CADASTER CAse studies on the Development and Application of in-Silico Techniques for Environmental hazard and Risk assessment

Implementation of REACH requires demonstration of the safe manufacture and use of chemicals. REACH aims to achieve  
a proper balance between societal, economic and environmental objectives, and attempts to efficiently use the scarce and  
scattered information available on the majority of substances. Thereupon REACH aims to reduce animal testing by  
optimized use of in silico and in vitro information on related compounds.The REACH proposals advocate the use of nonanimal  
testing methods, but guidance is needed on how these methods should be used. The procedures include alternative  
methods such as chemical and biological read-across, in vitro results, in vivo information on analogues, (Q)SARs, and  
exposure-based waiving. The concept of Intelligent Testing Strategies for regulatory endpoints has been outlined to  
facilitate the assessments. Intensive efforts are needed to translate the concept into a workable, consensually acceptable,  
and scientifically sound strategy.CADASTER aims at providing the practical guidance to integrated risk assessment by  
carrying out a full hazard and risk assessment for chemicals belonging to four compound classes. A DSS will be developed  
that will be updated on a regular basis in order to accommodate and integrate the alternative methods mentioned above.  
Operational procedures will be developed, tested, and disseminated that guide a transparent evaluation of four classes of  
emerging chemicals, explicitly taking account of variability and uncertainty in data and in models.The main goal is to  
exemplify the integration of information, models and strategies for carrying out safety-, hazard- and risk assessments for  
large numbers of substances. Real risk estimates will be delivered according to the basic philosophy of REACH of  
minimizing animal testing, costs, and time. CADASTER will show how to increase the use of non-testing information for  
regulatory decision whilst meeting the main challenge of quantifying and reducing uncertainty.