Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T00:51:34.709Z Has data issue: false hasContentIssue false

A critical virus production rate for efficiency of oncolytic virotherapy

Published online by Cambridge University Press:  08 May 2020

YOUSHAN TAO
Affiliation:
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, P.R. China. email: taoys@sjtu.edu.cn
MICHAEL WINKLER
Affiliation:
Institut für Mathematik, Universität Paderborn, 33098 Paderborn, Germany. email: michael.winkler@math.uni-paderborn.de

Abstract

In a planar smoothly bounded domain $\Omega$ , we consider the model for oncolytic virotherapy given by

$$\left\{ \begin{array}{l} u_t = \Delta u - \nabla \cdot (u\nabla v) - uz, \\[1mm] v_t = - (u+w)v, \\[1mm] w_t = d_w \Delta w - w + uz, \\[1mm] z_t = d_z \Delta z - z - uz + \beta w, \end{array} \right.$$
with positive parameters $ D_w $ , $ D_z $ and $\beta$ . It is firstly shown that whenever $\beta \lt 1$ , for any choice of $M \gt 0$ , one can find initial data such that the solution of an associated no-flux initial-boundary value problem, well known to exist globally actually for any choice of $\beta \gt 0$ , satisfies
$$u\ge M \qquad \mbox{in } \Omega\times (0,\infty).$$
If $\beta \gt 1$ , however, then for arbitrary initial data the corresponding is seen to have the property that
$$\liminf_{t\to\infty} \inf_{x\in\Omega} u(x,t)\le \frac{1}{\beta-1}.$$
This may be interpreted as indicating that $\beta$ plays the role of a critical virus replication rate with regard to efficiency of the considered virotherapy, with corresponding threshold value given by $\beta = 1$ .

Type
Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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

