A literature curated dataset containing 24 distinct metal oxide (Me
O
) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic ...computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of Me
O
NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by Me
O
NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the Me
O
conduction band (
), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).
The physicochemical characterisation data from a library of 69 engineered nanomaterials (ENMs) has been exploited in silico following enrichment with a set of molecular descriptors that can be easily ...acquired or calculated using atomic periodicity and other fundamental atomic parameters. Based on the extended set of twenty descriptors, a robust and validated nanoinformatics model has been proposed to predict the ENM ζ-potential. The five critical parameters selected as the most significant for the model development included the ENM size and coating as well as three molecular descriptors, metal ionic radius (rion), the sum of metal electronegativity divided by the number of oxygen atoms present in a particular metal oxide (Σχ/nO) and the absolute electronegativity (χabs), each of which is thoroughly discussed to interpret their influence on ζ-potential values. The model was developed using the Isalos Analytics Platform and is available to the community as a web service through the Horizon 2020 (H2020) NanoCommons Transnational Access services and the H2020 NanoSoveIT Integrated Approach to Testing and Assessment (IATA).
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•A NM library was enriched with molecular descriptors calculated from atomic data.•20 descriptors were used to develop and validate a model to predict ζ-potential.•Critical parameters for predicting z-potential included the NM size and coating.•Three molecular descriptors related to metal and oxygen content were also critical.•The model, developed using Isalos Analytics Platform, is available as a web service.
Mesocosms allow the simulation of environmentally relevant conditions and can be used to establish more realistic scenarios of organism exposure to nanoparticles. An indoor mesocosm experiment ...simulating an aquatic stream ecosystem was conducted to assess the toxicokinetics and bioaccumulation of silver sulfide nanoparticles (Ag2S NPs) and AgNO3 in the freshwater invertebrates Girardia tigrina, Physa acuta and Chironomus riparius, and determine if previous single-species tests can predict bioaccumulation in the mesocosm. Water was daily spiked at 10 μg Ag L−1. Ag concentrations in water and sediment reached values of 13.4 μg Ag L−1 and 0.30 μg Ag g−1 in the Ag2S NP exposure, and 12.8 μg Ag L−1 and 0.20 μg Ag g−1 in the AgNO3. Silver was bioaccumulated by the species from both treatments, but with approximately 1.5, 3 and 11 times higher body Ag concentrations in AgNO3 compared to Ag2S NP exposures in snails, chironomids and planarians, respectively. In the Ag2S NP exposures, the observed uptake was probably of the particulate form. This demonstrates that this more environmentally relevant Ag nanoform may be bioavailable for uptake by benthic organisms. Interspecies interactions likely occurred, namely predation (planarians fed on chironomids and snails), which somehow influenced Ag uptake/bioaccumulation, possibly by altering organisms´ foraging behaviour. Higher Ag uptake rate constants were determined for AgNO3 (0.64, 80.4 and 1.12 Lwater g−1organism day−1) than for Ag2S NPs (0.05, 2.65 and 0.32 Lwater g−1organism day−1) for planarians, snails and chironomids, respectively. Biomagnification under environmentally realistic exposure seemed to be low, although it was likely to occur in the food chain P. acuta to G. tigrina exposed to AgNO3. Single-species tests generally could not reliably predict Ag bioaccumulation in the more complex mesocosm scenario. This study provides methodologies/data to better understand exposure, toxicokinetics and bioaccumulation of Ag in complex systems, reinforcing the need to use mesocosm studies to improve the risk assessment of environmental contaminants, specifically NPs, in aquatic environments.
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•Mesocosm tests allow more realistic exposure scenarios to nanoparticles (NPs).•Artificial streams were used to study toxicokinetics of Ag2S NP in benthic species.•Higher uptake and bioaccumulation of AgNO3 than Ag2S NPs by benthic invertebrates.•Ag2S NP may be bioavailable for uptake by benthic invertebrates.•Single-species tests did not reliably predict Ag bioaccumulation in the mesocosm.
