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Evaluating the efficacies of maximum tolerated dose and metronomic chemotherapies: a mathematical approach. (English) Zbl 1400.92257

Summary: We present a mathematical model based on partial differential equations that is applied to understand tumor development and its response to chemotherapy. Our primary aim is to evaluate comparatively the efficacies of two chemotherapeutic protocols, Maximum Tolerated Dose (MTD) and metronomic, as well as two methods of drug delivery. Concerning therapeutic outcomes, the metronomic protocol proves more effective in prolonging the patient’s life than MTD. Moreover, a uniform drug delivery method combined with the metronomic protocol is the most efficient strategy to reduce tumor density.

MSC:

92C50 Medical applications (general)
35K51 Initial-boundary value problems for second-order parabolic systems
35Q92 PDEs in connection with biology, chemistry and other natural sciences
92C45 Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.)

References:

[1] Siegel, R.; Ward, E.; Brawley, O.; Jermal, A., Cancer statistics, 2011: the impact of eliminating socioeconomic and racial disparities on premature cancer deaths, CA Cancer J. Clin., 61, 4, 212-236 (2011)
[2] Murray, C. J.; Lopez, A. D., Alternative projections of mortality and disability by cause, 1990-2020: global burden of disease study, Lancet, 349, 9064, 1498-1504 (1997)
[4] Baruchel, S.; Stempak, D., Low-dose metronomic chemotherapy: myth or truth?, Onkologie, 29, 305-307 (2006)
[5] André, N.; Barbolosi, D.; Billy, F.; Chapuisat, G.; Hubert, F.; Grenier, E.; Rovini, A., Mathematical model of cancer growth controled by metronomic chemotherapies, ESAIM: Proceed, 41, 77-94 (2013) · Zbl 1336.92035
[6] Lien, K.; Georgsdottir, S.; Sivanathan, L.; Chan, K.; Emmenegger, U., Low-dose metronomic chemotherapy: a systematic literature analysis, Eur. J. Cancer, 49, 16, 3387-3395 (2013)
[7] Chaplain, M. A.J.; Matzavinos, A., Mathematical modelling of spatio-temporal phenomena in tumour immunology, (Tutorials in Mathematical Biosciences III, vol. 1872 (2006), Springer: Springer The Netherlands), 131-183
[8] Yang, H. M., Mathematical modeling of solid cancer growth with angiogenesis, Theor. Biol. Med. Model., 9, 2, 3-39 (2012)
[9] Reis, E. A.; Santos, L. B.L.; Pinho, S. T.R., A cellular automata model for avascular solid tumor growth under the effect of therapy, Physica A, 388, 7, 1303-1314 (2009)
[10] Ferreira, S. C.; Martins, M. L.; Vilela, M. J., A growth model for primary cancer, Physica A, 261, 569-580 (1998)
[11] Ferreira, S. C.; Martins, M. L.; Vilela, M. J., A growth model for primary cancer (ii). new rules, progress curves and morphology transitions, Physica A, 272, 245-256 (1999)
[12] Fister, K. R.; Panetta, J. C., Optimal control applied to competing chemotherapeutic cell-kill strategies, SIAM J. Appl. Math., 63, 6, 1954-1971 (2003) · Zbl 1058.92025
[13] Madhavi, K.; Rao, T.; Reddy, P. R.S., Optimal drug administration for cancer chemotherapy through stochastic programming, AJAMMS, 2, 1, 37-45 (2013)
[14] Ledzewicz, U.; Schattler, H.; Gahrooi, M. R.; Dehkordi, S. M., On the mtd paradigm and optimal control for multi drug cancer chemotherapy, Math. Biosci. Eng., 10, 3, 803-819 (2013) · Zbl 1268.92065
[15] Stamatakos, G. S.; Kolokotroni, E. A.; Dionysiou, D. D.; Georgiadi, E. C.; Desmedt, C., An advanced discrete state-discrete event multiscale simulation model of the response of a solid tumor to chemotherapy: mimicking a clinical study, J. Theoret. Biol., 266, 1, 124-139 (2010) · Zbl 1407.92067
[16] Martins, M. L.; Ferreira, S. C.; Vilela, M. J., Multiscale models for biological systems, Curr. Opin. Colloid Interface, 15, 18-23 (2010)
[17] Hahnfeldt, P.; Panigrahy, D.; Folkman, J.; Hlatky, L., Tumor development under angiogenic signaling: a dynamical theory of tumor growth, treatment response, and postvascular dormancy, Cancer Res., 59, 19, 4770-4775 (1999)
[18] d’Onofrio, A.; Gandolfi, A., Tumour eradication by antiangiogenic therapy: analysis and extensions of the model by Hahnfeldt et al. (1999), Math. Biosci., 191, 2, 159-184 (2004) · Zbl 1050.92039
[19] Pinho, S. T.R.; Bacelar, F. S.; Andrade, R. F.S.; Freedman, H. I., A mathematical model for the effect of anti-angiogenic therapy in the treatment of cancer tumours by chemotherapy, Nonlinear Anal. RWA, 14, 1, 815-828 (2013) · Zbl 1254.92049
[20] Phipps, C., Combination of chemotherapy and antiangiogenic therapies: a mathematical modelling approach (2009), University of Waterloo: University of Waterloo Waterloo, (Master’s thesis)
