In Silico Assessment of Acute Oral Toxicity for Mixtures.

While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains in vivo acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure-Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.

Broad spectrum proteomics analysis of the inferior colliculus following acute hydrogen sulfide exposure.

Acute exposure to high concentrations of H2S causes severe brain injury and long-term neurological disorders, but the mechanisms involved are not known. To better understand the cellular and molecular mechanisms involved in acute H2S-induced neurodegeneration we used a broad-spectrum proteomic analysis approach to identify key molecules and molecular pathways involved in the pathogenesis of acute H2S-induced neurotoxicity and neurodegeneration. Mice were subjected to acute inhalation exposure of up to750 ppm of H2S. H2S induced behavioral deficits and severe lesions including hemorrhage in the inferior colliculus (IC). The IC was microdissected for proteomic analysis. Tandem mass tags (TMT) liquid chromatography mass spectrometry (LC-MS/MS)-based quantitative proteomics was applied for protein identification and quantitation. LC-MS/MS identified 598, 562, and 546 altered proteomic changes at 2 h, and on days 2 and 4 post-H2S exposure, respectively. Of these, 77 proteomic changes were statistically significant at any of the 3 time points. Mass spectrometry data were subjected to Perseus 1.5.5.3 statistical analysis, and gene ontology heat map clustering. Expressions of several key molecules were verified to confirm H2S-dependent proteomics changes. Webgestalt pathway overrepresentation enrichment analysis with Panther engine revealed H2S exposure disrupted several biological processes including metabotropic glutamate receptor group 1 and inflammation mediated by chemokine and cytokine signaling pathways among others. Further analysis showed that energy metabolism, integrity of blood-brain barrier, hypoxic, and oxidative stress signaling pathways were also implicated. Collectively, this broad-spectrum proteomics data has provided important clues to follow up in future studies to further elucidate mechanisms of H2S-induced neurotoxicity.

Potential new therapeutic modality revealed through agent-based modeling of the neuromuscular junction and acetylcholinesterase inhibition.

BACKGROUND: One of the leading causes of death and illness within the agriculture industry is through unintentionally ingesting or inhaling organophosphate pesticides. OP intoxication directly inhibits acetylcholinesterase, resulting in an excitatory signaling cascade leading to fasciculation, loss of control of bodily fluids, and seizures. METHODS: Our model was developed using a discrete, rules-based modeling approach in NetLogo. This model includes acetylcholinesterase, the nicotinic acetylcholine receptor responsible for signal transduction, a single release of acetylcholine, organophosphate inhibitors, and a theoretical novel medical countermeasure. We have parameterized the system considering the molecular reaction rate constants in an agent-based approach, as opposed to apparent macroscopic rates used in differential equation models. RESULTS: Our model demonstrates how the cholinergic crisis can be mitigated by therapeutic intervention with an acetylcholinesterase activator. Our model predicts signal rise rates and half-lives consistent with in vitro and in vivo data in the absence and presence of inhibitors. It also predicts the efficacy of theoretical countermeasures acting through three mechanisms: increasing catalytic turnover of acetylcholine, increasing acetylcholine binding affinity to the enzyme, and decreasing binding rates of inhibitors. CONCLUSION: We present a model of the neuromuscular junction confirming observed acetylcholine signaling data and suggesting that developing a countermeasure capable of reducing inhibitor binding, and not activator concentration, is the most important parameter for reducing organophosphate (OP) intoxication.

Quantitative structure-activity relationships for organophosphates binding to acetylcholinesterase.

Organophosphates are a group of pesticides and chemical warfare nerve agents that inhibit acetylcholinesterase, the enzyme responsible for hydrolysis of the excitatory neurotransmitter acetylcholine. Numerous structural variants exist for this chemical class, and data regarding their toxicity can be difficult to obtain in a timely fashion. At the same time, their use as pesticides and military weapons is widespread, which presents a major concern and challenge in evaluating human toxicity. To address this concern, a quantitative structure-activity relationship (QSAR) was developed to predict pentavalent organophosphate oxon human acetylcholinesterase bimolecular rate constants. A database of 278 three-dimensional structures and their bimolecular rates was developed from 15 peer-reviewed publications. A database of simplified molecular input line entry notations and their respective acetylcholinesterase bimolecular rate constants are listed in Supplementary Material, Table I. The database was quite diverse, spanning 7 log units of activity. In order to describe their structure, 675 molecular descriptors were calculated using AMPAC 8.0 and CODESSA 2.7.10. Orthogonal projection to latent structures regression, bootstrap leave-random-many-out cross-validation and y-randomization were used to develop an externally validated consensus QSAR model. The domain of applicability was assessed by the William's plot. Six external compounds were outside the warning leverage indicating potential model extrapolation. A number of compounds had residuals >2 or <-2, indicating potential outliers or activity cliffs. The results show that the HOMO-LUMO energy gap contributed most significantly to the binding affinity. A mean training R (2) of 0.80, a mean test set R (2) of 0.76 and a consensus external test set R (2) of 0.66 were achieved using the QSAR. The training and external test set RMSE values were found to be 0.76 and 0.88. The results suggest that this QSAR model can be used in physiologically based pharmacokinetic/pharmacodynamic models of organophosphate toxicity to determine the rate of acetylcholinesterase inhibition.

