An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.
An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.
复制标题
DOI：
10.1186/1752-153x-4-s1-s2
复制
发表时间：
2010-07-29
影响因子：
--
通讯作者：
Gini G
中科院分区：
文献类型：
--
作者：
Ferrari T;Gini G
致突变性是物质引起基因突变的能力。这一特性引起了公众的高度关注，因为它与致癌性和潜在的生殖毒性密切相关。在实验上，可以通过沙门氏菌的艾姆斯试验评估致突变性，估计实验重现性为85%;体外试验的这种固有局限性，沿着对更快、更便宜的替代品的需求，为其他类型的评估方法开辟了道路，例如计算机结构-活性预测模型。一种广泛使用的方法检查是否存在致突变性的已知警示结构。然而，仅存在此类警示并不是证明化合物对沙门氏菌致突变性的确定方法，因为分子的其他部分可能影响并可能改变分类。因此，将提出基于统计的方法，最终目标是获得具有定制属性的建模步骤级联，例如减少假阴性。已经开发了级联模型，并在大量公开的分子结构及其相关沙门氏菌致突变性结果上进行了验证。第一步包括推导统计模型和致突变性预测，然后进一步检查预测结果空间的“安全”子集中的特定结构警报。在准确性方面（即，阴性和阳性的总体正确预测），所获得的模型接近实验致突变性艾姆斯试验的85%再现性。该模型和用于监管目的的文件可在CAESAR网站上免费获取。输入是简单的分子结构文件，输出是分类结果。
Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models. A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives. A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test. The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result.