ArticleJournal of hazardous materials2022
Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.
Article in Journal of hazardous materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Prediction of Ocular Toxicity of Prostaglandin FMolecules (Basel, Switzerland) · 2026Article
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.bioRxiv : the preprint server for biology · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view.Nature communications · 2025Article
- A multi-dimensional computational framework of drug-induced hepatotoxicity: integrating molecular structure features with disease pathogenesis.Briefings in bioinformatics · 2025Article
- Nanoparticles from grape seed extract inhibit inflammatory cytokines and ameliorate CClBMC complementary medicine and therapies · 2025Article
- Drug metabolizing enzymes and transporters, and their roles for the development of drug-induced liver injury.Expert opinion on drug metabolism & toxicology · 2025Review
- An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.Nano letters · 2024Article
- 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
- Roadmap to DILI research in Europe. A proposal from COST action ProEuroDILINet.Pharmacological research · 2024Article
- In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Review
- 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
- Integrating structure annotation and machine learning approaches to develop graphene toxicity models.Carbon · 2023Article
- Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.Toxicology and applied pharmacology · 2022Article
- Modeling and insights into the structural characteristics of drug-induced autoimmune diseases.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.
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Registered trials
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