Comparison of Deconvolution-Based and Absorption Modeling IVIVC for Extended Release Formulations of a BCS III Drug Development Candidate
Journal Title: The AAPS Journal - Year 2015, Vol 17, Issue 6
Abstract
In vitro–in vivo correlations (IVIVC) are predictive mathematical models describing the relationship between dissolution and plasma concentration for a given drug compound. The traditional deconvolution/convolution-based approach is the most common methodology to establish a level A IVIVC that provides point to point relationship between the in vitro dissolution and the in vivo input rate. The increasing application of absorption physiologically based pharmacokinetic model (PBPK) has provided an alternative IVIVC approach. The current work established and compared two IVIVC models, via the traditional deconvolution/convolution method and via absorption PBPK modeling, for two types of modified release (MR) formulations (matrix and multi-particulate tablets) of MK-0941, a BCS III drug development candidate. Three batches with distinct release rates were studied for each formulation technology. A two-stage linear regression model was used for the deconvolution/convolution approach while optimization of the absorption scaling factors (a model parameter that relates permeability and input rate) in GastroplusTM Advanced Compartmental Absorption and Transit model was used for the absorption PBPK approach. For both types of IVIVC models established, and for either the matrix or the multiparticulate formulations, the average absolute prediction errors for AUC and Cmax were below 10% and 15%, respectively. Both the traditional deconvolution/convolution-based and the absorption/PBPK-based level A IVIVC model adequately described the compound pharmacokinetics to guide future formulation development. This case study highlights the potential utility of absorption PBPK model to complement the traditional IVIVC approaches for MR products.
Authors and Affiliations
Filippos Kesisoglou, Binfeng Xia, Nancy G. B. Agrawal
Keywords
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