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Coating, stabilization layers, functionalization of particles or simple contamination are common variants of a core-shell system. For smaller nanoparticles this is of major importance. A particle with 16 nm diameter and a usual surface layer of 2 nm will have the same volume for the core as for the shell. In this case the material of the particle doesn’t have a clear definition. It is a common case that a particle consists of four different layers: Core, shell, stabilization layer and contamination. The properties of the particles differ according to this structure. For example silver particles might have a different dissolution rate for pure particles and for particles which are grown on top of a core.
Different solubility or defined other properties of materials is a common reason for producing core-shell systems. Gold cores are surrounded by silica to stabilize them or to get a defined distance between the cores. Silica might be surrounded by gold and the silica dissolved afterwards. This delivers hollow shells. Another important example for core-shell systems are quantum dots. A small core is surrounded by a different material for increasing the photoluminescence. Furthermore there a stabilization layer is needed. The smallest part of the final particles is the initial core. The photoluminescence is based on this core, but the shells contain much more material. Categorization should address this.
Core-shell systems are not covered by most of the existing decision trees for grouping. They are either regarded as special case or a singular layer. This disqualifies core-shell systems for grouping within the common models. There might be a very easy way to avoid this problem and even to combine some of the different decision trees. Starting the decision tree with the solubility of the outer shell and subsequently addressing the inner layers will be a pragmatic approach to solve the problem. If there is no shell, the categorization can start with a tiered approach or with the proposed “stawman” chemical categorization. If a shell is covering the surface there is a need to check if the shell is stable. If it is stable, the particle can be categorized based on this shell. If it is soluble, the ions need to be addressed as in the classic case. Furthermore the shell might increase the uptake by the cells. If the ions and the uptake are not critical the categorization can continue with the next layer.
With this not perfect but pragmatic approach, the surface layers can be addressed with very limited additional efforts. Most criteria are based on classically tabulated data. Including a rating system like the precautionary matrix approach might even address the fact that some parameters are not always Yes/No, e.g. solubility, ion toxicity and uptake.
Grouping of nanomaterials (NM) promises to serve effectively to reduce the extensive safety testing needs associated with regulatory risk assessment. Key challenges in this task are how to rapidly and cost-efficiently generate the needed data, and how to best combine structural material characteristics with biological effects data. Herein, we performed NM grouping from combining existing physiochemical data with high-throughput screening (HTS)-derived hazard assessment data generated in the human lung epithelial cell line BEAS-2B. Twenty-one NMs from the European Joint Research Centre´s Representative Nanomaterials Repository (diverse nanoforms of substances ZnO, SiO2 and TiO2) and five reference chemicals were analyzed by HTS assays for cytotoxicity/cell viability (CellTiterGlo, Dapi-staining), oxidative stress (8-OHdG), apoptosis (Caspase-3), and DNA damage repair (γH2AX). Additionally, physicochemical data relevant for grouping of NMs under REACH (ECHA, 2017 Appendix R.6-1) were collated for 15 of the NMs, including from EU-funded projects (NanoReg2, caLIBRAte) and the OECD Testing Programme of Nanomaterials. The diverse data types were scaled, normalized and integrated using a newly developed scoring pipeline inspired by the US-EPA Toxicological Prioritization Index (ToxPi). Results demonstrated that the in vitro-derived hazard data permitted substance-based grouping of the selected NMs, whereas integration of physicochemical data deepened the grouping of specific nanoforms within each substance group. Furthermore, a case study on 10 TiO2 NMs showed that hazard-based grouping allowed for read across of physicochemical data between 6 NMs acting as source nanoforms and 4 NMs acting as target nanoforms. The ToxPi tool and scoring pipeline permitted transparent visualization of the final grouping, while giving equal weight to different types of data/results related to structure and biology. Overall, this study aligns fully with the ECHA recommendations for grouping of NM (Appendix R.6-1), i.e. i) to aim at identification of criteria for grouping nanoforms (and non-nanoforms) within one substance, and ii) to provide additional information beyond physicochemical data to support read across between nanoforms.