Physiologically-Based Biopharmaceutics Modeling for Ibuprofen: Identifying Key Formulation Parameter and Virtual...

Background: Physiologically based pharmacokinetic (PBPK) modeling for biopharmaceutics applications (i.e., physiologically based biopharmaceutics modeling (PBBM)) enables mechanistic modeling from dissolution to absorption and disposition, facilitating the prediction of bioequivalence (BE) outcomes and the delimitation of the safe space. This study aims to identify the product-related parameter driving ibuprofen dissolution to upgrade an existing PBPK model, so that an in vitro safe space and virtual BE (VBE) predictions of IR ibuprofen tablets can be performed. Methods: C_max within- and between-subject variabilities of a previous PBPK model were optimized after identifying crucial physiological parameters for ibuprofen absorption and disposition. In vitro data modeling was performed to estimate the value of the parameter driving ibuprofen dissolution. A safe space was defined for this parameter and the sample size to declare BE was calculated. Finally, VBE simulations were performed to explore the effect of sample size as well as number of trial replicates and runs. Results: C_max variability was adequately predicted after changing V_ss and MRT in stomach and small intestine CV (%) to 10 and 150%, respectively. Particle surface pH was identified as the dissolution key parameter for ibuprofen. A safe space for test product surface pH values of 5.64-6.40 was defined in order to achieve a 90%CI for the C_max ratio within the 80-125% range when the reference product surface pH is 6.02. R-ibuprofen was identified as the most discriminative enantiomer. VBE studies with 24 individuals showed BE outcomes that are sensitive to the number of trial replicates and runs. Conclusions: Ibuprofen particle surface pH has been identified as the in vitro parameter governing dissolution in maleate buffer 7 mM with HCl pH 2.0 pretreatment, allowing to establish an in vitro safe space useful for calculating sample sizes and to evaluate the BE success rate through PBBM/PBPK model-informed VBE simulations.