Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.
big data
hepatotoxicity
mechanism-driven
read-across
risk assessment
Journal
Toxicological sciences : an official journal of the Society of Toxicology
ISSN: 1096-0929
Titre abrégé: Toxicol Sci
Pays: United States
ID NLM: 9805461
Informations de publication
Date de publication:
01 04 2020
01 04 2020
Historique:
pubmed:
20
2
2020
medline:
2
6
2021
entrez:
20
2
2020
Statut:
ppublish
Résumé
Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.
Identifiants
pubmed: 32073637
pii: 5740592
doi: 10.1093/toxsci/kfaa005
pmc: PMC7098374
doi:
Substances chimiques
Xenobiotics
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
178-188Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES005022
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES031080
Pays : United States
Organisme : NIEHS NIH HHS
ID : R15 ES023148
Pays : United States
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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