Evidence map›Paper›PMID 22268004›Full record

ArticleToxicological sciences : an official journal of the Society of Toxicology2012

Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model.

Eric F Lock, Nour Abdo, Ruili Huang, Menghang Xia, Oksana Kosyk, Shannon H O'Shea, Yi-Hui Zhou, Alexander Sedykh, Alexander Tropsha, Christopher P Austin and 3 more

Abstract read
In one paragraph

Article in Toxicological sciences : an official journal of the Society of Toxicology, 2012. 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
–field-weighted citation impact
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.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives.Toxicological sciences : an official journal of the Society of Toxicology · 2018
    Review
  7. Article
  8. Bridging the Data Gap FromFrontiers in public health · 2018
    Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. A genetic model of differential susceptibility to human respiratory syncytial virus (RSV) infection.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2014
    Article
  18. FutureTox: building the road for 21st century toxicology and risk assessment practices.Toxicological sciences : an official journal of the Society of Toxicology · 2014
    Article
  19. 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

13 authors.

Eric F LockUniversity of North Carolina, Chapel Hill, North Carolina 27599, USA.
Nour Abdo
Ruili Huang
Menghang Xia
Oksana Kosyk
Shannon H O'Shea
Yi-Hui Zhou
Alexander Sedykh
Alexander Tropsha
Christopher P Austin
Raymond R Tice
Fred A Wright
Ivan Rusyn

Funding

NIH Chemical Genomics CenterZIBTR000004 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI MCKEW, JOHN · 2012 to 2014
$52.3M
STOCHASTIC ANALYSIS OF FLOW AND TRANSPORT PHENOMENAP42ES005948 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRY, REBECCA · 1992 to 2017
$45.4M
UNC-CH CENTER FOR ENVIRONMENTAL HEALTH &SUSCEPTIBILITYP30ES010126 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Melissa A. Troester · 2001 to 2026
$36.3M
TRAINING GRANTS IN ENVIROMENTAL TOXICOLOGYT32ES007020 · NIEHS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI ESSIGMANN, JOHN M · 1985 to 2024
$13.1M
Bioengineering partnership to improve chemical hazard testing paradigmsR01ES015241 · NIEHS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RUSYN, IVAN · 2008 to 2012
$3.1M
NIEHS NIH HHS P30 ES010126NIEHS NIH HHS P42 ES005948NIEHS NIH HHS R01 ES015241NIEHS NIH HHS Y2-ES-7020-01
6 · The paper itself

Abstract

A shift in toxicity testing from in vivo to in vitro may efficiently prioritize compounds, reveal new mechanisms, and enable predictive modeling. Quantitative high-throughput screening (qHTS) is a major source of data for computational toxicology, and our goal in this study was to aid in the development of predictive in vitro models of chemical-induced toxicity, anchored on interindividual genetic variability. Eighty-one human lymphoblast cell lines from 27 Centre d'Etude du Polymorphisme Humain trios were exposed to 240 chemical substances (12 concentrations, 0.26nM-46.0μM) and evaluated for cytotoxicity and apoptosis. qHTS screening in the genetically defined population produced robust and reproducible results, which allowed for cross-compound, cross-assay, and cross-individual comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited interindividual differences in cytotoxicity. Specifically, the qHTS in a population-based human in vitro model system has several unique aspects that are of utility for toxicity testing, chemical prioritization, and high-throughput risk assessment. First, standardized and high-quality concentration-response profiling, with reproducibility confirmed by comparison with previous experiments, enables prioritization of chemicals for variability in interindividual range in cytotoxicity. Second, genome-wide association analysis of cytotoxicity phenotypes allows exploration of the potential genetic determinants of interindividual variability in toxicity. Furthermore, highly significant associations identified through the analysis of population-level correlations between basal gene expression variability and chemical-induced toxicity suggest plausible mode of action hypotheses for follow-up analyses. We conclude that as the improved resolution of genetic profiling can now be matched with high-quality in vitro screening data, the evaluation of the toxicity pathways and the effects of genetic diversity are now feasible through the use of human lymphoblast cell lines.

Indexed as

Models, TheoreticalToxicity TestsApoptosisCell LineHumansIn Vitro TechniquesReproducibility of Results

Identifiers

PMID22268004
PMCPMC3307611

What OpenQuestion holds

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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.