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首页> 外文期刊>Journal of pharmacokinetics and biopharmaceutics >Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability.
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Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability.

机译：通过非参数人群方法进行药物间药效相互作用的检测。设计和个体差异的影响。

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Population approaches are appealing methods for detecting then assessing drug-drug interactions mainly because they can cope with sparse data and quantify the interindividual pharmacokinetic (PK) and pharmacodynamic (PD) variability. Unfortunately these methods sometime fail to detect interactions expected on biochemical and/or pharmacological basis and the reasons of these false negatives are somewhat unclear. The aim of this paper is firstly to propose a strategy to detect and assess PD drug-drug interactions when performing the analysis with a nonparametric population approach, then to evaluate the influence of some design variates (i.e., number of subjects, individual measurements) and of the PD interindividual variability level on the performances of the suggested strategy. Two interacting drugs A and B are considered, the drug B being supposed to exhibit by itself a pharmacological action of no interest in this work but increasing the A effect. Concentrations of A and B after concomitant administration are simulated as well as the effect under various combinations of design variates and PD variability levels in the context of a controlled trial. Replications of simulated data are then analyzed by the NPML method, the concentration of the drug B being included as a covariate. In a first step, no model relating the latter to each PD parameter is specified and the NPML results are then proceeded graphically, and also by examining the expected reductions of variance and entropy of the estimated PD parameter distribution provided by the covariate. In a further step, a simple second stage model suggested by the graphic approach is introduced, the fixed effect and its associated variance are estimated and a statistical test is then performed to compare this fixed effect to a given value. The performances of our strategy are also compared to those of a non-population-based approach method commonly used for detecting interactions. Our results illustrate the relevance of our strategy in a case where the concentration of one of the two drugs can be included as a covariate and show that an existing interaction can be detected more often than with a usual approach. The prominent role of the interindividual PD variability level and of the two controlled factors is also shown.
机译：人口方法是用于检测然后评估药物相互作用的诱人方法，主要是因为它们可以处理稀疏数据并量化个体间药代动力学（PK）和药效学（PD）的变异性。不幸的是，这些方法有时无法检测到基于生化和/或药理学的相互作用，并且这些假阴性的原因还不清楚。本文的目的是首先提出一种策略，以便在使用非参数总体方法进行分析时检测和评估PD药与药之间的相互作用，然后评估一些设计变量（例如，受试者人数，个体测量）的影响，以及PD个人差异性水平对建议策略的绩效的影响。考虑了两种相互作用的药物A和B，应该认为药物B本身表现出对这项工作没有兴趣的药理作用，但会增加A作用。模拟了同时给药后A和B的浓度，以及在对照试验中设计变量和PD变异水平的各种组合下的效果。然后通过NPML方法分析模拟数据的复制，将药物B的浓度作为协变量包括在内。在第一步中，没有指定将后者与每个PD参数相关的模型，然后以图形方式进行NPML结果，并且还通过检查由协变量提供的估计PD参数分布的方差和熵的预期减少。在进一步的步骤中，引入了由图形方法建议的简单的第二阶段模型，估计了固定效果及其相关的方差，然后执行统计测试以将该固定效果与给定值进行比较。我们还将该策略的性能与通常用于检测交互的基于非人口的方法的性能进行了比较。我们的结果说明了在将两种药物之一的浓度作为协变量包括在内的情况下，我们策略的相关性，并表明与常规方法相比，可以更频繁地检测到现有的相互作用。还显示了个体间PD变异水平和两个控制因素的突出作用。

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