Ravi P. Subramaniam

USE OF COMPUTATIONAL FLUID DYNAMICS MODELS FOR DOSIMETRY OF INHALED GASES IN THE NASAL PASSAGES

Computational fluid dynamics (CFD) models of the nasal passages of a rat, monkey, and human are being used (1) to determine important factors affecting nasal uptake, (2) to make interspecies dosimetric comparisons, (3) to provide detailed anatomical information for the rat, monkey, and human nasal passages, and (4) to provide estimates of regional air-phase mass transport coefficients (a measure of the resistance to gas transport from inhaled air to airway walls) in the nasal passages of all three species. For many inhaled materials, lesion location in the nose follows patterns that are both site and species specific. For reactive, water-soluble (Category 1) gases, regional uptake can be a major factor in determining lesion location. Since direct measurement of airflow and uptake is experimentally difficult, CFD models are used here to predict uptake patterns quantitatively in three-dimensional reconstructions of the F344 rat, rhesus monkey, and human nasal passages. In formaldehyde uptake simulations, absorption processes were assumed to be as rapid as possible, and regional flux (transport rate) of inhaled formaldehyde to airway walls was calculated for rats, primates, and humans. For uptake of gases like vinyl acetate and acrylic acid vapors, physiologically based pharmacokinetic uptake models incorporating anatomical and physical information from the CFD models were developed to estimate nasal tissue dose in animals and humans. The use of biologically based models in risk assessment makes sources of uncertainty explicit and, in doing so, allows quantification of uncertainty through sensitivity analyses. Limited resources can then be focused on reduction of important sources of uncertainty to make risk estimates more accurate.Computational fluid dynamics (CFD) models of the nasal passages of a rat, monkey, and human are being used (1) to determine important factors affecting nasal uptake, (2) to make interspecies dosimetric comparisons, (3) to provide detailed anatomical information for the rat, monkey, and human nasal passages, and (4) to provide estimates of regional air-phase mass transport coefficients (a measure of the resistance to gas transport from inhaled air to airway walls) in the nasal passages of all three species. For many inhaled materials, lesion location in the nose follows patterns that are both site and species specific. For reactive, water-soluble (Category 1) gases, regional uptake can be a major factor in determining lesion location. Since direct measurement of airflow and uptake is experimentally difficult, CFD models are used here to predict uptake patterns quantitatively in three-dimensional reconstructions of the F344 rat, rhesus monkey, and human nasal passages. In formaldehyde uptake simulations, absorption processes were assumed to be as rapid as possible, and regional flux (transport rate) of inhaled formaldehyde to airway walls was calculated for rats, primates, and humans. For uptake of gases like vinyl acetate and acrylic acid vapors, physiologically based pharmacokinetic uptake models incorporating anatomical and physical information from the CFD models were developed to estimate nasal tissue dose in animals and humans. The use of biologically based models in risk assessment makes sources of uncertainty explicit and, in doing so, allows quantification of uncertainty through sensitivity analyses. Limited resources can then be focused on reduction of important sources of uncertainty to make risk estimates more accurate.

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. Results BBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling. Conclusions The problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems.

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. Understanding what role these mechanisms play in various species and lifestages (e.g., child vs. adult) could be crucial for making more meaningful inter- and intra-species dosimetric extrapolations in human health risk assessment.

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. The method consists of solving four coupled ordinary differential equations derived from the Kolmogorov backward equations for this process. The relationship of this method to previously proposed solutions is discussed.

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). HVD modeling estimates log-normal distributions from data on pharmacokinetic and pharmacodynamic variables that affect individual sensitivities to the toxic response. These distributions are combined into an overall log-normal distribution for the threshold dose (dose below which there is no contribution to a toxic response) by assuming the variables act independently and multiplicatively. This distribution is centered at a point-of-departure dose that is usually estimated from animal data. The resulting log-normal distribution is used to quantify low-dose risk. Objective We examined the implications of various assumptions in HVD modeling for estimating low-dose risk. Methods The assumptions and data used in HVD modeling were subjected to rigorous scrutiny. Results We found that the assumption that the variables affecting human sensitivity vary log normally is not scientifically defensible. Other distributions that are equally consistent with the data provide very different estimates of low-dose risk. HVD modeling can also involve an assumption that a threshold dose defined by dichotomizing a continuous apical response has a log-normal distribution. This assumption is shown to be incompatible (except under highly specialized conditions) with assuming that the continuous apical response itself is log normal. However, the two assumptions can lead to very different estimates of low-dose risk. The assumption in HVD modeling that the threshold dose can be expressed as a function of a product of independent variables lacks phenomenological support. We provide an example that shows that this assumption is generally invalid. Conclusion In view of these problems, we recommend caution in the use of HVD modeling as a general approach to estimating low-dose risks from human exposures to toxic chemicals.

