| Literature DB >> 30988136 |
Stephen Lynch1, Chris S Pridgeon1, Carrie A Duckworth1, Parveen Sharma1, B Kevin Park1, Chris E P Goldring2.
Abstract
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.Entities:
Keywords: cardiotoxicity; gastrointestinal toxicity; hepatotoxicity; organoid; stem cell; toxicology
Mesh:
Year: 2019 PMID: 30988136 PMCID: PMC6463389 DOI: 10.1042/BCJ20170780
Source DB: PubMed Journal: Biochem J ISSN: 0264-6021 Impact factor: 3.857
Enzyme metabolic rates, depending on the concentration of product produced, comparing immortalised HepaRG cells against iPSC-Hep cell lines [14], showing the different systems responding to a known compound over 8 and 29 days. IPSCs responded in a far more stable manner over 29 days, as HepaRG cells metabolic rates were too low to measure
| Cell model | Day | Cell per well | Drug oxidation activity | ||||
|---|---|---|---|---|---|---|---|
| CYP1A2 | CYP2C9 | CYP2C19 | CYP2D6 | CYP3A4 | |||
| HepaRG | 8 | 1.8 | 0.93 ± 0.03 | 0.040 ± 0.004 | 4.1 ± 1.0 | 11 ± 2.0 | 20 ± 2.0 |
| iPSC #1 | 8 | 1.5 | 0.93 ± 0.24 | 0.004 ± 0.001 | 1.2 ± 0.20 | 7.0 ± 2.0 | 94 ± 20.0 |
| 29 | 1.1 | 1.2 ± 0.10 | 0.053 ± 0.006 | 7.9 ± 0.80 | 24 ± 4.0 | 26 ± 2.0 | |
| iPSC #2 | 8 | 1.5 | 0.78 ± 0.19 | 0.002 ± 0.0004 | 1.0 ± 0.10 | 4.4 ± 1.1 | 77 ± 6.0 |
| 29 | 1.3 | 1.2 ± 0.10 | 0.027 ± 0.005 | 6.5 ± 0.70 | 16 ± 1.0 | 24 ± 3.0 | |
Cardiac diseases which have been modelled in vitro using iPSC-CMs. Many drugs have been tested using disease state iPSC-CMs
| Disease | Tested drug(s) |
|---|---|
| LQT1 | Propranolol [ |
| LQT2 | Nifedipine [ |
| LQT8 | Roscovitine [ |
| CPTV1 | Isoprenaline, forskolin [ |
| DCM | Metoprolol, norepinephrine [ |
| HCM | Propranolol, verapamil, nifedipine [ |
| ARVC | Nifedipine [ |