geometric methods in optimal control theory applied to problems
TRANSCRIPT
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Geometric Methods
in Optimal Control Theory Applied
to Problems in Biomedicine
Urszula Ledzewicz
Southern Illinois University
Edwardsville, USA
Miedzy Teoria a
Zastosowaniami-
Matematyka w Dzialaniu,
Bedlewo,16-22 czerwca 2013
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Co-author and Support Heinz Schättler
Washington University St. Louis, MO USA
Research supported by collaborative research
NSF grants DMS 0405827/0405848
DMS 0707404/0707410
DMS 1008209/1008221
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• Heinz Schättler and Urszula Ledzewicz,
Geometric Optimal Control – Theory, Methods, Examples
Springer Verlag, July 2012
• Urszula Ledzewicz and Heinz Schättler,
Geometric Optimal Control Applied to Biomedical Models
Springer Verlag, 2014
• Mathematical Methods and Models in Biomedicine
Urszula Ledzewicz, Heinz Schättler, Avner Friedman and Eugene Kashdan, Eds.
Springer Verlag, October 2012
Books
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Main Collaborators
Alberto d’Onofrio
European Institute for Oncology, Milano, Italy
Helmut Maurer
Rheinisch Westfälische Wilhelms-Universität Münster, Münster, Germany
Andrzej Swierniak
Silesian University of Technology, Gliwice, Poland
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Recent Collaborators
Eddy Pasquier
Children Cancer Institute Australia,
University of New South Wales, Sydney
Yangjin Kim
University of Michigan Dearborn, MI USA
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Graduate Student Collaborators
John Marriott
University of Hawaii
Mohammad Naghnaeian, Mostafa Reisi
University of Illinois at Urbana-Champaign
Mozdeh Sadat Faraji
Georgia Tech
Sia Mahmoudian Dehkordi
North Carolina State University
Omeiza Olumoye
Southern Illinois University Edwardsville
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Optimal Control Problems
dynamics
(model)
min or max
objective
control
response
disturbance
(unmodelled dynamics)
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• a model for chemotherapy for heterogeneous tumors
• a model for antiangiogenic therapy
(alone and in combination with chemotherapy)
• a model for chemotherapy and immune boost
• future directions metronomic chemotherapy
Outline – An Optimal Control Approach to …
• a model for growth and invasion in glioblastoma
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Optimal Drug Treatment Protocols
Main Questions
HOW MUCH? (dosage)
HOW OFTEN? (timing)
IN WHAT ORDER? (sequencing)
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A Model for Growth and
Invasion in Glioblastoma
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• a particularly aggressive form of
brain cancer characterized by
alternating phases of rapid growth
and tissue invasion with a mean
survival time of just about one
year
• treatment: surgery
• problem: distant tumor satellites
• normal glucose levels up-regulate the microRNA miR-451
level leading to cell proliferation and decreased cell
migration
• low glucose levels induce a down-regulation of miR-451,
which, in turn, promotes cell motility and invasion,
but inhibits proliferation.
Glioblastoma
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Dynamics of the miR-451 AMPK Complex
[Kim, Roh, Lawler and Friedman, PLoS One, (2011)
M
A M – concentration of miR-451
A – concentration of AMPK complex
G – glucose level
S – source of AMPK complex
miR 451 - micro RNA’s regulate the expression levels
of genes
AMPK – adenosine monophosphate-activated protein
kinase
an enzyme that plays a role in cellular
energy homeostasis including glucose uptake
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Dynamics of the miR-451 AMPK Complex
[Kim and Roh, Discr. and Cont. Dyn. Syst., (2013)
α – inhibition of miR-451 by AMPK (Hill-type, scaled)
β - inhibition of AMPK by miR-451 (Hill-type, scaled)
κ1 - autocatalytic strength of miR-451 (scaled)
κ3 - autocatalytic strength of AMPK (scaled)
ε - scaling factor related to the degradation rates of miR-451 and AMPK,
non-dimensionalized,
minimally parameterized
version of the model
A degrades much faster than M (half-life of miR 451 is in
the range of 100-200 hours, for AMPK about 6 hours)
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Differential-Algebraic Model
low glucose level
high glucose
level
multi-stability for
intermediate
glucose level
blue curve = slow manifold
red curve = equilibrium manifold
G constant
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Hysteresis for a constant glucose level, G=const, the
following hysteresis picture for the equilibria
and their stability arises
growth
(over-expression)
invasion
(under-expression)
cycle for differential
algebraic model
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Goal
• maintain the miR-451 level M above a threshold Mth so that
glioma cells remain in the proliferation phase and do not
switch into their invasive migratory phase
• effective control: the level of glucose
we allow for both bolus injections or continuous infusions and
do not limit the control variable in its size
• use as little glucose as possible in order to limit the cancer growth
minimize the overall amount of glucose given
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Optimal Control Problem
[M] for a fixed terminal time T, minimize the objective
over all times , bolus dosages , and all
Lebesgue measurable functions
subject to the dynamics
and state-space constraint for all
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State-Space Constraint
• the state-space constraint is of order 2:
• this makes transitions with the constraint difficult
(possibly chattering, … )
• consider a modified problem with an order 1 state-space constraint
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Optimal Control Problem II
[G] for a fixed terminal time T, minimize the objective
over all times , bolus dosages , and all
Lebesgue measurable functions
subject to the dynamics
and state-space constraint for all
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Solution for [G]
• for problem [M] to be well-posed, we assume that
• according to the fast dynamics for A we also assume that
Theorem [SchKimLed, 52nd, CDC 2013]
For these initial conditions the solution to the
optimal control problem [G] is given by
administering an initial bolus dose G1 of glucose
that brings the system to the threshold level Gth
at time 0 (if it lies below) followed by constant
infusion at rate u* ≡ λGth. This control maintains
the level of M above its lower threshold Mth.
