Nano-bio Interactions in an In Vitro System: Implications for Dosimetry and Nanotoxicology

Transcription

1 Nano-bio Interactions in an In Vitro System: Implications for Dosimetry and Nanotoxicology Joel Cohen, ScD Center for Nanotechnology and Nanotoxicology Harvard School of Public Health 1

2 Background (1 of 2) Wide spread manufacture and incidental generation of nanoparticles (NPs) has led to concerns of environmental and occupational exposure NP s exhibit unique physico-chemical properties distinct from their micron-sized counterparts Greater particle number per unit mass Greater reactive surface area per unit mass Quantum effects size dependent material properties Preliminary tox and epi data suggest inhalation of NPs leads to adverse biological effects to the heart, lungs, and other organs 1-3. Information about safety and potential hazards is urgently needed 1. Choi et al Brain et al Mills et al

3 Background (2 of 2) Development of cheap, accurate, and reproducible in vitro screening assays is a major goal for nano- EH&S research in the next decade 1 US National Academy of Science s Approach to Toxicity Testing in the 21 st Century 1,2 : Predictive in vitro tests that utilize toxicity pathways and mechanistic systems biology approaches High throughput screening approaches to facilitate testing of large batches of materials In vitro results to be confirmed in vivo Problems: Lack of data for industrially relevant materials Flame generated metal oxides account for >90% by volume ENMs on market 3 Lack of harmonized (standardized and shared) in vitro methodologies Discrepancies in the literature may be due to inaccurate reporting of dose 4,5 Lack of tools available to improve reported dose values 1. National Nanotechnology Initiative Wegner et al Teeguarden et al. 2007

4 Common Practices In Vitro Nanotoxicology: Common Prac=ces 4

5 In Vitro Screening Methodology 1. Prepara)on of NP Suspension in Liquid 2. NP Transforma)ons in Physiological Media Minimize ROS Genera)on Agglomerate Stability Zeta Poten)al Sonica)on Monodispersity Agglomera)on Effec)ve Density Par)cokine)cs 3. Dosimetry Admin vs Delivered Exposure Time 5

6 1. Prepara=on of NPs in Liquid Suspension Features of Protocol: Calorimetric calibration 1 and standardized reporting of delivered sonication energy (DSE) reproducible with any sonicator Sonication only in DI H 2 O Minimize ROS generation Identification of critical DSEà energy required to break ENMs down to smallest possible agglomerates monodisperse suspensions Characterization by DLS Suspension stability over time Dilute stock suspension in cell culture media Characterize with DLS stability/monodispserity Expose cells to final liquid suspension 1. Taurozzi et al Cohen et al

7 In Vitro Screening Methodology Sonica)on 1. Prepara)on of NP Suspension in Liquid ROS Genera)on Monodispersity Stability 2. NP Transforma)ons in Physiological Media Zeta Poten)al Agglomera)on Effec)ve Density Par)cokine)cs 3. Dosimetry Admin vs Delivered Exposure Time 7

8 2. NP Transformations in Physiological Media Agglomerate Diameter and Effective Density Effective density and agglomeration state are required to estimate settling rate over time delivered to cell dose 8 (Cohen et al. submitted 2013)

9 Agglomerate Effective Density Es)mate: Sterling Method 1 : "" ρ E = d H $ $ ## d BET ρ E is effec)ve density; d H is hydrodynamic diameter; d BET is primary par)cle diameter; DF is fractal dimension (unknown); ρ Material is material density; ρ Media is media density % ' & DF 3 % ' ρ " " Material + 1 d H $ $ & # # d % ' & DF 3 % ' ρ Media & Measure Directly: Analy)cal Ultracentrifuga)on (AUC) several hours per sample Op)miza)on for ENM- specific sexngs required: rotor speed, spin )me, absorbance wavelength Cells can leak, forfei)ng experiment Not ideal for characterizing large panels of industrially relevant materials Volumetric Centrifuga)on Method (VCM) 100nm 9 1. Sterling et al. 2005

