ReviewHepatology (Baltimore, Md.)2017
Stem cell-derived models to improve mechanistic understanding and prediction of human drug-induced liver injury.
Review in Hepatology (Baltimore, Md.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 60 citations in OpenAlex.
- Engineering Multilayered Hepatic Cell Sheet Model Using Oxygen-Supplying MeHA/CPO Hydrogel.Bioengineering (Basel, Switzerland) · 2025Article
- In vitro liver models for toxicological research.Drug metabolism and pharmacokinetics · 2025Review
- The evolving role of investigative toxicology in the pharmaceutical industry.Nature reviews. Drug discovery · 2023Review
- Liver-humanized mice: A translational strategy to study metabolic disorders.Journal of cellular physiology · 2022Review
- Fluorescent tagging of endogenous Heme oxygenase-1 in human induced pluripotent stem cells for high content imaging of oxidative stress in various differentiated lineages.Archives of toxicology · 2021Article
- Three-Dimensional Liver Culture Systems to Maintain Primary Hepatic Properties for Toxicological Analysis In Vitro.International journal of molecular sciences · 2021Review
- Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features.ACS omega · 2020Article
- Hepatocyte-like cells derived from human induced pluripotent stem cells using small molecules: implications of a transcriptomic study.Stem cell research & therapy · 2020Article
- Advances in generating liver cells from pluripotent stem cells as a tool for modeling liver diseases.Stem cells (Dayton, Ohio) · 2020Review
- Characterization of Human Induced Pluripotent Stem Cell-Derived Hepatocytes with Mature Features and Potential for Modeling Metabolic Diseases.International journal of molecular sciences · 2020Article
- Human Pluripotent Stem Cells: A Unique Tool for Toxicity Testing in Pancreatic Progenitor and Endocrine Cells.Frontiers in endocrinology · 2020Article
- Stem cell models as anThe Biochemical journal · 2019Review
- Comprehensive Evaluation of Organotypic and Microphysiological Liver Models for Prediction of Drug-Induced Liver Injury.Frontiers in pharmacology · 2019Review
- Clinical Trials in a Dish: A Perspective on the Coming Revolution in Drug Development.SLAS discovery : advancing life sciences R & D · 2018Review
- Application of hepatocyte-like cells to enhance hepatic safety risk assessment in drug discovery.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Review
- Co-stimulation of LPARStem cell research & therapy · 2018Article
- Hepatic differentiation of human iPSCs in different 3D models: A comparative study.International journal of molecular medicine · 2017Article
- Pluripotent stem cells to hepatocytes, the journey so far.Biomedical reports · 2017Article
- Differentiation of hepatocyte-like cells from human pluripotent stem cells using small molecules.Differentiation; research in biological diversityArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 13 institutions in 9 countries.
Funding
Abstract
Current preclinical drug testing does not predict some forms of adverse drug reactions in humans. Efforts at improving predictability of drug-induced tissue injury in humans include using stem cell technology to generate human cells for screening for adverse effects of drugs in humans. The advent of induced pluripotent stem cells means that it may ultimately be possible to develop personalized toxicology to determine interindividual susceptibility to adverse drug reactions. However, the complexity of idiosyncratic drug-induced liver injury means that no current single-cell model, whether of primary liver tissue origin, from liver cell lines, or derived from stem cells, adequately emulates what is believed to occur during human drug-induced liver injury. Nevertheless, a single-cell model of a human hepatocyte which emulates key features of a hepatocyte is likely to be valuable in assessing potential chemical risk; furthermore, understanding how to generate a relevant hepatocyte will also be critical to efforts to build complex multicellular models of the liver. Currently, hepatocyte-like cells differentiated from stem cells still fall short of recapitulating the full mature hepatocellular phenotype. Therefore, we convened a number of experts from the areas of preclinical and clinical hepatotoxicity and safety assessment, from industry, academia, and regulatory bodies, to specifically explore the application of stem cells in hepatotoxicity safety assessment and to make recommendations for the way forward. In this short review, we particularly discuss the importance of benchmarking stem cell-derived hepatocyte-like cells to their terminally differentiated human counterparts using defined phenotyping, to make sure the cells are relevant and comparable between labs, and outline why this process is essential before the cells are introduced into chemical safety assessment. (Hepatology 2017;65:710-721).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.