ArticleArchives of toxicology2014
A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the 'rule-of-two' model.
Article in Archives of toxicology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Integration of microphysiological systems with computational and digital twin modeling for pharmaceutical development: a systematic review.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2026Pooled it
- Randomized phase 2a trial assessing a novel septin molecular glue in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Review
- Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development.Toxicology research · 2023Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs.PloS one · 2021Article
- Intense bitterness of molecules: Machine learning for expediting drug discovery.Computational and structural biotechnology journal · 2021Article
- Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.Nature reviews. Drug discovery · 2020Review
- Review of high-content screening applications in toxicology.Archives of toxicology · 2019Review
- Salsolinol Attenuates Doxorubicin-Induced Chronic Heart Failure in Rats and Improves Mitochondrial Function in H9c2 Cardiomyocytes.Frontiers in pharmacology · 2019Article
- Synergistic Cytotoxicity from Drugs and Cytokines In Vitro as an Approach to Classify Drugs According to Their Potential to Cause Idiosyncratic Hepatotoxicity: A Proof-of-Concept Study.The Journal of pharmacology and experimental therapeutics · 2017Article
- Identification of informative features for predicting proinflammatory potentials of engine exhausts.Biomedical engineering online · 2017Article
- A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.Nature communications · 2017Article
- Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.Journal of chemical information and modeling · 2017Article
- Drug-induced liver injury: Do we know everything?World journal of hepatology · 2017Review
- Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.Journal of computer-aided molecular design · 2016Article
- Case Characterization, Clinical Features and Risk Factors in Drug-Induced Liver Injury.International journal of molecular sciences · 2016Review
- Comedications alter drug-induced liver injury reporting frequency: Data mining in the WHO VigiBase™.Regulatory toxicology and pharmacology : RTP · 2015Article
- Drug-induced mitochondrial impairment in liver cells.EXCLI journal · 2015Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 5 countries.
Funding
Abstract
Drug-induced liver injury (DILI) is a major cause of drug failures in both the preclinical and clinical phase. Consequently, improving prediction of DILI at an early stage of drug discovery will reduce the potential failures in the subsequent drug development program. In this regard, high-content screening (HCS) assays are considered as a promising strategy for the study of DILI; however, the predictive performance of HCS assays is frequently insufficient. In the present study, a new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model (i.e., 'rule-of-two' defined by daily dose ≥100 mg/day & logP ≥3). The RO2 model was derived from the observation that high daily doses and lipophilicity of an oral medication were associated with significant DILI risk in humans. In the developed testing strategy, the RO2 model was used for the rational selection of candidates for HCS assays, and only the negatives predicted by the RO2 model were further investigated by HCS. Subsequently, the effects of drug treatment on cell loss, nuclear size, DNA damage/fragmentation, apoptosis, lysosomal mass, mitochondrial membrane potential, and steatosis were studied in cultures of primary rat hepatocytes. Using a set of 70 drugs with clear evidence of clinically relevant DILI, the testing strategy improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone. Moreover, the testing strategy was further validated by including published data (Cosgrove et al. in Toxicol Appl Pharmacol 237:317-330, 2009) on drug-cytokine-induced hepatotoxicity, which improved the accuracies by 7 %. Taken collectively, the proposed testing strategy can significantly improve the prediction of in vitro assays for detecting DILI liability in an early drug discovery phase.
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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.