Amines, QSAR

Vracko M (2006) QSAR Approach in Study of Mutagenicity of Aromatic and Heteroaromatic Amines. 4 85-106... 

Basak SC, Mills DR, Balaban AT, Gute BD (2001) Prediction of mutagenicity of aromatic and heteroaromatic amines from structure A hierarchical QSAR approach. J. Chem. Inf. Comput. Sci. 41 671-678. 

Chen P, Doweyko AM, Norris D, et al. Imidazo-quinoxahne Src-family kinase p56 inhibitors SAR, QSAR, and the discovery of (S)-N-(2-chloro-6-methylphenyl)-2-(3-methyl-l-piper-azinyl) imidazo [l,5-a]pyrido[3,2-e]pyrazin-6-amine (BMS-279700) as a potent and orally active inhibitor with excellent in vivo antiinflammatory activity J Med Chem 2004 47, 4517-29. 

Based on their chemical structure, the organic chemicals were divided into a number of categories alkanes, alkenes, amines, aromatic hydrocarbons, benzenes, carboxylic acids, halides, phenols, and sulfonic acid. Linear regression analysis has been applied using the method of least-squares fit. Each correlation required at least three datapoints, and the parameters chosen were important to ensure comparable experimental conditions. Most vital parameters in normalizing oxidation rate constants for QSAR analysis are the overall liquid volume used in the treatment system, the source of UV light, reactor type, specific data on substrate concentration, temperature, and pH of the solution during the experiment. 

The remaining chemical classes such as alkene (R2 < 0.51), amine (R2 < 0.90), aromatic hydrocarbon (R2 < 0.29), carboxylic acid (R2 < 0.06), and sulfonic acid (R2 < 0.77) do not correlate well. Table 7.6 shows all the QSAR models using ELUMO as the molecular descriptor. 

R. Benigni et al., QSAR models for both mutagenic potency and activity Application to nitroarenes and aromatic amines. Environ. Mol. Mutagen. 24, 208-219 (1994)... 

Ambrose Amin E, Welsh WJ (2001) Three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a novel class of piperazine-based stromelysin-1 (MMP-3) inhibitors applying a divide and conquer strategy. J Med Chem 44 3849-3855... 

Recent applications of CPSA descriptors include QSAR investigations of the genotoxicity of thiophene derivatives (Mosier et al., 2003) as well as of secondary and aromatic amines (Mattioni et al., 2003) and a study to classify phenols with respect to toxic modes of action (Aptula et al., 2003). A somewhat different route has been explored with the concept of dynamic molecular surface areas (Lipkowitz et al., 1989) that represent Boltzmann-weighted means of surface areas of different conformations within a preset energy window (e.g., within an excess of 10.5 kJ/mol above the lowest energy found for the particular molecule). Following this strategy, so-called dynamic polar... 

The next section presents in detail the results of QSAR analyses of the most studied chemical class the aromatic amines. 

In addition to the rodent bioassay, the aromatic amines have been studied in the shorter term test Salmonella typhimurium mutagenicity as well as in a variety of acute toxicity assays. A number of QSARs have been generated from such data. The work of Hansch in recent years has demonstrated that the comparison of the QSAR models obtained in different systems, by putting them in a wider perspective, can provide useful clues in the study of the mechanisms of action of individual chemical classes, and can give precious hints on how appropriate the specific models and parameters selected are (Hansch, 2001 Hansch et al., 2002). An exercise of the mechanistic comparison of QSARs has been performed on aromatic amines (Benigni and Passerini, 2002). The results are detailed below. 

The QSARs show that for both bacterial mutagenicity and rodent carcinogenicity, the gradation of the potency of the active aromatic amines first depends on hydrophobicity, and then depends on electronic and steric properties. This confirms the existence of a common first step in the mutagenicity and carcinogenicity of these compounds, and adds confidence to the models obtained. 

The QSARs for the potency of the active aromatic amines in Salmonella typhimurium were not suitable to differentiate the inactives from the actives, and appropriate QSAR models were derived. The QSAR models specific for the separation were based on electronic and steric terms, and hydrophobicity was not found to be significant (Benigni et al., 1998). This result is analogous to that obtained for the rodent carcinogenicity of amines (see above), and provides further evidence of the similarity of the mechanisms of action in Salmonella and rodents. This similarity obviously applies only to the first steps of the process by which the aromatic amines provoke cancer on one side (rodents), and mutations on the other side (bacteria). Once the initial insult to the cells has occurred, the process follows different pathways in the two biological systems the strength of the QSAR model is that it is able to describe quantitatively the first step, which appears to be rate limiting in both systems. 

The QSARs for the acute toxicity of the aromatic amines in a variety of other experimental systems (e.g., fathead minnow, guppy, etc.) were also considered. They were much simpler than those for bacterial mutagenicity and rodent carcinogenicity, and usually relied only on hydrophobicity these findings point to a specific mechanism of action, different from the mechanisms of genotoxic carcinogenicity (Benigni and Passerini, 2002). 

A number of QSAR models for individual classes of congeneric chemicals (e.g., aromatic amines) are available in the literature (Table 8.2). These models can be used to predict the biological activity of untested chemicals belonging to the same class. 

Benigni, R., Passerini, L., Gallo, G., Giorgi, F., and Cotta-Ramusino, M., QSAR models for discriminating between mutagenic and nonmutagenic aromatic and heteroaromatic amines, Environ. Mol. Mutagenesis, 32, 75-83, 1998. 

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