Zhang Tao, Chen Qi, Li Li, Liu Limin Angela, Wei Dong-Qing
Key Laboratory of Microbial Metabolism, Luc Montagnier Biomedical Research Institute , College of Life Science and Biotechnology, Shanghai Jiaotong University, PR China.
Comb Chem High Throughput Screen. 2011 Jun 1;14(5):388-95. doi: 10.2174/138620711795508412.
The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.
组合化学和高通量筛选技术的应用使得能够同时生成和测试大量化学物质，这将促进药物的开发和发现。与此同时，它带来了一个挑战，即如何从数千种化合物中高效识别潜在的候选药物。应对这一挑战的一种方法是在药物开发的早期阶段考虑药物的药代动力学性质，如吸收、分布、代谢和排泄（ADME）。在ADME性质中，代谢由于与药物的疗效和安全性密切相关而至关重要。本综述将聚焦于用于预测细胞色素P450介导的药物代谢的计算机模拟方法。我们将从基于结构和基于数据两个方面描述这些预测方法。此外，还将讨论各种方法的应用和局限性。最后，我们为提高这些计算机模拟方法的预测准确性提供了进一步的方向。
