Representing and describing nanomaterials in predictive nanoinformatics

Wyrzykowska, Ewelina; Mikołajczyk, Alicja; Lynch, Iseult; Jeliazkova, Nina; Kochev, Nikolay; Sarimveis, Haralambos; Doganis, Philip; Karatzas, Pantelis; Afantitis, Antreas; Melagraki, Georgia; Serra, Angela; Greco, Dario; Subbotina, Julia O.; Lobaskin, Vladimir; Bañares, Miguel Á.; Valsami-Jones, Eugénia; Jagiełło, Karolina; Puzyn, Tomasz

doi:10.1038/s41565-022-01173-6

Cited by 34 publications

(39 citation statements)

References 73 publications

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“…181 Wyrzykowska et al reviewed the most promising directions for developing the appropriate nanostructure representation described by a set of nanodescriptors that enhance the reliability of computational methods for the Safe-and Sustainable-by-Design strategy. 179 This study answers how to represent and describe nano- materials in predictive nanoinformatics based on combined molecular modeling (MM) and machine learning (ML) techniques. 179 The concept 179 includes a system perspective that is crucial for nanomaterial characterization, i.e., (i) system-independent nanodescriptors that describe nanostructures (e.g., chemical composition, crystal structure, size, shape, and the surface structure), so-called S-descriptors; (ii) system dependent descriptors that describe particular elements of a nanoparticle and its environment in real-time (i.e., core, coating, and ligands), so-called E-descriptors, and (iii) multicomponent structures represented by numerical combinations of descriptors for individual components of the nanostructure and descriptors related to the interactions between these components.…”

Section: Safe-and Sustainable-by-design (Ssbd) Concept For Nanomateri...mentioning

confidence: 99%

Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review

et al. 2023

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Section: Safe-and Sustainable-by-design (Ssbd) Concept For Nanomateri...mentioning

confidence: 99%

Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review

et al. 2023

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“…It is worth to notice that the dataset solely refers to AgNFs, rendering those descriptors collectively unique, acting as a fingerprint. This enables NFs belonging in the same elemental composition group to be differentiated . Commission remarks the development of substance-specific hazard assessments and the exploration of the determinants that drive the toxicity.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, nanodescriptors should reflect the influence of the system (surrounding environment or experimental conditions) on those properties. One can differentiate between system-independent (intrinsic properties) and system-dependent (extrinsic properties) nanodescriptors . In this study, both are considered.…”

Section: Methodsmentioning

confidence: 99%

“…One can differentiate between system-independent (intrinsic properties) and system-dependent (extrinsic properties) nanodescriptors. 40 In this study, both are considered. It is worth to notice that the dataset solely refers to AgNFs, rendering those descriptors collectively unique, acting as a fingerprint.…”

Section: Data Collectionmentioning

confidence: 99%

“…This enables NFs belonging in the same elemental composition group to be differentiated. 40 Commission 4 remarks the development of substancespecific hazard assessments and the exploration of the determinants that drive the toxicity.…”

Section: Data Collectionmentioning

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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A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes

del Giudice,

Serra,

Pavel

et al. 2024

Advanced Science

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Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network‐based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network‐based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data‐driven approaches for the hazard assessment of nanomaterials and other advanced materials.

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Representing and describing nanomaterials in predictive nanoinformatics

Cited by 34 publications

References 73 publications

Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review

Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review

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

A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes

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