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Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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Compendial dissolution methods are standardized procedures defined by pharmacopeias to evaluate the rate at which a drug dissolves in a specific medium. These methods ensure batch-to-batch consistency, enable quality control, and support the prediction of drug bioavailability. They are critical for both immediate and modified-release drug products.The apparatuses used for dissolution testing differ in their design and mechanical function, but all aim to simulate the physiological environment of...
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Updated: Jan 16, 2026

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Modeling and predicting tablet dissolution slowdown using an acceleration factor approach and constrained neural

Yi Li1, Shalini Raj Unnikandam Veettil1, Tiffany Pham1

  • 1Gilead Sciences, Foster City, CA 94404, USA.

Journal of Pharmaceutical Sciences
|September 28, 2025
PubMed
Summary
This summary is machine-generated.

Predictive models can forecast tablet dissolution slowdown during storage. An acceleration factor (AF) approach and a constrained neural network effectively predicted changes, aiding formulation and packaging decisions.

Keywords:
Dissolution modelNeural networkPhysical stabilitySolid dosage form

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Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Tablet dissolution slowdown during storage can compromise drug release and bioavailability.
  • Predictive stability models using accelerated data are crucial for assessing long-term storage risks.
  • Understanding and mitigating dissolution changes are vital for pharmaceutical development.

Purpose of the Study:

  • To compare science-based and machine learning models for predicting tablet dissolution slowdown.
  • To evaluate the effectiveness of an acceleration factor (AF) model and a constrained neural network.
  • To identify strategies for controlling storage conditions to prevent dissolution changes.

Main Methods:

  • Applied an empirical acceleration factor (AF) model to open dish stability data.
  • Constrained a neural network with the AF model, leveraging Arrhenius relationships and first-order decay.
  • Compared prediction performance against an unconstrained neural network and evaluated packaged storage data.

Main Results:

  • Both the AF approach and the constrained neural network accurately predicted dissolution profiles in packaged tablets.
  • The AF model identified critical humidity boundary conditions to prevent dissolution slowdown.
  • Constraining the neural network with the AF model improved prediction performance.

Conclusions:

  • Accelerated stability modeling, particularly the AF approach and constrained neural networks, shows promise for predicting dissolution changes.
  • These methods can serve as valuable Modeling Approaches to Reimagine Stability (MARS) tools in pharmaceutical development.
  • Insights gained aid in formulation, packaging selection, and stability evaluations to mitigate dissolution challenges.