Hazard identification of nanomaterials: In silico unraveling of descriptors for cytotoxicity and genotoxicity

Yamani, Naouale El; Mariussen, Espen; Gromelski, Maciej; Wyrzykowska, Ewelina; Grabarek, Dawid; Puzyn, Tomasz; Tănăsescu, Speranţa; Dušinská, Maria; Rundén-Pran, Elise

doi:10.1016/j.nantod.2022.101581

Cited by 10 publications

(11 citation statements)

References 57 publications

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“…Frontiers in Toxicology frontiersin.org positive, negative and equivocal genotoxicity response (El Yamani et al, 2022).…”

Section: Evaluation and Interpretation Of The Resultsmentioning

confidence: 99%

“…In addition to fulfilment of the acceptance criteria, since in the case of NMs a concentration response is not always observed (due to agglomeration at higher concentrations), in the EU FP7 project NanoREG2 we developed modified criteria for positive, negative and equivocal genotoxicity response ( El Yamani et al, 2022 ).…”

Section: Evaluation and Interpretation Of The Resultsmentioning

confidence: 99%

“…Characterization of the NMs to be tested is a key to understanding their observed effects and their mechanism of action. The physicochemical properties of NMs have been linked to their effect or toxicity in several studies ( Huk et al, 2015b ; Magdolenova et al, 2015 ; El Yamani et al, 2022 ). It is, therefore, important to perform full characterization of the pristine NMs where intrinsic properties will be measured; this is what we refer to as primary characterization.…”

Section: Methodsmentioning

confidence: 99%

“…We are addressing the most relevant points that need to be taken into consideration when assessing NM genotoxicity. Interpretation of NM comet assay data is facilitated by a categorization approach for positive, negative and equivocal effects recently developed within the H2020 NanoREG2 project ( El Yamani et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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The miniaturized enzyme-modified comet assay for genotoxicity testing of nanomaterials

Yamani

Rundén-Pran²,

Collins³

et al. 2022

Front. Toxicol.

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The in vitro comet assay is a widely applied method for investigating genotoxicity of chemicals including engineered nanomaterials (NMs). A big challenge in hazard assessment of NMs is possible interference between the NMs and reagents or read-out of the test assay, leading to a risk of biased results. Here, we describe both the standard alkaline version of the in vitro comet assay with 12 mini-gels per slide for detection of DNA strand breaks and the enzyme-modified version that allows detection of oxidized DNA bases by applying lesion-specific endonucleases (e.g., formamidopyrimidine DNA glycosylase or endonuclease III). We highlight critical points that need to be taken into consideration when assessing the genotoxicity of NMs, as well as basic methodological considerations, such as the importance of carrying out physicochemical characterization of the NMs and investigating uptake and cytotoxicity. Also, experimental design—including treatment conditions, cell number, cell culture, format and volume of medium on the plate—is crucial and can have an impact on the results, especially when testing NMs. Toxicity of NMs depends upon physicochemical properties that change depending on the environment. To facilitate testing of numerous NMs with distinct modifications, the higher throughput miniaturized version of the comet assay is essential.

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“…Frontiers in Toxicology frontiersin.org positive, negative and equivocal genotoxicity response (El Yamani et al, 2022).…”

Section: Evaluation and Interpretation Of The Resultsmentioning

confidence: 99%

Section: Evaluation and Interpretation Of The Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The miniaturized enzyme-modified comet assay for genotoxicity testing of nanomaterials

Yamani

Rundén-Pran²,

Collins³

et al. 2022

Front. Toxicol.

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“…Use of 12-well plates will also reduce the amount of NMs needed, which can be critical when the supply of particles is short. The protocol has been thoroughly tested in our laboratory with a range of different NMs, including TiO 2 , SiO 2 , Ag, Au, BaSO 4 , CeO 2 , ZnO, CNT, graphene NMs ( El Yamani et al, 2022 ), and liposomes, in A549 lung cells and also in liver HepG2 cells (data not shown).…”

Section: Discussionmentioning

confidence: 99%

The colony forming efficiency assay for toxicity testing of nanomaterials—Modifications for higher-throughput

et al. 2022

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To cope with the high number of nanomaterials manufactured, it is essential to develop high-throughput methods for in vitro toxicity screening. At the same time, the issue with interference of the nanomaterial (NM) with the read-out or the reagent of the assay needs to be addressed to avoid biased results. Thus, validated label-free methods are urgently needed for hazard identification of NMs to avoid unintended adverse effects on human health. The colony forming efficiency (CFE) assay is a label- and interference-free method for quantification of cytotoxicity by cell survival and colony forming efficiency by CFE formation. The CFE has shown to be compatible with toxicity testing of NMs. Here we present an optimized protocol for a higher-throughput set up.

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Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Shirokii

Din

Petrov

et al. 2023

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Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio‐related research. To prevent excessive experiments, such properties have to be pre‐evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML‐based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10‐fold cross‐validation (CV) Q2 = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation‐based feature selection and grid search‐based model hyperparameters optimization. To provide further model flexibility, quantitative atom property‐based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision‐making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor‐intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

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Hazard identification of nanomaterials: In silico unraveling of descriptors for cytotoxicity and genotoxicity

Cited by 10 publications

References 57 publications

The miniaturized enzyme-modified comet assay for genotoxicity testing of nanomaterials

The miniaturized enzyme-modified comet assay for genotoxicity testing of nanomaterials

The colony forming efficiency assay for toxicity testing of nanomaterials—Modifications for higher-throughput

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

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