Demonstrating Contribution of Components of Fixed-Dose Drug Combinations Through Longitudinal Exposure-Response Analysis

Exposure-response (ER) modeling for fixed-dose combinations (FDC) has previously been found to have an inflated false positive rate (FP), i.e., observing a significant effect of FDC components when no true effect exists. Longitudinal exposure-response (LER) analysis utilizes the time course of the data and is valid for several clinical endpoints for FDCs. The aim of the study was to investigate if LER is applicable for the validation of FDCs by demonstrating the contribution of each component to the overall effect without inflation of FP rates. FP and FN rates associated with ER and LER analysis were investigated using stochastic simulation and estimation. Four hundred thirty-two scenarios with varying numbers of patients, duration, sampling frequency, dose distribution, design, and drug activity were analyzed using a range of linear, log-linear, and non-linear models to asses FP and FN rates. Lastly, the impact of the clinical trial parameters was investigated. LER analyses provided well-controlled FP rates of the expected 5% or less; however, in low information clinical trials consisting of 30 patients, 4 samples, and 20 days, LER analyses lead to inflated FN rates. Parameter investigation showed that when the clinical trial includes sufficient patients, duration, samples, and an appropriate trial design, the FN rates are in general below the expected 5% for LER analysis. Based on the results, LER analysis can be used for the validation of FDCs and fixed ratio drug combinations. The method constitutes a new avenue for providing evidence that demonstrates the contribution of each component to the overall clinical effect.