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Related Concept Videos

Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

123
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...
123
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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

277
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...
277
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

224
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
224
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

609
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
609
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

702
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...
702
Factors Affecting Dissolution: Particle Size and Effective Surface Area01:23

Factors Affecting Dissolution: Particle Size and Effective Surface Area

757
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...
757

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Related Experiment Video

Updated: Jun 12, 2025

Formation of Dispersible Taohong Siwu Tablets
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Advancing pharmaceutical Intelligence via computationally Prognosticating the in-vitro parameters of fast

Dhruv Gupta1, Anuj A Biswas1, Rohan Chand Sahu1

  • 1Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

European Journal of Pharmaceutics and Biopharmaceutics : Official Journal of Arbeitsgemeinschaft Fur Pharmazeutische Verfahrenstechnik E.V
|September 21, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning models predict fast-dissolving tablet properties like disintegration time, friability, and water absorption. These AI tools accelerate drug development, reducing costs and experimental iterations.

Keywords:
Data EngineeringFast disintegration TabletsK-nearest neighborMachine LearningRandom ForestVoting Regressor

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Chemistry
  • Data Science

Background:

  • Machine learning (ML) is increasingly used in drug development.
  • Tablet efficacy depends on physicochemical properties, formulation, and processing.
  • Predicting tablet characteristics aids in optimizing drug delivery.

Purpose of the Study:

  • Develop ML models to predict disintegration time, friability, and water absorption ratio for fast-dissolving tablets.
  • Evaluate model performance using RMSE and R-squared metrics.
  • Provide a novel approach for predicting tablet properties.

Main Methods:

  • Data visualization, pre-processing, and splitting.
  • Creation and evaluation of ML models including voting regressor, random forest, and KNN.
  • Hyperparameter tuning and cross-validation were employed.

Main Results:

  • Voting regressor achieved best disintegration time prediction (RMSE: 21.99, R²: 0.76).
  • Random forest regressor excelled in friability prediction (RMSE: 0.142, R²: 0.7).
  • KNN regressor demonstrated superior performance for water absorption ratio (RMSE: 10.07, R²: 0.94).

Conclusions:

  • ML models can accurately predict key tablet properties.
  • This approach offers a significant advancement, particularly for friability and water absorption prediction.
  • The developed models can streamline tablet development, reducing time and resources.