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

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning

Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical machine learning approaches. Multiple unsupervised learning algorithms (principal component analysis, agglomerative clustering, and K-means) as well as supervised linear and nonlinear regression algorithms (linear regression, AdaBoost ensemble learning, artificial neural network) have been applied. As a result, a small set of biomolecules has been identified, knowledge of adsorption free energies of which to a specific nanomaterial can be used to predict, within the developed machine learning model, adsorption free energies of other biomolecules. Furthermore, the methodology of grouping of nanomaterials according to their interactions with biomolecules has been presented.

Deimos: A novel automated methodology for optimal grouping. Application to nanoinformatics case studies

In this study we present deimos, a computational methodology for optimal grouping, applied on the read-across prediction of engineered nanomaterials' (ENMs) toxicity-related properties. The method is based on the formulation and the solution of a mixed-integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple-region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction.

Assessing regulated cell death modalities as an efficient tool for in vitro nanotoxicity screening: a review

Nanomedicine is a fast-growing field of nanotechnology. One of the major obstacles for a wider use of nanomaterials for medical application is the lack of standardized toxicity screening protocols for assessing the safety of newly synthesized nanomaterials. In this review, we focus on less frequently studied nanomaterials-induced regulated cell death (RCD) modalities, including eryptosis, necroptosis, pyroptosis, and ferroptosis, as a tool for in vitro nanomaterials safety evaluation. We summarize the latest insights into the mechanisms that mediate these RCDs in response to nanomaterials exposure. Comprehensive data from reviewed studies suggest that ROS (reactive oxygen species) overproduction and ROS-mediated pathways play a central role in nanomaterials-induced RCDs activation. On the other hand, studies also suggest that individual properties of nanomaterials, including size, shape, or surface charge, could determine specific toxicity pathways with consequent RCD induction as well. We anticipate that the evaluation of RCDs can become one of the mechanism-based screening methods in nanotoxicology. In addition to the toxicity assessment, evaluation of necroptosis-, pyroptosis-, and ferroptosis-promoting capacity of nanomaterials could simultaneously provide useful information for specific medical applications as could be their anti-tumor potential. Moreover, a detailed understanding of molecular mechanisms driving nanomaterials-mediated induction of immunogenic RCDs will substantially aid novel anti-tumor nanodrugs development.

The cell transformation assay to assess potential carcinogenic properties of nanoparticles

Nanoparticles (NPs) are present in many daily life products with particular physical-chemical properties (size, density, porosity, geometry …) giving very interesting technological properties. Their use is continuously growing and NPs represent a new challenge in terms of risk assessment, consumers being multi-exposed. Toxic effects have already been identified such as oxidative stress, genotoxicity, inflammatory effects, and immune reactions, some of which are leading to carcinogenesis. Cancer is a complex phenomenon implying multiple modes of action and key events, and prevention strategies in cancer include a proper assessment of the properties of NPs. Therefore, introduction of new agents like NPs into the market creates fresh regulatory challenges for an adequate safety evaluation and requires new tools. The Cell Transformation Assay (CTA) is an in vitro test able of highlighting key events of characteristic phases in the cancer process, initiation and promotion. This review presents the development of this test and its use with NPs. The article underlines also the critical issues to address for assessing NPs carcinogenic properties and approaches for improving its relevance.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

A Nanomedicine Structure-Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of science-informed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure-activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

Magnetic Iron Nanoparticles: Synthesis, Surface Enhancements, and Biological Challenges

