A.V. Bogatsky Physical-Chemical Institute, National Academy of Sciences of Ukraine, Odessa, Ukraine.
SAR QSAR Environ Res. 2011 Jul-Sep;22(5-6):575-601. doi: 10.1080/1062936X.2011.569950. Epub 2011 Jun 30.
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).
层次结构定量构效关系（HiT QSAR）技术被应用于 95 种不同的硝基芳香族化合物（包括一些广为人知的炸药），这些化合物的毒性（对草履虫 Tetrahymena pyriformis 的 50%抑制生长浓度，IGC₅₀）进行了测试。数据集根据毒性的潜在机制分为子集。HiT QSAR 中实施的分类和回归树（CART）方法已用于预测新化合物的毒性机制。结果模型对外部数据集的预测准确率约为 80%，表明机制数据集的划分是合理的。然后使用偏最小二乘（PLS）统计方法来开发 2D QSAR 模型。对验证后的 PLS 模型进行了探索，以：（1）阐明硝基芳香族化合物中不同取代基对毒性的影响；（2）根据其结构描述符区分具有不同毒性机制的化合物；（3）分析负责化合物毒性的各种物理化学因素的作用。模型根据分子片段对毒性的促进或干扰作用进行了解释。还表明，苯环中取代基的相互影响在毒性变化中起着决定性的作用。尽管基于化学机制的模型在统计学上是显著的，并且具有外部预测性（在完成所有计算后，外部的 63 种硝基芳香族化合物的外部集的 r²(ext) = 0.64），但它们的覆盖范围也有限（建模的 57%和外部集的 76%）。
