Towards an<i>in silico</i>integrated approach for testing and assessment of nanomaterials: from predicted indoor air concentrations to lung dose and biodistribution

Tsiros, Periklis; Cheimarios, Nikolaos; Tsoumanis, Andreas; Jensen, Alexander C. Ø.; Melagraki, Georgia; Lynch, Iseult; Sarimveis, Haralambos; Afantitis, Antreas

doi:10.1039/d1en00956g

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…For example, Tsiros et al proposed an integrated computational framework using two different in silico approaches to predict the internal deposition and distribution of NMs in the human respiratory system following inhalation exposure in an occupational setting, which can be used in risk assessment and life cycle impact assessment. 160 Rybińska-Fryca et al proposed structure–activity prediction networks (SAPNets), an approach that effectively links the description of ENM structure with toxicity through a series of layers built from nodes that correspond to predictive “meta-models” so as to overcome the limitations of traditional QSAR modeling. 161 Besides, the combined use of in silico tools (QSAR and RA) has demonstrated usefulness in improving the prediction accuracy for ENM cytotoxicity 162 (detailed in section 3.3.4).…”

Section: In Silico Tools Developed For Nanosafety Assessmentmentioning

confidence: 99%

The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools

Xie,

Xiong,

Wang

et al. 2024

Environ. Sci.: Nano

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Section: In Silico Tools Developed For Nanosafety Assessmentmentioning

confidence: 99%

The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools

Xie,

Xiong,

Wang

et al. 2024

Environ. Sci.: Nano

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“…Allosteric scaling is one approach used in PBPK modeling to account for differences in sizes of test organisms, 96 it can be used for NMs-induced impacts in lungs and even for aquatic cladoceran species by having the smallest species being more sensitive to CuNP compared to the larger species. 97,98 Another way of approaching is classifying species according to trophic level. In an earlier attempt, 20 this was ineffective because it lacked crucial information, such as the morphology, ecological traits and intrinsic sensitivities.…”

Section: Identification Of Descriptors Formentioning

confidence: 99%

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective

Zhou,

Wang,

Peijnenburg

et al. 2024

Environ. Sci. Technol.

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The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, in silico methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an in silico predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.

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“…NanoSolveIT [ 143 ] is a new informatics system for in silico nanosafety assessment [ 144 ] that, in particular, can be applied for PBPK modeling of nanomaterial biodistribution [ 145 ].…”

Section: Auxiliary Pbpk Modeling Softwarementioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova

Akberdin

Kiselev

et al. 2022

IJMS

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Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.

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Towards anin silicointegrated approach for testing and assessment of nanomaterials: from predicted indoor air concentrations to lung dose and biodistribution

Abstract: Integrated approaches to testing and assessment (IATA) provide a framework for combining different information (experimental or in silico) for hazard characterization of chemicals, including nanomaterials (NM), based on a weight of evidence approach.

Cited by 8 publications

References 48 publications

The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools

The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools

Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

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