Department of Pathology and Immunology, Washington University School of Medicine , Campus Box 8118, 660 S. Euclid Ave., St. Louis, Missouri 63110, United States.
Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences , Little Rock, Arkansas 72205, United States.
Chem Res Toxicol. 2018 Feb 19;31(2):68-80. doi: 10.1021/acs.chemrestox.7b00191. Epub 2018 Feb 6.
Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .
细胞色素 P450（CYPs）氧化烷基化胺，这些胺通常存在于药物和其他生物活性分子中，将其断裂为胺和醛。代谢研究通常忽略报告或研究醛，尽管它们可能有毒。人们认为它们可以有效地解毒为羧酸和醇。然而，一些醛是反应性的，并且通过形成 DNA 和蛋白质加合物逃避解毒途径，从而引起不良反应。在此，我们模拟了产生胺和醛代谢物的 N-去烷基化反应，然后预测了醛的反应性。该模型使用了我们小组先前开发的一种深度学习方法来预测其他类型的药物代谢。在这项研究中，我们训练模型来预测人肝微粒体（HLM）中的 N-去烷基化，发现将同工酶特异性代谢数据与 HLM 数据一起使用可显著提高结果。最终的 HLM 模型准确预测了代谢底物中 N-去烷基化的部位（97%前两位和 94%ROC 曲线下面积）。接下来，我们将代谢、代谢产物结构预测和以前发表的反应性模型结合到生物活化模型中。该组合模型预测了一小部分已知通过 N-去烷基化发生生物活化的药物样分子的验证集的最可能反应性代谢产物的结构。将该模型应用于批准和撤回的药物，我们发现 N-去烷基化产生的醛代谢物可能解释了几种药物的肝毒性：茚地那韦、哌拉西林、维拉帕米和齐拉西酮。我们的结果表明，N-去烷基化可能是一种被低估的生物活化途径，尤其是在醛类解毒途径被抑制的临床情况下。此外，这是第一个通过组合代谢和反应性模型构建生物活化模型的报告。这些结果表明，更全面的生物活化模型是可能的。本研究中开发的模型可在 http://swami.wustl.edu/xenosite/ 获得。
