Cross species extrapolation of the disruption of thyroid hormone synthesis by oxyfluorfen using in vitro data, physiologically based pharmacokinetic (PBPK), and thyroid hormone kinetics models.

The thyroid hormones play key roles in physiological processes such as regulation of the metabolic and cardiac systems as well as the development of the brain and surrounding sympathetic nervous system. Recent efforts to screen environmental chemicals for their ability to alter thyroid hormone synthesis, transport, metabolism and/or function have identified novel chemicals that target key processes in the thyroid pathway. One newly identified chemical, oxyfluorfen, is a diphenyl-ether herbicide used for control of annual broadleaf and grassy weeds in a variety of tree fruit, nut, vine, and field crops. Using in vitro high-throughput screening (HTS) assays, oxyfluorofen was identified to be a potent inhibitor of the thyroidal sodium-iodide symporter (NIS). To quantitatively assess this inhibition mechanism in vivo, we extrapolated in vitro NIS inhibition data to in vivo disruption of thyroid hormones synthesis in rats using physiologically based pharmacokinetic (PBPK) and thyroid hormone kinetics models. The overall computational model (chemical PBPK and THs kinetic sub-models) was calibrated against in vivo data for the levels of oxyfluorfen in thyroid tissue and serum and against serum levels of thyroid hormones triiodothyronine (T3) and thyroxine (T4) in rats. The rat thyroid model was then extrapolated to humans using human in vitro HTS data for NIS inhibition and the chemical specific hepatic clearance rate in humans. The overall species extrapolated PBPK-thyroid kinetics model can be used to predict dose-response (% drop in thyroid serum levels compared to homeostasis) relationships in humans. These relationships can be used to estimate points of departure for health risks related to a drop in serum levels of TH hormones based on HTS assays in vitro to in vivo extrapolation (IVIVE), toxicokinetics, and physiological principles.

Fate of arsenicals in mice carrying the human AS3MT gene exposed to environmentally relevant levels of arsenite in drinking water.

Although mice are widely used to study adverse effects of inorganic arsenic (iAs), higher rates of iAs methylation in mice than in humans may limit their utility as a model organism. A recently created 129S6 mouse strain in which the Borcs7/As3mt locus replaces the human BORCS7/AS3MT locus exhibits a human-like pattern of iAs metabolism. Here, we evaluate dosage dependency of iAs metabolism in humanized (Hs) mice. We determined tissue and urinary concentrations and proportions of iAs, methylarsenic (MAs), and dimethylarsenic (DMAs) in male and female Hs and wild-type (WT) mice that received 25- or 400-ppb iAs in drinking water. At both exposure levels, Hs mice excrete less total arsenic (tAs) in urine and retain more tAs in tissues than WT mice. Tissue tAs levels are higher in Hs females than in Hs males, particularly after exposure to 400-ppb iAs. Tissue and urinary fractions of tAs present as iAs and MAs are significantly greater in Hs mice than in WT mice. Notably, tissue tAs dosimetry in Hs mice resembles human tissue dosimetry predicted by a physiologically based pharmacokinetic model. These data provide additional support for use of Hs mice in laboratory studies examining effects of iAs exposure in target tissues or cells.

Advances in computational methods along the exposure to toxicological response paradigm.

Human health risk assessment is a function of chemical toxicity, bioavailability to reach target biological tissues, and potential environmental exposure. These factors are complicated by many physiological, biochemical, physical and lifestyle factors. Furthermore, chemical health risk assessment is challenging in view of the large, and continually increasing, number of chemicals found in the environment. These challenges highlight the need to prioritize resources for the efficient and timely assessment of those environmental chemicals that pose greatest health risks. Computational methods, either predictive or investigative, are designed to assist in this prioritization in view of the lack of cost prohibitive in vivo experimental data. Computational methods provide specific and focused toxicity information using in vitro high throughput screening (HTS) assays. Information from the HTS assays can be converted to in vivo estimates of chemical levels in blood or target tissue, which in turn are converted to in vivo dose estimates that can be compared to exposure levels of the screened chemicals. This manuscript provides a review for the landscape of computational methods developed and used at the U.S. Environmental Protection Agency (EPA) highlighting their potentials and challenges.

