ArticleJournal of toxicologic pathology2009
Toxicogenomic biomarkers for liver toxicity.
Article in Journal of toxicologic pathology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 28 citations in OpenAlex.
- Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity.Environmental and molecular mutagenesis · 2025Review
- High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model.International journal of molecular sciences · 2023Article
- Emerging Bioinformatics Methods and Resources in Drug Toxicology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Identification of Translational microRNA Biomarker Candidates for Ketoconazole-Induced Liver Injury Using Next-Generation Sequencing.Toxicological sciences : an official journal of the Society of Toxicology · 2021Article
- Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles.Molecular systems biology · 2019Article
- Iron overload by Superparamagnetic Iron Oxide Nanoparticles is a High Risk Factor in Cirrhosis by a Systems Toxicology Assessment.Scientific reports · 2016Article
- Successful drug development despite adverse preclinical findings part 2: examples.Journal of toxicologic pathology · 2010Article
- Practical application of toxicogenomics for profiling toxicant-induced biological perturbations.International journal of molecular sciences · 2010Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.
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.