Issues in assessing the health risks of n-butanol.

A literature review and health effects evaluation were conducted for n-butanol, a chemical that occurs naturally in some foods, which is an intermediate in the production of butyl esters and can be used as a gasoline additive or blend. Studies evaluating n-butyl acetate were included in the review as n-butyl acetate is rapidly converted to n-butanol following multiple routes of exposure. The primary n-butanol health effects identified were developmental and nervous system endpoints. In conducting the literature review and evaluating study findings, the following observations were made: (1) developmental findings were consistently identified; (2) neurodevelopmental findings were inconsistent; (3) evidence for nervous system effects was weak; (4) comparing internal doses from oral and inhalation exposures using physiologically based pharmacokinetic models introduces uncertainties; and (5) a lack of mechanistic information for n-butanol resulted in the reliance on mechanistic data for ethanol, which may or may not be applicable to n-butanol. This paper presents findings from a literature review on the health effects of n-butanol and proposes research to help reduce uncertainty that exists due to database limitations.

Revision of the affinity constant for perchlorate binding to the sodium-iodide symporter based on in vitro and human in vivo data.

A series of previously published physiologically based pharmacokinetic (PBPK) models describe the effect of perchlorate on iodide uptake by the thyroid, with the mechanism being competitive inhibition of iodide transport by the sodium-iodide symporter (NIS). Hence a key parameter of these models is the affinity of perchlorate for the NIS, characterized as the Michaelis-Menten kinetic constant, Km . However, when model predictions were compared to published results of a human study measuring radio-iodide uptake (RAIU) inhibition after controlled perchlorate exposures, it was found to only fit the lowest exposure level and underpredicted RAIU inhibition at higher levels. Published in vitro data, in which perchlorate-induced inhibition of iodide uptake via the NIS was measured, were re-analyzed. Km for binding of perchlorate to the NIS originally derived from these data, 1.5 µm, had been obtained using Lineweaver-Burk plots, which allow for linear regression but invert the signal-noise of the data. Re-fitting these data by non-linear regression of the non-inverted data yielded a 60% lower value for the Km , 0.59 µm. Substituting this value into the PBPK model for an average adult human significantly improved model agreement with the human RAIU data for exposures <100 µg kg-1 day-1 . Thus, this lower Km value both fits the in vitro NIS kinetics and provides better predictions of human in vivo RAIU data. This change in Km increases the predicted sensitivity of humans to perchlorate over twofold for low-level exposures. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

A revised model of ex-vivo reduction of hexavalent chromium in human and rodent gastric juices.

Chronic oral exposure to hexavalent chromium (Cr-VI) in drinking water has been shown to induce tumors in the mouse gastrointestinal (GI) tract and rat oral cavity. The same is not true for trivalent chromium (Cr-III). Thus reduction of Cr-VI to Cr-III in gastric juices is considered a protective mechanism, and it has been suggested that the difference between the rate of reduction among mice, rats, and humans could explain or predict differences in sensitivity to Cr-VI. We evaluated previously published models of gastric reduction and believe that they do not fully describe the data on reduction as a function of Cr-VI concentration, time, and (in humans) pH. The previous models are parsimonious in assuming only a single reducing agent in rodents and describing pH-dependence using a simple function. We present a revised model that assumes three pools of reducing agents in rats and mice with pH-dependence based on known speciation chemistry. While the revised model uses more fitted parameters than the original model, they are adequately identifiable given the available data, and the fit of the revised model to the full range of data is shown to be significantly improved. Hence the revised model should provide better predictions of Cr-VI reduction when integrated into a corresponding PBPK model.

The use of PBPK models to inform human health risk assessment: case study on perchlorate and radioiodide human lifestage models.

