图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
国际多尺度计算工程期刊
影响因子: 1.016 5年影响因子: 1.194 SJR: 0.554 SNIP: 0.82 CiteScore™: 2

ISSN 打印: 1543-1649
ISSN 在线: 1940-4352

国际多尺度计算工程期刊

DOI: 10.1615/IntJMultCompEng.2020033358
pages 335-359

A HIERARCHICAL MULTISCALE MODEL FOR PREDICTING THE VASCULAR BEHAVIOR OF BLOOD-BORNE NANOMEDICINES

F. Laurino
MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy; Laboratory of Nanotechnology for Precision Medicine, Italian Institute of Technology, Genova, Italy
A. Coclite
Scuola di Ingegneria, Università degli Studi della Basilicata, Potenza, Italy
A. Tiozzo
MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy
P. Decuzzi
Laboratory of Nanotechnology for Precision Medicine, Italian Institute of Technology, Genova, Italy
Paolo Zunino
MOX, Department of Mathematics, Politecnico di Milano, Milano, Italy

ABSTRACT

In the field of nanomedicine, there is a pressing need for predictive, quantitative tools to rationally design and optimize carriers for therapeutic and imaging applications. Current nano/microfabrication technologies allow us to control a large number of parameters, including the size, shape surface properties, and mechanical stiffness. These design parameters affect the biophysical behavior of nanomedicines in terms of blood longevity, tissue deposition, drug release, contrast imaging amplification, and more. Thus, sophisticated, multiscale and multiphysics computational models are needed to predict the behavior of nanomedicines and guide the fabrication process toward optimal delivery systems. This work is a first step toward the realization of a fully integrated simulation platform. Here a computational model for describing blood flow in the microvasculature, particle transport, and molecular interaction with the vascular walls is presented. The model predicts particle deposition within a tumor microvasculature as a function of different design parameters. The simulations show that there is a complex interaction between the morphology of the vascular network, the particle surface and mechanical properties, and the particle deposition on the vascular walls. Specifically, the computational model shows and provides interpretation of how the stiffness affects significantly the probability of adhesion onto the vascular walls and the distribution along the network of blood-borne nanomedicines.

REFERENCES

  1. Anselmo, A.C., Zhang, M., Kumar, S., Vogus, D.R., Menegatti, S., Helgeson, M.E., and Mitragotri, S., Elasticity ofNanoparticles Influences Their Blood Circulation, Phagocytosis, Endocytosis, and Targeting, ACS Nano, vol. 9, no. 3, pp. 3169-3177,2015.

  2. Antoniades, C., Psarros, C., Tousoulis, D., Bakogiannis, C., Shirodaria, C., and Stefanadis, C., Nanoparticles: A Promising Therapeutic Approach in Atherosclerosis, Curr. Drug Delivery, vol. 7, no. 4, pp. 303-311, 2010.

  3. Bao, G., Bazilevs, Y., Chung, J.H., Decuzzi, P., Espinosa, H.D., Ferrari, M., Gao, H., Hossain, S.S., Hughes, T.J.R., Kamm, R.D., Liu, W.K., Marsden, A., and Schrefler, B., USNCTAM Perspectives on Mechanics in Medicine, J. R. Soc, Interf., vol. 11, no. 97, 2014.

  4. Baxter, L.T. and Jain, R.K., Transport of Fluid and Macromolecules in Tumors. II. Role of Heterogeneous Perfusion and Lymphatics. Microvasc. Res., vol. 40, no. 2, pp. 246-263, 1990.

  5. Bhatnagar, P.L., Gross, E.P., and Krook, M., A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems, Phys. Rev., vol. 94, no. 3, pp. 511-525, 1954.

  6. Cattaneo, L. and Zunino, P., A Computational Model of Drug Delivery through Microcirculation to Compare Different Tumor Treatments, Int. J. Numer. Methods Biomed. Eng., vol. 30, no. 11, pp. 1347-1371, 2014a.

  7. Cattaneo, L. and Zunino, P., Computational Models for Fluid Exchange between Microcirculation and Tissue Interstitium, Networks Heterog Media, vol. 9, no. 1, pp. 135-159, 2014b.

  8. Cattaneo, L. and Zunino, P., Numerical Investigation of Convergence Rates for the FEM Approximation of 3D-1D Coupled Problems, Lect. Notes Comput. Sci. Eng., vol. 103, pp. 727-734, 2015.

  9. Cerroni, D., Laurino, F., and Zunino, P., Mathematical Analysis, Finite Element Approximation and Numerical Solvers for the Interaction of 3D Reservoirs with 1D Wells, GEM-Int. J. Geomath., vol. 10, no. 1, 2019.

