QSARS of mutagens and carcinogens: Two case studies illustrating problems in the construction of models for noncongeneric chemicals

There is a strong motivation to develop QSAR models for toxicity prediction for use in screening, for setting testing priorities, and for reducing reliance on animal testing. Decisions must be made daily by toxicologists in governments and industry to direct limited testing resources to the most urgent public health problems, and to direct the types of chemical synthesis and product development efforts undertaken. This need has motivated attempts to construct general QSAR models (e.g., for rodent carcinogenicity), not tailored to congeneric series of chemicals. These various attempts have provided interesting and important scientific evidence; however, they have also shared a limited overall performance. The goal of this paper is to illustrate, by two unrelated actual examples of QSARs for mutagens and carcinogens, some fundamental problems relative to the application of general QSAR approaches to noncongeneric chemicals. Both examples consider data sets that are noncongeneric in a chemical structure and mechanism of action sense: in the first case, a mean mutagenic potency defined as an average over multiple genetic toxicity endpoints, and, in the second case, the NTP two-sexes, two species rodent carcinogenicity bioassay results for 280 carcinogens and noncarcinogens. The problems encountered with the QSAR analyses of these two cases indicate that a successful approach to the problem of QSAR modeling of noncongeneric data will need to consider the multidimensional nature of the problem in both a chemical and a biological sense. Since different chemical classes represent largely independent action mechanisms, some means for extracting local QSARs for constituent classes will be necessary. Alternatively, a general QSAR derived for a noncongeneric data set will need to be scrutinized and decomposed along chemical class lines in order establish boundaries for application and confidence levels for prediction.