A biologically based model to quantitatively assess the role of the nuclear receptors liver X (LXR), and pregnane X (PXR) on chemically induced hepatic steatosis.

Hepatic steatosis is characterized by the intracellular increase of free fatty acids (FFAs) in the form of triglycerides in hepatocytes. This hepatic adverse outcome can be caused by many factors, including exposure to drugs or environmental toxicants. Mechanistically, accumulation of lipids in the liver can take place via several mechanisms such as de novo synthesis and/or uptake of FFAs from serum via high fat content diets. De novo synthesis of FFAs within the liver is mediated by the liver X receptor (LXR), and their uptake into the liver is mediated through the pregnane X receptor (PXR). We investigated the impact of chemical exposure on FFAs hepatic content via activation of LXR and PXR by integrating chemical-specific physiologically based pharmacokinetic (PBPK) models with a quantitative toxicology systems (QTS) model of hepatic lipid homeostasis. Three known agonists of LXR and/or PXR were modeled: T0901317 (antagonist for both receptors), GW3965 (LXR only), and Rifampicin (PXR only). Model predictions showed that T0901317 caused the most FFAs build-up in the liver, followed by Rifampicin and then GW3965. These modeling results highlight the importance of PXR activation for serum FFAs uptake into the liver while suggesting that increased hepatic FAAs de novo synthesis alone may not be enough to cause appreciable accumulation of lipids in the liver under normal environmental exposure levels. Moreover, the overall PBPK-hepatic lipids quantitative model can be used to screen chemicals for their potential to cause in vivo hepatic lipid content buildup in view of their in vitro potential to activate the nuclear receptors and their exposure levels.

Mechanistic Computational Model for Extrapolating In Vitro Thyroid Peroxidase (TPO) Inhibition Data to Predict Serum Thyroid Hormone Levels in Rats.

High-throughput in vitro assays are developed to screen chemicals for their potential to inhibit thyroid hormones (THs) synthesis. Some of these experiments, such as the thyroid peroxidase (TPO) inhibition assay, are based on thyroid microsomal extracts. However, the regulation of thyroid disruption chemicals is based on THs in vivo serum levels. This necessitates the estimation of thyroid disruption chemicals in vivo tissue levels in the thyroid where THs synthesis inhibition by TPO takes place. The in vivo tissue levels of chemicals are controlled by pharmacokinetic determinants such as absorption, distribution, metabolism, and excretion, and can be described quantitatively in physiologically based pharmacokinetic (PBPK) models. An integrative computational model including chemical-specific PBPK and TH kinetics models provides a mechanistic quantitative approach to translate thyroidal high-throughput in vitro assays to in vivo measures of circulating THs serum levels. This computational framework is developed to quantitatively establish the linkage between applied dose, chemical thyroid tissue levels, thyroid TPO inhibition potential, and in vivo TH serum levels. Once this link is established quantitatively, the overall model is used to calibrate the TH kinetics parameters using experimental data for THs levels in thyroid tissue and serum for the 2 drugs, propylthiouracil and methimazole. The calibrated quantitative framework is then evaluated against literature data for the environmental chemical ethylenethiourea. The linkage of PBPK and TH kinetics models illustrates a computational framework that can be extrapolated to humans to screen chemicals based on their exposure levels and potential to disrupt serum THs levels in vivo.

Thresholds Derived From Common Measures in Rat Studies Are Predictive of Liver Tumorigenic Chemicals.

