Structure-activity relationship analysis of rat mammary carcinogens

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe here the application of the cat-SAR (categorical-SAR) program to two learning sets of rat mammary carcinogens. One set of developed models was based on a comparison of rat mammary carcinogens to rat noncarcinogens (MC-NC), and the second set compared rat mammary carcinogens to rat nonmammary carcinogens (MC-NMC). On the basis of a leave-one-out validation, the best rat MC-NC model achieved a concordance between experimental and predicted values of 84%, a sensitivity of 79%, and a specificity of 89%. Likewise, the best rat MC-MNC model achieved a concordance of 78%, a sensitivity of 82%, and a specificity of 74%. The MC-NMC model was based on a learning set that contained carcinogens in both the active (i.e., mammary carcinogens) and the inactive (i.e., carcinogens to sites other than the mammary gland) categories and was able to distinguish between these different types of carcinogens (i.e., tissue specific), not simply between carcinogens and noncarcinogens. On the basis of a structural comparison between this model and one for Salmonella mutagens, there was, as expected, a significant relationship between the two phenomena since a high proportion of breast carcinogens are Salmonella mutagens. However, when analyzing the specific structural features derived from the MC-NC learning set, a dichotomy was observed between fragments associated with mammary carcinogenesis and mutagenicity and others that were associated with estrogenic activity. Overall, these findings suggest that the MC-NC and MC-NMC models are able to identify structural attributes that may in part address the question of "why do some carcinogens cause breast cancer", which is a different question than "why do some chemicals cause cancer".