continuous administration does better than spaced boli
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Periodic Bolus Injections
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Heterogeneous Tumor
Cell Populations
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Mathematical Model
[Hahnfeldt, Folkman and Hlatky, JTB, 2003]
for simplicity, just consider two populations of different chemotherapeutic
sensitivity and call them ‘sensitive’ and ‘resistant’
S – sensitive cell population
R – resistant cell population
α1 – growth rate of sensitive population
α2 – growth rate of resistant population
γ1 – transfer rate from sensitive to resistant population
γ2 – transfer rate from resistant to sensitive population
φ1 – linear log-kill parameter for sensitive population
φ2 – linear log-kill parameter for resistant population
β – pharmacokinetic parameter related to half-life of chemotherapeutic agent
S
R
c
N=(S,R)
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Mathematical Model: Objective
minimize the number of cancer cells N = (S,R)
left without causing too much harm to the healthy cells
Weighted average
of number of
cancer cells at
end of therapy
Weighted average
of cancer cells
during therapy
Toxicity of the
drug
(side effects on
healthy cells)
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For a fixed therapy horizon minimize
over all functions subject to the dynamics
where
As Optimal Control Problem
(LSch, JBS, 2013, submitted)
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Candidates for Optimal Protocols
• bang-bang controls
• singular controls
treatment protocols of
maximum dose therapy
periods with rest periods
in between
continuous infusions of
varying lower doses
umax
T T
MTD BOD
Φ(t) > 0
Φ(t) > 0 Φ(t) > 0 Φ(t) ≡ 0
switching function Φ(t)
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Singular Controls
• is singular on an open interval
switching function on
• all time derivatives must vanish as well
• “allows” to compute the singular control
• order : the control appears for the first time in
the derivative
• Legendre-Clebsch condition (minimize)
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• in the region BB,
the Legendre-Clebsch condition is violated and optimal controls are bang-
bang; in particular, this holds if
•outside the region BB,
the Legendre-Clebsch condition is satisfied, but singular controls are of
order 2 and concatenations with bang controls are through chattering arcs
singular controls become a viable option as the sensitive cells
get depleted through chemotherapy and the resistant population
becomes dominant
Bang-bang vs. Singular Solutions
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Numerical Simulations
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Numerical Simulations
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Numerical Simulations
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Numerical Simulations
0 20 40 60 80 100 120 140 1600
1
2
3
4
5
6
7
8
9
10x 10
4
time
popu
latio
ns R
and
S
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Tumor stimulating myeloid cell
Surveillance T-cell
Fibroblast
Endothelia Chemo-resistant tumor cell
Chemo-sensitive tumor cell
Tumor Microenvironment – Other Treatments
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Tumor Antiangiogenesis
avascular
growth angiogenesis
metastasis
http://www.gene.com/gene/research/focusareas/oncology/angiogenesis.html
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Tumor Anti-Angiogenesis
• suppress tumor growth by
preventing the recruitment of new
blood vessels that supply the
tumor with nutrients
indirect approach
• done by inhibiting the growth of
the endothelial cells that form the
