SLIDE 1
“To bridge the Mode of Action based computational modelling to the demands of grouping and safe by design of nanoparticles, and make it applicable for industry”.
Objective
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Science-based grouping of nanoparticles for industrial application - - PowerPoint PPT Presentation
Science-based grouping of nanoparticles for industrial application - - PowerPoint PPT Presentation
Science-based grouping of nanoparticles for industrial application of safe-by-design Katarzyna Odziomek, Tomas Puzyn, Piotr Urbaszek, Andrea Haase, Christian Riebeling, Agnieszka Gajewicz, Muhammed A. Irfan, Robert Landsiedel, Meike van der
SLIDE 2
SLIDE 3
Are there (any) principles for grouping of NM?
- ..not every chemical needs to be tested for every endpoint...overall
data for that category should prove adequate to support a hazard assessment... (OECD, 2014)
- Grouping should take into account all aspects of NM life cycle.. (Arts
et al., 2014)
- Structure and material properties, exposure, uptake and kinetics,
initiating cellular effects or apical effects.. (ECETOC 2014)
Oomen et al 2014
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SLIDE 4
Establishing science-based criteria for grouping
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Adapted from Agnes Oomen et al. 2014 In „Safety of nanomaterials along their life-cycle“ pp 358 – 379 ISBN 978-1-46-656786-3, 2014
SLIDE 5
ECETOC Grouping Strategy
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Arts et al., 2015, A decision-making framework for the grouping and testing of nanomaterials (DF4nanoGrouping). Regul Toxicol Pharmacol. 2015 Mar 15;71(2 Suppl):S1-27. doi: 10.1016/j.yrtph.2015.03.007.
SLIDE 6
Grouping Concepts
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No single property groups all materials – Need a multi-perspective grouping & testing strategy
Multi-perspective grouping
Refinement of grouping criteria
SLIDE 7
Tiered Testing
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SLIDE 8
Panel of nanoparticles
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Name Size SizeDLSw SSA Zeta XPSC XPSNa BaSO4.NM220 32.00 350.00 41.00
- 39.00
17.00 0.00 CeO2 200.00 N/A 33.00 6.00 9.00 0.00 CeO2.Al 81.00 N/A 46.00 18.00 9.00 0.00 CeO2.NM211 12.00 N/A 33.00 16.00 28.70 0.00 CeO2.NM212 40.00 N/A 27.00 42.00 79.90 0.00 SiO2.NH3 15.00 42.00 200.00 0.00 73.10 0.00 SiO2.PEG 15.00 50.00 200.00
- 26.00
73.60 0.00 SiO2.PO3 15.00 40.00 200.00 42.90 77.10 0.00 SiO2.UNMOD 15.00 40.00 200.00
- 39.00
0.00 0.00 TiO2.NM105 50.00 478.00 51.00
- 17.00
0.00 0.00 TiO2.TLSF 50.00 N/A 100.00
- 3.00
5.00 0.50 ZrO2 42.50 N/A 24.90
- 12.00
4.00 1.00 ZrO2.ACR 9.00 9.00 117.00
- 39.00
9.00 0.50 ZrO2.NH3 10.00 315.00 105.00
- 3.90
9.00 0.00 ZrO2.PEG 9.00 27.00 117.00
- 7.80
19.00 0.00 ZrO2.TOD 9.00 9.00 117.00
- 6.50
19.00 0.00 DPP.BULK 200.00 N/A 42.00
- 11.40
0.00 0.00 DPP.NANO 400.00 N/A 64.00
- 12.30
0.00 0.00 DPP.RED 43.00 N/A 30.00
- 16.00
11.00 0.00
SLIDE 9
With variations in surface modifications
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SLIDE 10
