ArticleFrontiers in pharmacology2022
Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Symptomatic Rescue or Disease Modification? A Critical Review of Levodopa and Phytochemical Interventions in Caenorhabditis elegans Models of Parkinson's Disease.Molecular neurobiology · 2026Review
- Article
- A tiered next-generation risk assessment framework integrating toxicokinetics and NAM-based toxicodynamics: "proof of concept" case study using pyrethroids.Archives of toxicology · 2025Article
- Systematic Approaches for the Encoding of Chemical Groups: A Case Study.Chemical research in toxicology · 2024Article
- Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.Regulatory toxicology and pharmacology : RTP · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and exposure data, called margins of exposure (MoEs). Exposures were estimated using a U.S. EPA's ExpoCast predictive model (SEEM3) results and estimates of bioactivity were predicted using: 1) Oral equivalent doses (OEDs) derived from U.S. EPA's ToxCast high-throughput screening program, together with
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Registered trials
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