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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

298
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...
298
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

31
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
31
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
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...
2.3K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

438
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...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

297
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...
297
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

23
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
23

You might also read

Related Articles

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

Sort by
Same author

Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter Estimation.

CPT: pharmacometrics & systems pharmacology·2026
Same author

SPIX: A new software package to reveal chemical reactions at trace amounts in very complex mixtures from high-resolution mass spectra dataset.

Rapid communications in mass spectrometry : RCM·2020
Same author

The Standard Output: A Tool-Agnostic Modeling Storage Format.

CPT: pharmacometrics & systems pharmacology·2018
See all related articles

Related Experiment Video

Updated: Feb 23, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

742

Pharmacometrics models with hidden Markovian dynamics.

Marc Lavielle1

  • 1Inria & Ecole Polytechnique, Université Paris-Saclay, Paris, France. Marc.Lavielle@inria.fr.

Journal of Pharmacokinetics and Pharmacodynamics
|September 2, 2017
PubMed
Summary
This summary is machine-generated.

This study overviews pharmacometric models with latent Markovian dynamics, including hidden Markov models and diffusion models. These models, extended to mixed-effects, aid in analyzing complex biological systems like disease progression and pharmacokinetics.

Keywords:
Diffusion modelEpilepsyHidden Markov modelMixed effects modelPharmacokineticsStochastic differential equation

More Related Videos

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Feb 23, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

742
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Pharmacometrics
  • Mathematical Biology
  • Statistical Modeling

Background:

  • Pharmacometric models often describe complex biological processes.
  • Latent processes with Markovian dynamics are crucial for modeling unobserved phenomena.
  • Existing models like hidden Markov models and diffusion models have limitations in population-level analysis.

Purpose of the Study:

  • To provide an overview of pharmacometric models incorporating latent Markovian dynamics.
  • To demonstrate the extension of these models to mixed-effects models for population analysis.
  • To illustrate the application of these models in disease state dynamics and pharmacokinetics.

Main Methods:

  • Overview of hidden Markov models (discrete-time, discrete-state) and diffusion models (continuous-time, continuous-state).
  • Extension of these models to mixed-effects models for population-level parameter estimation.
  • Integration of forward-backward algorithm (for HMMs) and extended Kalman filter (for diffusion models) with mixed-effects inference algorithms.

Main Results:

  • Demonstration of straightforward extension of latent Markovian models to mixed-effects models.
  • Successful application of combined inference algorithms for parameter estimation.
  • Illustrative examples using a hidden Markov model for epilepsy and a diffusion model for theophylline pharmacokinetics.

Conclusions:

  • Pharmacometric models with latent Markovian dynamics offer a flexible framework for complex biological systems.
  • Mixed-effects extensions and combined inference algorithms enable robust population-level analysis.
  • These models are valuable tools for understanding disease progression and drug behavior.