Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project.

The particular properties of nanomaterials have led to their rapidly increasing use in diverse fields of application. However, safety assessment is not keeping pace and there are still gaps in the understanding of their hazards. Computational models predicting nanotoxicity, such as (quantitative) structure-activity relationships ((Q)SARs), can contribute to safety evaluation, in line with general efforts to apply alternative methods in chemical risk assessment. Their development is highly dependent on the availability of reliable and high quality experimental data, both regarding the compounds' properties as well as the measured toxic effects. In particular, "nano-QSARs" should take the nano-specific characteristics into account. The information compiled needs to be well organized, quality controlled and standardized. Integrating the data in an overarching, structured data collection aims to (a) organize the data in a way to support modelling, (b) make (meta)data necessary for modelling available, and (c) add value by making a comparison between data from different sources possible.Based on the available data, specific descriptors can be derived to parameterize the nanomaterial-specific structure and physico-chemical properties appropriately. Furthermore, the interactions between nanoparticles and biological systems as well as small molecules, which can lead to modifications of the structure of the active nanoparticles, need to be described and taken into account in the development of models to predict the biological activity and toxicity of nanoparticles. The EU NanoPUZZLES project was part of a global cooperative effort to advance data availability and modelling approaches supporting the characterization and evaluation of nanomaterials.

Definition of the applicability domains of knowledge-based predictive toxicology expert systems by using a structural fragment-based approach.

The applicability domain of a (quantitative) structure-activity relationship ([Q]SAR) must be defined, if a model is to be used successfully for toxicity prediction, particularly for regulatory purposes. Previous efforts to set guidelines on the definition of applicability domains have often been biased toward quantitative, rather than qualitative, models. As a result, novel techniques are still required to define the applicability domains of structural alert models and knowledge-based systems. By using Derek for Windows as an example, this study defined the domain for the skin sensitisation structural alert rule-base. This was achieved by fragmenting the molecules within a training set of compounds, then searching the fragments for those created from a test compound. This novel method was able to highlight test chemicals which differed from those in the training set. The information was then used to designate chemicals as being either within or outside the domain of applicability for the structural alert on which that training set was based.