Bayer AG , Pharmaceuticals R&D , 13353 Berlin , Germany.
Bayer AG , Pharmaceuticals R&D , 42096 Wuppertal , Germany.
J Chem Inf Model. 2019 Feb 25;59(2):668-672. doi: 10.1021/acs.jcim.8b00758. Epub 2019 Feb 13.
Pharmaceutical products are often synthesized by the use of reactive starting materials and intermediates. These can, either as impurities or through metabolic activation, bind to the DNA. Primary aromatic amines belong to the critical classes that are considered potentially mutagenic in the Ames test, so there is a great need for good prediction models for risk assessment. How primary aromatic amines exert their mutagenic potential can be rationalized by the widely accepted nitrenium ion hypothesis of covalent binding to the DNA of reactive electrophiles formed out of the aromatic amines. Since the reactive chemical species is different in chemical structure from the actual compound, it is difficult to achieve good predictions via classical descriptor or fingerprint-based machine learning. In this approach, we use a combination of different molecular and atomic descriptors that is able to describe different mechanistic aspects of the metabolic transformation leading from the primary aromatic amine to the reactive metabolite that binds to the DNA. Applied to a test set, the combination shows significantly better performance than models that only use one of these descriptors and complemented the general internal Ames mutagenicity prediction model at Bayer.
药品通常通过使用反应性起始材料和中间体来合成。这些物质，无论是作为杂质还是通过代谢激活，都可能与 DNA 结合。芳基伯胺属于关键类别，被认为在 Ames 试验中具有潜在的致突变性，因此非常需要用于风险评估的良好预测模型。芳基伯胺如何发挥其致突变潜能，可以通过广泛接受的亲电反应性氮宾离子假说来合理化，该假说认为氮宾离子与由芳基伯胺形成的 DNA 发生共价结合。由于反应性化学物质在化学结构上与实际化合物不同，因此通过经典描述符或基于指纹的机器学习很难实现良好的预测。在这种方法中，我们使用不同的分子和原子描述符的组合，这些描述符能够描述从芳基伯胺到与 DNA 结合的反应性代谢物的代谢转化的不同机制方面。应用于测试集，该组合的性能明显优于仅使用这些描述符之一的模型，并补充了拜耳公司的一般内部 Ames 致突变性预测模型。
