Fighting cancer with mathematics and viruses

• Verfasst von: Santiago, Daniel [VerfasserIn]
• Verfasst von: Heidbüchel, Johannes P. W. [VerfasserIn]
• Verfasst von: Engeland, Christine Elisabeth [VerfasserIn]
• Titel: Fighting cancer with mathematics and viruses
• Verf.angabe: Daniel N. Santiago, Johannes P.W. Heidbuechel, Wendy M. Kandell, Rachel Walker, Julie Djeu, Christine E. Engeland, Daniel Abate-Daga, and Heiko Enderling
• E-Jahr: 2017
• Jahr: 23 August 2017
• Umfang: 26 S.
• Fussnoten: Artikel veröffentlicht im "Special issue: Mathematical modeling of virus infections" ; DOI funktioniert nicht ; Gesehen am 23.08.2018
• Titel Quelle: Enthalten in: Viruses
• Ort Quelle: Basel : MDPI, 2009
• Jahr Quelle: 2017
• Band/Heft Quelle: 9(2017,9), Artikel-Nummer 239, 26 Seiten
• ISSN Quelle: 1999-4915
• DOI: doi:10.3390/v9090239
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: cancer
• Sach-SW: combination therapy
• Sach-SW: immune system
• Sach-SW: immunotherapy
• Sach-SW: mathematical model
• Sach-SW: oncolytic virotherapy
• K10plus-PPN: 1580325831

Abstract

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.