Literature DB >> 12614566

Gene therapy for killing p53-negative cancer cells: use of replicating versus nonreplicating agents.

Dominik Wodarz1.   

Abstract

Research has focused on the use of viral vectors to attack p53-negative cancer cells. Such agents may be nonreplicating, whereas others are replicating. This paper uses mathematical models to study the conditions under which therapy can lead to tumor remission. It is found that the optimal characteristics of the vector can be quite different depending on whether the virus replicates or not. If it does not replicate, the rate of virus-induced tumor cell killing should be maximized. If the virus does replicate, the rate of virus-induced cell killing should be kept small. If the virus is too lytic in cancer cells, viral spread is compromised, resulting in persistence of both virus and tumor. This has important implications for choosing the correct techniques to evaluate replicating viruses in culture. A low multiplicity of infection must be used for evaluation, because this mimicks the spread of the virus through an established tumor. If a high multiplicity of infection is used, the virus that appears most efficient in this evaluation can be least efficient at eradicating the cancer in vivo. Theoretical results are discussed in the context of experimental data.

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Year:  2003        PMID: 12614566     DOI: 10.1089/104303403321070847

Source DB:  PubMed          Journal:  Hum Gene Ther        ISSN: 1043-0342            Impact factor:   5.695


  19 in total

1.  Perfusion Pressure Is a Critical Determinant of the Intratumoral Extravasation of Oncolytic Viruses.

Authors:  Amber Miller; Rebecca Nace; Camilo Ayala-Breton C; Michael Steele; Kent Bailey; Kah Whye Peng; Stephen J Russell
Journal:  Mol Ther       Date:  2015-12-09       Impact factor: 11.454

2.  Use of oncolytic viruses for the eradication of drug-resistant cancer cells.

Authors:  Dominik Wodarz
Journal:  J R Soc Interface       Date:  2009-02-06       Impact factor: 4.118

3.  In Vivo Estimation of Oncolytic Virus Populations within Tumors.

Authors:  Mi-Yeon Jung; Chetan P Offord; Matthew K Ennis; Iris Kemler; Claudia Neuhauser; David Dingli
Journal:  Cancer Res       Date:  2018-08-16       Impact factor: 12.701

4.  Pharmacodynamics of non-replicating viruses, bacteriocins and lysins.

Authors:  James J Bull; Roland R Regoes
Journal:  Proc Biol Sci       Date:  2006-11-07       Impact factor: 5.349

5.  Dynamics of melanoma tumor therapy with vesicular stomatitis virus: explaining the variability in outcomes using mathematical modeling.

Authors:  D M Rommelfanger; C P Offord; J Dev; Z Bajzer; R G Vile; D Dingli
Journal:  Gene Ther       Date:  2011-09-15       Impact factor: 5.250

6.  ODE models for oncolytic virus dynamics.

Authors:  Natalia L Komarova; Dominik Wodarz
Journal:  J Theor Biol       Date:  2010-01-18       Impact factor: 2.691

7.  Chemotherapy in conjoint aging-tumor systems: some simple models for addressing coupled aging-cancer dynamics.

Authors:  Mitra S Feizabadi; Tarynn M Witten
Journal:  Theor Biol Med Model       Date:  2010-06-15       Impact factor: 2.432

8.  Dynamics of multiple myeloma tumor therapy with a recombinant measles virus.

Authors:  D Dingli; C Offord; R Myers; K-W Peng; T W Carr; K Josic; S J Russell; Z Bajzer
Journal:  Cancer Gene Ther       Date:  2009-06-05       Impact factor: 5.987

9.  Mathematical modeling of tumor therapy with oncolytic viruses: regimes with complete tumor elimination within the framework of deterministic models.

Authors:  Artem S Novozhilov; Faina S Berezovskaya; Eugene V Koonin; Georgy P Karev
Journal:  Biol Direct       Date:  2006-02-17       Impact factor: 4.540

10.  Towards predictive computational models of oncolytic virus therapy: basis for experimental validation and model selection.

Authors:  Dominik Wodarz; Natalia Komarova
Journal:  PLoS One       Date:  2009-01-30       Impact factor: 3.240

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