The use of categorical regression in modeling copper exposure-response relationships

Effects of Copper or Zinc Organometallics on Cytotoxicity, DNA Damage and Epigenetic Changes in the HC-04 Human Liver Cell Line

Copper and zinc organometallics have multiple applications and many are considered "data-poor" because the available toxicological information is insufficient for comprehensive health risk assessments. To gain insight into the chemical prioritization and potential structure activity relationship, the current work compares the in vitro toxicity of nine "data-poor" chemicals to five structurally related chemicals and to positive DNA damage inducers (4-nitroquinoline-oxide, aflatoxin-B1). The HC-04 non-cancer human liver cell line was used to investigate the concentration-response effects (24 h and 72 h exposure) on cell proliferation, DNA damage (γH2AX and DNA unwinding assays), and epigenetic effects (global genome changes in DNA methylation and histone modifications using flow cytometry). The 24 h exposure screening data (DNA abundance and damage) suggest a toxicity hierarchy, starting with copper dimethyldithiocarbamate (CDMDC, CAS#137-29-1) > zinc diethyldithiocarbamate (ZDEDC, CAS#14324-55-1) > benzenediazonium, 4-chloro-2-nitro-, and tetrachlorozincate(2-) (2:1) (BDCN4CZ, CAS#14263-89-9); the other chemicals were less toxic and had alternate ranking positions depending on assays. The potency of CDMDC for inducing DNA damage was close to that of the human hepatocarcinogen aflatoxin-B1. Further investigation using sodium-DMDC (SDMDC, CAS#128-04-1), CDMDC and copper demonstrated the role of the interactions between copper and the DMDC organic moiety in generating a high level of CDMDC toxicity. In contrast, additive interactions were not observed with respect to the DNA methylation flow cytometry data in 72 h exposure experiments. They revealed chemical-specific effects, with hypo and hypermethylation induced by copper chloride (CuCl2, CAS#10125-13-0) and zinc-DMDC (ZDMDC, CAS#137-30-4), respectively, but did not show any significant effect of CDMDC or SDMDC. Histone-3 hypoacetylation was a sensitive flow cytometry marker of 24 h exposure to CDMDC. This study can provide insights regarding the prioritization of chemicals for future study, with the aim being to mitigate chemical hazards.

Ordinal dose-response modeling approach for the phthalate syndrome

BACKGROUND

The phthalate syndrome (PS) is a collection of related male reproductive developmental effects, ranging in severity, that have been observed in rats after gestational exposure to developmentally-toxic phthalates. For statistical purposes, the PS is defined as a single endpoint and one dose-response analysis is conducted, rather than conducting multiple analyses on each individual endpoint.

OBJECTIVE

To improve dose-response modeling approaches for the PS and other syndromes of effects by accounting for differing severity levels among the endpoints.

METHODS

Ordinal dose-response modeling was performed on PS data from a published study of diisobutyl phthalate (DIBP) gestational exposure to male Sprague-Dawley rats. To incorporate PS endpoint severity, the endpoints were categorized into ordinal levels based on the expected impact of male developmental endpoint's on fertility. Then, a benchmark dose was estimated for each ordinal level. A bootstrap procedure was used to account for the nested nature of the data, and a sensitivity analysis was performed to assess the bootstrap results. A comparison of the estimates between the ordinal and the dichotomous model was performed.

RESULTS

The ordinal version of the log-logistic model applied to the data categorized by PS endpoint severity level provided benchmark dose estimates that were closer to each other in value and had lower variability than the traditional dichotomous application. The sensitivity analysis confirmed the validity of the bootstrap results.

CONCLUSION

The ordinal dose-response modeling method accounts for severity differences among dichotomous PS endpoints, can be expanded in the future to include more severity levels, and can be used in both single and cumulative phthalate risk assessments.

Critical Review of Exposure and Effects: Implications for Setting Regulatory Health Criteria for Ingested Copper

Decades of study indicate that copper oral exposures are typically not a human health concern. Ingesting high levels of soluble copper salts can cause acute gastrointestinal symptoms and, in uncommon cases, liver toxicity in susceptible individuals with repeated exposure. This focused toxicological review evaluated the current literature since the last comprehensive reviews (2007-2010). Our review identified limitations in the existing United States and international guidance for determining an oral reference dose (RfD) for essential metals like copper. Instead, an alternative method using categorical regression analysis to develop an optimal dose that considers deficiency, toxicity, and integrates information from human and animal studies was reviewed for interpreting an oral RfD for copper. We also considered subchronic or chronic toxicity from genetic susceptibility to copper dysregulation leading to rare occurrences of liver and other organ toxicity with elevated copper exposure. Based on this approach, an oral RfD of 0.04 mg Cu/kg/day would be protective of acute or chronic toxicity in adults and children. This RfD is also protective for possible genetic susceptibility to elevated copper exposure and allows for background dietary exposures. This dose is not intended to be protective of patients with rare genetic disorders for copper sensitivity within typical nutritional intake ranges, nor is it protective for those with excessive supplement intake. Less soluble mineral forms of copper in soil have reduced bioavailability as compared with more soluble copper in water and diet, which should be considered in using this RfD for risk assessments of copper.