[21] Majumder, D., Tumor angiogenesis based analytical model for the assessment of MCT and MTD chemotherapeutic strategies in cancer, J. Biol. Syst., 18, 4, 749-761 (2010) · Zbl 1404.92099
[22] Benzekry, S.; André, N.; Benabdallah, A.; Ciccolini, J.; Faivre, C.; Hubert, F.; Barbolosi, D., Modelling the impact of anticancer agents on metastatic spreading, Math. Model. Nat. Phenom., 7, 1, 306-366 (2012) · Zbl 1320.92058
[23] Faivre, C.; Barbolosi, D.; Pasquier, E.; André, N., A mathematical model for the administration of temozolomide: comparative analysis of conventional and metronomic chemotherapy regimens, Cancer Chemother. Pharm., 71, 4, 1013-1019 (2013)
[25] Hanahan, D.; Bergers, G.; Bergsland, E., Less is more, regularly: metronomic dosing of cytotoxic drugs can target tumor angiogenesis in mice, J. Clin. Invest., 105, 8, 1045-1047 (2000)
[26] Kerbel, R. S.; Kamen, B. A., The anti-angiogenic basis of metronomic chemotherapy, Nat. Rev. Cancer, 4, 423-436 (2004)
[27] Rodrigues, D. S.; Mancera, P. F.A., Mathematical analysis and simulations involving chemotherapy and surgery on large human tumours under a suitable cell-kill functional response, Math. Biosci. Eng., 10, 1, 221-234 (2013) · Zbl 1259.92052
[28] Ribatti, D., Cancer stem cells and tumor angiogenesis, Cancer Lett., 321, 13-17 (2012)
[29] Bjerkvig, R.; Johansson, M.; Miletic, H.; Niclou, S. P., Cancer stem cells and angiogenesis, Sem. Cancer Biol., 19, 279-284 (2009)
[30] Ricci-Vitiani, L.; Pallini, R.; Biffoni, M.; Todaro, M.; Invernici, G.; Cenci, T.; Maira, G.; Parati, E. A.; Stassi, G.; Larocca, L. M.; De Maria, R., Tumour vascularization via endothelial differentiation of glioblastoma stem-like cells, Nature. Nature, Nature, 477, 238-828 (2011), (corrigendum)
[31] Bao, S.; Wu, Q.; Sathornsumetee, S.; Hao, Y.; Li, Z.; Hjelmeland, A. B.; Shi, Q.; McLendon, R. E.; Bigner, D. D.; Rich, J. N., Stem cell-like glioma cells promote tumor angiogenesis through vascular endothelial growth factor, Cancer Res., 66, 7843-7848 (2006)
[32] Anderson, A. R.A.; Chaplain, M. A.J., Continuous and discrete mathematical models of tumor-induced angiogenesis, Bull. Math. Biol., 60, 5, 857-899 (1998) · Zbl 0923.92011
[33] Martin, R.; Teo, K. L., Optimal Control of Drug Administration in Cancer Chemotherapy (1993), World Scientific: World Scientific Singapore · Zbl 0870.92006
[34] Burgess, P. K.; Kulesa, P. M.; Murray, J. D.; Alvord, E. C., The interaction of growth rates and diffusion coefficients in a three-dimensional mathematical model of gliomas, J. Neuropathol. Exp. Neurol., 56, 6, 704-713 (1997)
[35] Spratt, J. S.; Meyer, J. S.; Spratt, J. A., Rates of growth of human neoplasms: part II, J. Surg. Oncol., 61, 1, 68-83 (1996)
[36] Chaplain, M. A.J.; McDougall, S. R.; Anderson, A. R.A., Mathematical modeling of tumor-induced angiogenesis, Annu. Rev. Biomed. Eng., 8, 233-257 (2006)
[37] Weinberg, R. A., The Biology of Cancer (2006), Garland Science: Garland Science New York
[38] Friberg, S.; Mattson, S., On the growth rates of human malignant tumors: implications for medical decision making, J. Surg. Oncol., 65, 284-297 (1997)
[39] Robinson, P. J.; Rapoport, S. I., Model for drug uptake by brain tumors: effects of osmotic treatment and of diffusion in brain, J. Cereb. Blood Flow Metab., 10, 2, 153-161 (1990)
[40] Britton, N. F., Essential Mathematical Biology (2005), Springer-Verlag: Springer-Verlag London
[41] Fassoni, A. C.; Martins, M. L., Mathematical analysis of a model for plant invasion mediated by allelopathy, Ecol. Complexity, 18, 49-58 (2014)
[42] Jain, R. K., Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy, Science, 307, 5706, 58-62 (2005)
[43] Chauhan, V. P.; Martin, J. D.; Liu, H.; Lacorre, D. A.; Jain, S. R.; Kozin, S. V.; Stylianopoulos, T.; Mousa, A. S.; Han, X.; Adstamongkonkul, P.; Popović, Z.; Huang, P.; Bawendi, M. G.; Boucher, Y.; Jain, R. K., Angiotensin inhibition enhances drug delivery and potentiates chemoterapy by decompressing tumor blood vessels, Nature Commun., 4, 2516 (2013)
[44] Jain, R. K., Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers, J. Clin. Oncol., 31, 17, 2205-2218 (2013)
[45] Alarcón, T.; Owen, M. R.; Byrne, H. M.; Maini, P. K., Multiscale modelling of tumour growth and therapy: the influence of vessel normalisation on chemotherapy, Comput. Math. Methods Med., 7, 2-3, 85-119 (2006) · Zbl 1111.92023
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