Alzahrani, T., Eftimie, R. & Trucu, D. (2019) Multiscale modelling of cancer response to oncolytic viral therapy. Math. Biosci. 310, 7695.CrossRefGoogle ScholarPubMed
Bischoff, J. R., Kirn, D. H., Williams, A., Heise, C., Horn, S., Muna, M., Ng, L., Nye, J. A., Sampson-Johannes, A., Fattaey, A. & McCormick, F. (1996) An adenovirus mutant that replicates selectively in p53-deficient human tumor cells. Science 274, 373376.CrossRefGoogle ScholarPubMed
Camara, B. I., Mokrani, H. & Afenya, E. (2013) Mathematical modelling of glioma therapy using oncolytic viruses. Math. Biosci. Eng. 10(3), 565578.Google ScholarPubMed
Cao, X. (2016) Boundedness in a three-dimensional chemotaxis-haptotaxis system. Z. Angew. Math. Phys. 67, 11.CrossRefGoogle Scholar
Coffey, M. C., Strong, J. E., Forsyth, P. A. & Lee, P. W. K. (1998) Reovirus therapy of tumors with activated Ras pathways. Science 282, 13321334.CrossRefGoogle Scholar
Corrias, L., Perthame, B. & Zaag, H. (2003) A chemotaxis model motivated by angiogenesis. C. R. Math. Acad. Sci. Paris 336, 141146.CrossRefGoogle Scholar
Fontelos, M. A., Friedman, A. & Hu, B. (2002) Mathematical analysis of a model for the initiation of angiogenesis. SIAM J. Math. Anal. 33, 13301355.CrossRefGoogle Scholar
Ganly, I., Kirn, D., Eckhardt, G., Rodriguez, G.I., Soutar, D. S., Otto, R., Robertson, A. G., Park, O., Gulley, M. L., Heise, C.,Von Hoff, D. D., Kaye, S. B. & Eckhardt, S. G. (2000) A phase I study of Onyx-015, an E1B-attenuated adenovirus, administered intratumorally to patients with with recurrent head and neck cancer. Clinical Cancer Res. 6, 798806 Google ScholarPubMed
Heise, C., Sampson-Johannes, A., Williams, A., McCormick, F.,Von Hoff, D. D. & Kirn, D.H. (1997) ONYX-015, an E1B gene-attenuated adenovirus, causes tumor-specific cytolysis and antitumoral efficacy that can be augmented by standard chemotherapeutic agents. Nature Med. 3, 639645.CrossRefGoogle ScholarPubMed
Jackson, T. L. & Byrne, H. M. (2000) A mathematical model to study the effects of drug resistance and vasculature on the response of solid tumors to chemotherapy. Math. Biosci. 164, 1738.CrossRefGoogle Scholar
Jacobsen, K., Russell, L., Kaur, B. & Friedman, A. (2015) Effects of CCN1 and macrophage content on glioma virotherapy: a mathematical model. Bull. Math. Biol. 77(6), 9841012.CrossRefGoogle ScholarPubMed
Jain, R. (1994) Barriers to drug delivery in solid tumors. Sci. Am. 271, 5865.CrossRefGoogle ScholarPubMed
Lawler, S., Speranza, M., Cho, C. & Chiocca, E. (2017) Oncolytic viruses in cancer treatment: a review. JAMA Oncol. 3(6), 841849.CrossRefGoogle ScholarPubMed
Li, Y. & Lankeit, J. (2016) Boundedness in a chemotaxis-haptotaxis model with nonlinear diffusion. Nonlinearity 29, 15641595.CrossRefGoogle Scholar
Liţcanu, G. & Morales-Rodrigo, C. (2010) Asymptotic behavior of global solutions to a model of cell invasion. Math. Models Methods Appl. Sci. 20, 17211758.CrossRefGoogle Scholar
Malinzi, J., Sibanda, P. & Mambili-Mamboundou, H. (2015) Analysis of virotherapy in solid tumor invasion. Math. Biosci. 263, 102110.CrossRefGoogle ScholarPubMed
Martuza, R. L., Malick, A., Markert, J. M., Ruffner, K. L. & Coen, D. M. (1991) Experimental therapy of human glioma by means of a genetically engineered virus mutant. Science 252, 854856.CrossRefGoogle ScholarPubMed
Morales-Rodrigo, C. & Tello, J. I. (2014) Global existence and asymptotic behavior of a tumor angiogenesis model with chemotaxis and haptotaxis. Math. Models Methods Appl. Sci. 24, 427464.CrossRefGoogle Scholar
Nemunaitis, J., Ganly, I., Khuri, F., Arseneau, J., Kuhn, J., McCarty, T., Landers, S., Maples, P., Romel, L., Randley, B., Reid, T., Kaye, S. & Kirn, D. (2000) Selective replication and oncolysis in p53 mutant tumors with ONYX-015, an E1B-55kD gene-deleted adenovirus, in patients with advanced head and neck cancer: a phase II trial. Cancer Res. 60, 63596366.Google ScholarPubMed
