Abstract
This paper describes a method for the construction of pharmacokinetic sampling windows so that they are around the $D$-optimum time points. Here we consider the situation where a pharmacokinetic (PK) study is accompanied by a dose-finding study in phase I clinical trial. The D-optimal criterion is often used to determine the optimal time for collecting blood samples so that they provide maximum information regarding the population PK parameters. However, collecting blood samples at the D-optimal time points is often difficult. Instead, the sampling time point chosen from a suitable time interval or window can ease the process. The proposed method is conceptually simple and considers the average value and standard deviation of D-optimal time points up to create sampling windows. Random time points can be chosen from these windows then to collect blood samples from the next cohort. The nonlinear random-effects model has been used to model the PK data. Also, we employ the continual reassessment method for dose allocation to the patients. Comparisons of the accuracy and precision for the PK parameter estimates obtained at the D-optimal and random time points are also presented. The results are convincing enough to suggest the proposed method as a useful tool for blood sample collection.
Highlights
Clinical trials are prospective studies to evaluate the effect of interventions in humans under pre-specified conditions
This paper presents a simple way of constructing windows for PK sampling in a phase I trial
Blood samples can be collected at time points chosen from windows when patients or volunteers have complexity and hardship for giving samples to medical personnel at the exact D-optimal time points
Summary
Clinical trials are prospective studies to evaluate the effect of interventions in humans under pre-specified conditions. Ogungbenro and Aarons (2007) provided approaches to the designs of a population PK experiment and a review of the existing optimal design methodologies Another method of Ogungbenro and Aarons (2009) modified the Graham and Aarons (2006) approach in the sense that optimal sampling window design is determined by optimising conditional sampling windows for each sampling time, which results in a joint loss of efficiency compared to the fixed D-optimal time points. The design gives high efficiency in parameter estimation, the equivalence theorem makes it complicated and challenging to implement Another method of Foo, McGree, and Duffull (2012) provided the determination of sampling windows for parameter estimation based on Markov Chain Monte Carlo sampling techniques. It gives time-sensitive windows around the optimal design points.
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