How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

Jeliazkova, Nina; Bleeker, Eric A.J.; Cross, Richard; Haase, Andrea; Janer, Gemma; Peijnenburg, Willie J.G.M.; Pink, Mario; Rauscher, Hubert; Svendsen, Claus; Tsiliki, Georgia; Zabeo, Alex; Hristozov, Danail; Stone, Vicki; Wohlleben, Wendel

doi:10.1016/j.impact.2021.100366

Cited by 31 publications

(42 citation statements)

References 65 publications

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“…This information will help in better understanding the effect of processes like aggregation and agglomeration on bioaccumulation and will facilitate biodistribution modeling. The findings of our metaanalysis could be used to support the development of grouping strategies based on bioaccumulation as a basis for a safer-by-design approach toward ENMs (Jeliazkova et al, 2021;Stone et al, 2020;Wohlleben et al, 2019). Further research could be done to extend our database to the dissolvable ENMs and soil organisms.…”

Section: Discussionmentioning

confidence: 91%

Meta‐analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

Zheng

Nowack

2022

Enviro Toxic and Chemistry

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Understanding the bioaccumulation of engineered nanomaterials (ENMs) is essential for making regulatory decisions on potential environmental risks. Research in the field of ENM bioaccumulation has increased in recent years, but the compilation and statistical analysis of the available experimental data have not been updated. We therefore performed a meta-analysis of the existing literature on the bioaccumulation of eight types of nondissolvable ENMs (titanium dioxide [TiO 2 ], aluminum oxide [Al 2 O 3 ], gold [Au], fullerene [C 60 ], carbon nanotubes, iron oxide [FeO x ], graphene, and polystyrene) in nonmammalian freshwater aquatic organisms across three trophic levels including phytoplankton, zooplankton, and fish. Three typical endpoints were used to assess the bioaccumulation potential: the bioconcentration factor (BCF), the bioaccumulation factor (BAF), and the biomagnification factor (BMF). Our results suggest that zooplankton has greater mean logarithmic BCF and BAF values than phytoplankton (3.31 vs. 1.42) and fish (2.04). The ENMs are biomagnified in zooplankton, with a mean BMF of 17.4, whereas trophic transfer from primary consumers (zooplankton) to secondary consumers (fish) was not observed (mean BMF of 0.13). No clear dependency was identified between the physicochemical characteristics of ENMs (e.g., primary particle size, zeta potential, or shape) and bioaccumulation, except for coated versus uncoated particles accumulated in phytoplankton. Carbonaceous ENMs were found to be more bioaccumulated than the other ENMs we considered, except for TiO 2 . A meta-analysis of bioaccumulation data can (1) deepen the understanding of bioconcentration, bioaccumulation, and biomagnification of ENMs, (2) be used to support grouping strategies as a basis for a safer-by-design approach for ENMs, (3) be integrated into comprehensive hazard and risk assessments, (4) promote the standardization of testing guidelines, and (5) enhance future kinetic bioaccumulation modeling.

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Section: Discussionmentioning

confidence: 91%

Meta‐analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

Zheng

Nowack

2022

Enviro Toxic and Chemistry

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“…The similarity assessment between NRCWE006 and NM-401 was based on the pairwise difference (fold-change) of a summary metric selected as a representative output for each decision node endpoint measured at Tier 1 (Table 7 ). Interpretation of the degree of similarity between HARN was aided by considering the dynamic range of each assay and relative strength of signal generated by each MWCNT compared to the negative control and RTM where included [ 14 ]. Furthermore, 2 MWCNT (NM-403, NRCWE040) for which the grouping hypothesis was rejected were included in a pairwise similarity assessment with the source HARN (NRCWE006) to further aid in the interpretation of an acceptable level of similarity to support read-across between the grouped HARN (NRCWE006 and NM-401).…”

Section: Resultsmentioning

confidence: 99%

“…Pairwise similarity assessment was conducted between NM-401, NRCWE006, NM-403 and NRCWE040 according to Tier 1 PC characterisation and hazard assessment data to assess similarity between the 2 grouped MWCNT (NM-401 and NRCWE006) and compare with 2 MWCNT which rejected the grouping hypothesis (NM-403 and NRCWE040) [ 14 ]. As the data was largely extracted from the published literature the similarity assessment was based on the summary metrics provided rather than raw data e.g.…”

Section: Methodsmentioning

confidence: 99%

“…EC 50 ) to represent each of the hazard outcomes tested. Alternative approaches for modelling of similarity which utilise the full set of dose–response data could be more representative of the similarity of hazard responses elicited by the MWCNT in the Tier 1 in vitro assays as the shape and magnitude of the dose–response curves could also be taken into account, allowing temporal differences or differences in potency to be recognised [ 14 ]. Similarity results from more complete data sets could reduce uncertainty and add confidence to the interpretation of similarity of hazard potential.…”

