Physiologically based mathematical models of nanomaterials for regulatory toxicology: A review.

The development of physiologically based (PB) models to support safety assessments in the field of nanotechnology has grown steadily during the last decade. This review reports on the availability of PB models for toxicokinetic (TK) and toxicodynamic (TD) processes, including in vitro and in vivo dosimetry models applied to manufactured nanomaterials (MNs). In addition to reporting on the state-of-the-art in the scientific literature concerning the availability of physiologically based kinetic (PBK) models, we evaluate their relevance for regulatory applications, mainly considering the EU REACH regulation. First, we performed a literature search to identify all available PBK models. Then, we systematically reported the content of the identified papers in a tailored template to build a consistent inventory, thereby supporting model comparison. We also described model availability for physiologically based dynamic (PBD) and in vitro and in vivo dosimetry models according to the same template. For completeness, a number of classical toxicokinetic (CTK) models were also included in the inventory. The review describes the PBK model landscape applied to MNs on the basis of the type of MNs covered by the models, their stated applicability domain, the type of (nano-specific) inputs required, and the type of outputs generated. We identify the main assumptions made during model development that may influence the uncertainty in the final assessment, and we assess the REACH relevance of the available models within each model category. Finally, we compare the state of PB model acceptance for chemicals and for MNs. In general, PB model acceptance is limited by the absence of standardised reporting formats, psychological factors such as the complexity of the models, and technical considerations such as lack of blood:tissue partitioning data for model calibration/validation.

Computational models for the assessment of manufactured nanomaterials: Development of model reporting standards and mapping of the model landscape.

Different types of computational models have been developed for predicting the biokinetics, environmental fate, exposure levels and toxicological effects of chemicals and manufactured nanomaterials (MNs). However, these models are not described in a consistent manner in the scientific literature, which is one of the barriers to their broader use and acceptance, especially for regulatory purposes. Quantitative structure-activity relationships (QSARs) are in silico models based on the assumption that the activity of a substance is related to its chemical structure. These models can be used to provide information on (eco)toxicological effects in hazard assessment. In an environmental risk assessment, environmental exposure models can be used to estimate the predicted environmental concentration (PEC). In addition, physiologically based kinetic (PBK) models can be used in various ways to support a human health risk assessment. In this paper, we first propose model reporting templates for systematically and transparently describing models that could potentially be used to support regulatory risk assessments of MNs, for example under the REACH regulation. The model reporting templates include (a) the adaptation of the QSAR Model Reporting Format (QMRF) to report models for MNs, and (b) the development of a model reporting template for PBK and environmental exposure models applicable to MNs. Second, we show the usefulness of these templates to report different models, resulting in an overview of the landscape of available computational models for MNs.

Grouping of nanomaterials to read-across hazard endpoints: a review.

The use of non-testing strategies like read-across in the hazard assessment of chemicals and nanomaterials (NMs) is deemed essential to perform the safety assessment of all NMs in due time and at lower costs. The identification of physicochemical (PC) properties affecting the hazard potential of NMs is crucial, as it could enable to predict impacts from similar NMs and outcomes of similar assays, reducing the need for experimental (and in particular animal) testing. This manuscript presents a review of approaches and available case studies on the grouping of NMs to read-across hazard endpoints. We include in this review grouping frameworks aimed at identifying hazard classes depending on PC properties, hazard classification modules in control banding (CB) approaches, and computational methods that can be used for grouping for read-across. The existing frameworks and case studies are systematically reported. Relevant nanospecific PC properties taken into account in the reviewed frameworks to support grouping are shape and surface properties (surface chemistry or reactivity) and hazard classes are identified on the basis of biopersistence, morphology, reactivity, and solubility.

Grouping of nanomaterials to read-across hazard endpoints: from data collection to assessment of the grouping hypothesis by application of chemoinformatic techniques.

BACKGROUND: An 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. METHODS: The 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. RESULTS: A 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. CONCLUSION: The 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.

Improving the prediction of environmental fate of engineered nanomaterials by fractal modelling.

A critical analysis of the available engineered nanomaterials (ENMs) environmental fate modelling approaches indicates that existing tools do not satisfactorily account for the complexities of nanoscale phenomena. Fractal modelling (FM) can complement existing kinetic fate models by including more accurate interpretations of shape and structure, density and collision efficiency parameters to better describe homo- and heteroaggregation. Pathways to including hierarchical symmetry concepts and a route to establishing a structural classification of nanomaterials based on FM are proposed.

Introducing an integrated climate change perspective in POPs modelling, monitoring and regulation.

This paper presents a review on the implications of climate change on the monitoring, modelling and regulation of persistent organic pollutants (POPs). Current research gaps are also identified and discussed. Long-term data sets are essential to identify relationships between climate fluctuations and changes in chemical species distribution. Reconstructing the influence of climatic changes on POPs environmental behaviour is very challenging in some local studies, and some insights can be obtained by the few available dated sediment cores or by studying POPs response to inter-annual climate fluctuations. Knowledge gaps and future projections can be studied by developing and applying various modelling tools, identifying compounds susceptibility to climate change, local and global effects, orienting international policies. Long-term monitoring strategies and modelling exercises taking into account climate change should be considered when devising new regulatory plans in chemicals management.