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No detailed information on in vivo biokinetics of CeO2 nanoparticles (NPs) following chronic low-dose inhalation is available. The CeO2 burden for lung, lung-associated lymph nodes, and major non-pulmonary organs, blood, and feces, was determined in a chronic whole-body inhalation study in female Wistar rats undertaken according to OECD TG453 (6 h per day for 5 days per week for a 104 weeks with the following concentrations: 0, 0.1, 0.3, 1.0, and 3.0 mg/m3, animals were sacrificed after 3, 12, 24 months). Different spectroscopy methods (ICP-MS, ion-beam-microscopy) were used for the quantification of organ burden and for visualization of NP distribution patterns in tissues. After 24 months of exposure, the highest CeO2 lung burden (4.41 mg per lung) was associated with the highest aerosol concentration and was proportionally lower for the other groups in a dose-dependent manner. Imaging techniques confirmed the presence of CeO2 agglomerates of different size categories within lung tissue with a non-homogenous distribution. For the highest exposure group, after 24 months in total 1.2% of the dose retained in the lung was found in the organs and tissues analyzed in this study, excluding lymph nodes and skeleton. The CeO2 burden per tissue decreased from lungs > lymph nodes > hard bone > liver > bone marrow. For two dosage groups, the liver organ burden showed a low accumulation rate. Here, the liver can be regarded as depot, whereas kidneys, the skeleton, and bone marrow seem to be dumps due to steadily increasing NP burden over time.

The ECETOC NanoApp was developed to support industry in the registration of sets of nanoforms, as well as regulators in the evaluation of these registration dossiers. The ECETOC NanoApp uses a systematic approach to create and justify sets of similar nanoforms, following the ECHA guidance in a transparent and evidence-based manner. The rational and decision rules behind the ECETOC NanoApp are described in detail in ���Janer, G., R. Landsiedel, and W. Wohlleben. 2021. [Rationale and Decision Rules Behind the ECETOC NanoApp to Support Registration of Sets of Similar Nanoforms within REACH. Nanotoxicology 15 (2): 145���122. https://doi.org/10.1080/17435390.2020.1842933]���. The decision criteria apply to human health and environmental hazards and risks. Here, we focus mostly on human health hazards; the decision rules are applied to a series of case studies, each consisting of real nanoforms: two barium sulfate nanoforms, four colloidal silica nanoforms, eight ceria nanoforms, and four copper phthalocyanine nanoforms. For each of them, we show step by step how the ECETOC NanoApp rules are applied. The cases include nanoforms that are justified as members of the same set of similar nanoforms based on sufficient similarity of their intrinsic properties (Tier 1). They also include other nanoforms with a relatively high (but insufficient) similarity of intrinsic properties; their similarity could be justified by functional properties (Tier 2). The case studies also include nanoforms that are concluded not to belong to the same set of similar nanoforms. These outcomes of the NanoApp were overall consistent (sometimes conservative) with available in vivo data. We also noted that datasets for various nanoforms were limited and use of the NanoApp may require the generation of data relevant to the decision criteria.

New registration requirements for nanomaterials under REACH consider the possibility to form ‘sets of similar nanoforms’ for a joined human health and environmental hazard, exposure and risk assessment. We developed a tool to create and justify sets of similar nanoforms and to ensure that each of the nanoforms is sufficiently similar to all other nanoforms. The decision logic is following the ECHA guidance in a transparent and evidence-based manner. For each two nanoforms the properties under consideration are compared and corresponding thresholds for maximal differences are proposed. In tier1, similarity is assessed based on intrinsic properties that mostly correspond to those required for nanoform identification under REACH: composition, impurities/additives, size, crystallinity, shape and surface treatment. Moreover, potential differences in the agglomeration/aggregation state resulting from different production processes are considered. If nanoforms were not sufficiently similar based on tier1 criteria, additional data from functional assays are required in tier2. In rare cases, additional short-term in vivo rodent data could be required in a third tier. Data required by tier 2 are triggered by the intrinsic properties in the first tier that did not match the similarity criteria. Most often this will be data on dissolution and surface reactivity followed by in vitro toxicity, dispersion stability, dustiness. Out of several nanoforms given by the user, the tool concludes which nanoforms could be justified to be in the same set and which nanoforms are outside. It defines the boundaries of sets of similar nanoforms and generates a justification for the REACH registration.

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To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property ‘lowest unoccupied molecular orbital’) need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low– one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were well-constructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before.

Titanium dioxide and zinc oxide nanomaterials, used as UV protecting agents in sunscreens, were investigated for their potential genotoxicity in in vitro and in vivo test systems. Since standard OECD test methods are designed for soluble materials and genotoxicity testing for nanomaterials is still under revision, a battery of standard tests was used, covering different endpoints. Additionally, a procedure to disperse the nanomaterials in the test media and careful characterization of the dispersed test item was added to the testing methods. No genotoxicity was observed in vitro (Ames' Salmonella gene mutation test and V79 micronucleus chromosome mutation test) or in vivo (mouse bone marrow micronucleus test and Comet DNA damage assay in lung cells from rats exposed by inhalation). These results add to the still limited data base on genotoxicity test results with nanomaterials and provide congruent results of a battery of standard OECD test methods applied to nanomaterials.

Industrial nanoparticles are not developed to be compatible with in vitro cell culture assays which are carried out in isotonic solutions at physiological pH and often in the presence of proteins. ...

Dose-response by in vitro testing is only valid if the fraction of the particle dose that deposits onto adherent cells is known. Modeling tools such as the ‘distorted grid’ (DG) code are common pra...