138-151.7nmol/L and 7.9-8.7pmol/L, respectively. The average value of the ratio of the 97.5th percentile to the 2.5th percentile equaled 3.1 and agreed well with similar estimates from recent observations in third-trimester pregnant women in the U.S. In addition, the reconstructed distributions of iodine intake allowed us to estimate nutrient inadequacy for late-gestation pregnant women in the U.S. via the probability approach. The prevalence of iodine inadequacy for third-trimester pregnant women in the U.S. was estimated to be between 21% and 44%. Taken together, the current work provides an improved tool for evaluating iodine nutritional status and the corresponding thyroid function status in pregnant women in the U.S. This model enables future assessments of the relevant risk of thyroid hormone level perturbations due to exposure to thyroid-active chemicals at the population-level.

Derivation of biomonitoring equivalent for inorganic tin for interpreting population-level urinary biomonitoring data

Population-level biomonitoring of tin in urine has been conducted by the U.S. National Health and Nutrition Examination Survey (NHANES) and the National Nutrition and Health Study (ENNS - Étude nationale nutrition santé) in France. The general population is predominantly exposed to inorganic tin from the consumption of canned food and beverages. The National Institute for Public Health and the Environment of the Netherlands (RIVM) has established a tolerable daily intake (TDI) for chronic exposure to inorganic tin based on a NOAEL of 20 mg/kg bw per day from a 2-year feeding study in rats. Using a urinary excretion fraction (0.25%) from a controlled human study along with a TDI value of 0.2 mg/kg bw per day, a Biomonitoring Equivalent (BE) was derived for urinary tin (26 μg/g creatinine or 20 μg/L urine). The geometric mean and the 95th percentile tin urine concentrations of the general population in U.S. (0.705 and 4.5 μg/g creatinine) and France (0.51 and 2.28 μg/g creatinine) are below the BE associated with the TDI, indicating that the population exposure to inorganic tin is below the exposure guidance value of 0.2 mg/kg bw per day. Overall, the robustness of pharmacokinetic data forming the basis of the urinary BE development is medium. The availability of internal dose and kinetic data in the animal species forming the basis of the assessment could improve the overall confidence in the present assessment.

Animal-Free Chemical Safety Assessment

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

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

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

Effects of plumbing systems on human exposure to disinfection byproducts in water: a case study

Disinfection byproducts (DBPs) in water distribution systems (WDS) are monitored for regulatory compliance, while populations are exposed to DBPs in tap water that may be different due to stagnation of water in plumbing pipes (PP) and heating in hot water tanks (HWT). This study investigated the effects of water stagnation in PP and HWT on exposure and risk of DBPs to humans. Trihalomethanes (THMs) in PP and HWT were observed to be 1.1-2.4 and 1.6-3.0 times, respectively, to THMs in the WDS, while haloacetic acids (HAAs) were 0.9-1.8 and 1.2-1.9 times, respectively, to HAAs in the WDS. The chronic daily intakes of DBPs from PP and HWT were 0.6-1.8 and 0.5-2.3 times the intakes from WDS. The cancer risks from PP and HWT were 1.46 (0.40-4.3) and 1.68 (0.35-5.1) times the cancer risks from WDS. The findings may assist in regulating DBPs exposure concentrations.

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Development and application of a human PBPK model for bromodichloromethane to investigate the impacts of multi-route exposure

As a result of its presence in water as a volatile disinfection byproduct, bromodichloromethane (BDCM), which is mutagenic, poses a potential health risk from exposure via oral, dermal and inhalation routes. We developed a refined human physiologically based pharmacokinetic (PBPK) model for BDCM (including new chemical-specific human parameters) to evaluate the impact of BDCM exposure during showering and bathing on important measures of internal dose compared with oral exposure. The refined model adequately predicted data from the published literature for oral, dermal and bathing/showering exposures. A liter equivalency approach (L-eq) was used to estimate BDCM concentration in a liter of water consumed by the oral route that would be required to produce the same internal dose of BDCM resulting from a 20-min bath or a 10-min shower in water containing 10 µg l(-1) BDCM. The oral liter equivalent concentrations for the bathing scenario were 605, 803 and 5 µg l(-1) BDCM for maximum venous blood concentration (Cmax), the area under the curve (AUCv) and the amount metabolized in the liver per hour (MBDCM), respectively. For a 10-min showering exposure, the oral L-eq concentrations were 282, 312 and 2.1 µg l(-1) for Cmax, AUC and MBDCM, respectively. These results demonstrate large contributions of dermal and inhalation exposure routes to the internal dose of parent chemical reaching the systemic circulation, which could be transformed to mutagenic metabolites in extrahepatic target tissues. Thus, consideration of the contribution of multiple routes of exposure when evaluating risks from water-borne BDCM is needed, and this refined human model will facilitate improved assessment of internal doses from real-world exposures. Published 2015. This article has been contributed to by US Government employees and their work is in the public domain in the USA.