This systematic study investigates the minimal complexity of a first-principle-based disintegrationdissolution model (DDM) required to accurately estimate the physicochemical properties of final dosage
forms (FDFs) and active pharmaceutical ingredients (APIs). Since direct measurement of API particle
dissolution properties within an FDF is not feasible, simulated data sets were utilized. Dissolution
time profiles were predicted for three distinct FDF compositions presented across three drug loads,
considering both frequent and sparse sampling under stable and chemically degrading conditions.
These predictions were then analyzed using DDMs of increasing complexity, focusing on their ability to
capture critical FDF disintegration and API particle dissolution characteristics. The analysis employed
nonlinear mixed-effects modeling to infer these properties.
The study demonstrates that intensive sampling significantly enhances the identification of the optimal
DDM, ensuring minimal complexity while allowing reliable inference of properties. This mathematical
model provides a unique opportunity to gain understanding of the dissolution properties of particles
within the FDF, regardless of the API's chemical stability. By simulating and analyzing a broad range
of conditions, including both non-sink and sink scenarios, the DDM offers valuable insights into how
formulation disintegration, particle size distribution, and chemical degradation of the API in the medium
affect the overall performance of the drug product.
The findings suggest that identifying the ideal DDM, marked by a substantial drop in objective function
values, depends on selecting an appropriately complex model and utilizing intensive sampling. This
approach ensures robust characterization of dissolution profiles and offers a promising method for
optimizing FDF design and enhancing our understanding of drug release mechanisms at the particle
level.
