OECD Safe(r) Innovation Approach (SIA)
Moving Towards a Safe(r) Innovation Approach (SIA) for More Sustainable Nanomaterials and Nano-enabled Products
OECD Environment, Health and Safety Publications, Series on the Safety of Manufactured Nanomaterials No. 96
© OECD, 2020
[…] representatives from France (INERIS), the Netherlands (RIVM), the European Union (Joint Research Centre of the European Commission), and Industry (Business and Industry Advisory Committee) prepared a project proposal that was presented to the OECD’s Working Party on Manufactured Nanomaterials (hereafter WPMN). The WPMN agreed to contribute to the discussion on Safe(r)-by-Design by describing the state-of-the-art frameworks available and initiatives conducted in support of future decision-making when developing more sustainable products, processes and uses.
In practice, the design process of e.g. nanotechnology requires finding an acceptable balance between various factors including safety, functionality and profitability. Since the desired and the potentially hazardous properties of MNMs both tend to be linked to their reactivity with their surroundings, maximal safety (non-reactivity) would easily render a MNM non-functional and therefore unmarketable (Fadeel, 2013)(Schwarz-Plaschg et al., 2017). Ultimately, the regulatory requirements for safety in various sectors are in place to prevent products that are not safe enough to fulfil certain criteria from reaching the market. The previously mentioned SbD concepts for nanotechnology aim to help industry in deciding whether or not these requirements can be fulfilled while creating a functional and profitable product, and to continue or abandon a research and design process accordingly at any particular stage.
The SbD (Safe-by-Design, Safer-by-Design, or Safety-by-Design) concept refers to identifying the risks and uncertainties concerning humans and the environment at an early phase of the innovation process so as to minimize uncertainties, potential hazard(s) and/or exposure. The SbD approach addresses the safety of the material/product and associated processes through the whole life cycle: from the Research and Development (R&D) phase to production, use, recycling and disposal.
For SbD in nanotechnology, three pillars of design can be specified:
- Safe(r) material/product: minimising, in the R&D phase, possible hazardous properties of the nanomaterial or nano-enabled product while maintaining function;
- Safe(r) production: ensuring industrial safety during the production of nanomaterials and nano-enabled products, more specifically occupational, environmental and process safety aspects; and
- Safe(r) use and end-of-life: minimising exposure and associated adverse effects through the entire use life, recycling and disposal of the nanomaterial or nano-enabled product. This can also support circular economy.
OECD SIA Inventory of Tools for SbD Implementation
For the inventory of frameworks and tools conducted under the OECD SIA project and presented in [the OECD] report, a selection of tools was reviewed according to their use for SbD, considering the SbD concepts described under the Working Descriptions above.
Human health related tools, including hazard, exposure and risk assessment tools were selected […]. Only nano-specific tools available as a functional software tool (either online or as a software package) were included in this review. Dermal exposure tools which were not specifically developed for the risk assessment of MNMs were also taken into account given the scarcity of nano-specific models in this area.
To achieve [the] three pillars, the following health and safety aspects along the material life cycle have to be considered:
|Safety aspect||Safer material/product||Safer production||Safer use and end-of-life|
|Worker exposure (chemical hazards)||X|
|Worker safety during production (physical hazards)||X|
|Releases to the environment during production (outdoor air, liquid & solid waste)||X|
|Releases to the environment during product use & end-of-life processes||X|
|Consumer exposure (incl. professional & industrial use of the final product)||X|
However, SbD goes beyond the classical risk assessment of combining hazard and exposure. To achieve safer materials, their structure and physico-chemical properties have to be linked to their hazard at the design stage, so that the hazard can be designed out or the least hazardous form with the desired functionality can be taken to the next stage. The implementation of SbD has to be economically viable for the industry, and therefore cost-benefit analysis tools are also required. Furthermore, to assess the overall social benefits of developing SbD products as opposed to non-SbD products, a social impact assessment is necessary. Other aspects considered in the classification of the tools were the exposure route, life cycle stage covered, whether the tool performs a complete life cycle assessment and in the case of Environmental Assessment tools, the environmental compartments covered.
