Integrated intravital microscopy and mathematical modeling to optimize nanotherapeutics delivery to tumors.

Inefficient vascularization hinders the optimal transport of cell nutrients, oxygen, and drugs to cancer cells in solid tumors. Gradients of these substances maintain a heterogeneous cell-scale microenvironment through which drugs and their carriers must travel, significantly limiting optimal drug exposure. In this study, we integrate intravital microscopy with a mathematical model of cancer to evaluate the behavior of nanoparticle-based drug delivery systems designed to circumvent biophysical barriers. We simulate the effect of doxorubicin delivered via porous 1000 x 400 nm plateloid silicon particles to a solid tumor characterized by a realistic vasculature, and vary the parameters to determine how much drug per particle and how many particles need to be released within the vasculature in order to achieve remission of the tumor. We envision that this work will contribute to the development of quantitative measures of nanoparticle design and drug loading in order to optimize cancer treatment via nanotherapeutics.

[1]  Mauro Ferrari,et al.  Rapid tumoritropic accumulation of systemically injected plateloid particles and their biodistribution. , 2012, Journal of controlled release : official journal of the Controlled Release Society.

[2]  Philip K Maini,et al.  Angiogenesis and vascular remodelling in normal and cancerous tissues , 2009, Journal of mathematical biology.

[3]  Mauro Ferrari,et al.  Mathematical modeling of cancer progression and response to chemotherapy , 2006, Expert review of anticancer therapy.

[4]  J. Lowengrub,et al.  Nonlinear simulation of the effect of microenvironment on tumor growth. , 2007, Journal of theoretical biology.

[5]  H. Frieboes,et al.  Nonlinear modelling of cancer: bridging the gap between cells and tumours , 2010, Nonlinearity.

[6]  Alexander R. A. Anderson,et al.  Mathematical modelling of the influence of blood rheological properties upon adaptative tumour-induced angiogenesis , 2006, Math. Comput. Model..

[7]  J. Stevens,et al.  Pharmacokinetic factors influencing risk assessment: saturation of biochemical processes and cofactor depletion. , 1994, Environmental health perspectives.

[8]  Zongxi Li,et al.  Engineered design of mesoporous silica nanoparticles to deliver doxorubicin and P-glycoprotein siRNA to overcome drug resistance in a cancer cell line. , 2010, ACS nano.

[9]  Kristin R. Swanson,et al.  The Evolution of Mathematical Modeling of Glioma Proliferation and Invasion , 2007, Journal of neuropathology and experimental neurology.

[10]  V. Cristini.  Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation , 2006 .

[11]  Mauro Ferrari,et al.  Tailored porous silicon microparticles: fabrication and properties. , 2010, Chemphyschem : a European journal of chemical physics and physical chemistry.

[12]  V. Quaranta,et al.  Integrative mathematical oncology , 2008, Nature Reviews Cancer.

[13]  N. Ratliff,et al.  Modulation in vitro and in vivo of cytotoxicity but not cellular levels of doxorubicin by the calmodulin inhibitor trifluoperazine is dependent on the level of resistance. , 1988, British Journal of Cancer.

[14]  R K Jain,et al.  Transport of molecules, particles, and cells in solid tumors. , 1999, Annual review of biomedical engineering.

[15]  Mauro Ferrari,et al.  Tailoring the degradation kinetics of mesoporous silicon structures through PEGylation. , 2010, Journal of biomedical materials research. Part A.

[16]  S. V. Sotirchos,et al.  Variations in tumor cell growth rates and metabolism with oxygen concentration, glucose concentration, and extracellular pH , 1992, Journal of cellular physiology.

[17]  Alissa M. Weaver,et al.  Tumor Morphology and Phenotypic Evolution Driven by Selective Pressure from the Microenvironment , 2006, Cell.

[18]  A. Ventura,et al.  On the role of cell signaling models in cancer research. , 2009, Cancer research.

[19]  H. Byrne.  Dissecting cancer through mathematics: from the cell to the animal model , 2010, Nature Reviews Cancer.

[20]  V. Cristini,et al.  Nonlinear simulation of tumor growth , 2003, Journal of mathematical biology.

[21]  C. Bucana,et al.  Leukocyte-induced angiogenesis and subcutaneous growth of B16 melanoma. , 1994, Cancer biotherapy.

[22]  H. Frieboes,et al.  An integrated computational/experimental model of tumor invasion. , 2006, Cancer research.

