基于非线性混合效应模型的比较药动学分析方法研究

Comparative pharmacokinetic analysis based on nonlinear mixed effect model

比较药动学研究贯穿药物研发的整个阶段,通过求算个体药动学参数,推测各处理因素间AUC、Cmax比值的90%置信区间,然后与事先设定的等效区间进行比较,最终判断各处理因素间是否等效,为用药剂量的合理调整提供依据。然而,很多比较药动学研究为稀疏采样设计,传统的统计矩法很难对个体药动学参数进行估计,此时需要借助群体药动学的计算方法,利用非线性混合效应模型进行计算。本研究在密集采样设计比较药动学研究实例基础之上,模拟稀疏采样过程,对稀疏数据采用非线性混合效应模型分析,原密集数据采用统计矩法分析,通过Bootstrap法1 000次重复抽样,最终比较两种方法所得参数的90%置信区间。结果表明非线性混合效应模型对稀疏数据处理结果可靠,与统计矩法计算结果一致,为此类比较药动学研究提供了参考。

Comparative pharmacokinetic（PK） analysis is often carried out throughout the entire period of drug development,the common approach for the assessment of pharmacokinetics between different treatments requires that the individual PK parameters,which employs estimation of 90% confidence intervals for the ratio of average parameters,such as AUC and Cmax,these 90% confidence intervals then need to be compared with the pre-specified equivalent interval,and last we determine whether the two treatments are equivalent.Unfortunately in many clinical circumstances,some or even all of the individuals can only be sparsely sampled,making the individual evaluation difficult by the conventional non-compartmental analysis.In such cases,nonlinear mixed effect model（NONMEM） could be applied to analyze the sparse data.In this article,we simulated a sparsely sampling design trial based on the dense sampling data from a truly comparative PK study.The sparse data were analyzed with NONMEM method,and the original dense data were analyzed with non-compartment analysis.Although the trial design and analysis methods are different,the 90% confidence intervals for the ratio of PK parameters based on 1000 Bootstrap are very similar,indicated that the analysis based on NONMEM is a reliable method to treat with the sparse data in the comparative pharmacokinetic study.