  10. Coclite, A., de Tullio, M.D., Pascazio, G., and Decuzzi, P., A Combined Lattice Boltzmann and Immersed Boundary Approach for Predicting the Vascular Transport of Differently Shaped Particles, Comput. Fluids, vol. 136, pp. 260-271, 2016.

  11. Coclite, A., Mollica, H., Ranaldo, S., Pascazio, G., de Tullio, M.D., and Decuzzi, P., Predicting Different Adhesive Regimens of Circulating Particles at Blood Capillary Walls, Microfluid. Nanofluid., vol. 21, no. 11, p. 042205,2017.

  12. D'Angelo, C. and Quarteroni, A., On the Coupling of 1D and 3D Diffusion-Reaction Equations. Application to Tissue Perfusion Problems, Math. Models Methods Appl. Sci, vol. 18, no. 8, pp. 1481-1504, 2008.

  13. Decuzzi, P., Facilitating the Clinical Integration of Nanomedicines: The Roles of Theoretical and Computational Scientists, ACS Nano, vol. 10, no. 9, pp. 8133-8138,2016.

  14. Decuzzi, P. and Ferrari, M., The Adhesive Strength of Non-Spherical Particles Mediated by Specific Interactions, Biomaterials, vol. 27, no. 30, pp. 5307-5314, 2006.

  15. Decuzzi, P., Pasqualini, R., Arap, W., and Ferrari, M., Intravascular Delivery of Particulate Systems: Does Geometry Really Matter?, Pharm. Res, vol. 26, no. 1, pp. 235-243,2009.

  16. Ern, A. and Guermond, J.L., Theory and Practice of Finite Elements, New York: Springer, 2004.

  17. Fedosov, D.A., Caswell, B., and Karniadakis, G.E., A Multiscale Red Blood Cell Model with Accurate Mechanics, Rheology, Dynamics, Biophys. J, vol. 98, no. 10, pp. 2215-2225,2010.

  18. Filipovic, N., Isailovic, V., Dukic, T., Ferrari, M., and Kojic, M., Multiscale Modeling of Circular and Elliptical Particles in Laminar Shear Flow, IEEE Trans. Biomed. Eng., vol. 59, no. 1, pp. 50-53, 2012.

  19. Fish, M.B., Fromen, C.A., Lopez-Cazares, G., Golinski, A.W., Scott, T.F., Adili, R., Holinstat, M., and Eniola-Adefeso, O., Exploring Deformable Particles in Vascular-Targeted Drug Delivery: Softer is Only Sometimes Better, Biomaterials, vol. 124, pp. 169-179,2017.

  20. Friedman, M.H., Principles and Models of Biological Transport, Berlin-Heidelberg: Springer, 2012.

  21. Golneshan, A.A. and Lahonian M., The Effect of Magnetic Nanoparticle Dispersion on Temperature Distribution in a Spherical Tissue in Magnetic Fluid Hyperthermia Using the Lattice Boltzmann Method, Int. J. Hyperthermia, vol. 27, no. 3, pp. 266-274, 2011.

  22. Hossain, S.S., Hughes, T.J.R., and Decuzzi, P., Vascular Deposition Patterns for Nanoparticles in an Inflamed Patient-Specific Arterial Tree, Biomech. Model. Mechanobiol., vol. 13, no. 3, pp. 585-597,2014.

  23. Hossain, S.S., Kopacz, A.M., Zhang, Y., Lee, S., Lee, T., Ferrari, M., Hughes, T.J.R., Liu, W.K., and Decuzzi, P., Multiscale Modeling for the Vascular Transport of Nanoparticles, in Nano and Cell Mechanics: Fundamentals and Frontiers, H.D. Espinosa and G. Bao, Eds., New York: Wiley, pp. 437-459, 2012.

  24. Hossain, S.S., Zhang, Y., Fu, X., Brunner, G., Singh, J., Hughes, T.J.R., Shah, D., and Decuzzi, P., Magnetic Resonance Imaging-Based Computational Modelling of Blood Flow and Nanomedicine Deposition in Patients with Peripheral Arterial Disease, J. R. Soc, Interf., vol. 12, no. 106, p. 20150001, 2015.

  25. Hossain, S.S., Zhang, Y., Liang, X., Hussain, F., Ferrari, M., Hughes, T.J., and Decuzzi, P., In Silico Vascular Modeling for Personalized Nanoparticle Delivery, Nanomedicine, vol. 8, no. 3, pp. 343-357, 2013.

  26. Kojic, M., Milosevic, M., Kojic, N., Koay, E.J., Fleming, J.B., Ferrari, M., and Ziemys, A., Mass Release Curves as the Constitutive Curves for Modeling Diffusive Transport within Biological Tissue, Comput. Biol. Med., vol. 92, pp. 156-167, 2018.