[23]  Mauro Ferrari,et al.  Sustained small interfering RNA delivery by mesoporous silicon particles. , 2010, Cancer research.

[24]  H. Frieboes,et al.  Computer simulation of glioma growth and morphology , 2007, NeuroImage.

[25]  S. McDougall,et al.  Mathematical modelling of dynamic adaptive tumour-induced angiogenesis: clinical implications and therapeutic targeting strategies. , 2006, Journal of theoretical biology.

[26]  M. Ferrari.  Cancer nanotechnology: opportunities and challenges , 2005, Nature Reviews Cancer.

[27]  L. Preziosi,et al.  Multiphase models of tumour growth , 2008 .

[28]  Lothar Lilge,et al.  The Distribution of the Anticancer Drug Doxorubicin in Relation to Blood Vessels in Solid Tumors , 2005, Clinical Cancer Research.

[29]  S. Jonathan Chapman,et al.  Mathematical Models of Avascular Tumor Growth , 2007, SIAM Rev..

[30]  Helen M. Byrne,et al.  Towards a multiscale model of colorectal cancer , 2006 .

[31]  Mauro Ferrari,et al.  Frontiers in cancer nanomedicine: directing mass transport through biological barriers. , 2010, Trends in biotechnology.

[32]  M. Ferrari,et al.  Multistage Mesoporous Silicon-based Nanocarriers: Biocompatibility with Immune Cells and Controlled Degradation in Physiological Fluids. , 2008, Controlled release newsletter.

[33]  Vittorio Cristini,et al.  Three-dimensional multispecies nonlinear tumor growth-II: Tumor invasion and angiogenesis. , 2010, Journal of theoretical biology.

[34]  S. McDougall,et al.  Multiscale modelling and nonlinear simulation of vascular tumour growth , 2009, Journal of mathematical biology.

[35]  Michael J Sailor,et al.  Biodegradable luminescent porous silicon nanoparticles for in vivo applications. , 2009, Nature materials.

[36]  J. Lankelma,et al.  Doxorubicin gradients in human breast cancer. , 1999, Clinical cancer research : an official journal of the American Association for Cancer Research.

[37]  Mauro Ferrari,et al.  Prediction of drug response in breast cancer using integrative experimental/computational modeling. , 2009, Cancer research.

[38]  Shuming Nie,et al.  Understanding and overcoming major barriers in cancer nanomedicine. , 2010, Nanomedicine.

[39]  Mauro Ferrari,et al.  Intravascular Delivery of Particulate Systems: Does Geometry Really Matter? , 2008, Pharmaceutical Research.

[40]  V. Cristini,et al.  Nonlinear simulation of tumor necrosis, neo-vascularization and tissue invasion via an adaptive finite-element/level-set method , 2005, Bulletin of mathematical biology.

[41]  Thomas S Deisboeck,et al.  In silico cancer modeling: is it ready for prime time? , 2009, Nature Clinical Practice Oncology.

[42]  M. Chaplain,et al.  A mathematical model of breast cancer development, local treatment and recurrence. , 2007, Journal of theoretical biology.

[43]  Vittorio Cristini,et al.  Two-Dimensional Chemotherapy Simulations Demonstrate Fundamental Transport and Tumor Response Limitations Involving Nanoparticles , 2004 .

[44]  S. McDougall,et al.  Mathematical modelling of flow through vascular networks: Implications for tumour-induced angiogenesis and chemotherapy strategies , 2002, Bulletin of mathematical biology.

[45]  M. Chaplain,et al.  Continuous and discrete mathematical models of tumor-induced angiogenesis , 1998, Bulletin of mathematical biology.

[46]  Helen M. Byrne,et al.  Modelling the response of spatially structured tumours to chemotherapy: Drug kinetics , 2006, Math. Comput. Model..

[47]  M. Ferrari,et al.  What does physics have to do with cancer? , 2011, Nature Reviews Cancer.

[48]  Mauro Ferrari,et al.  Morphologic Instability and Cancer Invasion , 2005, Clinical Cancer Research.

[49]  Trachette L. Jackson,et al.  Intracellular accumulation and mechanism of action of doxorubicin in a spatio-temporal tumor model. , 2003, Journal of theoretical biology.

[50]  M Ferrari,et al.  Size and shape effects in the biodistribution of intravascularly injected particles. , 2010, Journal of controlled release : official journal of the Controlled Release Society.

[51]  Mauro Ferrari,et al.  Logic-embedded vectors for intracellular partitioning, endosomal escape, and exocytosis of nanoparticles. , 2010, Small.