Alternative methods for MOA classification in support of QSAR development

Aquatic toxicity information is essential in environmental risk assessment to determine the potential hazards of risks of new and existing chemicals. Prediction and modelling techniques such as quantitative structure activity relationships (QSARs) are applied to fill data gaps and to predict, assess and extrapolate the toxicity of various chemicals. Assigning a specific mode of action (MOA) to a chemical seems to be essential in the development and utilisation of QSARs. Besides physico-chemical characteristics, biological parameters are very important in the cascade of a toxicological response. Though in QSAR development, these biological descriptors are not always incorporated as much as the phyisico-chemical ones. Various structural-based MOA classification models recurrently turn out to be inadequate to appoint a compound to its particular MOA. This has led to an insistent need for biology-based and biologically interpretable MOA categorization tools. Gene profiling techniques that allow the simultaneous screening of a large set of genes have been suggested several times as reinforcement tools of the existing, physico-chemistry-based MOA classification methods. However, the requirement of advanced and complex analyses tools is a major limitation for the application of these gene profiling tools in a mechanistic context for environmental risk assessment.

Thus, despite the promising perspectives, introducing gene profiling tools in regulatory ecotoxicology is still in an exploratory phase. There is a need for distinct and unambiguous tools that are easy, predictive and straightforward to work with within the REACH framework. In this project, we try to evaluate the potential of alternative gene profiling techniques in offering additional biological information (i.e. bacterial gene profiling assay, custom made cDNA microarrays, full genome microarrays) on the toxic potency of the selected structural analogues. Additionally, it is investigated whether there is a direct input in a risk assessment context for this biological information in support of the QSAR based chemical grouping and MOA classification.

This project contributes to work conducted for the European OSIRIS project (http://www.osiris-reach.eu/)