ArticleEnvironmental health perspectives2018
Conditional Toxicity Value (CTV) Predictor: An
Article in Environmental health perspectives, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Human vs. cells vs. machine: A comparative analysis of toxicological points of departure derived from quantitative read-across (qRAx), in vitro data, and In silico predictions.Regulatory toxicology and pharmacology : RTP · 2026Article
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- Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025Review
- Advancing quantitative hazard banding using expanded probabilistic reference doses and high-throughput screening data for preliminary hazard assessment.NAM journal · 2025Article
- Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.Environmental science & technology · 2024Article
- Probabilistic Reference and 10% Effect Concentrations for Characterizing Inhalation Non-cancer and Developmental/Reproductive Effects for 2,160 Substances.Environmental science & technology · 2024Article
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.Regulatory toxicology and pharmacology : RTP · 2024Article
- Article
- Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment.Environment international · 2023Article
- A value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals.Environmental health perspectives · 2023Article
- Leveraging Multiple Data Streams for Prioritization of Mixtures for Hazard Characterization.Toxics · 2022Article
- A Framework that Considers the Impacts of Time, Cost, and Uncertainty in the Determination of the Cost Effectiveness of Toxicity-Testing Methodologies.Risk analysis : an official publication of the Society for Risk Analysis · 2022Article
- Implementing in vitro bioactivity data to modernize priority setting of chemical inventories.ALTEX · 2022Article
- Estimating mouthing exposure to chemicals in children's products.Journal of exposure science & environmental epidemiology · 2022Article
- Development and Evaluation of a Holistic and Mechanistic Modeling Framework for Chemical Emissions, Fate, Exposure, and Risk.Environmental health perspectives · 2021Article
- Cardiotoxicity Hazard and Risk Characterization of ToxCast Chemicals Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes from Multiple Donors.Chemical research in toxicology · 2021Article
- Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium.Chemical research in toxicology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundHuman health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data.
objectivesAs recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models.
methodsWe used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays.
resultsQSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q
conclusionsAn
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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.