Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

Factors Affecting Dissolution: Particle Size and Effective Surface Area

1.9K
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...
1.9K
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

834
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...
834
Factors Influencing Drug Absorption: Drug Dissolution01:27

Factors Influencing Drug Absorption: Drug Dissolution

1.8K
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...
1.8K
Drug Dissolution: Requirements and Profile Comparison01:14

Drug Dissolution: Requirements and Profile Comparison

500
The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
500
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

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

937
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...
937
In Vitro Drug Dissolution: Compendial Testing Models I01:13

In Vitro Drug Dissolution: Compendial Testing Models I

560
Compendial dissolution methods are standardized procedures defined by pharmacopeias to evaluate the rate at which a drug dissolves in a specific medium. These methods ensure batch-to-batch consistency, enable quality control, and support the prediction of drug bioavailability. They are critical for both immediate and modified-release drug products.The apparatuses used for dissolution testing differ in their design and mechanical function, but all aim to simulate the physiological environment of...
560

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Aqueous OH Kinetics of Aliphatic Carboxylic Acids: New Data and Updated Structure-Activity Relationship.

The journal of physical chemistry. A·2026
Same author

The Impact of the Core-Shell Fiber Composition on the Properties and Stability of the Electrospun Films.

Nanotechnology, science and applications·2026
Same author

Tiny Diamonds, Big Impact: Unlocking the Structure-Activity Relationship of Antimicrobial Nanodiamonds.

Nanotechnology, science and applications·2026
Same author

'Let's skip digital stuff and play cards'.

Medical education·2025
Same author

Target, Treat, and Track: Superparamagnetic Iron Oxide Nanoparticles (SPION) Driven Theranostic Delivery of Antimicrobials to the Lungs.

Nanotechnology, science and applications·2025
Same author

Evaluation of the Effect of Formulation Composition and Physicochemical Properties of Omeprazole and Bisoprolol Hemifumarate on Electrospun Nanofibers Characteristics.

Nanotechnology, science and applications·2025

Related Experiment Video

Updated: May 5, 2026

Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
09:59

Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution

Published on: July 4, 2014

19.0K

Explainable AI in Pharmaceutics: Grad-CAM Analysis of Surface Dissolution Imaging Using Convolutional Neural

Abdullah Al-Baghdadi1, Adam Pacławski1, Jakub Szlęk1

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

Pharmaceutics
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces surface dissolution imaging (SDi2) and a dual-wavelength convolutional neural network (CNN) to predict drug release from tablets. Explainable AI methods reveal how the model analyzes surface and bulk changes for better pharmaceutical development.

Keywords:
Grad-CAMconvolutional neural networksdrug dissolutionmachine learningsolid dosage formssurface dissolution imaging

More Related Videos

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.9K
Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging
11:07

Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging

Published on: November 24, 2021

2.6K

Related Experiment Videos

Last Updated: May 5, 2026

Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution
09:59

Coherent anti-Stokes Raman Scattering CARS Microscopy Visualizes Pharmaceutical Tablets During Dissolution

Published on: July 4, 2014

19.0K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.9K
Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging
11:07

Visualizing and Quantifying Pharmaceutical Compounds within Skin using Coherent Raman Scattering Imaging

Published on: November 24, 2021

2.6K

Area of Science:

  • Pharmaceutical Sciences
  • Drug Delivery
  • Computational Chemistry

Background:

  • Dissolution is critical for oral drug bioavailability.
  • Traditional methods lack real-time surface dynamics.
  • Novel imaging and AI approaches are needed.

Purpose of the Study:

  • To develop a predictive model for drug dissolution using surface imaging and AI.
  • To understand the real-time surface dynamics of drug release.
  • To interpret the AI model's predictions using explainable AI.

Main Methods:

  • Surface dissolution imaging (SDi2) was employed.
  • A dual-wavelength convolutional neural network (CNN) was developed.
  • Eight tablet formulations were tested under compendial conditions (pH 1.2 and 6.8).

Main Results:

  • The CNN model accurately predicted dissolution profiles (R²=0.89, RMSE=11.57).
  • Synergistic processing of spectral images, temporal data, and formulation composition improved predictions.
  • Explainable AI (Grad-CAM) identified model focus on tablet edges (280 nm) and bulk regions (520 nm).

Conclusions:

  • Real-time imaging combined with explainable AI enhances understanding of dissolution.
  • The developed framework supports pharmaceutical formulation development.
  • This approach offers insights into surface dissolution and structural changes.