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Cassano A.,Laboratory of Chemistry and Environmental Toxicology | Manganaro A.,Laboratory of Chemistry and Environmental Toxicology | Martin T.,U.S. Environmental Protection Agency | Young D.,U.S. Environmental Protection Agency | And 4 more authors.
Chemistry Central Journal | Year: 2010

Background: The new REACH legislation requires assessment of a large number of chemicals in the European market for several endpoints. Developmental toxicity is one of the most difficult endpoints to assess, on account of the complexity, length and costs of experiments. Following the encouragement of QSAR (in silico) methods provided in the REACH itself, the CAESAR project has developed several models.Results: Two QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods. Both models performed well. The first makes a classification based on a random forest algorithm, while the second is based on an adaptive fuzzy partition algorithm. The first model has been implemented and inserted into the CAESAR on-line application, which is java-based software that allows everyone to freely use the models.Conclusions: The CAESAR QSAR models have been developed with the aim to minimize false negatives in order to make them more usable for REACH. The CAESAR on-line application ensures that both industry and regulators can easily access and use the developmental toxicity model (as well as the models for the other four endpoints). © 2010 Cassano et al; licensee BioMed Central Ltd. Source

Ros F.,Prisme Institute | Harba R.,Prisme Institute | Piclin N.,BioChemics Consulting | Pintore M.,BioChemics Consulting
Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010 | Year: 2010

This paper investigates a method for instance selection in the context of supervised classification adapted to large databases. Based on the scale up concept, the method reduces the time required to perform the selection procedure by enabling the application of known condensation instance techniques to only small data sets instead of the whole set. The novelty of our approach relies in the way of hybridizing neighborhood and stratification approaches. The key idea is to consider instances found out for a given strata to generate sub populations for the other strata representing critical regions of the feature space. Experiments performed with various data sets revealed the effectiveness and applicability of the proposed approach. © 2010 IEEE. Source

Chaudhry Q.,UK Environment Agency | Piclin N.,BioChemics Consulting | Cotterill J.,UK Environment Agency | Pintore M.,BioChemics Consulting | And 3 more authors.
Chemistry Central Journal | Year: 2010

Background: The new European Regulation on chemical safety, REACH, (Registration, Evaluation, Authorisation and Restriction of CHemical substances), is in the process of being implemented. Many chemicals used in industry require additional testing to comply with the REACH regulations. At the same time EU member states are attempting to reduce the number of animals used in experiments under the 3 Rs policy, (refining, reducing, and replacing the use of animals in laboratory procedures). Computational techniques such as QSAR have the potential to offer an alternative for generating REACH data. The FP6 project CAESAR was aimed at developing QSAR models for 5 key toxicological endpoints of which skin sensitisation was one.Results: This paper reports the development of two global QSAR models using two different computational approaches, which contribute to the hybrid model freely available online.Conclusions: The QSAR models for assessing skin sensitisation have been developed and tested under stringent quality criteria to fulfil the principles laid down by the OECD. The final models, accessible from CAESAR website, offer a robust and reliable method of assessing skin sensitisation for regulatory use. © 2010 Chaudhry et al; licensee BioMed Central Ltd. Source

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