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Betraying Paracelsus, Ignoring Newton: A Flaw in the Nanotoxicology Paradigm Justin Teeguarden, PHD, DABT [Paul Hinderliter, Joel Pounds, Brian Thrall,,

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Presentation on theme: "Betraying Paracelsus, Ignoring Newton: A Flaw in the Nanotoxicology Paradigm Justin Teeguarden, PHD, DABT [Paul Hinderliter, Joel Pounds, Brian Thrall,,"— Presentation transcript:

1 Betraying Paracelsus, Ignoring Newton: A Flaw in the Nanotoxicology Paradigm Justin Teeguarden, PHD, DABT [Paul Hinderliter, Joel Pounds, Brian Thrall,, Galya Orr, Katrina Waters, Tom Weber, Barbara Tarasevich, Bobbie-Jo Webb Robertson] Funded by the Environmental Biomarkers Initiative, DOE

2 Nano, Nanomaterial, Nanotechnology Nano is 10 -9. Nanotechnology is the understanding and control of matter at dimensions of roughly 1 to 100 nanometers, where unique phenomena enable novel applications. Nanomaterials are 1-100 nm in at least one dimension National Nanotechnology Initiative, 2002 SARS Virus Avain Flu Virus 100 nm Sayes, Tox Sci 2006 TiO 2 Agglomerates

3 Nanotoxicology is in its Infancy Do the unique material properties of nanomaterials equate to unique biological properties as well? Which of these properties are responsible for the biological effects? Are the effects limited by the traditional defintion of nanomaterial (1-100 nm)? Who should be studying the biocompatibility or toxicity of these new materials? What studies should be done? Where do we obtain high quality materials for testing?

4 Nanotoxicology is in its Infancy 236 January ‘08

5 What We Know Nanomaterial Human Health Risks and Risk Assessment Unknown Insufficient Data

6 In Vitro Dosimetry

7 The State of The Science (Ignorance?) Response ?

8 Major Challenges for Dose-Response Assessment What is dose? Size, shape, particle number, agglomeration state, surface chemistry, reactivity, surface area? Dose rate is also important. Do researches have all the tools necessary to measure these characteristics? What is dose for in vitro studies? 67% of published NM toxicity studies use in vitro systems. Mass concentrations are typically reported. Cannot be compared to doses for in vivo studies. How do you measure NM characteristics in liquid systems? How do you extrapolate animal study doses to human doses This extrapolation is required for all risk assessments based on animal studies

9 Cell Culture is a Standard Tool for Mechanistic Work and Hazard Screening The paradigm for chemicals is being used without consideration of the unique kinetic differences between chemicals and particles in solution: Response is considered a function of the nominal media mass or surface area concentration (cm 2 /ml, µg/ml) Expose cells to selected concentrations of suspended particles Report dose-response on a nominal media concentration basis

10 Dosimetry for Particles is Important! Response is proportional to concentration at the target site!

11 So What’s the Problem? In solution, particles settle and diffuse at rates which depend on: Size Density Shape Agglomeration state Surface area changes with size Number concentrations change with size While nominal media concentrations stay the same across density and size, DELIVERY RATES DO NOT!

12 Nominal Media Concentration is A Poor Metric of Dose 1 nm Particle Size 1000 nm Mass Concentration (ug/ml) SA Concentration (cm 2 /ml) Decreases as the square of the radius 1 nm Particle Size 1000 nm Mass Concentration (ug/ml) # concentration (#/ml) Decreases as the cube of the radius

13 Nominal Media Concentration is A Poor Metric of Dose 1 Particle Size 1000 (nm) Diffusivity (Cm 2 /s) Settling Rate (cm/s) 1 Density 20 (g/cm 3) # concentration (#/ml) SA Concentration (cm 2 /ml) Mass Concentration is Constant

14 Nominal Media Concentration is A Poor Metric of Dose 1 Density 20 (g/cm 3) Settling Rate (cm/s) Diffusivity (Cm2/s) Mass Concentration is Constant

