Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials
Abstract
:1. Introduction
2. State-of-the-Art of In Silico Models Serving Hazard Assessment of ENMs
2.1. Development of (Q)SARs and Read-Across Models for Metallic ENMs
- (i)
- Descriptors regarding the intrinsic properties of metal (oxide):
- Surface catalytic properties and redox modifications related factors include: Wigner–Seitz radius, mass density, band gap energy, overlap of conduction band energy levels with the cellular redox potential, conduction band energy, average of the alpha and beta LUMO (lowest unoccupied molecular orbital) energies of the metal oxide, accessible surface area, absolute electronegativity of the metal and the metal oxide, aligned electronegativity, electronegativity, Mulliken’s electronegativity of the cluster, S2 (SiRMS-derived number of oxygen’s atoms in a molecule, which was described by their electronegativity), S3 (tri-atomic fragments[Me]-[O]-[Me], which were encoded by SiRMS-derived descriptors, encoding electronegativity), and metal electronegativity.
- Characteristics related to the capability of ion and electron detachment and the activity of ions include: covalent index, cation polarizing power, atomization energy, metal oxide ionization energy, ionic index of metal cation, enthalpy of formation of metal oxide nanocluster representing a fragment of the surface, cationic charge, enthalpy of formation of a gaseous cation, charge of the metal cation corresponding to a given oxide, solubility, polarizability, molar refractivity, and polarization force.
- (ii)
- The nano-specific descriptors employed in the developed models include:
- The size of ENMs; and
- Parameters characterizing the surface chemistry of ENMs, e.g., hydrophobicity of surface coating chemicals, surface-area-to-volume ratio, surface coating and charge, surface area, polar surface area.
- (iii)
- The parameters indicating the dynamic changes of ENMs in media include:
- Zeta potential;
- Concentration of ENMs; and
- Descriptors representing the dispersion and aggregation of ENMs in media, e.g., aggregation parameter, size in DMEM (Dulbecco’s Modified Eagle’s Medium), relaxivity (representing ENM magnetic properties), size in phosphate buffered saline, size in water, aggregation size.
2.2. Development of SSDs for Metal-Based ENMs
3. The Struggle of Data Availability
- (i)
- Details of the tested organisms, e.g., taxonomic categorization, name of species, exposure route, life-stage or bacterial strain (for bacteria);
- (ii)
- Conditions of the performed experiments, e.g., test guideline used (if available) and possible modifications of the test guideline, preparation of test medium, composition of the exposure medium, media pH, light condition, and time-dependent medium stability;
- (iii)
- Information on the specific toxicity endpoints, e.g., observed biological effects, type of endpoint, experimental value of toxicity endpoint, and unit in which the endpoint is expressed; and
- (iv)
- Characteristics of the ENMs tested, e.g., type of ENMs, composition of core, distribution of particle size, surface coating, purity, crystallinity, surface area, surface charge, shape, agglomerate size and material zeta potential in media, stability in test medium.
4. Profiling Nanotoxicity on the Basis of In Silico Models
5. Outlook
6. Conclusions
- (i)
- An overview is provided of the current advances towards the development of in silico predictive models and SSDs for metallic ENMs. Based on reported models, factors such as solubility, hydrophobicity of ENM surface coating, and polarizability were concluded as enhancing the toxicity elicited by metallic ENMs. Meanwhile factors such as conduction band energy, ionization energy, and cationic charge were shown to play an opposite role in this respect. The studies on SSDs for ENMs showed that marginal risks are associated with the presence of Ag, TiO2, and ZnO ENMs in surface water, whereas high environmental risks are foreseen for those ENMs in sewage treatment effluents.
- (ii)
- A proposal is presented for preparation of a thoroughly curated dataset related to reporting of future results of laboratory studies, in light of enclosing sufficient information to allow for optimal ENM-related modeling based on laboratory assays.
- (iii)
- The mechanism of biological activities of metal-based ENMs is profiled based on employed descriptors. The intrinsic properties of ENMs such as cationic charge and ionic radius are considered pivotal in affecting nanotoxicity. However, surface chemistry of ENMs is shown to also be able to significantly modify the toxicity or bioavailability of metallic ENMs.
