Rationale and decision rules behind the ECETOC NanoApp to support registration of sets of similar nanoforms within REACH

Janer, Gemma; Landsiedel, Robert; Wohlleben, Wendel

doi:10.1080/17435390.2020.1842933

Cited by 20 publications

(15 citation statements)

References 92 publications

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“…(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) ECETOC NanoApp (Janer et al, 2021). In Jeliazkova et al (2021)-this issue, a simplified version of the BF analysis was applied to a set of predefined PC and toxicological properties of interest to estimate pairwise similarities between NFs for benchmark materials.…”

Section: Resultsmentioning

confidence: 99%

Bayesian based similarity assessment of nanomaterials to inform grouping

Tsiliki¹,

Şeleci²,

Zabeo³

et al. 2022

NanoImpact

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Section: Resultsmentioning

confidence: 99%

Bayesian based similarity assessment of nanomaterials to inform grouping

Tsiliki¹,

Şeleci²,

Zabeo³

et al. 2022

NanoImpact

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“…We independently assessed the reproducibility of the CFS method on TiO 2 NM-104 and ZnO NM-110 (data not shown, NanoHarmony and PATROLS projects) and found about 40% reproducibility for both materials; this value is in the range of 67% found in earlier interlab comparisons on the CFS method, and thus a factor of 2 can be considered as the metrological limit of significance. Depending on the toxicity of the material studied, differences of a factor of 2 may already impact the hazard assessment; the ECETOC (European Centre for Ecotoxicology and Toxicology Of Chemicals) NanoApp recommended up to a factor of 3 difference in dissolution rates as acceptable for joint hazard assessment. , …”

Section: Discussionmentioning

confidence: 99%

Dissolution Rate of Nanomaterials Determined by Ions and Particle Size under Lysosomal Conditions: Contributions to Standardization of Simulant Fluids and Analytical Methods

Zanoni

Keller

Sauer³

et al. 2022

Chem. Res. Toxicol.

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Dissolution of inhaled engineered nanomaterials (ENM) under physiological conditions is essential to predict the clearance of the ENM from the lungs and to assess their biodurability and the potential effects of released ions. Alveolar macrophage (AM) lysosomes contain a pH 4.5 saline brine with enzymes and other components. Different types of artificial phagolysosomal simulant fluids (PSFs) have been developed for dissolution testing, but the consequence of using different media is not known. In this study, we tested to which extent six fundamentally different PSFs affected the ENM dissolution kinetics and particle size as determined by a validated transmission electron microscopy (TEM) image analysis. Three lysosomal simulant media were consistent with each other and with in vivo clearance. These media predict the quick dissolution of ZnO, the partial dissolution of SiO2, and the very slow dissolution of TiO2. The valid media use either a mix of organic acids (with the total concentration below 0.5 g/L, thereof citric acid below 0.15 g/L) or another organic acid (KH phthalate). For several ENM, including ZnO, BaSO4, and CeO2, all these differences induce only minor modulation of the dissolution rates. Only for TiO2 and SiO2, the interaction with specific organic acids is highly sensitive, probably due to sequestration of the ions, and can lead to wrong predictions when compared to the in vivo behavior. The media that fail on TiO2 and SiO2 dissolution use citric acid at concentrations above 5 g/L (up to 28 g/L). In the present selection of ENM, fluids, and methods, the different lysosomal simulant fluids did not induce changes of particle morphology, except for small changes in SiO2 and BaSO4 particles most likely due to ion dissolution, reprecipitation, and coalescence between neighboring particles. Based on the current evidence, the particle size by TEM analysis is not a sufficiently sensitive analytical method to deduce the rate of ENM dissolution in physiological media. In summary, we recommend the standardization of ENM dissolution testing by one of the three valid lysosomal simulant fluids with determination of the dissolution rate and halftime by the quantification of ions. This recommendation was established for a continuous flow system but may be relevant as well for static (batch) solubility testing.

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“…The rational and decision rules behind the ECETOC NanoApp are described in detail in Janer, Landsiedel, and Wohlleben (2021). The approach is based on pairwise similarity assessments to ensure that each of the nanoforms within a set is sufficiently similar to all other nanoforms within that set.…”

Section: Introductionmentioning

confidence: 99%

“…Tier 2 properties were selected, depending on the Tier 1 property not reaching sufficient similarity (see Janer, Landsiedel, and Wohlleben 2021 for details), and considering the need to evaluate potential impacts in nanoform exposure, toxicokinetics, human toxicity, fate, and ecotoxicity and ultimate risk to environment and humans (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Creating sets of similar nanoforms with the ECETOC NanoApp: real-life case studies

et al. 2021

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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 "

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Rationale and decision rules behind the ECETOC NanoApp to support registration of sets of similar nanoforms within REACH

Cited by 20 publications

References 92 publications

Bayesian based similarity assessment of nanomaterials to inform grouping

Bayesian based similarity assessment of nanomaterials to inform grouping

Dissolution Rate of Nanomaterials Determined by Ions and Particle Size under Lysosomal Conditions: Contributions to Standardization of Simulant Fluids and Analytical Methods

Creating sets of similar nanoforms with the ECETOC NanoApp: real-life case studies

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