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Abstract  Population pharmacodynamic experiments sometime involve repeated measurements of ordinal random variables at specific time points. Such longitudinal data presents a challenge during modelling due to correlation between measurements within an individual and often mixed-effects modelling approach may be used for the analysis. It is important that these studies are adequately powered by including an adequate number of subjects in order to detect a significant treatment effect. This paper describes a method for calculating sample size for repeated ordinal measurements in population pharmacodynamic experiments based on analysis by a mixed-effects modelling approach. The Wald test is used for testing the significance of treatment effects. This method is fast, simple and efficient. It can also be extended to account for differential allocation of subjects to the groups and unbalanced sampling designs between and within groups. The results obtained from two simulation studies using nonlinear mixed-effects modelling software (NONMEM) showed good agreement between the power obtained from simulation and nominal power used for sample size calculations. [ABSTRACT FROM AUTHOR]

Abstract  This paper describes an approach for optimising sampling windows for population pharmacokinetic experiments. Sampling windows designs are more practical in late phase drug development where patients are enrolled in many centres and in out-patient clinic settings. Collection of samples under the uncontrolled environment at these centres at fixed times may be problematic and can result in uninformative data. Population pharmacokinetic sampling windows design provides an opportunity to control when samples are collected by allowing some flexibility and yet provide satisfactory parameter estimation. This approach uses information obtained from previous experiments about the model and parameter estimates to optimise sampling windows for population pharmacokinetic experiments within a space of admissible sampling windows sequences. The optimisation is based on a continuous design and in addition to sampling windows the structure of the population design in terms of the proportion of subjects in elementary designs, number of elementary designs in the population design and number of sampling windows per elementary design is also optimised. The results obtained showed that optimal sampling windows designs obtained using this approach are very efficient for estimating population PK parameters and provide greater flexibility in terms of when samples are collected. The results obtained also showed that the generalized equivalence theorem holds for this approach. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of determining D-optimal designs for multiresponse pharmacokinetic–pharmacodynamic (PKPD) experiments where data on each response variable can be collected at different times. Most previous multiresponse model optimal design applications have considered the case where all response variables are measured at the same time points. However in practice it may not be possible to have all responses measured at the same sampling times. We propose an optimal design method to take into account the unbalanced nature of the problem. The method developed was applied to a PKPD problem that involved describing the time course of drug plasma concentrations, heart rate and mean arterial blood pressure for both a fixed effects and mixed effects regression model. Additionally a simulation study was carried out in NONMEM for one such population optimal design problem. [ABSTRACT FROM AUTHOR]

We address the problem of designing pharmacokinetic experiments in multivariate response situations. Criteria, based on the Fisher information matrix, whose inverse according to the Rao–Cramer inequality is the lower bound of the variance–covariance matrix of any unbiased estimator of the parameters, have previously been developed for univariate response for an individual and a population. We extend these criteria to design individual and population studies where more than one response is measured, for example, when both parent drug and metabolites are measured in plasma, multi-compartment models, where measurements are taken at more than one site, or when drug concentration and pharmacodynamic data are collected simultaneously. We assume that measurements made at distinct times are independent, but measurements made of each concentration are correlated with a response variance–covariance matrix. We investigated a number of optimisation algorithms, namely simplex, exchange, adaptive random search, simulated annealing and a hybrid, to maximise the determinant of the Fisher information matrix as required by the D-optimality criterion. The multiresponse optimal design methodology developed was applied in two case studies, where the aim was to suggest optimal sampling times. The first was a restrospective iv infusion experiment aimed to characterise the disposition kinetics of tolcapone and its two metabolites in healthy volunteers. The second was a prospective iv bolus experiment designed to estimate the tissue disposition kinetics of eight beta-blockers in rat. [ABSTRACT FROM AUTHOR]

The objective of this paper is to determine optimal blood sampling time windows for the estimation of pharmacokinetic (PK) parameters by a population approach within the clinical constraints. A population PK model was developed to describe a reference phase II PK dataset. Using this model and the parameter estimates, D-optimal sampling times were determined by optimising the determinant of the population Fisher information matrix (PFIM) using PFIM_ _M 1.2 and the modified Fedorov exchange algorithm. Optimal sampling time windows were then determined by allowing the D-optimal windows design to result in a specified level of efficiency when compared to the fixed-times D-optimal design. The best results were obtained when Ka and IIV on Ka were fixed. Windows were determined using this approach assuming 90% level of efficiency and uniform sample distribution. Four optimal sampling time windows were determined as follow: at trough between 22 h and new drug administration; between 2 and 4 h after dose for all patients; and for 1/3 of the patients only 2 sampling time windows between 4 and 10 h after dose, equal to [4 h–5 h 05] and [9 h 10–10 h]. This work permitted the determination of an optimal design, with suitable sampling time windows which was then evaluated by simulations. The sampling time windows will be used to define the sampling schedule in a prospective phase II study [ABSTRACT FROM AUTHOR]