This review focuses on the role of magnetic nanoparticles (MNPs), their physicochemical properties, their potential applications, and their association with the consequent toxicological effects in complex biologic systems. These MNPs have generated an accelerated development and research movement in the last two decades. They are solving a large portion of problems in several industries, including cosmetics, pharmaceuticals, diagnostics, water remediation, photoelectronics, and information storage, to name a few. As a result, more MNPs are put into contact with biological organisms, including humans, via interacting with their cellular structures. This situation will require a deeper understanding of these particles’ full impact in interacting with complex biological systems, and even though extensive studies have been carried out on different biological systems discussing toxicology aspects of MNP systems used in biomedical applications, they give mixed and inconclusive results. Chemical agencies, such as the Registration, Evaluation, Authorization, and Restriction of Chemical substances (REACH) legislation for registration, evaluation, and authorization of substances and materials from the European Chemical Agency (ECHA), have held meetings to discuss the issue. However, nanomaterials (NMs) are being categorized by composition alone, ignoring the physicochemical properties and possible risks that their size, stability, crystallinity, and morphology could bring to health. Although several initiatives are being discussed around the world for the correct management and disposal of these materials, thanks to the extensive work of researchers everywhere addressing the issue of related biological impacts and concerns, and a new nanoethics and nanosafety branch to help clarify and bring together information about the impact of nanoparticles, more questions than answers have arisen regarding the behavior of MNPs with a wide range of effects in the same tissue. The generation of a consolidative framework of these biological behaviors is necessary to allow future applications to be manageable.

Experimental and Computational Nanotoxicology-Complementary Approaches for Nanomaterial Hazard Assessment

The growing development and applications of nanomaterials lead to an increasing release of these materials in the environment. The adverse effects they may elicit on ecosystems or human health are not always fully characterized. Such potential toxicity must be carefully assessed with the underlying mechanisms elucidated. To that purpose, different approaches can be used. First, experimental toxicology consisting of conducting in vitro or in vivo experiments (including clinical studies) can be used to evaluate the nanomaterial hazard. It can rely on variable models (more or less complex), allowing the investigation of different biological endpoints. The respective advantages and limitations of in vitro and in vivo models are discussed as well as some issues associated with experimental nanotoxicology. Perspectives of future developments in the field are also proposed. Second, computational nanotoxicology, i.e., in silico approaches, can be used to predict nanomaterial toxicity. In this context, we describe the general principles, advantages, and limitations especially of quantitative structure–activity relationship (QSAR) models and grouping/read-across approaches. The aim of this review is to provide an overview of these different approaches based on examples and highlight their complementarity.

Ecotoxicological read-across models for predicting acute toxicity of freshly dispersed versus medium-aged NMs to Daphnia magna

Nanoinformatics models to predict the toxicity/ecotoxicity of nanomaterials (NMs) are urgently needed to support commercialization of nanotechnologies and allow grouping of NMs based on their physico-chemical and/or (eco)toxicological properties, to facilitate read-across of knowledge from data-rich NMs to data-poor ones. Here we present the first ecotoxicological read-across models for predicting NMs ecotoxicity, which were developed in accordance with ECHA's recommended strategy for grouping of NMs as a means to explore in silico the effects of a panel of freshly dispersed versus environmentally aged (in various media) Ag and TiO₂ NMs on the freshwater zooplankton Daphnia magna, a keystone species used in regulatory testing. The dataset used to develop the models consisted of dose-response data from 11 NMs (5 TiO₂ NMs of identical cores with different coatings, and 6 Ag NMs with different capping agents/coatings) each dispersed in three different media (a high hardness medium (HH Combo) and two representative river waters containing different amounts of natural organic matter (NOM) and having different ionic strengths), generated in accordance with the OECD 202 immobilization test. The experimental hypotheses being tested were (1) that the presence of NOM in the medium would reduce the toxicity of the NMs by forming an ecological corona, and (2) that environmental ageing of NMs reduces their toxicity compared to the freshly dispersed NMs irrespective of the medium composition (salt only or NOM-containing). As per the ECHA guidance, the NMs were grouped into two categories - freshly dispersed and 2-year-aged and explored in silico to identify the most important features driving the toxicity in each group. The final predictive models have been validated according to the OECD criteria and a QSAR model report form (QMRF) report included in the supplementary information to support adoption of the models for regulatory purposes.

