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

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,...
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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)...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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...
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...

You might also read

Related Articles

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

Sort by
Same author

Constrained numerical deconvolution using orthogonal polynomials.

Heliyon·2024
Same author

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose.

British journal of pharmacology·2017
Same author

Population Pharmacokinetics of Selumetinib and Its Metabolite N-desmethyl-selumetinib in Adult Patients With Advanced Solid Tumors and Children With Low-Grade Gliomas.

CPT: pharmacometrics & systems pharmacology·2017
Same author

Why has model-informed precision dosing not yet become common clinical reality? lessons from the past and a roadmap for the future.

Clinical pharmacology and therapeutics·2017
Same author

Mathematical model of T-cell lymphoblastic lymphoma: disease, treatment, cure or relapse of a virtual cohort of patients.

Mathematical medicine and biology : a journal of the IMA·2017
Same author

Identification of the effect of multiple polymorphisms on the pharmacokinetics of simvastatin and simvastatin acid using a population-modeling approach.

Clinical pharmacology and therapeutics·2014

Related Experiment Video

Updated: Jul 10, 2026

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs
10:02

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs

Published on: July 23, 2016

The estimation of population pharmacokinetic parameters using an EM algorithm

L Aarons1

  • 1Pharmacy Department, University of Manchester, UK.

Computer Methods and Programs in Biomedicine
|September 1, 1993
PubMed
Summary

This study introduces an Estimation-Maximization (EM) algorithm for non-linear mixed-effects models, enhancing pharmacokinetic analyses. The robust algorithm aids in estimating covariate relationships and pharmacokinetic-pharmacodynamic models from sparse data.

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

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

Related Experiment Videos

Last Updated: Jul 10, 2026

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs
10:02

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs

Published on: July 23, 2016

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

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

Area of Science:

  • Statistics
  • Pharmacokinetics
  • Computational Biology

Background:

  • Non-linear mixed-effects models (NLMEMs) are crucial for analyzing complex biological data, particularly in population pharmacokinetics.
  • Accurate estimation of fixed and random effects is essential for understanding population variability and individual responses.
  • Existing methods for NLMEMs can be computationally intensive and may face convergence issues.

Purpose of the Study:

  • To develop and evaluate an Estimation-Maximization (EM) algorithm for analyzing data from non-linear mixed-effects models.
  • To assess the utility of the EM algorithm in population pharmacokinetic studies, including covariate relationship investigation and pharmacokinetic-pharmacodynamic (PK/PD) modeling.
  • To provide a robust and practical computational tool for pharmacokinetic data analysis.

Main Methods:

  • Utilized an EM algorithm for estimating random effects after linearization of NLMEMs.
  • Employed simplex minimization for maximum likelihood estimation of fixed parameters.
  • Applied the method to a simple linear model and population pharmacokinetic data, including indomethacin.
  • Investigated the use of posterior parameter estimates for covariate analysis.

Main Results:

  • The implemented EM algorithm demonstrated robustness in practice, although it was found to be slow.
  • The algorithm facilitated the estimation of covariate relationships in population pharmacokinetic studies using individual posterior parameter estimates.
  • Posterior means derived from the algorithm proved useful for estimating PK/PD relationships from sparse data.
  • Comparison with an alternative linearization method (Lindstrom and Bates) showed similar results for indomethacin data, though the alternative was less stable.

Conclusions:

  • The developed EM algorithm provides a valuable tool for NLMEM analysis, particularly in population pharmacokinetics.
  • Individual posterior parameter estimates are highly beneficial for detecting covariate relationships.
  • The method enables the estimation of PK/PD relationships from limited pharmacokinetic data where individual modeling is not feasible.
  • Future work includes replacing the simplex method with a Newton-Raphson routine to improve convergence speed.