Mechanism-informed read-across assessment of skin sensitizers based on SkinSensDB

Chun Wei Tung, Chia Chi Wang, Shan Shan Wang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Integrative testing strategies using adverse outcome pathway (AOP)-based alternative assays for assessing skin sensitizers show the potential for replacing animal testing. However, the application of alternative assays for a large number of chemicals is still time-consuming and expensive. In order to facilitate the assessment of skin sensitizers based on integrative testing strategies, a mechanism-informed read-across assessment method was proposed and evaluated using data from SkinSensDB. First, the prediction performance of two integrated testing strategy models was evaluated giving the highest area under the receiver operating characteristic curve (AUC) values of 0.928 and 0.837 for predicting human and LLNA data, respectively. The proposed read-across prediction method achieves AUC values of 0.957 and 0.802 for predicting human and LLNA data, respectively, with interpretable activation statuses of AOP events. As data grows, a better prediction performance is expected. A user-friendly tool has been constructed and integrated into SkinSensDB that is publicly accessible at http://cwtung.kmu.edu.tw/skinsensdb.

Original languageEnglish
Pages (from-to)276-282
Number of pages7
JournalRegulatory Toxicology and Pharmacology
Volume94
DOIs
Publication statusPublished - Apr 1 2018
Externally publishedYes

Keywords

  • Adverse outcome pathway
  • Read-across
  • Skin sensitizer
  • SkinSensDB

ASJC Scopus subject areas

  • Toxicology

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