Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy

Artificial Intelligence in Medicine

Volumn 50, Issue 3, 2010, Pages 163-173

  Villasana, Minaya ^a   Ochoa, Gabriela ^b   Aguilar, Soraya ^c  

^a UNIVERSIDAD SIMÓN BOLÍVAR   (Venezuela)

^b UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^c UNIVERSIDAD DE CARABOBO   (Venezuela)

Author keywords

Cancer chemotherapy; Combined chemotherapy; Cytostatic drug; Cytotoxic drug; Delay differential equation system; Evolution strategies; Evolutionary algorithms; Singular optimal control; Tumor model

Indexed keywords

CANCER CHEMOTHERAPY; COMBINED CHEMOTHERAPY; CYTOSTATIC; CYTOTOXIC DRUGS; DELAY DIFFERENTIAL EQUATIONS; EVOLUTION STRATEGIES; SINGULAR OPTIMAL CONTROL; TUMOR MODEL;

CHEMOTHERAPY; CONTROL; COVARIANCE MATRIX; DECISION SUPPORT SYSTEMS; DIFFERENTIAL EQUATIONS; DRUG INTERACTIONS; ENCODING (SYMBOLS); IMMUNOLOGY; MATHEMATICAL MODELS; OPTIMIZATION; TUMORS;

EVOLUTIONARY ALGORITHMS;

CYTOSTATIC AGENT; CYTOTOXIC AGENT; GEFITINIB; PACLITAXEL;

ANTINEOPLASTIC ACTIVITY; ARTICLE; CANCER CHEMOTHERAPY; CELL CYCLE ARREST; CELL CYCLE PHASE; CELL CYCLE PROGRESSION; CYTOTOXICITY; DRUG EFFICACY; EVOLUTIONARY ALGORITHM; MATHEMATICAL MODEL; PRIORITY JOURNAL; TREATMENT OUTCOME;

ANTINEOPLASTIC AGENTS; HUMANS; MODELS, THEORETICAL; NEOPLASMS;

EID: 78149465757     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2010.05.009     Document Type: Article

References (32)

