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Related Concept Videos

Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

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

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

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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

Theories of Dissolution: Diffusion Layer Model

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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.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Factors Affecting Dissolution: Particle Size and Effective Surface Area01:23

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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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Factors Affecting Dissolution: Polymorphism, Amorphism and Pseudopolymorphism01:21

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
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Factors Influencing Drug Absorption: Drug Dissolution01:27

Factors Influencing Drug Absorption: Drug Dissolution

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The pharmacokinetic journey of drugs from solid oral dosage forms into systemic circulation is multifaceted. It begins with disintegration, a prerequisite ensuring a solid dosage form's subdivision into minute particles. Dissolution occurs next as these granulated entities solubilize in gastrointestinal fluids. This solubilization is crucial for the succeeding stage, permeation, which describes the traversal of the drug across the intestinal membrane and its subsequent entry into the blood...
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Puzzle out Machine Learning Model-Explaining Disintegration Process in ODTs.

Jakub Szlęk1, Mohammad Hassan Khalid1, Adam Pacławski1

  • 1Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9, 30-688 Kraków, Poland.

Pharmaceutics
|April 23, 2022
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Summary
This summary is machine-generated.

Machine learning optimizes orally disintegrating tablet (ODT) disintegration times, overcoming patient swallowing difficulties. This approach improves upon traditional methods, offering a more efficient way to ensure ODT quality and patient compliance.

Keywords:
AutoMLODTsexplainable modelsmachine learningorally disintegrating tabletspartial dependence plotsshapley values

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Drug Delivery Systems

Background:

  • Conventional tablets pose swallowing difficulties for many patients, impacting compliance.
  • Orally disintegrating tablets (ODTs) offer a solution for patients with dysphagia.
  • Optimizing ODT disintegration time is crucial for their efficacy and patient acceptance.

Purpose of the Study:

  • To develop and validate a machine learning (ML) approach for optimizing orally disintegrating tablet (ODT) disintegration times.
  • To enhance traditional trial-and-error methods for ODT formulation development.
  • To identify critical quality attributes influencing ODT disintegration.

Main Methods:

  • Utilized the H2O AutoML platform for diverse ML model development.
  • Integrated chemical descriptors of active pharmaceutical ingredients (APIs) into the ML models.
  • Employed the SHAP method for interpretability and identification of key influencing parameters.

Main Results:

  • Achieved a deep learning model with a 10-fold cross-validation NRMSE of 8.1% and R² of 0.84.
  • Identified critical parameters affecting the disintegration of directly compressed ODTs.
  • Developed a reusable, open-source ODT calculator tool.

Conclusions:

  • Machine learning provides an effective alternative to optimize ODT disintegration times.
  • The developed ML models accurately predict ODT disintegration, improving formulation efficiency.
  • The open-source ODT calculator facilitates wider adoption and further research in ODT development.