Pillar 1: Safer nanomaterials/nano-enabled products
Computational methods to predict hazard
There are several computational methodologies available to predict hazard from the physicochemical properties of a material. These methods can be very useful for SbD, although they do not have the added element of predicting functionality. A full review was published by the Nanocomput project (Worth et al., 2017). A brief description is provided below:
- Bayesian methodologies: these are based on a method of statistical inference in which Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian networks can be applied to hazard identification and ranking of MNMs by capturing the (inter) relationships between the exposure route, the MNM’s physico-chemical properties and the ultimate biological effects. Marvin et al., 2017 applied a Bayesian network (BN) construction, parameterisation, and uncertainty analysis to metal and metal-oxide MNMs. The physico-chemical properties used were dissolution, shape, surface area, surface reactivity, particle size, surface coating, surface charge, aggregation and exposure route and the biological effects genotoxicity, neurological effects, immunological effects, cytotoxicity, pulmonary effects, inflammation, central nervous system effects and fibrosis. This BN tool showed high accuracy, with 72% hazard prediction precision in an out-of-sample test, however it is not commercially available. To our knowledge there are no commercially available tools of this type.
- Q-SARs: Quantitative Structure-Activity Relationship methods establish relationships between physicochemical properties and the behaviour of MNMs in biological systems. This methodology is well known and applied under REACH (ECHA, 2008a). However, its use for MNMs is still limited due to the lack of data that correlates with the Mode of Action (MOA) or Adverse Outcome Pathway (AOP). The Enalos InSilicoTox Platform holds some tools for the prediction of solubility and TNF (specific NF-kB Induction) Prediction. The OECD has developed a Q-SAR Toolbox to make (Q)SAR technology readily accessible, transparent, and less demanding in terms of infrastructure costs.
- OMIC technologies and systems biology: OMICs are primarily aimed at the universal detection of genes (genomics), mRNA (transcriptomics: the total mRNA in a cell or organism), proteins (proteomics: the set of all expressed proteins in a cell, tissue or organism) and metabolites (metabolomics: the study of global metabolite profiles in a system (cell, tissue or organism) under a given set of conditions) in a specific biological sample. Systems biology and omics experiments differ from traditional studies, which are largely hypothesis driven or reductionist. The reasoning is that a complex system can be understood more thoroughly if considered as a whole. The strategy is to analyse all data from an experiment to define a hypothesis that can be further tested (Kell & Oliver, 2004). The application of OMICs technologies to nanotoxicology has been hampered by the lack of standard operating procedures for the experimental analysis. However, some studies have used OMICs strategies to successfully predict MOAs (Scala et al., 2018; Pan et al., 2018). Given the large amount of data generated in these studies and the challenges of integrating the data from the different OMICs techniques, sophisticated bioinformatics and dedicated statistics are essential.
- The company OMicX holds several software tools for big biodata analysis and interpretation including ToxFlow (Varsou et al., 2018). However, expert knowledge is required for the use and interpretation of the outputs.
- INSIdE nano is a web-based tool that highlights connections between phenotypic entities based on their effects on genes. The database behind INSIdE nano is a network whose nodes are grouped into four categories:
- Nanomaterial exposures
- Drug treatments
- Chemical exposures
Currently there are no methods for validating QSARs, but there are some useful principles that are described in OECD Report from the Expert Group on (Q)SARs on Principles for the Validation of (Q)SARs, (OECD, 2004) and OECD Guidance Document 69 on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models (OECD, 2007).
