ArticleThe pharmacogenomics journal2018
Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity.
Article in The pharmacogenomics journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Meta-analysis and Consolidation of Farnesoid X Receptor Chromatin Immunoprecipitation Sequencing Data Across Different Species and Conditions.Hepatology communications · 2021Pooled it
- 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
- A genomic-led strategy to anticipate drug safety effects.PLoS genetics · 2026Article
- Weighted network analysis of adverse outcome pathways decodes the multiscale mechanisms of environmental toxicity.Environmental science and ecotechnology · 2026Article
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Utilizing rat kidney gene co-expression networks to enhance safety assessment biomarker identification and human translation.iScience · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening.Environmental health perspectives · 2025Article
- Spatio-temporal transcriptomic analysis reveals distinct nephrotoxicity, DNA damage, and regeneration response after cisplatin.Cell biology and toxicology · 2025Article
- Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities.Toxicological sciences : an official journal of the Society of Toxicology · 2025Review
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
- AI-driven Discovery of Morphomolecular Signatures in Toxicology.bioRxiv : the preprint server for biology · 2024Article
- Biomarkers of NRF2 signalling: Current status and future challenges.Redox biology · 2024Review
- A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches.Frontiers in genetics · 2024Article
- A systems approach reveals species differences in hepatic stress response capacity.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver.Toxicologic pathology · 2023Review
- The evolving role of investigative toxicology in the pharmaceutical industry.Nature reviews. Drug discovery · 2023Review
- Identifying multiscale translational safety biomarkers using a network-based systems approach.iScience · 2023Article
- Phosphatidylcholine (18:0/20:4), a potential biomarker to predict ethionamide-induced hepatic steatosis in rats.Journal of applied toxicology : JAT · 2022Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the 'TXG-MAP'). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses complement traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.
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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.