title: Establishing the level of safety concern for chemicals in food without the need for toxicity testing.
abstract: There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.
authors.affiliation: Nestlé Research Centre, Vers-Chez-Les-Blanc, Lausanne, Switzerland.
authors.forename: Benoît
authors.initials: B
authors.lastname: Schilter
authors.affiliation: Istituto Superiore di Sanità, Rome, Italy.
authors.forename: Romualdo
authors.initials: R
authors.lastname: Benigni
authors.affiliation: Imperial College London, London, United Kingdom.
authors.forename: Alan
authors.initials: A
authors.lastname: Boobis
authors.affiliation: ILSI Europe, Brussels, Belgium. Electronic address: publications@ilsieurope.be.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Chiodini
authors.affiliation: University of Newcastle, Newcastle, United Kingdom.
authors.forename: Andrew
authors.initials: A
authors.lastname: Cockburn
authors.affiliation: Liverpool John Moores University, Liverpool, United Kingdom.
authors.forename: Mark T D
authors.initials: MT
authors.lastname: Cronin
authors.affiliation: Nestlé Research Centre, Vers-Chez-Les-Blanc, Lausanne, Switzerland.
authors.forename: Elena
authors.initials: E
authors.lastname: Lo Piparo
authors.affiliation: Unilever, Bedford, United Kingdom.
authors.forename: Sandeep
authors.initials: S
authors.lastname: Modi
authors.affiliation: DSM Nutritional Products, Kaiseraugst, Switzerland.
authors.forename: Anette
authors.initials: A
authors.lastname: Thiel
authors.affiliation: European Commission - Joint Research Centre, Institute for Health & Consumer Protection, Ispra, Italy.
authors.forename: Andrew
authors.initials: A
authors.lastname: Worth
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003663:Decision Trees; D005502:Food / Q000633:toxicity*; D059022:Food Safety / Q000379:methods*; D006801:Humans; D019055:No-Observed-Adverse-Effect Level; D021281:Quantitative Structure-Activity Relationship; D018570:Risk Assessment / Q000379:methods; D012984:Software
keywords: (Q)SAR; Chemical grouping; Food; In silico toxicology; Incident management; Predictive toxicology; Read across; Risk assessment; Uncertainties