Speciation of copper in a range of food types by X-ray absorption spectroscopy

Copper (Cu) is an essential element and the effects of diets deficient in it are well established. However, the effects of long-term high copper intake are less clear. The chemical form of copper from food sources and its resultant bioavailability is a potentially important factor in its biological activity. X-ray Absorption Near-Edge Structure (XANES) was used to determine the chemical forms of Cu in a range of foods that would make significant contributions to total copper absorption in a standard diet, as well as a chlorinated tap water sample. Analysis of the Cu K-edge XANES spectra suggested that Cu existed in both Cu(I) and Cu(II) forms, with the following five model compounds: Cu(I) acetate; Cu(II) acetate; Cu(I)-glutathione; Cu(I)-cysteine; and, Cu(II)-histidine being fitted to the sample spectra. This research suggested that the absorption of dietary copper could vary markedly dependent on the types of food consumed and the different bioavailability of the Cu species they contain.

Using sparse dose-response data for wildlife risk assessment

Hazard quotients based on a point-estimate comparison of exposure to a toxicity reference value (TRV) are commonly used to characterize risks for wildlife. Quotients may be appropriate for screening-level assessments but should be avoided in detailed assessments, because they provide little insight regarding the likely magnitude of effects and associated uncertainty. To better characterize risks to wildlife and support more informed decision making, practitioners should make full use of available dose-response data. First, relevant studies should be compiled and data extracted. Data extractions are not trivial--practitioners must evaluate the potential use of each study or its components, extract numerous variables, and in some cases, calculate variables of interest. Second, plots should be used to thoroughly explore the data, especially in the range of doses relevant to a given risk assessment. Plots should be used to understand variation in dose-response among studies, species, and other factors. Finally, quantitative dose-response models should be considered if they are likely to provide an improved basis for decision making. The most common dose-response models are simple models for data from a particular study for a particular species, using generalized linear models or other models appropriate for a given endpoint. Although simple models work well in some instances, they generally do not reflect the full breadth of information in a dose-response data set, because they apply only for particular studies, species, and endpoints. More advanced models are available that explicitly account for variation among studies and species, or that standardize multiple endpoints to a common response variable. Application of these models may be useful in some cases when data are abundant, but there are challenges to implementing and interpreting such models when data are sparse.

An exposure-response curve for copper excess and deficiency

There is a need to define exposure-response curves for both Cu excess and deficiency to assist in determining the acceptable range of oral intake. A comprehensive database has been developed where different health outcomes from elevated and deficient Cu intakes were assigned ordinal severity scores to create common measures of response. A generalized linear model for ordinal data was used to estimate the probability of response associated with dose, duration and severity. The model can account for differences in animal species, the exposure medium (drinking water and feed), age, sex, and solubility. Using this model, an optimal intake level of 2.6 mg Cu/d was determined. This value is higher than the current U.S. recommended dietary intake (RDI; 0.9 mg/d) that protects against toxicity from Cu deficiency. It is also lower than the current tolerable upper intake level (UL; 10 mg/d) that protects against toxicity from Cu excess. Compared to traditional risk assessment approaches, categorical regression can provide risk managers with more information, including a range of intake levels associated with different levels of severity and probability of response. To weigh the relative harms of deficiency and excess, it is important that the results be interpreted along with the available information on the nature of the responses that were assigned to each severity score.

Risk-benefit analysis of mineral intakes: case studies on copper and iron

Dietary reference values for essential trace elements are designed to meet requirements with minimal risk of deficiency and toxicity. Risk-benefit analysis requires data on habitual dietary intakes, an estimate of variation and effects of deficiency and excess on health. For some nutrients, the range between the upper and lower limits may be extremely narrow and even overlap, which creates difficulties when setting safety margins. A new approach for estimating optimal intakes, taking into account several health biomarkers, has been developed and applied to selenium, but at present there are insufficient data to extend this technique to other micronutrients. The existing methods for deriving reference values for Cu and Fe are described. For Cu, there are no sensitive biomarkers of status or health relating to marginal deficiency or toxicity, despite the well-characterised genetic disorders of Menkes and Wilson's disease which, if untreated, lead to lethal deficiency and overload, respectively. For Fe, the wide variation in bioavailability confounds the relationship between intake and status and complicates risk-benefit analysis. As with Cu, health effects associated with deficiency or toxicity are not easy to quantify, therefore status is the most accessible variable for risk-benefit analysis. Serum ferritin reflects Fe stores but is affected by infection/inflammation, and therefore additional biomarkers are generally employed to measure and assess Fe status. Characterising the relationship between health and dietary intake is problematic for both these trace elements due to the confounding effects of bioavailability, inadequate biomarkers of status and a lack of sensitive and specific biomarkers for health outcomes.