KREATiS
Kreatis replacing experimentation for reach - REACH alternative laboratory testing
Kreatis - an alternative to experimentation for REACH - REACH alternative experimentation

Research Projects

DAMIER

Développement et Applications
de Modèles Informatiques pour REACH

Duration: 3 years
Project description
DAMIER project was financed by BPI France and the main objective was to develop alternative and in silico methods so as to replace main experimentations needed for regulatory purposes (REACH regulation). The project aimed to provide QSAR models to predict several physico-chemical and ecotoxicological properties for non-reactive substances.

CRANCS

Compatmentalised Risk Assessment
for Natural Complex Substances

Duration: 18 months
Project description
CRANCS is an R&D project supported by FranceAgriMer as part of “Projets Agricoles et Agroalimentaires d'Avenir” - “Initiatives Innovantes dans l'Agriculture et l'Agroalimentaire” (2I2A). The objective of CRANCS project was to develop a predictive method for environmental risk assessment with partitioning between the aqueous and the sediment compartments of Natural Complex Substances (NCS), notably essential oils. The main characteristic of CRANCS was to provide a more realistic picture of the fate of mixtures and their ecotoxicity when they are released into the aquatic environment.

NC3Rs CRACK-IT QSARs Mix Challenge

Duration: 12 months
Project description
This project was sponsored by Shell and the aim objective of this work was to develop a High Accuracy QSAR (HA-QSAR) model to predict skin and eye irritation for mono-constituent substances and mixtures. The HA-QSARs developed during this project provides a new approach to quantify the skin and eye irritancy potential of individual chemicals and their mixtures, based on substance properties, exposure to the biological surfaces and the maximum impact of the structural alert for each structure.
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KREATiS, Knowledge & research in environment and toxicology in silico

Knowledge & research in environment and toxicology in silico