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

996
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...
996
Sampling Plans01:23

Sampling Plans

1.5K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.5K
Random Sampling Method01:09

Random Sampling Method

11.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.8K
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

363
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...
363
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359

You might also read

Related Articles

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

Sort by
Same author

Naoxintong Capsule for Secondary Prevention of Ischemic Stroke: A Multicenter, Randomized, and Placebo-Controlled Trial.

Chinese journal of integrative medicine·2022
Same author

Relationship between antofloxacin concentration and QT prolongation and estimation of the possible false-positive rate.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2020
Same author

Population pharmacokinetics of moxifloxacin and its concentration-QT interval relationship modeling in Chinese healthy volunteers.

Acta pharmacologica Sinica·2017
Same author

Effect of integrated Chinese and Western medicine therapy on severe hand, foot and mouth disease: A prospective, randomized, controlled trial.

Chinese journal of integrative medicine·2016
Same author

Sample sizes in dosage investigational clinical trials: a systematic evaluation.

Drug design, development and therapy·2015
Same author

High expression of erythropoietin-producing hepatoma cell line-B2 (EphB2) predicts the efficiency of the Qingyihuaji formula treatment in pancreatic cancer CFPAC-1 cells through the EphrinB1-EphB2 pathway.

Oncology letters·2014

Related Experiment Video

Updated: May 3, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

9.4K

Random sparse sampling strategy using stochastic simulation and estimation for a population pharmacokinetic study.

Xiao-Hui Huang1, Kun Wang2, Ji-Han Huang2

  • 1Center of Pharmacokinetics, College of Pharmacy, Anhui Medical University, Hefei 230032, Anhui, China.

Saudi Pharmaceutical Journal : SPJ : the Official Publication of the Saudi Pharmaceutical Society
|February 5, 2014
PubMed
Summary
This summary is machine-generated.

Optimizing sparse sampling in pharmacokinetic modeling is crucial. This study recommends specific sample sizes and points, demonstrating the value of stochastic simulation for efficient model development.

Keywords:
AmlodipineModel estimationPopulation pharmacokineticsSparse sampling

More Related Videos

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

15.0K
Intraventricular Drug Delivery and Sampling for Pharmacokinetics and Pharmacodynamics Study
09:18

Intraventricular Drug Delivery and Sampling for Pharmacokinetics and Pharmacodynamics Study

Published on: March 31, 2022

2.2K

Related Experiment Videos

Last Updated: May 3, 2026

An Unbiased Approach of Sampling TEM Sections in Neuroscience
10:56

An Unbiased Approach of Sampling TEM Sections in Neuroscience

Published on: April 13, 2019

9.4K
The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

15.0K
Intraventricular Drug Delivery and Sampling for Pharmacokinetics and Pharmacodynamics Study
09:18

Intraventricular Drug Delivery and Sampling for Pharmacokinetics and Pharmacodynamics Study

Published on: March 31, 2022

2.2K

Area of Science:

  • Pharmacokinetics
  • Pharmacometric modeling
  • Clinical trial design

Background:

  • Population pharmacokinetic (PopPK) models are essential for understanding drug behavior.
  • Sparse sampling strategies significantly impact the accuracy and efficiency of PopPK model development.
  • Amlodipine clinical trial data provides a basis for evaluating sampling scenarios.

Purpose of the Study:

  • To evaluate the impact of sample size and sampling frequency on population pharmacokinetic model development.
  • To determine optimal sparse sampling strategies for accurate pharmacokinetic parameter estimation.
  • To assess the utility of stochastic simulation and estimation for model evaluation.

Main Methods:

  • Utilized stochastic simulation and estimation techniques.
  • Generated 1000 simulated datasets based on amlodipine pharmacokinetic parameters.
  • Investigated 55 different sparse sampling scenarios, varying sample size and points per individual.
  • Fitted three candidate models to the simulated data.

Main Results:

  • Identified optimal sparse sampling strategies: 60 samples with three collection points or 20 samples with five collection points.
  • Demonstrated that stochastic simulation and estimation are effective for evaluating model development.
  • Showcased the efficiency of the proposed quantitative methodology for compartment model estimation.

Conclusions:

  • The stochastic simulation and estimation method is valuable for efficient compartment model development and evaluation.
  • Recommended sparse sampling strategies can improve the precision of pharmacokinetic parameter estimates.
  • This quantitative approach can be applied to other similar model development and evaluation studies.