Evidence map›Paper›PMID 39419704›Full record

ArticleReproductive toxicology (Elmsford, N.Y.)2025

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

Julia R Varshavsky, Juleen Lam, Courtney Cooper, Patrick Allard, Jennifer Fung, Ashwini Oke, Ravinder Kumar, Joshua F Robinson, Tracey J Woodruff

Abstract read
PubMed Publisher
In one paragraph

Article in Reproductive toxicology (Elmsford, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Julia R VarshavskyDepartment of Public Health and Health Sciences and Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA. Electronic address: j.varshavsky@northeastern.edu.
Juleen LamDepartment of Public Health, California State University, East Bay, Hayward, CA, USA.
Courtney CooperUniversity of California, San Francisco (UCSF), Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, CA, USA.
Patrick AllardInstitute for Society and Genetics, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
Jennifer FungCenter for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
Ashwini OkeCenter for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
Ravinder KumarCenter for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
Joshua F RobinsonUniversity of California, San Francisco (UCSF), Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, CA, USA; Center for Reproductive Sciences and Department of Obstetrics, Gynecology & Reproductive Sciences, UCSF, San Francisco, CA, USA.
Tracey J WoodruffUniversity of California, San Francisco (UCSF), Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, San Francisco, CA, USA. Electronic address: Tracey.Woodruff@ucsf.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95 % CI: 0.28-1.00, p<0.001) and r

Indexed as

Caenorhabditis elegansEnvironmental PollutantsHigh-Throughput Screening AssaysReproductionSaccharomyces cerevisiaeToxicity TestsAnimalsDatabases, FactualHumansEnvironmental PollutantsAneuploidyBenchmark dose modelingBenchmark responseChemical assessmentDevelopmental toxicologyGerm cell developmentMeiosisNew approach methodologies (NAMs)

Identifiers

PMID39419704

What OpenQuestion holds

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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.