Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, Brazil.
J Mol Model. 2012 May;18(5):2065-78. doi: 10.1007/s00894-011-1219-9. Epub 2011 Sep 8.
In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In silico methods based on docking, molecular dynamics and quantum chemical calculations can bring us closer to understand drug metabolism and predict drug-drug interactions. We report herein on a combined methodology to explore the site of metabolism prediction of a new cardioactive drug prototype, LASSBio-294 (1), using MetaPrint2D to predict the most likely metabolites, combined with structure-based tools using docking, molecular dynamics and quantum mechanical calculations to predict the binding of the substrate to CYP2C9 enzyme, to estimate the binding free energy and to study the energy profiles for the oxidation of (1). Additionally, the computational study was correlated with a metabolic fingerprint profiling using LC-MS analysis. The results obtained using the computational methods gave valuable information about the probable metabolites of (1) (qualitatively) and also about the important interactions of this lead compound with the amino acid residues of the active site of CYP2C9. Moreover, using a combination of different levels of theory sheds light on the understanding of (1) metabolism by CYP2C9 and its mechanisms. The metabolic fingerprint profiling of (1) has shown that the metabolites founded in highest concentration in different species were metabolites M1, M2 and M3, whereas M8 was found to be a minor metabolite. Therefore, our computational study allowed a qualitative prediction for the metabolism of (1). The approach presented here has afforded new opportunities to improve metabolite identification strategies, mediated by not only CYP2C9 but also other CYP450 family enzymes.
在现代药物发现过程中,ADME/Tox 性质应尽早在测试级联中确定,以便及时评估其性质特征。为了帮助药物化学家设计具有改善药代动力学性质的新化合物,需要了解软点位置或代谢部位(SOM)。基于对接、分子动力学和量子化学计算的计算方法可以帮助我们更深入地了解药物代谢并预测药物相互作用。在此,我们报告了一种联合方法,用于探索新型心脏活性药物原型 LASSBio-294(1)的代谢部位预测,使用 MetaPrint2D 预测最可能的代谢物,结合基于结构的工具,使用对接、分子动力学和量子力学计算预测底物与 CYP2C9 酶的结合,估算结合自由能,并研究(1)氧化的能量分布。此外,计算研究与使用 LC-MS 分析的代谢指纹图谱分析相关联。使用计算方法获得的结果提供了有关(1)代谢物(定性)的有价值信息,并且还提供了有关该先导化合物与 CYP2C9 活性位点氨基酸残基的重要相互作用的信息。此外,使用不同理论水平的组合阐明了对 CYP2C9 代谢(1)及其机制的理解。(1)的代谢指纹图谱分析表明,在不同物种中发现的浓度最高的代谢物是代谢物 M1、M2 和 M3,而 M8 被发现是一种次要代谢物。因此,我们的计算研究允许对(1)的代谢进行定性预测。此处呈现的方法为改善代谢物鉴定策略提供了新的机会,这些策略不仅由 CYP2C9 介导,还由其他 CYP450 家族酶介导。