Probabilistic hazard assessment for skin sensitiza tion potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships

Thomas Luechtefeld, Alexandra Maertens, James M. Mckim, Thomas Hartung, Andre Kleensang, Vanessa Sá-Rocha

Research output: Contribution to journalArticlepeer-review

Abstract

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced "false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets.

Original languageEnglish (US)
Pages (from-to)1361-1371
Number of pages11
JournalJournal of Applied Toxicology
Volume35
Issue number11
DOIs
StatePublished - Nov 1 2015

Keywords

  • Feature selection
  • Hidden Markov model
  • In vitro
  • Integrated testing strategy
  • LLNA
  • Machine learning
  • QSAR
  • Skin sensitization

ASJC Scopus subject areas

  • Toxicology

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