ArticleNature communications2025
A large-scale human toxicogenomics resource for drug-induced liver injury prediction.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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
7 citing papers in PubMed.
- A multiparametric in vitro strategy for small molecule drug-induced liver injury risk mitigation in early drug development.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Hepatic responses as potential biomarkers of idiosyncratic drug reaction risk.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Systematic comparison of temporal hepatotoxicant-induced gene network responses across 3 liver test systems.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Fondaparinux attenuates methotrexate-induced hepatotoxicity by regulating coagulation, endothelial dysfunction, and inflammatory signaling via the TLR4/NLRP3 and NF-κB/IL-1β/MCP-1 pathways.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-Induced Liver Injury (DILI) remains one of the most critical challenges in drug development, causing patient safety concerns, clinical trial failures and drug withdrawals. We introduce ToxPredictor, a toxicogenomics framework combining RNA-seq data from primary human hepatocytes with pharmacokinetic data to predict dose-resolved DILI risks and safety margins. At its core is DILImap, an RNA-seq library tailored for DILI research, comprising 300 compounds at multiple concentrations. ToxPredictor achieves 88% sensitivity at 100% specificity in blind validation, outperforming state-of-the-art methods. It flagged recent phase III clinical failures, including Evobrutinib, TAK-875, and BMS-986142, overlooked by animal studies. Beyond prediction, ToxPredictor provides mechanistic insights into hepatotoxic pathways, enabling early de-risking and actionable safety decisions. Unlike single-endpoint readouts-even from 3D models-transcriptomics offers a multi-dimensional system-level view of hepatocyte responses, capable of detecting diverse DILI mechanisms not captured by conventional assays. Scalable, actionable, and integrated into a broader AI/ML drug discovery platform, this work establishes toxicogenomics as a promising tool for developing safer therapeutics and addressing one of the most pressing challenges in toxicology.
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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 Socratic graph.