Search

The objective of this stimulation study was to evaluate effect of simoidicity of the concentration-effect (C-E) relationship on the efficiency of population parameter estimation from sparse sampling and is a continuation of previous work that addressed the effect of sample size and number of samples on parameters estimation from sparse sampling for drugs with C-E relationship characterized by high sigmoidicity (gamma > 5). The findings are based on observed C-E relationships for two drugs, octreotide and remifentanil, characterized by simple E (max) and sigmoid E (max) models (gamma = ~2.5), respectively. For each model, C-E profiles (100 replicates of 100 subjects each) were simulated for several sampling designs, with four or five samples/individual randomly obtained from within sampling windows based on EC(50)-normalized plasma drug concentrations, PD parameters based on observed population mean values, and inter-individual and residual variability of 30% and 25%, respectively. The C-E profiles were fitted using non-linear mixed effect modeling with the first-order conditional estimation method; variability parameters were described by an exponential error model. The results showed that, for the sigmoid E (max) model, designs with four or five samples reliably estimated the PD parameters (EC(50), E (max), E (0), and gamma), whereas the five-sample design, with two samples in the 2-3 E (max) region, provided in addition more reliable estimates of inter-individual variability; increasing the information content of the EC(50) region was not critical as long as this region was covered by a single sample in the 0.5-1.5 EC(50) window. For the simple E (max) model, because of the shallower profile, enriching the EC(50) region was more important. The impact of enrichment of appropriate regions for the two models can be explained based on the shape (sigmoidicity) of the concentration-effect relationships, with shallower C-E profiles requiring data enrichment in the EC(50) region and steeper curves less so; in both cases, the E (max) region needs to be adequately delineated, however. The results provide a general framework for population parameter estimation from sparse sampling in clinical trials when the underlying C-E profiles have different degrees of sigmoidicity.

The purpose of this study was to evaluate the effects of population size, number of samples per individual, and level of interindividual variability (IIV) on the accuracy and precision of pharmacodynamic (PD) parameter estimates. Response data were simulated from concentration input data for an inhibitory sigmoid drug efficacy (E(max)) model using Nonlinear Mixed Effect Modeling, version 5 (NONMEM). Seven designs were investigated using different concentration sampling windows ranging from 0 to 3 EC(50) (EC(50) is the drug concentration at 50% of the E(max)) units. The response data were used to estimate the PD and variability parameters in NONMEM. The accuracy and precision of parameter estimates after 100 replications were assessed using the mean and SD of percent prediction error, respectively. Four samples per individual were sufficient to provide accurate and precise estimate of almost all of the PD and variability parameters, with 100 individuals and IIV of 30%. Reduction of sample size resulted in imprecise estimates of the variability parameters; however, the PD parameter estimates were still precise. At 45% IIV, designs with 5 samples per individual behaved better than those designs with 4 samples per individual. For a moderately variable drug with a high Hill coefficient, sampling from the 0.1 to 1, 1 to 2, 2 to 2.5, and 2.5 to 3 EC(50) window is sufficient to estimate the parameters reliably in a PD study.