On the incorporation of biokinetic and mechanistic data in modeling for risk assessment

The goal of the studies described in this thesis was to foster the increased use of emerging scientific information and innovative methods in chemical risk assessments, in order to assure the protection of public health while limiting the economic and social consequences of over-regulation. The first part of the thesis presents three studies that were performed to demonstrate approaches for incorporating biokinetic and mechanistic data and mathematical modeling in risk assessment, focusing on mode of action evaluation, physiologically based biokinetic (PBBK) modeling, and benchmark dose-response (BMD) modeling. The second part of the thesis documents three studies that were performed to show the ability of PBBK modeling to describe the interaction between chemical-specific properties, physiology, and age-dependent biochemical processes and the resulting variability in risks across individuals in a population at different ages. The final part of the thesis describes three studies that attempted to make optimal use of available biokinetic and mechanistic data in cancer risk assessments for vinyl chloride, trichloroethylene, and perchloroethylene using the three methodologies discussed in the first section of the thesis. Overall, the research described in this thesis demonstrates that the use of mode-of-action evaluation, PBBK modeling, and quantitative dose-response modeling greatly increases the opportunity for the use of biokinetic and mechanistic data in risk assessment. The resulting risk assessment approaches are more appropriately tailored to the specific chemical and are likely to provide a more accurate assessment of the potential hazards associated with human exposures. The studies in this thesis also demonstrate that the application of PBBK models in risk assessment demands well-formulated statements about the chemical mode of action. It is this requirement for an explicit, mechanistic hypothesis that gives biologically motivated models their power, but at the same time serves as the greatest impediment to the acceptance of a chemical-specific risk assessment approach by regulators

Dose-response for assessing the cancer risk of inorganic arsenic in drinking water: the scientific basis for use of a threshold approach

The biologic effects of inorganic arsenic predominantly involve reaction of the trivalent forms with sulfhydryl groups in critical proteins in target cells, potentially leading to various toxicologic events including cancer. This mode of action is a threshold process, requiring sufficient concentrations of trivalent arsenic to disrupt normal cellular function. Nevertheless, cancer risk assessments for inorganic arsenic have traditionally utilized various dose-response models that extrapolate risks from high doses assuming low-dose linearity without a threshold. We present here an approach for a cancer risk assessment for inorganic arsenic in drinking water that involves considerations of this threshold process. Extensive investigations in mode of action analysis, in vitro studies (>0.1 \ub5M), and in animal studies (>2 mg/L in drinking water or 2 mg/kg of diet), collectively indicate a threshold basis for inorganic arsenic-related cancers. These studies support a threshold for the effects of arsenic in humans of 50\u2013100 \ub5g/L in drinking water (about 65 \ub5g/L). We then evaluate the epidemiology of cancers of the urinary bladder, lung, and skin and non-cancer skin changes for consistency with this calculated value, focusing on studies involving low-level exposures to inorganic arsenic primarily in drinking water (approximately <150 \ub5g/L). Based on the relevant epidemiological studies with individual-level data, a threshold level for inorganic arsenic in the drinking water for these cancers is estimated to be around 100 \ub5g/L, with strong evidence that it is between 50 and 150 \ub5g/L, consistent with the value calculated based on mechanistic, in vitro and in vivo investigations. This evaluation provides an alternative mode of action-based approach for assessing health-protective levels for oral arsenic exposure based on the collective in vitro, in vivo, and human evidence rather than the use of a linear low-dose extrapolation based on default assumptions and theories

Development of good modelling practice for physiologically based pharmacokinetic models for use in risk assessment : the first steps

International audienceThe increasing use of tissue dosimetry estimated using pharmacokinetic models in chemical risk assessments in various jurisdictions necessitates the development of internationally recognized good modelling practice (GMP). These practices would facilitate sharing of models and model evaluations and consistent applications in risk assessments. Clear descriptions of good practices for model development i.e., research and analysis activities, model characterization i.e., methods to describe how consistent the model is with biology and the strengths and limitations of available models and data, such as sensitivity analyses, model documentation, and model evaluation i.e., independent review that will assist risk assessors in their decisions of whether and how to use the models, and also model developers to understand expectations for various purposes e.g., research versus application in risk assessment. Next steps in the development of guidance for GMP and research to improve the scientific basis of the models are described based on a review of the current status of the application of physiologically based pharmacokinetic (PBPK) models in risk assessments in Europe, Canada, and the United States at the International Workshop on the Development of GMP for PBPK

Excretion of di-2-ethylhexyl phthalate (DEHP) metabolites in urine is related to body mass index because of higher energy intake in the overweight and obese.

