Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
Basic Clin Pharmacol Toxicol. 2010 Mar;106(3):234-42. doi: 10.1111/j.1742-7843.2009.00520.x. Epub 2009 Dec 30.
One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity.
一种常用于降低抗癌药物方案引起的严重中性粒细胞减少症的方法是按固定步长减少连续剂量，通常减少 25%。另一种方法是使用药代动力学 (PK) 采样来调整剂量，但很少使用基于模型的计算机方法来利用收集的 PK 和/或药效动力学 (PD) 数据。一种用于骨髓抑制的半机械模型，可以描述广泛的抗癌药物给药后中性粒细胞时间过程中的个体间和个体间变异性，可用于临床基于模型的个体化剂量。本研究的目的是通过模拟比较当前的逐步程序和基于模型的剂量适应，并研究是否可以在不增加严重毒性风险的情况下增加人群中的总体剂量强度。使用 NONMEM 中的最大后验程序比较了各种 PK 和/或 PD 信息的价值与标准剂量策略。结果表明，当有中性粒细胞计数信息时，PK 采样的额外改进可忽略不计。与单侧 25%剂量调整方案相比，在基线和预测最低点附近进行中性粒细胞采样可将目标范围内的患者数量增加 27%，同时在五轮治疗后将经历严重毒性的患者数量保持在可比的低水平。个体间高度变异性不会限制基于模型的剂量适应的获益，而个体间高度变异性预计会使任何剂量适应方法的成功率降低。本研究表明，成功的基于模型的剂量适应在临床上不需要药物浓度采样，并且在治疗前值之外额外进行一次中性粒细胞测量就足以限制严重中性粒细胞减少症并增加剂量强度。
