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Title: Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showedmore » a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation. - Highlights: • It was compiled the largest publicly-available skin sensitization dataset. • Predictive QSAR models were developed for skin sensitization. • Developed models have higher prediction accuracy than OECD QSAR Toolbox. • Putative chemical hazards in the Scorecard database were found using our models.« less
Authors:
 [1] ;  [2] ;  [3] ;  [4] ;  [3] ; ;  [5] ;  [1] ;  [3]
  1. Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220 (Brazil)
  2. (United States)
  3. Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 (United States)
  4. (Ukraine)
  5. ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709 (United States)
Publication Date:
OSTI Identifier:
22465743
Resource Type:
Journal Article
Resource Relation:
Journal Name: Toxicology and Applied Pharmacology; Journal Volume: 284; Journal Issue: 2; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ACCURACY; COMPARATIVE EVALUATIONS; COMPUTER CODES; DATASETS; FORECASTING; HAZARDS; IMMUNE REACTIONS; OECD; RANDOMNESS; SCREENING; SENSITIZERS; SIMULATION; SKIN; STRUCTURE-ACTIVITY RELATIONSHIPS; TOOLS; VALIDATION