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

Variability: Analysis01:11

Variability: Analysis

433
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
433
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

1.1K
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...
1.1K
Variance01:15

Variance

12.0K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
12.0K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

383
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
383
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.1K

You might also read

Related Articles

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

Sort by
Same author

The evolution of nonlinear mixed effects modeling in pharmacometrics: toward AI-based variational autoencoders.

Journal of pharmacokinetics and pharmacodynamics·2026
Same author

Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Evaluation of adalimumab tapering regimens in children and adolescents with juvenile idiopathic arthritis in remission: a simulation study.

Pediatric rheumatology online journal·2026
Same author

Predictive AI in Clinical Pharmacology: A Call to Action to Develop Robust Benchmarking Practices.

CPT: pharmacometrics & systems pharmacology·2025
Same author

Population Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Analysis for Clazakizumab in Patients With End-Stage Kidney Disease Undergoing Dialysis.

Clinical and translational science·2025
Same author

Influence of Disease Type and Activity on Adalimumab Exposure in Children with Inflammatory Rheumatic Diseases.

Journal of clinical pharmacology·2025

Related Experiment Video

Updated: Jan 12, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

Redefining Parameter Estimation and Covariate Selection via Variational Autoencoders: One Run Is All You Need.

Jan Rohleff1, Freya Bachmann1, Uri Nahum2,3

  • 1Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany.

CPT: Pharmacometrics & Systems Pharmacology
|November 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel generative Artificial Intelligence (AI) framework using Variational Autoencoders (VAEs) for nonlinear mixed effects (NLME) pharmacometrics (PMX) modeling. The AI-powered VAE efficiently automates covariate selection and parameter estimation in a single run.

Keywords:
Bayesian inferencecovariate selectiondata‐driven modelinggenerative artificial intelligence (AI)machine learningnonlinear mixed effects modelingparameter estimationvariational autoencoder

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

Related Experiment Videos

Last Updated: Jan 12, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

Area of Science:

  • Pharmacometrics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Nonlinear mixed effects (NLME) modeling is crucial in pharmacometrics (PMX) for drug development.
  • Traditional NLME covariate selection is manual and iterative, often requiring repeated model fitting.
  • Generative AI frameworks like Variational Autoencoders (VAEs) excel at learning from complex data.

Purpose of the Study:

  • To integrate generative AI (VAE) with mechanism-based PMX modeling.
  • To develop an automated approach for covariate selection and parameter estimation in NLME models.
  • To enhance efficiency and robustness in pharmacometric model development.

Main Methods:

  • A VAE framework was specifically designed for NLME modeling in pharmacometrics.
  • The Evidence Lower Bound objective in VAEs was replaced with a corrected Bayesian information criterion.
  • This enables simultaneous evaluation of covariate-parameter combinations for automated joint estimation.

Main Results:

  • The proposed AI-PMX approach successfully automates covariate selection and parameter estimation in a single run.
  • The VAE-based method demonstrated high-quality results, outperforming traditional stepwise procedures in efficiency.
  • Manual selection and repeated model fitting were rendered unnecessary.

Conclusions:

  • The generative AI-based VAE framework offers an efficient and automated solution for NLME modeling in pharmacometrics.
  • This approach advances automated model development, supporting model-informed drug development.
  • The VAE framework integrates generative AI flexibility with PMX interpretability and robustness.