Institute of Physiologically Active Compounds of the Russian Academy of Sciences, 142432, Chernogolovka, Moscow region, Russia.
Institute for Health and Consumer Protection, European Commission - Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (Va), Italy.
Mol Inform. 2011 Mar 14;30(2-3):267-75. doi: 10.1002/minf.201000145. Epub 2011 Mar 17.
A development of the Arithmetic Mean Toxicity (AMT) approach is presented in this article. Twenty six physicochemical descriptors, calculated by using the HYBOT program, along with molecular weight and lipophilicity were included in the selection of structural and physicochemical neighbours (analogues). Toxicity predictions of 906 chemicals from the REACH Pre-Registration Substance (PRS) list were carried out with the application of six nearest structural neighbours and three pairs of structural/physicochemical neighbours on the basis of molecular polarizability, the sum of negative atomic charges in a molecule, the sum of H-bond acceptor and donor factors and the octanol-water partition coefficient. The best prediction results were obtained three pair structural neighbours were applied (each pair contains one chemical with a higher and one chemical with a lower descriptor value). The prediction of toxicity as the mean arithmetic toxicity value of the nearest structural and physicochemical neighbours can be considered a robust approach for the read-across of properties (toxicity data) between analogues. Traditionally, analogues would be selected by expert judgement, but increasingly the availability of large databases and the application of nearest neighbour approaches such as the AMT approach provide a means of automating such assessments.
本文提出了一种算术平均毒性(AMT)方法的发展。HYBOT 程序计算的 26 个物理化学描述符,以及分子量和脂溶性,被包括在结构和物理化学邻居(类似物)的选择中。应用 6 个最近的结构邻居和 3 对结构/物理化学邻居,基于分子极化率、分子中负原子电荷的总和、氢键接受体和供体因子的总和以及辛醇-水分配系数,对 906 种来自 REACH 预注册物质(PRS)清单的化学品进行了毒性预测。当应用三对结构邻居时,得到了最佳的预测结果(每对包含一个具有较高描述符值的化学品和一个具有较低描述符值的化学品)。可以认为,将最近的结构和物理化学邻居的平均算术毒性值作为毒性预测值是一种在类似物之间进行属性(毒性数据)外推的稳健方法。传统上,类似物是通过专家判断选择的,但越来越多的大型数据库的可用性和最近邻方法的应用,如 AMT 方法,为这种评估提供了一种自动化的手段。