In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products.... Show moreIn recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation.This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We reviewmodern multiscale physics-based models that generate advanced system-dependent (intrinsic) or time -and environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs' data generation, curation, representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing. Show less
Rybińska-Fryca, A.; Gromelski, M.; Vijver, M.G.; Peijnenburg, W.J.G.M.; Châtel, A.; Barrick, A.; ... ; Puzyn, T. 2022
Risk assessment of chemical substances is always a challenging process. It can be supported by using the potential of the in silico methods such as the read-across approach. Several frameworks and... Show moreRisk assessment of chemical substances is always a challenging process. It can be supported by using the potential of the in silico methods such as the read-across approach. Several frameworks and methodologies can be found, e.g. the Read-across Assessment Framework (RAAF) developed by ECHA, which describes how the analysis is carried out using the read-across approach. However, they are focused on classical chemical substances, not nanomaterials. Thus, our goal was to evaluate publicly available read-across frameworks in the context of ENM. Especially, in view of the recent update of the REACH regulations (Annex VI), which introduced the concept of “nanoform” of the substance. We examined the possibilities as well as the challenges for nanomaterials when applying available frameworks by carrying out readacross case studies for selected nanoforms of nano-SiO2. Structural properties of five ENMs and data related to their ecotoxicity were extracted from the JRC Repository characterization dossier on nanoSiO2 amorphous materials and the corresponding NanoReg2 H2020 project deliverable. From all endpoints available, toxicity results towards the Carp leucocyte cell line were considered as the most appropriate. For the purposes of the case study, we decided to treat one of the nano-SiO2 as a target (NM200) and the four others (NM 201- 204) as source analogues. The analysis consisted of several steps: i) identification and characterization of all nanoforms; ii) development of grouping hypothesis; iii) assignment to groups; iv) data gathering; v) applicability assessment; vi) filling data gaps. After passing through all the stages we were able to estimate the toxicity of target ENM. The formulated hypothesis of the read-across approach for the assessment of ecotoxicity was as follows: SiO2 nanoforms can be separated into two distinct groups based on how the following properties influenced cytotoxicity in fish cells: i) surface area, ii) coating mass, iii) size distribution in stock and media solutions, iv) polydispersity in stock and media solutions. This leads to the follow-up hypothesis of novel SiO2 ENMs with similar physicochemical/structural parameters inducing similar toxicological activities in fish cells. Subsequently, we employed similarity analysis in the space of the mentioned properties. Based on the calculated Euclidean distances, the target nanoform (NM200), has been placed within the group of toxic source analogues (NM201 and 204). Therefore, according to the worst-case approach, one can assume that the target nanoform will be highly toxic to fish cells. The results and lessons learned from this exercise will be discussed further in the context of the work carried out in the PATROLS project. Show less
