Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-19T01:48:27.632Z Has data issue: false hasContentIssue false

A Computational Framework to Assess the Efficacy of CytotoxicMolecules and Vascular Disrupting Agents against Solid Tumours

Published online by Cambridge University Press:  25 January 2012

M. Pons-Salort
Affiliation:
UJF-Grenoble 1, CNRS, Laboratory TIMC-IMAG UMR 5525 DyCTiM research team, 38041 Grenoble, France
B. van der Sanden
Affiliation:
INSERM U836, Grenoble Institut des Neurosciences, UJF-Grenoble 1 CHU Michallon, 38042 Grenoble, France
A. Juhem
Affiliation:
Ecrins therapeutics, BIOPOLIS, 38700 La Tronche, France
A. Popov
Affiliation:
Ecrins therapeutics, BIOPOLIS, 38700 La Tronche, France
A. Stéphanou*
Affiliation:
UJF-Grenoble 1, CNRS, Laboratory TIMC-IMAG UMR 5525 DyCTiM research team, 38041 Grenoble, France
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

A computational framework for testing the effects of cytotoxic molecules, specific to agiven phase of the cell cycle, and vascular disrupting agents (VDAs) is presented. Themodel is based on a cellular automaton to describe tumour cell states transitions fromproliferation to death. It is coupled with a model describing the tumour vasculature andits adaptation to the blood rheological constraints when alterations are induced by VDAstreatment. Several therapeutic protocols in two structurally different vascular networkswere tested by varying the duration of cytotoxic drug perfusion and the periodicity oftreatment cycles. The impact of VDAs were also tested both experimentally from intravitalmicroscopy through a dorsal skinfold chamber on a mouse and numerically. Simulationresults show that combining cytotoxic treatment with a post treatment of VDA through ajudicious timing could favour the rapid eradication of the tumour. The computationalframework thus gives some insights into the outcome of cytotoxic and VDAs treatments on aqualitative basis. Future validation from our experimental setup could open up newperspectives towards Computer-Assisted Therapeutic Strategies.

