Original data: Use of absorption/formulation PBPM modeling and in vitro dumping test data to explain a bioequivalence failure of valsartan formulations, a BCS class IV drug

Background: In recent years, significant efforts have been made to develop new dissolution methods capable of predicting the in vivo behavior of pharmaceutical formulations. This is essential for establishing valid in vitro-in vivo correlations (IVIVCs) and biopredictive models. However, many of these methodologies involve sophisticated, costly, and complex devices designed to replicate human physiology. The objective of this study was to predict in vivo bioequivalence (BE) using a simplified dynamic dissolution methodology and to establish a physiologically based biopharmaceutics model (PBBM). Methods: An oral fixed-dose combination of valsartan (VALS, BCS Class IV) and hydrochlorothiazide (HCTZ, BCS Class III) was used for the study. The reference product (Co-Diovan® Forte) and two generic formulations were evaluated using an in vivo predictive dissolution approach. A simple dynamic dissolution technique, known as the "dumping test," was applied to assess drug release profiles. The in vitro dissolution data obtained were incorporated into the PBBM, with two scaling factors—one for time and another for absorption—to enhance predictive accuracy. Results: The "dumping test" successfully simulated the different dissolution behaviors of the formulations and explained the failure of bioequivalence in certain cases. The established IVIVC demonstrated both validity and biopredictive capability. The percentage prediction errors (% PEs) for the PBBM, after incorporating in vitro data and the scaling factors, remained within acceptable regulatory limits. Conclusions: The study demonstrated that a simple dynamic dissolution methodology can effectively predict in vivo bioequivalence and formulation performance. The application of the "dumping test" provided valuable insights into the dissolution characteristics of different formulations, highlighting its potential as a cost-effective and practical tool for biopredictive studies