Physiologically-based pharmacokinetic (PBPK) models are often submitted to or selected by agencies, such as the U.S. Environmental Protection Agency (U.S. EPA) and Agency for Toxic Substances and Disease Registry, for consideration for application in human health risk assessment (HHRA). Recently, U.S. EPA evaluated the human PBPK models for perchlorate and radioiodide for their ability to estimate the relative sensitivity of perchlorate inhibition on thyroidal radioiodide uptake for various population groups and lifestages. The most well-defined mode of action of the environmental contaminant, perchlorate, is competitive inhibition of thyroidal iodide uptake by the sodium-iodide symporter (NIS). In this analysis, a six-step framework for PBPK model evaluation was followed, and with a few modifications, the models were determined to be suitable for use in HHRA to evaluate relative sensitivity among human lifestages. Relative sensitivity to perchlorate was determined by comparing the PBPK model predicted percent inhibition of thyroidal radioactive iodide uptake (RAIU) by perchlorate for different lifestages. A limited sensitivity analysis indicated that model parameters describing urinary excretion of perchlorate and iodide were particularly important in prediction of RAIU inhibition; therefore, a range of biologically plausible values available in the peer-reviewed literature was evaluated. Using the updated PBPK models, the greatest sensitivity to RAIU inhibition was predicted to be the near-term fetus (gestation week 40) compared to the average adult and other lifestages; however, when exposure factors were taken into account, newborns were found to be populations that need further evaluation and consideration in a risk assessment for perchlorate.

A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models: Extension to Volatile Organic Compounds.

Chemical risk assessors use physiologically based pharmacokinetic (PBPK) models to perform dosimetric calculations, including extrapolations between exposure scenarios, species, and populations of interest. Assessors should complete a thorough quality assurance (QA) review to ensure biological accuracy and correct implementation prior to using these models. This process can be time-consuming, and we developed a PBPK model template that allows for faster, more efficient QA review. The model template consists of a single model "superstructure" with equations and logic commonly found in PBPK models, allowing users to implement a wide variety of chemical-specific PBPK models. QA review can be completed more quickly than for conventional PBPK model implementations because the general model equations have already been reviewed and only parameters describing chemical-specific model and exposure scenarios need review for any given model implementation. We have expanded a previous version of the PBPK model template by adding features commonly included in PBPK models for volatile organic compounds (VOCs). We included multiple options for representing concentrations in blood, describing metabolism, and modeling gas exchange processes to allow for inhalation exposures. We created PBPK model template implementations of published models for seven VOCs: dichloromethane, methanol, chloroform, styrene, vinyl chloride, trichloroethylene, and carbon tetrachloride. Simulations performed using our template implementations matched published simulation results to a high degree of accuracy (maximum observed percent error: 1%). Thus, the model template approach can now be applied to a broader class of chemical-specific PBPK models while continuing to bolster efficiency of QA processes that should be conducted prior to using models for risk assessment applications.

A Generic Pharmacokinetic Model for Quantifying Mother-to-Offspring Transfer of Lipophilic Persistent Environmental Chemicals.

Lipophilic persistent environmental chemicals (LPECs) can accumulate in a woman's body and transfer to her developing child across the placenta and via breast milk. To assess health risks associated with developmental exposures to LPECs, we developed a pharmacokinetic (PK) model that quantifies mother-to-offspring transfer of LPECs during pregnancy and lactation and facilitates internal dosimetry calculations for offspring. We parameterized the model for mice, rats, and humans using time-varying functions for body mass and milk consumption rates. The only required substance-specific parameter is the elimination half-life of the LPEC in the animal species of interest. We used the model to estimate whole-body concentrations in mothers and offspring following maternal exposures to hexachlorobenzene (HCB) and 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) and compared these with measured concentrations from animal studies. We also compared estimated concentrations for humans to those generated using a previously published human LPEC PK model. Finally, we compared human equivalent doses (HEDs) calculated using our model and an allometric scaling method. Estimated and observed whole-body concentrations of HCB and PCB 153 in offspring followed similar trends and differed by less than 60%. Simulations of human exposure yielded concentration estimates comparable to those generated using the previously published model, with concentrations in offspring differing by less than 12%. HEDs calculated using our PK model were about 2 orders of magnitude lower than those generated using allometric scaling. Our PK model can be used to calculate internal dose metrics for offspring and corresponding HEDs and thus informs assessment of developmental toxicity risks associated with LPECs.

Approaches to cancer assessment in EPA's Integrated Risk Information System.