Type
Research Article
Copyright
© EDP Sciences, 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alarcon, T., Byrne, H., Maini, P.K.. A cellular automaton model for tumour growth in inhomogeneous environment. J. Theor. Biol., 225 (2003), No. 2, 25774. CrossRefGoogle ScholarPubMed
Alarcon, T., Byrne, H., Maini, P.K.. A mathematical model of the effects of hypoxia on the cell-cycle of normal and cancer cells. J. Theor. Biol., 229 (2004), No. 3, 395411. CrossRefGoogle ScholarPubMed
Altinok, A., Gonze, D., Lévi, F., Goldbeter, A.. An automaton model for the cell cycle. Interface Focus, 1 (2011), 3647. CrossRefGoogle ScholarPubMed
Anderson, A.R.A., Rejniak, K.A., Gerlee, P., Quaranta, V.. Modelling of cancer growth, evolution and invasion : bridging scales and models. Math. Mod. Nat. Phenom., 2 (2007), No. 3, 129. CrossRefGoogle Scholar
Baguley, B.C., Siemann, D.W.. Temporal aspects of the action of ASA404 (vadimezan ; DMXAA). Expert Opin. Investig. Drugs., 19 (2010), No. 11, 141325. CrossRefGoogle Scholar
Byrne, H.M.. Dissecting cancer through mathematics : from cell to the animal model. Nat. Rev. Cancer, 10 (2010), 22130. CrossRefGoogle ScholarPubMed
Carmeliet, P.. Angiogenesis in life, disease and medicine. Nature, 438 (2005), No. 7070, 9326. CrossRefGoogle Scholar
Casciari, J.J., Sotirchos, S.V., Sutherland, R.M.. Variations in tumor cell growth rates and metabolism with oxygen concentration, glucose concentration, and extracellular PH. J. Cell Physiol., 151 (1992), No. 2, 38694. CrossRefGoogle ScholarPubMed
d’Onofrio, A., Gandolfi, A.. Chemotherapy of vascularised tumours : role of vessel density and the effect of vascular "pruning". J. Theor. Biol., 264 (2010), 25365. CrossRefGoogle ScholarPubMed
Eichholz, A., Merchant, S., Gaya, A.M.. Anti-angiogenesis therapies : their potential in cancer management. OncoTragets and Therapy, 3 (2010), 6982. Google ScholarPubMed
Folkman, J.. Tumor angiogenesis : therapeutic implications. N. Engl. J. Med., 285 (1971), No. 21, 11826. Google ScholarPubMed
Freyer, J.P., Tustanoff, E., Franko, A.J., Sutherland, R.M.. In situ oxygen consumption rates of cells in V-79 multicellular spheroids during growth. J. Cell Physiol., 118 (1984), 5361. CrossRefGoogle ScholarPubMed
Freyer, J.P., Sutherland, R.M.. Regulation of growth saturation and development of necrosis in EMT6/Ro multicellular spheroids by the glucose and oxygen supply. Cancer Res., 46 (1986), 35043512. Google ScholarPubMed
Gevertz, J.L.. Computational modeling of tumor response to vascular-targeting therapies - part I : validation. Comput. Math. Methods Med., (2011), 830515. CrossRefGoogle ScholarPubMed
Grote, J., Süsskind, R., Vaupel, P.. Oxygen diffusivity in tumor tissue (DS-carcinosarcoma) under temperature conditions within the range of 20-40 degrees C. Pflugers Arch., 372 (1977), No. 1, 3742. CrossRefGoogle ScholarPubMed
Honstvet, C.A.. Targeting tumour vasculature as a cancer treatment. Comp. Math. Meth. Med., 8 (2007), No. 1, 19. CrossRefGoogle Scholar
Hoshino, T., Wilson, C.B., Rosenblum, M.L., Barker, M.J.. Chemotherapeutic implications of growth fraction and cell cycle time in glioblastomas. Neurosurg., 43 (1975), 12735. CrossRefGoogle ScholarPubMed
Jain, R.K.. Normalizing tumor vasculature with anti-angiogenic therapy : a new paradigm for combination therapy. Nat. Med., 7 (2001), No. 9, 9879. CrossRefGoogle ScholarPubMed
Lippert, J.W.. Vascular disrupting agents. Bioorg. Med. Chem., 15 (2007), 2, 60515. CrossRefGoogle ScholarPubMed
Lowengrub, J.S., Frieboes, H.B., Jin, F., Chuang, Y.L., Li, X., Macklin, P., Wise, S.M., Cristini, V.. Nonlinear modelling of cancer : bridging the gap between cells and tumours. Nonlinearity, 23 (2010), R1R91. CrossRefGoogle ScholarPubMed
Macklin, P., McDougall, S.R., Anderson, A.R.A., Chaplain, M.A.J., Cristini, V., Lowengrub, J.. Multiscale modelling and nonlinear simulation of vascular tumour growth. J. Math. Biol., 58 (2009), No. 4-5, 76598. CrossRefGoogle Scholar
Maurin, M., Stéphan, O., Vial, J.C., Marder, S.R., van der Sanden, B.. Deep in vivo Two-Photon Imaging of Blood Vessels with a new Dye encapsulated in Pluronic Nanomicelles. J. Biomed. Opt., 16 (2011), 036001. CrossRefGoogle ScholarPubMed
McDougall, S.R., Anderson, A.R.A., Chaplain, M.A.J.. Mathematical modelling of dynamic adaptative tumour-induced angiogenesis : clinical implications and therapeutic targeting strategies. J. Theor. Biol., 241 (2006), 56489. CrossRefGoogle Scholar
McDougall, S.R., Chaplain, M.A.J., Stéphanou, A., Anderson, A.R.A. Modelling the impact of pericyte migration and coverage of vessels on the efficacy of vascular disrupting agents. Math. Mod. Nat. Phenom., 5 (2010), No. 1, 163202. CrossRefGoogle Scholar
Morimura, T.. Prolongation of G1 phase in cultured glioma cells by cis-dichlorodiammineplatinum (II) (CDDP) : Analysis using bromodeoxyuridine (BrdU)-Hoechst technique. J. Neuro-Oncol., 7 (1989), 7179. CrossRefGoogle ScholarPubMed
