Evolution of resistance to anti-cancer therapy during general dosing schedules.
Evolution of resistance to anti-cancer therapy during general dosing schedules.
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DOI：
10.1016/j.jtbi.2009.11.022
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发表时间：
2010-03-21
影响因子：
2
通讯作者：
Michor, Franziska
中科院分区：
文献类型：
--
作者：
Foo, Jasmine;Michor, Franziska
靶向特定致癌通路的抗癌药物在过去几年中显示出有希望的治疗结果;然而，耐药性仍然是这些治疗的重要障碍。对这些药物的耐药性可能由于多种原因而出现，包括改变药物靶标结合位点的遗传或表观遗传变化、细胞代谢或输出机制。更好地了解治疗期间耐药人群的演变可能有助于设计更有效的治疗方案，预防或延迟由于耐药导致的疾病进展。在本文中，我们使用随机数学模型来研究时变给药方案和药代动力学效应下的耐药性的演化动力学。敏感细胞和耐药细胞群体被建模为多类型非齐次出生-死亡过程，其中药物浓度在连续时间内影响敏感细胞和耐药细胞群体的出生率和死亡率。这种灵活的模型使我们能够考虑广义治疗策略的影响以及详细的药代动力学现象，如药物消除和累积多剂量。我们开发的概率估计发展阻力和耐药细胞群体的大小的时刻。有了这些估计，我们在耐受时间表的子空间上优化治疗时间表，以最大限度地减少由于耐药性导致疾病进展的风险，并在耐药性不可避免的情况下找到控制耐药性克隆群体大小的理想时间表。我们的方法可用于描述任何肿瘤类型中由于单一（表观）遗传改变而产生的耐药性动态。
Anti-cancer drugs targeted to specific oncogenic pathways have shown promising therapeutic results in the past few years; however, drug resistance remains an important obstacle for these therapies. Resistance to these drugs can emerge due to a variety of reasons including genetic or epigenetic changes which alter the binding site of the drug target, cellular metabolism or export mechanisms. Obtaining a better understanding of the evolution of resistant populations during therapy may enable the design of more effective therapeutic regimens which prevent or delay progression of disease due to resistance. In this paper, we use stochastic mathematical models to study the evolutionary dynamics of resistance under time-varying dosing schedules and pharmacokinetic effects. The populations of sensitive and resistant cells are modeled as multi-type non-homogeneous birth-death processes in which the drug concentration affects the birth and death rates of both the sensitive and resistant cell populations in continuous time. This flexible model allows us to consider the effects of generalized treatment strategies as well as detailed pharmacokinetic phenomena such as drug elimination and accumulation over multiple doses. We develop estimates for the probability of developing resistance and moments of the size of the resistant cell population. With these estimates, we optimize treatment schedules over a subspace of tolerated schedules to minimize the risk of disease progression due to resistance as well as locate ideal schedules for controlling the population size of resistant clones in situations where resistance is inevitable. Our methodology can be used to describe dynamics of resistance arising due to a single (epi)genetic alteration in any tumor type.
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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。
影响因子：
4.3
作者：
Foo J;Michor F
通讯作者：
Michor F
影响因子：
3.3
作者：
Haeno, Hiroshi;Iwasa, Yoh;Michor, Franziska
通讯作者：
Michor, Franziska
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：
56.9
作者：
Engelman, Jeffrey A.;Zejnullahu, Kreshnik;Janne, Pasi A.
通讯作者：
Janne, Pasi A.
影响因子：
4.3
作者：
Coldman, AJ;Murray, JM
通讯作者：
Murray, JM