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Douglas W. Bristol,¹ Joseph T. Wachsman,² and Arnold Greenwell¹

¹Cancer Biology Group, Laboratory of Environmental Carcinogenesis and Mutagenesis, Division of Intramural Research, ²Environmental Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina

Abstract

The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estimate the true error of models that have been applied to make prospective predictions on previously untested, noncongeneric-chemical substances. The PTE project first identifies a group of standardized NTP chembioassays either scheduled to be conducted or are ongoing, but not yet complete. The project then announces and advertises the evaluation experiment, disseminates information about the chembioassays, and encourages researchers from a wide variety of disciplines to publish their predictions in peer-reviewed journals, using whatever approaches and methods they feel are best. A collection of such papers is published in this Environmental Health Perspectives Supplement, providing readers the opportunity to compare and contrast PT approaches and models, within the context of their prospective application to an actual-use situation. This introduction to this collection of papers on predictive toxicology summarizes the predictions made and the final results obtained for the 44 chemcarcinogenesis bioassays of the first PTE experiment (PTE-1) and presents information that identifies the 30 chemcarcinogenesis bioassays of PTE-2, along with a table of prediction sets that have been published to date. It also provides background about the origin and goals of the PTE project, outlines the special challenge associated with estimating the true error of models that aspire to predict open-system behavior, and summarizes what has been learned to date. -- Environ Health Perspect 104(Suppl 5) :1001-1010 (1996)

Key words: predictive toxicology, carcinogenesis, decision support, hazard identification, activity classification, risk assessment, pattern recognition, human heuristic, expert system, machine learning, artificial intelligence