We hypothesized that typical tissue and clinical chemistry (ClinChem) end points measured in rat toxicity studies exhibit chemical-independent biological thresholds beyond which cancer occurs. Using the rat in vivo TG-GATES study, 75 chemicals were examined across chemical-dose-time comparisons that could be linked to liver tumor outcomes. Thresholds for liver weight to body weight (LW/BW) and 21 serum ClinChem end points were defined as the maximum and minimum values for those exposures that did not lead to liver tumors in rats. Upper thresholds were identified for LW/BW (117%), aspartate aminotransferase (195%), alanine aminotransferase (141%), alkaline phosphatase (152%), and total bilirubin (115%), and lower thresholds were identified for phospholipids (82%), relative albumin (93%), total cholesterol (82%), and total protein (94%). Thresholds derived from the TG-GATES data set were consistent across other acute and subchronic rat studies. A training set of ClinChem and LW/BW thresholds derived from a 38 chemical training set from TG-GATES was predictive of liver tumor outcomes for a test set of 37 independent TG-GATES chemicals (91%). The thresholds were most predictive when applied to 7d treatments (98%). These findings provide support that biological thresholds for common end points in rodent studies can be used to predict chemical tumorigenic potential.

Arsenic Metabolism in Mice Carrying a BORCS7/AS3MT Locus Humanized by Syntenic Replacement.

BACKGROUND: Chronic exposure to inorganic arsenic (iAs) is a significant public health problem. Methylation of iAs by arsenic methyltransferase (AS3MT) controls iAs detoxification and modifies risks of iAs-induced diseases. Mechanisms underlying these diseases have been extensively studied using animal models. However, substantive differences between humans and laboratory animals in efficiency of iAs methylation have hindered the translational potential of the laboratory studies. OBJECTIVES: The goal of this study was to determine whether humanization of the As3mt gene confers a human-like pattern of iAs metabolism in mice. METHODS: We generated a mouse strain in which the As3mt gene along with the adjacent Borcs7 gene was humanized by syntenic replacement. We compared expression of the mouse As3mt and the human AS3MT and the rate and pattern of iAs metabolism in the wild-type and humanized mice. RESULTS: AS3MT expression in mouse tissues closely modeled that of human and differed substantially from expression of As3mt. Detoxification of iAs was much less efficient in the humanized mice than in wild-type mice. Profiles for iAs and its methylated metabolites in tissues and excreta of the humanized mice were consistent with those reported in humans. Notably, the humanized mice expressed both the full-length AS3MT that catalyzes iAs methylation and the human-specific AS3MTd2d3 splicing variant that has been linked to schizophrenia. CONCLUSIONS: These results suggest that AS3MT is the primary genetic locus responsible for the unique pattern of iAs metabolism in humans. Thus, the humanized mouse strain can be used to study the role of iAs methylation in the pathogenesis of iAs-induced diseases, as well as to evaluate the role of AS3MTd2d3 in schizophrenia. https://doi.org/10.1289/EHP6943.

Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens.

Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3-29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%-97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%-94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or "molecular tipping points" that are predictive of later-life health outcomes.

Extrapolating In Vitro Screening Assay Data for Thyroperoxidase Inhibition to Predict Serum Thyroid Hormones in the Rat.

Thyroperoxidase (TPO) is an enzyme essential for thyroid hormone (TH) synthesis and a target site for a number of xenobiotics that disrupt TH homeostasis. An in vitro high-throughput screening assay for TPO inhibition, the Amplex UltraRed-TPO (AUR-TPO), has been used to screen the ToxCast chemical libraries for this action. Output from this assay would be most useful if it could be readily translated into an in vivo response, namely a reduction of TH in serum. To this end, the relationship between TPO inhibition in vitro and serum TH decreases was examined in rats exposed to 2 classic TPO inhibitors, propylthiouracil (PTU) and methimazole (MMI). Serum and gland PTU, MMI, and TH levels were quantified using tandem liquid chromatography mass spectrometry. Thyroperoxidase activity was determined in thyroid gland microsomes treated with PTU or MMI in vitro and ex vivo from thyroid gland microsomes prepared from exposed animals. A quantitative model was constructed by contrasting in vitro and ex vivo AUR-TPO results and the in vivo time-course and dose-response analysis. In vitro:ex vivo correlations of AUR-TPO outputs indicated that less than 30% inhibition of TPO in vitro was sufficient to reduce serum T4 by 20%, a degree of regulatory significance. Although further testing of model estimates using other TPO inhibitors is essential for verification of these initial findings, the results of this study provide a means to translate in vitro screening assay results into predictions of in vivo serum T4 changes to inform risk assessment.

