A probabilistic framework for the reference dose (probabilistic RfD).

Determining the probabilistic limits for the uncertainty factors used in the derivation of the Reference Dose (RfD) is an important step toward the goal of characterizing the risk of noncarcinogenic effects from exposure to environmental pollutants. If uncertainty factors are seen, individually, as "upper bounds" on the dose-scaling factor for sources of uncertainty, then determining comparable upper bounds for combinations of uncertainty factors can be accomplished by treating uncertainty factors as distributions, which can be combined by probabilistic techniques. This paper presents a conceptual approach to probabilistic uncertainty factors based on the definition and use of RfDs by the U.S. EPA. The approach does not attempt to distinguish one uncertainty factor from another based on empirical data or biological mechanisms but rather uses a simple displaced lognormal distribution as a generic representation of all uncertainty factors. Monte Carlo analyses show that the upper bounds for combinations of this distribution can vary by factors of two to four when compared to the fixed-value uncertainty factor approach. The probabilistic approach is demonstrated in the comparison of Hazard Quotients based on RfDs with differing number of uncertainty factors.

An approach for modeling noncancer dose responses with an emphasis on uncertainty.

This paper presents an approach for characterizing the probability of adverse effects occurring in a population exposed to dose rates in excess of the Reference Dose (RfD). The approach uses a linear threshold (hockey stick) model of response and is based on the current system of uncertainty factors used in setting RfDs. The approach requires generally available toxicological estimates such as No-Observed-Adverse-Effect Levels (NOAELs) or Benchmark Doses and doses at which adverse effects are observed in 50% of the test animals (ED50s). In this approach, Monte Carlo analysis is used to characterize the uncertainty in the dose response slope based on the range and magnitude of the key sources of uncertainty in setting protective doses. The method does not require information on the shape of the dose response curve for specific chemicals, but is amenable to the inclusion of such data. The approach is applied to four compounds to produce estimates of response rates for dose rates greater than the RfD.

On reference dose (RfD) and its underlying toxicity data base.

The toxicity data of pesticides were summarized and compared amongst different animal species and types of bioassays. These comparisons showed the expected inter-species and inter-bioassay variability. After quantitative and statistical analysis of these data, it was concluded that, on the average, a 2-year dog bioassay detected toxic responses at similar doses as a 2-year rat study, and that both of these bioassays detected toxic responses at lower doses than either a rat 2-generation bioassay, a rat developmental toxicity study, or a 2-year mouse bioassay. Although these chronic dog and rat bioassays were found to detect toxic responses at lower doses than the other studies listed, this analysis does not reflect the seriousness of the effects that were compared. Within the confines of this analysis, then, it appears that a 2-year dog and rat study, reproductive and developmental bioassays are a sufficient data base on which to estimate high confidence Reference Doses (RfDs), and furthermore, that an additional uncertainty factor is needed to estimate RfDs to account for this inter-species and inter-bioassay variability when fewer than this number of bioassays are available.