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基于治疗药物监测的每周紫杉醇给药算法的药效学-药代动力学模拟研究:评价。

Evaluation of a pharmacology-driven dosing algorithm of 3-weekly paclitaxel using therapeutic drug monitoring: a pharmacokinetic-pharmacodynamic simulation study.

机构信息

Department of Oncology & Hematology, Cantonal Hospital, St Gallen, Switzerland.

出版信息

Clin Pharmacokinet. 2012 Sep 1;51(9):607-17. doi: 10.1007/BF03261934.

DOI:10.1007/BF03261934
PMID:22804749
Abstract

BACKGROUND AND OBJECTIVE

Severe neutropenia is the most frequent and important toxicity of 3-weekly paclitaxel and puts patients at substantial risk of infectious complications. It is well known that the time during which paclitaxel plasma concentrations exceed 0.05 μmol/L (T(C>0.05)) correlates with the extent of neutropenia. This study was initiated to develop a dosing algorithm that would be able to reduce severe neutropenia by targeting an individual paclitaxel T(C>0.05) between 26 and 31 hours, and could be validated in a prospective randomized trial by comparing it to conventional dosing of paclitaxel.

METHODS

Paclitaxel plasma concentration-time (n = 273) and absolute neutrophil count (ANC) data (152 of the 273 patients) were pooled from two previous studies and submitted to population pharmacokinetic and pharmacodynamic modelling using nonlinear mixed-effects modelling software NONMEM® version VII. To fit the data, we used a previously described 3-compartment model with saturable elimination and distribution, coupled to a semiphysiological model with a linear function to describe the myelotoxic effect of paclitaxel (E(paclitaxel)) on circulating neutrophils (neutropenia). Patient age, sex, body surface area (BSA), bilirubin and renal function were tested as potential covariates on the maximum elimination capacity of paclitaxel (VM(EL)). Limited sampling strategies were tested on the pharmacokinetic model for their accuracy to predict paclitaxel T(C>0.05). Subsequently, we proposed a first-cycle dosing algorithm that accounted for BSA, patient age and sex, while later cycles accounted for the previous-cycle paclitaxel T(C>0.05) (target: 26 to 31 hours) and ANC nadir to adapt the paclitaxel dose for the next treatment cycle. To test the adequacy of the proposed dosing algorithm, we used extensive data simulations on the final pharmacokinetic/pharmacodynamic model, generating datasets of 1000 patients for six subsequent treatment cycles. Grade 4 neutropenia was tested as a potential endpoint for a prospective clinical trial and simulated for two scenarios, i.e. conventional dosing of paclitaxel 200 mg/m(2) every 3 weeks, and personalized, pharmacology-driven dosing as outlined above.

RESULTS

Concentration-time data for paclitaxel were adequately described by the 3-compartment model. Also, individual ANC counts were adequately described by the semiphysiological model using a linear function to describe E(paclitaxel) on neutropenia. Patient age, sex, bilirubin and BSA were significant and independent covariates on the elimination of paclitaxel. Paclitaxel VM(EL) was 16% higher in males than in female patients, and a 10-year increase in age led to a 13% decrease in VM(EL). A single paclitaxel plasma concentration 24 hours after the start of infusion was adequate to predict paclitaxel T(C>0.05) (root squared mean error [RSME] = +0.5%), and the addition of an end-of-infusion sample did not further improve precision (RSME = -0.6%). Data simulations on the final pharmacokinetic/pharmacodynamic model and using the proposed dosing algorithm resulted in a first-cycle paclitaxel dose ranging from 150 to 185 mg/m(2) for women and from 165 to 200 mg/m(2) for men. Dose adaptations for cycles two to six ranged from -40% to +30%, with a final median paclitaxel dose of 167 mg/m(2) (range 76 to 311 mg/m(2)). When compared with conventional dosing (paclitaxel 200 mg/m(2) every 3 weeks), personalized dosing reduced grade 4 neutropenia in cycle one from 15% to 7%, and further to 4% in cycle 2.

