A combined approach to drug metabolism and toxicity assessment
A combined approach to drug metabolism and toxicity assessment
复制标题
DOI：
10.1124/dmd.105.008458
复制
发表时间：
2006-03-01
影响因子：
3.9
通讯作者：
Nikolskaya, T
中科院分区：
文献类型：
--
作者：
Ekins, S;Andreyev, S;Nikolskaya, T
预测药物在人体中的代谢或毒性的挑战已经使用体内动物模型、体外系统、高通量基因组学和蛋白质组学方法以及最近的计算方法来解决。理解生物系统的复杂性需要更广阔的视角，而不是仅仅关注一种孤立的预测方法。因此，多种方法可能是必要的，并结合起来进行更准确的预测。在药物代谢和毒理学领域，近年来，我们已经看到了计算定量结构活性关系（QSAR）的增长，以及来自微阵列的经验数据。在当前的研究中，我们进一步开发了一种新型计算方法MetaDrug，该方法1）根据分子的化学结构预测分子的代谢物，2）通过各种吸收、分布、代谢、排泄预测原始化合物及其代谢物的活性。毒性模型，3）将预测与人类细胞信号传导和代谢途径和网络结合起来，以及4）将网络和代谢物与相关的毒理基因组学或其他高通量数据整合。我们已经使用最近发表的阿瑞匹坦2（S）-甲硫氨酸的体外代谢和微阵列研究数据证明了这种方法的实用性。（（3，5-双（三氟甲基）苄基）-氧基）-3（S）苯基-4-甲酰胺（（3-氧代-1，2，4-三唑-5-基）甲基）吗啉（L-742694）、曲伏氧氟沙星、4-羟基他莫昔芬和青蒿素以及其它青蒿素类似物，以显示预测的与细胞色素P450的相互作用，受影响的代谢物和基因网络。作为比较，我们使用了第二种计算方法MetaCore，用可用的表达数据生成统计学显著的基因网络。这些案例研究证明了QSAR和系统生物学方法的结合。
The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4((3-oxo-1,2,4-triazol-5-yl)methyl) morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.