The U.S. Environmental Protection Agency's (EPA) Integrated Risk Information System (IRIS) Program develops assessments of health effects that may result from chronic exposure to chemicals in the environment. The IRIS database contains more than 540 assessments. When supported by available data, IRIS assessments provide quantitative analyses of carcinogenic effects. Since publication of EPA's 2005 Guidelines for Carcinogen Risk Assessment, IRIS cancer assessments have implemented new approaches recommended in these guidelines and expanded the use of complex scientific methods to perform quantitative dose-response assessments. Two case studies of the application of the mode of action framework from the 2005 Cancer Guidelines are presented in this paper. The first is a case study of 1,2,3-trichloropropane, as an example of a chemical with a mutagenic mode of carcinogenic action thus warranting the application of age-dependent adjustment factors for early-life exposure; the second is a case study of ethylene glycol monobutyl ether, as an example of a chemical with a carcinogenic action consistent with a nonlinear extrapolation approach. The use of physiologically based pharmacokinetic (PBPK) modeling to quantify interindividual variability and account for human parameter uncertainty as part of a quantitative cancer assessment is illustrated using a case study involving probabilistic PBPK modeling for dichloromethane. We also discuss statistical issues in assessing trends and model fit for tumor dose-response data, analysis of the combined risk from multiple types of tumors, and application of life-table methods for using human data to derive cancer risk estimates. These issues reflect the complexity and challenges faced in assessing the carcinogenic risks from exposure to environmental chemicals, and provide a view of the current trends in IRIS carcinogenicity risk assessment.

A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances.

Physiologically based pharmacokinetic (PBPK) models are commonly used in risk assessments to perform inter- and intraspecies extrapolations as well as to extrapolate between different dosing scenarios; however, they must first undergo quality assurance review, which can be a time-consuming process, especially when model code is not readily available. We developed and implemented (using R and MCSim) a PBPK model template capable of replicating published model results for several chemical-specific PBPK models. This model template allows for faster quality assurance review because the general model equations only need to be reviewed once, and application to a specific chemical then only requires reviewing input parameters. The model template can implement PBPK models with oral and intravenous exposure routes, varying numbers of tissue compartments, renal reabsorption, and multiple elimination pathways, including fecal, urinary, and biliary. Using the model template, we reproduced published model simulation results for perfluorohexanesulfonic acid, perfluorononanoic acid, perfluorodecanoic acid, perfluorooctanoate, and perflouorooctane sulfonate. We also show that the template can be a useful tool for identifying potential model errors. Thus, the model template allows for faster evaluation and review of published PBPK models and provides a proof of concept for using this approach with broader classes of chemical-specific PBPK models.

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making.

OBJECTIVES: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). STUDY DESIGN AND SETTING: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. RESULTS: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose-response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either "off-the-shelf" or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. CONCLUSION: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care-related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics).

A multicompartment liver-based pharmacokinetic model for benzene and its metabolites in mice.

Benzene is a highly flammable, colorless liquid. Ubiquitous exposures result from its presence in gasoline vapors, cigarette smoke, and industrial processes. After uptake into the body, benzene undergoes a series of metabolic transformations to multiple metabolites that exert toxic effects on the bone marrow. We developed a physiologically based pharmacokinetic model for the uptake and elimination of benzene in mice to relate the concentration of inhaled and orally administered benzene to the tissue doses of benzene and its key metabolites. This model takes into account the zonal distribution of enzymes and metabolism in the liver rather than treating the liver as one homogeneous compartment, and considers metabolism in tissues other than the liver. Analysis was done to examine the existence and uniqueness of solutions of the system. We then formulated an inverse problem to obtain estimates for the unknown parameters; data from multiple laboratories and experiments were used. Despite the sources of variability, the model simulations matched the data reasonably well in most cases. Our study shows that the multicompartment metabolism model does improve predictions over the previous model (Cole et al. in J. Toxicol. Environ. Health, 439-465, 2001) and that in vitro metabolic constants can be successfully extrapolated to predict in vivo data for benzene metabolism and dosimetry.

A Physiologically Based Pharmacokinetic Model for Naphthalene With Inhalation and Skin Routes of Exposure.

