Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Furxhi, Irini

doi:10.1016/j.impact.2021.100378

Cited by 21 publications

(12 citation statements)

References 93 publications

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“…This FAIR challenge requires the active involvement, participation, and collaboration of participants with different expertise. In this work, we used data captured by the data shepherd, which demonstrates that this role is essential in a project where data are generated, modeled, and used . The role of the shepherd is to capture data, protocols, and instrumentations and to help with data reporting, merging, and harmonization.…”

Section: Discussionmentioning

confidence: 99%

“…By incorporating the polydispersity index, we increases the quality of the measurements and consequently the modeling . The data is targeting cellular viability, which is the majority of the re-usable data that exist in the literature and databases, , implying a potentiality of further merging. Also, exposure conditions and in vitro characteristics are commonly considered as input features .…”

Section: Discussionmentioning

confidence: 99%

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Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

Furxhi

Bengalli

Motta

et al. 2023

ACS Appl. Nano Mater.

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The current European (EU) policies, that is, the Green Deal, envisage safe and sustainable practices for chemicals, which include nanoforms (NFs), at the earliest stages of innovation. A theoretically safe and sustainable by design (SSbD) framework has been established from EU collaborative efforts toward the definition of quantitative criteria in each SSbD dimension, namely, the human and environmental safety dimension and the environmental, social, and economic sustainability dimensions. In this study, we target the safety dimension, and we demonstrate the journey toward quantitative intrinsic hazard criteria derived from findable, accessible, interoperable, and reusable data. Data were curated and merged for the development of new approach methodologies, that is, quantitative structure−activity relationship models based on regression and classification machine learning algorithms, with the intent to predict a hazard class. The models utilize system (i.e., hydrodynamic size and polydispersity index) and non-system (i.e., elemental composition and core size)-dependent nanoscale features in combination with biological in vitro attributes and experimental conditions for various silver NFs, functional antimicrobial textiles, and cosmetics applications. In a second step, interpretable rules (criteria) followed by a certainty factor were obtained by exploiting a Bayesian network structure crafted by expert reasoning. The probabilistic model shows a predictive capability of ≈78% (average accuracy across all hazard classes). In this work, we show how we shifted from the conceptualization of the SSbD framework toward the realistic implementation with pragmatic instances. This study reveals (i) quantitative intrinsic hazard criteria to be considered in the safety aspects during synthesis stage, (ii) the challenges within, and (iii) the future directions for the generation and distillation of such criteria that can feed SSbD paradigms. Specifically, the criteria can guide material engineers to synthesize NFs that are inherently safer from alternative nanoformulations, at the earliest stages of innovation, while the models enable a fast and cost-efficient in silico toxicological screening of previously synthesized and hypothetical scenarios of yet-to-be synthesized NFs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

Furxhi

Bengalli

Motta

et al. 2023

ACS Appl. Nano Mater.

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“…-The ASINA-SMM is embedded into and sets the frame for a roadmap, a simple guideline for improving NEPs in a data-driven way, providing a trusted environment, in combination with regulatory preparedness (Soeteman-Hernandez et al, 2019). The roadmap will provide the state-of-the-art scientific knowledge by artificial intelligence using FAIRified data (the process that assures Findability, Accessibility, Interoperability, and Reusability of the generated data) (Furxhi, 2022), specific methods for the production of NEPs, characterization, and testing with relevant regulatory endpoints (Botte et al, 2021). FAIR data are based on FAIR principles; these FAIR principles acquired popularity in the scientific community and are expected to grow into a cornerstone of research policy and requirements for research data management plan (Dunning et al, 2017).…”

Section: Extensions From the Primary Sbd Approachmentioning

confidence: 99%

“…Data curation is by far the most major issue addressed, regarding its central role in nanoinformatics, workflow, data completeness, and quality (Karcher et al, 2018;Papadiamantis et al, 2020;Furxhi et al, 2020;Jeliazkova et al, 2021). Experimental quantitative and computational derived data will be captured, curated, and used for optimization purposes prior to analysis (Furxhi, 2022;Furxhi et al, 2021a). FAIRified primary data are stored in the project's database 10 (ASINA-DB) where data logging templates, specific to data generation, are designed and populated for each working package and partner.…”