10 Volumetric Centrifugation Method (VCM) Features of VCM 1,2 : Fast Ø easy to screen large panels of materials Inexpensive Ø spin at low speeds using low cost common laboratory centrifuges Accurate >98% of particles are collected in pellet Measurements validated with gold standard AUC data Applicable for soluble and partially soluble ENMs Requires time-resolved dissolution analysis to determine M ENMsol Results significantly improve accuracy of fate and transport models ρ E = ρ media " V pellet SF M M % " ENM ENMsol $ '+ ρ ENM $ # ρ ENM & # V pellet SF SF = V agg M ENM M ENMsol ρ ENM 1. Cohen et al. submitted 2013 V 2. U.S. Provisional Patent Application 10 pellet No. 61/661,895, filed June 20, 2012 ) % ' &

11 Volumetric Centrifugation: Stacking Factor is Not a New Concept Theore)cal Stacking Factor (SF) for perfectly stacked spheres 1 : 0.74 Theore)cal Stacking Factor (SF) for irregularly stacked spheres 2 : Gauss et al Song et al

12 Volumetric Centrifugation: Stacking Factor Validation by AUC AUC valida)on of Stacking Factor for Reference Material: Non agglomera)ng Gold Nanospheres Monodisperse sedimenta)on coefficient distribu)on Weighted average sedimenta)on coefficient used to back calculate effec)ve density and SF SF = Very close to theore)cal value for perfectly stacked spheres of 0.74! 12 (Cohen et al. submitted 2013)

13 Volumetric Centrifugation: Stacking Factor Validation by AUC AUC valida)on of Stacking Factor for irregular, fractal, flame generated nanometals: CeO 2 (d XRD =9.5nm) Monodisperse sedimenta)on coefficient distribu)on Weighted average sedimenta)on coefficient used to back calculate effec)ve density and SF SF = close to theore)cal value for irregularly stacked spheres of consistent with irregular fractal geometry of flame generated class of ENMs 100nm 13 (Cohen et al. submitted 2013)

14 Effec=ve Density of ENM Agglomerates Material ρ m (g/cm 3 ) Material Density VENGES SiO 2 d BET =18.6nm VENGES Fe 2 O VENGES CeO 2 d XRD =9.5nm VENGES CeO 2 d XRD =28.4nm VENGES CeO 2 d BET =71.3nm EVONIK SiO EVONIK TiO ρ EV (g/cm 3 ) Dispersed following previously described protocol in RPMI/10%FBS Effective density can be almost 1/6 th that of the material density For non-agglomerating spheres, effective density is very close to material density Sigma Aldrich CuO Alfa Aesar ZnO Gold Nanospheres d H =42.2nm (Cohen et al. submitted 2013) 14

15 In Vitro Screening Methodology Sonica)on 1. Prepara)on of ENM Suspension in Liquid ROS Genera)on Monodispersity Stability 2. ENM Transforma)ons in Physiological Media Zeta Poten)al Agglomera)on Effec)ve Density Par)cokine)cs 3. Dosimetry Admin vs Delivered Exposure Time 15

16 3. Particokinetics and Dosimetry In Vitro Transport of ENM in liquid suspension determined by two fundamental transport mechanisms 1. Diffusion Stokes Einstein Equation: D = RT 3N A πµd 2. Sedimentation Stokes Law: V = g ( ρ ρ E media)d 2 18µ ENM mobility and particokinetics determine delivered dose what the cells see - which is also time dependent 1. Hinderliter et al

17 Impact of Agglomerate Diameter On Dosimetry In Vitro Case Study:Gold Nanospheres Agglomerate size impacts NP delivery to cells in vitro! 17

18 Impact of Effective Density On Dosimetry in vitro VENGES SiO 2 (d BET =18.6nm) dispersed in RPMI/10%FBS Administered Dose <20% Administered dose is actually delivered to cells after 24 hours (Cohen et al. submitted 2013) 18