  27. Kojic, M., Milosevic, M., Simic, V., Koay, E.J., Fleming, J.B., Nizzero, S., Kojic, N., Ziemys, A., and Ferrari, M., A Composite Smeared Finite Element for Mass Transport in Capillary Systems and Biological Tissue, Comput. Methods Appl. Mech. Eng., vol. 324, pp. 413-437, 2017.

  28. Koppl, T., Vidotto, E., Wohlmuth, B., and Zunino, P., Mathematical Modeling, Analysis and Numerical Approximation of Second-Order Elliptic Problems with Inclusions, Math. Models Methods Appl. Sci., vol. 28, no. 5, pp. 953-978,2018.

  29. Kumar, A., Henriquez Rivera, R.G., and Graham, M.D., Flow-Induced Segregation in Confined Multicomponent Suspensions: Effects of Particle Size and Rigidity, J. Fluid Mech, vol. 738, pp. 423-462,2013.

  30. Laurino, F. and Zunino, P., Derivation and Analysis of Coupled PDEs on Manifolds with High Dimensionality Gap Arising from Topological Model Reduction, ESAIM: Math. Modell. Numer. Anal, vol. 53, no. 6, pp. 2047-2080, 2019.

  31. Liu, W.K., Liu, Y., Farrell, D., Zhang, L., Wang, X.S., Fukui, Y., Patankar, N., Zhang, Y., Bajaj, C., Lee, J., Hong, J., Chen, X., and Hsu, H., Immersed Finite Element Method and Its Applications to Biological Systems, Comput. Methods Appl. Mech. Eng., vol. 195, nos. 13-16, pp. 1722-1749,2006.

  32. Merkel, T.J., Jones, S.W., Herlihy, K.P., Kersey, F.R., Shields, A.R., Napier, M., Luft, J.C., Wu, H., Zamboni, W.C., Wang, A.Z., Bear, J.E., and DeSimone, J.M., Using Mechanobiological Mimicry of Red Blood Cells to Extend Circulation Times of Hydro- gel Microparticles, Proc. Natl. Acad. Sci. U. S. A., vol. 108, no. 2, pp. 586-591,2011.

  33. Milosevic, M., Simic, V., Milicevic, B., Koay, E.J., Ferrari, M., Ziemys, A., and Kojic, M., Correction Function for Accuracy Improvement of the Composite Smeared Finite Element for Diffusive Transport in Biological Tissue Systems, Comput. Methods Appl. Mech. Eng., vol. 338, pp. 97-116, 2018.

  34. Moghimi, S.M., Peer, D., and Langer, R., Reshaping the Future of Nanopharmaceuticals: AdIudicium, ACSNano, vol. 5, no. 11, pp. 8454-8458,2011.

  35. Nabil, M., Decuzzi, P., and Zunino, P., Modelling Mass and Heat Transfer in Nano-Based Cancer Hyperthermia, R. Soc. Open Sci, vol. 2, no. 10, p. 150447,2015.

  36. Nabil, M. and Zunino, P., A Computational Study of Cancer Hyperthermia based on Vascular Magnetic Nanoconstructs, R. Soc. Open Sci, vol. 3, no. 9, p. 160287, 2016.

  37. Obrist, D., Weber, B., Buck, A., and Jenny P., Red Blood Cell Distribution in Simplified Capillary Networks, Philos. Trans. R. Soc., A, vol. 368, no. 1921, pp. 2897-2918,2010.

  38. Peer, D., Karp, J.M., Hong, S., Farokhzad, O.C., Margalit, R., and Langer, R., Nanocarriers as an Emerging Platform for Cancer Therapy, Nat. Nanotechnol., vol. 2, no. 12, pp. 751-760, 2007.

  39. Petros, R.A. and Desimone, J.M., Strategies in the Design of Nanoparticles for Therapeutic Applications, Nat. Rev. Drug Discovery, vol. 9, no. 8, pp. 615-627, 2010.

  40. Popel, A.S. and Johnson, P.C., Microcirculation and Hemorheology, Annu. Rev. Fluid Mech, vol. 37, pp. 43-69, 2005.

  41. Possenti, L., Modeling of Microvasculature in Uremic Patients, PhD, Politecnico di Milano, 2018.

  42. Possenti, L., Casagrande, G., di Gregorio, S., Zunino, P., and Laura Costantino, M., Numerical Simulations of the Microvascular Fluid Balance with a Non-Linear Model of the Lymphatic System, Microvasc. Res, vol. 122, pp. 101-110,2018.

