A search for data that concurrently report biological effects and whole-body residues of chemicals of interest in aquatic organisms can be accomplished by consulting the U.S. Army Corps of Engineers Environmental Residue Effects Database (ERED) (USACE 2004) and the U.S. Environmental Protection Agency (USEPA) Office of Research and Development's effect residue database (Jarvinen and Ankley 1999). These 2 databases provide information on tissue residues in various organisms exposed to individual compounds and the magnitude and nature of biological effects associated with those residues. The database investigations should be augmented by a thorough literature search covering primary scientific journals (e.g., DialogTM database) to confirm the accuracy of database citations and identify new studies published since the latest database update. Gray literature studies are not generally recommended for inclusion because of the uncertain nature and level of peer-review.
Identified studies must be screened before statistical analyses using a set of specific criteria designed to retain only those inputs that will maximize the relevance and the reliability of the derived benchmarks. The specific criteria used to screen the data in this study are discussed in the Data screening criteria section. If a benchmark is to be developed for a different substance, the list of screening criteria that were used for screening studies with TCDD and dioxin-like compounds might need to be modified to account for any chemical-specific considerations (e.g., mechanism of action, speciation, and target tissue).
Data screening criteria
Laboratory studies with single chemical exposures only—Only data that are obtained from well-designed and controlled laboratory studies with exposures to the individual chemical of interest should be used to generate an SSD. Data from studies with exposure to multiple chemicals or field-collected sediments or waters should not be used because of the potential confounding effects associated with other chemicals present in the mixture. In some cases, such as in this study, data can be pooled for different compounds that share a common toxic effect and mechanism of toxicity. In such cases, however, it is important that the relative toxicities of those compounds be accounted for in generating the SSD curve.
For this study, data for all compounds having a dioxin-like mechanism of toxicological action (aryl hydrocarbon [Ah] receptor active compounds) and published TCDD TEFs were considered. These compounds include the 17 chlorinated dioxins and furans and 12 PCB congeners having TEFs listed by the World Health Organization (van den Berg et al. 1998). Most of the studies that met the screening criteria and retained in the analysis were conducted with TCDD. Although the SSD is primarily derived using TCDD responses, it may appropriately be used to evaluate the total risk (i.e., toxic equivalent quotient [TEQ]) associated with all Ah-receptor active compounds that have published TEFs by using the following equation (van den Berg et al. 1998):
where TEQ is the toxic equivalent of 2,3,7,8-TCDD, PCDDn is polychlorinated dibenzo-p-dioxin congener concentration, PCDFp is polychlorinated dibenzo-p-furan congener concentration, PCBq is polychlorinated biphenyl congener concentration, and TEFn,p,q is the toxic equivalency factor for appropriate individual PCDD, PCDF, and PCB congeners, respectively.
Ecologically significant endpoints—In calculating an SSD, preference should be given to those effect residue data that link residues with organism effects that can be most confidently associated with ecological consequences at the population level. These potential endpoints include significant reductions in survival, growth, or reproduction. Although the SSD approach could potentially be applied to generate benchmarks based on other physiological responses and effects (i.e., metabolic induction and avoidance behavior), the ecological relevance of those effects in terms of affecting the survival and reproduction of species are usually harder to ascertain (McCarty and Mackay 1993).
Exposure route and duration—It is important that the exposure route and duration be carefully evaluated in screening studies for inclusion before generating an SSD. Namely, the exposure duration should be sufficient to allow for the internal distribution of the chemical to reach the target organ or to equilibrate within the organism (van Wezel et al. 1995; Landrum et al. 2004). A static equilibrium is preferred, but a well-parameterized dynamic equilibrium (showing empirical evidence of a steady state) can be used to determine the approximate time to steady state. To minimize variability associated with the use of residue effect data, the exposure route should be considered (Barron et al. 2002). For ecological risk assessment, it may be preferable to generate residue-based SSDs using studies that report effects resulting from chronic exposures through dietary, waterborne, or maternal exposure routes.
Egg and embryo development was the preferred endpoint in this study; therefore, maternal TCDD uptake and transfer to eggs was deemed the most ecologically relevant exposure pathway, followed by direct absorption. Because there is evidence that TCDD effect residues in early life stage fish are independent of the route of exposure (including egg injection) (Walker et al. 1992), studies were not excluded based on this criterion. Studies using maternal, egg injection, and water exposures were used for the final SSD calculation.
Measured data only—To minimize uncertainty, only those residue and effect data that are measured using acceptable methods and directly reported in the results should be used to generate SSDs. Use of estimated or predicted tissue residue or effect data can introduce potentially large uncertainties into the SSD curve. Similarly, when data are represented graphically, concentrations should not be estimated from the axes.
Whole body versus organ residues—The SSDs can be generated using residues reported for specific tissues or using whole-body residues. In making the choice, it is important to carefully consider the mechanism of toxicological action of the contaminant and how the resulting TRB will be incorporated into an ecological risk assessment.
In the current framework, an effect residue distribution is developed for the life stage of fish that is most sensitive to the Ah-receptor mediated toxicity (egg and embryo). Therefore, only residues reported on a whole egg and embryo basis were considered further in the derivation of the effect residue distribution. Because of their lipophilicity, however, effect residues of TCDD and dioxin-like compounds in fish eggs could readily be related to maternal tissue concentrations after lipid normalization. For nonpolar organic compounds, the ratio of chemical on a lipid-normalized basis was found to be approximately 1:1 egg to adult fish. (Russell et al. 1999).
Dose-dependent response—Only data from studies with demonstrated dose-dependent response should be included in an SSD. The residues reported in candidate studies should support a dose-dependent response in which significant effects are consistently observed at higher residues. This screening ensures that the derived benchmark relies on data that indicate incremental effects and that the observed response is related to the concentration of chemical in the tissue of the organism.
NOER and LOER—Only 1 toxicity metric (e.g., LOEC and LC50) should be used for development of the SSD. If the geometric mean of NOER and LOER is to be used to generate the SSD, only studies that simultaneously report these endpoints should be included in the database. Many studies report one or the other and are, therefore, rejected based on this criterion. Although it has been suggested that NOER values can be estimated from LOER by applying a correction factor (Sample et al. 1996), it is not recommended in this proposed framework because it introduces additional uncertainty. In the current study, the geometric mean of the LOER and NOER was calculated by taking the square root of the product of the 2 values.