The application of new HARD-descriptor available from the CORAL software to building up NOAEL models. (February 2018)

• Record Type: Journal Article
• Authors: Toropova, Alla P.; Toropov, Andrey A.; Marzo, Marco; Escher, Sylvia E.; Dorne, Jean Lou; Georgiadis, Nikolaos; Benfenati, Emilio
• Is Part Of: Food and chemical toxicology. Volume 112(2018)
• Page Start: 544
• Page End: 550
• Publication Date: 2018-02
• Subjects: QSAR -- NOAEL -- OECD principles -- Monte Carlo method -- CORAL software; Toxicology -- Periodicals; Food poisoning -- Periodicals; Food Poisoning -- Periodicals; Toxicology -- Periodicals; Toxicologie -- Périodiques; Intoxications alimentaires -- Périodiques; Food poisoning; Toxicology; Periodicals; Electronic journals; 615.9
• DOI: 10.1016/j.fct.2017.03.060
• Languages: English
• ISSNs: 0278-6915

Abstract

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. Graphical abstract: Image 1 Highlights: Predictive model for NOAEL (sub-chronic toxicity) is suggested. The Monte Carlo method is basis to build up the model. New descriptor (HARD) calculated from SMILES is suggested. The model is built up in accordance with OECD principles.