Wang Yulan, Xing Jing, Xu Yuan, Zhou Nannan, Peng Jianlong, Xiong Zhaoping, Liu Xian, Luo Xiaomin, Luo Cheng, Chen Kaixian, Zheng Mingyue, Jiang Hualiang
Drug Discovery and Design Center, State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,555 Zuchongzhi Road,Shanghai 201203,China.
State Key Laboratory of Bioreactor Engineering and Shanghai Key Laboratory of Chemical Biology,School of Pharmacy,East China University of Science and Technology,Shanghai 200237,China.
Q Rev Biophys. 2015 Nov;48(4):488-515. doi: 10.1017/S0033583515000190. Epub 2015 Sep 2.
In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.
近几十年来,计算机模拟吸收、分布、代谢、排泄(ADME)和毒性(T)建模作为合理药物设计的一种工具,受到了制药科学家的广泛关注,并且已经报道了各种与ADME/T相关的预测模型。这些模型高通量和低成本的特性使得药物开发过程更加精简,在这个过程中,可以通过对生物利用度、安全性以及活性的并行研究来指导活性化合物的识别或其结构优化。然而,这些工具的有效性高度依赖于它们应对不同阶段需求的能力,例如,由于缺乏所需的可预测性,它们在候选药物选择中的应用受到了限制。对于一些涉及更复杂机制的事件或终点,当前的计算机模拟方法仍需进一步改进。在这篇综述中,我们将简要介绍用于一些物理化学参数、ADME性质和毒性评估的计算机模拟模型的发展,重点介绍其建模方法、在药物发现中的应用以及这些模型的潜在优点或不足。最后,将讨论基于大数据分析和系统科学的未来ADME/T建模的前景。