Nano-QTTR development for interspecies aquatic toxicity of silver nanoparticles between daphnia and fish

Limited studies of quantitative toxicity-toxicity relationship (QTTR) modeling have been conducted to predict interspecies toxicity of engineered nanomaterials (ENMs) between aquatic test species. A meta-analysis of 66 publications providing acute toxicity data of silver nanoparticles (AgNPs) to daphnia and fish was performed, and the toxicity data, physicochemical properties, and experimental conditions were collected and curated. Based on Euclidean distance (ED) grouping, a meaningful correlation of logarithmic lethal concentrations between daphnia and fish was derived for bare (R²_bare = 0.47) and coated AgNPs (R²_coated = 0.48) when a distance of 10 was applied. The correlation of coated AgNPs was improved (R²_coated = 0.55) by the inclusion of descriptors of the coating materials. The correlations were further improved by R²_bare = 0.57 and R²_coated = 0.81 after additionally considering particle size only, and by R²_bare = 0.59 and R²_coated = 0.92 after considering particle size and zeta potential simultaneously. The developed ED-based nano-QTTR model demonstrated that inclusion of the coating material descriptors and physicochemical properties improved the goodness-of-fit to predict interspecies aquatic toxicity of AgNPs between daphnia and fish. This study provides insight for future in silico research on QTTR model development in ENM toxicology.

FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study on in vitro Comet assay genotoxicity data

(Quantitative) structure-activity relationship ([Q]SAR) methodologies are widely applied to predict the (eco)toxicological effects of chemicals, and their use is envisaged in different regulatory frameworks for filling data gaps of untested substances. However, their application to the risk assessment of nanomaterials is still limited, also due to the scarcity of large and curated experimental datasets. Despite a great amount of nanosafety data having been produced over the last decade in international collaborative initiatives, their interpretation, integration and reuse has been hampered by several obstacles, such as poorly described (meta)data, non-standard terminology, lack of harmonized reporting formats and criteria. Recently, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles have been established to guide the scientific community in good data management and stewardship. The EU H2020 Gov4Nano project, together with other international projects and initiatives, is addressing the challenge of improving nanosafety data FAIRness, for maximizing their availability, understanding, exchange and ultimately their reuse. These efforts are largely supported by the creation of a common Nanosafety Data Interface, which connects a row of project-specific databases applying the eNanoMapper data model. A wide variety of experimental data relating to characterization and effects of nanomaterials are stored in the database; however, the methods, protocols and parameters driving their generation are not fully mature. This article reports the progress of an ongoing case study in the Gov4nano project on the reuse of in vitro Comet genotoxicity data, focusing on the issues and challenges encountered in their FAIRification through the eNanoMapper data model. The case study is part of an iterative process in which the FAIRification of data supports the understanding of the phenomena underlying their generation and, ultimately, improves their reusability.

Safe-by-Design part II: A strategy for balancing safety and functionality in the different stages of the innovation process

Manufactured nanomaterials have the potential to impact an exceedingly wide number of industries and markets ranging from energy storage, electronic and optical devices, light-weight construction to innovative medical approaches for diagnostics and therapy. In order to foster the development of safer nanomaterial-containing products, two main aspects are of major interest: their functional performance as well as their safety towards human health and the environment. In this paper a first proposal for a strategy is presented to link the functionality of nanomaterials with safety aspects. This strategy first combines information on the functionality and safety early during the innovation process and onwards, and then identifies Safe-by-Design (SbD) actions that allow for optimisation of both aspects throughout the innovation process. The strategy encompasses suggestions for the type of information needed to balance functionality and safety to support decision making in the innovation process. The applicability of the strategy is illustrated using a literature-based case study on carbon nanotube-based transparent conductive films. This is a first attempt to identify information that can be used for balancing functionality and safety in a structured way during innovation processes.

A concise review: the synergy between artificial intelligence and biomedical nanomaterials that empowers nanomedicine

Nanomedicine has recently experienced unprecedented growth and development. However, the complexity of operations at the nanoscale introduces a layer of difficulty in the clinical translation of nanodrugs and biomedical nanotechnology. This problem is further exacerbated when engineering and optimizing nanomaterials for biomedical purposes. To navigate this issue, artificial intelligence (AI) algorithms have been applied for data analysis and inference, allowing for a more applicable understanding of the complex interaction amongst the abundant variables in a system involving the synthesis or use of nanomedicine. Here, we report on the current relationship and implications of nanomedicine and AI. Particularly, we explore AI as a tool for enabling nanomedicine in the context of nanodrug screening and development, brain-machine interfaces and nanotoxicology. We also report on the current state and future direction of nanomedicine and AI in cancer, diabetes, and neurological disorder therapy.

