ReviewClinical and translational science2019
The DILI-sim Initiative: Insights into Hepatotoxicity Mechanisms and Biomarker Interpretation.
Review in Clinical and translational science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 55 citations in OpenAlex.
- Quantitative systems toxicology (QST) reproduces species differences in PF-04895162 liver safety due to combined mitochondrial and bile acid toxicity.Pharmacology research & perspectives · 2019Trial
- Quantitative Systems Toxicology Model Predicts Obeticholic Acid-Associated Liver Injury in Metabolic Dysfunction-Associated Steatotic Liver Disease.Clinical pharmacology and therapeutics · 2026Article
- Quantitative Systems Toxicology Modeling with DILIsym to Support Phase 3 Dose Selection for Fezolinetant.Clinical pharmacology and therapeutics · 2026Article
- Quantitative systems toxicology: modelling to mechanistically understand and predict drug safety.Nature reviews. Drug discovery · 2026Review
- Development and prospective validation of a machine learning model for risk stratification of drug-induced liver injury using real-world clinical data.Therapeutic advances in drug safety · 2026Article
- Subclinical Hepatic Injury and Dysfunction Following Surgery: Evidence From a Young and Aged Laparotomy Murine Model.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Drug metabolizing enzymes and transporters, and their roles for the development of drug-induced liver injury.Expert opinion on drug metabolism & toxicology · 2025Review
- Mechanisms of drug induced liver injury.Cellular and molecular life sciences : CMLS · 2025Review
- Application of Mechanistic Mathematical Modeling to Toxicology: Quantitative Systems Toxicology (QST).Handbook of experimental pharmacology · 2025Review
- Voriconazole-associated liver injury: clinical risk factor identification and predictive nomogram construction.Frontiers in pharmacology · 2025Article
- Tolvaptan safety in autosomal-dominant polycystic kidney disease; a focus on idiosyncratic drug-induced liver injury liabilities.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values.Drug safety · 2024Article
- Advances and Challenges in Modeling Cannabidiol Pharmacokinetics and Hepatotoxicity.Drug metabolism and disposition: the biological fate of chemicals · 2024Review
- Role of Hepatocyte Transporters in Drug-Induced Liver Injury (DILI)-In Vitro Testing.Pharmaceutics · 2022Review
- Review
- Challenges and Future of Drug-Induced Liver Injury Research-Laboratory Tests.International journal of molecular sciences · 2022Review
- Operationalizing the Use of Biofabricated Tissue Models as Preclinical Screening Platforms for Drug Discovery and Development.SLAS discovery : advancing life sciences R & D · 2021Article
- Biomarker-Guided Individualization of Antibiotic Therapy.Clinical pharmacology and therapeutics · 2021Review
- Systems Modeling to Quantify Safety Risks in Early Drug Development: Using Bifurcation Analysis and Agent-Based Modeling as Examples.The AAPS journal · 2021Review
- A general model for cell death and biomarker release from injured tissues.Journal of pharmacokinetics and pharmacodynamics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The drug-induced liver injury (DILI)-sim Initiative is a public-private partnership involving scientists from industry, academia, and the US Food and Drug Administration (FDA). The Initiative uses quantitative systems toxicology (QST) to build and refine a model (DILIsym) capable of understanding and predicting liver safety liabilities in new drug candidates and to optimize interpretation of liver safety biomarkers used in clinical studies. Insights gained to date include the observation that most dose-dependent hepatotoxicity can be accounted for by combinations of just three mechanisms (oxidative stress, interference with mitochondrial respiration, and alterations in bile acid homeostasis) and the importance of noncompetitive inhibition of bile acid transporters. The effort has also provided novel insight into species and interpatient differences in susceptibility, structure-activity relationships, and the role of nonimmune mechanisms in delayed idiosyncratic hepatotoxicity. The model is increasingly used to evaluate new drug candidates and several clinical trials are underway that will test the model's ability to prospectively predict liver safety. With more refinement, in the future, it may be possible to use the DILIsym predictions to justify reduction in the size of some clinical trials. The mature model could also potentially assist physicians in managing the liver safety of their patients as well as aid in the diagnosis of DILI.
Indexed as
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What OpenQuestion holds
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.