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The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.
Golden, Emily; Ukaegbu, Daniel C; Ranslow, Peter; Brown, Robert H; Hartung, Thomas; Maertens, Alexandra.
Affiliation
  • Golden E; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States.
  • Ukaegbu DC; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States.
  • Ranslow P; Consortium for Environmental Risk Management (CERM), Hallowell, Maine 04347, United States.
  • Brown RH; School of Medicine, Johns Hopkins University, Baltimore, Maryland 21287, United States.
  • Hartung T; Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States.
  • Maertens A; CAAT-Europe, University of Konstanz, 78464, Konstanz, Germany.
Chem Res Toxicol ; 36(5): 734-746, 2023 05 15.
Article in En | MEDLINE | ID: mdl-37126467
In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Skin / Quantitative Structure-Activity Relationship Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Chem Res Toxicol Journal subject: TOXICOLOGIA Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Skin / Quantitative Structure-Activity Relationship Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Chem Res Toxicol Journal subject: TOXICOLOGIA Year: 2023 Type: Article Affiliation country: United States