American Institute of Mathematical Sciences

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May  2019, 24(5): 2383-2405. doi: 10.3934/dcdsb.2019100

Optimal control for cancer chemotherapy under tumor heterogeneity with Michealis-Menten pharmacodynamics

Shuo Wang 1, and  Heinz Schättler 2,,

1. 

Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX, 76010, USA
2. 

Dept. of Electrical and Systems Engineering, Washington University, St. Louis, Mo, 63130, USA

* Corresponding author: Heinz Schättler

Received  January 2018 Revised  January 2019 Published  March 2019

We analyze mathematical models for cancer chemotherapy under tumor heterogeneity as optimal control problems. Tumor heterogeneity is incorporated into the model through a potentially large number of states which represent sub-populations of tumor cells with different chemotherapeutic sensitivities. In this paper, a Michaelis-Menten type functional form is used to model the pharmacodynamic effects of the drug concentrations. In the objective, a weighted average of the tumor volume and the total amounts of drugs administered (taken as an indirect measurement for the side effects) is minimized. Mathematically, incorporating a Michaelis-Menten form creates a nonlinear structure in the controls with partial convexity properties in the Hamiltonian function for the optimal control problem. As a result, optimal controls are continuous and this fact can be utilized to set up an efficient numerical computation of extremals. Second order Jacobi type necessary and sufficient conditions for optimality are formulated that allow to verify the strong local optimality of numerically computed extremal controlled trajectories. Examples which illustrate the cases of both strong locally optimal and non-optimal extremals are given.

Keywords: Optimal control, tumor heterogeneity, pharmacodynamics, Michaelis-Menten kinetics, ensemble systems.
Mathematics Subject Classification: Primary: 92C50, 49K15; Secondary: 93C95.
Citation: Shuo Wang, Heinz Schättler. Optimal control for cancer chemotherapy under tumor heterogeneity with Michealis-Menten pharmacodynamics. Discrete & Continuous Dynamical Systems - B, 2019, 24 (5) : 2383-2405. doi: 10.3934/dcdsb.2019100
References:
[1]

B. Bonnard and M. Chyba, Singular Trajectories and their Role in Control Theory, Mathématiques & Applications, vol. 40, Springer Verlag, Paris, 2003.  Google Scholar

[2]

A. Bressan and B. Piccoli, Introduction to the Mathematical Theory of Control, American Institute of Mathematical Sciences, 2007.  Google Scholar

[3]

A. E. Bryson and Y. C. Ho, Applied Optimal Control, Hemisphere Publishing, 1975.  Google Scholar

[4]

U. Felgenhauer, L. Poggiolini and G. Stefani, Optimality and stability result for bang-bang optimal controls with simple and double switch behaviour, in: 50 Years of Optimal Control, A. Ioffe, K. Malanowski, F. Tröltzsch, Eds., Control and Cybernetics, 38 (2009), 1305–1325.  Google Scholar

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R. A. Gatenby, A change of strategy in the war on cancer, Nature, 459 (2009), 508-509.  doi: 10.1038/459508a.  Google Scholar

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J. H. Goldie, Drug resistance in cancer: A perspective, Cancer and Metastasis Review, 20 (2001), 63-68.   Google Scholar

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J. H. Goldie and A. Coldman, A model for resistance of tumor cells to cancer chemotherapeutic agents, Mathematical Biosciences, 65 (1983), 291-307.   Google Scholar

[8]

R. Grantab, S. Sivananthan and I. F. Tannock, The penetration of anticancer drugs through tumor tissue as a function of cellular adhesion and packing density of tumor cells, Cancer Research, 66 (2006), 1033-1039.  doi: 10.1158/0008-5472.CAN-05-3077.  Google Scholar

[9]

J. Greene, O. Lavi, M. M. Gottesman and D. Levy, The impact of cell density and mutations in a model of multidrug resistance in solid tumors, Bull. Math. Biol., 74 (2014), 627-653.  doi: 10.1007/s11538-014-9936-8.  Google Scholar

[10]

