Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies.
Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies.
复制标题
在连续和脉冲给药策略期间对靶向抗癌疗法的抵抗力的演变。
DOI：
10.1371/journal.pcbi.1000557
复制
发表时间：
2009-11
影响因子：
4.3
通讯作者：
Michor F
中科院分区：
文献类型：
--
作者：
Foo J;Michor F
靶向特定致癌途径的小分子的发现彻底改变了抗癌疗法。然而，这种疗法往往由于获得性耐药性的演变而失败。临床癌症研究中一个长期存在的问题是确定最佳的治疗给药策略，以便将耐药风险降至最低。在本文中，我们研究了最佳药物给药方案，以防止或至少延迟耐药性的出现。我们设计和分析了一个随机数学模型，描述了治疗过程中肿瘤细胞群体的进化动力学。我们考虑由于单一（epi）遗传改变而出现的耐药性，并计算在特定给药策略期间出现耐药性的概率。然后，我们优化治疗方案，使耐药的风险最小，同时考虑药物的毒性和副作用作为限制。我们的方法可用于确定最佳药物给药方案，以避免任何癌症和治疗类型的一种（epi）遗传改变所产生的耐药性。近年来，靶向治疗的发现使抗癌治疗领域发生了一场革命，靶向治疗是指靶向导致癌细胞异常生长的特定途径的化合物。这些药物的临床成功受到对这些化合物的获得性耐药性的演变的限制，这导致对治疗的初始反应后复发。目前的给药程序不是为了最佳地延迟耐药性的出现而设计的;确定这种最佳给药方案是临床癌症研究中的一个重要挑战。在这里，我们设计了一种新的方法来确定最佳的药物管理策略，达到这一临床目标。我们的模型描述了治疗过程中肿瘤细胞群的进化动力学。我们考虑由于单一（epi）遗传改变而出现的耐药性，并计算在特定给药策略期间出现耐药性的概率。然后，我们优化治疗方案，使耐药的风险最小，同时考虑药物的毒性和副作用作为限制。由于这种方法也可以扩展到描述细胞毒性化疗给药期间出现的情况，因此它可以用于确定最佳药物给药方案，以避免对任何癌症和治疗类型的耐药性。
The discovery of small molecules targeted to specific oncogenic pathways has revolutionized anti-cancer therapy. However, such therapy often fails due to the evolution of acquired resistance. One long-standing question in clinical cancer research is the identification of optimum therapeutic administration strategies so that the risk of resistance is minimized. In this paper, we investigate optimal drug dosing schedules to prevent, or at least delay, the emergence of resistance. We design and analyze a stochastic mathematical model describing the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Our methodology can be used to identify optimum drug administration schedules to avoid resistance conferred by one (epi)genetic alteration for any cancer and treatment type. Recently, the field of anti-cancer therapy has witnessed a revolution by the discovery of targeted therapy, which refers to compounds targeting specific pathways causing abnormal growth of cancer cells. The clinical success of such drugs has been limited by the evolution of acquired resistance to these compounds, which leads to a relapse after initial response to therapy. Current dosing procedures are not designed to optimally delay the emergence of resistance; the identification of such optimal dosing schedules represents an important challenge in clinical cancer research. Here, we design a novel methodology to identify the optimum drug administration strategies that reach this clinical goal. Our model describes the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Since this methodology can be extended to describe situations arising during administration of cytotoxic chemotherapy as well, it can be used to identify optimum drug administration schedules to avoid resistance for any cancer and treatment type.
登录
查看更多内容
影响因子：
3.3
作者：
Haeno, Hiroshi;Iwasa, Yoh;Michor, Franziska
通讯作者：
Michor, Franziska
影响因子：
4.3
作者：
COSTA, MIS;BOLDRINI, JL;BASSANEZI, RC
通讯作者：
BASSANEZI, RC
影响因子：
6.2
作者：
Milton, Daniel I.;Azzoli, Christopher G.;Miller, Vincent A.
通讯作者：
Miller, Vincent A.
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：
2
作者：
Hahnfeldt, P;Folkman, J;Hlatky, L
通讯作者：
Hlatky, L