Using short-term tests to predict carcinogenic activity in the long-term bioassay

A method for classifying chemicals with respect to carcinogenic potential b ased on short-term test results is presented. The method utilizes the logis tic regression model to translate results from short-term toxicity assays i nto predictions of the likelihood that a chemical will be carcinogenic if t ested in a long-term bioassay. The proposed method differs from previous ap proaches in two ways. First, statistical confidence limits on probabilities of cancer rather than central estimates of those probabilities are used fo r classification. Second, the method does not classify all chemicals in a d ata base with respect to carcinogenic potential. Instead, it identifies che micals with highest and lowest likelihood of testing positive for carcinoge nicity in the bioassay. A subset of chemicals with intermediate likelihood of being positive remains unclassified, and will require further testing, p erhaps in a long-term bioassay. Two data bases of binary short-term and lon g-term test results from the literature are used to illustrate and evaluate the proposed procedure. A cross-validation analysis of one of the data set s suggests that, for a sufficiently rich data base of chemicals, the develo pment of a robust predictive system to replace the bioassay for some unknow n chemicals is a realistic goal.