P. Hahnfeldt and L. Hlatky, Cell resensitization during protracted dosing of heterogeneous cell populations, Radiation Research, 150 (1998), 681-687.  doi: 10.2307/3579891.  Google Scholar

[11]

P. Hahnfeldt, J. Folkman and L. Hlatky, Minimizing long-term burden: The logic for metronomic chemotherapeutic dosing and its angiogenic basis, J. of Theoretical Biology, 220 (2003), 545-554.  doi: 10.1006/jtbi.2003.3162.  Google Scholar

[12]

O. Lavi, J. Greene, D. Levy and M. Gottesman, The role of cell density and intratumoral heterogeneity in multidrug resistance, Cancer Research, 73 (2013), 7168-7175.  doi: 10.1158/0008-5472.CAN-13-1768.  Google Scholar

[13]

U. Ledzewicz and H. Schättler, The influence of PK/PD on the structure of optimal control in cancer chemotherapy models, Mathematical Biosciences and Engineering (MBE), 2 (2005), 561-578.  doi: 10.3934/mbe.2005.2.561.  Google Scholar

[14]

U. Ledzewicz, K. Bratton and H. Schättler, A 3-compartment model for chemotherapy of heterogeneous tumor populations, Acta Applicandae Matematicae, 135 (2015), 191-207.  doi: 10.1007/s10440-014-9952-6.  Google Scholar

[15]

U. Ledzewicz and H. Schättler, On optimal chemotherapy for heterogeneous tumors, J. of Biological Systems, 22 (2014), 177-197.  doi: 10.1142/S0218339014400014.  Google Scholar

[16]

M. Leszczy\'nski, E. Ratajczyk, U. Ledzewicz and H. Schättler, Sufficient conditions for optimality for a mathematical model of drug treatment with pharmacodynamics, Opuscula Math., 37 (2017), 403-419.  doi: 10.7494/OpMath.2017.37.3.403.  Google Scholar

[17]

J. S. Li and N. Khaneja, Ensemble control of linear systems, Proc. of the 46th IEEE Conference on Decision and Control, 2007, 3768–3773. Google Scholar

[18]

J. S. Li and N. Khaneja, Ensemble control of Bloch equations, IEEE Transactions on Automatic Control, 54 (2009), 528-536.  doi: 10.1109/TAC.2009.2012983.  Google Scholar

[19] D. Liberzon, Calculus of Variations and Optimal Control Theory, Princeton University Press, 2012.   Google Scholar
[20]

A. Lorz, T. Lorenzi, M. E. Hochberg, J. Clairambault and B. Berthame, Population adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies, ESAIM: Mathematical Modelling and Numerical Analysis, 47 (2013), 377-399.  doi: 10.1051/m2an/2012031.  Google Scholar

[21]

A. Lorz, T. Lorenzi, J. Clairambault, A. Escargueil and B. Perthame, Effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors, Bull. Math. Biol., 77 (2015), 1-22.  doi: 10.1007/s11538-014-0046-4.  Google Scholar

[22]

L. Norton and R. Simon, Tumor size, sensitivity to therapy, and design of treatment schedules, Cancer Treatment Reports, 61 (1977), 1307-1317.   Google Scholar

[23]

L. Norton and R. Simon, The Norton-Simon hypothesis revisited, Cancer Treatment Reports, 70 (1986), 41–61. Google Scholar

[24]

L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes, Macmillan, New York, 1964.  Google Scholar

[25]

H. Schättler, On classical envelopes in optimal control theory, Proc. of the 49th IEEE Conference on Decision and Control, Atlanta, USA, (2010), 1879–1884. Google Scholar

[26]

H. Schättler and U. Ledzewicz, Geometric Optimal Control, Interdisciplinary Applied Mathematics, vol. 38, Springer, 2012. doi: 10.1007/978-1-4614-3834-2.  Google Scholar

[27]

H. Schättler and U. Ledzewicz, Optimal Control for Mathematical Models of Cancer Therapies, Interdisciplinary Applied Mathematics, vol. 42, Springer, 2015. doi: 10.1007/978-1-4939-2972-6.  Google Scholar