10
DPP Orange 1 (bulk) DPP Orange 2 (nano) Pigment Red 254- 2 (nano) MWNT NM400 Graphen e Graphen e nano- platelets Carbon black SiO2- naked SiO2 PEG SiO2 Amino SiO2 Phosphat e SiO2 FITC CeO2 NM212 CeO2 NM211 CeO2 Al-doped CeO2 BaSO4 NM220 ZnO NM111 ZnO NM110 TiO2 NM105 TiO2 (T- Lite SF) ZrO2.Acr ylate ZrO2.PE G ZrO2.Am ino ZrO2.TO Dacid AG50 AG50.mo no AG200.m
- no
AG50.citr ate Ag.Braak uis Ag.Braak uis Ag.Braak uis Ag.Braak uis ZrO2 CuO
Material Properties Particle size DistributionTE M/SEM: Primary Particle Diameter 0.3-3 µm x 70-200 nm (TEM) 30-400 nm x 10-50 nm (TEM) 43 nm 15 nm Fiber Up to10 µm Flakes Up to 30 µm Flakes 50-100 nm Globular 15 nm 15 nm 15 nm 15 nm 25 nm 40 nm 4-15 nm Up to 200 nm Globular 2-160 nm Globular 32nm 80 nm Globular 80 15x50 nm 50x10 nm Fiber 9 nm 9 nm 10 nm 9 nm 7 nm 97 nm 134 nm 20 nm 18 nm 34 nm 60 nm 134 nm 25-60 nm 10 nm Surface Area (BET/Hg intrusion) 42 m²/g 64 m²/g 30 m²/g 161 m2/mg 131 m2/mg 74 m2/mg 32 m2/mg 200 m2/g 200 m2/g 200 m2/g 200 m2/g 178 m2/g 30 m²/g (Hg) 27 m²/g (BET) 33 m2/g 33 m2/g 46 m2/g 41 m2/g 12 m2/g 12 m2/g 51 m2/g 100 m2/g 117 m2/g 117 m2/g 105 m2/g 117 m2/g 86 m2/g 6.2 m2/g 4.5 m2/g 30 m2/g 32 m2/g 17 m2/g 9 m2/g 1 m2/g 24.9 m2/g N/A Surface Chemistry (XPS element %) C 73.1 Cl 9 N 9.5 O 8.4 C 73.6 Cl 8.8 N 8.7 O 8.8 C 77.1 O 10.9 N 5.9 Cl 6.1 C 99 O1 C 84.1 O 8.8 S 5.4 Na 0.6 Si 0.4 Cl 0.6 C 84.3 O 9.0 S 1.7 Na 3.0 Ca 1.5 Si 0.6 C 98 O 1 S 1 Cl <1 O 66 Si 29 C 4 Na 1 PEG identified (SIMS) Amino identified (SIMS) O 66 Si 29 C 5 Na 0,5 PO2,PO3 fragments N/A C 79.9 O 17.7 Ce 2.4 Ce 28.7 O 57.2 C 14.1 Ce 16 O 61 C 9 Al 9 Zr 5 Ce 21 Al 9 O 56 C 9 Zr 4 N 1 O 52 Ba 13 C 17 S 11 Cl, P 3 N 1 O 38 Zn 35 C 20 Cl 3 Na 3 O 38 Zn 35 C 30 Cl 3 Na 3 Ti 16 O 63 C 9 Al 7 Si 5 Na <1 Ti 16 O 63 C 9 Al 7 Si5 Na <1 dimethicone / methicone copolymer as coating Zr 23 O 58 C 19 SIMS: expected acrylic acid PEG identified (SIMS) Amino identified (SIMS) Zr 24 O 63 C 11 N 0.7 S 0.2 SIMS: expected trioxadecan- ic acid
- 11.4 mV
- 12.3 mV
- 16 mV
- 39 mV
- 26 mV
- 42,9 mV
- 39 mV
- 17 mV
- 3 mV
- 39 mV
- 7.8 mV
- 3.9 mV
- 6.5 mV
- 20 mV
- 7 mV
- 7 mV
- 45 mV
- 45 mV
- 45 mV
- 45 mV
- 45 mV
- 12 mV
- f
- steopontin
SLIDE 11
Solubility of nanoparticles in realistic environments
Biological matrix Water, Lung, Intestine Nanomaterial
Ultrafiltration (UF) – ICP-MS
- Accessible, (easy to use), cheap
methods based on ICP-MS, elementary detection
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SLIDE 12
Results
% Dissolved Concentration
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SLIDE 13
Approach failed in this project!
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- Detection (at low concentrations) is challenging:
- polyatomic interference,
- relatively low analytical recovery,
- Complex matrix is very difficult, interactions with the matrix or filter
- “Lessons learned”:
- SP-ICP-MS: promising technique, but the size detection limit may be
limiting: thus not used here.
- Very interesting developments in AF4 or HDC-SP-ICP-hrMS method
development outside of this project!