Tools that predict the overall risk or a hazard band
- Precautionary Matrix for MNMs: The Precautionary Matrix helps to assess the need for nano-specific measures (“need for precautions”) in connection with synthetic MNMs and applications of these materials for employees, consumers and the environment. In addition, it helps to identify potential sources of risk in the development, production, use and disposal of synthetic nanomaterials. It is based on a limited number of parameters and intended for those situations where data is lacking. Users may carry out their own guided investigations on human exposure, emissions into the environment and the effects of MNMs based on results obtained from the matrix. The tool is not a risk assessment tool.
- LICARA NanoScan: The main goal of LICARA is to develop a structured life cycle approach for MNMs which enables a qualitative evaluation of the benefits and risks associated with new or existing nano-products. It further allows a comparison with the risks and benefits of conventional (non-nano) products. The tool stimulates economic, environmental and social opportunities. This tool is specifically intended for use by SMEs to support them in communicating with regulators, potential clients and investors.
- GUIDEnano tool: The tool guides the user (i.e. industrial nano-enabled product developers) in the design and application of the most appropriate risk assessment and mitigation strategy for a specific product. The tools predicts the overall risk from the nanomaterial along its life cycle. The tool is being improved as part of the H2020 SAbyNA project.
- SUNDS - The Sustainable Nanotechnologies Project Decision Support System: SUNDS is a cloud-based nano-product sustainability assessment Decision Support System. SUNDS supports decisions on the assessment and management of MNMs and nano-enabled products along with their life cycles in industry, regulatory bodies and insurance companies. It applies a two-tiered approach which, on the basis of the supplied information, is able to generate qualitative or quantitative results. The first assessment tier is based on the LICARA NanoScan tool. The second assessment tier, based on an adaptation of the authorisation process currently in operation under the EU REACH regulation, allows applying Risk Control (RC) measures and demonstrating adequate control of risk due to a substance’s use and that according to the Socio-economic Analysis (SEA), the benefits of using the substance significantly outweigh the societal costs. SEA analyses are based on the triple bottom line approach, which comprises the environmental, economic, and societal ‘pillars’.
SbD Implementation Platform: The SbD Implementation Platform was developed to perform SbD. It helps the user to identify hot spots regarding risks at different stages of the product’s life cycle. The platform follows the stage-gate model and produces summary outputs and graphs comparing the results of different control banding tools. The user may also introduce safety thresholds to better understand where they are in terms of risks. The platform contains a repository of guidelines, guidance documents and links to relevant sites.
- NanoSafety Classifier (NANOSOLUTIONS): The Nanosafety Classifier is a computational tool that can predict the environmental and health impact of MNMs based on their characteristics and behaviour. The tool is continuously learning, and the predictions keep improving as new data is fed into it.
The Precautionary Matrix and the Licara NanoScan can be used at the early stages of the value chain when there is not yet a prototype material/product. Their uncertainties are also larger. For later stages when there is more information available, the GUIDEnano tool, SUNDS and NanoSafety Classifier are more suitable, as they produce a quantitative assessment. SUNDS goes further by assessing the societal benefits of incorporating a SbD approach.
Pillar 2: Safer production processes
Safer processes imply protection from chemical risks, such as releases of MNMs to the indoor workplace and the outdoor environment (either as solid or liquid waste or to the outdoor air) and from physical hazards such as fire and explosions. Because these hazards have different natures and are subject to different legislation, there is no single tool that can assess them all together. There are tools for assessing the exposure of workers, and tools that deal with the physical safety hazards. The reviewed literature did not include tools that estimate potential releases into the environment taking into account the process parameters.
Tools that cover occupational exposure
Several of the risk assessment tools included in this document also include the assessment of the occupational risk of exposure to MNMs during their production:
- Precautionary Matrix for MNMs (qualitative output)
- LICARA NanoScan (qualitative output)
- GUIDEnano tool (quantitative output)
- SUNDs system (quantitative output)
There are a number of control banding and risk assessment tools that have been developed specifically to assess the risk of workers and only include risk in occupational settings.
