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. 2024 May;17(5):e13819.
doi: 10.1111/cts.13819.

Physiologically based absorption modeling to predict the bioequivalence of two apixaban formulations

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Physiologically based absorption modeling to predict the bioequivalence of two apixaban formulations

Ting Luo et al. Clin Transl Sci. 2024 May.

Abstract

The equivalence of absorption rates and extents between generic drugs and their reference formulations is crucial for ensuring therapeutic comparability. Bioequivalence (BE) studies are widely utilized and play a pivotal role in substantiating the approval and promotional efforts for generic drugs. Virtual BE simulation is a valuable tool for mitigating risks and guiding clinical BE studies, thereby minimizing redundant in vivo BE assessments. Herein, we successfully developed a physiologically based absorption model for virtual BE simulations, which precisely predicts the BE of the apixaban test and reference formulations. The modeling results confirm that the test and reference formulations were bioequivalent under both fasted and fed conditions, consistent with clinical studies. This highlights the efficacy of physiologically based absorption modeling as a powerful tool for formulation screening and can be adopted as a methodological and risk assessment strategy to detect potential clinical BE risks.

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Conflict of interest statement

The authors declared no competing interests for this work.

Figures

FIGURE 1
FIGURE 1
In vitro dissolution profiles of two apixaban formulations in acetate buffer media with 0.05% SDS (n = 12).
FIGURE 2
FIGURE 2
Model simulation and pharmacokinetic assessment of apixaban reference formulation: (a, c) in fasted state, (b, d) in fed state.
FIGURE 3
FIGURE 3
Representative virtual BE profile of 24 subjects after oral administration of 2.5 mg apixaban reference and test formulations: (a) in fasted state, (b)in fed state.
FIGURE 4
FIGURE 4
Mean plasma concentration–time curves for apixaban test and reference formulations in fasted state (a) and fed state (b). Data present mean ± standard deviation (SD). n = 26.

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