Friberg Lena E, Karlsson Mats O
Division of Pharmacokinetics and Drug Therapy, Uppsala University, Uppsala, Sweden.
Invest New Drugs. 2003 May;21(2):183-94. doi: 10.1023/a:1023573429626.
As myelosuppression is the dose-limiting toxicity for most chemotherapeutic drugs, modelers attempt to find relationships between drug and toxicity to optimize treatment. Mechanistic models, i.e. models based on physiology and pharmacology, are preferable over empirical models, as prior information can be utilized and as they generally are more reliable for extrapolations. To account for different dosing-regimens and possible schedule-dependent effects, the whole concentration-time profile should be used as input into the pharmacokinetic-pharmacodynamic model. It is also of importance to model the whole time course of myelosuppression to be able to predict both the degree and duration of toxicity as well as consecutive courses of therapy. A handful of (semi)-mechanistic pharmacokinetic-pharmacodynamic models with the above properties have been developed and are reviewed. Ideally, a model of myelosuppression should separate drug-specific parameters from system related parameters to be applicable across drugs and useful under different clinical settings. Introduction of mechanistic models of myelosuppression in the design and evaluation of clinical trials can guide in the decision of optimal sampling times, contribute to knowledge of optimal doses and treatment regimens at an earlier time point and identify sub-groups of patients at a high risk of myelosuppression.
由于骨髓抑制是大多数化疗药物的剂量限制性毒性,建模者试图寻找药物与毒性之间的关系以优化治疗。基于生理学和药理学的机制模型优于经验模型,因为可以利用先验信息,并且通常对外推更可靠。为了考虑不同的给药方案和可能的疗程依赖性效应,应将整个浓度-时间曲线用作药代动力学-药效学模型的输入。对骨髓抑制的整个时间过程进行建模也很重要,以便能够预测毒性的程度和持续时间以及连续的治疗疗程。已经开发并综述了一些具有上述特性的(半)机制药代动力学-药效学模型。理想情况下,骨髓抑制模型应将药物特异性参数与系统相关参数分开,以便适用于不同药物并在不同临床环境中有用。在临床试验的设计和评估中引入骨髓抑制机制模型可以指导最佳采样时间的决策,有助于在更早的时间点了解最佳剂量和治疗方案,并识别骨髓抑制高风险患者亚组。