The potential of the computer program PASS (Prediction Activity Spectra for Substances) to predict rodent carcinogenicity for chemical compounds was studied. PASS predicts carcinogenicity of chemical compounds on the basis of their structural formula and of structure–activity relationship analysis of known carcinogens and non-carcinogens. The data on structures and experimental results of 2-year carcinogenicity assays for 412 chemicals from the NTP (National Toxicological Program) and 1190 chemicals from the CPDB (Carcinogenic Potency Database) were used in our study. The predictions take into consideration information about species and sex of animals. For evaluation of the predictive accuracy we used two procedures: leave-one-out cross-validation (LOO CV) and leave-20%-out cross-validation. In the last case we randomly divided the studied data set 20 times into two subsets. The data from the first subset, containing 80% of the compounds, were added to the PASS training set (which includes about 46,000 compounds with about 1500 biological activity types collected during the last 20 years to predict biological activity spectra), the second subset with 20% of the compounds was used as an evaluation set. The mean accuracy of prediction calculated by LOO CV is about 73% for NTP compounds in the ‘equivocal’ category of carcinogenic activity and 80% for NTP compounds in the ‘evidence’ category of carcinogenicity. The mean accuracy of prediction for the CPDB database is 89.9% calculated by LOO CV and 63.4% calculated by leave-20%-out cross-validation. Influence of incorporation of species and sex data on the accuracy of carcinogenicity prediction was also investigated. It was shown that the accuracy was increased only for data on male animals.
Mutation Research, 2005, 586 (2), 138-146.

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.
SAR and QSAR in Environ. Res., 2007, 18 (7-8), 629-643