Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).

Combination of cancer data in quantitative risk assessments: case study using bromodichloromethane.

There are often several data sets that may be used in developing a quantitative risk estimate for a carcinogen. These estimates are usually based, however, on the dose-response data for tumor incidences from a single sex/strain/species of animal. When appropriate, the use of more data should result in a higher level of confidence in the risk estimate. The decision to use more than one data set (e.g., representing different animal sexes, strains, species, or tumor sites) can be made following biological and statistical analyses of the compatibility of the these data sets. Biological analysis involves consideration of factors such as the relevance of the animal models, study design and execution, dose selection and route of administration, the mechanism of action of the agent, its pharmacokinetics, any species- and/or sex-specific effects, and tumor site specificity. If the biological analysis does not prohibit combining data sets, statistical compatibility of the data sets is then investigated. A generalized likelihood ratio test is proposed for determining the compatibility of different data sets with respect to a common dose-response model, such as the linearized multistage model. The biological and statistical factors influencing the decision to combine data sets are described, followed by a case study of bromodichloromethane.

A statistical test of compatibility of data sets to a common dose-response model.

Quantitative estimates of cancer risk generally involve low-dose extrapolation based on an exponential dose-response model for dichotomous response data. Frequently more than one data set is available. If a careful analysis of the biological issues indicates that more than one of the available data sets could be used in the quantitative estimate of cancer risk, it is reasonable to think of combining the data. Before combining data, however, it would be prudent to test whether the data sets are compatible with a common dose-response model. If they are not, it could be concluded that an underlying biological factor is responsible. If they are statistically compatible, the decision to combine data sets based on biological issues would be reinforced. A statistical test based on the generalized likelihood ratio method is proposed for evaluating the compatibility of different data sets with a common dose-response model. This method of constructing a statistical test and the associated asymptotic theory is consistent with the approach used by GLOBAL86 (R. B. Howe, K. S. Crump, and C. Van Landingham, GLOBAL86: A Computer Program to Extrapolate Quantal Animal Toxicity Data to Low Doses, K. S. Crump & Co., Ruston, LA, 1986) for estimating the confidence limits that are used as a basis for quantitative estimates.

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.