Nanotechnology and artificial intelligence to enable sustainable and precision agriculture

Climate change, increasing populations, competing demands on land for production of biofuels and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, in which farmers respond in real time to changes in crop growth with nanotechnology and artificial intelligence, offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery, nutrient capture and long-term impacts on soil microbial communities will enable design of nanoscale agrochemicals that combine optimal safety and functionality profiles.

Variation in dissolution behavior among different nanoforms and its implication for grouping approaches in inhalation toxicity

Different nanoforms (NF) of the same substance each need to be registered under REACH, but similarities in physiological interaction -among them biodissolution- can justify read-across within a group of NFs, thereby reducing the need to perform animal studies. Here we focused on the endpoint of inhalation toxicity and explored how differences in physical parameters of 17 NFs of silica, and organic and inorganic pigments impact dissolution rates, half-times, and transformation under both pH 7.4 lung lining conditions and pH 4.5 lysosomal conditions. We benchmarked our observations against well-known TiO₂, BaSO₄ and ZnO nanomaterials, representing very slow, partial and quick dissolution respectively. By automated image evaluation, structural transformations were observed for dissolution rates in the order of 0.1 to 10 ng/cm²/h, but did not provide additional decision criteria on the similarity of NFs. Dissolution half-times spanned nearly five orders of magnitude, mostly dictated by the substance and simulant fluid, but modulated up to ten-fold by the subtle differences between NFs. Physiological time scales and benchmark materials help to frame the biologically relevant range, proposed as 1 h to 1 y. NFs of ZnO, Ag, SiO₂, BaSO₄ were in this range. We proposed numerical rules of pairwise similarity within a group, of which the worst case NF would be further assessed by in vivo inhalation studies. These rules divided the colloidal silica NFs into two separate candidate groups, one with Al-doping, one without. Shape or silane surface treatment were less important. The dissolution halftimes of many organic and inorganic pigment NFs were longer than the biologically relevant range, such that dissolution behavior is not an obstacle for their groupings.

Utilizing literature-based rodent toxicology data to derive potency estimates for quantitative risk assessment

Evaluating the potential occupational health risk of engineered nanomaterials is an ongoing need. The objective of this meta-analysis, which consisted of 36 studies containing 86 materials, was to assess the availability of published in vivo rodent pulmonary toxicity data for a variety of nanoscale and microscale materials and to derive potency estimates via benchmark dose modeling. Additionally, the potency estimates based on particle mass lung dose associated with acute pulmonary inflammation were used to group materials based on toxicity. The commonalities among the physicochemical properties of the materials in each group were also explored. This exploration found that a material's potency tended to be associated primarily with the material class based on chemical composition and form (e.g. carbon nanotubes, TiO₂, ZnO) rather than with particular physicochemical properties. Limitations in the data available precluded a more extensive analysis of these associations. Issues such as data reporting and appropriate experimental design for use in quantitative risk assessment are the main reasons publications were excluded from these analyses and are discussed.

Towards FAIR nanosafety data

Nanotechnology is a key enabling technology with billions of euros in global investment from public funding, which include large collaborative projects that have investigated environmental and health safety aspects of nanomaterials, but the reuse of accumulated data is clearly lagging behind. Here we summarize challenges and provide recommendations for the efficient reuse of nanosafety data, in line with the recently established FAIR (findable, accessible, interoperable and reusable) guiding principles. We describe the FAIR-aligned Nanosafety Data Interface, with an aggregated findability, accessibility and interoperability across physicochemical, bio-nano interaction, human toxicity, omics, ecotoxicological and exposure data. Overall, we illustrate a much-needed path towards standards for the optimized use of existing data, which avoids duplication of efforts, and provides a multitude of options to promote safe and sustainable nanotechnology.

