Santiago Daniel N, Heidbuechel Johannes P W, Kandell Wendy M, Walker Rachel, Djeu Julie, Engeland Christine E, Abate-Daga Daniel, Enderling Heiko
Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
Viruses. 2017 Aug 23;9(9):239. doi: 10.3390/v9090239.
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.
经过数十年的研究，溶瘤病毒疗法最近已推进到临床应用阶段，目前有多种新型药物和联合治疗方案正在接受癌症治疗评估。溶瘤药物优先在肿瘤细胞中复制，诱导肿瘤细胞裂解并产生复杂的抗肿瘤效应，如先天性和适应性免疫反应以及肿瘤血管的破坏。随着不同载体平台的出现以及基因工程和联合治疗方案在增强安全性和有效性特定方面的潜力，为患者亚群甚至个体患者确定最佳治疗方案成为当务之急。数学建模可以通过利用实验和临床数据来生成关于复杂生物学潜在机制的假设，并最终预测最佳治疗方案，从而在这一领域提供支持。越来越复杂的模型可用于考虑治疗相关参数，如免疫系统的组成部分。在本综述中，我们描述了溶瘤病毒疗法和数学建模的当前进展，以讨论将不同建模方法整合到生物学和临床实验中的益处。最后，我们建议将这些研究领域相互结合，以提高临床前开发的价值和最终治疗的疗效。
