Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity
-
1.
Dept. of Electrical and Systems Engineering, Washington University, St. Louis, Mo, 63130
-
2.
Dept. of Electrical and Systems Engineering, Washington University, St. Louis, Mo 63130
-
Received:
01 December 2015
Accepted:
29 June 2018
Published:
01 August 2016
-
-
MSC :
Primary: 49K15, 92B05; Secondary: 93C95.
-
-
We consider cancer chemotherapy as an optimal control problem with the aim to minimize a combination of the tumor volume and side effects over an a priori specified therapy horizon when the tumor consists of a heterogeneous agglomeration of many subpopulations. The mathematical model, which accounts for different growth and apoptosis rates in the presence of cell densities, is a finite-dimensional approximation of a model originally formulated by Lorz et al. [18,19] and Greene et al. [10,11] with a continuum of possible traits. In spite of an arbitrarily high dimension, for this problem singular controls (which correspond to time-varying administration schedules at less than maximum doses) can be computed explicitly in feedback form. Interestingly, these controls have the property to keep the entire tumor population constant. Numerical computations and simulations that explore the optimality of bang-bang and singular controls are given. These point to the optimality of protocols that combine a full dose therapy segment with a period of lower dose drug administration.
Citation: Shuo Wang, Heinz Schättler. Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity[J]. Mathematical Biosciences and Engineering, 2016, 13(6): 1223-1240. doi: 10.3934/mbe.2016040
-
Abstract
We consider cancer chemotherapy as an optimal control problem with the aim to minimize a combination of the tumor volume and side effects over an a priori specified therapy horizon when the tumor consists of a heterogeneous agglomeration of many subpopulations. The mathematical model, which accounts for different growth and apoptosis rates in the presence of cell densities, is a finite-dimensional approximation of a model originally formulated by Lorz et al. [18,19] and Greene et al. [10,11] with a continuum of possible traits. In spite of an arbitrarily high dimension, for this problem singular controls (which correspond to time-varying administration schedules at less than maximum doses) can be computed explicitly in feedback form. Interestingly, these controls have the property to keep the entire tumor population constant. Numerical computations and simulations that explore the optimality of bang-bang and singular controls are given. These point to the optimality of protocols that combine a full dose therapy segment with a period of lower dose drug administration.
References
[1]
|
Future Oncology, 7 (2011), 385-394.
|
[2]
|
Springer Verlag, Series: Mathematics and Applications, Vol. 40, 2003.
|
[3]
|
American Institute of Mathematical Sciences, 2007.
|
[4]
|
Bull. of Math. Biology, 65 (2003), 407-424.
|
[5]
|
Cancer Research, 69 (2009), 4894-4903.
|
[6]
|
Nature, 459 (2009), 508-509.
|
[7]
|
Cancer and Metastasis Review, 20 (2001), 63-68.
|
[8]
|
Mathematical Biosciences, 65 (1983), 291-307.
|
[9]
|
Cancer Research, 66 (2006), 1033-1039.
|
[10]
|
Bull. Math. Biol., 76 (2014), 627-653.
|
[11]
|
Cancer Research, 73 (2013), 7168-7175.
|
[12]
|
in Recent Advances in Optimization and its Applications in Engineering, M. Diehl, F. Glineur, E. Jarlebring and W. Michiels, Eds., (2010), 267-276.
|
[13]
|
Mathematical Biosciences and Engineering (MBE), 2 (2005), 561-578.
|
[14]
|
SIAM J. Contr. Optim., 46 (2007), 1052-1079.
|
[15]
|
Control and Cybernetics, 38 (2009), 1501-1523.
|
[16]
|
Proc. of the 46th IEEE Conference on Decision and Control, 2007, 3768-3773.
|
[17]
|
IEEE Transactions on Automatic Control, 54 (2009), 528-536.
|
[18]
|
ESAIM: Mathematical Modelling and Numerical Analysis, 47 (2013), 377-399.
|
[19]
|
Bull. Math. Biol., 77 (2015), 1-22.
|
[20]
|
Cancer Treatment Reports, 61 (1977), 1307-1317.
|
[21]
|
Cancer Treatment Reports, 70 (1986), 41-61.
|
[22]
|
Nature Reviews|Clinical Oncology, 7 (2010), 455-465.
|
[23]
|
Macmillan, New York, 1964.
|
[24]
|
Springer, New York, 2012
|
[25]
|
Springer, 2015.
|
-
-
-
-