Peer reviewed: evaluating impacts of hormonally active agents in the environment.

Information about endocrine disrupters is limited, with sparse data, few answers, great uncertainties, and a definite need for further research.

Research needs for the risk assessment of health and environmental effects of endocrine disruptors: a report of the U.S. EPA-sponsored workshop.

The hypothesis has been put forward that humans and wildlife species adverse suffered adverse health effects after exposure to endocrine-disrupting chemicals. Reported adverse effects include declines in populations, increases in cancers, and reduced reproductive function. The U.S. Environmental Protection Agency sponsored a workshop in April 1995 to bring together interested parties in an effort to identify research gaps related to this hypothesis and to establish priorities for future research activities. Approximately 90 invited participants were organized into work groups developed around the principal reported health effects-carcinogenesis, reproductive toxicity, neurotoxicity, and immunotoxicity-as well as along the risk assessment paradigm-hazard identification, dose-response assessment, exposure assessment, and risk characterization. Attention focused on both ecological and human health effects. In general, group felt that the hypothesis warranted a concerted research effort to evaluate its validity and that research should focus primarily on effects on development of reproductive capability, on improved exposure assessment, and on the effects of mixtures. This report summarizes the discussions of the work groups and details the recommendations for additional research.

Structure-activity considerations in risk assessment: a simulation study.

The use of quantitative structure-activity relationships (QSAR) is considered with respect to estimating the carcinogenic risk of untested chemicals. SAR derived from a retrospective classification of a series of aromatic amines were used to study the estimation of carcinogenic risk by analogy. Using pattern recognition methods, a series of molecular descriptors were developed for a data set of aromatic amines that supported a linear discriminant function capable of separating compounds testing positively for carcinogenicity from those testing negatively. Linear discriminant analysis correctly categorized the compounds as positive or negative in 94.9% of the cases. For each aromatic amine within the subset of positive compounds, the most appropriate analogue was identified using physicochemical, topological, geometric and electronic molecular descriptors as variables. An upper-limit unit risk estimate was calculated for each compound that was a positive carcinogen within the data set using the linearized multistage model. The actual risk and the risk estimated by analogy to a congener were compared for each compound within the positive subset. The results support estimating both qualitative and quantitative carcinogenic risk by analogy for this particular data set.