Comparison of Cancer Slope Factors Using Different Statistical Approaches

The U.S. Environmental Protection Agencys 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. In the past, the cancer slope factor has been calculated as the upper 95% confidence limit on the coefficient (q*1) of the linear term of the multistage model for the extra cancer risk over background. To what extent do the two approaches differ in practice? We addressed this issue by calculating s* and q*1 for 102 data sets for 60 carcinogens using the constrained multistage model to fit the dose-response data. We also examined how frequently the fitted dose-response curves departed appreciably from linearity at low dose by comparing q1, the coefficient of the linear term in the multistage polynomial, with a slope factor, sc, derived from a point of departure based on the maximum likelihood estimate of the dose-response. Another question we addressed is the extent to which s* exceeded sc for various levels of extra risk. For the vast majority of chemicals, the prescribed default EPA methodology for the cancer slope factor provides values very similar to that obtained with the traditionally estimated q*1. At 10% extra risk, q*1/s* is greater than 0.3 for all except one data set; for 82% of the data sets, q*1 is within 0.9 to 1.1 of s*. At the 10% response level, the interquartile range of the ratio, s*/sc, is 1.4 to 2.0.

An Exploratory Study of Variations in Exposure to Environmental Tobacco Smoke in the United States

There is considerable interest in assessing exposure to environmental tobacco smoke (ETS) and in understanding the factors that affect exposure at various venues. The impact of these complex factors can be researched only if monitoring studies are carefully designed. Prior work by Jenkins et al. gathered personal monitor and diary data from 1,564 nonsmokers in 16 metropolitan areas of the United States and compared workplace exposures to ETS with exposures away from work. In this study, these data were probed further to examine (1) the correspondence between work and away-from-work exposure concentrations of ETS; (2) the variability in exposure concentration levels across cities; and (3) the association of ETS exposure concentrations with select socioeconomic, occupation, and lifestyle variables. The results indicate (1) at the population level, there was a positive association between ETS concentrations at the work and away-from-work environments; (2) exposure concentration levels across the 16 cities under consideration were highly variable; and (3) exposure concentration levels were significantly associated with occupation, education, household income, age, and dietary factors. Workplace smoking restrictions were associated with low ETS concentration levels at work as well as away from work. Generally, the same cities that exhibited either lower or higher away-from-work exposure concentration levels also showed lower or higher work exposure concentration levels. The observations suggest that similar avoidance characteristics as well as socioeconomic and other lifestyle factors that affect exposure to ETS may have been in operation in both away-from-work and work settings.

High-to-Low Dose Extrapolation: Issues and Approaches

The practice of risk assessment at the U.S. Environmental Protection Agency (USEPA) often includes the estimation of risks at exposures or doses below the range of observation (USEPA 2004), a challenge given the type of data typically available from standard toxicological paradigms. For instance, a number of programs involve regulation at the 10−6 to 10−4 risk level—risks (and by implication, exposures) that are well below those observable in experimental or epidemiological settings. As a general rule, fewer studies are available as one goes to lower and lower exposures. Rodent bioassays and pharmacokinetic studies and human occupational studies are typically the most plentiful, while at the same time probing exposures often several orders of magnitude above those found in the environment. At the other end of the spectrum, environmental epidemiology and exposure biomarker studies may begin to probe exposures of regulatory interest, but are currently few in number. Although in vitro studies have typically also been at higher exposures, data from “omics” technologies may potentially expand the availability of data in the “low dose” range. When considering what “low-dose extrapolation” means, it is instructive to consider the difference between “individual” dose-response (i.e ., the probability for a particular individual to exhibit an effect at a given dose) and “population” doseresponse (i.e ., the fraction of a variable population to exhibit an effect at a given dose). In particular, the same “population” dose-response might originate from different distributions of different-shaped “individual” dose-responses. At one extreme, a common interpretation for cancer dose response curves is “purely” stochastic with all individuals having the same probability of cancer at a given dose (USEPA

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. We work with a previously developed PBPK model for the formation and disposition of DNA–protein cross-links formed by inhaled formaldehyde in the nasal lining of rats and rhesus monkeys. We purposefully choose this model because it is based on sparse time-course data.