ArticleArchives of toxicology2018
Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.
Article in Archives of toxicology, 2018. 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.
- Advances in the application of computational toxicology in cosmetic safety assessment.Archives of toxicology · 2026Review
- ProfhEX: AI-based platform for small molecules liability profiling.Journal of cheminformatics · 2023Article
- The effect of noise on the predictive limit of QSAR models.Journal of cheminformatics · 2021Article
- Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure.Computational toxicology (Amsterdam, Netherlands) · 2020Article
- Variability inComputational toxicology (Amsterdam, Netherlands) · 2020Article
- Ensemble QSAR Modeling to Predict Multispecies Fish Toxicity Lethal Concentrations and Points of Departure.Environmental science & technology · 2019Article
- ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses.Reproductive toxicology (Elmsford, N.Y.) · 2019Article
- The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.Toxicological sciences : an official journal of the Society of Toxicology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log
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.