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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

24
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...
24
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
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...
54
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

214
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
214
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
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.
38
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

543
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...
543
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

58
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...
58

You might also read

Related Articles

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

Sort by
Same author

Direct Consideration of Process History During Intensified Design of Experiments Planning Eases Interpretation of Mammalian Cell Culture Dynamics.

Bioengineering (Basel, Switzerland)·2025
Same author

Lifecycle DoE-The Companion for a Holistic Development Process.

Bioengineering (Basel, Switzerland)·2024
Same author

High-Throughput Determination of Infectious Virus Titers by Kinetic Measurement of Infection-Induced Changes in Cell Morphology.

International journal of molecular sciences·2024
Same author

Bounded rational decision-making models suggest capacity-limited concurrent motor planning in human posterior parietal and frontal cortex.

PLoS computational biology·2022

Related Experiment Video

Updated: May 25, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.3K

Bayesian Hierarchical Modeling for Variance Estimation in Biopharmaceutical Processes.

Sonja Schach1, Tobias Eilert2, Beate Presser1

  • 1CMC Statistics Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riß, Germany.

Bioengineering (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

A new Bayesian model enhances biopharmaceutical process variance estimation using meta-analysis. This approach improves critical quality attribute reliability with limited data, aiding faster drug development and ensuring patient safety.

Keywords:
Bayesian borrowingbiopharmaceutical process variabilityheteroskedastic process modelinglocation-scale modelmeta-analysisrandom-effect variances

More Related Videos

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

8.3K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

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

568

Related Experiment Videos

Last Updated: May 25, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

4.3K
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

8.3K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

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

568

Area of Science:

  • Biopharmaceutical Manufacturing
  • Statistical Modeling
  • Quality by Design

Background:

  • Accurate process variance estimation is crucial in biopharmaceutical manufacturing.
  • Limited data availability poses a significant challenge for reliable variance determination.
  • Existing methods struggle with data scarcity, impacting process development and quality assessment.

Purpose of the Study:

  • To introduce a Bayesian hierarchical model for meta-analysis of process variance.
  • To improve the estimation of process variances and critical quality attributes (CQAs) in data-scarce scenarios.
  • To enhance the evaluation of process models and support quality in biopharmaceutical development.

Main Methods:

  • Development of a Bayesian hierarchical model for meta-analysis.
  • Integration of data from multiple products to enhance variance estimation.
  • Application of the model to both upstream and downstream manufacturing processes.

Main Results:

  • The model provides more reliable process variance estimates, especially with limited data.
  • Demonstrated effectiveness through a simulation study.
  • Potential to leverage historical data for future CMC (Chemistry, Manufacturing, and Controls) drug development.

Conclusions:

  • The proposed statistical model effectively addresses data scarcity in biopharmaceutical process variance analysis.
  • It facilitates more robust process evaluation and quality assurance.
  • The method can expedite the introduction of new therapies to market while upholding patient safety and product quality.