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

Background: The U.S. Environmental Protection Agency (EPA) completed a toxicological review of tetrachloroethylene (perchloroethylene, PCE) in February 2012 in support of the Integrated Risk Information System (IRIS).

Statistical inferences from formaldehyde DNA-protein cross-link data: improving methods for characterization of uncertainty.

Physiologically based pharmacokinetic (PBPK) modeling has reached considerable sophistication in its application to pharmacological and environmental health problems. Yet, mature methodologies for making statistical inferences have not been routinely incorporated in these applications except in a few data-rich cases. This paper demonstrates how improved statistical inference on estimated model parameters from both frequentist and Bayesian points of view can be routinely carried out.

Approaches to advancing quantitative human health risk assessment of environmental chemicals in the post-genomic era.

The contribution of genomics and associated technologies to human health risk assessment for environmental chemicals has focused largely on elucidating mechanisms of toxicity, as discussed in other articles in this issue. However, there is interest in moving beyond hazard characterization to making more direct impacts on quantitative risk assessment (QRA)--i.e.

Issues in using human variability distributions to estimate low-dose risk.

Background: The National Research Council (NRC) Committee on Improving Risk Analysis Approaches Used by the U.S. EPA (Environmental Protection Agency) recommended that low-dose risks be estimated in some situations using human variability distributions (HVDs).

What role for biologically based dose-response models in estimating low-dose risk?

Background: Biologically based dose-response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect.

Objectives: Our goal was to examine the utility of BBDR models in estimating low-dose risk.

Methods: We reviewed the utility of BBDR models in risk assessment.

Mechanistic and dose considerations for supporting adverse pulmonary physiology in response to formaldehyde.

Induction of airway hyperresponsiveness and asthma from formaldehyde inhalation exposure remains a debated and controversial issue. Yet, recent evidences on pulmonary biology and the pharmacokinetics and toxicity of formaldehyde lend support for such adverse effects. Specifically, altered thiol biology from accelerated enzymatic reduction of the endogenous bronchodilator S-nitrosoglutathione and pulmonary inflammation from involvement of Th2-mediated immune responses might serve as key events and cooperate in airway pathophysiology.

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.

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.

Comparison of cancer slope factors using different statistical approaches.

The U.S. Environmental Protection Agency's cancer guidelines (USEPA, 2005) present the default approach for the cancer slope factor (denoted here as s*) as the slope of the linear extrapolation to the origin, generally drawn from the 95% lower confidence limit on dose at the lowest prescribed risk level supported by the data.

A numerical solution to the nonhomogeneous two-stage MVK model of cancer.

In this article, we describe a straightforward method for solving the probability of at least one malignant cell by time t, and the associated hazard function, in the general (i.e., nonhomogeneous) two-stage Moolgavkar-Venzon-Knudson (MVK) model of cancer.

Analysis of lobar differences in particle deposition in the human lung.

Lung diseases caused by the inhalation of various particulate pollutants have often been reported to occur at specific sites in the lung with some diseases preferentially occurring in one of the lobes. Models for the dosimetry of particulate matter in the lung, therefore, need to be developed at a level of resolution that allows for the study of lobar- and airway-specific patterns of deposition. Using an approach best described as a combination of asymmetric and symmetric approaches to modeling lung geometry, we calculated deposition of particulate matter (PM) ranging from ultrafine to coarse particles in each airway down to the level of the lobar bronchi.

Use of a hybrid computational fluid dynamics and physiologically based inhalation model for interspecies dosimetry comparisons of ester vapors.

Numerous inhalation studies have demonstrated that exposure to high concentrations of a wide range of volatile acids and esters results in cytotoxicity to the nasal olfactory epithelium. Previously, a hybrid computational fluid dynamics (CFD) and physiologically based pharmacokinetic (PBPK) dosimetry model was constructed to estimate the regional tissue dose of organic acids in the rodent and human nasal cavity. This study extends this methodology to a representative volatile organic ester, ethyl acrylate (EA).