No abstract

No abstract

No abstract

The chronic toxicity is fundamental for toxicological risk assessment, but its correlation with the chemical structures has been studied only little. This is partly due to the complexity of such an experimental test that embraces a plethora of different biological effects and mechanisms of action, making (Q)SAR studies extremely challenging. In this paper we report a predictive in silico study of more than 400 compounds based on two-dimensional chemical descriptors and multivariate analysis. The root mean squared error of the predictive model is 0.73 (in a logarithmic scale) on a leave-one-out cross-validation and is close to the estimated variability of experimental values (0.64). The analysis of the model revealed that the chronic toxicity effects are driven by the bioavailability of the compound that constitutes a baseline effect plus excess toxicity possible described by a few chemical moieties. The results obtained give confidence that this model can be useful for establishing a level of safety concern in the absence of hard toxicological data.

This article addresses a number of concepts related to the selection and modelling of carcinogenicity data for the calculation of a Margin of Exposure. It follows up on the recommendations put forward by the International Life Sciences Institute - European branch in 2010 on the application of the Margin of Exposure (MoE) approach to substances in food that are genotoxic and carcinogenic. The aims are to provide practical guidance on the relevance of animal tumour data for human carcinogenic hazard assessment, appropriate selection of tumour data for Benchmark Dose Modelling, and approaches for dealing with the uncertainty associated with the selection of data for modelling and, consequently, the derived Point of Departure (PoD) used to calculate the MoE. Although the concepts outlined in this article are interrelated, the background expertise needed to address each topic varies. For instance, the expertise needed to make a judgement on biological relevance of a specific tumour type is clearly different to that needed to determine the statistical uncertainty around the data used for modelling a benchmark dose. As such, each topic is dealt with separately to allow those with specialised knowledge to target key areas of guidance and provide a more in-depth discussion on each subject for those new to the concept of the Margin of Exposure approach.