Advanced computational modeling for in vitro nanomaterial dosimetry.
BACKGROUND
Accurate and meaningful dose metrics are a basic requirement for in vitro screening to assess potential health risks of engineered nanomaterials (ENMs). Correctly and consistently quantifying what cells "see," during an in vitro exposure requires standardized preparation of stable ENM suspensions, accurate characterizatoin of agglomerate sizes and effective densities, and predictive modeling of mass transport. Earlier transport models provided a marked improvement over administered concentration or total mass, but included assumptions that could produce sizable inaccuracies, most notably that all particles at the bottom of the well are adsorbed or taken up by cells, which would drive transport downward, resulting in overestimation of deposition.


METHODS
Here we present development, validation and results of two robust computational transport models. Both three-dimensional computational fluid dynamics (CFD) and a newly-developed one-dimensional Distorted Grid (DG) model were used to estimate delivered dose metrics for industry-relevant metal oxide ENMs suspended in culture media. Both models allow simultaneous modeling of full size distributions for polydisperse ENM suspensions, and provide deposition metrics as well as concentration metrics over the extent of the well. The DG model also emulates the biokinetics at the particle-cell interface using a Langmuir isotherm, governed by a user-defined dissociation constant, K(D), and allows modeling of ENM dissolution over time.


RESULTS
Dose metrics predicted by the two models were in remarkably close agreement. The DG model was also validated by quantitative analysis of flash-frozen, cryosectioned columns of ENM suspensions. Results of simulations based on agglomerate size distributions differed substantially from those obtained using mean sizes. The effect of cellular adsorption on delivered dose was negligible for K(D) values consistent with non-specific binding (> 1 nM), whereas smaller values (≤ 1 nM) typical of specific high-affinity binding resulted in faster and eventual complete deposition of material.


CONCLUSIONS
The advanced models presented provide practical and robust tools for obtaining accurate dose metrics and concentration profiles across the well, for high-throughput screening of ENMs. The DG model allows rapid modeling that accommodates polydispersity, dissolution, and adsorption. Result of adsorption studies suggest that a reflective lower boundary condition is appropriate for modeling most in vitro ENM exposures.
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA. gdeloid@hsph.harvard.edu.
Glen M
GM
DeLoid
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA.
Joel M
JM
Cohen
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA.
Georgios
G
Pyrgiotakis
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA.
Sandra V
SV
Pirela
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA.
Anoop
A
Pal
Department of Architectural Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.|School of Thermal Engineering, Shandong Jianzhu University, 1000 Fengming Rd, Jinan, China.
Jiying
J
Liu
Department of Architectural Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.|Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA.
Jelena
J
Srebric
Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA. pdemokri@hsph.harvard.edu.
Philip
P
Demokritou
D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D049329:Nanostructures