Evidence map›Paper›PMID 32521033›Full record

ArticleALTEX2020

Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs

Zunwei Chen, Yizhong Liu, Fred A Wright, Weihsueh A Chiu, Ivan Rusyn

Open access · goldAbstract read
In one paragraph

Article in ALTEX, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
5.2field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Article
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  4. Advancing systemic toxicity risk assessment: Evaluation of a NAM-based toolbox approach.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  5. 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
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Computational toxicology (Amsterdam, Netherlands) · 2021
    Article
  19. Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes.Toxicological sciences : an official journal of the Society of Toxicology · 2021
    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

5 authors at 1 institution in 1 country.

Zunwei ChenInterdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Yizhong LiuInterdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Fred A WrightBioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
Weihsueh A ChiuInterdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Ivan RusynInterdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Texas A&M University · US

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
Texas A&M Center for Environmental Health Research (TiCER)P30ES029067 · NIEHS · TEXAS A&M UNIVERSITY · PI Sakhila Banu · 2019 to 2026
$13.0M
NIEHS NIH HHS P30 ES029067NIEHS NIH HHS P42 ES027704
6 · The paper itself

Abstract

The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.

Identifiers

PMID32521033
PMCPMC7941183
OpenAlexW3033388808

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.