A higher body mass index (BMI) has been positively associated with the rate of excretion of di-2-ethylhexyl phthalate (DEHP) metabolites in urine in data from the National Health and Nutrition Examination Survey (NHANES), suggesting an association between DEHP exposure and BMI. The association, however, may be due to the association between body mass maintenance and higher energy intake, with higher energy intake being accompanied by a higher intake of DEHP. To examine this hypothesis, we ran a Monte Carlo simulation with a DEHP physiologically-based pharmacokinetic (PBPK) model for adult humans. A realistic exposure sub-model was used, which included the relation of body weight to energy intake and of energy intake to DEHP intake. The model simulation output, when compared with urinary metabolite data from NHANES, supported good model validity. The distribution of BMI in the simulated population closely resembled that in the NHANES population. This indicated that the simulated subjects and DEHP exposure model were closely aligned with the NHANES population of interest. In the simulated population, the ordinary least squares regression coefficient for log(BMI) as a function of log(DEHP nmol/min) was 0.048 (SE 0.001), as compared with the reported value of 0.019 (SE 0.005). In other words, given our model structure, the higher energy intake in the overweight and obese, and the concomitant higher DEHP exposure, describes the reported relationship between BMI and DEHP

Derivation of inhalation toxicity reference values for propylene oxide using mode of action analysis: Example of a threshold carcinogen.

Propylene oxide (PO) is an important industrial chemical used primarily in the synthesis of other compounds. Inhalation carcinogenesis studies in rodents, with no-observed-adverse-effect levels (NOAELs) of 100 and 200 ppm, have revealed that chronic, high exposure to PO can induce tumors at the site of contact. Despite these characteristics, there is no evidence that typical environmental or occupational exposures to PO constitute a health risk for humans. The nongenotoxic effects of PO (glutathione depletion and cell proliferation) that augment its DNA-reactive and non-DNA-reactive genotoxicity are expected to be similar in humans and rodents. Available evidence on mode-of-action suggests that cancer induction by PO at the site of contact in rodents is characterized by a practical threshold. Human toxicity reference values for potential carcinogenic effects of PO were derived based on nasal tumors identified in rodent studies and specified uncertainty factors. The 95% lower confidence limit on the dose producing a 10% increase in additional tumor risk (LED10) was calculated using the rat and mouse data sets. The human reference values derived from the rat and mouse LED10 values were 0.7 and 0.5 ppm PO, respectively. A similar noncancer reference value, 0.4 ppm, was derived on the basis of non-neoplastic nasal effects in rats

The use of biomarkers of toxicity for integrating in vitro hazard estimates into risk assessment for humans

The role that in vitro systems can play in toxicological risk assessment is determined by the appropriateness of the chosen methods, with respect to the way in which in vitro data can be extrapolated to the in vivo situation. This report presents the results of a workshop aimed at better defining the use of in vitro-derived biomarkers of toxicity (BoT) and determining the place these data can have in human risk assessment. As a result, a conceptual framework is presented for the incorporation of in vitro-derived toxicity data into the risk assessment process. The selection of BoT takes into account that they need to distinguish adverse and adaptive changes in cells. The framework defines the place of in vitro systems in the context of data on exposure, structural and physico-chemical properties, and toxicodynamic and biokinetic modeling. It outlines the determination of a proper point-of-departure (PoD) for in vitro-in vivo extrapolation, allowing implementation in risk assessment procedures. A BoT will need to take into account both the dynamics and the kinetics of the compound in the in vitro systems. For the implementation of the proposed framework it will be necessary to collect and collate data from existing literature and new in vitro test systems, as well as to categorize biomarkers of toxicity and their relation to pathways-of-toxicity. Moreover, data selection and integration need to be driven by their usefulness in a quantitative in vitro-in vivo extrapolation (QIVIVE)