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

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

185
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
185
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

152
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
152
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.0K
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...
1.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

83
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
83
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

104
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
104
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
121

You might also read

Related Articles

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

Sort by
Same author

Digital and AI-enabled models in pharmaceutical development and manufacturing: a regulatory-focused industry survey.

International journal of pharmaceutics·2026
Same author

Achieving Fermi-Level Depinning and Ideal Metal Contact in <i>β</i>-Ga<sub>2</sub>O<sub>3</sub> Devices via MXene Integration.

Nano letters·2026
Same author

Liquid biopsy technologies offer new insights and approaches for canine cancer detection and management.

Journal of the American Veterinary Medical Association·2026
Same author

Navigating polymorph generation and distilled-potential development via entropy-symmetry landscapes for metal plasticity mechanisms.

Nature communications·2026
Same author

Resolving drug release mechanisms of amorphous solid dispersions using optical coherence tomography.

Journal of pharmaceutical sciences·2026
Same author

Predicting the viability of pharmaceutical formulations for continuous direct compression using machine learning approaches.

International journal of pharmaceutics·2026

Related Experiment Video

Updated: Aug 31, 2025

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

739

Linked experimental and modelling approaches for tablet property predictions.

Hikaru G Jolliffe1, Ebenezer Ojo1, Carlota Mendez1

  • 1EPSRC CMAC Future Manufacturing Research Hub, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK; Strathclyde Institute of Pharmacy & Biomedical Sciences (SIPBS), University of Strathclyde, Glasgow G4 0RE, UK.

International Journal of Pharmaceutics
|August 20, 2022
PubMed
Summary

This study introduces a combined modeling and experimental approach to predict multicomponent tablet properties, reducing the need for extensive trials. The method uses extrapolation from binary data and specific mixing rules for efficient pharmaceutical development.

Keywords:
CompactionDirect compressionFormulationModelsPredictionsTabletsTensile strength

More Related Videos

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.8K
An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
05:42

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Published on: October 5, 2020

3.3K

Related Experiment Videos

Last Updated: Aug 31, 2025

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing
15:00

A Tablet-Based Curriculum-Based Measurement Protocol for Kindergarten Writing

Published on: February 7, 2025

739
Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
10:13

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

13.8K
An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
05:42

An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones

Published on: October 5, 2020

3.3K

Area of Science:

  • Pharmaceutical Sciences
  • Materials Science
  • Chemical Engineering

Background:

  • Quality-by-Design (QbD) principles are increasingly adopted for pharmaceutical manufacturing to ensure quality and efficiency.
  • Direct Compression is a key tablet manufacturing method, but traditional development approaches are time- and resource-intensive.
  • Developing robust models for multicomponent systems is challenging due to complex interactions.

Purpose of the Study:

  • To evaluate a combined modeling and experimental approach for predicting multicomponent tablet properties.
  • To assess the feasibility of extrapolating pure component model parameters from binary tablet data.
  • To identify suitable mixing rules and volume fraction estimation methods for accurate predictions.

Main Methods:

  • Utilized extrapolation from binary tablet data to determine pure component model parameters for difficult-to-compact materials.
  • Evaluated various mixing rules (e.g., linear averaging, power law) for predicting compression and compaction properties.
  • Assessed different methods for estimating component volume fractions, including theoretical relative compression rates.

Main Results:

  • Extrapolation from binary tablet data proved feasible for obtaining model parameters of challenging materials.
  • Linear averaging using pre-compression volume fractions was most suitable for compression parameters.
  • Averaging using a power law equation form showed best agreement for compaction parameters.
  • Estimations based on theoretical relative compression rates slightly outperformed constant volume fractions.

Conclusions:

  • The developed framework enables prediction of multicomponent tablet properties without extensive experimental fitting.
  • This approach accelerates the evaluation of the tablet's knowledge space and identifies key regions for targeted experimentation.
  • It offers a pathway to establish design and control spaces, potentially bypassing laborious initial Design-of-Experiments.