Key Laboratory for Wetland Ecology and Vegetation Restoration of National Environmental Protection, Department of Environmental Sciences, Northeast Normal University, Changchun, Jilin 130024, PR China.
Chemosphere. 2010 Mar;79(1):72-7. doi: 10.1016/j.chemosphere.2009.12.055. Epub 2010 Jan 15.
A large toxicity data set containing the toxicities of 250 phenols and 252 aliphatic compounds to Tetrahymena pyriformis was classified into different groups based on the structure and substituted functional groups. QSAR analysis was performed between the toxicity and calculated descriptors, expressed as hydrophobicity, polarity and ionization. Through an analysis of these class-based compounds, significant relationships were developed between the toxicity and hydrophobicity for non-polar and polar narcotic compounds. A single model for both non-polar and polar narcotics was developed by inclusion of a polar descriptor as well as the hydrophobic parameter logP. The highly hydrophobic polar narcotics can be treated as non-polar narcotics because their polar functional group(s) makes a relatively small contribution as compared to their hydrophobicity. A cut-off to classify the polar narcotics is difficult because polarity of a chemical not only depends on one or two functional groups (i.e. amino- or hydroxyl-) substituted on the compound, but also on the overall hydrophobicity of the compound. The toxicity increases with increasing the ionization by increasing the interaction between ionisable compounds and macromolecules at the target sites. However, the toxicity decreases with increasing the ionization by decreasing the bio-uptake for extremely ionisable compounds. A significant QSAR equation has been developed between the toxicity to T. pyriformis and the descriptors of hydrophobic, polarity/polarizability and ionization parameters for 457 compounds (R(2)=0.87). These compounds contain non-polar, polar and reactive compounds, and some of them are extremely ionisable. The models developed are simple, interpretable and transparent, using a small number of descriptors.
一个包含 250 种酚类化合物和 252 种脂肪族化合物对梨形四膜虫毒性的大型毒性数据集,根据结构和取代官能团进行了分类。QSAR 分析在毒性和计算描述符之间进行,描述符表示为疏水性、极性和电离度。通过对这些基于类别的化合物进行分析,发现非极性和极性麻醉化合物的毒性与疏水性之间存在显著关系。通过包含极性描述符以及疏水性参数 logP,为非极性和极性麻醉化合物开发了一个单一模型。由于其极性官能团与疏水性相比贡献相对较小,因此高度疏水性的极性麻醉化合物可以被视为非极性麻醉化合物。由于化学物质的极性不仅取决于化合物上取代的一个或两个官能团(即氨基或羟基),还取决于化合物的整体疏水性,因此很难确定将极性麻醉化合物分类的截止值。随着可电离化合物与靶部位的大分子之间相互作用的增加,电离度的增加会导致毒性增加。然而,对于非常可电离的化合物,由于生物摄取的减少,电离度的增加会导致毒性降低。已经为 457 种化合物(R(2)=0.87)之间的毒性与疏水性、极性/极化和电离参数描述符之间建立了一个显著的 QSAR 方程。这些化合物包含非极性、极性和反应性化合物,其中一些化合物具有极高的电离度。所开发的模型简单、可解释且透明,使用了少量描述符。