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

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

Physiocognitive Modeling: Explaining the Effects of Caffeine on Fatigue.

Most computational theories of cognition lack a representation of physiology. Understanding the cognitive effects of compounds present in the environment is important for explaining and predicting changes in cognition and behavior given exposure to toxins, pharmaceuticals, or the deprivation of critical compounds like oxygen. This research integrates physiologically based pharmacokinetic (PBPK) model predictions of caffeine concentrations in blood and tissues with ACT-R's fatigue module to predict the effects of caffeine on fatigue. Mapping between the PBPK model parameters and ACT-R model parameters is informed by the neurophysiological literature and established associations between ACT-R modules and brain regions. The results from three such parameter mappings are explored to explain observed data from sleep-deprived participants performing the psychomotor vigilance test with and without caffeine. Predicted caffeine concentrations in the brain are used to modulate procedural parameters in the fatigue module to explain caffeine's effects on multiple performance metrics.

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.

Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner.

Quantitative structure-activity relationships for organophosphates binding to trypsin and chymotrypsin.

Organophosphate (OP) nerve agents such as sarin, soman, tabun, and O-ethyl S-[2-(diisopropylamino) ethyl] methylphosphonothioate (VX) do not react solely with acetylcholinesterase (AChE). Evidence suggests that cholinergic-independent pathways over a wide range are also targeted, including serine proteases. These proteases comprise nearly one-third of all known proteases and play major roles in synaptic plasticity, learning, memory, neuroprotection, wound healing, cell signaling, inflammation, blood coagulation, and protein processing. Inhibition of these proteases by OP was found to exert a wide range of noncholinergic effects depending on the type of OP, the dose, and the duration of exposure. Consequently, in order to understand these differences, in silico biologically based dose-response and quantitative structure-activity relationship (QSAR) methodologies need to be integrated. Here, QSAR were used to predict OP bimolecular rate constants for trypsin and α-chymotrypsin. A heuristic regression of over 500 topological/constitutional, geometric, thermodynamic, electrostatic, and quantum mechanical descriptors, using the software Ampac 8.0 and Codessa 2.51 (SemiChem, Inc., Shawnee, KS), was developed to obtain statistically verified equations for the models. General models, using all data subsets, resulted in R(2) values of .94 and .92 and leave-one-out Q(2) values of 0.9 and 0.87 for trypsin and α-chymotrypsin. To validate the general model, training sets were split into independent subsets for test set evaluation. A y-randomization procedure, used to estimate chance correlation, was performed 10,000 times, resulting in mean R(2) values of .24 and .3 for trypsin and α-chymotrypsin. The results show that these models are highly predictive and capable of delineating the complex mechanism of action between OP and serine proteases, and ultimately, by applying this approach to other OP enzyme reactions such as AChE, facilitate the development of biologically based dose-response models.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Considerations for Development of Exposure Limits for Chemicals Encountered During Aircraft Operation.

BACKGROUND: Military aircrews' health status is critical to their mission readiness, as they perform physically and cognitively demanding tasks in nontraditional work environments. Research Objectives: Our objective is to develop a broad operational risk assessment framework and demonstrate its applicability to health risks to aircrews because of airborne chemical exposure, considering stressors such as heat and exertion. METHODS: Extrapolation of generic exposure standards to military aviation-specific conditions can include computation of risk-relevant internal dosimetry estimates by incorporating changes in breathing patterns and blood flow distribution because of aspects of the in-flight environment. We provide an example of the effects of exertion on peak blood concentrations of 1,2,4-trimethylbenzene computed using a physiologically based pharmacokinetic model. RESULTS: Existing published collections on the effects of flight-related stressors on breathing patterns and blood flow address only a limited number of stressors. Although data exist that can be used to develop operational exposure limits specific to military aircrew activities, efforts to integrate this information in specific chemical assessments have been limited. CONCLUSIONS: Efforts to develop operational exposure limits would benefit from guidance on how to make use of existing assessments and expanded databases of the impact of environmental stressors on adult human physiology.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Perchlorate and radioiodide kinetics across life stages in the human: using PBPK models to predict dosimetry and thyroid inhibition and sensitive subpopulations based on developmental stage.

