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.

在过去的十年中，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发现任何药物的重要组成部分。由于在细胞培养，测定孵育，样品分析和数据分析方面软件和自动化工具的广泛开发，HT-ADME筛选的行为已被“工业化”。HT-ADME分析产品组合在新兴领域不断扩展，例如药物与转运蛋白的相互作用，早期软点识别以及候选肽药物的ADME筛选。此外，得益于许多生物制药公司提供的超大型高质量HT-ADME数据集，近年来使用机器学习对ADME属性进行计算机预测的趋势也越来越多。在这篇评论中 我们将讨论HT-ADME筛选的当前最新实践，包括分析产品组合，分析自动化，样品分析，数据处理和预测模型构建。此外，我们还提供了这一激动人心领域的未来发展前景。