Bioactivity Signatures of Drugs vs. Environmental Chemicals Revealed by Tox21 High-Throughput Screening Assays.
Tox21
drug
environmental chemical
in vitro assay
quantitative high throughput screening
Journal
Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603
Informations de publication
Date de publication:
2019
2019
Historique:
received:
29
05
2019
accepted:
29
11
2019
entrez:
11
3
2021
pubmed:
13
12
2019
medline:
13
12
2019
Statut:
epublish
Résumé
Humans are exposed to tens of thousands of chemicals over the course of a lifetime, yet there remains inadequate data on the potential harmful effects of these substances on human health. Using quantitative high-throughput screening (qHTS), we can test these compounds for potential toxicity in a more efficient and cost-effective way compared to traditional animal studies. Tox21 has developed a library of ~10,000 chemicals (Tox21 10K) comprising one-third approved and investigational drugs and two-thirds environmental chemicals. In this study, the Tox21 10K was screened in a qHTS format against a panel of 70 cell-based assays with 213 readouts covering a broad range of biological pathways. Activity profiles were compared with chemical structure to assess their ability to differentiate drugs from environmental chemicals, and structure was found to be a better predictor of which chemicals are likely to be drugs. Drugs and environmental chemicals were further analyzed for diversity in structure and biological response space and showed distinguishable, but not distinct, responses in the Tox21 assays. Inclusion of other targets and pathways to further diversify the biological response space covered by these assays is likely required to better evaluate the safety profile of drugs and environmental chemicals to prioritize for in-depth toxicological studies.
Identifiants
pubmed: 33693373
doi: 10.3389/fdata.2019.00050
pmc: PMC7931954
doi:
Types de publication
Journal Article
Langues
eng
Pagination
50Informations de copyright
Copyright © 2019 Ngan, Ye, Wu, Xia, Rossoshek, Simeonov and Huang.
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