Controlling the Evolution of Resistance.
Controlling the Evolution of Resistance.
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DOI：
10.1016/j.jprocont.2010.11.010
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发表时间：
2011-03-01
影响因子：
4.2
通讯作者：
Zurakowski, Ryan
中科院分区：
文献类型：
--
作者：
Luo, Rutao;Cannon, LaMont;Hernandez, Jason;Piovoso, Michael J.;Zurakowski, Ryan
长期以来，进化一直被理解为许多医学问题的驱动力。艾滋病毒和细菌感染的耐药性演变被认为是医学上最重要的新兴问题之一。在癌症治疗中，化疗药物耐药性的演变往往是有效治疗和疾病进展或死亡的区别因素。到目前为止，管理耐药性演变的干预措施一直基于对突变和选择模型的稳态分析。本文综述了数学方法在研究抗病进化中的应用。我们对进化的数学模型的几个经典应用进行了广泛的回顾，并深入回顾了最近的两个问题，这两个问题证明了利用抗性进化模型的动态行为进行干预的潜力。第一个问题涉及艾滋病毒连续治疗失败的问题；我们回顾了我们最近解决这一问题的出版物。第二个问题涉及胰腺癌基因治疗的新方法，其中选择被用来鼓励易感基因通过目标肿瘤的最佳传播，然后在第二个治疗阶段被根除。我们回顾了最近在这一主题上的体外实验室工作，提出了一个新的数学模型来描述治疗过程，并说明了为什么基于模型的方法将是成功实施这一新的和有前途的方法所必需的。
Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in Vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach.
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影响因子：
7.6
作者：
Colgrove, R;Japour, A
通讯作者：
Japour, A
影响因子：
5.4
作者：
KORBER, BTM;KUNSTMAN, KJ;WOLINSKY, SM
通讯作者：
WOLINSKY, SM
DOI：
10.1073/pnas.0501870102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Komarova, NL;Wodarz, D
通讯作者：
Wodarz, D
影响因子：
9.7
作者：
Ishida, Daisuke;Nawa, Akihiro;Kikkawa, Fumitaka
通讯作者：
Kikkawa, Fumitaka
定向进化产生了一种新型的溶瘤病毒，用于治疗结肠癌。
影响因子：
3.7
作者：
Kuhn, Irene;Harden, Paul;Bauzon, Maxine;Chartier, Cecile;Nye, Julie;Thorne, Steve;Reid, Tony;Ni, Shaoheng;Lieber, Andre;Fisher, Kerry;Seymour, Len;Rubanyi, Gabor M.;Harkins, Richard N.;Hermiston, Terry W.
通讯作者：
Hermiston, Terry W.