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

Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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...
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...
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...
Nonlinear Pharmacokinetics: Michaelis-Menten Equation01:18

Nonlinear Pharmacokinetics: Michaelis-Menten Equation

The Michaelis–Menten equation is a fundamental model for describing capacity-limited kinetics in drug metabolism. It offers insights into the rate of decline of plasma drug concentration Cp over time, with Vmax and KM as pivotal parameters.
Vmax represents the maximum achievable process rate, while KM, known as the Michaelis constant, signifies the drug concentration at which the process rate reaches half its maximum. This relationship between Vmax, KM, and Cp gives rise to three distinct...

You might also read

Related Articles

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

Sort by
Same author

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.

Nature communications·2026
Same author

Corrigendum to "Abemaciclib plus a nonsteroidal aromatase inhibitor as initial therapy for HR+, HER2- advanced breast cancer: final overall survival results of MONARCH 3": [Ann Oncol 2024; 35: 718-727].

Annals of oncology : official journal of the European Society for Medical Oncology·2025
Same author

Nuclear model calculations on alpha-induced reactions on <sup>nat</sup>Cu: Evaluation of excitation functions of the <sup>nat</sup>Cu(α, x)<sup>67,66</sup>Ga, <sup>65</sup>Zn reactions.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2025
Same author

Analysis of flap sugar as an objective monitoring of intra-operative flap vascularity following a single vein vs. a double vein anastomosis.

Acta chirurgiae plasticae·2025
Same author

Systematic study of effect of theoretical models on cross sections for <sup>nat</sup>Ca(α, x)<sup>47,46,44g,44m,43</sup>Sc reactions.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2025
Same author

Combining structural modeling and deep learning to calculate the <i>E. coli</i> protein interactome and functional networks.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jul 22, 2026

Quantitative FRET (F&#246;rster Resonance Energy Transfer) Analysis for SENP1 Protease Kinetics Determination
16:02

Quantitative FRET (Förster Resonance Energy Transfer) Analysis for SENP1 Protease Kinetics Determination

Published on: February 21, 2013

Use of ridge regression for improved estimation of kinetic constants from PET data.

F O'Sullivan1, A Saha

  • 1Department of Statistics, University College, Cork, Ireland.

IEEE Transactions on Medical Imaging
|May 8, 1999
PubMed
Summary

Ridge regression improves parameter estimation in positron emission tomography (PET) by adding constraints to nonlinear least squares. This method significantly reduces errors in kinetic modeling for FDG-PET studies.

More Related Videos

Kinetic Screening of Nuclease Activity using Nucleic Acid Probes
06:52

Kinetic Screening of Nuclease Activity using Nucleic Acid Probes

Published on: November 1, 2019

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the &#181;s-ms Timescale
08:09

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

Related Experiment Videos

Last Updated: Jul 22, 2026

Quantitative FRET (F&#246;rster Resonance Energy Transfer) Analysis for SENP1 Protease Kinetics Determination
16:02

Quantitative FRET (Förster Resonance Energy Transfer) Analysis for SENP1 Protease Kinetics Determination

Published on: February 21, 2013

Kinetic Screening of Nuclease Activity using Nucleic Acid Probes
06:52

Kinetic Screening of Nuclease Activity using Nucleic Acid Probes

Published on: November 1, 2019

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the &#181;s-ms Timescale
08:09

15N CPMG Relaxation Dispersion for the Investigation of Protein Conformational Dynamics on the µs-ms Timescale

Published on: April 19, 2021

Area of Science:

  • Nuclear Medicine
  • Biophysics
  • Computational Biology

Background:

  • Parameter estimation in positron emission tomography (PET) using nonlinear least squares (NLS) often yields unacceptable mean square error.
  • Physiological constraints can improve the accuracy of parameter estimates in PET kinetic modeling.
  • Existing methods for parameter estimation in PET require refinement to enhance accuracy and reliability.

Purpose of the Study:

  • To evaluate the effectiveness of a ridge-regression technique for improving parameter estimation in PET studies.
  • To assess the performance of a data-dependent method for selecting the degree of penalization in ridge regression.
  • To quantify the reduction in mean square error for kinetic parameters using ridge regression in FDG-PET studies.

Main Methods:

  • Augmenting the standard NLS criterion with a penalty term for physiologically unreasonable estimates.
  • Applying a variation of the Hoerl et al. plug-in methodology for data-dependent selection of the penalty term.
  • Conducting a simulation study using a three-compartment model for fluoro-deoxyglucose (FDG) PET data.

Main Results:

  • Ridge regression reduced the root mean square error of parameter estimates by approximately 60% across various noise levels.
  • The performance improvement was consistent and not highly dependent on the specific penalty function formulation.
  • The proposed ridge-regression approach demonstrated significant potential for enhancing kinetic parameter estimation in PET.

Conclusions:

  • Ridge regression is a promising tool for improving the accuracy of kinetic parameter estimation in PET studies.
  • The method offers a substantial reduction in estimation errors, particularly in the presence of noise.
  • Further application of ridge regression can lead to more reliable quantitative analyses in PET imaging.