Tumor growth rate determines the timing of optimal chronomodulated treatment schedules.
Tumor growth rate determines the timing of optimal chronomodulated treatment schedules.
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DOI：
10.1371/journal.pcbi.1000712
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发表时间：
2010-03-19
影响因子：
4.3
通讯作者：
Herzel H
中科院分区：
文献类型：
--
作者：
Bernard S;Cajavec Bernard B;Lévi F;Herzel H
在宿主和癌组织中，药物代谢和对药物的敏感性以昼夜（24小时）方式变化。特别地，细胞周期特异性（CCS）细胞毒性剂的功效受到靶组织中细胞周期活性的日常调节的影响。抗癌时间疗法，其中在每天的特定时间施用治疗，旨在利用这些生物节律来减少毒性并提高治疗的功效。昼夜节律状态是相对于日常环境线索的生理和行为活动的时间，在很大程度上决定了治疗的最佳时间。然而，在确定适当的治疗方案时，尚未考虑肿瘤动力学变化的影响。我们使用了一个简单的模型的细胞群体的时间调节治疗，以确定哪些生物参数是重要的时间治疗策略的成功设计。我们表明，治疗靶向的细胞周期阶段的持续时间和细胞增殖率在确定CCS药物给药的最佳时间方面至关重要。因此，最佳治疗时间不仅取决于患者的昼夜节律状态，还取决于肿瘤的细胞周期动力学。然后，我们开发了治疗结果的理论分析（TATO），将昼夜节律状态和细胞周期动力学参数与治疗结果联系起来。我们发现，最好和最差的CCS药物给药时间表是那些与24小时的间隔，这意味着，24小时的时间调节治疗可以是无效的，甚至有害的，如果在错误的昼夜节律时间。我们表明，对于某些肿瘤，间隔不同于24小时的给药时间可以降低这些风险，而不会影响总体疗效。癌症的时间疗法旨在利用日常生理节律，通过在一天中的特定时间给予治疗来提高抗癌功效和对药物的耐受性。最近的临床试验表明，时间疗法有助于提高患者的生活质量和中位寿命，但如果时机不对，它也会产生负面影响。合理制定个体化治疗方案的理论基础仍然缺乏。在这里，我们使用一个简单的细胞群体模型来展示生物节律和细胞周期如何相互作用来调节对癌症治疗的反应。特别是，我们表明癌细胞的增殖率决定了治疗何时最有效。我们提供了一个简单的配方的问题，可以用来计算一个客观的反应函数的基础上的药物敏感性和肿瘤细胞的增殖率。最后，我们表明，在某些情况下，每天在不同的时间进行治疗可能比标准的每日时间疗法更合适。这些结果构成了设计个体化时间疗法治疗的重要一步，并指出了设计更好的临床试验的方法。
In host and cancer tissues, drug metabolism and susceptibility to drugs vary in a circadian (24 h) manner. In particular, the efficacy of a cell cycle specific (CCS) cytotoxic agent is affected by the daily modulation of cell cycle activity in the target tissues. Anti-cancer chronotherapy, in which treatments are administered at a particular time each day, aims at exploiting these biological rhythms to reduce toxicity and improve efficacy of the treatment. The circadian status, which is the timing of physiological and behavioral activity relative to daily environmental cues, largely determines the best timing of treatments. However, the influence of variations in tumor kinetics has not been considered in determining appropriate treatment schedules. We used a simple model for cell populations under chronomodulated treatment to identify which biological parameters are important for the successful design of a chronotherapy strategy. We show that the duration of the phase of the cell cycle targeted by the treatment and the cell proliferation rate are crucial in determining the best times to administer CCS drugs. Thus, optimal treatment times depend not only on the circadian status of the patient but also on the cell cycle kinetics of the tumor. Then, we developed a theoretical analysis of treatment outcome (TATO) to relate the circadian status and cell cycle kinetic parameters to the treatment outcomes. We show that the best and the worst CCS drug administration schedules are those with 24 h intervals, implying that 24 h chronomodulated treatments can be ineffective or even harmful if administered at wrong circadian times. We show that for certain tumors, administration times at intervals different from 24 h may reduce these risks without compromising overall efficacy. Chronotherapy of cancers aims at exploiting daily physiological rhythms to improve anti-cancer efficacy and tolerance to drugs by administering treatments at a specific time of the day. Recent clinical trials have shown that chronotherapy can be beneficial in improving quality of life and median life span in patients, but that it can also have negative effects if the timing is wrong. A theoretical basis for the rational development of individualized therapy schedules is still lacking. Here, we use a simple cell population model to show how biological rhythms and the cell cycle interact to modulate the response to cancer therapy. In particular, we show that the proliferation rate of cancer cells determines when treatments are most effective. We provide a simple formulation of the problem that can be used to compute an objective response function based on the drug sensitivity and the proliferation rate of tumor cells. Finally, we show that in some cases, treating at a different time every day may be more appropriate than standard daily chronotherapy. These results constitute an important step in designing individualized chronotherapy treatments, and point out to ways to design better clinical trials.
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DOI：
10.1073/pnas.0707772105
发表时间：
2008-02-05
影响因子：
11.1
作者：
Brown, Steven A.;Kunz, Dieter;Kramer, Achim
通讯作者：
Kramer, Achim
影响因子：
0.8
作者：
Clairambault, J;Michel, P;Perthame, B
通讯作者：
Perthame, B
影响因子：
45.3
作者：
Giacchetti, Sylvie;Bjarnason, Georg;Levi, Francis
通讯作者：
Levi, Francis
影响因子：
2
作者：
Dibrov, BF
通讯作者：
Dibrov, BF
影响因子：
3.5
作者：
Falcone, A;Allegrini, G;Conte, P
通讯作者：
Conte, P