1. Baxevanis C.N., Perez S.A., Papamichail M. Combinatorial treatments including vaccines, chemotherapy and monoclonal antibodies for cancer therapy. Cancer Immunology Immunotherapy 2009, 58(3):317-324.
2. Kumar S., Gao L., Yeagy B., Reid T. Virus combinations and chemotherapy for the treatment of human cancers. Current Opinion in Molecular Therapeutics 2008, 10(4):371-379.
3. Park J.K., Lee S., Kang J., Nishio K., Saijo N., Kuh H. Synergistic interaction between gefitinib (iressa zd1839) and paclitaxel against human gastric carcinoma cells. Anticancer Drugs 2004, 15(8):809-819.
4. Villasana M., Radunskaya A. A delay differential equation model for tumor growth. Journal of Mathematical Biology 2003, 47:270-294.
5. Villasana M., Ochoa G. Heuristic design of cancer chemotherapies. IEEE Transactions on Evolutionary Computation 2004, 8(6):513-521.
6. Ochoa G., Villasana M., Burke E. An evolutionary approach to cancer chemotherapy. Genetic Programming and Evolvable Machines 2007, 8(4):301-318.
7. Panetta J., Adam J. A mathematical model of cycle-specific chemotherapy. Mathematical and Computer Modelling 1995, 22(2):67-82.
8. de Pillis L., Radunskaya A. A mathematical tumor model with immune resistance and drug therapy: an optimal control approach. Journal of Theoretical Medicine 2001, 3:79-100.
9. de Pillis L. The dynamics of an optimally controlled tumnor model: a case study. Mathematical and Computer Modeling 2003, 37(11):1221-1244.
10. Petrovski A., McCall J. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. Evolutionary multi-criterion optimization, First international conference, EMO 2001, Proceedings, vol. 1993 of LNCS 2001, 531-545. Springer, Berlin. E. Zitzler, K. Deb, L. Thiele, C.A.C. Coello, D. Corne (Eds.).
11. Petrovski A., Sudha B., McCall J. Optimising cancer chemotherapy using particle swarm optimisation and genetic algorithms. Parallel problem solving from nature-PPSN VIII, vol. 3242 of LNCS 2004, 633-641. Springer, Berlin.
12. Petrovski A., Shakya S., McCall J. Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms. Genetic and evolutionary computation conference, GECCO 2006 2006, vol. 1:413-418. ACM Press, New York.
13. McCall J., Petrovski A., Shakya S. Computational intelligence in bioinformatics 2007, Wiley, Hoboken, NJ, p. 263-96, Chapter 15.
14. Martin R.B. Optimal control drug scheduling of cancer chemotherapy. Automatica 1992, 28(6):1113-1123.
15. Tse S.-M., Liang Y., Leung K.-S., Lee K.-H., Mok T.-S.-K. A memetic algorithm for multiple drugs cancer chemotherapy schedule optimization. IEEE Transcations on Systems, Man, and Cybernetics-Part B: Cybernetics 2007, 37(1):84-91.
16. Liang Y., Leung K.-S., Mok T.-S.-K. A novel evolutionary drug scheduling model in cancer chemotherapy. IEEE Transactions on Information Technology in Biomedicine 2006, 10(2):237-245.
17. Swierniak A., Polanski A., Kimmel M. Optimal control problem arising in cell-cycle-specific cancer chemotherapy. Cell Proliferation 1996, 29:117-139.
18. Hardman J., Limbird L. Goodman and Gilman's the pharmacological basis of therapeutics 1996, McGraw Hill, Health Profession Division, New York. 9th ed.
19. Ciardiello F., Caputo R., Bianco R., Damiano V., Pomatico G., De Placido S. Antitumor effect and potentiation of cytotoxic drugs activity in human cancer cells by zd-1839 (iressa), an epidermal growth factor receptor-selective tyrosine kinase inhibitor. Clinical Cancer Research 2000, 6(5):2053-2063.
20. Piechocki M.P, Yoo G.H., Dibbley S.K., Amjad E.H., Lonardo F. Iressa induces cytostasis and augments fas-mediated apoptosis in acinic cell adenocarcinoma overexpressing her2/neu. International Journal of Cancer 2006, 119:441-454.
21. KIP1 and induces g1 arrest in oral squamous cell carcinoma cell lines. Oral Oncology 2004, 40:43-51.
22. Zoli W, Flamigni A., Frassineti G., Bajorko P., De Paola F., Milandri C.E.A. In vitro activitity of taxol and toxotere in comparison with doxorubicin and cisplatin on primary cell cultures of human breast cancers. Breast Cancer Research and Treatment 1995, 34:63-69.
23. Chuang L., Lotzova E., Cook K., Cristoforoni P., Morris M., Wharton T. Effect of new investigational drug taxol on oncolytic activity and stimulation of human lymphocytes. Gynecologic Oncology 1993, 49:291-298.
24. Luus R. Iterative dynamic programming vol. 110 of monographs and surveys in pure and applied mathematics 2000, Chapman and Hall, Boca Raton.
25. Eiben A., Smith J. Introduction to evolutionary computing 2003, Springer-Verlag, Berlin.
26. Hansen N., Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 2001, 9(2):159-195.
27. Hansen N., Kern S. Evaluating the CMA evolution strategy on multimodal test functions. Parallel problem solving from nature PPSN VIII, vol. 3242 of LNCS 2004, 282-291. Springer, Berlin.
28. Auger A., Hansen N. A restart CMA evolution strategy with increasing population size. The 2005 IEEE international congress on evolutionary computation (CEC'05) 2005, vol. 2:1777-1784. IEEE Press, Piscataway, NJ.
29. Auger A., Hansen N. Performance evaluation of an advanced local search evolutionary algorithm. The 2005 IEEE international congress on evolutionary computation (CEC'05) 2005, vol. 2:1769-1776. IEEE Press, Piscataway, NJ.
30. Rechenberg I. Evolution strategy: optimization of technical systems by means of biological evolution 1973, Fromman-Holzboog, Stuttgart.
31. Hansen N. The CMA evolution strategy 1996, Website, http://www.lri.fr/hansen/cmaesintro.html.
32. Hansen N. The CMA evolution strategy: a comparing review. Vol. 192 of towards a new evolutionary computation. Advances on estimation of distribution algorithms 2006, Springer, Berlin, p. 75-102, Chapter 4.