Naphthalene, a volatile organic compound present in moth repellants and petroleum-based fuels, has been shown to induce toxicity in mice and rats during chronic inhalation exposures. Although simpler default methods exist for extrapolating toxicity points of departure from animals to humans, using a physiologically based pharmacokinetic (PBPK) model to perform such extrapolations is generally preferred. Confidence in PBPK models increases when they have been validated using both animal and human in vivo pharmacokinetic (PK) data. A published inhalation PBPK model for naphthalene was previously shown to predict rodent PK data well, so we sought to evaluate this model using human PK data. The most reliable human data available come from a controlled skin exposure study, but the inhalation PBPK model does not include a skin exposure route; therefore, we extended the model by incorporating compartments representing the stratum corneum and the viable epidermis and parameters that determine absorption and rate of transport through the skin. The human data revealed measurable blood concentrations of naphthalene present in the subjects prior to skin exposure, so we also introduced a continuous dose-rate parameter to account for these baseline blood concentration levels. We calibrated the three new parameters in the modified PBPK model using data from the controlled skin exposure study but did not modify values for any other parameters. Model predictions then fell within a factor of 2 of most (96%) of the human PK observations, demonstrating that this model can accurately predict internal doses of naphthalene and is thus a viable tool for use in human health risk assessment.

Uncertainties in biologically-based modeling of formaldehyde-induced respiratory cancer risk: identification of key issues.

In a series of articles and a health-risk assessment report, scientists at the CIIT Hamner Institutes developed a model (CIIT model) for estimating respiratory cancer risk due to inhaled formaldehyde within a conceptual framework incorporating extensive mechanistic information and advanced computational methods at the toxicokinetic and toxicodynamic levels. Several regulatory bodies have utilized predictions from this model; on the other hand, upon detailed evaluation the California EPA has decided against doing so. In this article, we study the CIIT model to identify key biological and statistical uncertainties that need careful evaluation if such two-stage clonal expansion models are to be used for extrapolation of cancer risk from animal bioassays to human exposure. Broadly, these issues pertain to the use and interpretation of experimental labeling index and tumor data, the evaluation and biological interpretation of estimated parameters, and uncertainties in model specification, in particular that of initiated cells. We also identify key uncertainties in the scale-up of the CIIT model to humans, focusing on assumptions underlying model parameters for cell replication rates and formaldehyde-induced mutation. We discuss uncertainties in identifying parameter values in the model used to estimate and extrapolate DNA protein cross-link levels. The authors of the CIIT modeling endeavor characterized their human risk estimates as "conservative in the face of modeling uncertainties." The uncertainties discussed in this article indicate that such a claim is premature.

Human health effects of dichloromethane: key findings and scientific issues.

BACKGROUND: The U.S. EPA's Integrated Risk Information System (IRIS) completed an updated toxicological review of dichloromethane in November 2011. OBJECTIVES: In this commentary we summarize key results and issues of this review, including exposure sources, identification of potential health effects, and updated physiologically based pharmacokinetic (PBPK) modeling. METHODS: We performed a comprehensive review of primary research studies and evaluation of PBPK models. DISCUSSION: Hepatotoxicity was observed in oral and inhalation exposure studies in several studies in animals; neurological effects were also identified as a potential area of concern. Dichloromethane was classified as likely to be carcinogenic in humans based primarily on evidence of carcinogenicity at two sites (liver and lung) in male and female B6C3F1 mice (inhalation exposure) and at one site (liver) in male B6C3F1 mice (drinking-water exposure). Recent epidemiologic studies of dichloromethane (seven studies of hematopoietic cancers published since 2000) provide additional data raising concerns about associations with non-Hodgkin lymphoma and multiple myeloma. Although there are gaps in the database for dichloromethane genotoxicity (i.e., DNA adduct formation and gene mutations in target tissues in vivo), the positive DNA damage assays correlated with tissue and/or species availability of functional glutathione S-transferase (GST) metabolic activity, the key activation pathway for dichloromethane-induced cancer. Innovations in the IRIS assessment include estimation of cancer risk specifically for a presumed sensitive genotype (GST-theta-1+/+), and PBPK modeling accounting for human physiological distributions based on the expected distribution for all individuals 6 months to 80 years of age. CONCLUSION: The 2011 IRIS assessment of dichloromethane provides insights into the toxicity of a commonly used solvent.