Identification of differentially expressed genes and networks related to hepatic lipid dysfunction.

A range of chemical exposures that resulted in the specific pathology of hepatic lipid dysfunction in rats were selected from DrugMatrix, a publicly available toxicogenomic database. Raw microarray data collected from these exposures were further analyzed using bioinformatic tools to generate a differentially expressed genes (DEGs) dataset associated with hepatic lipid dysfunction. Further analysis of the DEGs dataset resulted in 324 upregulated genes, and 275 genes that were down regulated. Meanwhile, 36 genes were either up regulated or down regulated in different chemical treatments. All identified genes were uploaded in the web application for Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology enrichments and to identify Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Some of the identified pathways included glycolysis/gluconeogenesis, steroid hormone biosynthesis, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. The same DEGs dataset was also analyzed using Ingenuity Pathway Analysis (IPA) software. IPA identified several pathways including PXR/RXR activation, Aryl hydrocarbon receptor signaling, and xenobiotic metabolism signaling. Furthermore, the generated DEGs lists were uploaded into NCATS BioPlanet platform. Some of the identified pathways were related to fatty acid omega oxidation, lipid and lipoprotein metabolism, and adipogenesis. The enrichment and clarification of the pathways and biological networks obtained from the DEGs dataset provide prior knowledge on the underlying biological key events and molecular mechanisms for the computational development of putative adverse outcome pathways (AOPs) for hepatic lipid dysfunction as a precursor to hepatic steatosis.

Advancing internal exposure and physiologically-based toxicokinetic modeling for 21st-century risk assessments.

Scientifically sound, risk-informed evaluation of chemicals is essential to protecting public health. Systematically leveraging information from exposure, toxicology, and epidemiology studies can provide a holistic understanding of how real-world exposure to chemicals may impact the health of populations, including sensitive and vulnerable individuals and life-stages. Increasingly, public health policy makers are employing toxicokinetic (TK) modeling tools to integrate these data streams and predict potential human health impact. Development of a suite of tools for predicting internal exposure, including physiologically-based toxicokinetic (PBTK) models, is being driven by needs to address large numbers of data-poor chemicals efficiently, translate bioactivity, and mechanistic information from new in vitro test systems, and integrate multiple lines of evidence to enable scientifically sound, risk-informed decisions. New modeling approaches are being designed "fit for purpose" to inform specific decision contexts, with applications ranging from rapid screening of hundreds of chemicals, to improved prediction of risks during sensitive stages of development. New data are being generated experimentally and computationally to support these models. Progress to meet the demand for internal exposure and PBTK modeling tools will require transparent publication of models and data to build credibility in results, as well as opportunities to partner with decision makers to evaluate and build confidence in use of these for improved decisions that promote safe use of chemicals.

Evaluation of a Physiologically Based Pharmacokinetic (PBPK) Model for Inorganic Arsenic Exposure Using Data from Two Diverse Human Populations.

