Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

Variables and Equations of State

Variables and Equations of State

The physical state of a pure substance can be defined by certain state variables such as volume (V), pressure (p), temperature (T), and amount of substance (n). When two gases are separated by a movable wall, the gas with the higher pressure naturally compresses the gas with the lower pressure. This causes the high-pressure gas to expand and the low-pressure gas to compress until both gases achieve mechanical equilibrium. At this point, their pressures equalize, and the movement of the wall...

Model Approaches for Pharmacokinetic Data: Compartment Models

Model Approaches for Pharmacokinetic Data: Compartment Models

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...

Precipitation Titration Curve: Analysis

Precipitation Titration Curve: Analysis

The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

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 assumptions,...

You might also read

Related Articles

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

Sort by

Same author

Patients in-use stability and safety of repacked delayed-release capsules of sodium divalproex.

International journal of pharmaceutics·2026

Same author

Stability Assessment of FDA Approved Varenicline Tartrate Products for Critical Quality Attributes - N-Nitroso Varenicline, Solid Form, and Dissolution.

AAPS PharmSciTech·2026

Same author

Addressing printability of poorly flowable drug by wet granulation: understanding interplays of formulation and process variables on critical quality attributes of sulfadiazine printlets.

International journal of pharmaceutics·2026

Same author

Appropriateness of Dissolution Methods on Evaluation of Digoxin Product Quality.

AAPS PharmSciTech·2026

Same author

Effect of Process and Formulation Variables on Quality, Stability, and Taste of Lamivudine Printlets Manufactured by Binder Jetting 3D Printing Method.

AAPS PharmSciTech·2026

Same author

A Comparative Study in Metformin Tablet Quality Assessment: LC-MS and LC-MS/MS Method Quantification of N-Nitroso-Dimethylamine in the Presence of Dimethyl Formamide.

International journal of analytical chemistry·2025

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

A QbD case study: Bayesian prediction of lyophilization cycle parameters.

Linas Mockus¹, David LeBlond, Prabir K Basu

¹Purdue University, Discovery Park, West Lafayette, Indiana, USA. lmockus@purdue.edu

AAPS Pharmscitech

|March 5, 2011

Summary

This study introduces a Bayesian model to predict primary drying duration in lyophilization, optimizing product development. Experimental verification confirms the model

More Related Videos

Mass Spectrometric Approaches to Study Protein Structure and Interactions in Lyophilized Powders

Mass Spectrometric Approaches to Study Protein Structure and Interactions in Lyophilized Powders

Published on: April 14, 2015

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Jun 3, 2026

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

Mass Spectrometric Approaches to Study Protein Structure and Interactions in Lyophilized Powders

Mass Spectrometric Approaches to Study Protein Structure and Interactions in Lyophilized Powders

Published on: April 14, 2015

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

Area of Science:

Pharmaceutical Science
Chemical Engineering
Process Development

Background:

Quality by Design (QbD) principles, guided by ICH Q8 R2, emphasize predicting critical process parameters.
Accurate prediction of primary drying duration is crucial for efficient lyophilization cycle development.

Purpose of the Study:

To develop and validate a Bayesian model for predicting the primary drying phase duration in lyophilization.
To enhance the prediction of critical process parameters using a product-specific approach.

Main Methods:

A Bayesian model was developed, assuming dry layer mass transfer resistance is product-specific and dependent on nucleation temperature.
The model's predictions for primary drying duration were experimentally validated using lab-scale lyophilization.

Main Results:

The Bayesian model successfully predicted primary drying phase duration.
Experimental validation confirmed the model's predictive accuracy on a lab scale.

Conclusions:

The proposed Bayesian model offers a reliable method for predicting primary drying duration.
Implementing this model during scale-up can minimize trial-and-error, reduce costs, and streamline lyophilization process development.