Pang, P. Y. H. & Wang, Y. (2018) Global boundedness of solutions to a chemotaxis-haptotaxis model with tissue remodeling. Math. Mod. Meth. Appl. Sci. 28, 22112235.CrossRefGoogle Scholar
Rodriguez, R., Schuur, E. R., Lim, H. Y., Henderson, G. A., Simons, J. W. & Henderson, D. R. (1997) Prostateattenuatedreplicationcompetentadenovirus(ARCA)CN706: aselective cytotoxic for prostate-specific antigen-positive prostate cancer cells. Cancer Res. 57, 25592563.Google Scholar
Russell, S. J., Peng, K.-W. & Bell, J. C. (2012) Oncolytic virotherapy. Nature Biotechnol. 30, 658670.CrossRefGoogle ScholarPubMed
Swabb, E. A., Wei, J. & Gullino, P. M. (1974) Diffusion and convection in normal and neoplastic tissues. Cancer Res. 34, 28142822.Google ScholarPubMed
Tao, Y. & Winkler, M. (2014) Energy-type estimates and global solvability in a two-dimensional chemotaxis-haptotaxis model with remodeling of non-diffusible attractant. J. Diff. Eq. 257, 784815.CrossRefGoogle Scholar
Tao, Y. & Winkler, M. (2015) Large time behavior in a mutidimensional chemotaxis-haptotaxis model with slow signal diffusion. SIAM J. Math. Anal. 47, 42294250.CrossRefGoogle Scholar
Tao, Y. & Winkler, M. (2020) Global classical solutions to a doubly haptotactic cross-diffusion system modeling oncolytic virotherapy. J. Diff. Eq. 268, 49734997.CrossRefGoogle Scholar
Tao, Y. & Winkler, M. (to appear) Critical mass for infinite-time blow-up in a haptotaxis system with nonlinear zero-order interaction. Discr. Cont. Dyn. Syst. A. doi: 10.3934/dcds.2020216.Google Scholar
Vähä-Koskela, M. & Hinkkanen, A. (2014) Tumor restrictions to oncolytic virus. Biomedicines 2(2), 163194.CrossRefGoogle ScholarPubMed
Walker, C. & Webb, G. F. (2007) Global existence of classical solutions for a haptotaxis model. SIAM J. Math. Anal. 38, 16941713.CrossRefGoogle Scholar
Ward, J. P. & King, J. R. (2003) Mathematical modelling of drug transport in tumour multicelll spheroids and monolayer cultures. Math. Biosci. 181, 177207.CrossRefGoogle ScholarPubMed
Winkler, M. (2010) Aggregation vs. global diffusive behavior in the higher-dimensional Keller-Segel model. J. Diff. Eq. 248, 28892905.Google Scholar
Winkler, M. (2018) Singular structure formation in a degenerate haptotaxis model involving myopic diffusion. J. Math. Pures Appl. 112, 118169.CrossRefGoogle Scholar
Wodarz, D. (2001) Viruses as antitumor weapons: defining conditions for tumor remission. Cancer Res. 61, 35013507.Google ScholarPubMed
Wodarz, D., Hofacre, A., Lau, J. W., Sun, Z., Fan, H. & Komarova, N. L. (2012) Complex spatial dynamics of oncolytic viruses in vitro: mathematical and experimental approaches. PLoS Comput. Biol. 8(6), (15pp).CrossRefGoogle ScholarPubMed
Wong, H., Lemoine, N. & Wang, Y. (2010) Oncolytic viruses for cancer therapy: overcoming the obstacles. Viruses 2(1), 78106.CrossRefGoogle ScholarPubMed
Wu, J. T., Byrne, H. M., Kirn, D. H. & Wein, L. M. (2001) Modeling and analysis of a virus that replicates selectively in tumor cells. Bull. Math. Biol. 63, 731768.CrossRefGoogle ScholarPubMed
Wu, J. T., Kirn, D. H. & Wein, L. M. (2004) Analysis of a three-way race between tumor growth, a replication-competent virus and an immune response. Bull. Math. Biol. 66, 605625.CrossRefGoogle Scholar
Yoon, S. S., Carroll, N. M., Chiocca, E. A. & Tanabe, K. K. (1998) Cancer gene therapy using a replication-competent herpes simplex virus type I vector. Ann. Surg. 228, 366374.CrossRefGoogle Scholar
Zhigun, A., Surulescu, C. & Hunt, A. (2018) A strongly degenerate diffusion-haptotaxis model of tumour invasion under the go-or-grow dichotomy hypothesis. Math. Methods Appl. Sci. 41, 24032428.Google Scholar
Zhigun, A., Surulescu, C. & Uatay, A. (2016) Global existence for a degenerate haptotaxis model of cancer invasion. Z. Angew. Math. Phys. 67, Art. 146, 29 pp.CrossRefGoogle Scholar