BACKGROUND: Multiple epidemiological studies exist for some of the well-studied health endpoints associated with inorganic arsenic (iAs) exposure; however, results are usually expressed in terms of different exposure/dose metrics. Physiologically based pharmacokinetic (PBPK) models may be used to obtain a common exposure metric for application in dose-response meta-analysis. OBJECTIVE: A previously published PBPK model for inorganic arsenic (iAs) was evaluated using data sets for arsenic-exposed populations from Bangladesh and the United States. METHODS: The first data set was provided by the Health Effects of Arsenic Longitudinal Study cohort in Bangladesh. The second data set was provided by a study conducted in Churchill County, Nevada, USA. The PBPK model consisted of submodels describing the absorption, distribution, metabolism and excretion (ADME) of iAs and its metabolites monomethylarsenic (MMA) and dimethylarsenic (DMA) acids. The model was used to estimate total arsenic levels in urine in response to oral ingestion of iAs. To compare predictions of the PBPK model against observations, urinary arsenic concentration and creatinine-adjusted urinary arsenic concentration were simulated. As part of the evaluation, both water and dietary intakes of arsenic were estimated and used to generate the associated urine concentrations of the chemical in exposed populations. RESULTS: When arsenic intake from water alone was considered, the results of the PBPK model underpredicted urinary arsenic concentrations for individuals with low levels of arsenic in drinking water and slightly overpredicted urinary arsenic concentrations in individuals with higher levels of arsenic in drinking water. When population-specific estimates of dietary intakes of iAs were included in exposures, the predictive value of the PBPK model was markedly improved, particularly at lower levels of arsenic intake. CONCLUSIONS: Evaluations of this PBPK model illustrate its adequacy and usefulness for oral exposure reconstructions in human health risk assessment, particularly in individuals who are exposed to relatively low levels of arsenic in water or food. https://doi.org/10.1289/EHP3096.

Neurodevelopment and Thyroid Hormone Synthesis Inhibition in the Rat: Quantitative Understanding Within the Adverse Outcome Pathway Framework.

Adequate levels of thyroid hormone (TH) are needed for proper brain development, deficiencies may lead to adverse neurologic outcomes in humans and animal models. Environmental chemicals have been linked to TH disruption, yet the relationship between developmental exposures and decline in serum TH resulting in neurodevelopmental impairment is poorly understood. The present study developed a quantitative adverse outcome pathway where serum thyroxin (T4) reduction following inhibition of thyroperoxidase in the thyroid gland are described and related to deficits in fetal brain TH and the development of a brain malformation, cortical heterotopia. Pregnant rats were exposed to 6-propylthiouracil (PTU 0, 0.1, 0.5, 1, 2, or 3 parts per million [ppm]) from gestational days 6-20, sequentially increasing PTU concentrations in maternal thyroid gland and serum as well as in fetal serum. Dams exposed to 0.5 ppm PTU and higher exhibited dose-dependent decreases in thyroidal T4. Serum T4 levels in the dam were significantly decreased with exposure to 2 and 3 ppm PTU. In the fetus, T4 decrements were first observed at a lower dose of 0.5 ppm PTU. Based on these data, fetal brain T4 levels were estimated from published literature sources, and quantitatively linked to increases in the size of the heterotopia present in the brains of offspring. These data show the potential of in vivo assessments and computational descriptions of biologic responses to predict the development of this structural brain malformation and use of quantitative adverse outcome pathway approach to evaluate brain deficits that may result from exposure to other TH disruptors.

Integration of Life-Stage Physiologically Based Pharmacokinetic Models with Adverse Outcome Pathways and Environmental Exposure Models to Screen for Environmental Hazards.

A computational framework was developed to assist in screening and prioritizing chemicals based on their dosimetry, toxicity, and potential exposures. The overall strategy started with contextualizing chemical activity observed in high-throughput toxicity screening (HTS) by mapping these assays to biological events described in Adverse Outcome Pathways (AOPs). Next, in vitro to in vivo (IVIVE) extrapolation was used to convert an in vitro dose to an external exposure level, which was compared with potential exposure levels to derive an AOP-based margins of exposure (MOE). In this study, the framework was applied to estimate MOEs for chemicals that can potentially cause developmental toxicity following a putative AOP for fetal vasculogenesis/angiogenesis. A physiologically based pharmacokinetic (PBPK) model was developed to describe chemical disposition during pregnancy, fetal, neonatal, and infant to adulthood stages. Using this life-stage PBPK model, maternal exposures were estimated that would yield fetal blood levels equivalent to the chemical concentration that altered in vitro activity of selected HTS assays related to the most sensitive vasculogenesis/angiogenesis putative AOP. The resulting maternal exposure estimates were then compared with potential exposure levels using literature data or exposure models to derive AOP-based MOEs.

Application of Biologically Based Lumping To Investigate the Toxicokinetic Interactions of a Complex Gasoline Mixture.

