Evidence map›Paper›PMID 26917256›Full record

ArticleScientific reports2016

Internal exposure dynamics drive the Adverse Outcome Pathways of synthetic glucocorticoids in fish.

Luigi Margiotta-Casaluci, Stewart F Owen, Belinda Huerta, Sara Rodríguez-Mozaz, Subramanian Kugathas, Damià Barceló, Mariann Rand-Weaver, John P Sumpter

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
5.0field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 73 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. A critical review on environmental presence of pharmaceutical drugs tested for the covid-19 treatment.Process safety and environmental protection : transactions of the Institution of Chemical Engineers, Part B · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. A review of the pharmaceutical exposome in aquatic fauna.Environmental pollution (Barking, Essex : 1987) · 2018
    Review
  17. Adverse outcome pathway networks II: Network analytics.Environmental toxicology and chemistry · 2018
    Article
  18. Adverse outcome pathway networks I: Development and applications.Environmental toxicology and chemistry · 2018
    Article
  19. Article
  20. The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2017
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Luigi Margiotta-CasaluciBrunel University London, Institute of Environment, Health and Societies, London, UB8 3PH, United Kingdom.
Stewart F OwenAstraZeneca, Global Environment, Alderley Park, Macclesfield, SK10 4TF, United Kingdom.
Belinda HuertaCatalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, Girona, 17003, Spain.
Sara Rodríguez-MozazCatalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, Girona, 17003, Spain.
Subramanian KugathasBrunel University London, Institute of Environment, Health and Societies, London, UB8 3PH, United Kingdom.
Damià BarcelóCatalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, Girona, 17003, Spain.
Mariann Rand-WeaverBrunel University London, College of Health and Life Sciences, London, UB8 3PH, United Kingdom.
John P SumpterBrunel University London, Institute of Environment, Health and Societies, London, UB8 3PH, United Kingdom.
Brunel University of London · GBAstraZeneca (United Kingdom) · GBCatalan Institute for Water Research · ESInstitute of Environmental Assessment and Water Research · ESUniversitat de Girona · ES

Funding

BB BB/100646X/1
6 · The paper itself

Abstract

The Adverse Outcome Pathway (AOP) framework represents a valuable conceptual tool to systematically integrate existing toxicological knowledge from a mechanistic perspective to facilitate predictions of chemical-induced effects across species. However, its application for decision-making requires the transition from qualitative to quantitative AOP (qAOP). Here we used a fish model and the synthetic glucocorticoid beclomethasone dipropionate (BDP) to investigate the role of chemical-specific properties, pharmacokinetics, and internal exposure dynamics in the development of qAOPs. We generated a qAOP network based on drug plasma concentrations and focused on immunodepression, skin androgenisation, disruption of gluconeogenesis and reproductive performance. We showed that internal exposure dynamics and chemical-specific properties influence the development of qAOPs and their predictive power. Comparing the effects of two different glucocorticoids, we highlight how relatively similar in vitro hazard-based indicators can lead to different in vivo risk. This discrepancy can be predicted by their different uptake potential, pharmacokinetic (PK) and pharmacodynamic (PD) profiles. We recommend that the development phase of qAOPs should include the application of species-specific uptake and physiologically-based PK/PD models. This integration will significantly enhance the predictive power, enabling a more accurate assessment of the risk and the reliable transferability of qAOPs across chemicals.

Indexed as

Models, BiologicalAnimalsBeclomethasoneFishesGlucocorticoidsGluconeogenesisMaleModels, AnimalReproductionBeclomethasoneGlucocorticoids

Identifiers

PMID26917256
PMCPMC4768075
OpenAlexW2291783248

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.