WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound
WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound
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DOI：
10.1007/s10822-018-0107-0
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发表时间：
2018-04-01
影响因子：
3.5
通讯作者：
Tyzack, Jonathan D.
中科院分区：
文献类型：
--
作者：
Hunt, Peter A.;Segall, Matthew D.;Tyzack, Jonathan D.
在新药开发中，了解化合物的I相代谢中涉及多少种细胞色素P450亚型以及哪些亚型是重要的。如果一种化合物主要由单一亚型代谢，就药物相互作用或遗传多态性而言，可能会出现潜在的问题，这将导致一般人群中暴露量的变化。结合各亚型代谢的区域选择性模型，这种模型也有助于预测可能由P450介导的代谢形成的代谢物。我们描述了一个多类随机森林模型的生成，以预测其中，七个领先的细胞色素P450亚型的列表，将是一种新的化合物的主要代谢亚型。该模型在前1项标准下的成功率为76%，在前2项标准下的成功率为88%，并显示出比随机模型显著的丰富性。
In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.