TOMLAB
Dynamic optimization of bioprocesses: efficient and robust numerical strategies 2003, Julio R. Banga, Eva Balsa-Cantro, Carmen G. Moles and Antonio A. Alonso
Case Study III: Optimal Drug Scheduling for Cancer Chemotherapy
Many researches have devoted their efforts to determine whether current methods for drugs administration during cancer chemotherapy are optimal, and if alternative regimens should be considered. Martin (1992) considered the interesting problem of determining the optimal cancer drug scheduling to decrease the size of a malignant tumor as measured at some particular time in the future. The drug concentration must be kept below some level throughout the treatment period and the cumulative (toxic) effect of the drug must be kept below the ultimate tolerance level. Bojkov et al. (1993) and Luus et al. (1995) also studied this problem using direct search optimization. More recently, Carrasco and Banga (1997) have applied stochastic techniques to solve this problem, obtaining better results (Carrasco & Banga 1998). The mathematical statement of this dynamic optimization problem is: Find u(t) over t in [t0; t_f ] to maximize:
| J = x1(tf) |
subject to:
| = −k1*x1+k2*(x2−k3)*H(x2−k3) |
| = u−k4*x2 |
| = x2 |
where the tumor mass is given by N = 10^12 * exp (-x1) cells, x2 is the drug concentration in the body in drug units [D] and x3 is the cumulative effect of the drug. The parameters are taken as k1 = 9.9e-4 days, k2 = 8.4e-3 days-1 [De-1], k3 = 10 [De-1], and k4 = 0.27 days-1. The initial state considered is:
| x(t0) = [log(100) 0 0]′ |
where,
H(x2-k3) = 1 if x2 >= k3 or 0 if x2 < k3
and the final time t_f = 84 days. The optimization is subject to the following constraints on the drug delivery (control variable):
| u >= 0 |
There are the following path constraints on the state variables:
| x2(t) <= 50 |
| x3(t) <= 2.1e3 |
Also, there should be at least a 50% reduction in the size of the tumor every three weeks, so that the following point constraints must be considered:
| x1(21) >= log(200) |
| x1(42) >= log(400) |
| x1(63) >= log(800) |
State number 3 is converted to an integral constraints in the formulation.
Reference: [3]
toms t nn = [20 40 120]; for i = 1:length(nn)
n = nn(i);
p = tomPhase('p', t, 0, 84, n);
setPhase(p);
tomStates x1 x2
tomControls u
% Initial guess
if i==1
x0 = {icollocate(x2 == 10)
collocate(u == 20)};
else
x0 = {icollocate({x1 == x1opt; x2 == x2opt})
collocate(u == uopt)};
end
% Box constraints
cbox = {
0 <= mcollocate(x1)
0 <= mcollocate(x2) <= 50
0 <= collocate(u) <= 80};
% Boundary constraints
cbnd = initial({x1 == log(100); x2 == 0});
% ODEs and path constraints
k1 = 9.9e-4; k2 = 8.4e-3;
k3 = 10; k4 = 0.27;
ceq = {collocate({
dot(x1) == -k1*x1+k2*max(x2-k3,0)
dot(x2) == u-k4*x2})
% Point-wise conditions
atPoints([21;42;63],x1) >= log([200;400;800])
% Integral constr.
integrate(x2) == 2.1e3};
% Objective
objective = -final(x1);
options = struct;
options.name = 'Drug Scheduling';
options.solver = 'multiMin';
options.xInit = 130-n;
solution = ezsolve(objective, {cbox, cbnd, ceq}, x0, options);
x1opt = subs(x1, solution);
x2opt = subs(x2, solution);
uopt = subs(u, solution);
Problem type appears to be: lpcon
Starting numeric solver
===== * * * =================================================================== * * *
TOMLAB - Tomlab Optimization Inc. Development license 999001. Valid to 2010-02-05
=====================================================================================
Problem: --- 1: Drug Scheduling - Trial 110 f_k -17.116123415306220000
sum(|constr|) 0.000000000002368399
f(x_k) + sum(|constr|) -17.116123415303850000
Solver: multiMin with local solver snopt. EXIT=0. INFORM=0.
Find local optima using multistart local search
Did 110 local tries. Found 1 global, 87 minima. TotFuncEv 110. TotConstrEv 2361
FuncEv 110 ConstrEv 2361 ConJacEv 23 Iter 1084
CPU time: 5.250000 sec. Elapsed time: 5.313000 sec.
Problem type appears to be: lpcon
Starting numeric solver
===== * * * =================================================================== * * *
TOMLAB - Tomlab Optimization Inc. Development license 999001. Valid to 2010-02-05
=====================================================================================
Problem: --- 1: Drug Scheduling - Trial 90 f_k -17.242322437683242000
sum(|constr|) 0.000000000005218615
f(x_k) + sum(|constr|) -17.242322437678023000
Solver: multiMin with local solver snopt. EXIT=0. INFORM=0.
Find local optima using multistart local search
Did 90 local tries. Found 1 global, 90 minima. TotFuncEv 90. TotConstrEv 3146
FuncEv 90 ConstrEv 3146 ConJacEv 61 Iter 1353
CPU time: 13.328125 sec. Elapsed time: 13.500000 sec.
Problem type appears to be: lpcon
Starting numeric solver
===== * * * =================================================================== * * *
TOMLAB - Tomlab Optimization Inc. Development license 999001. Valid to 2010-02-05
=====================================================================================
Problem: --- 1: Drug Scheduling - Trial 10 f_k -17.407072045338278000
sum(|constr|) 0.000002782646977696
f(x_k) + sum(|constr|) -17.407069262691302000
Solver: multiMin with local solver snopt. EXIT=0. INFORM=0.
Find local optima using multistart local search
Did 10 local tries. Found 1 global, 10 minima. TotFuncEv 10. TotConstrEv 911
FuncEv 10 ConstrEv 911 ConJacEv 96 Iter 358
CPU time: 33.625000 sec. Elapsed time: 34.250000 sec.
end
subplot(2,1,1)
ezplot([x1;x2]);
legend('x1','x2');
title('Drug Scheduling state variable');
subplot(2,1,2)
ezplot(u);
legend('u');
title('Drug Scheduling control');
