Romualdo Benigni

Structure-Activity Relationship in carcinogenesis and mutagenesis: What is their true role and value?

Istituto Superiore di Sanita', Lab. TCE, Viale Regina Elena 299, 00161 Rome, Italy. E-mail:; Fax: +39 6 49902355 or 49387139

The subject of structure-activity relationships is central to the understanding of the chemical/life interaction; in particular, the science of Quantitative Structure-Activity Relationships (QSAR) is the tool of choice for the rationalization of these phenomena. As in classical QSAR applications to medicinal chemistry, various studies relative to individual classes of mutagens and carcinogens described how the potency of the active chemicals in each class varies according to the variation of chemical structure/properties. However, in mutagenicity and carcinogenicity QSAR studies, the need for investigating the difference between active and nonactive chemicals has a primary importance, since risk assessment is concerned first with this issue, and second with the potency of the active compounds. Moreover, the needs relative to the practice of risk assessment have motivated attempts to construct general QSAR models (e.g., for predicting chemical carcinogenicity), not tailored to congeneric series of chemicals, with the ambitious hope that these models would be valid for all kind of chemicals. Within this context, an exercise which was aimed at comparing different prediction approaches for carcinogenicity by challenging them on a common set of chemicals, represented a unique occasion for evaluating the state of the art in this field. Particularly important, the carcinogenic potential of the chemicals was unknown at the time the predictions were formulated. The exercise considered 44 chemicals, which were in the process of being tested by the U.S. National Toxicology Program (NTP). The accuracy of the (Q)SAR approaches prediction was in the range 50-65%. When breaking the chemicals into the classes of: a) most powerful carcinogens (homogeneously positive in the four rodent systems); b) chemicals with mixed result profiles; and c) noncarcinogens; most of the prediction systems were concordant in the identification of class a) chemicals. The chemicals with mixed carcinogenicity profiles were mostly predicted to be positive. The real problems were found with the noncarcinogens, since many of them were predicted to be positive by different systems. Thus the clear limitation of almost all the prediction systems was their excessive sensitivity. The results also showed that the various (Q)SAR approaches essentially acted as gross "class-identifiers": they pointed to the presence or absence of alerting chemical functionalities, but were not able to make gradations within each potentially harmful class. The positive side of this overall picture is that, at this stage, we are capable of identifying most of the possible Structural Alerts (SA): this type of knowledge can already be used to set priorities for the experimentation, and to design safer chemicals by avoiding the SAs. The big challenge of the present research is to acquire the ability of distinguishing between potential and actual activity of the chemicals presenting the SAs. Preliminary results of a second comparative exercise will be presented as well.

Keywords: QSAR; Mutagenicity; Carcinogenicity

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