Korea Institute of Science and TechnologyEurope

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1 OpenTox Asia 2017 Deajeon, Korea, May 2017 How well can the mixture toxicity of engineered nanoparticles be predicted? Jongwoon Kim, PhD Environmental Safety Group KIST Europe, Germany Korea Institute of Science and TechnologyEurope KIST Europe 1

2 Prologue For Humans For the Environ. KIST Europe 2

3 Research Need Why necessary to study the prediction of mixture toxicity? Regulatory Perspective Risk assessment often focus on single chemicals (Syberg et al., 2009) Enhancing chemical regulations Industrial Perspective 100,000 commercial substances (Hartung & Rovida, 2009) Economic Burden Mixture Toxicity Prediction Scientific Perspective Mixture toxicity as combined effects at levels below NOECs (Koretnkamp & Altenburger, 1999) Lack of knowledge for interactions (Xu & Nirmalakhandan, 1998) (NOEC: No-Observed-Effect-Concentration) KIST Europe 3

4 What is a mixture? Definition of Mixtures Mixture is composed of two or more chemicals, but not chemically bonded Mixture of 2 Elements - Two Types of Atoms Mixture of 2 Elements - Atoms & Molecules Mixture of 2 Elements - Two Types of Molecules Mixture of Elem.& Comp. - Molecules and Compound Atom Molecule Compound KIST Europe 4

5 Nano-mixtures Types of Nano-Mixtures? Type I: NM + Organic Compound Type II: NM + Inorganic Compound Type III: NM+NM (incl. Multi-NMs) KIST Europe 5 (Source: Azevedo et al., 2016)

6 Interactions Non-interaction Mixture Toxicity What is mixture toxicity? Additivity Toxicity of the mixture of toxicants A and B is the same as the sum of toxicities of toxicants A and B when applied individually Ex) 2+2=4 Synergism Toxicity of the mixture of toxicants A and B is higher than the sum of toxicities of toxicants A and B when applied individually Ex) 2+2=6 Antagonism Toxicity of the mixture of toxicants A and B is lower than the sum of toxicities of toxicants A and B when applied individually Ex) 2+2=2 Potentiation One chemical, which is not toxic on its own, enhances toxicity of the second chemical in the mixture Ex) 2+0=4 KIST Europe 6

7 Computational Toxicology Advantages of Computational Toxicology A discipline combining variety chemo- & bioinformatics data to develop computer-based models to better understand and predict adverse health effects caused by chemicals The main functions of computational toxicology (US EPA 2007): 1. To effectively assess hazards posed by chemicals in the environment; 2. To determine the risk of chemicals at low levels of exposure; 3. To more accurately predict the effects of chemicals across species; and 4. To reduce experimental costs. Reliable, Cost- & Time-Saving KIST Europe 7 (Source:

8 Conventional Models Conventional Models of Mixture Toxicity: Classical Models for Non-Interaction For Similarly Acting Chemicals: Similar modes of toxic action Conc. Addition (Dose Addition) No interaction between mixture components Substances act as simple dilutions Sum of doses, scaled for relative toxicity Equation (Loewe and Muischnek, 1926) Independent Action (Response Addition) For Dissimilarly Acting Chemicals: Different modes of action Statistical independence Relative effect of A not influenced by B Sum of effects from each substance (sum of probabilistic risks) Equation (Bliss, 1939) KIST Europe 8

9 Predictive Models for Nano-Mixtures Application of CA and IA models to prediction of Toxicity of Nano-Mixtures Under REACH and CLP Regulations, mixture toxicity can be predicted based on the CA model. But some papers showed IA model predicted the nano-mixture toxicity better than CA (Wang et al., 2016) Approximately, 20 empirical papers on nano-mixture toxicity have been published. Lack of model papers on nano-mixture toxicity KIST Europe 9

