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. 2011 Jul-Sep;22(5-6):575-601.
doi: 10.1080/1062936X.2011.569950. Epub 2011 Jun 30.

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action

A G Artemenko  1 , E N Muratov, V E Kuz'min, N N Muratov, E V Varlamova, A V Kuz'mina, L G Gorb, A Golius, F C Hill, J Leszczynski, A Tropsha
Affiliations

QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action

A G Artemenko et al. SAR QSAR Environ Res. 2011 Jul-Sep.

Abstract

The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).

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Figures

Figure 1
Figure 1
QSAR models similarity/dissimilarity in the structural space estimated by correlation (A, C) and Euclidian distances methods (B, D) for modeling (A, B) and external test (C, D) sets.
Figure 1
Figure 1
QSAR models similarity/dissimilarity in the structural space estimated by correlation (A, C) and Euclidian distances methods (B, D) for modeling (A, B) and external test (C, D) sets.
Figure 1
Figure 1
QSAR models similarity/dissimilarity in the structural space estimated by correlation (A, C) and Euclidian distances methods (B, D) for modeling (A, B) and external test (C, D) sets.
Figure 1
Figure 1
QSAR models similarity/dissimilarity in the structural space estimated by correlation (A, C) and Euclidian distances methods (B, D) for modeling (A, B) and external test (C, D) sets.
Figure 2
Figure 2
Local AD approach.
Figure 3
Figure 3
Decision trees for mechanisms A (left) and B (right).
Figure 4
Figure 4
Workflow of consensus models development.
Figure 5
Figure 5
Relative influence of some physicochemical factors on variation of toxicity estimated on the basis of consensus model 16.
Figure 6
Figure 6
Contributions of different substituents in benzene ring to nitroaromatics toxicity change.
Figure 7
Figure 7
Contributions of insertion of nitro- (a) and chlorine- (b) groups to nitroaromatics toxicity change.
Figure 8
Figure 8
The analysis of structural fragment influence on toxicity for some substituted nitrobenzenes.
Figure 8
Figure 8
The analysis of structural fragment influence on toxicity for some substituted nitrobenzenes.
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References

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