Section: Implications Of the Similarity Assessmentmentioning

confidence: 99%

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Grouping MWCNTs based on their similar potential to cause pulmonary hazard after inhalation: a case-study

et al. 2022

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Background The EU-project GRACIOUS developed an Integrated Approach to Testing and Assessment (IATA) to support grouping high aspect ratio nanomaterials (HARNs) presenting a similar inhalation hazard. Application of grouping reduces the need to assess toxicity on a case-by-case basis and supports read-across of hazard data from substances that have the data required for risk assessment (source) to those that lack such data (target). The HARN IATA, based on the fibre paradigm for pathogenic fibres, facilitates structured data gathering to propose groups of similar HARN and to support read-across by prompting users to address relevant questions regarding HARN morphology, biopersistence and inflammatory potential. The IATA is structured in tiers, allowing grouping decisions to be made using simple in vitro or in silico methods in Tier1 progressing to in vivo approaches at the highest Tier3. Here we present a case-study testing the applicability of GRACIOUS IATA to form an evidence-based group of multiwalled carbon nanotubes (MWCNT) posing a similar predicted fibre-hazard, to support read-across and reduce the burden of toxicity testing. Results The case-study uses data on 15 different MWCNT, obtained from the published literature. By following the IATA, a group of 2 MWCNT was identified (NRCWE006 and NM-401) based on a high degree of similarity. A pairwise similarity assessment was subsequently conducted between the grouped MWCNT to evaluate the potential to conduct read-across and fill data gaps required for regulatory hazard assessment. The similarity assessment, based on expert judgement of Tier 1 assay results, predicts both MWCNT are likely to cause a similar acute in vivo hazard. This result supports the possibility for read-across of sub-chronic and chronic hazard endpoint data for lung fibrosis and carcinogenicity between the 2 grouped MWCNT. The implications of accepting the similarity assessment based on expert judgement of the MWCNT group are considered to stimulate future discussion on the level of similarity between group members considered sufficient to allow regulatory acceptance of a read-across argument. Conclusion This proof-of-concept case-study demonstrates how a grouping hypothesis and IATA may be used to support a nuanced and evidence-based grouping of ‘similar’ MWCNT and the subsequent interpolation of data between group members to streamline the hazard assessment process.

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“…For this reason, use of standardized protocols is preferred. A description of the methods used to assess similarity are beyond the scope of this paper; they are however provided in a special issue of NanoImpact, summarized in the White paper by (Jeliazkova et al, 2022).…”

mentioning

confidence: 99%

How to formulate hypotheses and IATA to support grouping and read-across of nanoforms

Murphy

Johnston

Dekkers

et al. 2022

ALTEX

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Manufacturing and functionalizing materials at the nanoscale has led to the generation of a whole array of nanoforms (NFs) of substances varying in size, morphology and surface characteristics. Due to financial, time and ethical considerations, testing every unique NF for adverse effects is virtually impossible. Use of hypothesis-driven grouping and read-across approaches, as supported by the GRACIOUS Framework, represent a promising alternative to case-by-case testing which will make the risk assessment process more efficient. Through application of appropriate grouping hypotheses, the Framework facilitates the assessment of similarity between NFs, thereby supporting grouping and read-across of information, minimizing the need for new testing and aligning with 3R principles of Replacement, Reduction and Refinement of animals in toxicology studies. For each grouping hypothesis an Integrated Approach to Testing and Assessment (IATA) guides the user in data gathering and acquisition to test the hypothesis, following a structured format to facilitate efficient decision-making. Here we present the template used to generate the GRACIOUS grouping hypotheses encompassing information relevant to "Lifecycle, environmental release and human exposure", "What they are: physicochemical characteristics", "Where they go: Environmental fate, uptake and toxicokinetics", and "What they do: human and environmental toxicity". A summary of the template-derived hypotheses focusing on human health is provided, along with an overview of the IATAs generated by the GRACIOUS project. We discuss the application and flexibility of the template, providing the opportunity to expand the application of grouping and read-across, in a logical evidencebased manner, to a wider range of NFs and substances.

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How can we justify grouping of nanoforms for hazard assessment? Concepts and tools to quantify similarity

Cited by 31 publications

References 65 publications

Meta‐analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

Meta‐analysis of Bioaccumulation Data for Nondissolvable Engineered Nanomaterials in Freshwater Aquatic Organisms

Grouping MWCNTs based on their similar potential to cause pulmonary hazard after inhalation: a case-study

How to formulate hypotheses and IATA to support grouping and read-across of nanoforms

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