[28]

A. Swierniak and J. Smieja, Cancer chemotherapy optimization under evolving drug resistance, Nonlinear Analysis, 47 (2000), 375-386.  doi: 10.1016/S0362-546X(01)00184-5.  Google Scholar

[29]

S. Wang and J. S. Li, Fixed-endpoint optimal control of bilinear ensemble systems, SIAM J. Control and Optimization (SICON), 55 (2017), 3039-3065.  doi: 10.1137/15M1044151.  Google Scholar

[30]

S. Wang and H. Schättler, Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity, Mathematical Biosciences and Engineering - MBE, 13 (2016), 1223-1240.  doi: 10.3934/mbe.2016040.  Google Scholar

show all references

References:
[1]

B. Bonnard and M. Chyba, Singular Trajectories and their Role in Control Theory, Mathématiques & Applications, vol. 40, Springer Verlag, Paris, 2003.  Google Scholar

[2]

A. Bressan and B. Piccoli, Introduction to the Mathematical Theory of Control, American Institute of Mathematical Sciences, 2007.  Google Scholar

[3]

A. E. Bryson and Y. C. Ho, Applied Optimal Control, Hemisphere Publishing, 1975.  Google Scholar

[4]

U. Felgenhauer, L. Poggiolini and G. Stefani, Optimality and stability result for bang-bang optimal controls with simple and double switch behaviour, in: 50 Years of Optimal Control, A. Ioffe, K. Malanowski, F. Tröltzsch, Eds., Control and Cybernetics, 38 (2009), 1305–1325.  Google Scholar

[5]

R. A. Gatenby, A change of strategy in the war on cancer, Nature, 459 (2009), 508-509.  doi: 10.1038/459508a.  Google Scholar

[6]

J. H. Goldie, Drug resistance in cancer: A perspective, Cancer and Metastasis Review, 20 (2001), 63-68.   Google Scholar

[7]

J. H. Goldie and A. Coldman, A model for resistance of tumor cells to cancer chemotherapeutic agents, Mathematical Biosciences, 65 (1983), 291-307.   Google Scholar

[8]

R. Grantab, S. Sivananthan and I. F. Tannock, The penetration of anticancer drugs through tumor tissue as a function of cellular adhesion and packing density of tumor cells, Cancer Research, 66 (2006), 1033-1039.  doi: 10.1158/0008-5472.CAN-05-3077.  Google Scholar

[9]

J. Greene, O. Lavi, M. M. Gottesman and D. Levy, The impact of cell density and mutations in a model of multidrug resistance in solid tumors, Bull. Math. Biol., 74 (2014), 627-653.  doi: 10.1007/s11538-014-9936-8.  Google Scholar

[10]

P. Hahnfeldt and L. Hlatky, Cell resensitization during protracted dosing of heterogeneous cell populations, Radiation Research, 150 (1998), 681-687.  doi: 10.2307/3579891.  Google Scholar

[11]

P. Hahnfeldt, J. Folkman and L. Hlatky, Minimizing long-term burden: The logic for metronomic chemotherapeutic dosing and its angiogenic basis, J. of Theoretical Biology, 220 (2003), 545-554.  doi: 10.1006/jtbi.2003.3162.  Google Scholar

[12]

O. Lavi, J. Greene, D. Levy and M. Gottesman, The role of cell density and intratumoral heterogeneity in multidrug resistance, Cancer Research, 73 (2013), 7168-7175.  doi: 10.1158/0008-5472.CAN-13-1768.  Google Scholar

[13]

U. Ledzewicz and H. Schättler, The influence of PK/PD on the structure of optimal control in cancer chemotherapy models, Mathematical Biosciences and Engineering (MBE), 2 (2005), 561-578.  doi: 10.3934/mbe.2005.2.561.  Google Scholar

[14]

U. Ledzewicz, K. Bratton and H. Schättler, A 3-compartment model for chemotherapy of heterogeneous tumor populations, Acta Applicandae Matematicae, 135 (2015), 191-207.  doi: 10.1007/s10440-014-9952-6.  Google Scholar

