CORAL: model for no observed adverse effect level (NOAEL)

Andrey A. Toropov, Alla P. Toropova, Fabiola Pizzo, Anna Lombardo, Domenico Gadaleta, Emilio Benfenati

Research output: Contribution to journalArticle


The in vivo repeated dose toxicity (RDT) test is intended to provide information on the possible risk caused by repeated exposure to a substance over a limited period of time. The measure of the RDT is the no observed adverse effect level (NOAEL) that is the dose at which no effects are observed, i.e., this endpoint indicates the safety level for a substance. The need to replace in vivo tests, as required by some European Regulations (registration, evaluation authorization and restriction of chemicals) is leading to the searching for reliable alternative methods such as quantitative structure–activity relationships (QSAR). Considering the complexity of the RDT endpoint, for which data quality is limited and depends anyway on the study design, the development of QSAR for this endpoint is an attractive task. Starting from a dataset of 140 organic compounds with NOAEL values related to oral short term toxicity in rats, we developed a QSAR model based on optimal descriptors calculated with simplified molecular input-line entry systems and the graph of atomic orbitals by the Monte Carlo method, using CORAL software. Three different splits into the training, calibration, and validation sets are studied. The mechanistic interpretation of these models in terms of molecular fragment with positive or negative contributions to the endpoint is discussed. The probabilistic definition for the domain of applicability is suggested.

Original languageEnglish
Pages (from-to)563-575
Number of pages13
JournalMolecular Diversity
Issue number3
Publication statusPublished - Apr 8 2015


  • CORAL software
  • Ecology
  • Monte Carlo method
  • QSAR
  • Repeated dose toxicity

ASJC Scopus subject areas

  • Organic Chemistry
  • Catalysis
  • Inorganic Chemistry
  • Physical and Theoretical Chemistry
  • Information Systems
  • Drug Discovery
  • Molecular Biology

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