Assessment of the efficient strategies for applying antitumor viral vaccine therapy based on mathematical modeling
Matematičeskaâ biologiâ i bioinformatika, Tome 14 (2019) no. 1, pp. 34-53

Voir la notice de l'article provenant de la source Math-Net.Ru

The paper presents the mathematical description of the two stages of tumor cells’ death as a result of immune response after antitumor viral vaccine introduction. This mathematical description is presented by the system of nonlinear equations implemented in the MatLab-Simulink system. As a result of the computing experiment, two strategies for effective application of the antitumor viral vaccine were identified. The first strategy leads to complete elimination of the tumor cells after a single-shot administration of the vaccine. The second strategy makes it possible to stabilize tumor size through the recurrent introductions of the vaccine. Using the mathematical model of antitumor therapy, appropriate dosages were identified based on the number of tumor cells that die at the two stages of immune response. Dynamics of tumor growth for the two strategies of the viral vaccine application was forecasted based on the mathematical model of antitumor therapy with discontinuous trajectories of tumor growth. The computing experiments made it possible to identify initial tumor size at the start of the therapy and the dosages that allow complete elimination of the tumor cells after the single-shot introduction. For the second strategy, dosages and intervals between recurrent vaccine introductions required to stabilize tumor size at the initial level were also identified. The proposed approach to exploring the effectiveness of vaccine therapy may be applied to different types of experimental tumors and antitumor vaccines.
@article{MBB_2019_14_1_a7,
     author = {N. A. Babushkina and E. A. Kuzina and A. A. Loos and E. V. Belyaeva},
     title = {Assessment of the efficient strategies for applying antitumor viral vaccine therapy based on mathematical modeling},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {34--53},
     publisher = {mathdoc},
     volume = {14},
     number = {1},
     year = {2019},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2019_14_1_a7/}
}
TY  - JOUR
AU  - N. A. Babushkina
AU  - E. A. Kuzina
AU  - A. A. Loos
AU  - E. V. Belyaeva
TI  - Assessment of the efficient strategies for applying antitumor viral vaccine therapy based on mathematical modeling
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2019
SP  - 34
EP  - 53
VL  - 14
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2019_14_1_a7/
LA  - ru
ID  - MBB_2019_14_1_a7
ER  - 
%0 Journal Article
%A N. A. Babushkina
%A E. A. Kuzina
%A A. A. Loos
%A E. V. Belyaeva
%T Assessment of the efficient strategies for applying antitumor viral vaccine therapy based on mathematical modeling
%J Matematičeskaâ biologiâ i bioinformatika
%D 2019
%P 34-53
%V 14
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2019_14_1_a7/
%G ru
%F MBB_2019_14_1_a7
N. A. Babushkina; E. A. Kuzina; A. A. Loos; E. V. Belyaeva. Assessment of the efficient strategies for applying antitumor viral vaccine therapy based on mathematical modeling. Matematičeskaâ biologiâ i bioinformatika, Tome 14 (2019) no. 1, pp. 34-53. http://geodesic.mathdoc.fr/item/MBB_2019_14_1_a7/

[1] A. Ya. Mutsenietse, Onkotropizm virusov i problema viroterapii zlokachestvennykh opukholei, Riga, 1972, 435 pp.

[2] A. Yu. Gromova, Protivoopukholevye svoistva vaktsinnogo shtamma virusa venesuelskogo entsefalomielita i ego onkolizata, dis. ... kand. biol. nauk, Sankt-Peterburg, 1999, 114 pp. | Zbl | Zbl

[3] L. N. Urazova, Effektivnost i mekhanizmy protivoopukholevogo deistviya virusnykh vaktsin pri eksperimentalnom onkogeneze, dis. ... d-ra. biol. nauk, Sankt-Peterburg, 2003, 196 pp.

[4] I. G. Vidyaeva, Virusnye vaktsiny i ikh onkolizaty v terapii eksperimentalnykh opukholei, dis. ... kand. med. nauk, Tomsk, 2005, 134 pp.

[5] V. B. Loktev, T. Yu. Ivankina, S. V. Netesov, P. M. Chumakov, “Onkoliticheskie parvovirusy. Novye podkhody k lecheniyu rakovykh zabolevanii”, Vestnik Rossiiskoi akademii meditsinskikh nauk, 2 (2012), 42–47

[6] Yu. N. Lezhnin, Yu. E. Kravchenko, E. I. Frolova, P. M. Chumakov, S. P. Chumakov, “Onkotoksicheskie belki v protivorakovoi terapii: Mekhanizmy deistviya”, Molekulyarnaya biologiya, 49:2 (2015), 264–278

[7] G. Hristov, M. Krämer, J. Li, N. El-Andaloussi, R. Mora, L. Daeffler, H. Zentgraf, J. Rommelaere, A. Marchini, “Through its non-structural protein NS1, parvovirus H-1 induces apoptosis via accumulation of reactive oxygen species”, Journal of Virology, 84:12 (2010), 5909–5922 | DOI | DOI

[8] J. Rommelaere, K. Geletneky, A. L. Angelova, L. Daeffler, C. Dinsart, I. Kiprianova, J. R. Schlehofer, Z. Raykov, “Oncolytic parvoviruses as cancer therapeutics”, Cytokine Growth Factor Reviews, 21:2 (2010), 185–195 | DOI | DOI

