Robustness of optimal controls for a class of mathematical models for tumor anti-angiogenesis

Heinz Schättler; Urszula Ledzewicz; Benjamin Cardwell

doi:10.3934/mbe.2011.8.355

Article Contents

2011, Volume 8, Issue 2: 355-369. Doi: 10.3934/mbe.2011.8.355

This issue Previous Article A model of competing saturable kinetic processes with application to the pharmacokinetics of the anticancer drug paclitaxel Next Article Tumor cells proliferation and migration under the influence of their microenvironment

Robustness of optimal controls for a class of mathematical models for tumor anti-angiogenesis

1.
Dept. of Electrical and Systems Engineering, Washington University, St. Louis, Missouri, 63130-4899
2.
Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois, 62026-1653
3.
Dept. of Mathematics and Statistic, Southern Illinois University Edwardsville, Edwardsville, Illinois, 62026-1653

Received: January 31, 2010

Revised: August 31, 2010

Published: March 2011

Abstract Related Papers Cited by

Abstract

We describe optimal protocols for a class of mathematical models for tumor anti-angiogenesis for the problem of minimizing the tumor volume with an a priori given amount of vessel disruptive agents. The family of models is based on a biologically validated model by Hahnfeldt et al. [9] and includes a modification by Ergun et al. [6], but also provides two new variations that interpolate the dynamics for the vascular support between these existing models. The biological reasoning for the modifications of the models will be presented and we will show that despite quite different modeling assumptions, the qualitative structure of optimal controls is robust. For all the systems in the class of models considered here, an optimal singular arc is the defining element and all the syntheses of optimal controlled trajectories are qualitatively equivalent with quantitative differences easily computed.

Keywords:

Mathematics Subject Classification: Primary: 49N35; Secondary: 49K15, 92C50.

Citation:

Related Papers

Cited by

References

[1]	T. Boehm, J. Folkman, T. Browder and M. S. O'Reilly, Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance, Nature, 390 (1997), 404-407.doi: 10.1038/37126.
[2]	B. Bonnard and M. Chyba, "Singular Trajectories and Their Role in Control Theory," Springer Verlag, Paris, 2003
[3]	A. Bressan and B. Piccoli, "Introduction to the Mathematical Theory of Control," American Institute of Mathematical Sciences, 2007.
[4]	S. Davis and G. D. Yancopoulos, The angiopoietins: Yin and Yang in angiogenesis, Current Topics in Microbiology and Immunology, 237 (1999), 173-185.
[5]	A. d'Onofrio and A. Gandolfi, Tumour eradication by antiangiogenic therapy: Analysis and extensions of the model by Hahnfeldt et al. (1999), Mathematical Biosciences, 191 (2004), 159-184.doi: 10.1016/j.mbs.2004.06.003.
[6]	A. Ergun, K. Camphausen and L. M. Wein, Optimal scheduling of radiotherapy and angiogenic inhibitors, Bulletin of Mathematical Biology, 65 (2003), 407-424.doi: 10.1016/S0092-8240(03)00006-5.
[7]	J. Folkman, Antiangiogenesis: New concept for therapy of solid tumors, Annals of Surgery, 175 (1972), 409-416.doi: 10.1097/00000658-197203000-00014.
[8]	J. Folkman, Angiogenesis inhibitors generated by tumors, Mol. Med., 1 (1995), 120-122.
[9]	P. Hahnfeldt, D. Panigrahy, J. Folkman and L. Hlatky, Tumor development under angiogenic signaling: A dynamical theory of tumor growth, treatment response, and postvascular dormancy, Cancer Research, 59 (1999), 4770-4775.
[10]	R. K. Jain, Normalizing tumor vasculature with anti-angiogenic therapy: A new paradigm for combination therapy, Nature Medicine, 7 (2001), 987-989.doi: 10.1038/nm0901-987.
[11]	R. K. Jain and L. L. Munn, Vascular normalization as a rationale for combining chemotherapy with antiangiogenic agents, Principles of Practical Oncology, 21 (2007), 1-7.
[12]	M. Klagsburn and S. Soker, VEGF/VPF: The angiogenesis factor found?, Current Biology, 3 (1993), 699-702.doi: 10.1016/0960-9822(93)90073-W.
[13]	R. S. Kerbel, A cancer therapy resistant to resistance, Nature, 390 (1997), 335-336.doi: 10.1038/36978.
[14]	U. Ledzewicz, J. Marriott, H. Maurer and H. Schättler, Realizable protocols for optimal administration of drugs in mathematical models for anti-angiogenic treatment, Mathematical Medicine and Biology, 27 (2010), 157-179.doi: 10.1093/imammb/dqp012.
[15]	U. Ledzewicz, J. Munden and H. Schättler, Scheduling of angiogenic inhibitors for Gompertzian and logistic tumor growth models, Discrete and Continuous Dynamical Systems, Series B, 12 (2009), 415-438.doi: 10.3934/dcdsb.2009.12.415.
[16]	U. Ledzewicz and H. Schättler, A synthesis of optimal controls for a model of tumor growth under angiogenic inhibitors, Proc. 44th IEEE Conference on Decision and Control, Sevilla, Spain, (2005), 934-939.doi: 10.1109/CDC.2005.1582277.
[17]	U. Ledzewicz and H. Schättler, Anti-angiogenic therapy in cancer treatment as an optimal control problem, SIAM J. on Control and Optimization, 46 (2007), 1052-1079.doi: 10.1137/060665294.
[18]	U. Ledzewicz and H. Schättler, Analysis of a mathematical model for tumor anti-angiogenesis, Optimal Control Applications and Methods, 29 (2008), 41-57.doi: 10.1002/oca.814.
[19]	U. Ledzewicz and H. Schättler, Optimal and suboptimal protocols for a class of mathematical models of tumor anti-angiogenesis, J. of Theoretical Biology, 252 (2008), 295-312.doi: 10.1016/j.jtbi.2008.02.014.
[20]	L. Norton and R. Simon, Growth curve of an experimental solid tumor following radiotherapy, J.of the National Cancer Institute, 58 (1977), 1735-1741.
[21]	L. Norton, A Gompertzian model of human breast cancer growth, Cancer Research, 48 (1988), 7067-7071.
[22]	L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze and E. F. Mishchenko, "The Mathematical Theory of Optimal Processes," MacMillan, New York, 1964.
[23]	A. Swierniak, Direct and indirect control of cancer populations, Bulletin of the Polish Academy of Sciences, Technical Sciences, 56 (2008), 367-378.
[24]	A. Swierniak, Comparison of six models of antiangiogenic therapy, Applied Mathematics, 36 (2009), 333-348.