Academic journal article Environmental Health Perspectives

What Role for Biologically Based Dose-Response Models in Estimating Low-Dose Risk?

Academic journal article Environmental Health Perspectives

What Role for Biologically Based Dose-Response Models in Estimating Low-Dose Risk?

Article excerpt

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.

KEY WORDS: biologically based dose response, dose--response model, low-dose risk, risk assessment, two-stage model. Environ Health Perspect 118:585-588 (2010). doi:10.1289/ehp.0901249 available via http://dx.doi.org/[Online 4 January 2010]

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A decision regarding an acceptable level of exposure to a toxic agent can be informed by quantitative estimates of risk to humans from low exposures (e.g., exposures corresponding to increased risks of [less than or equal to] [10.sup.-3]). Such estimates are often based on responses in animals subjected to much higher exposures. The generally accepted gold standard for making these estimates has been biologically based dose-response (BBDR) models, which incorporate information on intermediate steps in the disease process [National Research Council (NRC) 1994; U.S. Environmental Protection Agency (EPA) 2005]. The U.S. EPA (2005) cancer guidelines state that "The preferred approach [to estimating low-dose risk] is to develop a toxicodynamic model of the agent's mode of action (MOA) and use that model for extrapolation to lower doses." However, the National Academy of Sciences (NAS) Science and Decisions Committee (NRC 2008), which was charged to develop "scientific and technical recommendations for improving risk analysis approaches used by the U.S. EPA," did not discuss BBDR modeling. The NAS committee on Toxicity Testing in the 21 Century (NRC 2007), which reviewed toxicity testing methods and strategies and proposed a long-range vision and strategy for toxicity testing, concluded that BBDR modeling is "still in its infancy" and "the committee ... does not see routine development of the models from toxicity-pathway testing data in the foreseeable future."

BBDR models are predictive models that describe biological processes at the cellular and molecular level to link external exposure to an adverse apical response. Such models can provide estimates of the probability of an adverse response in humans, expressed as a function of quantitative biological variables involved in the adverse response. …

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