[15]

U. Ledzewicz and H. Schättler, On optimal chemotherapy for heterogeneous tumors, J. of Biological Systems, 22 (2014), 177-197.  doi: 10.1142/S0218339014400014.  Google Scholar

[16]

M. Leszczy\'nski, E. Ratajczyk, U. Ledzewicz and H. Schättler, Sufficient conditions for optimality for a mathematical model of drug treatment with pharmacodynamics, Opuscula Math., 37 (2017), 403-419.  doi: 10.7494/OpMath.2017.37.3.403.  Google Scholar

[17]

J. S. Li and N. Khaneja, Ensemble control of linear systems, Proc. of the 46th IEEE Conference on Decision and Control, 2007, 3768–3773. Google Scholar

[18]

J. S. Li and N. Khaneja, Ensemble control of Bloch equations, IEEE Transactions on Automatic Control, 54 (2009), 528-536.  doi: 10.1109/TAC.2009.2012983.  Google Scholar

[19] D. Liberzon, Calculus of Variations and Optimal Control Theory, Princeton University Press, 2012.   Google Scholar
[20]

A. Lorz, T. Lorenzi, M. E. Hochberg, J. Clairambault and B. Berthame, Population adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies, ESAIM: Mathematical Modelling and Numerical Analysis, 47 (2013), 377-399.  doi: 10.1051/m2an/2012031.  Google Scholar

[21]

A. Lorz, T. Lorenzi, J. Clairambault, A. Escargueil and B. Perthame, Effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors, Bull. Math. Biol., 77 (2015), 1-22.  doi: 10.1007/s11538-014-0046-4.  Google Scholar

[22]

L. Norton and R. Simon, Tumor size, sensitivity to therapy, and design of treatment schedules, Cancer Treatment Reports, 61 (1977), 1307-1317.   Google Scholar

[23]

L. Norton and R. Simon, The Norton-Simon hypothesis revisited, Cancer Treatment Reports, 70 (1986), 41–61. Google Scholar

[24]

L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze and E. F. Mishchenko, The Mathematical Theory of Optimal Processes, Macmillan, New York, 1964.  Google Scholar

[25]

H. Schättler, On classical envelopes in optimal control theory, Proc. of the 49th IEEE Conference on Decision and Control, Atlanta, USA, (2010), 1879–1884. Google Scholar

[26]

H. Schättler and U. Ledzewicz, Geometric Optimal Control, Interdisciplinary Applied Mathematics, vol. 38, Springer, 2012. doi: 10.1007/978-1-4614-3834-2.  Google Scholar

[27]

H. Schättler and U. Ledzewicz, Optimal Control for Mathematical Models of Cancer Therapies, Interdisciplinary Applied Mathematics, vol. 42, Springer, 2015. doi: 10.1007/978-1-4939-2972-6.  Google Scholar

[28]

A. Swierniak and J. Smieja, Cancer chemotherapy optimization under evolving drug resistance, Nonlinear Analysis, 47 (2000), 375-386.  doi: 10.1016/S0362-546X(01)00184-5.  Google Scholar

[29]

S. Wang and J. S. Li, Fixed-endpoint optimal control of bilinear ensemble systems, SIAM J. Control and Optimization (SICON), 55 (2017), 3039-3065.  doi: 10.1137/15M1044151.  Google Scholar

[30]

S. Wang and H. Schättler, Optimal control of a mathematical model for cancer chemotherapy under tumor heterogeneity, Mathematical Biosciences and Engineering - MBE, 13 (2016), 1223-1240.  doi: 10.3934/mbe.2016040.  Google Scholar

