Image-based high-content screening in drug discovery
| Autor: | Ina Rothenaigner, Kenji Schorpp, Sean Lin, Kamyar Hadian |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Pharmacology Computer science Drug discovery Phenotypic screening Computational biology behavioral disciplines and activities High-Throughput Screening Assays Machine Learning 03 medical and health sciences Identification (information) Phenotype 030104 developmental biology 0302 clinical medicine Workflow 030220 oncology & carcinogenesis High-content screening mental disorders Drug Discovery Humans Image acquisition Classical pharmacology Image based |
| Zdroj: | Drug Discov. Today 25, 1348-1361 (2020) |
| ISSN: | 1359-6446 |
| Popis: | While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis. |
| Databáze: | OpenAIRE |
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