[9] S. F. Cotmore, P. Tattersall, “Parvoviral hostrangeand cell entry mechanisms”, Advances in Virus Research, 70 (2007), 183–232 | DOI | DOI

[10] S. P. Grekova, M. Aprahamian, L. Daeffler, B. Leuchs, A. Angelova, T. Giese, A. Galabov, A. Heller, N. A. Giese, J. Rommelaere, Z. Raykov, “Interferon improves the vaccination potential of oncolytic parvovirus H-1PV for the treatment of peritoneal carcinomatosis in pancreatic cancer”, Cancer Biology Therapy, 12:10 (2011), 888–895 | DOI | DOI

[11] Z. Raykov, S. Grekova, A. S. Galabov, G. Balboni, U. Koch, M. Aprahamian, J. Rommelaere, “Combined oncolytic and vaccination activities of parvovirus H-1 in a metastatic tumor model”, Oncology Reports, 17:6 (2007), 1493–1500

[12] A. L. Angelova, M. Aprahamian, G. Balboni, H. J. Delecluse, R. Feederle, I. Kiprianova, S. Grekova, A. Galabov, M. Witzens-Harig, A. D. Ho, J. Rommelaere, Z. Raykov, “Oncolytic rat parvovirus H-1PV, a candidate for the treatment of human lymphoma: In vitro and in vivo studies”, Molecular Therapy, 17:7 (2009), 1164–1172 | DOI | DOI

[13] L. De Pillis, A. Radunskaya, C. Wiseman, “A Validated Mathematical Model of Cell-Mediated Immune Response to Tumor Growth”, Cancer Res., 65 (2005), 7950–7958 | DOI | DOI

[14] L. De Pillis, A. Gallegos, A. Radunskaya, “A model of dendritie cell therapy for melanoma”, Front. Oncol., 3 (2013), 56–77

[15] E. Kose, S. Moore, C. Ofodile, A. Radunskaya, R. Ellen, “Immuno-kinetics of immunotherapy: dosing with DCs”, Letters in Biomathematics, 4:1 (2017), 39–58 | DOI | MR | DOI | MR

[16] R. Kim, T. Woods, A. Radunskaya, “Mathematical Modeling of Tumor Immune Interactions: A Closer Look at a PD-L1 Inhibitor in Cancer Immunotherapy”, SPORA: A Journal of Biomathematics, 4:1 (2018), 25–41

[17] N. A. Babushkina, “Otsenka upravlyayuschikh dozovykh vozdeistvii protivoopukholevoi vaktsinoterapii s pomoschyu matematicheskogo modelirovaniya”, Problemy upravleniya, 5 (2013), 60–65

[18] N. A. Glumov V. M. Babushkina, “Matematicheskoe modelirovanie mekhanizmov protivoopukholevogo deistviya virusnykh vaktsin”, Trudy XI Mezhdunarodnoi. nauchnoi konferentsii «Fizika i radioelektronika v meditsine i ekologii», FREME-2014 (Rossiya, Suzdal), v. 1, VGU, Vladimir, 2014, 153–158

[19] N. A. Babushkina, V. M. Glumov, E. A. Kuzina, “Primenenie kompyuternykh tekhnologii pri eksperimentalnom izuchenii effektivnosti protivoopukholevykh virusnykh vaktsin”, Trudy XII Mezhdunarodnoi nauchnoi konferentsii «Fizika i radioelektronika v meditsine i ekologii», FREME-2016 (Rossiya, Suzdal), v. 1, VGU, Vladimir, 2016, 116–121

[20] N. A. Babushkina, V. M. Glumov, E. A. Kuzina, “Primenenie matematicheskogo modelirovaniya dlya otsenki effektivnosti metoda protivoopukholevoi terapii”, Problemy upravleniya, 3 (2017), 49–56

[21] N. Babushkina, E. Kuzina, “Analytical study of the antitumor viral vaccine introduction regimens based on mathematical modeling”, Advances in Systems Science and Applications, 18:1 (2018), 59–84

[22] G. I. Marchuk, Matematicheskie modeli v immunologii. Vychislitelnye metody i eksperimenty, Nauka, M., 1991, 304 pp.

[23] N. V. Pertsev, “Globalnaya razreshimost i otsenki reshenii zadachi Koshi dlya funktsionalno-differentsialnykh uravnenii s zapazdyvaniem, ispolzuemykh v modelyakh zhivykh sistem”, Sibirskii matematicheskii zhurnal, 59:1 (2018), 143–157 | MR | Zbl | MR | Zbl

[24] A. A. Romanyukha, Matematicheskie modeli v immunologii i epidemiologii infektsionnykh zabolevanii, BINOM. Laboratoriya znanii, M., 2011, 293 pp.

[25] H. F. Skipper, “Kinetics of mammary tumor cell-growth and implications for therapy”, Cancer, 28:6 (1971), 1479–1499 | 3.0.CO;2-M class='badge bg-secondary rounded-pill ref-badge extid-badge'>DOI | DOI

[26] S. V. Rusakov, M. V. Chirkov, “Matematicheskaya model vliyaniya immunoterapii na dinamiku immunnogo otveta”, Problemy upravleniya, 6 (2012), 45–50

[27] S. V. Rusakov, M. V. Chirkov, “Identifikatsiya parametrov i upravlenie v matematicheskikh modelyakh immunnogo otveta”, Rossiiskii zhurnal biomekhaniki, 18:2 (2014), 259–269