Prediction of rodent carcinogenicity of aromatic amines: a quantitative structure-activity relationships model.

The aromatic amines are widely used industrial chemicals and can be found in tobacco smoke as well as in products generated during cooking. In a previous study, we established quantitative structure-activity relationship (QSAR) models linking the carcinogenic potency of non-heterocyclic carcinogenic aromatic amines to a series of molecular determinants. We also found that QSAR models for carcinogenic potency were inadequate in describing the difference between carcinogenic and non-carcinogenic amines [Benigni,R., Giuliani,A., Franke,R. and Gruska,A. (2000) CHEM: Rev., 100, 3697-3714]. In this paper, we derived specific QSAR models for separating active from inactive amines. It appeared that hydrophobicity (as measured by the octanol/water partition coefficient, logP) played a major role in modulating the potency of the carcinogens, whereas mainly electronic (reactivity) and steric characteristics separated the carcinogens from the non-carcinogens. Interestingly, a similar pattern was previously demonstrated by us regarding their mutagenic activity [Benigni,R., Passerini,L., Gallo,G., Giorgi,F. and Cotta-Ramusino,M. (1998) ENVIRON: Mol. Mutagen., 32, 75-83]. Based on the QSAR models found, the molecular determinants of the mechanisms of action of aromatic amines are discussed in detail. The QSAR models obtained can be used directly for estimating the carcinogenicity of other non-heterocyclic aromatic amines for which experimental data are not available. With the QSARs in Benigni et al. (2000) and the present results, a two-step prediction of carcinogenicity of aromatic amines is possible: (i) step 1, yes/no activity from the discriminant functions; and (ii) step 2, if the answer from step 1 is yes then prediction of the degree of potency from the equations in Benigni et al. (2000). Thus, QSAR models can contribute to the following: the direct synthesis of safer chemicals; the estimation of the risk posed by amines present in the environment; setting priorities for further experimentation, thus also reducing the use of experimental animals. Whereas the quality of in vivo experimental data is often questioned, the robustness and interpretability of the present results strongly support the reliability of the rodent carcinogenicity assay.

Synthesis, pharmacological characterization, and quantitative structure-activity relationship analyses of 3,7,9,9-tetraalkylbispidines: derivatives with specific bradycardic activity.

A series of 3,7,9,9-tetraalkyl-3,7-diazabicyclo[3.3.1]nonane derivatives (bispidines) was synthesized and identified as potential antiischemic agents. Pharmacological experiments in vitro as well as in vivo are described, and the results are listed. For selection of those compounds fitting best to the desired profile of a specific bradycardic antianginal agent--decrease in heart rate without affecting contractility and blood pressure--these results were scored and ranked. Quantitative structure--activity relationship (QSAR) analyses were performed and discussed a posteriori by means of Hansch, nonelementary discriminant and factor analysis to get insight into the molecular features determining the biological profile. Highly significant equations were obtained, indicating hydrophobic and steric effects. Both pharmacological ranking and QSAR considerations showed compound 6 as the optimum within the structural class under investigation. Compound 6 (tedisamil, KC8857) has been selected as the most promising compound and was chosen for further pharmacological and clinical investigations as an antiischemic drug.

Combined factor and QSAR analysis for antibacterial and pharmacokinetic data from parallel biological measurements.

Antibacterial potency data for a variety of bacterial strains and pharmacokinetic parameters of naphthyridines taken from the literature were submitted to factor and QSAR analyses. Factor analysis of antibacterial potencies unraveled a distinct data structure. The information obtained about the internal relatedness of tests can be used in QSAR analyses and reflects itself in the QSAR results. Factor analysis of pharmacokinetic data also led to a clear grouping of measurements with a strong relationship of half life time and urinary recovery, respectively, with log D.