Evidence map›Paper›PMID 35533074›Full record

ReviewEnvironmental health perspectives2022

On the Utility of ToxCast-Based Predictive Models to Evaluate Potential Metabolic Disruption by Environmental Chemicals.

Dayne L Filer, Kate Hoffman, Robert M Sargis, Leonardo Trasande, Christopher D Kassotis

Abstract readReview
In one paragraph

Review in Environmental health perspectives, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
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.

Dayne L FilerDepartment of Genetics, School of Medicine, and Renaissance Computing Institute, University of North Carolina, Chapel Hill, North Carolina, USA.
Kate HoffmanNicholas School of the Environment, Duke University, Durham, North Carolina, USA.
Robert M SargisDepartment of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID 0000-0001-7061-2983
Leonardo TrasandeDepartment of Pediatrics, New York University (NYU) School of Medicine, New York, New York, USA.ORCID 0000-0002-1928-597X
Christopher D KassotisInstitute of Environmental Health Sciences and Department of Pharmacology, School of Medicine, Wayne State University, Detroit, Michigan, USA.ORCID 0000-0002-0990-2428

Funding

Pilot and Feasibility ProgramP30DK020595 · NIDDK · UNIVERSITY OF CHICAGO · PI Matthew J Brady · 2013 to 2026
$20.9M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM008719 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DARVILLE, TONI, DESHMUKH, MOHANISH P · 1999 to 2023
$15.1M
Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6M
UNC Predoc Training Progr in Bioinformatics/Comp BiologyT32GM067553 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ELSTON, TIMOTHY C · 2005 to 2019
$2.5M
Mechanisms of Environmental-Mixture Induced Metabolic DisruptionR00ES030405 · NIEHS · WAYNE STATE UNIVERSITY · PI KASSOTIS, CHRISTOPHER DENNIS · 2020 to 2022
$744k
Expanding the utility of prenatal genetic testingF30HD101228 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FILER, DAYNE LEWIS · 2019 to 2022
$121k
Interrogation of molecular mechanisms involved in driving adipogenesis in environmental mixtures and novel analytical techniques for identifying putative causative chemicalsF32ES027320 · NIEHS · DUKE UNIVERSITY · PI KASSOTIS, CHRISTOPHER DENNIS · 2017 to 2018
$117k
NICHD NIH HHS F30 HD101228NIDDK NIH HHS P30 DK020595NIEHS NIH HHS F32 ES027320NIEHS NIH HHS P30 ES027792NIEHS NIH HHS R00 ES030405NIGMS NIH HHS T32 GM008719NIGMS NIH HHS T32 GM067553
6 · The paper itself

Abstract

backgroundResearch suggests environmental contaminants can impact metabolic health; however, high costs prohibit

objectivesWe sought to

methodsUsing a set of 38 chemicals with previously published results in 3T3-L1 cells, we performed a metabolism-targeted literature review to determine consensus activity determinations. To assess ToxCast predictive utility, we used two published ToxPi models:

resultsThe literature review revealed relevant publications for 30 out of the 38 chemicals (the remaining 8 chemicals were only examined in our previous 3T3-L1 testing). We observed a balanced accuracy (average of sensitivity and specificity) of 0.86 comparing our previous DISCUSSION: Using the most recent ToxCast data and an updated ToxPi model, we found ToxCast performed similarly to that of our own 3T3-L1 testing in predicting consensus calls. Furthermore, we provide the full ranked list of largely untested chemicals with ToxPi scores that predict adipogenic activity and that require further investigation. https://doi.org/10.1289/EHP6779.

Indexed as

AdipogenesisHigh-Throughput Screening Assays3T3-L1 CellsAnimalsIn Vitro TechniquesMice

Identifiers

PMID35533074
PMCPMC9084331

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.