19 A Tool for In Vitro Nanotoxicologists: Relevant In Vitro Dose Func=ons (RID) RID Function: RID F=M,N,SA = AD i=m,n,sa (1 e α t ) AD M = γ A V Administered mass (µg): ϒ A = mass concentration (µg/ml) of ENM dispersion V= volume of ENM dispersion administered to cells Administered Dose Value: mass, particle number, surface area Administered particle number (#): r H = agglomerate radius (nm) ρ E = effective density (g/cm 3 ) AD N =! # " AD M 4 3 πr 3 H $ & ρ % e Administered surface area (cm 2 ): AD SA = ( 2 4πr H ) AD N Convert to delivered doseà simply plug in ENM dispersion-specific values for: Agglomerate radius (r H ) Effective Density (ρ E ) Exposure time (t) Deposition Constant (α) 19

20 A Tool for In Vitro Nanotoxicologists: Relevant In Vitro Dose Func=ons (RID) Deposition Constant (α) describes ENM deposition over time for a given material-mediaconcentration-cell culture well plate system Derivation of α parameter: Model particle fate and transport over time Fit data to Gompertz sigmoidal function: f (t) =1 e α t Apply value to RID function to estimate delivered dosimetry for any exposure duration RID F=M,N,SA = AD i=m,n,sa (1 e α t ) 20

21 Validation of Delivered Dose Methodology Neutron ac)vated flame generated CeO 2 and SiO 2 coated CeO 2 ENMs Industrially relevant, account for >90% by volume ENMs on market 1 Gamma emixng isotope 141 Ce easy to trace and quan)fy by gamma spectroscopy Measured passage of agglomerates across transwell insert with 3μm pores <2% par)cle loss on transwell membrane à easy passage across 3μm pores 1. Wegner et al

22 Validation of Delivered Dose Methodology Close agreement (within 5%) between estimated and measured delivered doses 22

23 Features of Methodology for In Vitro Dosimetry Versa=le method for improved dosimetry Industry relevant flame generated nanometal agglomerates account for 90% by volume ENMs on the market 1 Non- agglomera)ng nanospheres for drug development Soluble and par)ally soluble ENMs - requires dissolu)on analysis in applicable media Can be used as a plasorm to inves=gate all types of nano- bio interac=ons in vitro Toxicity screens Cellular uptake Transloca)on across cellular monolayers 1. Wegner et al

24 Case Study: Effect of Dosimetry on Nanotoxicology Objective: evaluate implications of dosimetry for in vitro nanotoxicology Applied integrated approach for 24 metal oxide ENMs previously characterized for toxicity and hazard ranking without dosimetry considerations 1 Characterize and report ENM dispersion properties that determine fate and transport: hydrodynamic diameter effective density deposition fraction parameter (α) All parameters measured under various conditions 2 : Two widely used cell culture media (BEGM, DMEM) Two mass concentrations (0-50ug/ml and ug/ml) Two well plate systems (96 well and 384 well plates) Derived RID functions and delivered dose values from reported parameters 1. Zhang et al Cohen and Ji et al. in progress 24

25 Case Study: Relevant In Vitro Dose Func=ons (RID) Example: SiO 2 (d BET 13nm); BEGM media; 50µg/ml; 384 well plate 25

26 Effective Density; Hydrodynamic Diameter 26

27 α 27

28 Relevant In Vitro Dose Func=ons (RID) Identify: SiO 2 (d BET 13nm); BEGM media; 50µg/ml; 384 well plate Values reported from tables: d H =182nm; ρ EV =1.23g/cm 3 ; α= 0.03 hours -1 ; γ=1.25 µg t=4 hours Delivered Dose values for 4 hour exposure period: RID M = 0.105µg RIS N =2.75x10 10 particles RID SA = 28.6cm 2 28

29 1. Prepara)on of NP Suspension in Liquid 2. NP Transforma)ons in Physiological Media 3. Dosimetry Implica=ons for Nanotoxicology 29

30 Implications for In Vitro Nanotoxicology: Effective Density and Agglomeration Influence Particle Delivery to Cells Challenge: high-throughput toxicity screens must account for dosimetry and differential mobility when comparing large libraries of NPs (Cohen et al. submitted 2013) 30

31 Implications for In Vitro Nanotoxicology: Mass Concentration Influences Particokinetics Mass concentration influences hydrodynamic diameter Changes to hydrodynamic diameter influence particle delivery to cells (Cohen et al. submitted 2013) 31

