NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment

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This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (ENMs). A workflow applying two different deep learning architectures on microscopic images of Daphnia magna is proposed that can automatically detect possible malformations, such as effects on the length of the tail, and the overall size, and uncommon lipid concentrations and lipid deposit shapes, which are due to direct or parental exposure to ENMs. Next, classification models assign specific objects (heart, abdomen/claw) to classes that depend on lipid densities and compare the results with controls. The models are statistically validated in terms of their prediction accuracy on external D. magna images and illustrate that deep learning technologies can be useful in the nanoinformatics field, because they can automate time‐consuming manual procedures, accelerate the investigation of adverse effects of ENMs, and facilitate the process of designing safer nanostructures. It may even be possible in the future to predict impacts on subsequent generations from images of parental exposure, reducing the time and cost involved in long‐term reproductive toxicity assays over multiple generations.

Section: Resultsmentioning

confidence: 99%

“…The application is available through the NanoSolveIT cloud platform, developed in the context of the H2020 project NanoSolveIT. [ 31 ]…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna

Karatzas

Melagraki

Ellis

et al. 2020

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“…Achieving these milestones in this roadmap required the maintenance of a network of experimental and computational researchers, regulators, and policymakers. This was done through a series of EU Projects and Actions (e.g., MODENA, MARINA, NANOSOLUTIONS) and more recently by the funding of at least three EU Horizon 2020 projects, Nano-SolveIT (https://nanosolveit.eu/), [28] SABYDOMA (https:// www.bnn.at/projects/sabydoma), and NanoCommons (https:// www.nanocommons.eu/). The milestones also assumed that high throughput experimentation methods for synthesis and characterization of nanomaterials would arise to provide data to train ML models, Achieving this ambitious set of milestones would provide the tools for the development of nanomaterials that were both functional and safe using rational "safe-bydesign" principles.…”

Section: Computational Nanosafety Roadblocks Milestones and Contextmentioning

confidence: 99%

“…This work is being extended by the development of cloud services under a new EU Horizon 2020 project, NanoSolveIT, in which the author is a member. [28]…”

Section: Use Of Web Cloud Servicesmentioning

confidence: 99%

Role of Artificial Intelligence and Machine Learning in Nanosafety

Winkler

2020

105

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.

“…[66] The specific system requirements and specifications are currently being defined in collaboration with the Nano-SolveIT project. [96] Concrete exploitation and sustainability plans are also foreseen and are currently under development to ensure continuity, beyond the specific project duration. data security, the need for easily extendable and adaptable approaches to keep pace with knowledge and technological development, and its hand-over to the RGC, in any of the possible forms it might take, as part of the long term operationalization.…”

Section: A Scientific View On a Universal Risk Governance Framework Amentioning

confidence: 99%

Risk Governance of Emerging Technologies Demonstrated in Terms of its Applicability to Nanomaterials

Isigonis

Afantitis

Antunes³

et al. 2020

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Nanotechnologies have reached maturity and market penetration that require nano‐specific changes in legislation and harmonization among legislation domains, such as the amendments to REACH for nanomaterials (NMs) which came into force in 2020. Thus, an assessment of the components and regulatory boundaries of NMs risk governance is timely, alongside related methods and tools, as part of the global efforts to optimise nanosafety and integrate it into product design processes, via Safe(r)‐by‐Design (SbD) concepts. This paper provides an overview of the state‐of‐the‐art regarding risk governance of NMs and lays out the theoretical basis for the development and implementation of an effective, trustworthy and transparent risk governance framework for NMs. The proposed framework enables continuous integration of the evolving state of the science, leverages best practice from contiguous disciplines and facilitates responsive re‐thinking of nanosafety governance to meet future needs. To achieve and operationalise such framework, a science‐based Risk Governance Council (RGC) for NMs is being developed. The framework will provide a toolkit for independent NMs' risk governance and integrates needs and views of stakeholders. An extension of this framework to relevant advanced materials and emerging technologies is also envisaged, in view of future foundations of risk research in Europe and globally.