恶性肿瘤患者多西他赛群体药动学模型的建立及验证

Establishment and validation of population pharmacokinetic model of docetaxel in malignant tumor patients

目的建立多西他赛在恶性肿瘤患者中的群体药动学模型并进行验证。方法回顾性收集我院2019年6月-2021年12月收治的接受含多西他赛化疗方案治疗的恶性肿瘤患者的临床资料,结合其血药浓度检测结果,利用非线性混合效应模型法,以三室模型为基础,采用“向前纳入、向后剔除”法筛选对清除率(CL)有影响的协变量(年龄、体质量、身高、体表面积、卡氏评分、总蛋白、白蛋白、总胆红素、天冬氨酸转氨酶、丙氨酸转氨酶、血清肌酐),建立多西他赛群体药动学模型,并进行拟合优度诊断、Bootstrap内部验证。结果共纳入132例恶性肿瘤患者化疗期间的264个血药浓度实测值。对多西他赛CL有显著影响的协变量为血清肌酐、总胆红素(P<0.01);Bootstrap分析的结果(参数中位值及95%置信区间)与所建模型的预测结果接近;最终模型估算出多西他赛CL的群体典型值为37.82 L/h。结论成功建立了恶性肿瘤患者多西他赛的群体药动学模型,可用于临床个体化给药方案的制订和优化。

OBJECTIVE To establish and validate a population pharmacokinetic model of docetaxel in malignant tumor patients.METHODS The clinical data of malignant tumor patients treated with chemotherapy regimen containing docetaxel in our hospital from June 2019 to December 2021 were retrospectively collected.According to the results of blood concentration detection,based on the three-compartment model the nonlinear mixed effect model(NONMEM)was used;covariates(age,weight,height,body surface area,Karnofsky performance scale,total protein,albumin,total bilirubin,aspartate aminotransferase,alanine aminotransferase and serum creatinine)affecting clearance(CL)were screened by“forward inclusion and backward exclusion”;the population pharmacokinetic model of docetaxel was established.The model was tested for goodness-of-fit diagnosis and internal validation by Bootstrap.RESULTS A total of 264 measured blood concentrations of 132 patients with malignant tumors during chemotherapy were included.The covariates that had significant effect on CL of docetaxel were serum creatinine and total bilirubin(P<0.01).The results of Bootstrap analysis(parameter median values and 95%confidence intervals)were close to predict results of the established model;the final model estimated that the population typical value of docetaxel CL was 37.82 L/h.CONCLUSIONS The population pharmacokinetic model of docetaxel in malignant tumor patients is established successfully,which can be used for the formulation and optimization of clinical individualized regimen.