Optimizing Chemotherapeutic Anti-cancer Treatment and the Tumor Microenvironment: An Analysis of Mathematical Models.

We review results about the structure of administration of chemotherapeutic anti-cancer treatment that we have obtained from an analysis of minimally parameterized mathematical models using methods of optimal control. This is a branch of continuous-time optimization that studies the minimization of a performance criterion imposed on an underlying dynamical system subject to constraints. The scheduling of anti-cancer treatments has all the features of such a problem: treatments are administered in time and the interactions of the drugs with the tumor and its microenvironment determine the efficacy of therapy. At the same time, constraints on the toxicity of the treatments need to be taken into account. The models we consider are low-dimensional and do not include more refined details, but they capture the essence of the underlying biology and our results give robust and rather conclusive qualitative information about the administration of optimal treatment protocols that strongly correlate with approaches taken in medical practice. We describe the changes that arise in optimal administration schedules as the mathematical models are increasingly refined to progress from models that only consider the cancerous cells to models that include the major components of the tumor microenvironment, namely the tumor vasculature and tumor-immune system interactions.

[1]  E Glatstein,et al.  High-time chemotherapy or high time for low dose. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  H. Schättler,et al.  Geometric Optimal Control , 2012 .

[3]  H. Schättler,et al.  Tumor Microenvironment and Anticancer Therapies: An Optimal Control Approach , 2014 .

[4]  H. Schättler,et al.  Dynamical properties of a minimally parameterized mathematical model for metronomic chemotherapy , 2016, Journal of mathematical biology.

[5]  Gabriele Bergers,et al.  Less is more, regularly: metronomic dosing of cytotoxic drugs can target tumor angiogenesis in mice. , 2000, The Journal of clinical investigation.

[6]  P. Hahnfeldt,et al.  Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy. , 1999, Cancer research.

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

[8]  Peter Bohlen,et al.  Continuous low-dose therapy with vinblastine and VEGF receptor-2 antibody induces sustained tumor regression without overt toxicity , 2000 .

[9]  H. Schättler,et al.  On optimal delivery of combination therapy for tumors. , 2009, Mathematical biosciences.

[10]  Avner Friedman,et al.  Tumor cells proliferation and migration under the influence of their microenvironment. , 2011, Mathematical biosciences and engineering : MBE.

[11]  H. Schättler,et al.  Dynamics and control of a mathematical model for metronomic chemotherapy. , 2015, Mathematical biosciences and engineering : MBE.

[12]  B. Perthame,et al.  Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies , 2012, 1207.0923.

[13]  Philip Hahnfeldt,et al.  Maximum tolerated dose versus metronomic scheduling in the treatment of metastatic cancers. , 2013, Journal of theoretical biology.

[14]  Urszula Ledzewicz,et al.  A 3-Compartment Model for Chemotherapy of Heterogeneous Tumor Populations , 2015 .

[15]  R. Kerbel A cancer therapy resistant to resistance , 1997, Nature.

[16]  Rakesh K. Jain,et al.  Normalizing tumor vasculature with anti-angiogenic therapy: A new paradigm for combination therapy , 2001, Nature Medicine.

[17]  U. Ledzewicz,et al.  The influence of pk/pd on the structure of optimal controls in cancer chemotherapy models. , 2005, Mathematical biosciences and engineering : MBE.

[18]  Kristian Pietras,et al.  A multitargeted, metronomic, and maximum-tolerated dose "chemo-switch" regimen is antiangiogenic, producing objective responses and survival benefit in a mouse model of cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  R. Kerbel Tumor angiogenesis: past, present and the near future. , 2000, Carcinogenesis.

[20]  Suzanne Lenhart,et al.  Optimal control of treatment in a mathematical model of chronic myelogenous leukemia. , 2007, Mathematical biosciences.

[21]  Urszula Ledzewicz,et al.  OPTIMAL CONTROLS FOR A MATHEMATICAL MODEL OF TUMOR-IMMUNE INTERACTIONS UNDER TARGETED CHEMOTHERAPY WITH IMMUNE BOOST , 2013 .

[22]  Urszula Ledzewicz,et al.  Optimal Bang-Bang Controls for a Two-Compartment Model in Cancer Chemotherapy , 2002 .

[23]  E. Glatstein,et al.  Back to the basics: the importance of concentration x time in oncology. , 1993, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  J. Coldman Drug resistance in cancer , 2016 .

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

[26]  Olivier Fercoq,et al.  Optimisation of Cancer Drug Treatments Using Cell Population Dynamics , 2013 .

[27]  Urszula Ledzewicz,et al.  Optimal response to chemotherapy for a mathematical model of tumor–immune dynamics , 2012, Journal of mathematical biology.

[28]  Harvey Thomas Banks,et al.  Preface. Distributed Parameter Systems in Immunology , 2012 .

[29]  Nicolas André,et al.  Metronomic chemotherapy: new rationale for new directions , 2010, Nature Reviews Clinical Oncology.

[30]  K. Nan,et al.  New insights into metronomic chemotherapy-induced immunoregulation. , 2014, Cancer letters.

[31]  Helen Moore,et al.  A mathematical model for chronic myelogenous leukemia (CML) and T cell interaction. , 2004, Journal of theoretical biology.

[32]  Urszula Ledzewicz,et al.  AntiAngiogenic Therapy in Cancer Treatment as an Optimal Control Problem , 2007, SIAM J. Control. Optim..

[33]  Urszula Ledzewicz,et al.  On the MTD paradigm and optimal control for multi-drug cancer chemotherapy. , 2013, Mathematical biosciences and engineering : MBE.

[34]  Jaroslaw Smieja,et al.  Cell Cycle as an Object of Control , 1995 .

[35]  R. Gatenby A change of strategy in the war on cancer , 2009, Nature.

[36]  Urszula Ledzewicz,et al.  ON OPTIMAL CHEMOTHERAPY FOR HETEROGENEOUS TUMORS , 2014 .

[37]  Quanxu Ge,et al.  CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES , 2014 .

[38]  Urszula Ledzewicz,et al.  Optimal Control for Mathematical Models of Cancer Therapies: An Application of Geometric Methods , 2015 .

[39]  A. Friedman Cancer as Multifaceted Disease , 2012 .

[40]  Nicolas André,et al.  Metronomic scheduling of anticancer treatment: the next generation of multitarget therapy? , 2011, Future oncology.

[41]  L. Norton,et al.  The Norton-Simon hypothesis revisited. , 1986, Cancer treatment reports.

[42]  B. Perthame,et al.  Modeling the Effects of Space Structure and Combination Therapies on Phenotypic Heterogeneity and Drug Resistance in Solid Tumors , 2014, Bulletin of Mathematical Biology.