Figure 1.  A numerically computed extremal control (top row, left) for the effectiveness function $ \varphi(x) = \frac{2}{1+x^2} $ (top row, right), weights $ \alpha = (1, \ldots, 1) $, $ \beta = 500\alpha $ and $ \gamma = 5000 $, and therapy horizon $ T = 10 $. The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density $ n(0, x)\equiv \frac{200}{21} $ and the terminal density $ n(10, x) $ shown as red curve is given in the bottom row on the left. This control is a strong local minimum as the entries of the solution $ S $ to the Riccati differential equation (13) along the extremal controlled trajectory (shown in the bottom row, right) remain finite over the full interval $ [0, T] $
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Figure 2.  A numerically computed extremal control (top row, left) for the effectiveness function $ \varphi_1 $ and $ \varphi_2 $ given in (22), (top row, right), $ \alpha = (1, \ldots, 1) $, $ \beta = 50 \alpha $, $ \gamma = (600, 1400) $ and time horizon $ T = 10 $. The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density $ n(0, x)\equiv \frac{200}{21} $ and the terminal density $ n(10, x) $ shown as red curve is given in the bottom row on the left. This control is a strong local minimum as the entries of the solution $ S $ to the Riccati differential equation (13) along the extremal controlled trajectory (shown in the bottom row, right) remain finite over the full interval $ [0, T] $
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Figure 3.  A numerically computed extremal control (top row, left) for the effectiveness function $ \varphi_1 $ and $ \varphi_2 $ given in (23), (top row, right), $ \alpha = (1, \ldots, 1) $, $ \beta = 50 \alpha $, $ \gamma = (600, 1400) $ and time horizon $ T = 10 $. The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density $ n(0, x)\equiv \frac{200}{21} $ and the terminal density $ n(10, x) $ shown as red curve is given in the bottom row on the left. This control is not a strong local minimum as the entries of the solution $ S $ to the Riccati differential equation (13) along the extremal controlled trajectory (shown in the bottom row, right) blow up in the interval
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Fig. 3, for the constant controls $ u_1 = 1 $ and $ u_2 = 2.5 $">Figure 4.  Examples of the system response, for the same parameters as in Fig. 3, for the constant controls $ u_1 = 1 $ and $ u_2 = 2.5 $
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Fig. 6 below at the time $ t = 19 $">Figure 5.  Illustration of the functions $ \Upsilon $ (top, left), $ \eta $ (top, right) and the full Hamiltonian $ H $ (bottom) as function of the controls. These graphs depict the actual values for the solution shown in Fig. 6 below at the time $ t = 19 $
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Figure 6.  A numerically computed locally optimal control (top row, left) for $ \alpha = (1, \ldots, 1) $, $ \beta = 15 \alpha $, $ \gamma = (90, 30) $ with therapy horizon $ T = 20 $ and effectiveness functions $ \varphi_1 = \frac{2}{1+x^2} $ and $ \varphi_2 = \frac{2}{1+3x^6} $ (top row, right). The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density $ n(0, x)\equiv \frac{200}{21} $ and the terminal density $ n(20, x) $ shown as red curve is given in the bottom row on the left. This control is a strong local minimum as the entries of the solution $ S $ to the Riccati differential equation (30) along the extremal controlled trajectory (shown in the bottom row, right) remain finite in the full interval $ [0, 20] $
