Power estimation using a population pharmacokinetics model with optimal design by clinical trial simulations: application in pharmacokinetic drug–drug interaction studies

Abstract

The aim of this article was to determine the power for pharmacokinetic interaction investigations using population a pharmacokinetic modelling approach with optimal sampling designs and clinical trial simulations. A clinical trial simulation approach was proposed to estimate the power for pharmacokinetic effects in drug-drug interaction (DDI) studies. This approach consisted of: (1) population pharmacokinetic (PK) model(s) was characterised for the drug(s) studied; (2) D-optimal design strategy was applied based on these model(s) to determine optimal sampling times for DDI investigation; (3) clinical trial simulations under particular study designs, for example a randomised parallel design, were used to evaluate the sample size needed for studying PK interaction. The approach was described using an example investigating the impact of a new anti-inflammatory drug on methotrexate (MTX) exposure in rheumatoid arthritis (RA) patients. The power for evaluating PK interaction largely depended on the interindividual variability (IIV) in PK parameters. Residual variability was also influential to a lesser degree in the sample size determination using the proposed approach. It required 40-60 participants for scenarios where IIV was relatively low in order to achieve 90% power. However, a sample size of 80 individuals was required to reach 90% power where both IIV and residual variances were high. Under the same IIV assumptions, the proposed approach in general required a smaller sample size compared with the standard noncompartmental analysis method with intensive blood samples to attain the target power. When IIV was low, the difference in the power between the two approaches was relatively small. Population PK modelling with optimal design and clinical trial simulation to determine sample size when designing drug-drug interaction studies was efficient and cost effective.