Human respiratory tract cancer risks of inhaled formaldehyde: dose-response predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset.

Formaldehyde inhalation at 6 ppm and above causes nasal squamous cell carcinoma (SCC) in F344 rats. The quantitative implications of the rat tumors for human cancer risk are of interest, since epidemiological studies have provided only equivocal evidence that formaldehyde is a human carcinogen. Conolly et al. (Toxicol. Sci. 75, 432-447, 2003) analyzed the rat tumor dose-response assuming that both DNA-reactive and cytotoxic effects of formaldehyde contribute to SCC development. The key elements of their approach were: (1) use of a three-dimensional computer reconstruction of the rat nasal passages and computational fluid dynamics (CFD) modeling to predict regional dosimetry of formaldehyde; (2) association of the flux of formaldehyde into the nasal mucosa, as predicted by the CFD model, with formation of DNA-protein cross-links (DPX) and with cytolethality/regenerative cellular proliferation (CRCP); and (3) use of a two-stage clonal growth model to link DPX and CRCP with tumor formation. With this structure, the prediction of the tumor dose response was extremely sensitive to cell kinetics. The raw dose-response data for CRCP are J-shaped, and use of these data led to a predicted J-shaped dose response for tumors, notwithstanding a concurrent low-dose-linear, directly mutagenic effect of formaldehyde mediated by DPX. In the present work the modeling approach used by Conolly et al. (ibid.) was extended to humans. Regional dosimetry predictions for the entire respiratory tract were obtained by merging a three-dimensional CFD model for the human nose with a one-dimensional typical path model for the lower respiratory tract. In other respects, the human model was structurally identical to the rat model. The predicted human dose response for DPX was obtained by scale-up of a computational model for DPX calibrated against rat and rhesus monkey data. The rat dose response for CRCP was used "as is" for the human model, since no preferable alternative was identified. Three sets of baseline parameter values for the human clonal growth model were obtained through separate calibrations against respiratory tract cancer incidence data for nonsmokers, smokers, and a mixed population of nonsmokers and smokers, respectively. Additional risks of respiratory tract cancer were predicted to be negative up to about one ppm for all three cases when the raw CRCP data from the rat were used. When a hockey-stick-shaped model was fit to the rat CRCP data and used in place of the raw data, positive maximum likelihood estimates (MLE) of additional risk were obtained. These MLE estimates were lower, for some comparisons by as much as 1,000-fold, than MLE estimates from previous cancer dose-response assessments for formaldehyde. Breathing rate variations associated with different physical activity levels did not make large changes in predicted additional risks. In summary, this analysis of the human implications of the rat SCC data indicates that (1) cancer risks associated with inhaled formaldehyde are de minimis (10(-6) or less) at relevant human exposure levels, and (2) protection from the noncancer effects of formaldehyde should be sufficient to protect from its potential carcinogenic effects.

Biologically motivated computational modeling of formaldehyde carcinogenicity in the F344 rat.

Formaldehyde inhalation at 6 ppm and above causes nasal squamous cell carcinoma (SCC) in F344 rats. The human health implications of this effect are of significant interest since human exposure to environmental formaldehyde is widespread, though at lower concentrations than those that cause cancer in rats. In this article, which is part of a larger effort to predict the human cancer risks of inhaled formaldehyde, we describe biologically motivated quantitative modeling of the exposure-tumor response continuum in the rat. An anatomically realistic, three-dimensional fluid dynamics model of the F344 rat nasal airways was used to predict site-specific flux of formaldehyde from inhaled air into tissue, since both SCC and preneoplastic lesions develop in a characteristic site-specific pattern. Flux into tissue was used as a dose metric for two modes of action, direct mutagenicity and cytolethality-regenerative cellular proliferation (CRCP), which in turn were linked to key parameters of a two-stage clonal growth model. The direct mutagenicity mode of action was represented by a low dose linear dose-response model of DNA-protein cross-link (DPX) formation. An empirical J-shaped dose-response model and a threshold model fit to the empirical data were used for CRCP. In the clonal growth model, the probability of mutation per cell generation was a function of the tissue concentration of DPX while the rate of cell division was calculated from the CRCP data. Maximum likelihood methods were used to estimate parameter values. Survivor (a nontumor outcome) and tumor data for controls from the National Toxicology Program database and from two formaldehyde inhalation bioassays were used for likelihood calculations. The J-shaped dose-response for CRCP provided a better description of the SCC data than did the threshold model. Sensitivity analyses indicated that the rodent tumor response is due to the CRCP mode of action, with the directly mutagenic pathway having little, if any, influence. When evaluated in light of modeling and database uncertainties, particularly the specification of the clonal growth model and the dose-response data for CRCP, this work provides suggestive though not definitive evidence for a J-shaped dose-response for formaldehyde-mediated nasal SCC in the F344 rat.

