Biomarkers of nanomaterials hazard from multi-layer data.
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ABSTRACT:
SUBMITTER: Fortino V
PROVIDER: S-EPMC9249793 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitative WoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs-TiO₂, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN and WoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework.
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Project description:BackgroundAn increasing number of manufactured nanomaterials (NMs) are being used in industrial products and need to be registered under the REACH legislation. The hazard characterisation of all these forms is not only technically challenging but resource and time demanding. The use of non-testing strategies like read-across is deemed essential to assure the assessment of all NMs in due time and at lower cost. The fact that read-across is based on the structural similarity of substances represents an additional difficulty for NMs as in general their structure is not unequivocally defined. In such a scenario, the identification of physicochemical properties affecting the hazard potential of NMs is crucial to define a grouping hypothesis and predict the toxicological hazards of similar NMs. In order to promote the read-across of NMs, ECHA has recently published "Recommendations for nanomaterials applicable to the guidance on QSARs and Grouping", but no practical examples were provided in the document. Due to the lack of publicly available data and the inherent difficulties of reading-across NMs, only a few examples of read-across of NMs can be found in the literature. This manuscript presents the first case study of the practical process of grouping and read-across of NMs following the workflow proposed by ECHA.MethodsThe workflow proposed by ECHA was used and slightly modified to present the read-across case study. The Read-Across Assessment Framework (RAAF) was used to evaluate the uncertainties of a read-across within NMs. Chemoinformatic techniques were used to support the grouping hypothesis and identify key physicochemical properties.ResultsA dataset of 6 nanoforms of TiO2 with more than 100 physicochemical properties each was collected. In vitro comet assay result was selected as the endpoint to read-across due to data availability. A correlation between the presence of coating or large amounts of impurities and negative comet assay results was observed.ConclusionThe workflow proposed by ECHA to read-across NMs was applied successfully. Chemoinformatic techniques were shown to provide key evidence for the assessment of the grouping hypothesis and the definition of similar NMs. The RAAF was found to be applicable to NMs.
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Project description:Agricultural food crop plants interact with engineered nanomaterials (ENMs) from the application of agri-food nanotechnologies and from unintentional emissions originating from other nanotechnologies. Both types of exposure present implications for agricultural yield and quality, food chain transfer, and environmental and human health. In this review, the most recent findings from agricultural plant-ENM studies published in 2017 and 2018 are summarized. The aim of this is to identify the current hazard potential of ENMs for plants grown under typical field conditions that originate from both intentional and unintentional exposures and to contribute to knowledge-based decisions on the application of ENMs in food-agriculture. We also address recent knowledge on ENM adsorption, internalization, translocation, and bioaccumulation by plants, ENM impacts on agricultural crop yield and nutrition, and ENM biotransformation. Using adverse effect level concentrations and data on ENM accumulation in environmental matrices, the literature analyses revealed that C-, Ag-, Ce-, and Ti-based ENMs are unlikely to pose a risk to plants grown under typical field conditions, whereas Cu- and Zn-based ENMs require surveillance. Since multiple factors (e.g., ENM concentration, route of exposure, and plant type) influence the effects of ENMs on plants, biomonitoring is recommended for tracking ENM environmental exposure in the future.
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