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.

COSMOS next generation - A public knowledge base leveraging chemical and biological data to support the regulatory assessment of chemicals.

The COSMOS Database (DB) was originally established to provide reliable data for cosmetics-related chemicals within the COSMOS Project funded as part of the SEURAT-1 Research Initiative. The database has subsequently been maintained and developed further into COSMOS Next Generation (NG), a combination of database and in silico tools, essential components of a knowledge base. COSMOS DB provided a cosmetics inventory as well as other regulatory inventories, accompanied by assessment results and in vitro and in vivo toxicity data. In addition to data content curation, much effort was dedicated to data governance - data authorisation, characterisation of quality, documentation of meta information, and control of data use. Through this effort, COSMOS DB was able to merge and fuse data of various types from different sources. Building on the previous effort, the COSMOS Minimum Inclusion (MINIS) criteria for a toxicity database were further expanded to quantify the reliability of studies. COSMOS NG features multiple fingerprints for analysing structure similarity, and new tools to calculate molecular properties and screen chemicals with endpoint-related public profilers, such as DNA and protein binders, liver alerts and genotoxic alerts. The publicly available COSMOS NG enables users to compile information and execute analyses such as category formation and read-across. This paper provides a step-by-step guided workflow for a simple read-across case, starting from a target structure and culminating in an estimation of a NOAEL confidence interval. Given its strong technical foundation, inclusion of quality-reviewed data, and provision of tools designed to facilitate communication between users, COSMOS NG is a first step towards building a toxicological knowledge hub leveraging many public data systems for chemical safety evaluation. We continue to monitor the feedback from the user community at support@mn-am.com.

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.

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.

Experimental verification of structural alerts for the protein binding of sulfur-containing compounds.

As often noted by Dr. Gilman Veith, a major barrier to advancing any model is defining its applicability domain. Sulfur-containing industrial organic chemicals can be grouped into several chemical classes including mercaptans (RSH), sulfides (RSR'), disulfides (RSSR'), sulfoxides (RS(=O)R'), sulfones (RS(=O)(=O)R'), sulfonates (ROS(=O)(=O)R') and sulfates (ROS(=O)(=O)OR'). In silico expert systems that predict protein binding reactions from 2D structure sub-divide these chemical classes into a variety of chemical reactive mechanisms and reactions which have toxic consequences. Using the protein binding profilers in version 3.1 of the OECD QSAR Toolbox, a series of sulfur-containing chemicals were profiled for protein binding potential. From these results it was hypothesized which sulfur-containing chemicals would be reactive or non-reactive in an in chemico glutathione assay and whether if reactive they would exhibit toxicity in excess of baseline in the Tetrahymena pyriformis population growth impairment assay. Subsequently, these hypotheses were tested experimentally. The in chemico data show that the in silico profiler predictions were generally correct for all chemical categories, where testing was possible. Mercaptans could not be assessed for GSH reactivity because they react directly with the chromophore 5,5'-dithiobis-(2-nitrobenzoic acid). With some exceptions, the major being disulfides, the in vitro toxicity data supported the in chemico findings.

Species analysis of metallothionein isoforms in human brain cytosols by use of capillary electrophoresis hyphenated to inductively coupled plasma-sector field mass spectrometry.

A new approach for the speciation of metallothioneins (MT) in human brain cytosols is described. The analysis is performed by application of a newly developed coupling of capillary electrophoresis (CE) with inductively coupled plasma-sector field mass spectrometry (ICP-SFMS). Isoforms of metallothioneins are separated from 30-100 microliter sample volumes by CE and the elements Cu, Zn, Cd, and S are detected by use of ICP-SFMS. The extraction of cytosols is the first step in the analytical procedure. Tissue samples from human brain are homogenized in a buffer solution and submitted to ultra-centrifugation. The supernatant is defatted and the cytosol pre-treatment is optimized for CE separation by matrix reduction. The buffer concentration and pH used for capillary electrophoretic separation of metallothionein from rabbit liver were optimized. CE with ICP-MS detection is compared to UV detection. In the electropherograms obtained from the cytosols three peaks can be assigned to MT-1, MT-2, and MT-3. As an additional method, size-exclusion chromatography (SEC) is applied. Fractions from an SEC separation of the cytosol are collected, concentrated, and then injected into the CE. The detection of sulfur by ICP-SFMS (medium resolution mode) and quantification by isotope dilution have also been investigated as a new method for the quantification of MT isoforms. The analytical procedure developed has been used for the first time in comparative studies of the distributions of MT-1, MT-2, and MT-3 in brain samples taken from patients with Alzheimer's disease and from a control group.