Unknown

Dataset Information

0

Current status and future directions of high-throughput ADME screening in drug discovery.


ABSTRACT: 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.

SUBMITTER: Shou WZ 

PROVIDER: S-EPMC7322755 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

Current status and future directions of high-throughput ADME screening in drug discovery.

Shou Wilson Z WZ  

Journal of pharmaceutical analysis 20200523 3


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,  ...[more]

Similar Datasets

| S-EPMC3458418 | biostudies-literature
| S-EPMC4849475 | biostudies-literature
| S-EPMC4113063 | biostudies-literature
| S-EPMC5992796 | biostudies-literature
| S-EPMC6700880 | biostudies-literature
| S-EPMC4256210 | biostudies-other
| S-EPMC4820069 | biostudies-literature
| S-EPMC7277983 | biostudies-literature
| S-EPMC7567762 | biostudies-literature
| S-EPMC5122991 | biostudies-literature