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Society For Risk Analysis Annual Meeting 2009

Risk Analysis: The Evolution of a Science

Session Schedule & Abstracts

* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

Symposium: Occupational Dose-Response for non-Cancer Sponsored by DRSG

Room: Federal Hill   8:30-10:00 AM

Chair(s): Andrew Maier

W1-E.1  8:30  The Need for Quantitative Estimates of Risk in Developing Occupational Health Standards. Schaeffer VH*; US Occupational Safety and Health Administration

Abstract: In order to promulgate an occupational standard to protect workers from material impairment of health, OSHA is required to make a determination that a significant health risk is present in the workplace and can be eliminated or lessened by a change in practice. For health standards that set permissible exposure limits (PELs) for hazardous chemical agents, it has been the Agency practice to use, where possible, quantitative risk assessment approaches to establish that significant risk exists at current exposure conditions and that the PEL and other health-protective provisions will substantially reduce that risk. Quantitative risk estimates are also used to calculate the projected benefits that employers will achieve in terms of health risk avoided by coming into compliance with the standard. Probabilistic approaches are typically used to assess risk for cancer endpoints. OSHA has employed these risk methodologies to support its occupational standards for hexavalent chromium, methylene chloride, and several other agents. Uncertainty factor-based risk approaches for non-cancer endpoints have focused on identifying exposures without harm rather than estimating the probability of risk at specific exposures. Such risk methods are less than optimal for occupational standard-setting, especially in situations that require extrapolation of risk from high dose levels in animals to lower occupational exposures. Reliable risk methods to estimate exposure-specific risks for non-cancer hazards will greatly improve OSHA’s ability to make its critical risk determinations.

W1-E.2  8:50  Applications of benchmark dose extrapolation, ordinal regression, and probabilistic uncertainty factor methods for characterizing occupational risks. Maier A*, Hertzberg R, Dourson M, Haber L; Toxicology Excellence for Risk Assessment; Emory University; Toxicology Excellence for Risk Assessment; Toxicology Excellence for Risk Assessment

Abstract: Characterizing risk probabilities is difficult from the approaches used by many organizations to develop occupational exposure limits (OELs). Traditional health-based OELs are often derived from the selection of a critical effect divided by safety or uncertainty factors. Comparison of actual exposures to the OEL provides guidance on whether exposure is above the derived human sub-threshold for adverse effects, but does not estimate the likelihood for adverse effects as exposure reaches and exceeds the OEL. We compare the application of three alternative approaches that provide information on risk probabilities to a common occupational scenario. Benchmark dose modeling with alternative strategies for extrapolation to exposure ranges of interest was tested as a simple method for estimating risk probabilities. Ordinal regression is a useful alternative to this approach that represents the toxicity in terms of four variables: dose, exposure duration, incidence, and toxic severity. Instead of a highly multivariate description of all possible toxic endpoints, the effects are recast in a small number of ordinal severity categories that reflect the overall impact on the affected animal. Ordinal regression is then used to model the exposure-response data. This approach adds two important features to dose-response assessment. First, it is able to reflect the striking difference from cancer dose-response by modeling the decrease in severity, not just incidence, as dose decreases. Second, it allows toxicological judgment to consider all the observed effects and their potential joint impact on the organism at all tested doses, not just at the low exposures near a practical threshold. A third approach for developing risk probabilities is to apply probabilistic approaches to the UF component of the traditional OEL approach. We illustrate the features of each of these three methodologies with actual and simulated occupational exposures.

W1-E.3  9:10  Dose-response modeling using biomarker data – TiO2 as a case study. Dankovic DA*, Allen B, Maier A, Willis A, Haber LT; NIOSH, BRUCE ALLEN CONSULTING, INC. AND TERA

Abstract: The use of precursor (biomarker of effect) data directly in risk assessments has been widely advocated, but suffers from the lack of validated approaches for quantitatively incorporating biomarker data into dose-response assessments. Although it is possible to apply dose-response modeling and benchmark dose methods to biomarker data, the quantitative relationship between the biomarker dose-response and the endpoint of interest is generally not obvious. In this study a dose-response modeling approach was used that incorporates non-cancer endpoints and biomarker data for the lung tumor response in rats exposed to titanium dioxide (TiO2). A series of linked "cause-effect" functions, fit using a likelihood approach, was used to describe the relationships between successive key events and the ultimate tumor response. This approach was used to evaluate a hypothesized pathway for biomarker progression from a biomarker of exposure (lung burden), through several intermediate potential biomarkers of effect, to the clinical effect of interest (lung tumor production). Although in this case the endpoint of concern was lung tumors, the biomarker-based modeling approach could be applied to non-cancer endpoints as well. The model evaluated the contribution of several intermediate effect biomarkers to the dose-response behavior for lung tumors. These effect biomarkers included polymorphonuclear lymphocyte (PMN) count, proteins in bronchoalveolar lavage fluid (BALF), pulmonary fibrosis incidence, and alveolar cell proliferation. Overall, the likelihood maximization approach allowed the calculation of a lung burden-based benchmark dose for lung tumors that directly incorporated data for biomarkers of exposure and effect.

W1-E.4  9:30  Applications of physiologically-based pharmacokinetic (PBPK) modeling to refining dose-response evaluations in occupational health risk assessment. Sweeney LM*; The Sapphire Group

Abstract: Physiologically-based pharmacokinetic (PBPK) modeling is a tool that can used be in a variety of applications in the health sciences, but has yet to achieve significant use in the occupational health arena. Using PBPK models, differences in metabolism, physiology, or chemical solubility in blood and tissues among individuals can be incorporated into internal dosimetry analyses, providing ranges of risks for representative populations rather than point estimates. Different exposure scenarios (e.g., different work schedules, level of exertion) can also be easily incorporated to provide refined risk comparisons. When data are available on the effects of chemical exposure to humans under known exposure scenarios, PBPK modeling can be used to calculate a variety of dose metrics, such as peak or time-weighted average blood or tissue concentrations, that can then be considered in dose-response evaluations of the data. PBPK modeling can be used to estimate internal dosimetry, thus providing measures of exposure that are likely to be better correlated to hazard than are external dosimetry estimates, providing a better scientific basis for occupational health risk assessment.

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