Updating the biologically based dose-response model for the nasal carcinogenicity of inhaled formaldehyde in the F344 rat.

Chronic inhalation of formaldehyde by F344 rats causes nasal squamous cell carcinoma (SCC). This outcome is well-characterized: including dose-response and time course data for SCC, mechanistic endpoints, and nasal dosimetry. Conolly et al. (Toxicol. Sci. 75, 432-447, 2003) used these resources to develop a biologically based dose-response (BBDR) model for SCC in F344 rats. This model, scaled up to humans, has informed dose-response conclusions reached by several international regulatory agencies. However, USEPA concluded that uncertainties precluded its use for cancer risk assessment. Here, we describe an updated BBDR model that addresses uncertainties through refined dosimetry modeling, revised analysis of labeling index data, and an extended dataset where both inhaled (exogenous) and endogenous formaldehyde (exogF, endoF) form DNA adducts. Further, since Conolly et al. (ibid) was published, it has become clear that, when controls from all F344 inhalation bioassays are considered, accounting for over 4000 rats, at most one nasal SCC occurred. This low spontaneous incidence constrains possible contribution of endoF to the formation of nasal SCC via DNA reactivity. Further, since both exogF and endoF form DNA adducts, this constraint also applies to exogF. The revised BBDR model therefore drives SCC formation through the cytotoxicity of high concentration exogF. An option for direct mutagenicity associated with DNA adducts is retained to allow estimation of an upper bound on adduct mutagenicity consistent with the lack of a spontaneous SCC incidence. These updates represent an iterative refinement of the 2003 model, incorporating new data and insights to reduce identified model uncertainties.

Relative contributions of endogenous and exogenous formaldehyde to formation of deoxyguanosine monoadducts and DNA-protein crosslink adducts of DNA in rat nasal mucosa.

Understanding the dose-response for formaldehyde-induced nasal cancer in rats is complicated by (1) the uneven distribution of inhaled formaldehyde across the interior surface of the nasal cavity and, (2) the presence of endogenous formaldehyde (endoF) in the nasal mucosa. In this work, we used computational fluid dynamics (CFD) modeling to predict flux of inhaled (exogenous) formaldehyde (exogF) from air into tissue at the specific locations where DNA adducts were measured. Experimental work has identified DNA-protein crosslink (DPX) adducts due to exogF and deoxyguanosine (DG) adducts due to both exogF and endoF. These adducts can be considered biomarkers of exposure for effects of endoF and exogF on DNA that may be part of the mechanism of tumor formation. We describe a computational model linking CFD-predicted flux of formaldehyde from air into tissue, and the intracellular production of endoF, with the formation of DPX and DG adducts. We assumed that, like exogF, endoF can produce DPX. The model accurately reproduces exogDPX, exogDG, and endoDG data after inhalation from 0.7 to 15 ppm. The dose-dependent concentrations of exogDPX and exogDG are predicted to exceed the concentrations of their endogenous counterparts at about 2 and 6 ppm exogF, respectively. At all concentrations examined, the concentrations of endoDPX and exogDPX were predicted to be at least 10-fold higher than that of their DG counterparts. The modeled dose-dependent concentrations of these adducts are suitable to be used together with data on the dose-dependence of cell proliferation to conduct quantitative modeling of formaldehyde-induced rat nasal carcinogenicity.

Rat Models of Virus-Induced Type 1 Diabetes.

Studies performed in humans and animal models have implicated the environment in the etiology of type 1 diabetes (T1D), but the nature and timing of the interactions triggering ß cell autoimmunity are poorly understood. Virus infections have been postulated to be involved in disease mechanisms, but the underlying mechanisms are not known. It is exceedingly difficult to establish a cause-and-effect relationship between viral infection and diabetes in humans. Thus, we have used the BioBreeding Diabetes-Resistant (BBDR) and the LEW1.WR1 rat models of virus-induced disease to elucidate how virus infection leads to T1D. The immunophenotype of these strains is normal, and spontaneous diabetes does not occur in a specific pathogen-free environment. However, ß cell inflammation and diabetes with many similarities to the human disease are induced by infection with the parvovirus Kilham rat virus (KRV). KRV-induced diabetes in the BBDR and LEW1.WR1 rat models is limited to young animals and can be induced in both male and female rats. Thus, these animals provide a powerful experimental tool to identify mechanisms underlying virus-induced T1D development.

An evaluation of the USEPA Proposed Approaches for applying a biologically based dose-response model in a risk assessment for perchlorate in drinking water.

The United States Environmental Protection Agency's (USEPA) 2017 report, "Draft Report: Proposed Approaches to Inform the Derivation of a Maximum Contaminant Level Goal for Perchlorate in Drinking Water", proposes novel approaches for deriving a Maximum Contaminant Level Goal (MCLG) for perchlorate using a biologically-based dose-response (BBDR) model. The USEPA (2017) BBDR model extends previously peer-reviewed perchlorate models to describe the relationship between perchlorate exposure and thyroid hormone levels during early pregnancy. Our evaluation focuses on two key elements of the USEPA (2017) report: the plausibility of BBDR model revisions to describe control of thyroid hormone production in early pregnancy and the basis for linking BBDR model results to neurodevelopmental outcomes. While the USEPA (2017) BBDR model represents a valuable research tool, the lack of supporting data for many of the model assumptions and parameters calls into question the fitness of the extended BBDR model to support quantitative analyses for regulatory decisions on perchlorate in drinking water. Until more data can be developed to address uncertainties in the current BBDR model, USEPA should continue to rely on the RfD recommended by the NAS (USEPA, 2005) when considering further regulatory action.