An integrated pathway based on in vitro data for the human hazard assessment of nanomaterials

In line with the 3R concept, nanotoxicology is shifting from a phenomenological to a mechanistic approach based on in vitro and in silico methods, with a consequent reduction in animal testing. Risk Assessment (RA) and Life Cycle Assessment (LCA) methodologies, which traditionally rely on in vivo toxicity studies, will not be able to keep up with the pace of development of new nanomaterials unless they adapt to use this new type of data. While tools and models are already available and show a great potential for future use in RA and LCA, currently none is able alone to quantitatively assess human hazards (i.e. calculate chronic NOAEL or ED₅₀ values). By highlighting which models and approaches can be used in a quantitative way with the available knowledge and data, we propose an integrated pathway for the use of in vitro data in RA and LCA. Starting with the characterization of nanoparticles' properties, the pathway then investigates how to select relevant in vitro human data, and how to bridge in vitro dose-response relationships to in vivo effects. If verified, this approach would allow RA and LCA to stir up the development of nanotoxicology by giving indications about the data and quality requirements needed in risk methodologies.

Nanotoxicology data for in silico tools: a literature review

The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies.

Potential Role of Soluble Metal Impurities in the Acute Lung Inflammogenicity of Multi-Walled Carbon Nanotubes

Multi-walled carbon nanotubes (MWCNTs) have variable metal impurities, but little is known about the impact of soluble metal impurities on the toxicity of MWCNTs. Here, we evaluated the role of soluble metal impurities to the acute inflammogenic potential of MWCNTs, using five types of high purity MWCNTs (>95%). MWCNTs and their soluble fractions collected at 24 h after incubation in phosphate-buffered saline showed diverse metal impurities with variable concentrations. The fiber-free soluble fractions produced variable levels of reactive oxygen species (ROS), and the iron level was the key determinant for ROS production. The acute inflammation at 24 h after intratracheal instillation of MWCNTs to rats at 0.19, 0.63, and 1.91 mg MWCNT/kg body weight (bw) or fiber-free supernatants from MWCNT suspensions at 1.91 and 7.64 mg MWCNT/kg bw showed that the number of granulocytes, a marker for acute inflammation, was significantly increased with a good dose-dependency. The correlation study showed that neither the levels of iron nor the ROS generation potential of the soluble fractions showed any correlations with the inflammogenic potential. However, the total concentration of transition metals in the soluble fractions showed a good correlation with the acute lung inflammogenic potential. These results implied that metal impurities, especially transitional metals, can contribute to the acute inflammogenic potential of MWCNTs, although the major parameter for the toxicity of MWCNTs is size and shape.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach

In the present study, a novel read-across methodology for the prediction of toxicity related end-points of engineered nanomaterials (ENMs) is developed. The proposed method lies in the interface between the two main read-across approaches, namely the analogue and the grouping methods, and can employ a single criterion or multiple criteria for defining similarities among ENMs. The main advantage of the proposed method is that there is no need of defining a prior read-across hypothesis. Based on the formulation and the solution of a mathematical optimization problem, the method searches over a space of alternative hypotheses, and determines the one providing the most accurate read-across predictions. The procedure is automated and only two parameters are user-defined: the balance between the level of predictive accuracy and the number of predicted samples, and the similarity criteria, which define the neighbors of a target ENM.

Developing OECD test guidelines for regulatory testing of nanomaterials to ensure mutual acceptance of test data

The OECD Working Party on Manufactured Nanomaterials (WPMN) provides a global forum for discussion of nano-safety issues. Together with the OECD Test Guidelines Programme (TGP) the WPMN has explored the need for adaptation of some of the existing OECD Test Guidelines (TGs) and Guidance Documents (GDs) as well as developing new TGs and GDs to specifically address NM issues. An overview is provided of progress in the TGP and WPMN, and information on supporting initiatives, regarding the development of TGs for nanomaterials addressing Physical Chemical Properties, Effects on Biotic Systems, Environmental Fate and Behaviour, and Health Effects. Three TGs specifically addressing manufactured nanomaterials have been adopted: a new TG318 ″Dispersion Stability of Nanomaterials in Simulated Environmental Media", and adaptation of TG412 and TG413 on Subacute Inhalation Toxicity: 28-Day Study/90-day Study. The associated GD39 on Inhalation Toxicity Testing has also been revised. The TGP current develops four new TGs and four GDs. One new TG and six GDs are developed in the WPMN. Six new proposals were submitted to the TGP in 2018. Furthermore, as TGs are accompanied by OECD harmonised templates (OHTs) for data collection, an outline of recently developed OHTs particularly relevant for NMs is also included.