Suitability of analytical methods to measure solubility for the purpose of nanoregulation

Tantra, Ratna; Bouwmeester, Hans; Bolea, Eduardo; Rey-Castro, Carlos; David, Calin; Dogné, Jean Michel; Jarman, John; Laborda, Francisco; Laloy, Julie; Robinson, Kenneth N.; Undas, Anna K.; Zande, Meike van der

doi:10.3109/17435390.2015.1038661

Cited by 18 publications

(19 citation statements)

References 110 publications

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“…All data collected falls into the first category (i.e., dissolution 1-25%; Figure 4). Furthermore, in the studies form literature that were analyzed in our study, only in 50% of the cases data on solubility was provided (Supplement 2), which may be caused by the current challenges to determine solubility of a NM in biological matrices (Tantra et al, 2016). It is evident that more states in the lower dissolution ranges combined with more data (i.e., more cases) will result in a higher contribution of dissolution node to the hazard noted of this BN.…”

Section: Discussionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal-and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO 2 , SiO 2 , Ag, CeO 2 , ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

et al. 2017

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“…37 An important advantage of AGNES is that it can be applied directly to the dispersion without the need of any previous solid/liquid separation step. 14,15 In this way, it allows a good control over the environmental variables (pH, temperature) as well as a relatively short time of analysis, which is essential for the measurement of dissolution rates. However, up to date, no direct experimental comparison between AGNES and other separation/analysis coupled protocols has been reported.…”

Section: On the Other Hand The Technique Absence Of Gradients And Nementioning

confidence: 99%

“…Different separation techniques have been proposed in the literature for this purpose. [13][14][15] One of the most common methods is ultrafiltration (UF), which involves the use of semipermeable filters (typically having pore sizes of 3 kDa) in a centrifugation tube.…”

Section: Introductionmentioning

confidence: 99%

Dissolution and Phosphate-Induced Transformation of ZnO Nanoparticles in Synthetic Saliva Probed by AGNES without Previous Solid–Liquid Separation. Comparison with UF-ICP-MS

David

Galceran

Quattrini

et al. 2019

Environ. Sci. Technol.

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The variation over time of free Zn 2+ ion concentration in stirred dispersions of ZnO nanoparticles (ZnO NPs) prepared in synthetic saliva at pH 6.80 and 37º C was followed in situ (without solid-liquid separation step) with the electroanalytical technique AGNES (Absence of Gradients and Nernstian Equilibrium Stripping). At these conditions, ZnO NPs are chemically unstable due to their reaction with phosphates. The initial stage of transformation (around 5-10 h) involves the formation of a metastable solid (presumably ZnHPO4), which later evolves into the more stable hopeite phase. The overall decay rate of ZnO NPs is significantly reduced in comparison with phosphate-free background solutions of the same ionic strength and pH. The effective equilibrium solubilities of ZnO (0.29-0.47 mg•L-1), as well as conditional excess-ligand stability constants and fractional distributions of soluble Zn species were determined in absence and presence of organic components. The results were compared with the conventional ultrafiltration and inductively coupled plasmamass spectrometry (UF-ICP-MS) methodology. AGNES proves to be advantageous in terms of speed, reproducibility and access to speciation information.

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“…To some extent, this approach combines intrinsic properties with systemdependent properties and toxicological effects. For example, the solubility of a nanomaterial can be regarded as an intrinsic property, but its dissolution rate is also highly dependent on the environmental conditions (Tantra et al 2016). Detailed subgrouping, especially in the group of active nanomaterials should be amended to this concept, and should be related to mechanisms of action, ideally integrated into the adverse outcome pathway concept (Ankley et al 2010;Becker et al 2015;Riebeling et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Decision tree models to classify nanomaterials according to theDF4nanoGroupingscheme

et al. 2017

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To keep pace with its rapid development an efficient approach for the risk assessment of nanomaterials is needed. Grouping concepts as developed for chemicals are now being explored for its applicability to nanomaterials. One of the recently proposed grouping systems is DF4nanoGrouping scheme. In this study, we have developed three structure-activity relationship classification tree models to be used for supporting this system by identifying structural features of nanomaterials mainly responsible for the surface activity. We used data from 19 nanomaterials that were synthesized and characterized extensively in previous studies. Subsets of these materials have been used in other studies (short-term inhalation, protein carbonylation, and intrinsic oxidative potential), resulting in a unique data set for modeling. Out of a large set of 285 possible descriptors, we have demonstrated that only three descriptors (size, specific surface area, and the quantum-mechanical calculated property 'lowest unoccupied molecular orbital') need to be used to predict the endpoints investigated. The maximum number of descriptors that were finally selected by the classification trees (CT) was very low-one for intrinsic oxidative potential, two for protein carbonylation, and three for NOAEC. This suggests that the models were wellconstructed and not over-fitted. The outcome of various statistical measures and the applicability domains of our models further indicate their robustness. Therefore, we conclude that CT can be a useful tool within the DF4nanoGrouping scheme that has been proposed before. ARTICLE HISTORY

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Suitability of analytical methods to measure solubility for the purpose of nanoregulation

Cited by 18 publications

References 110 publications

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment

Dissolution and Phosphate-Induced Transformation of ZnO Nanoparticles in Synthetic Saliva Probed by AGNES without Previous Solid–Liquid Separation. Comparison with UF-ICP-MS

Decision tree models to classify nanomaterials according to theDF4nanoGroupingscheme

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