32 Implications for In Vitro Nanotoxicology: Effect of ENM Solubility on Dosimetry Solubility of 24 industry relevant metal oxide ENMs suspended in cell culture media over 24 hours 32 Few materials exhibit dissolu=on >10% (CuO; WO 3; ZnO) (Zhang et al. 2012)

33 Implications for In Vitro Nanotoxicology: Effect of ENM Solubility on Dosimetry Ion shedding of soluble ENMs may lead to par)cle mass loss Implica)ons for par)cle delivery to cells in vitro Decreased effec)ve density (M/V) over )me Changes to par)cle surface chemistry changes to agglomera)on state over )me Soluble/Ionic component dis)nct from par)culate exposure component Nominal media concentra)on of dissolved chemical/ions is propor)onal to cellular dose Widely and successfully used paradigm to characterize drug potency and toxicity of industrial and environmental chemicals 1-3 Dosimetry modeling applicable to soluble materials: Requires =me- resolved dissolu=on analysis Must account for dynamic changes to agglomera=on state over =me 1. Teeguarden et al Allen et al Bakand et al

34 Implications for In Vitro Nanotoxicology: Ranking Toxicity of ENMs BEAS-2B bronchial epithelial cells exposed to CoO (d H =234nm; ρ EV =2.39g/cm 3 ) and Co 3 O 4 (d H =185nm; ρ EV =1.37g/cm 3 ) When particokinetics are accounted for in the reported dose: Adminstered dose overestimates by >100% for Co 3 O 4 20µg vs 10µg Dose response curves appear more similar (Cohen et al. submitted 2013) Differences in toxicity may be attributable to different delivered 34 doses

35 Conclusions Accurate repor)ng of dosimetry may improve validity and relevance of in vitro screens RID func)ons provide a simple tool for nanotoxicologists to improve reported dosimetry New RID func)ons can be derived by following our newly developed method: 1. Standardized ENM dispersion protocol Improve reproducibility and stability of dispersions applied to cells Achieve monodisperse suspensions Minimize undesired by products (ROS genera)on, protein denaturing, etc) 2. Comprehensive characteriza)on of ENM transforma)ons in liquid Agglomera)on, zeta poten)al, effec)ve density etc 3. Es)ma)on of par)cle dose delivered to cells VCM significantly improves accuracy of delivered dose es)mates 35

36 Acknowledgements Dr Philip Demokritou, HSPH Dr Glen DeLoid, HSPH Dr George Pyrgiotakis, HPSH Dr Ramon Molina, HSPH Dr Lester Kobzik, HSPH Dr Joe Brain, HSPH Dr John Godleski, HSPH Dr George Sotiriou, HSPH Dr Christa Watson, HSPH Samuel Gass, ETH 36

37 Ques)ons? 37

38 2. NP Transformations in Physiological Media Material Media d H (nm) PdI ζ (mv) σ (ms/cm) ph VENGES SiO 2 d BET =11.9nm VENGES CeO 2 d BET =13nm DI H 2 O ± ± ± ± ± 0.46 RPMI ± ± ± ± ± RPMI/10%FBS ± ± ± ± ± 0.13 RPMI/1%BSA ± ± ± ± ± DI H 2 O ± ± ± ± ± RPMI 1678 ± ± ± ± ± RPMI/10%FBS ± ± ± ± ± ± RPMI/1%BSA 369 ± ± ± ± Electrostatic and steric forces determine agglomerate size Implications for particle mobility and transport in vitro 38

39 Effect of Polydispersity on Dosimetry : NP Density Distribution vs. Average Value Case study: CeO 2 (d XRD =9.5nm) Measure sedimenta)on coefficient distribu)on for NPs in suspension by AUC Divide distribu)on into equal sized bins corresponding to 10% of the total suspension, select median sedimenta)on coefficient within each bin as representa)ve value Model par)cle transport over )me for each bin, and sum to es)mate total deposi)on Compare deposi)on kine)cs with those modeled using weighted average value for effec)ve density Only 6% difference in es)mated deposited frac)on arer 24 hr exposure between the two methods Dispersion protocol and monodispersity maser! (Deloid et al. submitted 2013) 39