lining of the new blood vessels
therapy “resistant to resistance”
Judah Folkman, 1972
• anti-angiogenic agents are
biological drugs (enzyme inhibitors
like endostatin) – very expensive
and with side effects
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Model [Hahnfeldt,Panigrahy,Folkman,
Hlatky],Cancer Research, 1999
p,q – volumes in mm3
Lewis lung
carcinoma
implanted
in mice
- tumor growth parameter
- endogenous stimulation (birth)
- endogenous inhibition (death)
- anti-angiogenic inhibition parameter
- natural death
p – tumor volume
q – carrying capacity
u – anti-angiogenic
dose rate
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For a free terminal time minimize
over all functions that satisfy
subject to the dynamics
Optimal Control Problem
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Singular Control
0 0.5 1 1.5 2 2.5 3-20
0
20
40
60
80
100
120
x
psi
feedback control
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0 0.5 1 1.5 2 2.5 3 3.5
x 104
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
carrying capacity of the vasculature, q
tum
or
volu
me,p
Admissible Singular Arc
q
p
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Synthesis of Optimal Controls
[LSch, SICON, 2007]
0 2000 4000 6000 8000 10000 12000 14000 16000 180000
2000
4000
6000
8000
10000
12000
14000
16000
18000
endothelial cells
tum
or c
ells
an optimal trajectory begin of
therapy
final point – minimum of p
end of “therapy”
p
q
u=a
u=0
typical synthesis: umax→s→0
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Some Practical Aspects
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An Optimal Controlled Trajectory
for [Hahnfeldt et al.]
Initial condition: p0 = 12,000 q0 = 15,000, umax=75
0 1 2 3 4 5 6 7
0
10
20
30
40
50
60
70
time
opti
mal
con
tro
l u
maximum dose rate
no dose
lower dose rate - singular
averaged optimal dose
u
q0
robust with respect to q0
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Minimum Tumor Volumes under Suboptimal
Constant Dose Protocols [LSch, JTB, 2008]
Values of the minimum tumor volume for a fixed initial tumor
volume as functions of the initial endothelial support
full dose
optimal control
averaged optimal dose
half dose
pmin
q0
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Response to Two Dose Protocols,
[LMMSch, Math. Med. &Biology, 2010]
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Optimal Daily Dosages
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Combination Therapy: Antiangiogenic
Treatment with Chemotherapy
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Minimize subject to
A Model for a Combination Therapy
[d’OLMSch, Mathematical Biosciences, 2009]
with d’Onofrio and H. Maurer
angiogenic inhibitors
cytotoxic agent or other killing term
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Optimal Protocols: what comes first?
4000 6000 8000 10000 12000 14000 16000
7000
8000
9000
10000
11000
12000
13000
carrying capacity of the vasculature, q
tum
or
volu
me,
p
optimal
angiogenic
monotherapy
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Controls and Trajectory [for dynamics from Hahnfeldt et al.]
0 1 2 3 4 5 6 7
0
10
20
30
40
50
60
70
time (in days)
dosag
e a
ng
io
4000 6000 8000 10000 12000 14000 16000
7000
8000
9000
10000
11000
12000
13000
carrying capacity of the vasculature, q
tum
or
vo
lum
e,
p
0 1 2 3 4 5 6 7-0.2
0
0.2
0.4
0.6
0.8
1
dosag
e c
he
mo
time (in days)
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Medical Aspect
Rakesh Jain,
Steele Lab, Harvard Medical School,
“there exists a therapeutic window
when changes in the tumor in response to
anti-angiogenic treatment may allow
chemotherapy to be particularly effective”
Connection between mathematical results
and experimental data ??