CONCLUSION

This study proposes a pharmacology-driven dosing algorithm of 3-weekly paclitaxel to reduce the incidence of grade 4 neutropenia. A randomized clinical trial comparing this dosing algorithm with conventional BSA-based dosing of paclitaxel in patients with advanced non-small cell lung cancer is currently ongoing.

摘要

背景与目的

三周紫杉醇疗法最常见且最重要的毒性反应是严重中性粒细胞减少,这使患者面临严重感染并发症的风险。众所周知,紫杉醇血药浓度超过 0.05 μmol/L(T(C>0.05))的时间与中性粒细胞减少的程度相关。本研究旨在开发一种给药算法,该算法能够将个体紫杉醇 T(C>0.05)时间设定在 26 至 31 小时之间,从而降低严重中性粒细胞减少的发生率,并通过与紫杉醇常规剂量进行前瞻性随机试验来验证。

方法

汇总了来自两项先前研究的 273 例紫杉醇血药浓度-时间(n=273)和绝对中性粒细胞计数(ANC)数据(152 例患者中的 152 例),并使用 NONMEM®版本 VII 的非线性混合效应建模软件进行群体药代动力学和药效动力学建模。为了拟合数据,我们使用了以前描述的 3 室模型,该模型具有饱和消除和分布,与线性函数相结合,描述了紫杉醇(E(paclitaxel))对循环中性粒细胞(中性粒细胞减少症)的骨髓抑制作用。测试了患者年龄、性别、体表面积(BSA)、胆红素和肾功能是否为紫杉醇最大消除能力(VM(EL))的潜在协变量。测试了有限采样策略对预测紫杉醇 T(C>0.05)的准确性。随后,我们提出了一种第一周期给药算法,该算法考虑了 BSA、患者年龄和性别,而后续周期则考虑了前一周期的紫杉醇 T(C>0.05)(目标:26 至 31 小时)和 ANC 最低点,以适应下一治疗周期的紫杉醇剂量。为了测试所提出的给药算法的充分性,我们在最终的药代动力学/药效动力学模型上进行了广泛的数据模拟,为后续的 6 个治疗周期生成了 1000 名患者的数据集。将 4 级中性粒细胞减少症作为前瞻性临床试验的潜在终点进行了测试,并模拟了两种情况,即紫杉醇 200 mg/m(2)每 3 周常规剂量,以及如上所述的个体化、基于药理学的剂量。

结果

紫杉醇的浓度-时间数据被 3 室模型充分描述。同样,使用线性函数描述 E(paclitaxel)对中性粒细胞减少症的影响,半生理模型也能充分描述个体 ANC 计数。患者年龄、性别、胆红素和 BSA 是紫杉醇消除的显著且独立的协变量。男性患者的紫杉醇 VM(EL)比女性患者高 16%,年龄每增加 10 岁,VM(EL)就会降低 13%。输注开始后 24 小时的单个紫杉醇血药浓度足以预测紫杉醇 T(C>0.05)(均方根误差[RSME]为+0.5%),添加终末输注样本不会进一步提高精度(RSME 为-0.6%)。基于最终药代动力学/药效动力学模型的数据模拟和使用所提出的给药算法,女性患者的第一周期紫杉醇剂量范围为 150 至 185 mg/m(2),男性患者的剂量范围为 165 至 200 mg/m(2)。第二至第六个周期的剂量调整范围为-40%至+30%,最终的紫杉醇中位数剂量为 167 mg/m(2)(范围 76 至 311 mg/m(2))。与常规剂量(紫杉醇 200 mg/m(2)每 3 周)相比,个体化剂量将第一周期的 4 级中性粒细胞减少症从 15%降低至 7%,并在第二周期进一步降低至 4%。

结论

本研究提出了一种基于紫杉醇的 3 周给药算法,以降低 4 级中性粒细胞减少症的发生率。目前正在进行一项比较这种给药算法与晚期非小细胞肺癌患者基于 BSA 的紫杉醇常规剂量的随机临床试验。

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