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Stem cell models as an in vitro model for predictive toxicology
Author(s) -
Stephen M. Lynch,
Chris S. Pridgeon,
Carrie A. Duckworth,
Parveen Sharma,
B. Kevin Park,
Christopher E. Goldring
Publication year - 2019
Publication title -
biochemical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 265
eISSN - 1470-8728
pISSN - 0264-6021
DOI - 10.1042/bcj20170780
Subject(s) - in vitro , predictive value , biology , computational biology , toxicology , chemistry , medicine , biochemistry
Adverse drug reactions (ADRs) are the unintended side effects of drugs. They are categorised as either predictable or unpredictable drug-induced injury and may be exhibited after a single or prolonged exposure to one or multiple compounds. Historically, toxicology studies rely heavily on animal models to understand and characterise the toxicity of novel compounds. However, animal models are imperfect proxies for human toxicity and there have been several high-profile cases of failure of animal models to predict human toxicity e.g. fialuridine, TGN1412 which highlight the need for improved predictive models of human toxicity. As a result, stem cell-derived models are under investigation as potential models for toxicity during early stages of drug development. Stem cells retain the genotype of the individual from which they were derived, offering the opportunity to model the reproducibility of rare phenotypes in vitro Differentiated 2D stem cell cultures have been investigated as models of hepato- and cardiotoxicity. However, insufficient maturity, particularly in the case of hepatocyte-like cells, means that their widespread use is not currently a feasible method to tackle the complex issues of off-target and often unpredictable toxicity of novel compounds. This review discusses the current state of the art for modelling clinically relevant toxicities, e.g. cardio- and hepatotoxicity, alongside the emerging need for modelling gastrointestinal toxicity and seeks to address whether stem cell technologies are a potential solution to increase the accuracy of ADR predictivity in humans.

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