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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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Theories of Dissolution: Diffusion Layer Model01:15

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Orally administered drugs primarily enter the systemic circulation via passive diffusion through the intestinal membranes. The drug's absorption is influenced by drug stability in the gastrointestinal GI tract, membrane permeability, the surface area available for absorption, luminal drug concentration, and residence time in the lumen. Drug permeability can be enhanced by adjusting the lipophilicity, polarity, or molecular size of the drug, promoting its passive transport across intestinal...
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Dissolution kinetics, an essential aspect of oral drug delivery, is significantly influenced by the drug's particle size. According to the Noyes-Whitney dissolution model, the dissolution rate correlates directly with the drug's surface area. The larger the surface area, the higher the drug's solubility in water, leading to a faster drug dissolution rate. Reducing particle size increases the effective surface area, enhancing the dissolution process. Micronization and nanosizing are...
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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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Updated: Jun 4, 2025

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Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient

Roshan A Patel1, Siddharth S Kesharwani1, Fady Ibrahim1

  • 1Drug Product Development, Synthetics Platform, Sanofi, 350 Water St., Cambridge, MA 02141, USA.

Journal of Controlled Release : Official Journal of the Controlled Release Society
|January 5, 2025
PubMed
Summary
This summary is machine-generated.

Gaussian process regression (GPR) and active learning significantly reduce data needs for pharmaceutical dissolution modeling. These methods improve predictive accuracy and identify critical processing parameters more efficiently than traditional approaches.

Keywords:
Active learningBiopharmaceuticsDissolution modelsGaussian process regressionMachine learningProcess parameter optimization

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Chemical Engineering

Background:

  • In vitro dissolution testing is crucial for pharmaceutical product quality control and formulation optimization.
  • Data-driven dissolution models offer efficiency by predicting outcomes, reducing physical experiments, and identifying key influencing factors.
  • Minimizing data requirements for model development enhances the practical utility of these predictive tools.

Purpose of the Study:

  • To investigate the efficacy of Gaussian process regression (GPR) and active learning in reducing data needs for predictive dissolution modeling.
  • To compare the performance of GPR against traditional polynomial models for dissolution prediction.
  • To assess the ability of these methods to identify critical processing parameters impacting drug dissolution.

Main Methods:

  • A Design of Experiments (DoE) study was conducted across five processing parameters to generate dissolution data for compound B.
  • Gaussian process regression (GPR) and polynomial models were trained and compared using the generated dataset.
  • Shapley additive explanations (SHAP) were employed for GPR model interpretation and parameter importance assessment.
  • Active learning strategies were retrospectively analyzed to evaluate their potential in selecting optimal experimental subsets.

Main Results:

  • GPR demonstrated higher fidelity dissolution predictions compared to polynomial models when trained on identical datasets.
  • Shapley additive explanations effectively identified and ranked the importance of various processing parameters influencing dissolution.
  • Retrospective analysis indicated that active learning can identify a smaller, more informative set of experiments than full DoE for model development and parameter identification.

Conclusions:

  • Gaussian process regression offers superior predictive performance for pharmaceutical dissolution compared to conventional polynomial models.
  • Active learning strategies can substantially reduce the experimental burden required for developing accurate dissolution models and understanding process-parameter relationships.
  • The combined use of GPR and active learning presents a powerful, data-efficient approach for pharmaceutical product development and quality control.