7 Materials
- NanoGem Silica 15 nm
- NanoGem Silica (amino) 15 nm
- NanoGem Silica (phosphate) 15 nm
- NM-202 (Silica)
- NM-203 (Silica)
- NM-104 (TiO2)
- NM-105 (TiO2)
SLIDE 14
Missing data to be addressed:
- Cytochrome c assay for surface reactivity
- Cytochrome c is oxidised by NP surface
14
N P
Ranking obtained from these results is in agreement with that from FRAS/FRAP assays CuO>Mn2O3>TiO2>CeO2>BaSO4
SLIDE 15
Classification tree for a short-term inhalation study on rats
15
Descriptors selected: LUMO_C, Size, XPSNa
SLIDE 16
NOAEC
Class NOAEC [mg/m
3]
TOX (1) ≤ 10 NTOX (2) > 10 NP LUMO_C Size XPSNa Split [S0] NOAEC [original value] NOAEC class [a priori] NOAEC class [predicted] Correct prediction BaSO4.NM220 0.128 32 T 50.00 2 2 TRUE SiO2.NH3
- 1.031
15 T 50.00 2 2 TRUE SiO2.PEG
- 1.031
15 V 50.00 2 2 TRUE SiO2.PO3
- 1.031
15 0.5 T 50.00 2 2 TRUE ZrO2.ACR
- 0.191
9 T 50.00 2 2 TRUE ZrO2.TOD
- 0.191
9 V 50.00 2 2 TRUE DPP.NANO
- 1.742
400 T 30.00 2 2 TRUE DPP.RED
- 1.879
43 T 30.00 2 2 TRUE ZrO2
- 0.191
42.5 V 10.00 1 2 FALSE DPP.BULK
- 1.742
3000 T 10.00 1 1 TRUE SiO2.UNMOD
- 1.031
15 1 T 2.50 1 1 TRUE TiO2.NM105
- 3.319
50 0.5 V 1.00 1 1 TRUE TiO2.TLSF
- 3.319
50 T 0.50 1 1 TRUE CeO2
- 20.582
200 T 0.25 1 1 TRUE CeO2.Al
- 20.582
81 V 0.25 1 1 TRUE CeO2.NM211 -20.582 12 T 0.25 1 1 TRUE CeO2.NM212 -20.582 40 T 0.25 1 1 TRUE ZrO2.NH3
- 0.191
10 P 2 ZrO2.PEG
- 0.191
9 P 2 T 1 2 1 6 2 6 T Accuracy 1.00 Error 0.00 Sensitivity 1.00 Specificity 1.00 V0 1 2 1 2 1 2 2 V0 Accuracy 0.80 Error 0.20 Sensitivity 1.00 Specificity 0.67
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SLIDE 17
NOAEC – multiple external validations
NP LUMO_C Size XPSNa NOAEC [original value] NOAEC class [a priori] S0 S1 S2 S3 S4 BaSO4.NM220 0.128 32 50 2 T T T V T SiO2.NH3
- 1.031
15 50 2 T T T T V SiO2.PEG
- 1.031
15 50 2 V T T T T SiO2.PO3
- 1.031
15 0.5 50 2 T V T T T ZrO2.ACR
- 0.191
9 50 2 T T V T T ZrO2.TOD
- 0.191
9 50 2 V T T V T DPP.NANO
- 1.742
400 30 2 T V T T V DPP.RED
- 1.879
43 30 2 T T V T T ZrO2
- 0.191
42.5 10 1 V T T V T DPP.BULK
- 1.742
3000 10 1 T V T T V SiO2.UNMOD
- 1.031
15 1 2.5 1 T T V T T TiO2.NM105
- 3.319
50 0.5 1 1 V T T V T TiO2.TLSF
- 3.319
50 0.5 1 T V T T V CeO2
- 20.582
200 0.25 1 T T V T T CeO2.Al
- 20.582
81 0.25 1 V T T V T CeO2.NM211
- 20.582
12 0.25 1 T V T T V CeO2.NM212
- 20.582
40 0.25 1 T T V T T ZrO2.NH3
- 0.191
10 P P P P P ZrO2.PEG
- 0.191
9 P P P P P Accuracy 80% 100% 100% 80% 100% Error 20% 0% 0% 20% 0% Sensitivity 100% 100% 100% 100% 100% Specificity 70% 100% 100% 70% 100%
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SLIDE 18
Towards Integrated Testing Strategies
18
Nanomaterial Protein carbonylation FRAS NOAEC BaSO4.NM220 I A I SiO2.NH3 A I I SiO2.PEG I I* I SiO2.PO3 A I I ZrO2.ACR I I* I ZrO2.TOD I I* I DPP.NANO I* A I DPP.RED I* A* I ZrO2 I* A* A DPP.BULK I* A A SiO2.UNMOD A I A TiO2.NM105 A A A TiO2.TLSF I* A* A CeO2 I* A* A CeO2.Al I* A* A CeO2.NM211 I* I A CeO2.NM212 I* A A ZrO2.NH3 I I* I* ZrO2.PEG I I* I*
SLIDE 19
Conclusions
- Optimization of analytical methods for determination of solubility in
complex matrices needed!
- Decision trees can be used for refining descriptor selection and
setting specific numerical thresholds of structural features related to the change in biological properties
- The identified key NP features (descriptors) can help in the design of
new nanomaterials, as they are the most relevant their safety
- A predictive model is now proposed, can be applied in decision-
making framework for the grouping and testing of nanomaterials (DF4nanoGrouping)
- Larger datasets on nanomaterials are needed: but now focus
- n specific endpoints!
- Consistent data and data quality remain important issues
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SLIDE 20
Thanks
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Hans.bouwmeester@wur .nl