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Related Concept Videos

Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

133
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...
133
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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

293
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...
293
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

745
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...
745
Factors Affecting Dissolution: Particle Size and Effective Surface Area01:23

Factors Affecting Dissolution: Particle Size and Effective Surface Area

828
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...
828
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

249
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...
249
Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time

83
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.
One of the key parameters is the mean transit time (MTT), which refers to the total duration required for drug molecules to transit through the body. MTT is determined by calculating the ratio of the area under the moment curve to the area...
83
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  11. A Prediction Model Based On Artificial Intelligence Techniques For Disintegration Time And Hardness Of Fast Disintegrating Tablets In Pre-formulation Tests.
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  11. A Prediction Model Based On Artificial Intelligence Techniques For Disintegration Time And Hardness Of Fast Disintegrating Tablets In Pre-formulation Tests.

Related Experiment Video

Formation of Dispersible Taohong Siwu Tablets
05:44

Formation of Dispersible Taohong Siwu Tablets

Published on: February 3, 2023

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A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.

Mehri Momeni1, Marziyeh Afkanpour1, Saleh Rakhshani2

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

BMC Medical Informatics and Decision Making
|March 28, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
Deep learningDrug designMachine learningOrally disintegrating tablets (ODTs)

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A Package of Established Analytical Tools to Investigate the Solid-State Alteration of Lipid-Based Excipients
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Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
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Related Experiment Videos

Formation of Dispersible Taohong Siwu Tablets
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A Package of Established Analytical Tools to Investigate the Solid-State Alteration of Lipid-Based Excipients
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Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
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Machine learning and deep learning significantly improve drug formulation by accurately predicting tablet disintegration time and hardness. This AI-driven approach optimizes the pre-formulation design process, reducing costs and time.

Area of Science:

  • Pharmaceutical Science
  • Computational Chemistry
  • Artificial Intelligence in Drug Development

Background:

  • The pharmaceutical industry seeks innovative drug development and formulation methods.
  • Orally disintegrating tablets (ODTs) offer patient-friendly benefits, but formulation relies on critical excipient choices.
  • Traditional trial-and-error formulation is costly and time-consuming.

Purpose of the Study:

  • To automate and enhance pre-formulation drug design using machine learning and deep learning.
  • To predict critical tablet quality attributes: disintegration time and hardness.
  • To overcome limitations of traditional, resource-intensive formulation methods.

Main Methods:

  • A dataset of 1983 formulations, including excipient details and physicochemical attributes, was compiled.
Pharmaceutical formulation
Pharmaceutical innovation
Prediction model
Tablet disintegration time
Tablet hardness
  • Various predictive models were compared, including deep learning, ANNs, SVMs, decision trees, MLR, and random forests.
  • The models predicted tablet disintegration time and hardness.

    • Main Results:

    • A 12-layer deep neural network achieved 73% accuracy for disintegration time and 99% for tablet hardness.
    • Deep learning models outperformed traditional machine learning techniques.
    • The AI approach demonstrated efficacy in predicting complex pharmaceutical factors.

    Conclusions:

    • Deep learning shows significant potential for optimizing pharmaceutical formulations, especially for predicting tablet hardness.
    • This AI-driven strategy can streamline drug formulation, reducing iterations and material usage.
    • Future research should expand datasets to enhance model performance and applicability in drug development.