Dose-response analysis of bromate-induced DNA damage and mutagenicity is consistent with low-dose linear, nonthreshold processes.

Mutagenic agents have long been inferred to act through low-dose linear, nonthreshold processes. However, there is debate about this assumption, with various studies interpreting datasets as showing thresholds for DNA damage and mutation. We have applied rigorous statistical analyses to investigate the shape of dose-response relationships for a series of in vitro and in vivo genotoxicity studies using potassium bromate (KBrO(3) ), a water ozonation byproduct that is bioactivated to a reactive species causing oxidative damage to DNA. We analyzed studies of KBrO(3) genotoxicity where no-effect/threshold levels were reported as well as other representative datasets. In all cases, the data were consistent with low-dose linear models. In the majority of cases, the data were fit either by a linear (straight line) model or a model which was linear at low doses and showed a saturation-like downward curvature at high doses. Other datasets with apparent upward curvature were still adequately represented by models that were linear at low dose. Sensitivity analysis of datasets showing upward curvature revealed that both low-dose linear and nonlinear models provide adequate fits. Additionally, a simple biochemical model of selected key processes in bromate-induced DNA damage was developed and illustrated a situation where response for early primary events suggested an apparent threshold while downstream events were linear. Overall, the statistical analyses of DNA damage and mutations induced by KBrO(3) are consistent with a low-dose linear response and do not provide convincing evidence for the presence of a threshold.

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.

State-of-the-science workshop report: issues and approaches in low-dose-response extrapolation for environmental health risk assessment.

Low-dose extrapolation model selection for evaluating the health effects of environmental pollutants is a key component of the risk assessment process. At a workshop held in Baltimore, Maryland, on 23-24 April 2007, sponsored by U.S. Environmental Protection Agency and Johns Hopkins Risk Sciences and Public Policy Institute, a multidisciplinary group of experts reviewed the state of the science regarding low-dose extrapolation modeling and its application in environmental health risk assessments. Participants identified discussion topics based on a literature review, which included examples for which human responses to ambient exposures have been extensively characterized for cancer and/or noncancer outcomes. Topics included the need for formalized approaches and criteria to assess the evidence for mode of action (MOA), the use of human versus animal data, the use of MOA information in biologically based models, and the implications of interindividual variability, background disease processes, and background exposures in threshold versus nonthreshold model choice. Participants recommended approaches that differ from current practice for extrapolating high-dose animal data to low-dose human exposures, including categorical approaches for integrating information on MOA, statistical approaches such as model averaging, and inference-based models that explicitly consider uncertainty and interindividual variability.