(509f) An Automated System for the Design of Optimal Personalized Chemotherapy Protocols for the Treatment of Leukemia

Authors: 
Velliou, E., Imperial College
Pefani, E., Imperial College of London
Panoskaltsis, N., Northwick Park Hospital
Fuentes, M., Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London
Mantalaris, A., Centre for Process Systems Engineering, Imperial College
Georgiadis, M. C., Imperial College
Pistikopoulos, S. N., Imperial College London



An
automated system for the design of optimal personalized chemotherapy protocols
for the treatment of Leukemia.

E. Pefania,
N. Panoskaltsisb, E. Vellioua,
M. Fuentesa, A. Mantalarisa,
M.C. Georgiadisa, E.N. Pistikopoulosa

aCentre for
Process Systems Engineering, Department of Chemical Engineering, Imperial
College London, South Kensington Campus
,
London SW7 2AZ, UK

b
Department of Hematology, Imperial College London,
Northwick Park & St. Mark's Campus, London, HA1 3UJ, UK

Keywords: Mathematical modeling,
chemotherapy optimization, cell cycle models, pharmacokinetics, pharmacodynamics

In the UK (Cancer Research UK data, 2008), it is
estimated that more than 1 in 3 people will be afflicted with cancer in their
lifetime. For one such cancer, leukemia - a neoplasm of the blood and bone marrow (BM) -
1 in 71 men and 1 in 105 women will be affected, with incidence sharply rising
in adults over the age of 50. Approximately 40% of those affected with leukemia
will have Acute Myeloid Leukemia (AML), a cancer of the BM and blood wherein blood cells are unable to develop or function
normally, are overproduced at an immature stage of development and overtake any
normal elements remaining in the BM and blood. This uncontrolled growth
compounds the morbidity and mortality due to the disease by inhibiting
development of healthy blood and immune cells through multiple mechanisms (Panoskaltsis 2003; Panoskaltsis,
2005).

The most common treatment for most types of leukemia
is intensive chemotherapy. It is well-known that this therapy can be
life-threatening with only relatively few patient-specific and
leukaemia-specific factors are considered in current protocols; choice of
chemotherapy, intensity and duration often depends on either the availability
of a clinical trial, the treating physician's experience or the collective
experience of the treating centre, with significant international protocol
variability.

There is therefore a
need to optimize current treatment schedules for cancers such as AML to limit
toxicities and to improve clinical trial pathways for new drugs to enable
personalized healthcare. Towards this end, mathematical modeling is undoubtedly a useful tool for the
automation of treatment design due its advantages in exploring extensive
datasets in combination with scientific principles in order to capture a
systems dynamics and subsequently provide better insight for process
enhancement (Dua et. al, 2008; Pefani et. al,
2013).

In this work, the prospective advances to the clinical treatment
design through a mathematical model are discussed. A mathematical model and model analysis is presented for
induction chemotherapy treatment for AML using Daunorubicin
(DNR) and Cytarabine (Ara-C)
anti-leukemic agents, a standard intensive treatment protocol for AML. The proposed model combines critical
targets of drug actions on the cell cycle, together with pharmacokinetic (PK)
and pharmacodynamic (PD) aspects providing a complete
description of drug diffusion and action after administration. The current developed model has been
created in the gPROMs environment (PSE, 2003) and
consists of a model simulator and an optimizer comprised of a closed-loop
system for the design of optimal personalized chemotherapy protocols (Figure
1).

Fig 1.jpg

Figure SEQ Figure \* ARABIC 1: Schematic representation of the proposed closed loop system for the design of optimal personalized chemotherapy protocols.

 

The
required inputs for both the simulator and the optimizer consist of patient,
disease and drug information. Patient information includes gender, age, weight
and height, whereas, disease information is that acquired as standard practice
from the BM diagnostic test ? leukemic blast percentage in the BM aspirate and
the BM cellularity from the trephine biopsy. Extra
information to that which is currently used in clinical practice will be the
cell cycle characteristics of the S-phase duration and the total cell cycle
duration. Moreover, PK information of drug elimination rate in liver and
kidneys is required and is readily available in the product specification provided
by the pharmaceutical manufacturer. PD information is also required that consists
of drug dependent parameters characterising the effect of the anticancer agent
on the cell population.

Results of sensitivity analysis indicate that
the cell cycle times are the critical model parameters affecting treatment
outcome. Clinical data of 6 patients are used in order to estimate the cell
cycle time distribution and characterise the intra- and inter- patient variability
on cycling times. Moreover, the derived mathematical model is used as an application
for an optimization problem. The benefits of optimization are presented and an
optimization problem is developed for chemotherapy treatment schedule. The objective of chemotherapy
treatment in AML is the reduction of cancer cells to eventual eradication and
to ensure that more normal cells remain in the BM.  Moreover, the number of normal cells is
constrained to have a maximum total reduction of 3-log during one chemotherapy
cycle.  An optimization algorithm is
presented in the current work for the scheduling of the optimal treatment design based on the control
drug use, dose load and dose duration. This optimization problem is solved for one of
the patient case studies (P016). P016 received two chemotherapy cycles of
treatment and by treatment completion residual disease was detected. For this
reason optimization is applied and the optimal treatment schedule for this
specific case study is derived. At
completion of the optimized treatment protocol, the leukemic cell population is
further minimized with a difference of 3·109 cells
when compared to that of the standard treatment protocol. Furthermore, at
treatment completion the normal cell population is also lower than that prior
to treatment, but it is still higher than the leukemic cell count with a
difference of 5.3·109 cells, as is the desired treatment outcome.

Model analysis reveals the utility of
mathematical modeling in gaining better insight into
the disease and into normal tissue dynamics during treatment with chemotherapy.
There is future potential for the amelioration of  treatment design that will be defined on a
case-by-case basis and would be dependent on disease characteristics (tumor-specific characteristics, tumor
burden, cell cycle times, normal cell population) as well as patient-specific
characteristics (gender, age, weight and height). This design would provide the
opportunity to personalize treatment protocols through the use of optimization
methods.

Acknowledgment

This
work is supported by European Research Council (MOBILE, ERC Advanced Grant, No:
226462), the Richard Thomas Leukaemia Fund and CPSE Industrial Consortium.

References

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P., Dua, V., Pistikopoulos,
E., 2008.
Optimal delivery of chemotherapeutic agents in cancer.
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gPROMS, Introductory user's
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Panoskaltsis N. Dendritic cells in MDS and AML ?
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Pefani E., Panoskaltsis N., Mantalaris A., Georgiadis M. C., Pistikopoulos
E. N.. Design of optimal patient-specific chemotherapy protocols for the
treatment of Acute Myeloid Leukaemia (AML). Computers and Chemical Engineering
Journal, In Press, Available Online 1 March 2013.