10 Data Collection on Nano-Mixture Toxicity Studies Empirical data status on nano-mixtures Type Target Mixture Endpoint Species Citation CeO 2 NP-florfenicol; Type I Algal growth Chlorella pyrenoidosa Wang et al. (2016) TiO 2 NP-florfenicol Type I SiO 2 NP-methyl mercury Cytotoxicity Lung adeno carcinoma cells Yu et al. (2015) Type I Ag NP-17α-ethinylestradiol Reproduction Potamopyrgus antipodarum Voelker et al. (2014) Type I TiO 2 NP-Bisphenol A Cytotoxicity; Genotoxicity Human liver cell( human embryo L-02 h epatocytes) Zheng et al. (2012) Type I ZnO NP-Propiconazole Cytotoxicity Mouse embryonic fibroblast cells Li et al. (2013) body weight; liver function; oxidative stress; Type II Fe 3O 4 NP-CdCl 2 Mice histopathological changes in tissues Zhang et al. (2016) Type II SiO 2 NP-Pb (Pb[AC] 2) Cytotoxicity; Genotoxicity Human lung epithelial cells (Lungadenoc Lu et al. (2015) arcinomaa549cells) Type II Cu + -TiO 2 NP Algal growth Microcystis aeruginosa Chen et al. (2015) Type II TiO 2 NP-Zn 2+ Algal growth Anabaena sp. Tang et al. (2013) Type II 5-fluorouracil (FL)- SiO 2 NP; 5-FL-Se NP; Cytotoxicity Human osteosarcoma MG-63 cells Jiang et al. (2011) 5-FL-Au NP Type II CdCl 2 NP-TiO 2 NP Cytotoxicity Human embryo kidney cells Xia et al. (2011) Type II TiO 2 NP-Cu + Bioaccumulation; Mortality; Metallothionein Daphnia Fan et al. (2011) Type II TiO 2 NP-As(V); Al 2O 3-As(V) Mortality Ceriodaphnia dubia Wang et al. (2011) Ag NP-AgNO 3 (Ag + ); Ag NP-ZnO NP; Type II/ III AgNP-ZnCl 2 (Zn+); AgNO 3 NP-ZnCl 2 NP Mobility; Feeding rate Daphnia magna Lopes et al. (2016) Type III CeO 2 NP-TiO 2 NP CeO 2 NP-ZnO NP Cytotoxicity; Genotoxicity Nitrosomonas europaea (Cytotoxicity) Yu et al. (2016) Type III TiO 2/ZnO NS Cytotoxicity E. Coli Tong et al. (2015) Type III ZnO NP-TiO 2 NP Cytotoxicity Human lung epithelial cells Hsiao et al. (2011) Type III ZnO NP-Ag NP Immobilization, Reproduction Daphnia magna Azevedo et al. (2016) Type III CuO NP-ZnO NP Mortality; Immobilization; Feeding rate Daphnia magna Zhao et al. (2012) KIST Europe 10

11 Result and Discussion Study on toxicity prediction of Type I nano-mixtures Adversity Ecotoxicological Type I: CeO 2 NP-florfenicol risks associated (FLO) with nceo2/ntio2 ; 2 NP-FLO were (CeO enhanced 2 : 1.07 by FLO. nm, TiO 2 : 3.9 ~ 4.26 nm) Endpoint / Species: Algal growth / Chlorella pyrenoidosa Joint toxicity of nceo2 and FLO was significantly higher than that of ntio2 and FLO. The IA and CA models underestimated the joint toxicity of nceo2/ntio2 and FLO. Predictions based on IA performed better than CA for the joint toxicity. Fig. 1. DRCs for algal growth of CeO 2 NP, FLO, and TiO 2 NP (Wang et al., Chemosphere, 2016) KIST Europe 11

12 Result and Discussion Study on toxicity prediction of Type II nano-mixtures Benefit Type II: 5-fluorouracil (FU)-SiO 2 NP; 5-FU-SeNP; 5-FU-AuNP (SiO 2 NP (25 nm); SeNP (60 nm); AuNP (10 nm)) Endpoint / Species: Cytotoxicity / Human osteosarcoma MG-63 cells Table 1. q values for combinations of 5-FU with NPs Antagonism Antagonism Antagonism Antagonism Addition Addition Addition Addition Synergism Antagonism Antagonism Antagonism The combination of 5-FU with high-concentration SiO2 NPs and Se NPs presented additive toxicity and synergistic effect, respectively. This kind of combination effects may be useful for the exploration of the applications of NPs in biomedicine and cancer treatment Fig 2. Inhibition eff. of SiO 2 NP (A); Se NP (B); Au NP (C); 5-FU (D) The inhibition efficiency of Au NPs was most prominent. Fig 3. Inhibition eff. of combined treatment of 5-FU with SiO 2 NP (A); Se NP (B); Au NP (C) (Jiang et al., Anal Lett, 2015) KIST Europe 12

13 Result and Discussion Study on toxicity prediction of Type II & III nano-mixtures Adversity Type II: AgNP-AgNO 3 (Ag + ); AgNP-ZnCl 2 (Zn+) Type III: AgNP-ZnONP Endpoint / Species: immobilization, reproduction / Daphnia Fig. 4. Isobolograms for immobilization (left) and feeding rate (right). NPs mixture patterns did not reflect their ionic counterparts mixture responses NP mixture prediction should not rely on available information for regular chemicals Ag+ was responsible for synergism on immobilization to the mixture of Ag NP and Ag+ (Lopes et al., Haz Mat, 2016) KIST Europe 13