People are often exposed to complex mixtures of environmental chemicals such as gasoline, tobacco smoke, water contaminants, or food additives. We developed an approach that applies chemical lumping methods to complex mixtures, in this case gasoline, based on biologically relevant parameters used in physiologically based pharmacokinetic (PBPK) modeling. Inhalation exposures were performed with rats to evaluate the performance of our PBPK model and chemical lumping method. There were 109 chemicals identified and quantified in the vapor in the chamber. The time-course toxicokinetic profiles of 10 target chemicals were also determined from blood samples collected during and following the in vivo experiments. A general PBPK model was used to compare the experimental data to the simulated values of blood concentration for 10 target chemicals with various numbers of lumps, iteratively increasing from 0 to 99. Large reductions in simulation error were gained by incorporating enzymatic chemical interactions, in comparison to simulating the individual chemicals separately. The error was further reduced by lumping the 99 nontarget chemicals. The same biologically based lumping approach can be used to simplify any complex mixture with tens, hundreds, or thousands of constituents.

Estimating Margin of Exposure to Thyroid Peroxidase Inhibitors Using High-Throughput in vitro Data, High-Throughput Exposure Modeling, and Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling.

Some pharmaceuticals and environmental chemicals bind the thyroid peroxidase (TPO) enzyme and disrupt thyroid hormone production. The potential for TPO inhibition is a function of both the binding affinity and concentration of the chemical within the thyroid gland. The former can be determined through in vitro assays, and the latter is influenced by pharmacokinetic properties, along with environmental exposure levels. In this study, a physiologically based pharmacokinetic (PBPK) model was integrated with a pharmacodynamic (PD) model to establish internal doses capable of inhibiting TPO in relation to external exposure levels predicted through exposure modeling. The PBPK/PD model was evaluated using published serum or thyroid gland chemical concentrations or circulating thyroxine (T4) and triiodothyronine (T3) hormone levels measured in rats and humans. After evaluation, the model was used to estimate human equivalent intake doses resulting in reduction of T4 and T3 levels by 10% (ED10) for 6 chemicals of varying TPO-inhibiting potencies. These chemicals were methimazole, 6-propylthiouracil, resorcinol, benzophenone-2, 2-mercaptobenzothiazole, and triclosan. Margin of exposure values were estimated for these chemicals using the ED10 and predicted population exposure levels for females of child-bearing age. The modeling approach presented here revealed that examining hazard or exposure alone when prioritizing chemicals for risk assessment may be insufficient, and that consideration of pharmacokinetic properties is warranted. This approach also provides a mechanism for integrating in vitro data, pharmacokinetic properties, and exposure levels predicted through high-throughput means when interpreting adverse outcome pathways based on biological responses.

A Workflow to Investigate Exposure and Pharmacokinetic Influences on High-Throughput in Vitro Chemical Screening Based on Adverse Outcome Pathways.

BACKGROUND: Adverse outcome pathways (AOPs) link adverse effects in individuals or populations to a molecular initiating event (MIE) that can be quantified using in vitro methods. Practical application of AOPs in chemical-specific risk assessment requires incorporation of knowledge on exposure, along with absorption, distribution, metabolism, and excretion (ADME) properties of chemicals. OBJECTIVES: We developed a conceptual workflow to examine exposure and ADME properties in relation to an MIE. The utility of this workflow was evaluated using a previously established AOP, acetylcholinesterase (AChE) inhibition. METHODS: Thirty chemicals found to inhibit human AChE in the ToxCast™ assay were examined with respect to their exposure, absorption potential, and ability to cross the blood-brain barrier (BBB). Structures of active chemicals were compared against structures of 1,029 inactive chemicals to detect possible parent compounds that might have active metabolites. RESULTS: Application of the workflow screened 10 "low-priority" chemicals of 30 active chemicals. Fifty-two of the 1,029 inactive chemicals exhibited a similarity threshold of ≥ 75% with their nearest active neighbors. Of these 52 compounds, 30 were excluded due to poor absorption or distribution. The remaining 22 compounds may inhibit AChE in vivo either directly or as a result of metabolic activation. CONCLUSIONS: The incorporation of exposure and ADME properties into the conceptual workflow eliminated 10 "low-priority" chemicals that may otherwise have undergone additional, resource-consuming analyses. Our workflow also increased confidence in interpretation of in vitro results by identifying possible "false negatives." CITATION: Phillips MB, Leonard JA, Grulke CM, Chang DT, Edwards SW, Brooks R, Goldsmith MR, El-Masri H, Tan YM. 2016. A workflow to investigate exposure and pharmacokinetic influences on high-throughput in vitro chemical screening based on adverse outcome pathways. Environ Health Perspect 124:53-60; http://dx.doi.org/10.1289/ehp.1409450.

