Recursive feature elimination in random forest classification supports nanomaterial grouping

Bahl, Aileen; Hellack, Bryan; Balaș, Mihaela; Dinischiotu, Anca; Wiemann, Martin; Brinkmann, Joep; Luch, Andreas; Renard, Bernhard Y.; Haase, Andrea

doi:10.1016/j.impact.2019.100179

Cited by 89 publications

(40 citation statements)

References 43 publications

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“…Given that density is directly proportional to mass, theoretically, the lower the effective density, the more CNT/F particle would be needed for equivalent dosing by mass. Recent computational modeling of engineered nanomaterials included density in the analyses [95][96][97] with some indication it was a primary driver of toxicity [96]. Measurements of bulk and tapped skeletal density were performed for all CNT/F ( Table 3).…”

Section: Densitymentioning

confidence: 99%

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities

et al. 2020

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Background Carbon nanotubes and nanofibers (CNT/F) have known toxicity but simultaneous comparative studies of the broad material class, especially those with a larger diameter, with computational analyses linking toxicity to their fundamental material characteristics was lacking. It was unclear if all CNT/F confer similar toxicity, in particular, genotoxicity. Nine CNT/F (MW #1–7 and CNF #1–2), commonly found in exposure assessment studies of U.S. facilities, were evaluated with reported diameters ranging from 6 to 150 nm. All materials were extensively characterized to include distributions of physical dimensions and prevalence of bundled agglomerates. Human bronchial epithelial cells were exposed to the nine CNT/F (0–24 μg/ml) to determine cell viability, inflammation, cellular oxidative stress, micronuclei formation, and DNA double-strand breakage. Computational modeling was used to understand various permutations of physicochemical characteristics and toxicity outcomes. Results Analyses of the CNT/F physicochemical characteristics illustrate that using detailed distributions of physical dimensions provided a more consistent grouping of CNT/F compared to using particle dimension means alone. In fact, analysis of binning of nominal tube physical dimensions alone produced a similar grouping as all characterization parameters together. All materials induced epithelial cell toxicity and micronuclei formation within the dose range tested. Cellular oxidative stress, DNA double strand breaks, and micronuclei formation consistently clustered together and with larger physical CNT/F dimensions and agglomerate characteristics but were distinct from inflammatory protein changes. Larger nominal tube diameters, greater lengths, and bundled agglomerate characteristics were associated with greater severity of effect. The portion of tubes with greater nominal length and larger diameters within a sample was not the majority in number, meaning a smaller percentage of tubes with these characteristics was sufficient to increase toxicity. Many of the traditional physicochemical characteristics including surface area, density, impurities, and dustiness did not cluster with the toxicity outcomes. Conclusion Distributions of physical dimensions provided more consistent grouping of CNT/F with respect to toxicity outcomes compared to means only. All CNT/F induced some level of genotoxicity in human epithelial cells. The severity of toxicity was dependent on the sample containing a proportion of tubes with greater nominal lengths and diameters.

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Section: Densitymentioning

confidence: 99%

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities

et al. 2020

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“…In this regard, NMs physicochemical features can provide important clues. Bahl, et al [45] used unsupervised and supervised machine learning methods to investigate which physicochemical properties were most predictive of inhalation toxicity for 11 NMs, including the four variants tested in the present study. The authors have identi ed redox potential, zeta potential and dissolution rate, as these physicochemical parameters re ected the highest predictivity on NMs biological activity.…”

Section: Discussionmentioning

confidence: 99%

“…Rutile-anatase titanium nanoparticles (TiO 2 _NM105) obtained from the JRC repository served as a benchmark. The tested NMs were sterilised by gamma-irradiation, extensively characterised using state-of-the-art techniques and con rmed to be endotoxin-free by the Limulus amebocyte lysate (LAL) test [45,46]. In Table S1 are presented some of their main physicochemical properties, namely zeta potential, redox potential and dissolution rate that have been shown to be good predictors of inhalation toxicity [45].…”

Section: Nanomaterials (Nms)mentioning

confidence: 99%

Genotoxicity and Gene Expression in the Rat Lung Tissue Following Instillation and Inhalation of Different Variants of Amorphous Silica Nanomaterials (aSiO2 NMs)

Brandão

Costa

Bessa

et al. 2020

Preprint

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Background: Synthetic amorphous silica (SAS) has been widely used in several industrial and consumer applications for decades and considered safe for humans. However, several reports on the in vitro and in vivo toxicity of amorphous silica nanomaterials (aSiO2 NMs) have been questioning its safety at the nanoscale. In the present study, we investigated the influence of the physicochemical properties (size and surface chemistry) on in vivo pulmonary toxicity using four variants of aSiO2 NMs (SiO2_15_Unmod, SiO2_15_Amino, SiO2_7 and SiO2_40). We focused on alterations in lung DNA and protein integrity, and gene expression following intratracheal instillation in rats. Additionally, a short-term inhalation study (STIS) was carried out for SiO2_7, using TiO2_NM105 as a benchmark NM.Results: Overall, single intratracheal instillation of all tested NMs did not cause a significant increase of DNA strand breaks in the rat lung. However, a significant but slight increase of oxidative DNA damage in rats exposed to the highest instilled dose (0.36 mg/rat) of SiO2_15_ Amino was observed at 21 days post-exposure. In turn, exposure to SiO2_7 or SiO2_40 markedly increased oxidative DNA lesions in the rat lung cells in every tested dose at 3 days post-exposure, even though in a dose-independent manner. Nevertheless, this damage seems to be repaired since no changes compared to controls were observed in the recovery groups. In STIS, a significant increase of DNA strand breaks in the lung cells of rats exposed to 0.5 mg/m3 of SiO2_7 or to 50 mg/m3 of TiO2_NM105 was observed at both assessed time-points. The gene expression changes detected suggest that oxidative stress and/or inflammation pathways are likely implicated in the induction of (oxidative) DNA damage.Conclusions: Overall, all tested aSiO2 NMs were not associated with marked in vivo toxicity following instillation or STIS. Under our experimental conditions, pyrogenic aSiO2 NMs (SiO2_7 and SiO2_40) showed a higher genotoxic potential compared to colloidal aSiO2 (SiO2_15_Unmod and SiO2_15_Amino). Overall, genotoxicity findings for SiO2_7 from instillation and STIS are concordant, however changes in STIS animals were more permanent/difficult to revert.

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“…However, the value of these models is rather different when it comes to grouping of nanomaterials based on their similarity in terms of physicochemical properties and biological activities. [ 44 ] Based on analyses drawn from the nanocluster growth rate and the physicochemical properties of the ENM a read across of the toxicological properties ENMs based on governance, risk, and compliance (GRC) transformation can be performed. [ 45 ] Such QSAR based models can be used in their own right but they can also be employed to provide input parameters to the more complex PBPK models.…”

Section: Models Predicting Colloidal Properties As Determinant To Nanmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Singh

Ansari

Rosenkranz

et al. 2020

Adv Healthcare Materials

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204

117

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Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial‐based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)‐based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure–activity relationships at nanoscale (nano‐QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

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Recursive feature elimination in random forest classification supports nanomaterial grouping

Cited by 89 publications

References 43 publications

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities

Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities

Genotoxicity and Gene Expression in the Rat Lung Tissue Following Instillation and Inhalation of Different Variants of Amorphous Silica Nanomaterials (aSiO2 NMs)

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

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