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Tumor Immune Interactions
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• Stepanova, Biophysics, 1980
Kuznetsov, Makalkin, Taylor and Perelson,
Bull. Math. Biology, 1994
de Vladar and Gonzalez, J. Theo. Biology, 2004,
d’Onofrio, Physica D, 2005
A Model for Tumor-Immune Interactions
renewed interest in the topic also in connection with
immune-dynamics and immuno-therapy
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Stepanova-Type Mathematical Models
for Tumor-Immune Dynamics
• STATE:
- primary tumor volume
- immunocompetent cell-density
(related to various types of T-cells)
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- tumor growth parameter - growth function
- rate at which cancer cells are eliminated through the activity of T-cells
- constant rate of influx of T-cells generated by primary organs
- natural death of T-cells
- calibrate the interactions between immune system and tumor
- threshold beyond which immune reaction becomes suppressed
by the tumor
Dynamical Model
[Stepanova]
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Growth Models on the Tumor Volume x
exponential growth
logistic growth
Gompertzian
Stepanova, 1980
Kuznetsov et al., 1994
de Vladar and
Gonzalez, 2004
d’Onofrio, 2005
L Sch Olumoye, 2013
F is positive, twice
continuously differentiable
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Phaseportrait for Gompertzian Model
asymptotically stable
focus – “good”,
benign equilibrium
[Kuznetsov et al., 1994
de Vladar et al., 2004]
saddle point
asymptotically stable
node – “bad”,
malignant equilibrium
multiple stable equilibrium points
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Phaseportrait of uncontrolled dynamics
• we want to move the state of the system into the region of
attraction of the benign equilibrium
minimize
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For a free terminal time T minimize
over all measurable functions and
subject to the dynamics
Optimal Control Problem (LSch, CDC 2012)
Chemotherapy – log-kill hypothesis
Immune boost
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Immunotherapy only
malignant region persists
Immunotherapy alone
is not successful in
this region
Chemotherapy is needed 0 100 200 300 400 500 600 700 800 900 1000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
x
y
Phase Portrait
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Dynamics revisited Write the system as
with
drift vector field
Lie bracket
control vector fields
g1 and g2 commute:
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Legendre-Clebsch Condition
for u (Chemotherapy)
The Legendre-Clebsch condition is satisfied if and only if
singular controls are locally optimal
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Suppose the control v is singular on an interval I . For optimality
we need that
But in this case
singular controls are maximizing, so not
optimal
the control v, i.e., immune boost, should
be bang-bang
Legendre-Clebsch Condition for Control v
(Immunotherapy)
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0 200 400 600 8000
0.5
1
1.5
2
2.5
3
0 2 4 6 8 10 12
0
0.2
0.4
0.6
0.8
1
Chemotherapy with Immune Boost [DCDSB, 2013]
• trajectory follows the optimal chemo monotherapy and provides final boosts
to the immune system and chemo at the end
• “cost” of immune boost is high and effects are low compared to chemo
* *
*
“free pass”
1s01 010
- chemo
- immune boost
* *
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Metronomics and Other
Alternatives to MTD
with Eddy Pasquier, CCIA, University of New South Wales
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The frequent administration of chemotherapy drugs at relatively low, non-toxic doses, without prolonged drug-free breaks (Hanahan et al., JCI 2000)
METRONOMICS =
Metronomic Chemotherapy + Drug Repositioning
Metronomic Chemotherapy
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Adapted from Pasquier et al., Nature Reviews Clinical Oncology, 2010
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Metronomic Chemotherapy: modeling challenge
• treatment at lower doses
( between 10% and 80% of the MTD)
• constant or not?
How is it administered?
Advantages (to be modelled):
1. lower, but continuous cytotoxic effects on tumor cells
• lower toxicity (in many cases, none)
• lower drug resistance and even resensitization effect
2. antiangiogenic effects
3. boost to the immune system
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How to optimize the anti-tumor, anti-angiogenic and pro-immune
effects of chemotherapy by modulating dose and administration
schedule?
Different therapeutic approaches:
- “Pure” metronomic / Metronomics
J Clin Oncol 2010
-Weekly VLB -Daily CPA -2x weekly MTX -Daily CLX
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How to optimize the anti-tumour, anti-angiogenic and pro-immune
effects of chemotherapy by modulating dose and administration
schedule?
Different therapeutic approaches:
- “Pure” metronomic / Metronomics
- MTD / Metronomic sequencing
(Bang-Bang-Metro, Metro-Bang-Bang…)
J Clin Oncol 2005
Lancet Oncol 2010
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How to optimize the anti-tumour, anti-angiogenic and pro-immune
effects of chemotherapy by modulating dose and administration
schedule?
Different therapeutic approaches:
- “Pure” metronomic / Metronomics
- MTD / Metronomic sequencing (Bang-Bang-
Metro, Metro-Bang-Bang…)
- Adaptive therapy
Cancer Research 2009
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Nature 2009
Nature 2009
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How to optimize the anti-tumour, anti-angiogenic and pro-immune
effects of chemotherapy by modulating dose and administration
schedule?
Different therapeutic approaches:
- “Pure” metronomic / Metronomics
- MTD / Metronomic sequencing (Bang-Bang-Metro,
Metro-Bang-Bang…)
- Adaptive therapy
- Chaos therapy
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Metronomics Global Health Initiative (MGHI)
http://metronomics.newethicalbusiness.org/
Nicolas Andre
Children’s Hospital of
Timone, France
Giannoula Klement
Tufts University School of
Medicine, Boston, USA
Eddy Pasquier
Children Cancer Institute
Australia Sydney, Australia
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