ReviewThe AAPS journal2012
Paradigm shift in toxicity testing and modeling.
Review in The AAPS journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Machine learning driven LDFrontiers in oncology · 2026Article
- Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis.Environmental science & technology · 2025Article
- MoltiTox: a multimodal fusion model for molecular toxicity prediction.Frontiers in toxicology · 2025Article
- Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment.Scientific data · 2024Article
- Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells.Journal of chemical information and modeling · 2023Article
- Expanding adverse outcome pathways towards one health models for nanosafety.Frontiers in toxicology · 2023Article
- Inter-Individual Variations: A Challenge for the Standardisation of Complement Activation Assays.International journal of nanomedicine · 2023Review
- Identifying Protein Features and Pathways Responsible for Toxicity Using Machine Learning and Tox21: Implications for Predictive Toxicology.Molecules (Basel, Switzerland) · 2022Article
- Comparison of Acute Effects of Neurotoxic Compounds on Network Activity in Human and Rodent Neural Cultures.Toxicological sciences : an official journal of the Society of Toxicology · 2021Article
- 3-D Human Renal Tubular Organoids Generated from Urine-Derived Stem Cells for Nephrotoxicity Screening.ACS biomaterials science & engineering · 2020Article
- Comparing Machine Learning Models for Aromatase (P450 19A1).Environmental science & technology · 2020Article
- Comparison of Machine Learning Models for the Androgen Receptor.Environmental science & technology · 2020Article
- Using Chemical Structure Information to Develop Predictive Models forComputational toxicology (Amsterdam, Netherlands) · 2020Article
- Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.Environmental science & technology · 2020Article
- Article
- Long QT syndrome in chromosome 7q35q36.3 deletion involving KCNH2 gene: Warning for chlorpheniramine prescription.Molecular genetics & genomic medicine · 2019Article
- Kinetics of Glutathione Depletion and Antioxidant Gene Expression as Indicators of Chemical Modes of Action Assessed in Vitro in Mouse Hepatocytes with Enhanced Glutathione Synthesis.Chemical research in toxicology · 2019Article
- Mussel-inspired 3D fiber scaffolds for heart-on-a-chip toxicity studies of engineered nanomaterials.Analytical and bioanalytical chemistry · 2018Article
- Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.Journal of proteome research · 2018Article
- Assembly of Hepatocyte Spheroids Using Magnetic 3D Cell Culture for CYP450 Inhibition/Induction.International journal of molecular sciences · 2017Article
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.
Funding
Abstract
The limitations of traditional toxicity testing characterized by high-cost animal models with low-throughput readouts, inconsistent responses, ethical issues, and extrapolability to humans call for alternative strategies for chemical risk assessment. A new strategy using in vitro human cell-based assays has been designed to identify key toxicity pathways and molecular mechanisms leading to the prediction of an in vivo response. The emergence of quantitative high-throughput screening (qHTS) technology has proved to be an efficient way to decompose complex toxicological end points to specific pathways of targeted organs. In addition, qHTS has made a significant impact on computational toxicology in two aspects. First, the ease of mechanism of action identification brought about by in vitro assays has enhanced the simplicity and effectiveness of machine learning, and second, the high-throughput nature and high reproducibility of qHTS have greatly improved the data quality and increased the quantity of training datasets available for predictive model construction. In this review, the benefits of qHTS routinely used in the US Tox21 program will be highlighted. Quantitative structure-activity relationships models built on traditional in vivo data and new qHTS data will be compared and analyzed. In conjunction with the transition from the pilot phase to the production phase of the Tox21 program, more qHTS data will be made available that will enrich the data pool for predictive toxicology. It is perceivable that new in silico toxicity models based on high-quality qHTS data will achieve unprecedented reliability and robustness, thus becoming a valuable tool for risk assessment and drug discovery.
Indexed as
Identifiers
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.