14 Result and Discussion Study on toxicity prediction of Type III nano-mixtures Adversity Type III: ZnO/Ag NS (Ag-NM (5~12 nm) decorated ZnO-NM (40~50 nm) Endpoint / Species: immobilization, reproduction / Daphnia Fig. 5. Isobolograms for immobilization (left) and reproduction (right) datasets. Parameters interpretation showed an occurrence for synergism at low concentrations, changing to antagonism at dose levels higher than the EC50 KIST Europe 14 (Azevedo et al., Sci Total Environ, 2016) ZnO/Ag NS surface showed higher toxicity than individual components toxicities

15 Result and Discussion Study on toxicity prediction of Type III nano-mixtures Adversity Type III: CuO NP-ZnO NP Endpoint / Species: immobilization, feeding rate / Daphnia KIST Europe 15 Fig. 6. DRCs of CuO NP (A, D); ZnO NP (B, E); CuO NP-ZnO NP (C, F). CuO NP-ZnO NP mixture was the most toxic followed by CuO and ZnO NPs Model deviation ratios for CA at EC50 : 0.99 on immobilization, 1.05 on feeding MDRs for IA at 50%-effect: 2.2 on immobilization and 1.4 on feeding (Zhao et al., Chem Res Chinese Univ, 2012)

16 Result and Discussion Study on toxicity prediction of Type III nano-mixtures Benefit Type III: ZnO NP-TiO 2 NP (ZnO NP decorated with TiO 2 NP) Endpoint / Species: Cytotoxicity / Human lung epithelial cells Fig. 9. Metabolism activity, production of pro-inflammatory factor, LDH activity, and intracellular H2O2 levels of cells. Fig. 8. Cytotoxicity patterns of ZnO and TiO2. Surface modifications and shell coatings (NS mixtures) can be used to reduce Fig. 7. TEM the images toxicity of ZnO of individual and TiO2 NPs NPs and their for core/shell developing structures. safer nano-products KIST Europe 16 (Hsiao et al., Chem Res Toxicol, 2011)

17 Conclusion & Outlook Conclusion 1. From literature survey, 19 datasets on toxicity of nano-mixtures were collected. 5 datasets (1 Type I NP, 2 Type II NPs, 3 Type III NS/NPs) could be selected and analyzed to investigate the applicability of component-based models: CA and IA; 2. For those nano-mixtures, their mixture toxicity could not be covered by the CA and IA models. Predicted toxicity data on nano-mixtures mostly tended to be underestimated by the models; 3. Under REACH/CLP, surface decorated NPs (NS) would be regarded as mixture. CA can be employed to predict mix toxicity, but this regulatory approach can underestimate the NS s toxicity; 4. If we could understand nano-mix. tox., surface modifications and shell coatings (NS mixtures) would be also used to reduce the toxicity of individual NPs for developing safer nano-products (e.g., TiO2 coatings decreased cytotoxicity of ZnO NP). 5. Therefore, further studies are strongly required not only to validate the accuracy of the existing prediction models, but also to develop more reliable models for predicting the toxicity of different nano-mixtures including synergistic effects. KIST Europe 17

18 Conclusion & Outlook: KIST Europe s deliverables for mixture toxicity prediction Industry Perspective Rendezvous Science Perspective Web Tools for Mixtures Predictive Models Training Workshop CLP-MixTool TM MixExpo Eco-PDS Tool TM SynToxTool TM MixToxDB TM CLP Estimation For Mixture Mixture Exposure Additive Toxicity Assessment Tool Prediction Tool Synergism Screening Tool DB on Mixture Toxicity Tech. Transfer Convergence of ET, BT, and IT for Science and Industry On-line DB Science-based BusinessSolution Point of Contact Leading Science Contribution KIST Europe 18

19 Acknowledgement Supported by Project Development of User-friendly Nanosafety Prediction System funded by Korea Ministry of Trade, Industry and Energy Thanks to Dr. Hyunpyo Jeon Ms. Hyeri Jeong Environmental Safety Group, Korea Institute of Science and Technology Europe, Germany Prof. Tae Hyun Yoon Mr. Mr. Xuan-Tung Trinh Mr. Chai Jin Man Nanoscale Characterization & Environmental Chemistry Lab Dept. of Chemistry Hanyang University, Korea KIST Europe 19

20 Thank you Dr. Jongwoon Kim Senior Researcher Contact: KIST Europe 20

21 KIST Europe 21