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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

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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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Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

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When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
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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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Predicting disintegration time in fast-disintegrating tablets using machine learning: a data-driven framework based

Mehri Momeni1, Hamed Tabesh2

  • 1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

International Journal of Medical Informatics
|April 17, 2026
PubMed
Summary

Developing data-driven models for fast-disintegrating tablets (FDTs) improves prediction of disintegration time. Functional excipient representation enhances model accuracy, enabling efficient pharmaceutical formulation development.

Keywords:
Decision supportDeep learningDisintegration time predictionFast-disintegrating tabletsMachine learningMedical informaticsPharmaceutical data modeling

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Drug Delivery Systems

Background:

  • Fast-disintegrating tablets (FDTs) are crucial oral dosage forms where disintegration time impacts drug release and patient compliance.
  • Formulation development for FDTs is complex due to intricate interactions between excipients, physicochemical properties, and tablet characteristics.
  • Traditional trial-and-error methods for FDT formulation are inefficient and time-consuming.

Purpose of the Study:

  • To establish a data-driven framework for predicting disintegration time in FDT formulations.
  • To evaluate the impact of different excipient data representations on predictive model performance.

Main Methods:

  • Analyzed a dataset of 1982 FDT formulations using three excipient representations: identity-based, excipient-specific functional, and functionally aggregated.
  • Employed regression models to predict continuous disintegration time and classification models for discretized intervals.
  • Assessed model performance using regression metrics, weighted F1-score, and Matthews correlation coefficient (MCC).

Main Results:

  • Deep neural networks yielded the highest predictive performance, particularly with the excipient-specific functional dataset (R² = 0.86, MAE = 8.53 s).
  • Random forest models also showed consistent performance.
  • Functional excipient representation improved predictions over identity-based encoding by mitigating sparsity and retaining key information.

Conclusions:

  • Functional excipient representation is an effective data abstraction strategy for enhancing predictive modeling in pharmaceutical formulation.
  • This approach facilitates more efficient data-driven decision-making in the early stages of FDT development.