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Nadia Alam
Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka
Bangladesh
Munira Sultana
Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka,
Bangladesh
M.S. Alam
Department of Applied Physics, Electronics and Communication Engineering, University of Dhaka
Bangladesh
M. A. Al-Mamun
Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle
United Kingdom
M. A. Hossain
Computational Intelligence Group, Faculty of Engineering and Environment, University of Northumbria at Newcastle
United Kingdom
Vol. 2 No. 2 (2013), Articles, pages 37-52
DOI: https://doi.org/10.14201/ADCAIJ2013253752
Accepted: Aug 31, 2013
Copyright

Abstract

In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA) is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD) and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

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References

ADAMS, R.A., MEADE A.M., SEYMOUR, M.T. et al. Intermittent versus continuous oxaliplatin and fluoropyrimidine combination chemotherapy for first-line treatment of advanced colorectal cancer: results of the randomized phase 3MRC COIN trial.

Lancet. Oncol., 12, pp-642–653, 2011.

ALAM, N., SULTANA, M., ALAM, M.S., et al. Chemotherapy Dose Schedule Optimization Using Genetic Algorithm.

Distributed Computing and Artificial Intelligence, Advances in Intelligent Systems and Computing, pp-503-511, Springer International Publishing, 2013.

ALGOUL, S., ALAM, M.S., HOSSAIN, M.A. et al. Multi-objective Optimal Chemotherapy Control Model for Cancer Treatment.

Springer Journal on MBEC, 49(1), pp-51-65, 2010. Springer-Verlag Berlin.

ALGOUL, S., ALAM, M.S., SAKIB, K. et al. MOGA-based Multi-drug Optimization for Cancer Chemotherapy.

AISC, 93, pp-133-140, 2011. Springer-Verlag Heidelberg.

Alam, M.S., Hossain M.A., Algoul, S., et al. Multi-Objective Multi-drug Scheduling Schemes for Cell Cycle Specific Cancer Treatment.

International Journal of Computers & Chemical Engineering, 58, pp-14–32, 2013.

Al-Mamun M.A., Kazmi, N., Hossain, M. A., Vickers, P., and Jiang, Y., An intelligent decision support system for personalized cancer treatment. In: CIS2012: 11th IEEE Conference on Cybernetic Intelligent Systems, 23-24 August 2012, Limerick, Ireland

Al-Mamun, M.A., Hossain, M.A., Alam, M.S., Bass, R., A Cellular automaton model of the effects of maspin on cell migration. Advances in Intelligent Systems and Computing Volume, 222, pp 53-60.

CLARE S.E., NAKHLIS F., PANETTA J.C. Molecular biology of breast cancer metastasis: The use of mathematical models to determine relapse and to predict response to chemotherapy in breast cancer. Breast Cancer Res. 2 (6), 2000, pp-430–435.

DEB, K. Introduction to evolutionary multiobjective optimization.

Multiobjective Optimization, pp. 59-96, 2008. Springer-Verlag Berlin Heidelberg

HARROLD, J.M. Model–based design of cancer chemotherapy treatment schedules.

Ph.D. Thesis, 2005, University of Pittsburgh, USA.

HOLLAND, J.H. Adaptation in natural and artificial system.

University of Michigan Press, 1975. USA

KIRAN, K., JAYACHANDRAN, D., LAKSHMINARAYANAN, S. Multi-objective Optimization of Cancer Immuno-Chemotherapy.

In Proc. ICBME, Springer, pp-1337–1340, 2008.

KAUFMAN, D.C., CHABNER, B.A. Clinical strategies for cancer treatment: the role of drug.

In Chabner, B.A., Longo, D. L. (eds.). Cancer Chemotherapy and Biotherapy: Principles and Practice, Lippincott, Williams and Wilkins, pp-1-16, 2001/

Kazmi, N., Hossain, MA., Phillips, RM., Al-Mamun, MA., Bass, R.,. Avascular tumour growth dynamics and the constraints of protein binding for drug transportation. J Theor Biol. Nov 21;313:142-52.

Kazmi, N., Hossain, MA., Phillips, RM., A hybrid cellular automaton model of solid tumor growth and bioreductive drug transport. IEEE/ACM Trans Comput Biol Bioinform. Nov-Dec;9(6):1595-606

MARTIN, R. B., FISHER, M. E, MINCHIN, R. F., TEO, K. L. (1990). A mathematical model of cancer chemotherapy with an optimal selection of parameters.

Math. Biosci., 99, pp-205–230.

MARTIN, R, TEO, K.L. Optimal Control of Drug Administration in Chemotherapy Tumor Growth.

World Scientific, 1994. River Edge, NJ, USA.

MCCALL, J., PETROVSKI, A. A decision support system for cancer chemotherapy using genetic algorithms. In Proceedings of the international conference on computational intelligence for modeling, control and automation, pp-65-70, 1999.

MCCALL, J., PETROVSKI, A. Multi-objective optimization of cancer chemotherapy using evolutionary algorithms. Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, 1993, pp-531-545, 2001, Springer-Verlag Berlin Heidelberg.

MCCALL, J., PETROVSKI, A. SUDHA B. Optimizing cancer chemotherapy using particle swarm optimization and genetic algorithms. In Proceedings of the 8th international conference on parallel problem solving from nature, Lecture notes in computer science. Springer, Berlin, 3242, pp-633–641, 2004.

MCCALL, J., PETROVSKI, A., SHAKYA, A. Evolutionary algorithms for cancer chemotherapy optimization. In Fogel, G.B., Corne, D.W., Pan Y. (eds.). Computational Intelligence in Bioinformatics, John Wiley & Sons, Inc., pp-265–296, 2008. Hoboken, NJ, USA.

NORTON, L. A Gompertzian model of human breast cancer growth.

Cancer Res., 48, pp-7067– 7071.

[SLINGERLAND, J.M. et al. 1998] SLINGERLAND, J.M., TANNOCK, I.F. Cell Proliferation and Cell Death.

The Basic Science of Oncology, McGraw-Hill, 1998, pp-134–165.

SWAN, G. W. Cancer chemotherapy: Optimal control using the Verhulst–Pearl equation.

Bull. Math. Biol., 48(3/4), pp-381–404, 1986.

THURSTON, D.E. Chemistry and pharmacology of anticancer drugs.

CRC Press, 2006. Boca Raton, Florida, USA

URQUHART J., KLERK E.D. Contending paradigms for the interpretation of data on patient compliance with therapeutic drug regimens.

Stat. Med., 17, 1998, pp-251–267.