- (iv)
- Several suggestions for further studies are provided in the outlook, with regard to the use of existing nanotoxicity data for modeling, computation of nano-specific descriptors, and consideration of the transformation of ENMs in media into modeling. Finally, a roadmap is depicted to optimize the use of computational toxicology in hazard assessment of ENMs and to further advance the broader field of ENM-related modeling.
Acknowledgments
Conflicts of Interest
References
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Reference | Indicated ENM Characteristics in Models | Theoretical Descriptor | Experimental Descriptor | ENMs | Tested Organism | Data Retrieved from |
---|---|---|---|---|---|---|
[18] * | Number of metal and oxygen atoms, molecular weight, atomization energy, group and period in the periodic table, size, isoelectric point, zeta potential, concentration | √ | √ | 9 metal oxide ENMs | BEAS-2B cells | N/A |
[19] | Band gap energy, overlap of conduction band energy levels with the cellular redox potential (−4.12 to −4.84 eV), solubility | √ | √ | 24 metal oxide ENMs | BEAS-2B cells, RAW 264.7 cells | N/A |
[20] | Mass density, molecular weight, aligned electronegativity, covalent index, cation polarizing power, Wigner–Seitz radius, surface area, surface-area-to-volume ratio, aggregation parameter, two-atomic descriptor of van der Waals interactions, tri-atomic descriptor of atomic charges, tetra-atomic descriptor of atomic charges, size in DMEM | √ | √ | [19] | ||
[21] * | Atomization energy, atomic mass, size, conduction band energy, metal oxide ionization energy, electronegativity, ionic index of metal cation | √ | ||||
[22] | Enthalpy of formation of metal oxide nanocluster representing a fragment of the surface, Mulliken’s electronegativity of the cluster | √ | 18 metal oxide ENMs | HaCaT cells | N/A | |
[23] * | Molar volume, polarizability, size | √ | √ | 41 metallic ENMs | Mammalian cells | Multiple resources |
[24] * | Size, relaxivities, zeta potential | √ | √ | 50 metallic ENMs | Endothelial cells, vascular smooth muscle cells, human HepG2 cells, RAW 264.7 cells | [25] |
[26] | Indicator variables of core material, surface coating, and surface charge | √ | ||||
[27] | (i) Size, relaxivities, zeta potential; (ii) Oxygen percent, molar refractivity, polar surface area | √ | √ | (i) 44; (ii) 17 metallic ENMs | (i) Endothelial cells, vascular smooth muscle cells, human HepG2 cells, RAW 264.7 cells; (ii) E. coli | [25,28] |
[29] * | Size, concentration, size in phosphate buffered saline, size in water, zeta potential | √ | √ | 24 TiO2, 18 ZnO ENMs | Rat L2 lung epithelial cells, rat lung alveolar macrophages | N/A |
[30] | Size, concentration, size in phosphate buffered saline, size in water | √ | √ | [29] | ||
[31] | Molecular weight, cationic charge, mass percentage of metal elements, size, aggregation size | √ | √ | (i) 17; (ii) 18 metal oxide ENMs | (i) E. coli; (ii) HaCaT cells | [22,28] |
[32] * | Enthalpy of formation of a gaseous cation, Mulliken’s electronegativity of the cluster | √ | ||||
[33] | (i) S1, Wigner–Seitz radius, mass density, cation polarizing power, S2, S3, proportion of surface molecules to molecules in volume; (ii) S1, Wigner–Seitz radius of oxide’s molecule, mass density, covalent index of the metal ion, S2, aggregation parameter | √ | √ | |||
[34] | Enthalpy of formation of a gaseous cation, enthalpy of formation of metal oxide nanocluster representing a fragment of the surface, Mulliken’s electronegativity of the cluster | √ | ||||
[28] | Enthalpy of formation of a gaseous cation | √ | 17 metal oxide ENMs | E. coli | N/A | |