Perchlorate (ClO4(-)) is a drinking-water contaminant, known to disrupt thyroid hormone homeostasis in rats. This effect has only been seen in humans at high doses, yet the potential for long term effects from developmental endocrine disruption emphasizes the need for improved understanding of perchlorate's effect during the perinatal period. Physiologically based pharmacokinetic/dynamic (PBPK/PD) models for ClO4(-) and its effect on thyroid iodide uptake were constructed for human gestation and lactation data. Chemical specific parameters were estimated from life-stage and species-specific relationships established in previously published models for various life-stages in the rat and nonpregnant adult human. With the appropriate physiological descriptions, these kinetic models successfully simulate radioiodide data culled from the literature for gestation and lactation, as well as ClO4(-) data from populations exposed to contaminated drinking water. These models provide a framework for extrapolating from chemical exposure in laboratory animals to human response, and support a more quantitative understanding of life-stage-specific susceptibility to ClO4(-). The pregnant and lactating woman, fetus, and nursing infant were predicted to have higher blood ClO4(-) concentrations and greater thyroid iodide uptake inhibition at a given drinking-water concentration than either the nonpregnant adult or the older child. The fetus is predicted to receive the greatest dose (per kilogram body weight) due to several factors, including placental sodium-iodide symporter (NIS) activity and reduced maternal urinary clearance of ClO4(-). The predicted extent of iodide inhibition in the most sensitive population (fetus) is not significant (approximately 1%) at the U.S. Environmental Protection Agency reference dose (0.0007 mg/kg-d).

Age-dependent partition coefficients for a mixture of volatile organic solvents in Sprague-Dawley rats and humans.

The absorption, distribution, metabolism, and excretion of volatile organic compounds (VOCs) are critically determined by a few chemical-specific factors, notably their blood and tissue partition coefficients (PC) and metabolism. Age-specific values for PCs in rats have rarely been reported or utilized in pharmacokinetic modeling for predicting dosimetry in toxicity studies with rats progressing through their lifestages. A mixture of six VOCs (benzene, chloroform, methyl ethyl ketone, methylene chloride, trichloroethylene, and perchloroethylene) was used to determine blood:air and tissue:air PCs in rats at three different ages (postnatal d 10, 60 d, and 2 yr) and blood:air PCs in pediatric and adult human blood. No differences with age in human blood:air PCs for the six compounds were observed. Rat blood:air PCs increased with age varying with compound. Tissue:air PCs showed tissue-specific changes with age. Water-soluble methyl ethyl ketone showed no age-dependent differences. Partition coefficients, particularly the blood:air PC, are key determinants of the rodent and human blood concentrations; age-appropriate values improve the accuracy of pharmacokinetic model predictions of population variability and age-specific exposures.

A systems dynamics approach to the efficacy of oxime therapy for mild exposure to sarin gas.

The use of nerve agents such as sarin is as much a threat today as any other time in our history. The events in Syria in 2013 are proof of this. "The Obama administration asserted Sunday for the first time that the Syrian government used the nerve gas sarin to kill more than 1,400 people (August 21, 2013) in the world's gravest chemical weapons attack in 25 years." With these recent events clear in our mind, we must focus on the horrific nature of these chemical agents to devise a strategy that will enable first responders to counteract these insidious chemicals. This paper presents research on a physiologically based pharmacokinetic model to determine whether the current treatment protocol prescribed by the Center for Disease Control (CDC) and the US Army is effective in treating victims suffering from acute exposure symptoms. The model was used to determine what treatment should be used for victims suffering from mild exposure symptoms. The results indicate that the current CDC and US Army treatment is effective, but treatment with oxime therapy was not effective in alleviating symptoms of mild exposure. By applying these results, an effective treatment protocol was developed.

Dietary Iodine Sufficiency and Moderate Insufficiency in the Lactating Mother and Nursing Infant: A Computational Perspective.

