Shou Wilson Z
Bristol-Myers Squibb, PO Box 4000, Princeton, NJ, 08540, USA.
J Pharm Anal. 2020 Jun;10(3):201-208. doi: 10.1016/j.jpha.2020.05.004. Epub 2020 May 23.
During the last decade high-throughput in vitro absorption, distribution, metabolism and excretion (HT-ADME) screening has become an essential part of any drug discovery effort of synthetic molecules. The conduct of HT-ADME screening has been "industrialized" due to the extensive development of software and automation tools in cell culture, assay incubation, sample analysis and data analysis. The HT-ADME assay portfolio continues to expand in emerging areas such as drug-transporter interactions, early soft spot identification, and ADME screening of peptide drug candidates. Additionally, thanks to the very large and high-quality HT-ADME data sets available in many biopharma companies, in silico prediction of ADME properties using machine learning has also gained much momentum in recent years. In this review, we discuss the current state-of-the-art practices in HT-ADME screening including assay portfolio, assay automation, sample analysis, data processing, and prediction model building. In addition, we also offer perspectives in future development of this exciting field.
在过去十年中,高通量体外吸收、分布、代谢和排泄(HT-ADME)筛选已成为合成分子药物研发工作的重要组成部分。由于细胞培养、分析孵育、样品分析和数据分析等软件及自动化工具的广泛发展,HT-ADME筛选的开展已实现“工业化”。HT-ADME分析方法组合在药物转运体相互作用、早期薄弱点识别以及肽类候选药物的ADME筛选等新兴领域不断扩展。此外,得益于许多生物制药公司拥有的大量高质量HT-ADME数据集,近年来利用机器学习对ADME性质进行计算机预测也获得了很大发展。在本综述中,我们讨论了HT-ADME筛选中的当前先进做法,包括分析方法组合、分析自动化、样品分析、数据处理和预测模型构建。此外,我们还对这一令人兴奋的领域的未来发展提出了展望。