ArticleComputational toxicology (Amsterdam, Netherlands)2020
Using Chemical Structure Information to Develop Predictive Models for
Article in Computational toxicology (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Applicability of next-generation risk assessment generic PBK models and their performance within the chemical domain.Archives of toxicology · 2026Article
- Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables.PloS one · 2026Article
- Machine learning to predict food effects during drug development: a comprehensive review.Journal of cheminformatics · 2025Review
- Interpretation of in vitro concentration-response data for risk assessment and regulatory decision-making: Report from the 2022 IWGT quantitative analysis expert working group meeting.Environmental and molecular mutagenesis · 2025Review
- Prediction of Standard Combustion Enthalpy of Organic Compounds Combining Machine Learning and Chemical Graph Theory: A Strategy.ACS omega · 2025Article
- 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
- Article
- In vitro to in vivo extrapolation from 3D hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrhythmia risk assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- A problem formulation framework for the application of in silico toxicology methods in chemical risk assessment.Archives of toxicology · 2024Article
- The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment.International journal of environmental research and public health · 2023Review
- Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- Article
- Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment.Archives of toxicology · 2022Article
- The chemical landscape of high-throughput new approach methodologies for exposure.Journal of exposure science & environmental epidemiology · 2022Review
- Evaluation of a rapid, generic human gestational dose model.Reproductive toxicology (Elmsford, N.Y.) · 2022Article
- Advances in computational methods along the exposure to toxicological response paradigm.Toxicology and applied pharmacology · 2022Article
- Machine Learning and Artificial Intelligence in Toxicological Sciences.Toxicological sciences : an official journal of the Society of Toxicology · 2022Review
- Comprehensive interpretation of in vitro micronucleus test results for 292 chemicals: from hazard identification to risk assessment application.Archives of toxicology · 2022Article
- IVIVE: Facilitating the Use ofToxics · 2022Review
- Considerations for Improving Metabolism Predictions forFrontiers in toxicology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The toxicokinetic (TK) parameters fraction of the chemical unbound to plasma proteins and metabolic clearance are critical for relating exposure and internal dose when building in vitro-based risk assessment models. However, experimental toxicokinetic studies have only been carried out on limited chemicals of environmental interest (~1000 chemicals with TK data relative to tens of thousands of chemicals of interest). This work evaluated the utility of chemical structure information to predict TK parameters in silico; development of cluster-based read-across and quantitative structure-activity relationship models of fraction unbound or fub (regression) and intrinsic clearance or Cl
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.