Chemotherapy for tumors: an analysis of the dynamics and a study of quadratic and linear optimal controls.

We investigate a mathematical model of tumor-immune interactions with chemotherapy, and strategies for optimally administering treatment. In this paper we analyze the dynamics of this model, characterize the optimal controls related to drug therapy, and discuss numerical results of the optimal strategies. The form of the model allows us to test and compare various optimal control strategies, including a quadratic control, a linear control, and a state-constraint. We establish the existence of the optimal control, and solve for the control in both the quadratic and linear case. In the linear control case, we show that we cannot rule out the possibility of a singular control. An interesting aspect of this paper is that we provide a graphical representation of regions on which the singular control is optimal.

[1]  Robert A. Gatenby,et al.  Modelling a new angle on understanding cancer , 2002, Nature.

[2]  E. Angelis,et al.  Qualitative analysis of a mean field model of tumor-immune system competition , 2003 .

[3]  R. Jain,et al.  Antibody-directed effector cell therapy of tumors: analysis and optimization using a physiologically based pharmacokinetic model. , 2002, Neoplasia.

[4]  Nicola Bellomo,et al.  MATHEMATICAL TOPICS ON THE MODELLING COMPLEX MULTICELLULAR SYSTEMS AND TUMOR IMMUNE CELLS COMPETITION , 2004 .

[5]  O. Sotolongo-Costa,et al.  Behavior of tumors under nonstationary therapy , 2002 .

[6]  Markus R. Owen,et al.  MATHEMATICAL MODELLING OF MACROPHAGE DYNAMICS IN TUMOURS , 1999 .

[7]  J. Stark,et al.  Mathematical models of the balance between apoptosis and proliferation , 2002, Apoptosis.

[8]  A. d’Onofrio A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences , 2005, 1309.3337.

[9]  Urszula Ledzewicz,et al.  Comparison of optimal controls for a model in cancer chemotherapy with L 1- and L 2-type objectives , 2004, Optim. Methods Softw..

[10]  M. Delitala Critical analysis and perspectives on kinetic (cellular) theory of immune competition , 2002 .

[11]  Shangbin Cui,et al.  Analysis of a mathematical model for the growth of tumors under the action of external inhibitors , 2002, Journal of mathematical biology.

[12]  J. M. Murray,et al.  Some optimal control problems in cancer chemotherapy with a toxicity limit. , 1990, Mathematical biosciences.

[13]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[14]  A. Dalgleish,et al.  The relevance of non-linear mathematics (chaos theory) to the treatment of cancer, the role of the immune response and the potential for vaccines. , 1999, QJM : monthly journal of the Association of Physicians.

[15]  Lawrence M Wein,et al.  Validation and analysis of a mathematical model of a replication-competent oncolytic virus for cancer treatment: implications for virus design and delivery. , 2003, Cancer research.

[16]  T. Oda,et al.  Accumulation of losses of heterozygosity and multistep carcinogenesis in pulmonary adenocarcinoma. , 2001, Cancer research.

[17]  L. D. Pillis,et al.  A Validated Mathematical Model of Cell-Mediated Immune Response to Tumor Growth , 2005 .

[18]  R. Luebke,et al.  Quantifying the Relationship Between Multiple Immunological Parameters and Host Resistance: Probing the Limits of Reductionism1 , 2001, The Journal of Immunology.

[19]  H. Gurney,et al.  How to calculate the dose of chemotherapy , 2002, British Journal of Cancer.

[20]  A. Perelson,et al.  Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. , 1994, Bulletin of mathematical biology.

[21]  A. Krener The High Order Maximal Principle and Its Application to Singular Extremals , 1977 .

[22]  D. Guyton,et al.  Oncologic mathematics: evolution of a new specialty. , 2002, Archives of surgery.

[23]  K. O'Byrne,et al.  Angiogenesis and the immune response as targets for the prevention and treatment of colorectal cancer (review). , 2003, Oncology reports.

[24]  J. Sherratt,et al.  Cells behaving badly: a theoretical model for the Fas/FasL system in tumour immunology. , 2002, Mathematical biosciences.

[25]  E. T. Gawlinski,et al.  The glycolytic phenotype in carcinogenesis and tumor invasion: insights through mathematical models. , 2003, Cancer research.

[26]  W. Fleming,et al.  Deterministic and Stochastic Optimal Control , 1975 .

[27]  K Renee Fister,et al.  Immunotherapy: an optimal control theory approach. , 2005, Mathematical biosciences and engineering : MBE.

[28]  S. Rosenberg,et al.  Cancer immunotherapy: moving beyond current vaccines , 2004, Nature Medicine.

[29]  O. V. Stryk,et al.  Numerical Solution of Optimal Control Problems by Direct Collocation , 1993 .

[30]  Urszula Ledzewicz,et al.  OPTIMAL CONTROL FOR A CLASS OF COMPARTMENTAL MODELS IN CANCER CHEMOTHERAPY , 2003 .

[31]  Umberto Lucia,et al.  Thermodynamical analysis of the dynamics of tumor interaction with the host immune system , 2002 .

