scholarly journals Transcriptional, Functional, and Mechanistic Comparisons of Stem Cell–Derived Hepatocytes, HepaRG Cells, and Three-Dimensional Human Hepatocyte Spheroids as Predictive In Vitro Systems for Drug-Induced Liver Injury

2017 ◽  
Vol 45 (4) ◽  
pp. 419-429 ◽  
Author(s):  
Catherine C. Bell ◽  
Volker M. Lauschke ◽  
Sabine U. Vorrink ◽  
Henrik Palmgren ◽  
Rodger Duffin ◽  
...  
Keyword(s):  
Stem Cell ◽  
Liver Injury ◽  
Three Dimensional ◽  
Human Hepatocyte ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Heparg Cells ◽  
In Vitro Systems ◽  
Hepatocyte Spheroids

scholarly journals Primary Human Hepatocyte Spheroids as Tools to Study the Hepatotoxic Potential of Non-Pharmaceutical Chemicals

2021 ◽  
Vol 22 (20) ◽  
pp. 11005
Author(s):  
Vânia Vilas-Boas ◽  
Eva Gijbels ◽  
Kaat Leroy ◽  
Alanah Pieters ◽  
Audrey Baze ◽  
...  
Keyword(s):  
Cyclosporine A ◽  
Prolonged Exposure ◽  
Human Hepatocyte ◽  
Drug Induced ◽  
Clinical Issue ◽  
Drug Induced Liver Injury ◽  
Primary Human Hepatocyte ◽  
Donor Variability ◽  
Hepatocyte Spheroids

Drug-induced liver injury, including cholestasis, is an important clinical issue and economic burden for pharmaceutical industry and healthcare systems. However, human-relevant in vitro information on the ability of other types of chemicals to induce cholestatic hepatotoxicity is lacking. This work aimed at investigating the cholestatic potential of non-pharmaceutical chemicals using primary human hepatocytes cultured in 3D spheroids. Spheroid cultures were repeatedly (co-) exposed to drugs (cyclosporine-A, bosentan, macitentan) or non-pharmaceutical chemicals (paraquat, tartrazine, triclosan) and a concentrated mixture of bile acids for 4 weeks. Cell viability (adenosine triphosphate content) was checked every week and used to calculate the cholestatic index, an indicator of cholestatic liability. Microarray analysis was performed at specific time-points to verify the deregulation of genes related to cholestasis, steatosis and fibrosis. Despite the evident inter-donor variability, shorter exposures to cyclosporine-A consistently produced cholestatic index values below 0.80 with transcriptomic data partially supporting its cholestatic burden. Bosentan confirmed to be hepatotoxic, while macitentan was not toxic in the tested concentrations. Prolonged exposure to paraquat suggested fibrotic potential, while triclosan markedly deregulated genes involved in different types of hepatotoxicity. These results support the applicability of primary human hepatocyte spheroids to study hepatotoxicity of non-pharmaceutical chemicals in vitro.

scholarly journals Characterization of primary human hepatocyte spheroids as a model system for drug-induced liver injury, liver function and disease

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Catherine C. Bell ◽  
Delilah F. G. Hendriks ◽  
Sabrina M. L. Moro ◽  
Ewa Ellis ◽  
Joanne Walsh ◽  
...  
Keyword(s):  
Liver Injury ◽  
Liver Function ◽  
Model System ◽  
Human Hepatocyte ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Primary Human Hepatocyte ◽  
Hepatocyte Spheroids

Comparison of three human liver cell lines for in vitro drug-induced liver injury assessment: Huh7, HepaRG, and stem cell-derived hepatocytes

2019 ◽  
Vol 15 (3) ◽  
pp. 271-285
Author(s):  
So Yoon Yun ◽  
Ju Yeun Kim ◽  
Moon Jung Back ◽  
Hee Soo Kim ◽  
Hae Chan Ha ◽  
...  
Keyword(s):  
Stem Cell ◽  
Liver Injury ◽  
Cell Lines ◽  
Liver Cell ◽  
Human Liver ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Human Liver Cell ◽  
Injury Assessment

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
Author(s):  
Danyel Jennen ◽  
Jan Polman ◽  
Mark Bessem ◽  
Maarten Coonen ◽  
Joost van Delft ◽  
...  
Keyword(s):  
Liver Injury ◽  
Clinical Chemistry ◽  
Classification Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Chemistry Data ◽  
Model Based ◽  
Injury Classification

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

2020 ◽  
Vol 8 (12) ◽  
pp. 3105-3109
Author(s):  
Miguel González‐Muñoz ◽  
Jaime Monserrat Villatoro ◽  
Eva Marín‐Serrano ◽  
Stefan Stewart ◽  
Belén Bardón Rivera ◽  
...  
Keyword(s):  
Case Report ◽  
Liver Injury ◽  
Causality Assessment ◽  
Assessment Method ◽  
In Vitro Testing ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

2020 ◽  
Vol 21 (6) ◽  
pp. 2114
Author(s):  
Robert Ancuceanu ◽  
Marilena Viorica Hovanet ◽  
Adriana Iuliana Anghel ◽  
Florentina Furtunescu ◽  
Monica Neagu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Computational Models ◽  
Liver Toxicity ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Drug Induced ◽  
Reference Drug ◽  
Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

scholarly journals Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Rosa Chan ◽  
Leslie Z. Benet
Keyword(s):  
Liver Injury ◽  
Drug Development ◽  
Safety Concern ◽  
Drug Induced ◽  
Development Review ◽  
Drug Induced Liver Injury ◽  
Underlying Mechanisms ◽  
Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

Sign in / Sign up

Export Citation Format

Share Document