Australian Centre for Pharmacometrics Comparison of non-compartmental analysis and non-linear mixed effects ability to determine bioequivalence in drugs with two-compartment kinetics Jim H Hughes1, Richard N Upton1, David J R Foster1 Australian Centre for Pharmacometrics Australian Centre for Pharmacometrics, School of Pharmacy and Medical Sciences, Sansom Institute for Health Research, University of South Australia Email: jim.hughes@mymail.unisa.edu.au Introduction and Aims 66.7% 47.9% 49.2% 67% 83.6% 49.4% Bioequivalence testing is employed by regulatory agencies to ensure generic drug products are the equivalent to their branded counterparts This is commonly done using non-compartmental analysis (NCA) NCA does not account for random unexplained variability (RUV) in measured concentrations and discards values below the limit of quantification (below LOQ) Non-linear mixed effects modelling (NLMEM) can account for this but has been found to be no better in drugs with one-compartment kinetics Drugs with two-compartment kinetics are more likely to be affected by NCA’s limitations due to the added complexity of the terminal phase The aim was to determine if NLMEM has a greater ability to determine bioequivalence than NCA in drugs with two-compartment kinetics Methods A comparison tool was developed with R and NONMEM® able to simulate 500 bioequivalence studies for a hypothetical (or real) drug Each bioequivalence study contained 24 participants The hypothetical drug had two-compartment kinetics and was simulated with 30% coefficient of variation (CV) on parameters split between subject variability (BSV) and between occasion variability (BOV) Simulations were made in R with very intense sampling to find the percentage of truly bioequivalent studies using the individual relative bioavailability parameters that were used for the simulation This was then truncated for use with NCA and NLMEM Truncated sampling schedule times were: 0.25, 0.5, 1, 2, 4, 6, 8, 12, 16, 24, 36, 48, 72 and 96 hours NCA was undertaken in R using the same method as WinNonlin® NLMEM was performed using NONMEM® fitting the original model to the data for each study and provide MAP Bayes parameter estimates The M1 and M3 methods were employed to compare the simple removal of below LOQ values with a maximum likelihood estimation method Relative bioavailability (Frel) was determined in two ways: 'F estimate' – individual estimate of generic drug’s bioavailability 'Post-Hoc' – ratio of AUCs as determined by individual estimates of both clearance and bioavailability Bioequivalence was determined using 90% confidence intervals calculating using a one-way ANOVA with acceptance limits of 80-125% Multiple sets of 500 studies were tested with varying additive RUV, LLOQ and relative bioavailability of the generic formulation. Eighteen sets were tested, half with the original sampling schedule, while the other half had a reduced sampling schedule Figure 1: Relative bioavailability confidence intervals for 500 simulated studies Each study contributes two dots representing the upper and lower 90% confidence intervals. If both dots are within the limits of 80-125% (represented by the dashed lines) then the study is bioequivalent. Dots within the limits are coloured blue while those outside are coloured red. Each plot represents a different method, with the 'Simulation' plot representing the analysis of individual relative bioavailability parameters used to simulate the intensely sampled data and 'Frel' plots representing the 'F estimate' method. Figure 2: Accuracy of bioequivalence methods for differing additive RUV Each plot shows the change in each methods accuracy as the percent of below LOQ values increases, split up by the amount of additive RUV that was used for simulation. 'Frel' – 'F estimate' method, 'PH' – 'Post-Hoc' method Results The tool allowed for simulation and analysis of 18 sets of 500 simulated studies testing differing simulated Frel, RUV, LOQ and sampling schedule Use of M1 and M3 with NLMEM showed little difference with the chosen drug, while the 'F estimate' method was superior to the 'Post-Hoc' method (Figure 1) due to additional error introduced by using the individual estimate both clearance NLMEM showed a 10-20% higher accuracy (Figure 2) and sensitivity in correctly identifying bioequivalent drugs when compared to NCA However NLMEM exhibited a 1-20% lower specificity (Figure 3) compared to NCA when determining bioequivalence Conclusion Due to its higher accuracy and sensitivity non-linear mixed effect modelling is less likely to reject bioequivalent drugs Given the higher specificity of non-compartmental analysis it is less likely to accept non-bioequivalent drugs Figure 3: Specificity of bioequivalence methods for differing additive RUV Each plot shows the change in each methods specificity as the percent of below LOQ values increases, split up by the amount of additive RUV that was used for simulation. 'Frel' – 'F estimate' method, 'PH' – 'Post-Hoc' method