ArticleToxicological sciences : an official journal of the Society of Toxicology2020
Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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.
Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.bioRxiv : the preprint server for biology · 2026Article
- Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms.Environment & health (Washington, D.C.) · 2024Article
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways.Environmental science & technology · 2023Article
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning.Molecules (Basel, Switzerland) · 2022Article
- Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.Environmental science & technology · 2022Article
- In Silico Models for Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2022Review
- High-Throughput Screening Assay Profiling for Large Chemical Databases.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- Revealing Adverse Outcome Pathways from Public High-Throughput Screening Data to Evaluate New Toxicants by a Knowledge-Based Deep Neural Network Approach.Environmental science & technology · 2021Article
- Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.ACS sustainable chemistry & engineering · 2021Article
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.Drug discovery today · 2020Review
- Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon.Frontiers in toxicology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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.
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What OpenQuestion holds
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.