BackgroundTo assess the risk of all nanomaterials (NMs) on a case-by-case basis is challenging in terms of financial, ethical and time resources. Instead a more intelligent approach to knowledge gain and risk assessment is required.MethodsA framework of future research priorities was developed from the accorded opinion of experts covering all major stake holder groups (government, industry, academia, funders and NGOs). It recognises and stresses the major topics of physicochemical characterisation, exposure identification, hazard identification and modelling approaches as key components of the current and future risk assessment of NMs.ResultsThe framework for future research has been developed from the opinions of over 80 stakeholders, that describes the research priorities for effective development of an intelligent testing strategy (ITS) to allow risk evaluation of NMs. In this context, an ITS is a process that allows the risks of NMs to be assessed accurately, effectively and efficiently, thereby reducing the need to test NMs on a case-by-case basis.For each of the major topics of physicochemical characterisation, exposure identification, hazard identification and modelling, key-priority research areas are described via a series of stepping stones, or hexagon diagrams structured into a time perspective. Importantly, this framework is flexible, allowing individual stakeholders to identify where their own activities and expertise are positioned within the prioritisation pathway and furthermore to identify how they can effectively contribute and structure their work accordingly. In other words, the prioritisation hexagon diagrams provide a tool that individual stakeholders can adapt to meet their own particular needs and to deliver an ITS for NMs risk assessment. Such an approach would, over time, reduce the need for testing by increasing the reliability and sophistication of in silico approaches.The manuscript includes an appraisal of how this framework relates to the current risk assessment approaches and how future risk assessment could adapt to accommodate these new approaches. A full report is available in electronic format (pdf) at http://www.nano.hw.ac.uk/research-projects/itsnano.html.ConclusionITS-NANO has delivered a detailed, stakeholder driven and flexible research prioritisation (or strategy) tool, which identifies specific research needs, suggests connections between areas, and frames this in a time-perspective.
Species sensitivity distributions (SSD) and 5% hazardous concentrations (HC5) are distribution-based approaches for assessing environmental risks of pollutants. These methods have potential for application in pesticide risk assessments, but their applicability for assessing pesticide risks to soil invertebrate communities has not been evaluated. Using data obtained in a systematic review, the present study investigates the relevance of SSD and HC5 for predicting pesticide risks to soil invertebrates. Altogether, 1950 laboratory toxicity data were obtained, representing 250 pesticides and 67 invertebrate taxa. The majority (96%) of pesticides have toxicity data for fewer than five species. Based on a minimum of five species, the best available endpoint data (acute mortality median lethal concentration) enabled SSD and HC5 to be calculated for 11 pesticides (atrazine, carbendazim, chlorpyrifos, copper compounds, diazinon, dimethoate, gamma-hexachlorocyclohexane, lambda-cyhalothrin, parathion, pentachlorophenol, and propoxur). Arthropods and oligochaetes exhibit pronounced differences in their sensitivity to most of these pesticides. The standard test earthworm species, Eisenia fetida sensu lato, is the species that is least sensitive to insecticides based on acute mortality, whereas the standard Collembola test species, Folsomia candida, is among the most sensitive species for a broad range of toxic modes of action (biocide, fungicide, herbicide, and insecticide). These findings suggest that soil arthropods should be tested routinely in regulatory risk assessments. In addition, the data indicate that the uncertainty factor for earthworm acute mortality tests (i.e., 10) does not fully cover the range of earthworm species sensitivities and that acute mortality tests would not provide the most sensitive risk estimate for earthworms in the majority (95%) of cases.
The toxicity of a range of inorganic (Ag, Cu, Ni, Al(2)O(3), SiO(2), TiO(2) and ZrO(2)) nanoparticles (NP) and their corresponding metal salt or bulk metal oxide were screened for toxicity toward the earthworm Eisenia fetida using the limit-test design (1000 mg/kg). This study provides the first ecotoxicological life history trait data on earthworms for each these NPs, as well as for AgNO(3), Al(2)O(3), SiO(2), TiO(2) and ZrO(2). Significant effects were observed on survival for AgNO(3) (2.5% of controls), CuCl(2) (17.5% of controls) and NiCl(2) (32.5% of controls) and on reproduction (AgNO(3), CuCl(2), NiCl(2), Ag-NP, Cu-NP, TiO(2)-NP); with total reproductive failure in both silver treatments. Ag-NP, Cu-NP and TiO(2)-NP were the only NPs that caused toxic effects to E. fetida. The toxicity could not be singularly related to particle size or zeta potential or to the inherent element constituting the NPs (e.g. Ag).
The effects of eight polycyclic aromatic compounds on the survival and reproduction of the collembolan Folsomia fimetaria L. were investigated in a well-characterized Danish agricultural soil. With the exception of acridine, polycyclic aromatic hydrocarbons (PAHs) and neutral N-, S-, and O-monosubstituted analogues showed similar toxicities to soil collembolans when the results were expressed in relation to total soil concentrations (mg/kg). The estimated concentrations resulting in a 10% reduction of reproductive output (EC10 values) were based on measured initial concentrations and were for acridine 290 mg/kg, carbazole 10 mg/kg, dibenzofuran 19 mg/kg, dibenzothiophene 7.8 mg/kg, fluoranthene 37 mg/kg, fluorene 7.7 mg/kg, phenantrene 23 mg/kg, and pyrene 10 mg/kg. When the EC10 values were converted to soil pore-water concentrations, they showed a highly significant correlation (r2 = 0.71, p < 0.01) to no-observed-effect concentrations for the freshwater crustacean Daphnia magna, as estimated by a quantitative structure activity relation (QSAR) for baseline toxicity (nonpolar narcosis). Only carbazole and acridine were more than two times more toxic (4.9 and 3.1, respectively) than expected from the Daphnia QSAR data. The latter result indicates that the toxicity of the tested substances is close to that expected for compounds with nonpolar narcosis as the mode of action. However, the relatively large uncertainties in the extrapolation method prevent final conclusions from being drawn.
Physicochemical properties of chemicals affect their exposure, toxicokinetics/fate and hazard, and for nanomaterials, the variation of these properties results in a wide variety of materials with potentially different risks. To limit the amount of testing for risk assessment, the information gathering process for nanomaterials needs to be efficient. At the same time, sufficient information to assess the safety of human health and the environment should be available for each nanomaterial. Grouping and read-across approaches can be utilised to meet these goals. This article presents different possible applications of grouping and read-across for nanomaterials within the broader perspective of the MARINA Risk Assessment Strategy (RAS), as developed in the EU FP7 project MARINA. Firstly, nanomaterials can be grouped based on limited variation in physicochemical properties to subsequently design an efficient testing strategy that covers the entire group. Secondly, knowledge about exposure, toxicokinetics/fate or hazard, for example via properties such as dissolution rate, aspect ratio, chemical (non-)activity, can be used to organise similar materials in generic groups to frame issues that need further attention, or potentially to read-across. Thirdly, when data related to specific endpoints is required, read-across can be considered, using data from a source material for the target nanomaterial. Read-across could be based on a scientifically sound justification that exposure, distribution to the target (fate/toxicokinetics) and hazard of the target material are similar to, or less than, the source material. These grouping and read-across approaches pave the way for better use of available information on nanomaterials and are flexible enough to allow future adaptations related to scientific developments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.