Section: Asina-smmmentioning

confidence: 99%

“…FAIRified primary data are stored in the project's database 10 (ASINA-DB) where data logging templates, specific to data generation, are designed and populated for each working package and partner. Efforts have been put together with all the relevant stakeholders to capture the data in a harmonized manner, for example, to consent on the metrics used in quantitative data or importance of features regarding categorical variables (Furxhi, 2022;Furxhi et al, 2021a;Furxhi et al, 2021b;Furxhi et al, 2021c). The methodological and transparent data capturing approach allows the integration of data across NM/NEPs' LC.…”

Section: Asina-smmmentioning

confidence: 99%

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ASINA Project: Towards a Methodological Data-Driven Sustainable and Safe-by-Design Approach for the Development of Nanomaterials

Furxhi

Perucca²,

Blosi

et al. 2022

Front. Bioeng. Biotechnol.

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The novel chemical strategy for sustainability calls for a Sustainable and Safe-by-Design (SSbD) holistic approach to achieve protection of public health and the environment, industrial relevance, societal empowerment, and regulatory preparedness. Based on it, the ASINA project expands a data-driven Management Methodology (ASINA-SMM) capturing quality, safety, and sustainability criteria across the Nano-Enabled Products’ (NEPs) life cycle. We base the development of this methodology through value chains of highly representative classes of NEPs in the market, namely, (i) self-cleaning/air-purifying/antimicrobial coatings and (ii) nano-structured capsules delivering active phases in cosmetics. These NEPs improve environmental quality and human health/wellness and have innovative competence to industrial sectors such as healthcare, textiles, cosmetics, and medical devices. The purpose of this article is to visually exhibit and explain the ASINA approach, which allows identifying, combining, and addressing the following pillars: environmental impact, techno-economic performance, functionality, and human and environmental safety when developing novel NEPs, at an early stage. A metamodel supports the above by utilizing quality data collected throughout the NEPs’ life cycle, for maximization of functionality (to meet stakeholders needs) and nano-safety (regulatory obligations) and for the minimization of costs (to meet business requirements) and environmental impacts (to achieve sustainability). Furthermore, ASINA explores digitalization opportunities (digital twins) to speed the nano-industry translation into automatic progress towards economic, social, environmental, and governance sustainability.

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Predicting the potential toxicity of the metal oxide nanoparticles using machine learning algorithms

Sayed,

Alshater,

Hassanien

2024

Soft Comput

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Over the years, machine learning (ML) algorithms have proven their ability to make reliable predictions of the toxicity of metal oxide nanoparticles. This paper proposed a predictive ML model of the potential toxicity of metal oxide nanoparticles. A dataset consisting of 79 descriptors including 24 metal oxide nanoparticles (MexOy NPs) and their physicochemical and structural characteristics is adopted. The proposed model comprises of three main phases. The first phase is used to analyze the characteristics of nanoparticles along with their toxicity behavior. In the second phase, the problems associated with the metal oxide nanoparticles dataset are tackled. The first problem namely the class imbalance problem is handled through utilizing synthetic minority over-sampling technique (SMOTE). The second problem namely the outliers is handled through applying a novel feature selection algorithm based on the enhanced binary version of the sine tree-seed algorithm (EBSTSA). The proposed EBSTSA is used to find the relevant features affecting toxicity. The density-based spatial clustering of applications with noise (DBSCAN) is utilized as a tool for identifying outliers in the dataset and for visualizing the impact of the feature selection on the performance of the subsequent classification. Finally, in the third phase, the support vector machine (SVM) supervised machine learning algorithm and k-fold cross-validation method are applied to classify the mode of action of each instance of nanoparticle as toxic or nontoxic. The simulation results showed that the EBSTSA-based feature selection algorithm is reliable and robust across 23 benchmark datasets from the UCI machine learning repository. The results also showed that proposed EBSTSA can effectively find the relevant descriptors for nano-particles. Furthermore, the results demonstrated the efficacy of the proposed ML toxicity prediction model. It is obtained on average 1.02% of error rate, 100% of specificity, 98.87% of sensitivity, and 99.47% of f1-score.

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Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Cited by 21 publications

References 93 publications

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

Data-Driven Quantitative Intrinsic Hazard Criteria for Nanoproduct Development in a Safe-by-Design Paradigm: A Case Study of Silver Nanoforms

ASINA Project: Towards a Methodological Data-Driven Sustainable and Safe-by-Design Approach for the Development of Nanomaterials

Predicting the potential toxicity of the metal oxide nanoparticles using machine learning algorithms

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