A modeling platform for the lymphatic system.

We present a physiologically-based pharmacokinetic modeling platform capable of simulating the biodistribution of different therapeutic agents, including cells, their interactions within the immune system, redistribution across lymphoid compartments, and infiltration into tumor tissues. This transport-based platform comprises a distinctive implementation of a tumor compartment with spatial heterogeneity which enables the modeling of tumors of different size, necrotic state, and agent infiltration capacity. We provide three validating and three exploratory examples that illustrate the capabilities of the proposed approach. The results show that the model can recapitulate immune cell balance across different compartments, respond to antigen stimulation, simulate immune vaccine effects, and immune cell infiltration to tumors. Based on the results, the model can be used to study problems pertinent to current immunotherapies and has the potential to assist medical techniques that rely on the transport of biological species.

[1]  H. Herzel,et al.  Lymphocyte Circadian Clocks Control Lymph Node Trafficking and Adaptive Immune Responses , 2017, Immunity.

[2]  Jinyuan Zhou,et al.  Evaluation of human brain tumor heterogeneity using multiple T1‐based MRI signal weighting approaches , 2008, Magnetic resonance in medicine.

[3]  J. Altman,et al.  Counting antigen-specific CD8 T cells: a reevaluation of bystander activation during viral infection. , 1998, Immunity.

[4]  Dhaval K. Shah,et al.  Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human , 2011, Journal of Pharmacokinetics and Pharmacodynamics.

[5]  R. Ahmed,et al.  Cytotoxic T-cell memory without antigen , 1994, Nature.

[6]  Torsten Teorell,et al.  Kinetics of distribution of substances administered to the body, II : The intravascular modes of administration , 1937 .

[7]  T. Roose,et al.  A Model for Fluid Drainage by the Lymphatic System , 2013, Bulletin of mathematical biology.

[8]  C. Janeway Immunobiology: The Immune System in Health and Disease , 1996 .

[9]  F. Theil,et al.  Effects of Anti-VEGF on Predicted Antibody Biodistribution: Roles of Vascular Volume, Interstitial Volume, and Blood Flow , 2011, PloS one.

[10]  Stephen P. Schoenberger,et al.  Naïve CTLs require a single brief period of antigenic stimulation for clonal expansion and differentiation , 2001, Nature Immunology.

[11]  E. Wherry,et al.  Differential Sensitivity of Naive and Memory CD8+ T Cells to Apoptosis in Vivo1 , 2002, The Journal of Immunology.

[12]  Both Dendritic Cells and Macrophages Can Stimulate Naive CD8 T Cells In Vivo to Proliferate, Develop Effector Function, and Differentiate into Memory Cells1 , 2005, The Journal of Immunology.

[13]  Viktor Vladimirovich Nemytskii Qualitative theory of differential equations , 1960 .

[14]  Eelco F. J. Meijer,et al.  The Lymphatic System in Disease Processes and Cancer Progression. , 2016, Annual review of biomedical engineering.

[15]  M. Kinjo,et al.  Determination of diffusion coefficients in live cells using fluorescence recovery after photobleaching with wide-field fluorescence microscopy , 2018, Biophysics and physicobiology.

[16]  V. Cristini,et al.  Size-Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature-Based Differential Targeting in Triple Negative Breast Cancer. , 2019, Small.

[17]  R. Black,et al.  Modelling the lymphatic system: challenges and opportunities , 2012, Journal of The Royal Society Interface.

[18]  Martin Braun Differential equations and their applications , 1976 .

[19]  M. Detmar,et al.  Interaction of tumor cells and lymphatic vessels in cancer progression , 2012, Oncogene.

[20]  T. Foster,et al.  Oxygen consumption and diffusion effects in photodynamic therapy. , 1991, Radiation research.

[21]  M. Tegenge,et al.  A first-generation physiologically based pharmacokinetic (PBPK) model of alpha-tocopherol in human influenza vaccine adjuvant. , 2015, Regulatory toxicology and pharmacology : RTP.