The Institute of Medicine recommends that lactating women ingest 290 µg iodide/d and a nursing infant, less than two years of age, 110 µg/d. The World Health Organization, United Nations Children's Fund, and International Council for the Control of Iodine Deficiency Disorders recommend population maternal and infant urinary iodide concentrations ≥ 100 µg/L to ensure iodide sufficiency. For breast milk, researchers have proposed an iodide concentration range of 150-180 µg/L indicates iodide sufficiency for the mother and infant, however no national or international guidelines exist for breast milk iodine concentration. For the first time, a lactating woman and nursing infant biologically based model, from delivery to 90 days postpartum, was constructed to predict maternal and infant urinary iodide concentration, breast milk iodide concentration, the amount of iodide transferred in breast milk to the nursing infant each day and maternal and infant serum thyroid hormone kinetics. The maternal and infant models each consisted of three sub-models, iodide, thyroxine (T4), and triiodothyronine (T3). Using our model to simulate a maternal intake of 290 µg iodide/d, the average daily amount of iodide ingested by the nursing infant, after 4 days of life, gradually increased from 50 to 101 µg/day over 90 days postpartum. The predicted average lactating mother and infant urinary iodide concentrations were both in excess of 100 µg/L and the predicted average breast milk iodide concentration, 157 µg/L. The predicted serum thyroid hormones (T4, free T4 (fT4), and T3) in both the nursing infant and lactating mother were indicative of euthyroidism. The model was calibrated using serum thyroid hormone concentrations for lactating women from the United States and was successful in predicting serum T4 and fT4 levels (within a factor of two) for lactating women in other countries. T3 levels were adequately predicted. Infant serum thyroid hormone levels were adequately predicted for most data. For moderate iodide deficient conditions, where dietary iodide intake may range from 50 to 150 µg/d for the lactating mother, the model satisfactorily described the iodide measurements, although with some variation, in urine and breast milk. Predictions of serum thyroid hormones in moderately iodide deficient lactating women (50 µg/d) and nursing infants did not closely agree with mean reported serum thyroid hormone levels, however, predictions were usually within a factor of two. Excellent agreement between prediction and observation was obtained for a recent moderate iodide deficiency study in lactating women. Measurements included iodide levels in urine of infant and mother, iodide in breast milk, and serum thyroid hormone levels in infant and mother. A maternal iodide intake of 50 µg/d resulted in a predicted 29-32% reduction in serum T4 and fT4 in nursing infants, however the reduced serum levels of T4 and fT4 were within most of the published reference intervals for infant. This biologically based model is an important first step at integrating the rapid changes that occur in the thyroid system of the nursing newborn in order to predict adverse outcomes from exposure to thyroid acting chemicals, drugs, radioactive materials or iodine deficiency.

PBPK model for radioactive iodide and perchlorate kinetics and perchlorate-induced inhibition of iodide uptake in humans.

Detection of perchlorate (ClO4-) in several drinking water sources across the U.S. has lead to public concern over health effects from chronic low-level exposures. Perchlorate inhibits thyroid iodide (I-) uptake at the sodium (Na+)-iodide (I-) symporter (NIS), thereby disrupting the initial stage of thyroid hormone synthesis. A physiologically based pharmacokinetic (PBPK) model was developed to describe the kinetics and distribution of both radioactive I- and cold ClO4- in healthy adult humans and simulates the subsequent inhibition of thyroid uptake of radioactive I- by ClO4-. The model successfully predicts the measured levels of serum and urinary ClO4- from drinking water exposures, ranging from 0.007 to 12 mg ClO4-/kg/day, as well as the subsequent inhibition of thyroid 131I- uptake. Thyroid iodine, as well as total, free, and protein-bound radioactive I- in serum from various tracer studies, are also successfully simulated. This model's parameters, in conjunction with corresponding model parameters established for the male, gestational, and lactating rat, can be used to estimate parameters in a pregnant or lactating human, that have not been or cannot be easily measured to extrapolate dose metrics and correlate observed effects in perchlorate toxicity studies to other human life stages. For example, by applying the adult male rat:adult human ratios of model parameters to those parameters established for the gestational and lactating rat, we can derive a reasonable estimate of corresponding parameters for a gestating or lactating human female. Although thyroid hormones and their regulatory feedback are not incorporated in the model structure, the model's successful prediction of free and bound radioactive I- and perchlorate's interaction with free radioactive I- provide a basis for extending the structure to address the complex hypothalamic-pituitary-thyroid feedback system. In this paper, bound radioactive I- refers to I- incorporated into thyroid hormones or iodinated proteins, which may or may not be bound to plasma proteins.