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High Content Screening for Prediction of Human Drug-Induced Liver Injury

  • Mikael PerssonEmail author
Protocol
Part of the Methods in Pharmacology and Toxicology book series (MIPT)

Abstract

High content screening (HCS) has emerged as a powerful tool for predicting drug-induced liver injury (DILI) in the early phases of drug discovery. It combines automated imaging with image analysis to assess cell health and customized parameters in a multiparametric fashion, enabling coverage over several mechanisms important for DILI. In simple two-dimensional cell models, various HCS assays typically show a sensitivity of ~50% with a high specificity of >90%. With relatively high throughput and short turn-around times, this makes it ideal for early decision making in drug discovery. HCS for DILI has lately expanded into complex three-dimensional models to further improve predictivity. The wealth of HCS data make it particularly amenable for machine learning and systems biology approaches for building rational models for prediction of DILI.

Key words

Imaging Multiparametric DILI High content Predictivity 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Drug Safety and Metabolism, Innovative Medicines and Early DevelopmentAstraZeneca R&D GothenburgMölndalSweden

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