Active and peripheral anionic sites of acetylcholinesterase have differential modulation effects on cell proliferation, adhesion and neuritogenesis in the NG108-15 cell line.

The classical enzymatic role of acetylcholinesterase (AChE) is to terminate impulse transmission at cholinergic synapses through rapid hydrolysis of acetylcholine (ACh). Inactivation of this enzyme's catalytic site is the primary mechanism of acute toxicity of OP insecticides (e.g. parathion, chlorpyrifos). There is now sufficient evidence to suggest that AChE has a neurotrophic function that may be altered by organophosphate (OP) exposure, resulting in defects of neuronal growth and development, though the clarification of the mechanisms involved require further in vitro investigation. In the present study, the mouse neuroblastoma×rat glioma hybrid NG108-15 cell line was used to investigate the differential effects between inhibition of the catalytic site and peripheral anionic site (PAS) of acetylcholinesterase (AChE) on cell adhesion, proliferation and neuritogenesis, in the presence and absence of human red blood cell (hRBC) AChE (ED3.1.1.7). AChE active-site inhibitor paraoxon (PO; 0.1-1.0µM), when added to NG108-15 cells grown on AChE-coated plates, had no effect on cell proliferation, but exerted a significant reduction in strongly adherent viable cells accompanied by mostly short process formations, with 18% of cells considered to be neuritogenic, similar to that observed on uncoated plates. In contrast, PO had no significant effect on cell adhesion and proliferation of NG108-15 cells on uncoated plates. The PAS-ligand thioflavin-T (Th-T; 0.5-25µM), however, decreased cell adhesion and proliferation, on both uncoated and ACh-E coated plates, with less magnitude on AChE-coated plates. Taken together, these results suggest that strong cell adherence and neuritogenesis are sensitive to PO in this cell culture model, with no impact on proliferation, in the presence of membrane bound AChE-coating, while there is no sensitivity to PO on uncoated plates. On the other hand, binding of Th-T directly to the PAS affects both cell adherence and proliferation, with less magnitude in the presence of membrane-bound AChE. The current study indicates that PO is deleterious in neural development during critical periods of strong cell adhesion and differentiation, interfering with AChE trophic function.

Application of an updated physiologically based pharmacokinetic model for chloroform to evaluate CYP2E1-mediated renal toxicity in rats and mice.

Physiologically based pharmacokinetic (PBPK) models are tools for interpreting toxicological data and extrapolating observations across species and route of exposure. Chloroform (CHCl(3)) is a chemical for which there are PBPK models available in different species and multiple sites of toxicity. Because chloroform induces toxic effects in the liver and kidneys via production of reactive metabolites, proper characterization of metabolism in these tissues is essential for risk assessment. Although hepatic metabolism of chloroform is adequately described by these models, there is higher uncertainty for renal metabolism due to a lack of species-specific data and direct measurements of renal metabolism. Furthermore, models typically fail to account for regional differences in metabolic capacity within the kidney. Mischaracterization of renal metabolism may have a negligible effect on systemic chloroform levels, but it is anticipated to have a significant impact on the estimated site-specific production of reactive metabolites. In this article, rate parameters for chloroform metabolism in the kidney are revised for rats, mice, and humans. New in vitro data were collected in mice and humans for this purpose and are presented here. The revised PBPK model is used to interpret data of chloroform-induced kidney toxicity in rats and mice exposed via inhalation and drinking water. Benchmark dose (BMD) modeling is used to characterize the dose-response relationship of kidney toxicity markers as a function of PBPK-derived internal kidney dose. Applying the PBPK model, it was also possible to characterize the dose response for a recent data set of rats exposed via multiple routes simultaneously. Consistent BMD modeling results were observed regardless of species or route of exposure.