Nugent, L.J., Jain, R.K.. Extravascular diffusion in normal and neoplastic tissues. Cancer Res., 44 (1984), No. 1, 23844. Google ScholarPubMed
Osborne, J.M., Walter, A., Kershaw, S.K., Mirams, G.R., Fletcher, A.G., Pathmanathan, P., Gavaghan, D., Jensen, O.E., Maini, P.K., Byrne, H.M.. A hybrid approach to multi-scale modelling of cancer. Philos. Transact. A Math. Phys. Eng., 368 (2010), No. 1930, 501328. CrossRefGoogle Scholar
Owen, M.R., Stamper, I.J., Muthana, M., Richardson, G.W., Dobson, J., Lewis, C.E., Byrne, H.M. Mathematical modeling predicts synergistic antitumour effects of comibining a macrophage-based, hypoxia-targeted gene therapy with chemotherapy. Cancer. Res., 71 (2011), No. 8, 282637. CrossRefGoogle Scholar
Pàez-Ribes, M., Allen, E., Hudock, J., Takeda, T., Okuyama, H., Viñals, F., Inoue, M., Bergers, G., Hanahan, D., Casanovas, O.. Antiangogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis. Cancer Cell., 15 (2009), No. 3, 22031. CrossRefGoogle Scholar
Panovska, J., Byrne, H.M., Maini, P.K.. A theoretical study of the response of vascular tumours to different types of chemotherapy. Math. Comp. Mod., 47 (2008), 56079. CrossRefGoogle Scholar
Perfahl, H., Byrne, H.M., Chen, T., Estrella, V., Alarcon, T., Lapin, A., Gatenby, R.A., Gillies, R.J., Lloyd, M.C., Maini, P.K., Reuss, M., Owen, M.R.. Multiscale modelling of vascular tumour growth in 3D : the roles of the domain size and boundary conditions. PLoS ONE, 6 (2011), No. 4, e14790. CrossRefGoogle ScholarPubMed
Pertuiset, B., Dougherty, D., Cromeyer, C., Hoshino, T., Berger, M., Rosenblum, M.L. J. Stem cell studies of human malignant brain tumours. Part 2 : proliferation kinetics of brain-tumour cells in vitro in early-passage cultures. Neurosurg., 63 (1985), 42632. CrossRefGoogle ScholarPubMed
Rehman, F., Rustin, G.. ASA404 : update on drug development. Expert Opin. Investig. Drugs, 17 (2008), No. No. 10, 15471551. CrossRefGoogle ScholarPubMed
Rockne, R., Rockhill, J.K., Mrugula, M., Spence, A.M., Kalet, I., Hendrickson, K., Lai, A., Cloughesy, T., Alvord, E.C. Jr, Swanson, K.R.. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo : a mathematical modeling approach. Phys. Med. Biol., 55 (2010), 327185. CrossRefGoogle Scholar
Shipley, R.J., Chapman, S.J.. Multiscale modelling of fluid and drug transport in vascular tumours. Bull. Math. Biol., 72 (2010), No. 6, 146491. CrossRefGoogle ScholarPubMed
Siemann, D.W., Mercer, E., Lepler, S., Rojiani, A.M.. Vascular targeting agents enhance chemotherapeutic agent activities in solid tumor therapy. Int. J. Cancer, 99 (2002), 16. CrossRefGoogle ScholarPubMed
Siemann, D.W., Horsman, M.R.. Enhancement of radiation therapy by vascular targeting agents. Curr. Opin. Investig. Drugs, 3 (2002), 16605. Google ScholarPubMed
Siemann, D.W., Horsman, M.R.. Vascular targeted therapies in oncology. Cell Tissue Res. 335 (2009), No. 1, 241248. CrossRefGoogle Scholar
Stamatakos, G.S., Antipas, V.P., Uzunoglu, N.K., Dale, R.G.. A four-dimensional computer simulation model of the in vivo response to radiotherapy of glioblastoma multiforme : studies on the effect of clonogenic cell density. The British Journal of Radiology, 79 (2006), 389400. CrossRefGoogle Scholar
Stéphanou, A., McDougall, S.R., Anderson, A.R.A, Chaplain, M.A.J.. Mathematical modelling of flow in 2d and 3d vascular networks : applications to anti-angiogenic and chemotherapeutic drug strategies. Math. Comp. Mod., 41 (2005), No. 10, 113756. CrossRefGoogle Scholar
Stéphanou, A., McDougall, S.R., Anderson, A.R.A, Chaplain, M.A.J.. Mathematical modelling of the influence of blood rheological properties upon adaptative tumour-induced angiogenesis. Math. Comp. Mod., 44 (2006), No. 1-2, 96123. CrossRefGoogle Scholar
Swabb, E.A., Wei, J., Gullino, P.M.. Diffusion and convection in normal and neoplastic tissues. Cancer Res., 34 (1974), No. 10, 281422. Google ScholarPubMed
Swanson, K.R., Alvord, E.C., Murray, J.D.. Quantifying efficacy of chemotherapy of brain tumours with homogeneous and heterogeneous drug delivery. Acta. Biotheor., 50 (2002), No. 4, 22337. CrossRefGoogle ScholarPubMed
Tanaka, G., Hirata, Y., Goldenberg, S.L., Bruchovsky, N., Aihara, K.. Mathematical modelling of prostate cancer growth and its application to hormone therapy. Phil. Trans. R. Soc. A, 368 (2010), 502944. CrossRefGoogle ScholarPubMed
Tozer, G.M., Kanthou, C., Baguley, B.C.. Disrupting tumour blood vessels. Nat. rev. Cancer, 5 (2005), No. 6, 42335. CrossRefGoogle ScholarPubMed
Tracqui, P.. Biophysical models of tumour growth. Rep. Prog. Phys., 72 (2009), No. 5, 056701. CrossRefGoogle Scholar
Tyson, J.T., Novak, B.. Temporal organization of the cell cycle. Curr. Biol., 18 (2008), No. 17, R759R768. CrossRefGoogle ScholarPubMed
Wang, B., Rosano, J.M., Cheheltani, R., Achary, M.P., Kiani, M.F.. Towards a targeted multi-drug delivery approach to improve therapeutic efficacy in breast cancer. Expert Opin. Drug Deliv., 7 (2010), No. 10, 115973. CrossRefGoogle ScholarPubMed