Comparison of Alternative Population Modeling Approaches for Implementing a Level A IVIVC and for Assessing the Time-Scaling Factor Using Deconvolution and Convolution-Based Methods

Different approaches based on deconvolution and convolution analyses have been proposed to establish IVIVC. A new implementation of the convolution-based model was used to evaluate the time-scaled IVIVC using the convolution (method 1) and the deconvolution-based (method 2) approaches. With the deconvolution-based approach, time-scaling was detected and estimated using Levy's plots while with the convolution-based approach, time-scaling was directly determined by a time-scaling sub-model of the convolution integral model by nonlinear regression. The objectives of this study were (i) to show how time-scaled deconvolution and convolution-based approaches can be implemented using population modeling approach using standard nonlinear mixed-effect modeling software such as NONMEM and R, and (ii) to compare the performances of the two methods for assessing IVIVC using complex in vivo drug release process. The impact of different PK scenarios (linear and nonlinear PK disposition models, and increasing levels of inter-individual variability (IIV) on in vivo drug release process) was considered. The performances of the methods were assessed by computing the prediction error (%PE) on C_max, AUC, and partial AUC values. The mean %PE values estimated with the two methods were compliant with the IVIVC validation criteria. However, different from convolution-based, deconvolution-based approach showed that (i) the increase of IIV on in vivo drug release significantly affects the maximal %PE values of C_max leading to failure of IVIVC validation, and (ii) larger %PE values for C_max were associated to complex nonlinear PK disposition models. These results suggest that convolution-based approach could be considered at preferred approach for assessing time-scaled IVIVC.