Statistical Power Analysis to Detect Drug-Drug Interaction between Lorezapam and Probenecid in Healthy and Renal-impaired Populations Using PBPK Modelling and the Simcyp R Package
Theresa Cain, Janak Wedagedera, Masoud Jamei
SIMCYP Limited (a Certara Company), Sheffield, UK
Objectives: A tool is already available within the Simcyp Simultaor to calculate the power of studies to correctly detect the difference between PK parameters in two populations. We aim to extend this feature using the Simcyp V16 R package to calculate power in drug-drug interaction (DDI) studies using model compounds Lorezapam and Probenecid.
Methods: We have developed an R library package where the Simcyp Simulator is called from R and facilitates various scenarios [1] such as constrained sensitivity analysis or parameter estimation, and parameter estimation using multiple substrates or populations. The new R package was used to calculate the power of correctly detecting difference in the AUC value after a single dose of Lorezapam between a healthy volunteer and a renal impaired (GFR<30) population, given a set of sample sizes and significance level. These values were then compared with the Simcyp power calculation tool results. It is of interest to extend such power analysis to DDI studies which is not currently available in the Simulator but can be performed using the Simcyp R package. The physiologically-based PK (PBPK) model fora DDI between Lorezapam and Probenecid has previously been verified [2]. The power to correctly detect the difference in AUCs after taking Lorezapam alone and in combination with Probenecid was then determined using the Simcyp R package for the sample sizes 4, 6, 8, 10 and12, assuming a significance level of 0.05.
Results: Using the R package, a sample size of 50 achieved a power of 85.54% to correctly distinguish between the healthy volunteer and renal impaired (GFR<30) AUC after a single Lorezapam dose which is the same as derived using the Simcyp power calculation tool. In the healthy volunteer population the sample sizes 4, 6, 8, 10 and 12 gave a power of 55.18%, 67.21%, 76.16%, 82.78% and 87.64% respectively to correctly detect the difference in AUCs after taking Lorezapam alone and in combination with Probenecid. Using the renal impaired (GFR<30) population a smaller sample size is required to achieve the equivalent power as the healthy volunteer population. A sample size of 4 gives a power of 61.58% and a sample size of 8 gives a power of 82.90%.
Conclusions: An R library package for Simcyp has been developed that enables running virtual clinical trials from within R. This work shows that Simcyp’s power calculation tool can be extended to calculating power to correctly identify a DDI using the Simcyp V16 R package.
References:
[1] ] Cain et al. Application of Simcyp’s R Library Package in Simulation and Prediction of Metoprolol Compliance Using a Single Plasma Concentration Sample. 24th PAGE meeting, Crete, 2nd-5th June 2015 (poster presentation).
[2] Neuhoff et al, Application of a physiologically based pharmacokinetic (PBPK) model for prediction of the drug-drug interaction (DDI) between Lorazepam and Probenecid. 19th North American ISSX/29th JSSX Meeting, 19th October 2014