Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability

Population approaches are appealing methods for detecting then assessing dr ug-drug interactions mainly because they can cope with sparse data and quan tify the interindividual pharmacokinetic (PK) and pharmacodynamic (PD) vari ability. Unfortunately these methods sometime fail to detect interactions e xpected on biochemical and/or pharmacological basis and the reasons of thes e false negatives art somewhat unclear. The aim of this paper is firstly to propose a strategy to detect and assess PD drug-drug interactions when per forming the analysis with a nonparametric population approach, then to eval uate the influence of some design variates (i.e., number of subjects, indiv idual measurements) and of the PD interindividual variability level on the performances of the suggested strategy. Two interacting drugs A and B are c onsidered, the drug B being supposed to exhibit by itself a pharmacological action of no interest in this work but increasing the A effect. Concentrat ions of A and B after concomitant administration are simulated as well as t he 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 bein g 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 gra phically, and also by examining the expected reductions of variance and ent ropy of the estimated PD parameter distribution provided by the covariate. In a further step, a simple second stage model suggested by the graphic app roach is introduced, the fixed effect and its associated variance are estim ated 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 tho se of a non-population-based approach method commonly used for detecting in teractions. 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 covari ate and show that an existing interaction can be detected more often than w ith a usual approach. The prominent role of the interindividual PD variabil ity, level and of the two controlled factors is also shown.