  43. Possenti, L., di Gregorio, S., Gerosa, F.M., Raimondi, G., Casagrande, G., Costantino, M.L., and Zunino, P., A Computational Model for Microcirculation Including Fahraeus-Lindqvist Effect, Plasma Skimming and Fluid Exchange with the Tissue Interstitium, Int. J. Numer. Methods Biomed. Eng., vol. 35, no. 3, p. e3165, 2019.

  44. Pries, A.R. and Secomb, T.W., Microvascular Blood Viscosity in Vivo and the Endothelial Surface Layer, Am. J. Physiol, vol. 289, pp. H2657-H2664, 2005.

  45. Pries, A.R., Secomb, T.W., GeBner, T., Sperandio, M.B., Gross, J.F., and Gaehtgens, P., Resistance to Blood Flow in Microvessels in Vivo, Circ. Res, vol. 75, no. 5, pp. 904-915,1994.

  46. Qian, Y.H., Dhumieres, D., and Lallemand, P., Lattice BGK Models for Navier-Stokes Equation, Europhys. Lett., vol. 17, pp. 479-484, 1992.

  47. Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S., Variance based Sensitivity Analysis of Model Output. Design and Estimator for the Total Sensitivity Index, Comput. Phys. Commun., vol. 181, no. 2, pp. 259-270, 2010.

  48. Secomb, T.W., Microvascular Networks: 3D Structural Information, Department of Physiology, College of Medicine Tuscon, Tuscon, AZ, from https://physiology.arizona.edu/people/secomb/network, 1993.

  49. Shan, X.W., Yuan, X.F., and Chen, H.D., Kinetic Theory Representation of Hydrodynamics: A Way Beyond the Navier-Stokes Equation, J. FluidMech, vol. 550, pp. 413-441, 2006.

  50. Stuben, K., Algebraic Multigrid (AMG): Experiences and Comparisons, Appl. Math. Comput., vol. 13, no. 3, pp. 419-451, 1983.

  51. Stuben, K., Ruge, J.W., Clees, T., and Gries, S., Algebraic Multigrid: From Academia to Industry, in Scientific Computing and Algorithms in Industrial Simulations, Cham, Switzerland: Springer, pp. 83-119, 2017.

  52. Sun, C., Migliorini, C., and Munn, L.L., Red Blood Cells Initiate Leukocyte Rolling in Postcapillary Expansions: A Lattice Boltzmann Analysis, Biophys. J., vol. 85, no. 1, pp. 208-222, 2003.

  53. Sun, C. and Munn, L.L., Lattice Boltzmann Simulation of Blood Flow in Digitized Vessel Networks, Comput. Math. Appl., vol. 55, no. 7, pp. 1594-1600,2008.

  54. Vidotto, E., Koch, T., Koppl, T., Helmig, R. and Wohlmuth, B., Hybrid Models for Simulating Blood Flow in Microvascular Networks, Multiscale Model. Simul., vol. 17, no. 3, pp. 1076-1102,2019.

  55. Zou, Q.S. and He, X.Y., On Pressure and Velocity Boundary Conditions for the Lattice Boltzmann BGK Model, Phys. Fluids, vol. 9, no. 6, pp. 1591-1598, 1997.


Articles with similar content:

Multiscale Modeling of Gastrointestinal Electrophysiology and Experimental Validation
Critical Reviews™ in Biomedical Engineering, Vol.38, 2010, issue 3
Peng Du, Andrew J. Pullan, Greg O'Grady, John B. Davidson, Leo K. Cheng
Cardiovascular Tissue Engineering I. Perfusion Bioreactors: A Review
Journal of Long-Term Effects of Medical Implants, Vol.16, 2006, issue 2
Michael J. Yost, Vladimir A. Kasyanov, Arnolds Kadishs, Waleed Twal, Louis Terracio, Richard Visconti, Thomas Trusk, Xuejun Wen, Glenn D. Prestwich, Iveta Ozolanta, Roger R. Markwald, Vladimir Mironov
EXPERIMENTAL STUDIES ON THE ELECTROCHEMICAL EVOLUTION OF OXYGEN BUBBLE FROM AN ARTIFICIAL NUCLEATION CAVITY
First Thermal and Fluids Engineering Summer Conference, Vol.5, 2015, issue
Malay K Das, Babu Radhakrishnan
Coronary Arteries: Imaging, Reconstruction, and Fluid Dynamic Analysis
Critical Reviews™ in Biomedical Engineering, Vol.34, 2006, issue 1
Krishnan B. Chandran, Andreas Wahle, James D. Rossen, Mark E. Olszewski, Sarah C. Vigmostad, Milan Sonka
SOME REMARKS ON MECHANISMS OF PHASE DISTRIBUTION IN AN ADIABATIC BUBBLY PIPE FLOW
Multiphase Science and Technology, Vol.15, 2003, issue 1-4
Akimi Serizawa