ATSDR Science Panel on the Bioavailability of Mercury in Soils: Lessons Learned

On the basis of discussion and analysis during and following an ATSDR science panel on the bioavailability of mercury in soils, it is apparent that the default assumption of 100% relative bioavailability for mercury-contaminated soils is excessively conservative. However, current knowledge does not allow the development of default assumptions or guidelines for determining relative bioavailability of mercury in soils. Until such default assumptions or guidelines can be developed, site-specific assays of bioavailability, preferably using either animal bioassays or validated in vitro techniques, may provide the best approach for estimating soil-mercury bioavailability.     

Combined Effects of Contaminant Desorption and Toxicity on Risk from PAH Contaminated Sediments

A strong inverse correlation was observed between the polycyclic aromatic hydrocarbon (PAH) mass fraction desorbed, a surrogate measure of bioavailability, and relative carcinogenicity, as quantified by potency equivalency factors (PEFs), for two study sediments from the New York/New Jersey Harbor estuary. Because compounds with the highest toxicity, such as dibenz(a,h)anthracene and benzo(a)pyrene (BAP), also tended to be the least rapidly and least extensively desorbed, the U.S. Environmental Protection Agency (EPA) default guidance may dramatically overestimate risk from exposure to PAH-contaminated soils or sediments. A "relative risk index" (RRI) was developed to account for the combined effects of compound-specific bioavailability and toxic potency in estimating excess cancer risk. Using this approach, estimated excess cancer risk may be diminished by as much as a factor of 159 times versus default EPA guidance. Also, the hierarchy of estimated risk between study sediments and among treatment fractions of study sediments differed using the two approaches, implying that the default approach may inaccurately determine site clean-up priorities. The percentage contribution of each potentially carcinogenic priority PAH to total excess cancer risk was computed under various scenarios. In each case, the contribution of BAP to total excess cancer risk was remarkably invariable, for example, ranging from 48% to 52% in one sediment, and 44% to 54% in the other, over four different exposure durations. These results suggest that BAP may be an excellent indexing compound for gauging relative exposure risk across sediments. Other important contributors to total excess cancer risk were benz(a)anthracene and dibenz(a,h)anthracene. Together, these three compounds comprised nearly 90% of total excess cancer risk from all PAHs in every scenario. This integrated RRI approach may enable regulators to more accurately gauge relative risks and make more informed sediment management decisions.     

Use of a more general model for bioavailability studies

Use of a multivariate model for bioavailability crossover studies is discussed. The model affords a convenient way to obtain from untransformed data exact confidence intervals and general hypothesis tests for ratios that are pertinent for comparing formulations. Previously, confidence intervals for the case of a two period study of two formulations have been presented. This paper treats the case of three or more formulations in a study in which each subject receives each formulation. The model requires weaker assumptions than the assumptions of other models that have been commonly used. In addition to inferences on ratios, it is pointed out how to obtain from the model tests of the hypotheses of equal formulation effects, equal period effects, and equal sequence effects. The use of these concepts for studies in which a formulation is received more than once by subjects isalso discussed.     

Quasi-Likelihood Approach to Bioavailability and Bioequivalence Analysis

The outcomes AUCT (area-under-curve from time zero to time t) of n individuals randomized to one of two groups TR or RT, where the group name denotes the order in which the subjects receive a test formulation (T) or a reference formulation (R), are used to assess average bioequivalence for the two formulations. The classical method is the mixed model, for example, proc mixed or proc glm with random statement in SAS can be used to analyze this type of data. This is equivalent to the marginal likelihood approach in a normal–normal model. There are some limitations for this approach. It is not appropriate if the random effect is not normally distributed. In this article, we introduce a hierarchical quasi-likelihood approach. Instead of assuming the random effect is normal, we make assumptions only about the mean and the variance function of the random effect. Our method is flexible to model the random effect. Since we can estimate the random effect for each individual, we can check the adequacy of the distribution assumption about the random effect. This method can also be used to handle high-dimensional crossover data. Simulation studies are conducted to check the finite sample performance of the method under various conditions and two real data examples are used for illustration.