Evaluation of the Global Performance of Eight In Silico Skin Sensitization Models Using Human Data

Emily Golden, Donna S. Macmillan, Greg Dameron, Petra Kern, Thomas Hartung, Alexandra Maertens

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Allergic contact dermatitis, or the clinical manifestation of skin sensitization, is a leading occupational hazard. Several testing approaches exist to assess skin sensitization, but in silico models are perhaps the most advantageous due to their high speed and low-cost results. Many in silico skin sensitization models exist, though many have only been tested against results from animal studies (e.g., LLNA); this creates uncertainty in human skin sensitization assessments in both a screening and regulatory context. This project’s aim was to evaluate the accuracy of eight in silico skin sensitization models against two human data sets: one highly curated (Basketter et al., 2014) and one screening level (HSDB). The binary skin sensitization status of each chemical in each of the two data sets was compared to the prediction from eight in silico skin sensitization tools (Toxtree, PredSkin, OECD’s QSAR Toolbox, UL’s REACHAcross™, Danish QSAR Database, TIMES-SS, and Lhasa Limited’s Derek Nexus). Models were assessed for coverage, accuracy, sensitivity, and specificity, as well as optimization features (e.g., probability of accuracy, applicability domain, etc.), if available. While there was a wide range of sensitivity and specificity, the models generally performed comparably to the LLNA in predicting human skin sensitization status (i.e., approximately 70-80% accuracy). Additionally, the models did not mispredict the same compounds, suggesting there might be an advantage in combining models. In silico skin sensitization models offer accurate and useful insights in a screening context; however, further improvements are necessary so these models may be considered fully reliable for regulatory applications.

Original languageEnglish (US)
Pages (from-to)33-48
Number of pages16
Issue number1
StatePublished - 2021

ASJC Scopus subject areas

  • Pharmacology
  • Medical Laboratory Technology


Dive into the research topics of 'Evaluation of the Global Performance of Eight In Silico Skin Sensitization Models Using Human Data'. Together they form a unique fingerprint.

Cite this