15 Dose-Related Parameters are Not Constant Across Particle Types How Misleading can Nominal Media Concentrations Be?

16 Different Particles = Different Delivery Rates to Cells Slower Faster T = 23 Hours Post Mixing

17 From First Principles: TiO 2 and Gold Nanoparticle Dosimetry

18 October 3, 2007 California EPA Nominal Media Concentration Obscures Underlying Dose-Response Behavior

19 Material Particle Size (nm) Compartative Potency is affected by the Choice of Dose Metric

20 October 3, 2007 California EPA Delivered Dose Reveals Surface Area Relationship

21 Is Any of this Real, or is it Just a Theory? Ceria Oxide Nano Particles 25-300 nm

22 Cellular Uptake is Diffusion Driven for Small Nanoparticles and Gravity Driven for Larger particles

23 Which Particle is Most Toxic?

24 Settling Impacts CNT Toxicity? Supernatant Pellet Rapid Settling Slow Settling

25 Material Property Size (nm)Affect on Nanoparticle Transport 1 <1000>1000DiffusionGravitational Settling Size-/++↓ with ↑ Diameter↑ with Square of Diameter Shape-/++UncertainSpheres most efficient Density-/++-↑ with Density Surface Chemistry++Agglomeration 1 Agglomeration Zeta Potential 2 ++Agglomeration Concentration++Agglomeration Media Property Density-/++-↓ with ↑ Media Density Viscosity-/++↓ with ↑ Viscosity↓ with ↑ Media Viscosity

26 26 Add LD50 Table

27 27 The Evidence this is Nonsense

28 28 Delivery of These Particles is Driven by Diffusion Three Cell Types, Three Laboratories, Several Endpoints One Particle Size Dose Not Test The Hypothesis that Size and Density Affect Delivery

29 29 Response is proportional to Concentration! Wait, no its proportional to mass! Lison et al. 2008 Changing mass & concentration Changing mass Changing concentration

30 30 Lison et al. 2008 Response is proportional to Concentration! Wait, no its proportional to mass! Changing mass & concentration Changing mass Changing concentration

31 31 Cellular Dose is Related to Mass and Concentration Lison et al. 2008

32 32 Response Could be Related to Mass and Concentration if Agglomerates are Settling Out But we really need to know better what is going on in these systems!

33 Conclusions In vitro, untested assumptions and a flawed paradigm are the norm Biologically and kinetically relevant measures of dose are not being used, imperiling interpretation of many studies.. What should the future be? Research to improve our understanding of kinetics and dosimetry in vitro is essential, but not often appreciated Direct measures of particle behavior in solution Direct measures of cellular particle dose Computational models of delivery These studies will support interpretation as well as extrapolation (NRC Vision for Toxicology)

34 What is the Future of Nanomaterial Dosimetry at PNNL? Establish a Paradigm for Rapid Development of Biokinetic Models From In Vitro Kinetics and Cellular Uptake Data Complete development of predictive models of in vitro kinetics and cellular uptake (Macrophages, and other elements of the RES system) Correlate material properties and serum protein binding to rates of uptake in tissues of the RES system Scale models of cellular uptake to full tissues/in vivo and integrate within our PBPK models Test and revise predictive models of in vivo dosimetry This is best accomplished as one element of an integrated program in nanomaterial biocompatibility.

35 PBPK Model Development and In Vivo Rodent Kinetics Completes our Dosimetry Program 18 nm gold +, - and PEG 28 day blood, tissue kinetics in rats

36 36 Particle Settling Rate Equation: Where Partial differential equations representing Navier-Stokes settling and Fickian diffusion are combined to form a single partial differential equation describing macroscopic particle transport. The solution to this equation is the basis for the computational model of nanoparticle solution dynamics and dosimetry in vitro (NanoDose). The model is written in MatLab. Parameters: n, number-density of particles at place x and time t; t, time; x, vertical distance from bottom of well; R, gas constant; T, temperature; N, Avogadro’s number; m, viscosity of liquid, a particle radius; g, gravitational acceleration; d, effective particle density (density of particle – liquid density). From Mason and Weaver (1924). Computational In Vitro Dosimetry Galya’s Cellular Uptake Data


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