Evidence map›Paper›PMID 29385628›Full record

ArticleToxicological sciences : an official journal of the Society of Toxicology2018

Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics.

John F Wambaugh, Michael F Hughes, Caroline L Ring, Denise K MacMillan, Jermaine Ford, Timothy R Fennell, Sherry R Black, Rodney W Snyder, Nisha S Sipes, Barbara A Wetmore and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in Toxicological sciences : an official journal of the Society of Toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
9.4field-weighted citation impact, top 1% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed, 142 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Informatics for toxicokinetics.Journal of pharmacokinetics and pharmacodynamics · 2025
    Article
  6. Article
  7. Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.Toxicological sciences : an official journal of the Society of Toxicology · 2023
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 2 countries.

John F WambaughNational Center for Computational Toxicology.
Michael F HughesNational Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
Caroline L RingNational Center for Computational Toxicology.
Denise K MacMillanNational Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
Jermaine FordNational Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
Timothy R FennellRTI International, Research Triangle Park, North Carolina.
Sherry R BlackRTI International, Research Triangle Park, North Carolina.
Rodney W SnyderRTI International, Research Triangle Park, North Carolina.
Nisha S SipesNational Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27717.
Barbara A WetmoreNational Exposure Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
Joost WesterhoutThe Netherlands Organisation for Applied Scientific Research (TNO), AJ Zeist 3700, The Netherlands.
R Woodrow SetzerNational Center for Computational Toxicology.
Robert G PearceNational Center for Computational Toxicology.
Jane Ellen SimmonsNational Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
Russell S ThomasNational Center for Computational Toxicology.
Environmental Protection Agency · USRTI International · USOak Ridge Associated Universities · USNational Institute of Environmental Health Sciences · USNetherlands Organisation for Applied Scientific Research · NL

Funding

Biomolecular Screening and Alternative Approaches for the National Toxicology ProgramZIAES103318 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI CASEY, WARREN · 2016 to 2021
$25.0M
6 · The paper itself

Abstract

Prioritizing the risk posed by thousands of chemicals potentially present in the environment requires exposure, toxicity, and toxicokinetic (TK) data, which are often unavailable. Relatively high throughput, in vitro TK (HTTK) assays and in vitro-to-in vivo extrapolation (IVIVE) methods have been developed to predict TK, but most of the in vivo TK data available to benchmark these methods are from pharmaceuticals. Here we report on new, in vivo rat TK experiments for 26 non-pharmaceutical chemicals with environmental relevance. Both intravenous and oral dosing were used to calculate bioavailability. These chemicals, and an additional 19 chemicals (including some pharmaceuticals) from previously published in vivo rat studies, were systematically analyzed to estimate in vivo TK parameters (e.g., volume of distribution [Vd], elimination rate). For each of the chemicals, rat-specific HTTK data were available and key TK predictions were examined: oral bioavailability, clearance, Vd, and uncertainty. For the non-pharmaceutical chemicals, predictions for bioavailability were not effective. While no pharmaceutical was absorbed at less than 10%, the fraction bioavailable for non-pharmaceutical chemicals was as low as 0.3%. Total clearance was generally more under-estimated for nonpharmaceuticals and Vd methods calibrated to pharmaceuticals may not be appropriate for other chemicals. However, the steady-state, peak, and time-integrated plasma concentrations of nonpharmaceuticals were predicted with reasonable accuracy. The plasma concentration predictions improved when experimental measurements of bioavailability were incorporated. In summary, HTTK and IVIVE methods are adequately robust to be applied to high throughput in vitro toxicity screening data of environmentally relevant chemicals for prioritizing based on human health risks.

Indexed as

Models, BiologicalToxicokineticsAnimalsArea Under CurveBiological AvailabilityComputer SimulationEnvironmental PollutantsHigh-Throughput Screening AssaysHumansMaleMetabolic Clearance RatePredictive Value of TestsRats, Sprague-DawleyRisk AssessmentStructure-Activity RelationshipTissue DistributionEnvironmental Pollutants

Identifiers

PMID29385628
PMCPMC5920326
OpenAlexW2791653123

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.