The carcinogenicity prediction and battery selection (CPBS) method: a Bayesian approach.

Abstract

Recently, a large number of relatively inexpensive in vitro short-term tests have been developed to help predict the carcinogenicity of chemicals. The carcinogenicity prediction and battery selection (CPBS) method utilizes the results of such short-term tests to screen for chemicals that are most likely to cause cancer. The method is an integrated approach for analyzing large, often sparsely filled, data bases containing short-term test results, which often have only marginal representation of known non-carcinogens. The CPBS method is developed for the purpose of (i) determining the reliability and predictive capability of individual and batteries of short-term tests, and (ii) developing a strategy for formulating and selecting optimally preferred batteries of short-term tests for screening chemicals for further testing. The term 'optimally preferred' connotes the best acceptable combination of tests in terms of trade-offs among the multiple attributes of each test and resulting battery (e.g., cost, sensitivity, specificity, etc). The CPBS method consists of 5 major tasks: (1) data consolidation, (2) parameter estimation, (3) predictivity calculation, (4) battery selection and (5) risk assessment. Although there is a great need for more research and improvement, the CPBS method at its present stage should add an important method to the maze of the thousands of new chemicals that are introduced into drugs, foods, consumer goods and to the environment every year. This method should also provide an enhanced identification procedure for classifying chemicals more accurately as suspected carcinogens or non-carcinogens.