A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria

Applicability of OECD TG 201, 202, 203 for the aquatic toxicity testing and assessment of 2D Graphene material nanoforms to meet regulatory needs

Tests using algae and/or cyanobacteria, invertebrates (crustaceans) and fish form the basic elements of an ecotoxicological assessment in a number of regulations, in particular for classification of a substance as hazardous or not to the aquatic environment according to the Globally Harmonised System of Classification and Labelling of Chemicals (GHS-CLP) (GHS, 2022) and the REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemicals, EC, 2006). Standardised test guidelines (TGs) of the Organisation for Economic Co-operation and Development (OECD) are available to address the regulatory relevant endpoints of growth inhibition in algae and cyanobacteria (TG 201), acute toxicity to invertebrates (TG 202), and acute toxicity in fish (TG 203). Applying these existing OECD TGs for testing two dimensional (2D) graphene nanoforms may require more attention, additional considerations and/or adaptations of the protocols, because graphene materials are often problematic to test due to their unique attributes. In this review a critical analysis of all existing studies and approaches to testing used has been performed in order to comment on the current state of the science on testing and the overall ecotoxicity of 2D graphene materials. Focusing on the specific tests and available guidance's, a complete evaluation of aquatic toxicity testing for hazard classification of 2D graphene materials, as well as the use of alternative tests in an integrated approach to testing and assessment, has been made. This information is essential to ensure future assessments generate meaningful data that will fulfil regulatory requirements for the safe use of this "wonder" material.

The application of existing genotoxicity methodologies for grouping of nanomaterials: towards an integrated approach to testing and assessment

The incorporation of nanomaterials (NMs) in consumer products has proven to be highly valuable in many sectors. Unfortunately, however, the same nano specific physicochemical properties, which make these material attractive, might also contribute to hazards for people exposed to these materials. The physicochemical properties of NMs will impact their interaction with biological surroundings and influence their fate and their potential adverse effects such as genotoxicity. Due to the large and expanding number of NMs produced, their availability in different nanoforms (NFs) and their utilization in various formats, it is impossible for risk assessment to be conducted on an individual NF basis. Alternative methods, such as grouping are needed for streamlining hazard assessment. The GRACIOUS Framework provides a logical and science evidenced approach to group similar NFs, allowing read-across of hazard information from source NFs (or non-NFs) with adequate hazard data to target NFs that lack such data. Here, we propose a simple three-tiered testing strategy to gather evidence to determine whether different NFs are sufficiently similar with respect to their potential to induce genotoxicity, in order to be grouped. The tiered testing strategy includes simple in vitro models as well as a number of alternative more complex multi-cellular in vitro models to allow for a better understanding of secondary NM-induced DNA damage, something that has been more appropriate in vivo until recently.

A methodology for the automatic evaluation of data quality and completeness of nanomaterials for risk assessment purposes

This manuscript proposes a methodology to assess the completeness and quality of physicochemical and hazard datasets for risk assessment purposes. The approach is also specifically applicable to similarity assessment as a basis for grouping of (nanoforms of) chemical substances as well as for classification of the substances according to the Classification, Labeling and Packaging regulation. The unique goal of this approach is to assess data quality in such a way that all the steps are automatized, thus reducing reliance on expert judgment. The analysis starts from available (meta)data as provided in the data entry templates developed by the NanoSafety community and used for import into the eNanoMapper database. The methodology is implemented in the templates as a traffic light system-the providers of the data can see in real time the completeness scores calculated by the system for their datasets in green, yellow, or red. This is an interactive feedback feature that is intended to provide an incentive for anyone inserting data into the database to deliver more complete and higher quality datasets. The users of the data can also see this information both in the data entry templates and on the database interface, which enables them to select better datasets for their assessments. The proposed methodology has been partially implemented in the eNanoMapper database and in a Weight of Evidence approach for the regulatory classification of nanomaterials. It was fully implemented in a publicly available online R tool.