The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the ...environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies. In this study we present an automated machine learning (autoML) scheme that employs dose-response toxicity data for silver (Ag), titanium dioxide (TiO2), and copper oxide (CuO) NPs. This model is further enriched with atomistic descriptors to capture the NPs’ underlying structural properties. To overcome the issue of limited data availability, synthetic data generation techniques are used. These techniques help in broadening the dataset, thus improving the representation of different NP classes. A key aspect of this approach is a novel three-step applicability domain method (which includes the development of a local similarity approach) that enhances user confidence in the results by evaluating the prediction’s reliability. We anticipate that this approach will significantly expedite the nanosafety assessment process enabling regulation to keep pace with innovation, and will provide valuable insights for the design and development of safe and sustainable NPs. The ML model developed in this study is made available to the scientific community as an easy-to-use web-service through the Enalos Cloud Platform (www.enaloscloud.novamechanics.com/sabydoma/safenanoscope/), facilitating broader access and collaborative advancements in nanosafety.
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In this work, we evaluated the effect of protein corona formation on graphene oxide (GO) mixture toxicity testing (i.e., co-exposure) using the
model and assessing acute toxicity determined as ...immobilisation. Cadmium (Cd
) and bovine serum albumin (BSA) were selected as co-pollutant and protein model system, respectively. Albumin corona formation on GO dramatically increased its colloidal stability (ca. 60%) and Cd
adsorption capacity (ca. 4.5 times) in reconstituted water (
medium). The acute toxicity values (48 h-EC
) observed were 0.18 mg L
for Cd
-only and 0.29 and 0.61 mg L
following co-exposure of Cd
with GO and BSA@GO materials, respectively, at a fixed non-toxic concentration of 1.0 mg L
. After coronation of GO with BSA, a reduction in cadmium toxicity of 110 % and 238% was achieved when compared to bare GO and Cd
-only, respectively. Integration of datasets associated with graphene-based materials, heavy metals and mixture toxicity is essential to enable re-use of the data and facilitate nanoinformatics approaches for design of safer nanomaterials for water quality monitoring and remediation technologies. Hence, all data from this work were annotated and integrated into the NanoCommons Knowledge Base, connecting the experimental data to nanoinformatics platforms under the FAIR data principles and making them interoperable with similar datasets.
Aquatic environments, particularly sediments, can be important final sinks for engineered nanoparticles (ENPs), with benthic biota being potentially exposed. There is an increasing need for hazard ...data to improve the environmental risk assessment of ENPs regarding aquatic systems. The aim of this study was to determine the toxicokinetics of several pristine (as manufactured) Ag-NPs, Ag 2 S-NPs (used to simulate environmental aging of silver nanoparticles) and AgNO 3 as the ionic counterpart, in the freshwater snail Physa acuta . Snails were exposed through 1) contaminated water (without sediment), 2) contaminated water and clean sediment, and 3) contaminated sediment. Bioavailability of Ag to the snails was greatly influenced by Ag characteristics, as different uptake and elimination kinetics were found for the different Ag forms within the same exposure route. Snails exposed via water revealed, in general, similar uptake kinetics, differing from exposure via contaminated sediment, suggesting that exposure route also had a determining role in Ag bioavailability. The simulated aged form (Ag 2 S-NPs) revealed fast uptake and depuration in snails from all experiments. When considering the double exposure route, which provides a more realistic contamination scenario, water was the main route, except for Ag 2 S-NPs, for which sediment was more important. The remarkably low elimination and high stored fraction of Ag in some exposures emphasizes the bioaccumulation ability of P. acuta and may raise concerns about possible trophic transfer. Snail shells accumulated low amounts of Ag. The present study highlights the need for a proper examination of the overall exposure scenario of Ag-NPs to benthic organisms. Our results contribute to the environmental risk assessment of Ag-NPs in benthic environments.
