Molecular fragments associated with non-genotoxic carcinogens, as detected using a software program based on graph theory: their usefulness to predict carcinogenicity.

We assembled 390 chemicals with a structure non-alerting to DNA-reactivity (145 carcinogens and 245 non-carcinogens) for which rodent carcinogenicity data were available. These non-alerting chemicals were defined by the absence in their molecules of DNA-reactive (directly or after metabolic activation) alerting structures, as described by Ashby and coworkers (Mutat. Res., 204 (1988) 17-115; Mutat. Res., 223 (1989) 73-103; Mutat. Res., 257 (1991) 209-227; Mutat. Res., 286 (1993) 3-74). Using our software program based on graph theory we analyzed the compounds in order to estimate the program's ability to predict nonalerting carcinogens. Our software fragmented the structural formula of the chemicals into all possible fragments of contiguous atoms with size between 2 and 8 (non-hydrogen) atoms and learned about statistically significant fragments from a training set of chemicals. These fragments were used to predict carcinogenicity or lack thereof in a verification set of compounds. For 390 runs of the software program we used (n - 1) of the chemicals as a training set, to predict the excluded chemical at each run (as a test set). Using two different probability thresholds to select significant fragments (P = 0.05 and P = 0.125 1-tailed according to binomial distribution), we performed two analyses: in the better one (P = 0.05) 19% of the molecules tested lacked significant fragments, for the remaining 81% the observed level of accuracy of the prediction was 66.0% against an expected level of accuracy of 51.7%. The difference was highly significant (P < 0.0001). We also examined the more significant activating fragments (biophores) and discussed at length both their biological plausibility and the working hypothesis that additional alerting structures for carcinogenicity (not only those related to genotoxicity) can be detected using this type of SAR approach. This new class of alerting structures could identify subfamilies of congeneric analogs active through mechanisms of receptor mediated carcinogenesis.