[32]  Zuzanna Szyma ANALYSIS OF IMMUNOTHERAPY MODELS IN THE CONTEXT OF CANCER DYNAMICS , 2003 .

[33]  L. Preziosi,et al.  Modelling and mathematical problems related to tumor evolution and its interaction with the immune system , 2000 .

[34]  M. L. Martins,et al.  Reaction-diffusion model for the growth of avascular tumor. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  D. Pardoll,et al.  Cancer vaccines. , 1993, Trends in pharmacological sciences.

[36]  A. Diefenbach,et al.  Rae1 and H60 ligands of the NKG2D receptor stimulate tumour immunity , 2001, Nature.

[37]  D. Wodarz,et al.  Viruses as antitumor weapons: defining conditions for tumor remission. , 2001, Cancer research.

[38]  Soldano Ferrone,et al.  Tumors as elusive targets of T-cell-based active immunotherapy. , 2003, Trends in immunology.

[39]  John Carl Panetta,et al.  Optimal Control Applied to Cell-Cycle-Specific Cancer Chemotherapy , 2000, SIAM J. Appl. Math..

[40]  R. Begent,et al.  Recombinant anti-carcinoembryonic antigen antibodies for targeting cancer , 2000, Cancer Chemotherapy and Pharmacology.

[41]  Keith L Black,et al.  Clinical Responsiveness of Glioblastoma Multiforme to Chemotherapy after Vaccination , 2004, Clinical Cancer Research.

[42]  J. Gálvez,et al.  Study of the physical meaning of the binding parameters involved in effector-target conjugation using monoclonal antibodies against adhesion molecules and cholera toxin. , 2002, Cellular immunology.

[43]  Ami Radunskaya,et al.  The dynamics of an optimally controlled tumor model: A case study , 2003 .

[44]  J. M. Murray,et al.  Optimal control for a stochastic model of cancer chemotherapy. , 2000, Mathematical biosciences.

[45]  J. Couzin Select T Cells, Given Space, Shrink Tumors , 2002, Science.

[46]  Robert F. Stengel,et al.  Optimal control of innate immune response , 2002, Optimal Control Applications and Methods.

[47]  M. Kolev Mathematical modelling of the competition between tumors and immune system considering the role of the antibodies , 2003 .

[48]  M. L. Chambers The Mathematical Theory of Optimal Processes , 1965 .

[49]  Lobna Derbel,et al.  ANALYSIS OF A NEW MODEL FOR TUMOR-IMMUNE SYSTEM COMPETITION INCLUDING LONG-TIME SCALE EFFECTS , 2004 .

[50]  L. S. Pontryagin,et al.  Mathematical Theory of Optimal Processes , 1962 .

[51]  Luca Mesin,et al.  Modeling of the immune response: conceptual frameworks and applications , 2001 .

[52]  J. Blattman,et al.  Cancer Immunotherapy: A Treatment for the Masses , 2004, Science.

[53]  Andrey V. Savkin,et al.  Application of optimal control theory to analysis of cancer chemotherapy regimens , 2002, Syst. Control. Lett..

[54]  Dominik Wodarz,et al.  A dynamical perspective of CTL cross-priming and regulation: implications for cancer immunology. , 2003, Immunology letters.

[55]  M. Perry The Chemotherapy Source Book , 1992, Annals of Internal Medicine.

[56]  Azuma Ohuchi,et al.  A Mathematical Analysis of the Interactions between Immunogenic Tumor Cells and Cytotoxic T Lymphocytes , 2001, Microbiology and immunology.

[57]  D. Kirschner,et al.  Modeling immunotherapy of the tumor – immune interaction , 1998, Journal of mathematical biology.

[58]  J. Horton Medical Oncology: Basic Principles and Clinical Management of Cancer, , 1986 .

[59]  Jon Ernstberger,et al.  OPTIMAL CONTROL APPLIED TO IMMUNOTHERAPY , 2003 .

[60]  Bard Ermentrout,et al.  Simulating, analyzing, and animating dynamical systems - a guide to XPPAUT for researchers and students , 2002, Software, environments, tools.

[61]  Urszula Ledzewicz,et al.  Drug resistance in cancer chemotherapy as an optimal control problem , 2005 .

[62]  E. Polak,et al.  Amatlab Toolbox for Solving Optimal Control Problems , 1997 .

[63]  H. I. Freedman,et al.  A mathematical model of cancer treatment by immunotherapy. , 2000, Mathematical biosciences.

[64]  G. D. Knott,et al.  Modeling tumor regrowth and immunotherapy , 2001 .

[65]  John Carl Panetta,et al.  Optimal Control Applied to Competing Chemotherapeutic Cell-Kill Strategies , 2003, SIAM J. Appl. Math..

[66]  A. Radunskaya,et al.  Mixed Immunotherapy and Chemotherapy of Tumors: Modeling, Applications and Biological Interpretations , 2022 .

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

[68]  Rodrick Wallace,et al.  Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer , 2003, Open Syst. Inf. Dyn..