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Lin, S. ; Schorpp, K.K. ; Rothenaigner, I. ; Hadian, K.

Image-based high-content screening in drug discovery.

Drug Discov. Today 25, 1348-1361 (2020)
Postprint DOI
Open Access Green
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Korrespondenzautor
Schlagwörter Pluripotent Stem-cells; Neural-networks; In-vitro; Software; Assay; Polypharmacology; Validation; Industry; Platform; Genome
ISSN (print) / ISBN 1359-6446
e-ISSN 1878-5832
Zeitschrift Drug discovery today
Quellenangaben Band: 25, Heft: 8, Seiten: 1348-1361 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Cambridge
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Molecular Toxicology and Pharmacology (TOXI)