Copper (Cu) and its alloys are used extensively in domestic and industrial applications. Cu is also an essential element in mammalian nutrition. Since both copper deficiency and copper excess produce adverse health effects, the dose-response curve is U-shaped, although the precise form has not yet been well characterized. Many animal and human studies were conducted on copper to provide a rich database from which data suitable for modeling the dose-response relationship for copper may be extracted. Possible dose-response modeling strategies are considered in this review, including those based on the benchmark dose and categorical regression. The usefulness of biologically based dose-response modeling techniques in understanding copper toxicity was difficult to assess at this time since the mechanisms underlying copper-induced toxicity have yet to be fully elucidated. A dose-response modeling strategy for copper toxicity was proposed associated with both deficiency and excess. This modeling strategy was applied to multiple studies of copper-induced toxicity, standardized with respect to severity of adverse health outcomes and selected on the basis of criteria reflecting the quality and relevance of individual studies. The use of a comprehensive database on copper-induced toxicity is essential for dose-response modeling since there is insufficient information in any single study to adequately characterize copper dose-response relationships. The dose-response modeling strategy envisioned here is designed to determine whether the existing toxicity data for copper excess or deficiency may be effectively utilized in defining the limits of the homeostatic range in humans and other species. By considering alternative techniques for determining a point of departure and low-dose extrapolation (including categorical regression, the benchmark dose, and identification of observed no-effect levels) this strategy will identify which techniques are most suitable for this purpose. This analysis also serves to identify areas in which additional data are needed to better define the characteristics of dose-response relationships for copper-induced toxicity in relation to excess or deficiency.

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Noncancer health risk assessment involves the evaluation of multiple types of toxic effects. For regulatory recommendations, such as the Reference Dose (RjD), the U.S. EPA relies heavily on expert judgment. This toxicologic judgment mixes toxic impact with likelihood: what effects are adverse, which of these is "critical," and which dose is the highest reliable NOAEL (No-Observed-Adverse-Effect Level). Uncertainty is indicated by qualitative statements of confidence. Statistical regression using ordered categories of overall toxicity is proposed as a superior alternative: uncertainty and variability are represented by statistical models, all relevant data are used, not just the NOAEL for the critical effect, and health risk can be estimated at exposure levels above the RfD. 1: BackgroundMost health risk assessments by regulatory agencies have one of two goals: to estimate a regulatory exposure level where health risk is minimal or zero, or to estimate the health risk at existing or proposed exposure levels.For the first goal, the oral Reference Dose (RfD) has been the mainstay of noncancer risk assessment in the U.S. Environmental Protection Agency (EPA) for several years.The RfD is defined as [ll:An estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without appreciable risk of deleterious effect during a lifetime.The RfD has an inhalation counterpart, the Reference Concentration or RfC. The RfDs and RfCs are published in an electronic data base called IRIS [2]. For clarity without loss of generality, this paper will only discuss the RfD. The Reference Dose has official status in EPA as a scientifically derived exposure level that feeds into the standard-setting process that regulates oral exposures. The RfD has been applied most often to risk assessments for chronic or lifetime exposures.For the second goal, no procedures for noncancer risk have been universally adopted by the U.S. EPA.Statistically based approaches have been generally limited to characterizations of the experimental data set: extrapolation to probabilistic human risk estimates, as is performed for cancer risk, has not been advocated because of high uncertainty and lack of empirical verification. When human exposure-response data are available, three procedures have been used or recommended for obtaining a human risk estimate: expert panel judgment of risk, Bayesian modeling of the critical effect, and regression of adversity categories for all effects combined. Recently, the EPA's Risk Assessment Council issued a memorandum requiring adequate discussion of uncertainties in all risk characterizations. No standard guidance yet exists for conducting and reporting quantitative uncertainties in noncancer risk assessment.This paper presents some ideas on uncertainty that apply to noncancer risk assessments based on categorical regression of toxic adversity on exposure. The basic regression approach is contrasted with the Reference Dose app...

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