Combination of docking, molecular dynamics and quantum mechanical calculations for metabolism prediction of 3,4-methylenedioxybenzoyl-2-thienylhydrazone
| 详细信息 | 推荐本文 | |
- 作者：Rodolpho C. Braga (1)
Vin铆cius M. Alves (1)
Carlos A. M. Fraga (2)
Eliezer J. Barreiro (2)
Val茅ria de Oliveira (1)
Carolina H. Andrade (1) carolina@farmacia.ufg.br - 关键词：Cytochrome P450 – ; Docking – ; Metabolism prediction – ; Molecular dynamics – ; QM calculations
- 刊名：Journal of Molecular Modeling
- 出版时间：May 2012
- 出版年：2012
- 期刊代码：149_1610-2940
- 类别：bio
- 卷：18
- 期：5
- 页码：2065-2078
- 数据来源：sp
摘要
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.