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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

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.
Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters00:54

Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters

The noncompartmental approach is a widely used method in pharmacokinetics to assess drugs' behaviors in the body. It considers several factors, including clearance, bioavailability, and total volume of distribution.
One key aspect of the noncompartmental approach is determining a drug's total clearance. This can be done by dividing the drug dose by the area under the concentration-time curve from zero to infinity. The area under the concentration-time curve represents the drug's overall...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

You might also read

Related Articles

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

Sort by
Same author

Advancing quantitative clinical pharmacology competencies in Francophone Africa through an on-line learning framework.

Journal of pharmacokinetics and pharmacodynamics·2026
Same author

Application of modelling of cell monolayer permeation data to generate input parameters compatible with in vitro-in vivo translation of blood-brain-barrier disposition of drugs.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2026
Same author

Population Physiologically-Based Pharmacokinetic Modeling to Determine Ontogeny: A Quantitative Clinical Pharmacology Example in Pediatric Rare Disease.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Population pharmacokinetics and dose-response relationships of mitoxantrone in children with acute myeloid leukaemia.

British journal of clinical pharmacology·2026
Same author

Virtual Twin-PBPK Modelling: A Step Toward Precision Dosing in Patients with Obesity.

The AAPS journal·2026
Same author

A prospective cohort feasibility study of real-time beta-lactam antimicrobial therapeutic drug monitoring in critically ill patients with lower respiratory infection: The TDM-TIME study.

Journal of the Intensive Care Society·2025

Related Experiment Video

Updated: Jun 20, 2026

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

Sample-size calculations for multi-group comparison in population pharmacokinetic experiments.

Kayode Ogungbenro1, Leon Aarons

  • 1Centre for Applied Pharmacokinetics Research, The University of Manchester, Oxford Road, Manchester, UK. kayode.ogungbenro@manchester.ac.uk

Pharmaceutical Statistics
|August 29, 2009
PubMed
Summary

This study presents a novel method for sample size calculation in population pharmacokinetic studies using mixed-effects modeling. The approach efficiently determines sample sizes for hypothesis testing in complex nonlinear models, improving experimental design.

More Related Videos

Comprehensive Analysis of Drug Response using the FLICK Assay
09:42

Comprehensive Analysis of Drug Response using the FLICK Assay

Published on: June 6, 2025

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

Related Experiment Videos

Last Updated: Jun 20, 2026

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays
06:13

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Published on: February 21, 2020

Comprehensive Analysis of Drug Response using the FLICK Assay
09:42

Comprehensive Analysis of Drug Response using the FLICK Assay

Published on: June 6, 2025

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

Area of Science:

  • Pharmacokinetics
  • Statistical Modeling
  • Experimental Design

Background:

  • Population pharmacokinetic (PopPK) studies are crucial for understanding drug behavior.
  • Accurate sample size calculation is essential for robust hypothesis testing in PopPK.
  • Existing methods often lack applicability to complex nonlinear PopPK models.

Purpose of the Study:

  • To develop an efficient and fast approach for sample size calculation in population pharmacokinetic experiments.
  • To extend existing sample size calculation methods to complex nonlinear PopPK models with multi-group comparisons.
  • To facilitate hypothesis testing for model parameters in PopPK studies.

Main Methods:

  • Linearization of the structural nonlinear model around random effects to derive a marginal model.
  • Application of Wald's test for hypothesis testing on model parameters.
  • Development of an approach applicable to nonlinear PopPK models with multi-group comparisons and complex covariates.

Main Results:

  • The proposed approach efficiently calculates sample sizes for hypothesis testing in complex nonlinear PopPK models.
  • The method accommodates design challenges like unequal group allocation and unbalanced sampling schedules.
  • Simulations demonstrated good agreement between calculated and simulated power for a one-compartment model.

Conclusions:

  • This approach offers an efficient and versatile tool for sample size determination in advanced population pharmacokinetic studies.
  • It enhances the statistical rigor of hypothesis testing in PopPK by providing a reliable method for sample size calculation.
  • The method is suitable for complex nonlinear models and various experimental designs, advancing PopPK research.