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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

720
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...
720
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

267
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...
267
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

79
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.
79
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
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...
74
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

140
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
140
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

133
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
133

You might also read

Related Articles

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

Sort by
Same author

Patient-Specific Determinants of Response to BCMA- and GPRC5D-Targeted CAR T-Cell Therapy in Multiple Myeloma: A QSP Analysis of Clinical Trial and Real-World Data.

Clinical pharmacology and therapeutics·2026
Same author

Clinical Validation of a QSP Model for ISB 2001, a Trispecific T Cell Engager to Support Optimal FIH Study Design in RRMM Patients.

Clinical pharmacology and therapeutics·2026
Same author

Understanding quantitative effects of anti-amyloid therapies on tau biomarkers and functional outcome. Insights from a comprehensive mechanistic quantitative systems pharmacology study.

Frontiers in pharmacology·2026
Same author

Quantitative Systems Pharmacology Modeling Amid the Rise of Agentic AI.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Two phase 1 randomised studies investigating the safety and pharmacokinetics of bepranemab in healthy participants of different ethnicities.

BMJ neurology open·2026
Same author

Model-Based Patient Selection and Dosing Strategies for HRAS and PIK3CA Dysregulated HNSCC: A QSP Model for Alpelisib and Tipifarnib Combination.

Clinical pharmacology and therapeutics·2026

Related Experiment Video

Updated: Jul 11, 2025

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo
12:42

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo

Published on: January 7, 2019

9.5K

A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development.

Rajat Desikan1, Massimiliano Germani2, Piet H van der Graaf3

  • 1Clinical Pharmacology Modelling & Simulation, GSK, United Kingdom.

Journal of Pharmaceutical Sciences
|November 4, 2023
PubMed
Summary
This summary is machine-generated.

Model-informed vaccine dose-optimization and development (MIVD) offers a quantitative approach to improve vaccine efficacy and safety. This framework integrates dose, exposure, efficacy, and toxicity for better vaccine design compared to traditional methods.

Keywords:
Clinical trial simulation(s)Clinical trial(s)Dose-responseImmune response(s)ImmunogenicityIn silico modelingInterspecies (dose) scalingPharmacokinetic/pharmacodynamic (PK/PD) modelingToxicityVaccine(s)

More Related Videos

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
04:24

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

1.5K
Whole-animal Imaging and Flow Cytometric Techniques for Analysis of Antigen-specific CD8+ T Cell Responses after Nanoparticle Vaccination
11:07

Whole-animal Imaging and Flow Cytometric Techniques for Analysis of Antigen-specific CD8+ T Cell Responses after Nanoparticle Vaccination

Published on: April 29, 2015

13.3K

Related Experiment Videos

Last Updated: Jul 11, 2025

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo
12:42

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo

Published on: January 7, 2019

9.5K
Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
04:24

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application

Published on: June 16, 2023

1.5K
Whole-animal Imaging and Flow Cytometric Techniques for Analysis of Antigen-specific CD8+ T Cell Responses after Nanoparticle Vaccination
11:07

Whole-animal Imaging and Flow Cytometric Techniques for Analysis of Antigen-specific CD8+ T Cell Responses after Nanoparticle Vaccination

Published on: April 29, 2015

13.3K

Area of Science:

  • Immunology
  • Clinical Pharmacology
  • Vaccinology

Background:

  • Traditional vaccine development often relies on empirical methods, lacking robust clinical pharmacology integration.
  • Complex immunobiological mechanisms and limited quantitative frameworks hinder dose-exposure-efficacy-toxicity assessments.
  • The COVID-19 pandemic underscored issues with suboptimal vaccine dosing and immunogenicity, necessitating improved trial designs.

Purpose of the Study:

  • To introduce a quantitative, clinical pharmacology-based framework for vaccine development.
  • To integrate vaccine dose, exposure, efficacy, and toxicity into a unified model.
  • To propose model-informed vaccine dose-optimization and development (MIVD) as a strategic advantage.

Main Methods:

  • Developing a quantitative framework integrating key vaccine parameters.
  • Applying model-informed approaches to vaccine dose-efficacy-toxicity relationships.
  • Analyzing scenarios where MIVD offers advantages over conventional practices.

Main Results:

  • A unified framework, MIVD, is proposed for optimizing vaccine development.
  • MIVD facilitates a quantitative understanding of dose-exposure-efficacy-toxicity.
  • Scenarios demonstrating MIVD's strategic benefits are highlighted.

Conclusions:

  • MIVD represents a significant advancement over traditional empirical vaccine development.
  • Implementing MIVD can enhance the selection of optimal vaccine dosing regimens.
  • This model-informed approach is crucial for future vaccine design and efficacy.