Species extrapolation of life-stage physiologically-based pharmacokinetic (PBPK) models to investigate the developmental toxicology of ethanol using in vitro to in vivo (IVIVE) methods.

To provide useful alternatives to in vivo animal studies, in vitro assays for dose-response assessments of xenobiotic chemicals must use concentrations in media and target tissues that are within biologically-plausible limits. Determining these concentrations is a complex matter, which can be facilitated by applying physiologically-based pharmacokinetic (PBPK) models in an in vitro to in vivo extrapolation (IVIVE) paradigm. We used ethanol (EtOH), a ubiquitous chemical with defined metrics for in vivo and in vitro embryotoxicity, as a model chemical to evaluate this paradigm. A published series of life-stage PBPK models for rats was extended to mice, yielding simulations that adequately predicted in vivo blood EtOH concentrations (BECs) from oral, intraperitoneal, and intravenous routes in nonpregnant and pregnant adult mice. The models were then extrapolated to nonpregnant and pregnant humans, replicating BEC data within a factor of two. The rodent models were then used to conduct IVIVEs for rodent and whole-embryo culture embryotoxicity data (neural tube closure defects, morphological changes). A second IVIVE was conducted for exposure scenarios in pregnant women during critical windows of susceptibility for developmental toxicity, such as the first 6-to-8 weeks (prerecognition period) or mid-to-late pregnancy period, when EtOH consumption is associated with fetal alcohol spectrum disorders. Incorporation of data from human embryonic stem cell studies led to a model-supported linkage of in vitro concentrations with plausible exposure ranges for pregnant women. This effort demonstrates benefits and challenges associated with use of multispecies PBPK models to estimate in vivo tissue concentrations associated with in vitro embryotoxicity studies.

Use of novel inhalation kinetic studies to refine physiologically-based pharmacokinetic models for ethanol in non-pregnant and pregnant rats.

Ethanol (EtOH) exposure induces a variety of concentration-dependent neurological and developmental effects in the rat. Physiologically-based pharmacokinetic (PBPK) models have been used to predict the inhalation exposure concentrations necessary to produce blood EtOH concentrations (BEC) in the range associated with these effects. Previous laboratory reports often lacked sufficient detail to adequately simulate reported exposure scenarios associated with BECs in this range, or lacked data on the time-course of EtOH in target tissues (e.g. brain, liver, eye, fetus). To address these data gaps, inhalation studies were performed at 5000, 10 000, and 21 000 ppm (6 h/d) in non-pregnant female Long-Evans (LE) rats and at 21 000 ppm (6.33 h/d) for 12 d of gestation in pregnant LE rats to evaluate our previously published PBPK models at toxicologically-relevant blood and tissue concentrations. Additionally, nose-only and whole-body plethysmography studies were conducted to refine model descriptions of respiration and uptake within the respiratory tract. The resulting time-course and plethysmography data from these in vivo studies were compared to simulations from our previously published models, after which the models were recalibrated to improve descriptions of tissue dosimetry by accounting for dose-dependencies in pharmacokinetic behavior. Simulations using the recalibrated models reproduced these data from non-pregnant, pregnant, and fetal rats to within a factor of 2 or better across datasets, resulting in a suite of model structures suitable for simulation of a broad range of EtOH exposure scenarios.