In Vivo Predictive Dissolution and Biopharmaceutic-Based In Silico Model to Explain Bioequivalence Results of Valsartan, a Biopharmaceutics Classification System Class IV Drug
PHARMACEUTICS(2024)
Abstract
The purpose of this study was to predict the in vivo bioequivalence (BE) outcome of valsartan (VALS, BCS class IV) from three oral-fixed combination products with hydrochlorothiazide (HCTZ, BCS class III) (Co-Diovan (R) Forte as reference and two generic formulations in development) by conducting in vivo predictive dissolution with a gastrointestinal simulator (GIS) and a physiologically based biopharmaceutic model (PBBM). In the first BE study, the HCTZ failed, but the VALS 90% CI of Cmax and the AUC were within the acceptance limits, while, in the second BE study, the HCTZ 90% CI of Cmax and the AUC were within the acceptance limits, but the VALS failed. As both drugs belong to different BCS classes, their limiting factors for absorption are different. On the other hand, the gastrointestinal variables affected by the formulation excipients have a distinct impact on their in vivo exposures. Dissolution tests of the three products were performed in a GIS, and a PBBM was constructed for VALS by incorporating in the mathematical model of the in vitro-in vivo correlation (IVIVC) the gastrointestinal variables affected by the excipients, namely, VALS permeability and GI transit time. VALS permeability in presence of the formulation excipients was characterized using the in situ perfusion method in rats, and the impact of the excipients on the GI transit times was estimated from the HCTZ's in vivo results. The model was able to fit the in vivo BE results with a good prediction error. This study contributes to the field by showing the usefulness of PBBM in establishing in vitro-in vivo relationships incorporating not only dissolution data but also other gastrointestinal critical variables that affect drug exposure in BCS class IV compounds.
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Key words
gastrointestinal simulator,in vitro dissolution,weak acid,dissolution modeling
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