Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.

Journal: Artificial intelligence in the life sciences
Published Date: Dec 5, 2021

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.

Authors

  • Arthur C Silva
    LabMol-Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás-UFG, Goiânia, GO, Brazil.
  • Joyce V V B Borba
    LabMol-Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás-UFG, Goiânia, GO, Brazil.
  • Vinicius M Alves
    Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
  • Steven U S Hall
    LabMol-Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás-UFG, Goiânia, GO, Brazil.
  • Nicholas Furnham
    Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, United Kingdom.
  • Nicole Kleinstreuer
    National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, NIEHS, Durham, North Carolina 27560, USA.
  • Eugene Muratov
    Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
  • Alexander Tropsha
    Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
  • Carolina Horta Andrade
    LabMol-Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás-UFG, Goiânia, GO, Brazil.

Keywords

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