title: Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data.
abstract: Public data from ECHA online dossiers on 9,801 substances encompassing 326,749 experimental key studies and additional information on classification and labeling were made computable. Eye irritation hazard, for which the rabbit Draize eye test still represents the reference method, was analyzed. Dossiers contained 9,782 Draize eye studies on 3,420 unique substances, indicating frequent retesting of substances. This allowed assessment of the test’s reproducibility based on all substances tested more than once. There was a 10% chance of a non-irritant evaluation after a prior severe-irritant result according to UN GHS classification criteria. The most reproducible outcomes were the results negative (94% reproducible) and severe eye irritant (73% reproducible).
  To evaluate whether other GHS categorizations predict eye irritation, we built a dataset of 5,629 substances (1,931 “irritant” and 3,698 “non-irritant”). The two best decision trees with up to three other GHS classifications resulted in balanced accuracies of 68% and 73%, i.e., in the rank order of the Draize rabbit eye test itself, but both use inhalation toxicity data (“May cause respiratory irritation”), which is not typically available.
  Next, a dataset of 929 substances with at least one Draize study was mapped to PubChem to compute chemical similarity using 2D conformational fingerprints and Tanimoto similarity. Using a minimum similarity of 0.7 and simple classification by the closest chemical neighbor resulted in balanced accuracy from 73% over 737 substances to 100% at a threshold of 0.975 over 41 substances. This represents a strong support of read-across and (Q)SAR approaches in this area.
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.
authors: Thomas
authors: T
authors: Luechtefeld
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.
authors: Alexandra
authors: A
authors: Maertens
authors: The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.
authors: Daniel P
authors: DP
authors: Russo
authors: CAAT-Europe, University of Konstanz, Konstanz, Germany.
authors: Costanza
authors: C
authors: Rovida
authors: The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA.|Department of Chemistry, Rutgers University at Camden, NJ, USA.
authors: Hao
authors: H
authors: Zhu
authors: Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA.|CAAT-Europe, University of Konstanz, Konstanz, Germany.
authors: Thomas
authors: T
authors: Hartung
mesh_terms: D000826:Animal Testing Alternatives; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005131:Eye Injuries / Q000139:chemically induced*; D015386:Hazardous Substances / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship; D011817:Rabbits
chemical_list: D015386:Hazardous Substances
keywords: animal testing alternatives; chemical safety; dataset; in silico; ocular toxicity