[22]  T. Kodama,et al.  Lymphatic mapping of mice with systemic lymphoproliferative disorder: usefulness as an inter-lymph node metastasis model of cancer. , 2013, Journal of immunological methods.

[23]  Cheng Zhu,et al.  Measuring diffusion and binding kinetics by contact area FRAP. , 2008, Biophysical journal.

[24]  Mauro Ferrari,et al.  Mass partitioning effects in diffusion transport. , 2015, Physical chemistry chemical physics : PCCP.

[25]  J. Sirard,et al.  Delayed Expansion and Contraction of CD8+ T Cell Response during Infection with Virulent Salmonella typhimurium1 , 2006, The Journal of Immunology.

[26]  Raffaella Giavazzi,et al.  Syngeneic murine metastasis models: B16 melanoma. , 2014, Methods in molecular biology.

[27]  Hua Tan,et al.  Prediction of treatment efficacy for prostate cancer using a mathematical model , 2016, Scientific Reports.

[28]  Vittorio Cristini,et al.  Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements , 2013, Proceedings of the National Academy of Sciences.

[29]  F. Ghiringhelli,et al.  Cytotoxic effects of chemotherapy on cancer and immune cells: how can it be modulated to generate novel therapeutic strategies? , 2015, Future oncology.

[30]  C. Cohen,et al.  The definition of the sentinel lymph node in melanoma based on radioactive counts , 2002, Annals of Surgical Oncology.

[31]  R. Dominguez,et al.  STUDIES OF THE RENAL EXCRETION OF CREATININE I. ON THE FUNCTIONAL RELATION BETWEEN THE RATE OF OUTPUT AND THE CONCENTRATION IN THE PLASMA , 1934 .

[32]  A. Edginton,et al.  Population physiologically-based pharmacokinetic model incorporating lymphatic uptake for a subcutaneously administered pegylated peptide , 2016, In Silico Pharmacology.

[33]  Alan S. Perelson,et al.  Recruitment Times, Proliferation, and Apoptosis Rates during the CD8+ T-Cell Response to Lymphocytic Choriomeningitis Virus , 2001, Journal of Virology.

[34]  Michael J. Kerin,et al.  Effects of Age on the Detection and Management of Breast Cancer , 2015, Cancers.

[35]  Mauro Ferrari,et al.  Extension of the composite smeared finite element (CSFE) to include lymphatic system in modeling mass transport in capillary systems and biological tissue. , 2017, Journal of the Serbian Society for Computational Mechanics.

[36]  S. Rosenberg,et al.  Acquisition of full effector function in vitro paradoxically impairs the in vivo antitumor efficacy of adoptively transferred CD8+ T cells. , 2005, The Journal of clinical investigation.

[37]  Pu Chen,et al.  Numerical Modeling of Fluid Flow in Solid Tumors , 2011, PloS one.

[38]  A. Shaw,et al.  Tumour heterogeneity and resistance to cancer therapies , 2018, Nature Reviews Clinical Oncology.

[39]  A. Dhillon,et al.  Granulomatous vasculitis in Crohn's disease. , 1991, Gastroenterology.

[40]  N. Restifo,et al.  B16 as a Mouse Model for Human Melanoma , 2000, Current protocols in immunology.

[41]  Mauro Ferrari,et al.  Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill , 2016, PLoS Comput. Biol..

[42]  Vittorio Cristini,et al.  Mathematical modeling in cancer nanomedicine: a review , 2019, Biomedical Microdevices.

[43]  Graeme J. Pettet,et al.  A new approach to modelling the formation of necrotic regions in tumours , 1998 .

[44]  G. Glatting,et al.  Physiologically Based Pharmacokinetic Modeling Is Essential in 90Y-Labeled Anti-CD66 Radioimmunotherapy , 2015, PloS one.

[45]  L. Sherwood Human Physiology : From Cells to Systems , 1989 .

[46]  Mohammad Jafarnejad,et al.  Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes. , 2015, Lymphatic research and biology.

[47]  Vittorio Cristini,et al.  Understanding Drug Resistance in Breast Cancer with Mathematical Oncology , 2014, Current Breast Cancer Reports.