- ANSES: Control Banding Tool for MNMs: The control banding tool requires input data, irrespective of the phase of the nanomaterial’s life cycle, such as information collected at the workplace through observation of actual work situations, toxicology data, etc. The output data generated by the control banding process will impact other processes of the overall management system defined by the employer.
- Control Banding NanoTool: The tool estimates an emission probability (without considering exposure controls) and severity band and provides advice on engineering controls to use to prevent exposure. It deals with occupational exposures, including domains covering handling of liquids, powders, and abrasion of solids.
- Stoffenmanager Nano: The tool estimates a hazard band and exposure band that are combined in the output as a risk band to qualitatively assess occupational health risks from inhalation exposure to Manufactured Nano Objects (MNO). Risk Management Measures may be selected or included in the Action Plan.
- NanoSafer CB: Online control banding and risk management tool for manufactured MNMs. Hazard assessment and case-specific exposure potentials are combined into an integrated assessment of risk levels expressed in control bands with associated risk management recommendations. The tool can also be used to assess and manage emissions from nanoparticleforming processes. It uses data on material properties, processes and production facilities to estimate occupational risk. The tool uses the Risk Quotient (i.e. the ratio of an exposure dose to a human effect threshold) to estimate risk deterministically. The tool is capable of estimating exposure from spray processes and it can perform nano-specific Hazard Assessment based on readacross between nanoparticles based on specific material properties and hazard indicators, tested for performance against in vivo experiments.
Tools that cover risks from processing
Process safety deals with all the accident scenarios that may be encountered during processing and the possible injuries to workers and damage to the environment. Such scenarios may be triggered by accidental leakages (local and environmental) that could arise from a malfunctioning process, a chemical runaway reaction, self-overheating, a fire or an explosion. To assess the risk of such accidents, one has to know the physicochemical, toxicological and ecotoxicological hazards of the substances involved. This means process starting materials and also their normal and potential accidental transformations in the process. In accident risk analysis, triggering ignition sources (e.g. electrostatic, mechanical or thermal sources) leading to adverse outcomes (release of material, fires, explosion) should be considered with a detailed description of the involved triggering parameters. Presently, this kind of data for MNMs is not available for performing process safety assessment. Moreover, currently few developments on predictive modelling have been made to assess the impact of a massive accidental leak, a process fire or explosion involving MNMs. The information gaps may constitute serious barriers to the development of safe MNMs processes.
However, commonly used process risk assessment methodologies, such as PRA, HAZOP, FMEA, BOW-TIE approaches, LOPA, and MSRA that have been developed for processes involving conventional chemicals, can also be used for MNMs. As indicated above, some critical process safety parameters are still unknown. In addition, risk assessment outcomes indicate that for processes involving MNMs, proper design, installation and management of recognized safety barriers (such as explosion venting systems, counter fire actions, catch tanks for runaway reactions) is still lacking. For the development of safe nanomaterial processing, such methods for analysis and barriers need to be developed for MNMs and further standardized. Recognized predictive computational models and tools to design and assess the performances of such barriers are still lacking.
Pillar 3: Tools for safer use and end of life
Tools that cover exposure assessment to consumers
The following tools, previously described, cover exposure assessment to consumers:
- LICARA NanoScan
- Precautionary Matrix for MNMs
- GUIDEnano tool
- SUNDS -The Sustainable Nanotechnologies Project Decision Support System
Additional tools for safer use and end of life are:
- NanoRiskCat: A screening tool for evaluation of exposure and hazard of MNMs integrated into products for professional and private use. It categorises and ranks the possible exposure and hazards associated with a nanomaterial in a product. The primary focus is on MNMs relevant for professional end-users and consumers as well as MNMs released into the environment.
