The application of new HARD-descriptor available from the CORAL software to building up NOAEL models

Food Chem Toxicol. 2018 Feb;112:544-550. doi: 10.1016/j.fct.2017.03.060. Epub 2017 Mar 30.

Abstract

Continuous QSAR models have been developed and validated for the prediction of no-observed-adverse-effect (NOAEL) in rats, using training and test sets from the Fraunhofer RepDose® database and EFSA's Chemical Hazards Database: OpenFoodTox. This paper demonstrates that the HARD index, as an integrated attribute of SMILES, improves the prediction power of NOAEL values using the continuous QSAR models and Monte Carlo simulations. The HARD-index is a line of eleven symbols, which represents the presence, or absence of eight chemical elements (nitrogen, oxygen, sulfur, phosphorus, fluorine, chlorine, bromine, and iodine) and different kinds of chemical bonds (double bond, triple bond, and stereo chemical bond). Optimal molecular descriptors calculated with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give satisfactory predictive models for NOAEL. Optimal molecular descriptors calculated in this way with the Monte Carlo technique (maximization of correlation coefficient between the descriptor and endpoint) give amongst the best results available in the literature. The models are built up in accordance with OECD principles.

Keywords: CORAL software; Monte Carlo method; NOAEL; OECD principles; QSAR.

MeSH terms

  • Animals
  • Computer Simulation
  • Databases, Factual
  • Halogens / chemistry
  • Models, Chemical*
  • Monte Carlo Method
  • Nitrogen / chemistry
  • No-Observed-Adverse-Effect Level*
  • Oxygen / chemistry
  • Phosphorus / chemistry
  • Quantitative Structure-Activity Relationship
  • Rats
  • Software*
  • Sulfur / chemistry

Substances

  • Halogens
  • Phosphorus
  • Sulfur
  • Nitrogen
  • Oxygen