Derivation and validation of toxicophores for mutagenicity prediction
Derivation and validation of toxicophores for mutagenicity prediction
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DOI：
10.1021/jm040835a
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发表时间：
2005-01-13
影响因子：
7.3
通讯作者：
Bursi, R
中科院分区：
文献类型：
--
作者：
Kazius, J;McGuire, R;Bursi, R
致突变性是化合物的众多不利性质之一，它阻碍了其成为一种适销对路的药物的潜力。有毒性质往往与化学结构有关，更具体地说，与通常被确认为毒素载体的特定亚结构有关。文献中已经发现了许多毒素载体。这项研究的目的是通过在相当大的致突变性数据集上应用新的毒物规则推导和验证标准来识别新的毒物，从而提高当前突变预测的可靠性和准确性。为此，构建了一个包含4337个分子结构的数据集，以及相应的Ames测试数据(2401个诱变剂和1936个非诱变剂)。对该数据集的初步亚结构搜索显示，大多数诱变剂仅通过应用8个一般毒素基团来检测到。通过使用化学和机械知识并结合统计标准，从这八个毒物中得出并批准了更具体的毒物。最终组装了包含新亚结构的29个毒素组，可以对所调查的数据集的诱变性进行分类，总分类误差为18%。此外，对一组独立验证的化合物进行了突变预测，误差百分比为15%。由于这些误差百分比接近Ames测试的实验室间平均重复性误差15%，因此可以得出结论，这些毒素可以应用于风险评估过程，并可以指导HIT和LEAD优化的化学库的设计。
Mutagenicity is one of the numerous adverse properties of a compound that hampers its potential to become a marketable drug. Toxic properties can often be related to chemical structure, more specifically, to particular substructures, which are generally identified as toxicophores. A number of toxicophores have already been identified in the literature. This study aims at increasing the current degree of reliability and accuracy of mutagenicity predictions by identifying novel toxicophores from the application of new criteria for toxicophore rule derivation and validation to a considerably sized mutagenicity dataset. For this purpose, a dataset of 4337 molecular structures with corresponding Ames test data (2401 mutagens and 1936 nonmutagens) was constructed. An initial substructure-search of this dataset showed that most mutagens were detected by applying only eight general toxicophores. From these eight, more specific toxicophores were derived and approved by employing chemical and mechanistic knowledge in combination with statistical criteria. A final set of 29 toxicophores containing new substructures was assembled that could classify the mutagenicity of the investigated dataset with a total classification error of 18%. Furthermore, mutagenicity predictions of an independent validation set of 535 compounds were performed with an error percentage of 15%. Since these error percentages approach the average interlaboratory reproducibility error of Ames tests, which is 15%, it was concluded that these toxicophores can be applied to risk assessment processes and can guide the design of chemical libraries for hit and lead optimization.