- ConsExpo nano: This tool can be used to estimate inhalation exposure to MNMs in consumer spray products. To run the model, user input on different exposure determinants such as the product and its use, the nanomaterial and the environmental conditions is required. Exposure is presented in different measures. The outcome of the assessment is an alveolar load in the lungs as one of the most critical determinants of inflammation of the lungs is both the magnitude and duration of the alveolar load of a nanomaterial. To estimate the alveolar load arising from the use of nano-enabled spray products, ConsExpo nano combines models that estimate the external aerosol concentration in indoor air, with models that estimate the deposition in and clearance of inhaled aerosol from the alveolar region.
Future Nano Needs - Bayesian Belief Network (FNN-BBN) Shredding Model: The model is very useful for the exposure assessment of products containing MNMs during shredding (end-of-life), a part of the life cycle where there is little data available. With a Bayesian probabilistic nature in its core, it uses subjective judgement when data is unavailable or scarce while being able to adapt and update risk forecasts as new information becomes available. Its novelty lies in a simplistic approach which combines the material and process variables of the system to determine the probability of number, size, mass and composition of released particles. It is applicable to the shredding of a wide range of nano-enabled products and it aims to reduce the nanomaterial release by using the Safe(r)-by-Design approach. The model works with the Genie 2.1 software- a graphical interface that runs on Windows, OSX and Linux.
Tools that cover Environmental Assessment
In this section, we have included tools for risk quantification and tools that estimate some of the aspects required for the environmental risk assessment such as environmental fate, transport, and uptake as well as flow analysis tools. Most of these tools have been reviewed within the OECD project “Compilation of Available Tools and Models for The Assessment of Environmental and Consumer Exposure to MNs” and therefore here are only briefly described.
At the time of writing this report, their applicability in SbD had not been demonstrated. Clearly, they can be used to perform risk assessment, which is part of the SbD process. However, it is unknown whether they will be sensitive enough to perceive the difference in risk after the implementation of SbD measures. The fit of these tools to the different stages of the innovation process has been recently reviewed in Sørensen et al., 2019.
Two control banding tools evaluating environmental effects were identified: the Precautionary Matrix and the LICARA nanoscan (see description above), where more details relevant for the environmental assessment are added below. The LICARA nanoscan estimates the potential effect of nanoparticles on the environment by addressing the redox and/or catalytic activity. The stability of the nanoparticles under the relevant environmental conditions is considered based on the half-life of the nanoparticles. The potential emission into the environment is estimated by the volume of nanoparticles present in the marketed products, the physical surroundings of the nanoparticles or carrier material of the nanoparticles in the product as an indicator for the release potential of the nanoparticles and the possible disposal of nanomaterial in different life cycle stages (van Harmelen et al., 2016). The output is a risk band (low, medium or high). For a quantitative risk estimation, the GUIDEnano tool and SUNDS estimate the risk in the environment along the life cycle together with the risk to humans.
Other tools that estimate the material flow, fate, transport and uptake/bioavailability are:
- SimpleBox4Nano is a regulatory-relevant multimedia fate model that is specifically fit for use with MNMs. The tool predicts background concentrations of MNMs in air, water, sediment and soil. Designed originally as a research tool, SimpleBox4Nano has proven useful in dedicated environmental fate studies, focused at understanding and predicting environmental fate from fundamental physical and chemical substance properties. It is a screening-level quantitative model that expresses nanoparticle transport and concentrations in and across air, rain, surface waters, soil, and sediment, accounting for nano-specific processes such as aggregation, attachment, and dissolution. The SimpleBox4Nano is a nanomaterial-specific development of the SimpleBox model, which underpins the EU’s chemical risk and safety decision-support tool EUSES (European Union System for the Evaluation of Substances). SimpleBox4Nano simulates screening level fate assessments at regional to continental scales. It can also be used to determine the maximum allowed production volume of a specific NP in EU since production volume is linearly correlated with the predicted environmental concentration.
- NanoFASE: This model system performs complex, spatially-explicit simulations at smaller scales. It simulates geographical area(s) as a network of cells. Within each cell, environmental compartments will be linked by transport functions (e.g. sedimentation, deposition, effluent release, soil runoff, biota uptake). Implementation of material flow among cells (e.g. water flow, air movement) enables multimedia transport modelling and fate prediction.