Quantitative global sensitivity analysis of a biologically based dose-response pregnancy model for the thyroid endocrine system.

A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis.

Evaluation of perturbations in serum thyroid hormones during human pregnancy due to dietary iodide and perchlorate exposure using a biologically based dose-response model.

A biologically based dose-response model (BBDR) for the hypothalamic pituitary thyroid (HPT) axis was developed in the near-term pregnant mother and fetus. This model was calibrated to predict serum levels of iodide, total thyroxine (T4), free thyroxine (fT4), and total triiodothyronine (T3) in the mother and fetus for a range of dietary iodide intake. The model was extended to describe perchlorate, an environmental and food contaminant, that competes with the sodium iodide symporter protein for thyroidal uptake of iodide. Using this mode-of-action framework, simulations were performed to determine the daily ingestion rates of perchlorate that would be associated with hypothyroxinemia or onset of hypothyroidism for varying iodide intake. Model simulations suggested that a maternal iodide intake of 75 to 250 µg/day and an environmentally relevant exposure of perchlorate (~0.1 µg/kg/day) did not result in hypothyroxinemia or hypothyroidism. For a daily iodide-sufficient intake of 200 µg/day, the dose of perchlorate required to reduce maternal fT4 levels to a hypothyroxinemic state was estimated at 32.2 µg/kg/day. As iodide intake was lowered to 75 µg/day, the model simulated daily perchlorate dose required to cause hypothyroxinemia was reduced by eightfold. Similarly, the perchlorate intake rates associated with the onset of subclinical hypothyroidism ranged from 54.8 to 21.5 µg/kg/day for daily iodide intake of 250-75 µg/day. This BBDR-HPT axis model for pregnancy provides an example of a novel public health assessment tool that may be expanded to address other endocrine-active chemicals found in food and the environment.

An approach for integrating toxicogenomic data in risk assessment: the dibutyl phthalate case study.

An approach for evaluating and integrating genomic data in chemical risk assessment was developed based on the lessons learned from performing a case study for the chemical dibutyl phthalate. A case study prototype approach was first developed in accordance with EPA guidance and recommendations of the scientific community. Dibutyl phthalate (DBP) was selected for the case study exercise. The scoping phase of the dibutyl phthalate case study was conducted by considering the available DBP genomic data, taken together with the entire data set, for whether they could inform various risk assessment aspects, such as toxicodynamics, toxicokinetics, and dose-response. A description of weighing the available dibutyl phthalate data set for utility in risk assessment provides an example for considering genomic data for future chemical assessments. As a result of conducting the scoping process, two questions--Do the DBP toxicogenomic data inform 1) the mechanisms or modes of action?, and 2) the interspecies differences in toxicodynamics?--were selected to focus the case study exercise. Principles of the general approach include considering the genomics data in conjunction with all other data to determine their ability to inform the various qualitative and/or quantitative aspects of risk assessment, and evaluating the relationship between the available genomic and toxicity outcome data with respect to study comparability and phenotypic anchoring. Based on experience from the DBP case study, recommendations and a general approach for integrating genomic data in chemical assessment were developed to advance the broader effort to utilize 21st century data in risk assessment.

Competitive inhibition of thyroidal uptake of dietary iodide by perchlorate does not describe perturbations in rat serum total T4 and TSH.

BACKGROUND: Perchlorate (ClO4(-)) is an environmental contaminant known to disrupt the thyroid axis of many terrestrial and aquatic species. ClO4(-) competitively inhibits iodide uptake into the thyroid at the sodium/iodide symporter and disrupts hypothalamic-pituitary-thyroid (HPT) axis homeostasis in rodents. OBJECTIVE: We evaluated the proposed mode of action for ClO4(-)-induced rat HPT axis perturbations using a biologically based dose-response (BBDR) model of the HPT axis coupled with a physiologically based pharmacokinetic model of ClO4(-). METHODS: We configured a BBDR-HPT/ClO4(-) model to describe competitive inhibition of thyroidal uptake of dietary iodide by ClO4(-) and used it to simulate published adult rat drinking water studies. We compared model-predicted serum thyroid-stimulating hormone (TSH) and total thyroxine (TT4) concentrations with experimental observations reported in these ClO4(-) drinking water studies. RESULTS: The BBDR-HPT/ClO4(-) model failed to predict the ClO4(-)-induced onset of disturbances in the HPT axis. Using ClO4(-) inhibition of dietary iodide uptake into the thyroid, the model underpredicted both the rapid decrease in serum TT4 concentrations and the rise in serum TSH concentrations. CONCLUSIONS: Assuming only competitive inhibition of thyroidal uptake of dietary iodide, BBDR-HPT/ClO4(-) model calculations were inconsistent with the rapid decrease in serum TT4 and the corresponding increase in serum TSH. Availability of bound iodide in the thyroid gland governed the rate of hormone secretion from the thyroid. ClO4(-) is translocated into the thyroid gland, where it may act directly or indirectly on thyroid hormone synthesis/secretion in the rat. The rate of decline in serum TT4 in these studies after 1 day of treatment with ClO4(-) appeared consistent with a reduction in thyroid hormone production/secretion. This research demonstrates the utility of a biologically based model to evaluate a proposed mode of action for ClO4(-) in a complex biological process.