ReviewEnvironmental toxicology and chemistry2019
Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment.
Review in Environmental toxicology and chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Organizing mechanism-related information on chemical interactions using a framework based on the aggregate exposure and adverse outcome pathways.Environment international · 2020Pooled it
- Transcriptomic signature comparisons identify conserved key events and respiratory disease signatures most similar to wildfire-relevant exposure.Chemico-biological interactions · 2026Article
- Towards Bayesian-based quantitative adverse outcome pathways using in vitro data from open literature and continuous variables: a case example for liver fibrosis.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Transcriptomic profiling combined with pharmacological research of Panax notoginseng-derived nanovesicles for preliminary biocompatibility profiling and STAT5/SOCS3-driven immunomodulation in psoriasis.Journal of translational medicine · 2026Article
- Multimodal Cross-Attentive Graph-Based Framework for Predicting In Vivo Endocrine Disruptors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mechanism-based new approach methodologies for in vitro detection of chemical-induced liver steatosis.Archives of toxicology · 2026Review
- Machine Learning Models for High-Throughput Screening of Obesogens Based on Pathway Network.Environment & health (Washington, D.C.) · 2026Article
- Mapping DEHP-induced metabolic dysregulation through multi-omics and systems biology: an adverse outcome pathway network approach.Frontiers in toxicology · 2026Article
- Cardiotoxicity adverse outcome pathway network: towards mechanistic and quantitative modelling.Frontiers in toxicology · 2026Article
- Extrapolation steps in quantitativeCurrent research in toxicology · 2026Review
- An AOP-guidedNAM journal · 2026Article
- Computational characterisation of ecological hazard and risk of environmental mixtures.EFSA journal. European Food Safety Authority · 2025Article
- Article
- Enhancing ecological risk assessment of dioxins in aquatic environments: AHR diversity and species sensitivity differences in tiger puffer (Takifugu rubripes).Journal of hazardous materials · 2025Article
- Highlighting best practices to advance next-generation risk assessment of cosmetic ingredients.NAM journal · 2025Article
- Adverse Outcome Pathways Mechanistically Describing Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Continuous improvement towards environmental protection for pharmaceuticals: advancing a strategy for Europe.Environmental sciences Europe · 2025Review
- Integrating Time-ResolvedEnvironmental science & technology · 2024Article
- Interdependency of estradiol-mediated ERα activation and subsequent PR and GREB1 induction to control cell cycle progression.Heliyon · 2024Article
- Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.International journal of molecular sciences · 2024Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
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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.