- nanoRelease estimates the annual releases of MNMs from manufacturing, use, and disposal of a product explicitly taking stock and flow dynamics into account. Given the variabilities in key parameters (e.g., service life of products and annual release rate during use), nanoRelease is designed as a stochastic model.
- nanoFATE is a screening-level dynamic multimedia model for predicting concentrations in different environmental compartments at a local scale. The model considers emissions to the air, freshwater, coastal water and different solid compartments and interactions of the nanomaterial and the environment. Ten regions of the USA and Europe can be used to simulate environmental scenarios.
- nanoDuFlow is a spatially resolved hydrological ENP fate model. The model simulates advection, aggregation–sedimentation, resuspension, dissolution and burial for singular ENPs, 5 classes of ENP homoaggregates and 25 classes of heteroaggregates, dynamically in space and time, and uses actual hydrological data of the river, 5 tributaries and a waste water treatment plant effluent.
LearNano estimates release rates using an LCIA (Lifecycle Inventory Assessment, described in Keller et al., 2013, and Gottschalk et al., 2009) approach. It considers nanomaterial production rates, product applications, treatment plants (WWTP, WIP, etc…) and estimates release rates to environmental compartments such as air, water, soil, including a landfill compartment
MendNano: This model is used to assess the multimedia environmental distribution of nanomaterials based on a mechanistic description of various intermedia transport and reaction processes. It also allows users to perform rapid “what if” evaluations of the potential environmental implications of ENMs.
- RedNano is a combination of MendNano and LearNano. It is an integrated simulation tool for assessing the potential release and environmental distribution of MNMs based on a life cycle assessment approach and multimedia compartmental modelling coupled with mechanistic intermedia transport processes. The RedNano simulation tool and its web-based software implementation enables rapid “what-if?” scenario analysis, in order to assess the response of an environmental system to various release scenarios.
- WSM/WASP7: This model evaluates the effect of stream dynamics and chemical transformations on the environmental fate of MNMs in a watershed-scale model. The James River Basin portion of the Phase 5.3.2 Chesapeake Bay Watershed Model (WSM) is coupled with EPA water quality modelling suite WASP7 and configured to model NM fate.
- Rhone/Rhine Model: the novelty of this model is that it incorporates spatial variability in environmental conditions in an existing ENP fate model for aquatic environments. The model is parameterised for the Rhine river.
- ECHA, 2008a: ECHA, Guidance on information requirements and chemical safety assessment Chapter R.6: QSARs and grouping of chemicals, 2008. https://echa.europa.eu/documents/10162/13632/information_requirements_r6_en.pdf/77f49f81-b76d-40ab-8513-4f3a533b6ac9
- Fadeel, 2013: Fadeel, B. Nanosafety: Towards Safer Design of Nanomedicines. Journal of Internal Medicine 2013, 274 (6), 578–580. https://doi.org/10.1111/joim.12137.
- Gottschalk et al., 2009: Gottschalk, F.; Sonderer, T.; Scholz, R. W.; Nowack, B. Modeled Environmental Concentrations of Engineered Nanomaterials (TiO2, ZnO, Ag, CNT, Fullerenes) for Different Regions. Environ. Sci. Technol. 2009, 43 (24), 9216–9222. https://doi.org/10.1021/es9015553.
- Hjorth et al. 2017: Hjorth, R.; van Hove, L.; Wickson, F. What Can Nanosafety Learn from Drug Development? The Feasibility of “Safety by Design.” Nanotoxicology 2017, 11 (3), 305–312. https://doi.org/10.1080/17435390.2017.1299891.
- Kell & Oliver, 2004: Kell, D. B.; Oliver, S. G. Here Is the Evidence, Now What Is the Hypothesis? The Complementary Roles of Inductive and Hypothesis-Driven Science in the Post-Genomic Era. BioEssays 2004, 26 (1), 99–105. https://doi.org/10.1002/bies.10385.
