ArticleRegulatory toxicology and pharmacology : RTP2019
Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers.
Article in Regulatory toxicology and pharmacology : RTP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
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- Multi-omics bioactivity profile-based chemical grouping and read-across: a case study with Daphnia magna and azo dyes.Archives of toxicology · 2024Article
- Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol.Environment international · 2023Article
- Substantiate a read-across hypothesis by using transcriptome data-A case study on volatile diketones.Frontiers in toxicology · 2023Article
- Analysis of 275 DIY recipes for eye cosmetics and their possible safety issues.International journal of cosmetic science · 2022Article
- Model systems and organisms for addressing inter- and intra-species variability in risk assessment.Regulatory toxicology and pharmacology : RTP · 2022Review
- Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays.ALTEX · 2022Article
- Potential Human Health Hazard of Post-Hurricane Harvey Sediments in Galveston Bay and Houston Ship Channel: A Case Study of UsingInternational journal of environmental research and public health · 2021Article
- Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case study.Computational toxicology (Amsterdam, Netherlands) · 2021Article
- High-Throughput Transcriptomics Platform for Screening Environmental Chemicals.Toxicological sciences : an official journal of the Society of Toxicology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
High-content screening data derived from physiologically-relevant in vitro models promise to improve confidence in data-integrative groupings for read-across in human health safety assessments. The biological data-based read-across concept is especially applicable to bioactive chemicals with defined mechanisms of toxicity; however, the challenge of data-derived groupings for chemicals that are associated with little or no bioactivity has not been explored. In this study, we apply a suite of organotypic and population-based in vitro models for comprehensive bioactivity profiling of twenty E-Series and P-Series glycol ethers, solvents with a broad variation in toxicity ranging from relatively non-toxic to reproductive and hematopoetic system toxicants. Both E-Series and P-Series glycol ethers elicited cytotoxicity only at high concentrations (mM range) in induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. Population-variability assessment comprised a study of cytotoxicity in 94 human lymphoblast cell lines from 9 populations and revealed differences in inter-individual variability across glycol ethers, but did not indicate population-specific effects. Data derived from various phenotypic and transcriptomic assays revealed consistent bioactivity trends between both cardiomyocytes and hepatocytes, indicating a more universal, rather than cell-type specific mode-of-action for the tested glycol ethers in vitro. In vitro bioactivity-based similarity assessment using Toxicological Priority Index (ToxPi) showed that glycol ethers group according to their alcohol chain length, longer chains were associated with increased bioactivity. While overall in vitro bioactivity profiles did not correlate with in vivo toxicity data on glycol ethers, in vitro bioactivity of E-series glycol ethers were indicative of and correlated with in vivo irritation scores.
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Registered trials
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.