The increasing focus on open and FAIR (Findable, Accessible, Interoperable and Re-useable) data is driving a step-change in how research communities and governments think about data and knowledge, ...and the potential for re-use of data. It has long been recognised that international data sharing is essential for regulatory harmonisation and commercialisation,
via
the Mutual Acceptance of Data (MAD) principle of the Organisation for Economic Cooperation and Development (OECD) for example. However, it is interesting to note that despite the power of data and data-driven software to support the achievement of the United Nations Sustainable Development Goals (UN SDGs), there appears to be limited awareness of how nanomaterials environmental health and safety (nano EHS) data can drive progress towards many of the SDGs. The goal of the NanoCommons research infrastructure project was to increase FAIRness and impact of nanoEHS data through development of services, including data shepherding to support researchers across the data life cycle and tools such as user-friendly nanoinformatics predictive models. We surveyed both service providers and service users on their ideas regarding how nanoEHS data might support the SDGs, and discovered a significant lack of awareness of the SDGs in general, and the potential for impact from NanoCommons tools and services. To address this gap, a workshop on the SDGs was prepared and delivered to support the NanoCommons service providers to understand the SDGs and how nanosafety data and nanoinformatics can support their achievement. Following the workshop, providers were invited to update their questionnaire responses. The results from the workshop discussions are presented, along with a summary of the 12 SDGs identified where increasingly accessible nanoEHS data will have a significant impact, and the 5 that are indirectly benefited along with some recommendations for EU-funded projects on how they can maximise and monitor their contributions to the SDGs.
Increasingly Findable, Accessible, Reusable and Interoperable (FAIR) nanomaterials environmental health and safety (nanoEHS) data and demoncratised access to nanoinformatics models will directly support 12 SDGs and indireclty benefit the other 5 SDGs.
Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC ...International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analyzed issues involved in developing an InChI for NMs (
). The layers needed to capture NM structures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested
layers are intended to stimulate further analysis that will lead to the first version of a "nano" extension to the InChI standard.
Correction for ‘Toxicokinetics of pristine and aged silver nanoparticles in Physa acuta ’ by Patrícia V. Silva et al. , Environ. Sci.: Nano , 2020, 7 , 3849–3868, DOI: 10.1039/D0EN00946F.
The reliable quantification of nanomaterials (NMs) in complex matrices such as food, cosmetics and biological and environmental compartments can be challenging due to interactions with matrix ...components and analytical equipment (vials and tubing). The resulting losses along the analytical process (sampling, extraction, clean-up, separation and detection) hamper the quantification of the target NMs in these matrices as well as the compatibility of results and meaningful interpretations in safety assessments. These issues can be overcome by the addition of known amounts of internal/recovery standards to the sample prior to analysis. These standards need to replicate the behaviour of target analytes in the analytical process, which is mainly defined by the surface properties. Moreover, they need to carry a tag that can be quantified independently of the target analyte. As inductively coupled plasma mass spectrometry is used for the identification and quantification of NMs, doping with isotopes, target analytes or with chemically related rare elements is a promising approach. We present the synthesis of a library of TiO2 NMs doped with hafnium (Hf) and zirconium (Zr) (both low in environmental abundance). Zirconia NMs doped with Hf were also synthesized to complement the library. NMs were synthesized with morphological and size properties similar to commercially available TiO2. Characterization included: transmission electron microscopy coupled with energy-dispersive X-ray spectroscopy, X-ray diffraction spectroscopy, Brunauer–Emmett–Teller total specific surface area analysis, cryofixation scanning electron microscopy, inductively coupled plasma optical emission spectroscopy and UV–visible spectrometry. The Ti : Hf and Ti : Zr ratios were verified and calculated using Rietveld refinement. The labelled NMs can serve as internal standards to track the extraction efficiency from complex matrices, and increase method robustness and traceability of characterization/quantification.
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