ArticleArtificial intelligence in the life sciences2021
Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.
Article in Artificial intelligence in the life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the potential of computer simulation models in drug testing and biomedical research: a systematic review.Frontiers in pharmacology · 2025Pooled it
- Molecular Modeling of Surfactant Interaction on Phospholipid Bilayers Mimicking Corneal Epithelium.Journal of chemical theory and computation · 2026Article
- Qualitative and quantitative in silico toxicity profiling of "angel dust": phencyclidine (PCP) analogues as new psychoactive substances (3-HO-PCP, 3-MeO-PCP, 4-MeO-PCP, 3-HO-PCE, 3-MeO-PCE, 4-MeO-PCE).Archives of toxicology · 2026Article
- MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity.Artificial intelligence in the life sciences · 2025Article
- Comprehensive reexamination of the acute toxicity of nitrogen mustards: HN-1, HN-2 and HN-3 as blister agents: application of multi in silico approach.Archives of toxicology · 2025Article
- Application of in silico methods to predict the acute toxicity of bicyclic organophosphorus compounds as potential chemical weapon.Archives of toxicology · 2025Article
- The estimation of acute oral toxicity (LDArchives of toxicology · 2024Article
- Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes.International journal of molecular sciences · 2024Article
- The acute toxicity of Novichok's degradation products using quantitative and qualitative toxicology in silico methods.Archives of toxicology · 2024Article
- Review
- Article
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- Machine-learning based prediction models for assessing skin irritation and corrosion potential of liquid chemicals using physicochemical properties by XGBoost.Toxicological research · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68-0.88), sensitivity (SE of 0.61-0.84), positive predictive value (PPV of 0.65-0.90), specificity (SP of 0.56-0.91), and negative predictive value (NPV of 0.68-0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds' irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds.
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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.