ReviewArchives of toxicology2016
Evidence-based selection of training compounds for use in the mechanism-based integrated prediction of drug-induced liver injury in man.
Review in Archives of toxicology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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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.
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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.
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Who cites it
26 citing papers in PubMed.
- Are New Approach Methodologies (NAMs) the Holy Grail of toxicology?Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Comparative Proteomic Characterization of Serum-Free Cultivated HepG2 Cells Reveals Upregulated Drug Metabolism and Increased Oxidative Stress Protection.Journal of proteome research · 2025Article
- Development of an AI model for DILI-level prediction using liver organoid brightfield images.Communications biology · 2025Article
- In vitro liver models for toxicological research.Drug metabolism and pharmacokinetics · 2025Review
- Current hPSC-derived liver organoids for toxicity testing: Cytochrome P450 enzymes and drug metabolism.Toxicological research · 2025Review
- Limitations of acetaminophen as a reference hepatotoxin for the evaluation of in vitro liver models.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- 3D human tissue models and microphysiological systems for HIV and related comorbidities.Trends in biotechnology · 2024Review
- Increasing sustainability and reproducibility ofFrontiers in toxicology · 2024Article
- Review
- Metabolic Priming as a Tool in Redox and Mitochondrial Theragnostics.Antioxidants (Basel, Switzerland) · 2023Review
- Drug Metabolism of Hepatocyte-like Organoids and Their Applicability in In Vitro Toxicity Testing.Molecules (Basel, Switzerland) · 2023Article
- Effect of total glycosides ofFrontiers in nutrition · 2023Article
- In Vitro Models for Studying Chronic Drug-Induced Liver Injury.International journal of molecular sciences · 2022Review
- Pre-clinical 2D and 3D toxicity response to a panel of nanomaterials; comparative assessment of NBM-induced liver toxicity.Drug delivery and translational research · 2022Article
- Oxidative-stress and long-term hepatotoxicity: comparative study in Upcyte human hepatocytes and hepaRG cells.Archives of toxicology · 2022Article
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- High-Content Screening for the Detection of Drug-Induced Oxidative Stress in Liver Cells.Antioxidants (Basel, Switzerland) · 2021Review
- An in vitro coculture system of human peripheral blood mononuclear cells with hepatocellular carcinoma-derived cells for predicting drug-induced liver injury.Archives of toxicology · 2021Article
- Robustness testing and optimization of an adverse outcome pathway on cholestatic liver injury.Archives of toxicology · 2020Article
- Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.Nature reviews. Drug discovery · 2020Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The 'MIP-DILI' project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.
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