- Keller et al., 2013 Keller, A. A.; McFerran, S.; Lazareva, A.; Suh, S. Global Life Cycle Releases of Engineered Nanomaterials. J Nanopart Res 2013, 15 (6), 1692. https://doi.org/10.1007/s11051-013-1692-4.
- OECD, 2004: The Report from the Expert Group on (Quantitative) Structure-Activity Relationships [(Q)SARs] on the Principles for the Validation of (Q)ARs. Retrieved from https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2004)24&doclanguage=en (Accessed: 28/06/22)
- Pan et al., 2018: Pan, L.; Lee, Y. M.; Lim, T. K.; Lin, Q.; Xu, X. Quantitative Proteomics Study Reveals Changes in the Molecular Landscape of Human Embryonic Stem Cells with Impaired Stem Cell Differentiation upon Exposure to Titanium Dioxide Nanoparticles. Small 2018, 14 (23), 1800190. https://doi.org/10.1002/smll.201800190.
- Scala et al., 2018: Scala, G.; Kinaret, P.; Marwah, V.; Sund, J.; Fortino, V.; Greco, D. Multi-Omics Analysis of Ten Carbon Nanomaterials Effects Highlights Cell Type Specific Patterns of Molecular Regulation and Adaptation. NanoImpact 2018, 11, 99–108. https://doi.org/10.1016/j.impact.2018.05.003.
- Schwarz-Plaschg et al., 2017: Schwarz-Plaschg, C.; Kallhoff, A.; Eisenberger, I. Making Nanomaterials Safer by Design? Nanoethics 2017, 11 (3), 277–281. https://doi.org/10.1007/s11569-017-0307-4.
- Sørensen et al., 2019: Sørensen, S. N.; Baun, A.; Burkard, M.; Maso, M. D.; Hansen, S. F.; Harrison, S.; Hjorth, R.; Lofts, S.; Matzke, M.; Nowack, B.; Peijnenburg, W.; Poikkimäki, M.; Quik, J. T. K.; Schirmer, K.; Verschoor, A.; Wigger, H.; Spurgeon, D. J. Evaluating Environmental Risk Assessment Models for Nanomaterials According to Requirements along the Product Innovation Stage-Gate Process. Environ. Sci.: Nano 2019, 6 (2), 505–518. https://doi.org/10.1039/C8EN00933C.
- van Harmelen et al., 2016: van Harmelen, T.; Zondervan-van den Beuken, E. K.; Brouwer, D. H.; Kuijpers, E.; Fransman, W.; Buist, H. B.; Ligthart, T. N.; Hincapié, I.; Hischier, R.; Linkov, I.; Nowack, B.; Studer, J.; Hilty, L.; Som, C. LICARA NanoSCAN - A Tool for the Self-Assessment of Benefits and Risks of Nanoproducts. Environment International 2016, 91, 150–160. https://doi.org/10.1016/j.envint.2016.02.021.
- Varsou et al., 2018: Varsou, D.-D.; Tsiliki, G.; Nymark, P.; Kohonen, P.; Grafström, R.; Sarimveis, H. ToxFlow: A Web-Based Application for Read-Across Toxicity Prediction Using Omics and Physicochemical Data. J. Chem. Inf. Model. 2018, 58 (3), 543–549. https://doi.org/10.1021/acs.jcim.7b00160.
- Worth et al., 2022: Worth, A.; Aschberger, K.; Asturiol, B. D.; Bessems, J.; Gerloff, K. B.; Graepel, R.; Joossens, E.; Lamon, L.; Palosaari, T.; Richarz, A. Evaluation of the availability and applicability of computational approaches in the safety assessment of nanomaterials: Final report of the Nanocomput project (accessed 2022 -06 -28). https://publications.jrc.ec.europa.eu/repository/handle/JRC106386.