Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa 13133, Jordan.
School of Pharmacy, Graphic Era Hill University, Dehradun-248002, Uttarakhand, India.
Curr Drug Metab. 2021;22(7):503-522. doi: 10.2174/1389200222666210705122913.
In the drug discovery setting, undesirable ADMET properties of a pharmacophore with good predictive power obtained after a tedious drug discovery and development process may lead to late-stage attrition. The earlystage ADMET profiling has brought a new dimension to lead drug development. Although several high-throughput in vitro models are available for ADMET profiling, the in silico methods are gaining more importance because of their economic and faster prediction ability without the requirements of tedious and expensive laboratory resources. Nonetheless, in silico ADMET tools alone are not accurate, and therefore, ideally adopted along with in vitro and or in vivo methods in order to enhance the predictability power. This review summarizes the significance and challenges associated with the application of in silico tools as well as the possible scope of in vitro models for integration to improve the ADMET predictability power of these tools.
在药物发现环境中，在经过繁琐的药物发现和开发过程后获得的具有良好预测能力的药效团，如果具有不良的 ADMET 特性，可能会导致后期淘汰。早期的 ADMET 分析为药物开发带来了新的维度。尽管有几种高通量的体外模型可用于 ADMET 分析，但由于其具有经济、快速预测能力，且不需要繁琐和昂贵的实验室资源，因此基于计算的方法越来越受到重视。尽管如此，基于计算的 ADMET 工具本身并不准确，因此，为了提高预测能力，理想情况下应结合体外和/或体内方法使用。本文综述了应用基于计算的工具的意义和挑战，以及整合体外模型的可能范围，以提高这些工具的 ADMET 预测能力。
