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用于确定依托泊苷曲线下面积的有限采样模型的验证

Validation of a limited sampling model to determine etoposide area under the curve.

作者信息

Lum B L, Lane K J, Synold T W, Goram A, Charnick S B, Sikic B I

机构信息

Division of Oncology and Clinical Pharmacology, Stanford University School of Medicine, California 94037, USA.

出版信息

Pharmacotherapy. 1997 Sep-Oct;17(5):887-90.

PMID:9324178
Abstract

STUDY OBJECTIVE

To validate the utility of a previously reported 3-point limited sampling model (LSM) for determining etoposide area under the curve to infinity (AUC(infinity)).

DESIGN

Secondary analysis of data from two clinical trials of etoposide.

SETTING

University medical center clinical research center.

PATIENTS

Thirty-four patients with different malignancies.

INTERVENTIONS

Etoposide was administered as a 2-hour infusion to 34 patients. Serial plasma samples were drawn over 24 hours after the infusion and analyzed for etoposide by high-performance liquid chromatography.

MEASUREMENTS AND MAIN RESULTS

The 3-point LSM AUC was compared with a 14-point actual AUC calculated by the linear trapezoidal rule. Actual and predicted AUC(infinity) by the LSM were highly correlated (r=0.97, p<0.0001). The LSM predictions had a mean absolute error of 10.9% (95% CI -14.1, -5.3) and a mean error of -9.7% (95% CI 6.9, 14.9). Nine patients with poor AUC(infinity) estimations by the LSM (error > 12%) tended to have abnormally low or high peak concentrations.

CONCLUSION

Our findings suggest the development of more robust LSM using other techniques, such as pharmacostatistical models, that can accommodate a greater degree of pharmacokinetic variability.

摘要

研究目的

验证先前报道的三点有限采样模型(LSM)在确定依托泊苷至无穷大曲线下面积(AUC(∞))方面的效用。

设计

对两项依托泊苷临床试验的数据进行二次分析。

地点

大学医学中心临床研究中心。

患者

34例患有不同恶性肿瘤的患者。

干预措施

对34例患者进行2小时的依托泊苷静脉输注。输注后24小时内采集系列血浆样本,并用高效液相色谱法分析依托泊苷。

测量指标和主要结果

将三点LSM AUC与通过线性梯形法则计算的14点实际AUC进行比较。LSM预测的实际AUC(∞)与预测值高度相关(r = 0.97,p < 0.0001)。LSM预测的平均绝对误差为10.9%(95%CI -14.1,-5.3),平均误差为-9.7%(95%CI 6.9,14.9)。9例LSM对AUC(∞)估计不佳(误差> 12%)的患者往往具有异常低或高的峰浓度。

结论

我们的研究结果表明,需要利用其他技术(如药代统计学模型)开发更稳健的LSM,以适应更大程度的药代动力学变异性。

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