Abstract：
All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.
摘要：
现代社会的各个领域都受到氟化学的影响。特别是,氟在医疗中起着重要的作用，制药和农业化学科学。在各种氟有机化合物中，三氟甲基（CF3）基团在制药等应用中很有价值，农用化学品和工业化学品。在本研究中,遵循严格的经合组织建模原则，通过遗传算法-多元线性回归（GA-MLR）方法，建立了大鼠三氟甲基化合物（TFM）急性口服毒性的定量结构-毒性关系（QSTR）模型。所有开发的模型都通过各种最新的验证指标和OECD原则进行了评估。最佳QSTR模型包括9个易于解释的2D分子描述符，具有明确的物理和化学意义。机理解释表明，原子型电拓扑状态指数，分子连通性，电离电势,亲脂性和一些自相关系数是TFM对大鼠急性口服毒性的主要因素。为了验证选定的2D描述符可以有效地表征毒性，我们进行了化学阅读分析.我们还将最佳QSTR模型与公共OPERA工具进行了比较，以证明预测的可靠性。为了进一步提高QSTR模型的预测范围,我们进行了共识建模。最后,最佳QSTR模型首次用于预测包含许多未测试/未知TFM的真实外部集。总的来说,所开发的模型有助于对新型含CF3的药物或化学品进行更全面的安全评估方法，减少不必要的化学合成，同时节省新药的开发成本。
