Fighting Cancer with Mathematics and Viruses.
Fighting Cancer with Mathematics and Viruses.
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DOI：
10.3390/v9090239
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发表时间：
2017-08-23
期刊：
影响因子：
--
通讯作者：
Enderling H
中科院分区：
文献类型：
--
作者：
Santiago DN;Heidbuechel JPW;Kandell WM;Walker R;Djeu J;Engeland CE;Abate-Daga D;Enderling H
经过几十年的研究，溶瘤病毒疗法最近已进入临床应用，目前正在评估多种新型药物和联合治疗用于癌症治疗。溶瘤剂优先在肿瘤细胞中复制，诱导肿瘤细胞裂解和复杂的抗肿瘤作用，例如先天性和适应性免疫应答以及肿瘤血管的破坏。随着不同载体平台的可用性以及基因工程和组合方案增强安全性和有效性的特定方面的潜力，确定患者亚群或甚至个体患者的最佳治疗成为最优先考虑的问题。数学建模可以在这个竞技场中提供支持，通过利用实验和临床数据来生成关于复杂生物学基础机制的假设，并最终预测最佳治疗方案。越来越复杂的模型可以应用于解释治疗相关的参数，如免疫系统的组成部分。在这篇综述中，我们描述了溶瘤病毒治疗和数学建模的最新进展，讨论了将不同的建模方法整合到生物和临床实验中的好处。最后，我们提出了这些研究领域的相互结合，以增加临床前开发的价值和所得治疗的疗效。
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.
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DOI：
10.3390/v8100294
发表时间：
2016-10-22
期刊：
Viruses
影响因子：
--
作者：
Aref S;Bailey K;Fielding A
通讯作者：
Fielding A
DOI：
10.1073/pnas.1703355114
发表时间：
2017-08-01
影响因子：
11.1
作者：
Barish, Syndi;Ochs, Michael F.;Gevertz, Jana L.
通讯作者：
Gevertz, Jana L.
影响因子：
12.4
作者：
Bridle, Byram W.;Boudreau, Jeanette E.;Wan, Yonghong
通讯作者：
Wan, Yonghong
影响因子：
5
作者：
Anacker DC;Moody CA
通讯作者：
Moody CA
影响因子：
11.2
作者：
Anderson, BD;Nakamura, T;Peng, KW
通讯作者：
Peng, KW