Intelligent testing strategy and analytical techniques for the safety assessment of nanomaterials

Chen, Rui; Qiao, Jiyan; Bai, Ru; Yang, Zhao; Chen, Chunying

doi:10.1007/s00216-018-0940-y

Cited by 54 publications

(29 citation statements)

References 149 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oomen et al 2018) or on in silico tools applied to NMs (e.g. Chen et al 2018). In this manuscript, we have made a selection of approaches that are relevant for developing an acceptable grouping hypothesis based on experimental evidence.…”

Section: Methodsmentioning

confidence: 99%

Grouping of nanomaterials to read-across hazard endpoints: a review

et al. 2018

View full text Add to dashboard Cite

The use of non-testing strategies like read-across in the hazard assessment of chemicals and nanomaterials (NMs) is deemed essential to perform the safety assessment of all NMs in due time and at lower costs. The identification of physicochemical (PC) properties affecting the hazard potential of NMs is crucial, as it could enable to predict impacts from similar NMs and outcomes of similar assays, reducing the need for experimental (and in particular animal) testing. This manuscript presents a review of approaches and available case studies on the grouping of NMs to read-across hazard endpoints. We include in this review grouping frameworks aimed at identifying hazard classes depending on PC properties, hazard classification modules in control banding (CB) approaches, and computational methods that can be used for grouping for read-across. The existing frameworks and case studies are systematically reported. Relevant nanospecific PC properties taken into account in the reviewed frameworks to support grouping are shape and surface properties (surface chemistry or reactivity) and hazard classes are identified on the basis of biopersistence, morphology, reactivity, and solubility.

show abstract

Section: Methodsmentioning

confidence: 99%

Grouping of nanomaterials to read-across hazard endpoints: a review

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Reduced glutathione (GSH) and antioxidant enzymes such as glutathione peroxidase (GSH-PX) and superoxide dismutase (SOD) are normally used to scavenge ROS. Oxidative stress occurs when there is an imbalance between ROS production and the cellular antioxidant defence system [17,18].…”

Section: Introductionmentioning

confidence: 99%

Role of Autophagy in Zinc Oxide Nanoparticles-Induced Apoptosis of Mouse LEYDIG Cells

Shen

Yang

Zhou

et al. 2019

IJMS

Self Cite

View full text Add to dashboard Cite

Zinc oxide nanoparticles (ZnO NPs) have shown adverse health impact on the human male reproductive system, with evidence of inducing apoptosis. However, whether or not ZnO NPs could promote autophagy, and the possible role of autophagy in the progress of apoptosis, remain unclear. In the current study, in vitro and in vivo toxicological responses of ZnO NPs were explored by using a mouse model and mouse Leydig cell line. It was found that intragastrical exposure of ZnO NPs to mice for 28 days at the concentrations of 100, 200, and 400 mg/kg/day disrupted the seminiferous epithelium of the testis and decreased the sperm density in the epididymis. Furthermore, serum testosterone levels were markedly reduced. The induction of apoptosis and autophagy in the testis tissues was disclosed by up-regulating the protein levels of cleaved Caspase-8, cleaved Caspase-3, Bax, LC3-II, Atg 5, and Beclin 1, accompanied by down-regulation of Bcl 2. In vitro tests showed that ZnO NPs could induce apoptosis and autophagy with the generation of oxidative stress. Specific inhibition of autophagy pathway significantly decreased the cell viability and up-regulated the apoptosis level in mouse Leydig TM3 cells. In summary, ZnO NPs can induce apoptosis and autophagy via oxidative stress, and autophagy might play a protective role in ZnO NPs-induced apoptosis of mouse Leydig cells.

show abstract

“…Nanomaterials/nanoforms (NMs) display high heterogeneity regarding their physicochemical (p-chem) properties, quantum-mechanical properties, and, as such, their toxicological impact, which renders assessing their risk a case-by-case challenge. Traditional hazard assessment relies mostly on in vivo testing that poses technical challenges, e.g., regarding the validity of extrapolation to humans, ethical dilemmas, but also comes with high resource demands in cost and time [1]. Such an approach is not conducive to efficient identification and mitigation of possible risks, especially within emerging technologies where the pace of development is rapid.…”

Section: Introductionmentioning

confidence: 99%

Practices and Trends of Machine Learning Application in Nanotoxicology

et al. 2020

View full text Add to dashboard Cite

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.

show abstract

Intelligent testing strategy and analytical techniques for the safety assessment of nanomaterials

Cited by 54 publications

References 149 publications

Grouping of nanomaterials to read-across hazard endpoints: a review

Grouping of nanomaterials to read-across hazard endpoints: a review

Role of Autophagy in Zinc Oxide Nanoparticles-Induced Apoptosis of Mouse LEYDIG Cells

Practices and Trends of Machine Learning Application in Nanotoxicology

Contact Info

Product

Resources

About