Sensory irritation response in rats: modeling, analysis and validation.

Inhaled gases can cause respiratory depression by irritating (stimulating) nerves in the nasal cavity. Respiratory depression, in turn, decreases the rate of delivery of those gases to the stimulated nerves, potentially leading to a complex feedback response. In order to better understand how the nervous system responds to such chemicals, a mathematical model is created to describe how the presence of irritants affects respiration in the rat. The ordinary differential equation model describes the dosimetry of these reactive gases in the respiratory tract, with particular focus on the physiology of the upper respiratory tract, and on the neurological control of respiration rate due to signaling from the irritant-responsive nerves in the nasal cavity. The ventilation equation is altered to account for an apparent change in dynamics between the initial ventilation decrease and the recovery to steady state as seen in formaldehyde exposure data. Further, the model is evaluated and improved through optimization of particular parameters to describe formaldehyde-induced respiratory response data and through sensitivity analysis. The model predicts the formaldehyde data well, and hence the model is thought to be a reasonable description of the physiological system of sensory irritation. The model is also expected to translate well to other irritants.

A delayed nonlinear PBPK model for genistein dosimetry in rats.

Genistein is an endocrine-active compound (EAC) found in soy products. It has been linked to beneficial effects such as mammary tumor growth suppression and adverse endocrine-related effects such as reduced birth weight in rats and humans. In its conjugated form, genistein is excreted in the bile, which is a significant factor in its pharmacokinetics. Experimental data suggest that genistein induces a concentration-dependent suppression of biliary excretion. In this article, we describe a physiologically based pharmacokinetic (PBPK) model that focuses on biliary excretion with the goal of accurately simulating the observed suppression. The mathematical model is a system of nonlinear differential equations with state-dependent delay to describe biliary excretion. The model was analyzed to examine local existence and uniqueness of a solution to the equations. Furthermore, unknown parameters were estimated, and the mathematical model was compared against published experimental data.

Uncertainties in the CIIT model for formaldehyde-induced carcinogenicity in the rat: a limited sensitivity analysis-I.

Scientists at the CIIT Centers for Health Research (Conolly et al., 2000, 2003; Kimbell et al., 2001a, 2001b) developed a two-stage clonal expansion model of formaldehyde-induced nasal cancers in the F344 rat that made extensive use of mechanistic information. An inference of their modeling approach was that formaldehyde-induced tumorigenicity could be optimally explained without the role of formaldehyde's mutagenic action. In this article, we examine the strength of this result and modify select features to examine the sensitivity of the predicted dose response to select assumptions. We implement solutions to the two-stage cancer model that are valid for nonhomogeneous models (i.e., models with time-dependent parameters), thus accounting for time dependence in variables. In this reimplementation, we examine the sensitivity of model predictions to pooling historical and concurrent control data, and to lumping sacrificed animals in which tumors were discovered incidentally with those in which death was caused by the tumors. We found the CIIT model results were not significantly altered with the nonhomogeneous solutions. Dose-response predictions below the range of exposures where tumors occurred in the bioassays were highly sensitive to the choice of control data. In the range of exposures where tumors were observed, the model attributed up to 74% of the added tumor probability to formaldehyde's mutagenic action when our reanalysis restricted the use of the National Toxicology Program (NTP) historical control data to only those obtained from inhalation exposures. Model results were insensitive to hourly or daily temporal variations in DNA protein cross-link (DPX) concentration, a surrogate for the dose-metric linked to formaldehyde-induced mutations, prompting us to utilize weekly averages for this quantity. Various other biological and mathematical uncertainties in the model have been retained unmodified in this analysis. These include model specification of initiated cell division and death rates, and uncertainty and variability in the dose response for cell replication rates, issues that will be considered in a future paper.