[35] | Polarization force, enthalpy of formation of a gaseous cation | √ | [28] | |||
[36] | Charge of the metal cation corresponding to a given oxide, metal electronegativity | √ | ||||
[37] | Dark: absolute electronegativity of the metal and the metal oxide; Light: molar heat capacity, average of the alpha and beta LUMO (lowest unoccupied molecular orbital) energies of the metal oxide | √ | N/A | |||
[15] * | Molecular polarizability, accessible surface area, solubility | √ | 400; 450; 166 metallic ENMs | Various species | [38]; OCHEM | |
[16] * | Molar volume, electronegativity, polarizability, size, hydrophobicity, polar surface area | √ | √ | 229 metallic ENMs | Various species | Multiple resources |
[17] | Concentration, shell composition, surface functional groups, purity, core structure, and surface charge | √ | √ | 82 ENMs including metal and metal oxide ENMs, dendrimer, polymeric etc. | Zebrafish embryo | NBI knowledgebase |
Reference | Type of ENMs | Reported HC5s | Number of Species in SSDs | Environmental Compartment |
---|---|---|---|---|
Jacobs et al., 2016 [46] | TiO2 | N/A | 31 | Water |
Wang et al., 2016 [47] | FeOx | 0.218 (0.169–0.267) mg/L, 15–85% percentiles | 12 | Water |
Kwak et al., 2016 [48] | Ag | 0.03173 mg/L (acute toxicity); 0.000614 mg/L (chronic toxicity) | 8 (acute toxicity); 5 (chronic toxicity) | Water |
Coll et al., 2016 [49] | (i) Ag; (ii) TiO2; (iii) ZnO | (i) 0.000017 (0.000014–0.000021) mg/L in freshwater, 8.2 (4.3–12.5) mg/kg in soil; (ii) 0.0157 (0.0106–0.0207) mg/L in fresh water, 91.1 (47.6–134.9) mg/kg in soil; (iii) 0.001 (0.0006–0.00138) mg/L in freshwater, 1.1 (0.6–1.6) mg/kg in soil, 95% confidence intervals | (i) 33 (water), 4 (soil); (ii) 31 (water), 2 (soil); (iii) 21 (water), 7 (soil) | Water, soil |
Wang et al., 2016 [50] | Silica | 1.023 (0.787–1.265) mg/L, 15–85% percentiles | 8 | Water |
Mahapatra et al., 2015 [51] | Au | N/A | 8 (water) | Water, soil |
Semenzin et al., 2015 [52] | TiO2 | 0.02 mg/L | 34 | Water |
Adam et al., 2015 [53] | (i) ZnO; (ii) CuO | (i) 0.07 (0.04–0.19) mg/L; (ii) 0.19 (0.06–0.59) mg/L, 90% confidence intervals | (i) 12; (ii) 13 | Water |
Garner et al., 2015 [45] | (i) Ag; (ii) Cu; (iii) CuO; (iv) ZnO; (v) Al2O3; (vi) CeO2; (vii) TiO2 | N/A | (i) Uncoated-Ag: 8, PVP-Ag: 6; (ii) 4; (iii) 5; (iv) 7; (v) 9; (vi) 7; (vii) 8 | Water |
Nam et al., 2015 [54] | Au | 0.29 mg/L | 7 | Water |
Botha et al., 2015 [55] | Au | 42.78 mg/L | 4 | Water |
Haulik et al., 2015 [56] | (i) Ag; (ii) TiO2; (iii) ZnO | (i) 0.00015; (ii) 0.275; (iii) 3.246 mg/L | (i) 14; (ii) 11; (iii) 10 | Water |
Gottschalk et al., 2013 [57] | (i) Ag; (ii) TiO2; (iii) ZnO | (i) 0.00001; (ii) 0.06151; (iii) 0.00985 mg/L | (i) 12; (ii) 18; (iii) 17 | Water |
Chen et al., 2017 [58] | (i) Ag; (ii) CuO; (iii) ZnO; (iv) CeO2; (v) TiO2 | HC5s were calculated for various SSDs | Different hierarchies of species were used | Water |
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Chen, G.; Peijnenburg, W.; Xiao, Y.; Vijver, M.G. Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials. Int. J. Mol. Sci. 2017, 18, 1504. https://doi.org/10.3390/ijms18071504
Chen G, Peijnenburg W, Xiao Y, Vijver MG. Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials. International Journal of Molecular Sciences. 2017; 18(7):1504. https://doi.org/10.3390/ijms18071504
Chicago/Turabian StyleChen, Guangchao, Willie Peijnenburg, Yinlong Xiao, and Martina G. Vijver. 2017. "Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials" International Journal of Molecular Sciences 18, no. 7: 1504. https://doi.org/10.3390/ijms18071504