a Department of Science , Babol University of Technology , Babol , Mazandaran , Iran .
SAR QSAR Environ Res. 2013;24(12):1041-50. doi: 10.1080/1062936X.2013.840678.
Aldehydes are toxic environmental contaminants which cause severe health hazards. There is a growing need by industries and regulatory agencies for the development of tools able to assess the potential hazardous effects of chemicals on living organisms. In this background, multivariate image analysis combined with quantitative structure-toxicity relationships (MIA-QSTR) was used to evaluate the toxicity of aromatic aldehydes to Tetrahymena pyriformis. The techniques of genetic algorithm-partial least squares (GA-PLS) were applied effectively as MIA descriptor selection and mapping tools. In MIA-QSTR evaluation, pixels of 2D images of chemical structures could be used to recognize physicochemical information and predict changes in the toxicities. The resulting MIA-QSTR explains 90.3% leave-one-out predicted variance and 93.1% external predicted variance. The MIA-QSTR/GA-PLS performances were validated using various evaluation techniques such as cross-validation, applicability domain and Y-scrambling procedures, suggesting that the present methodology together with mechanistic interpretation may be useful to evaluate toxicity, safety and risk assessment of toxic environmental contaminants.
醛类是有毒的环境污染物，会对健康造成严重危害。工业界和监管机构越来越需要开发能够评估化学品对生物体潜在危害的工具。在此背景下，多元图像分析结合定量结构-毒性关系（MIA-QSTR）被用于评估芳香醛类对梨形四膜虫的毒性。遗传算法-偏最小二乘法（GA-PLS）技术被有效地应用于 MIA 描述符选择和映射工具。在 MIA-QSTR 评估中，可以使用化学结构的 2D 图像的像素来识别物理化学信息并预测毒性的变化。所得的 MIA-QSTR 解释了 90.3%的留一法预测方差和 93.1%的外部预测方差。通过交叉验证、适用域和 Y 打乱程序等各种评估技术验证了 MIA-QSTR/GA-PLS 的性能，表明该方法结合机制解释可能有助于评估有毒环境污染物的毒性、安全性和风险评估。