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Figure 7.  A numerically computed locally optimal control (top row, left) for α = (1, ...., 1), β = 15α, γ = (90, 30) with therapy horizon T = 20 and effectiveness functions ${\varphi _1}{\rm{ = }}\frac{2}{{1 + 5{x^2}}}$ and ${\varphi _2}{\rm{ = }}\frac{2}{{1 + 60{{\left( {1 - x} \right)}^6}}}$ (top row, right). The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density n(0, x) ≡ $\frac{{200}}{{21}}$ and the terminal density n(20, x) shown as red curve is given in the bottom row on the left. This control is a strong local minimum as the entries of the solution S to the Riccati differential equation (30) along the extremal controlled trajectory (shown in the bottom row, right) blow up in the interval.
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Figure 8.  A numerically computed extremal control (top row, left) for α = (1, ..., 1), β = 15α, γ = (90, 30) with shortened therapy horizon T = 5 and effectiveness function ${\varphi _1}{\rm{ = }}\frac{2}{{1 + 5{x^2}}}$ and ${\varphi _2}{\rm{ = }}\frac{2}{{1 + 60{{\left( {1 - x} \right)}^6}}}$ (top row, right). The corresponding evolution of the individual cell populations is shown in the middle row (left) along with the evolution of the average of the populations (right). A comparison of the initial density n(0, x) ≡ $\frac{{200}}{{21}}$ and the terminal density n(5, x) shown as red curve is given in the bottom row on the left. This control still is a strong local minimum as the entries of the solution S to the Riccati differential equation (30) along the extremal controlled trajectory (shown in the bottom row, right) remain finite in the full interval [0, T].
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Algorithm 1.  Shooting Method
Require: $ N(t)>0 $, $ \forall t \in [0, T] $ and pick a threshold $ \epsilon >0 $.
Ensure: optimal control
  start with an initial guess $ \lambda(0) $ and the stepsize $ s=0.01 $.
while $ \|\lambda(T)-\alpha\|>\epsilon $ do
  solve $ N, \; \lambda $.
   $ \lambda(0)\Leftarrow \lambda(0)-s\Big( \frac{\delta \lambda(T)}{\delta \lambda(0)}\Big) ^{-1} (\lambda(T)-\alpha) $
end while
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Require: $ N(t)>0 $, $ \forall t \in [0, T] $ and pick a threshold $ \epsilon >0 $.
Ensure: optimal control
  start with an initial guess $ \lambda(0) $ and the stepsize $ s=0.01 $.
while $ \|\lambda(T)-\alpha\|>\epsilon $ do
  solve $ N, \; \lambda $.
   $ \lambda(0)\Leftarrow \lambda(0)-s\Big( \frac{\delta \lambda(T)}{\delta \lambda(0)}\Big) ^{-1} (\lambda(T)-\alpha) $
end while
Algorithm 2.  Iterative Method
Require: $ N(t)>0 $, $ \forall t \in [0, T] $ and pick a threshold $ \epsilon >0 $.
Ensure: initial guess
  Start with an initial trajectory $ N^{(0)} $ and $ k=1 $.
while $ \|N^{(k)}-N^{(k-1)}\|>\epsilon $ do
   $ A^{(k-1)} \Leftarrow $ function of $ N^{(k-1)} $
   $ O^{(k-1)} \Leftarrow $ function of $ N^{(k-1)} $
   Solve $ S^{(k)}, \; \nu^{(k)} $ backward in time by assuming $ \lambda^{(k)} = S^{(k)}N^{(k)}+\nu^{(k)} $.
  Update $ N^{(k)} $ forward in time:
  $ \qquad \dot{N}^{(k)} =[A^{(k-1)}+O^{(k-1)}S^{(k)}]N^{(k)}+O^{(k-1)}\nu^{(k)} $)
  $ u^{(k)} \Leftarrow $ function of $ N^{(k)} $, $ \lambda^{(k)} $
  $ k+1 \Leftarrow k $
end while
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Require: $ N(t)>0 $, $ \forall t \in [0, T] $ and pick a threshold $ \epsilon >0 $.
Ensure: initial guess
  Start with an initial trajectory $ N^{(0)} $ and $ k=1 $.
while $ \|N^{(k)}-N^{(k-1)}\|>\epsilon $ do
   $ A^{(k-1)} \Leftarrow $ function of $ N^{(k-1)} $
   $ O^{(k-1)} \Leftarrow $ function of $ N^{(k-1)} $
   Solve $ S^{(k)}, \; \nu^{(k)} $ backward in time by assuming $ \lambda^{(k)} = S^{(k)}N^{(k)}+\nu^{(k)} $.
  Update $ N^{(k)} $ forward in time:
  $ \qquad \dot{N}^{(k)} =[A^{(k-1)}+O^{(k-1)}S^{(k)}]N^{(k)}+O^{(k-1)}\nu^{(k)} $)
  $ u^{(k)} \Leftarrow $ function of $ N^{(k)} $, $ \lambda^{(k)} $
  $ k+1 \Leftarrow k $
end while
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