Mazzatorta Paolo, Estevez Manuel Dominguez, Coulet Myriam, Schilter Benoit
Department of Quality and Safety, Nestlè Research Center, Vers-Chez-les-Blanc 44, 1000 Lausanne 26, Vaud, Switzerland.
J Chem Inf Model. 2008 Oct;48(10):1949-54. doi: 10.1021/ci8001974. Epub 2008 Sep 20.
The chronic toxicity is fundamental for toxicological risk assessment, but its correlation with the chemical structures has been studied only little. This is partly due to the complexity of such an experimental test that embraces a plethora of different biological effects and mechanisms of action, making (Q)SAR studies extremely challenging. In this paper we report a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis. The root mean squared error of the predictive model is 0.73 (in a logarithmic scale) on a leave-one-out cross-validation and is close to the estimated variability of experimental values (0.64). The analysis of the model revealed that the chronic toxicity effects are driven by the bioavailability of the compound that constitutes a baseline effect plus excess toxicity possible described by a few chemical moieties. The results obtained give confidence that this model can be useful for establishing a level of safety concern in the absence of hard toxicological data.
慢性毒性是毒理学风险评估的基础，但它与化学结构的相关性研究较少。部分原因在于这种实验测试的复杂性，它涵盖了大量不同的生物学效应和作用机制，使得（定量）构效关系研究极具挑战性。在本文中，我们报告了一项基于二维化学描述符和多变量分析对400多种化合物进行的预测性计算机模拟研究。预测模型在留一法交叉验证中的均方根误差为0.73（对数尺度），接近实验值的估计变异性（0.64）。对模型的分析表明，慢性毒性效应由化合物的生物利用度驱动，生物利用度构成基线效应，再加上可能由一些化学基团描述的过量毒性。所获得的结果让人相信，在缺乏确凿毒理学数据的情况下，该模型可用于确定安全关注水平。
