Allergic contact dermatitis and its relationship to carcinogenicity

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".

Lack of predictivity of the rat lethality (LD50) test for ecological and human health effects

The relationship between acute toxicity in rats (LD50 values) and indicators of potential health hazards in humans was investigated, based on a chemical population-based paradigm (i.e. the "chemical diversity approach"). These structure-activity relationship-based analyses indicate that high toxicity in rats (i.e. a low LD50 value) is not a good predictor of health effects in humans. In fact, it was found that high acute toxicity to minnows, as well as toxicity to cultured cells, showed significantly greater associations with the potential for health effects than rat LD50 values.

Synergy between systemic toxicity and genotoxicity: relevance to human cancer risk

The health risk manager and policy analyst must frequently make recommendations based upon incomplete toxicity data. This is a situation which is encountered in the evaluation of human carcinogenic risks as animal cancer bioassay results are often not available. In this study, in order to assess the relevance of other possible indicators of carcinogenic risks, we used the "chemical diversity approach" to estimate the magnitude of the human carcinogenic risk based upon Salmonella mutagenicity and systemic toxicity data of the "universe of chemicals" to which humans have the potential to be exposed. Analyses of the properties of 10,000 agents representative of the "universe of chemicals" suggest that chemicals that have genotoxic potentials as well as exhibiting greater systemic toxicity are more likely to be carcinogens than non-genotoxicants or agents that exhibit lesser toxicity. Since "genotoxic" carcinogenicity is a hallmark of recognized human carcinogens, these findings are relevant to human cancer risk assessment.