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

Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

195
The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
195
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

73
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
73
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

141
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
141
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

1.5K
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...
1.5K
Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

2.5K
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
2.5K
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

864
The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
864

You might also read

Related Articles

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

Sort by
Same author

Precision Profile Weighted Deming Regression for Methods Comparison.

The journal of applied laboratory medicine·2026
Same author

Plasma pTau 217/β-amyloid 1-42 ratio for enhanced accuracy and reduced uncertainty in detecting amyloid pathology.

Brain : a journal of neurology·2026
Same author

Quantile-Quantile Toolbox.

The journal of applied laboratory medicine·2024
Same author

A Quantile-Quantile Toolbox for Reference Intervals.

The journal of applied laboratory medicine·2024
Same author

Multimodality Imaging for Cardiac Surveillance of Cancer Treatment in Children: Recommendations From the American Society of Echocardiography.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography·2023
Same author

Burden of prostate cancer in the Middle East: A comparative analysis based on global cancer observatory data.

Cancer medicine·2023
Same journal

Bioactive Compounds and Mechanistic Insights of Chuanxiong Rhizoma and Angelicae Sinensis Radix in Endometriosis Treatment: A Network Pharmacology and Experimental Validation Study.

Current computer-aided drug design·2026
Same journal

Identification of Potential Compounds from Medicinal Plants using Molecular Docking and Molecular Dynamics Simulation with Special Reference to Autism Spectrum Disorder.

Current computer-aided drug design·2026
Same journal

Molecular Docking, Molecular Dynamics Simulation, DFT, and ADMET Prediction of 3-Carbonyl-3-Hydroxyl-Isoindolin-1-ones, Revealing Potential Inhibitors of MAO-B.

Current computer-aided drug design·2026
Same journal

Drug Repurposing Using Machine Learning and Deep Learning: A Systematic Literature Review.

Current computer-aided drug design·2026
Same journal

Augmented Chemical Language Meets Descriptor Space: A Hybrid Deep-learning Pipeline for Predicting Blood-brain Barrier Penetration of Drug-like Molecules.

Current computer-aided drug design·2026
Same journal

Integrating Network Pharmacology and Molecular Docking to Decipher the Anti-fibrotic Role of Fuzheng Xiezhuo Decoction via NLRP3/TNF-α/IL-6 Pathway in Renal Fibrosis.

Current computer-aided drug design·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.8K

Development of a two-step indirect method for modeling Ecom50.

Lowell H Hall1, L Mark Hall, Dennis W Hill

  • 1Department of Chemistry, Eastern Nazarene College, 23 East Elm Avenue, Quincy, MA 02170, USA. lowell.hall@enc.edu.

Current Computer-Aided Drug Design
|January 1, 2015
PubMed
Summary
This summary is machine-generated.

A new two-step modeling approach improves predictions by transforming mass spectrometry collision energy (CE50) data. This method reduces prediction error and confidence intervals for enhanced metabolite modeling.

More Related Videos

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput
12:22

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

Published on: September 22, 2019

9.1K
A Streamlined, Label-Free Real-Time 50% Tissue Culture Infectious Dose (TCID50) Assay using Impedance for Automated Viral Titer Quantification
08:01

A Streamlined, Label-Free Real-Time 50% Tissue Culture Infectious Dose (TCID50) Assay using Impedance for Automated Viral Titer Quantification

Published on: January 9, 2026

501

Related Experiment Videos

Last Updated: Apr 19, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.8K
A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput
12:22

A Strategy to Identify Compounds that Affect Cell Growth and Survival in Cultured Mammalian Cells at Low-to-Moderate Throughput

Published on: September 22, 2019

9.1K
A Streamlined, Label-Free Real-Time 50% Tissue Culture Infectious Dose (TCID50) Assay using Impedance for Automated Viral Titer Quantification
08:01

A Streamlined, Label-Free Real-Time 50% Tissue Culture Infectious Dose (TCID50) Assay using Impedance for Automated Viral Titer Quantification

Published on: January 9, 2026

501

Area of Science:

  • Analytical Chemistry
  • Computational Chemistry
  • Metabolomics

Background:

  • Modeling experimental data often involves transformations.
  • Data range significantly impacts modeling statistics.
  • Collision energy (CE50) data in mass spectrometry has a wide range.

Purpose of the Study:

  • To develop and validate a two-step modeling approach for algebraically transformed data.
  • To assess the impact of data range reduction on modeling accuracy.
  • To improve predictions of mass spectrometry collision energy (CE50) derived values.

Main Methods:

  • Utilized a two-step modeling process: standard modeling (PLS) followed by algebraic transformation.
  • Applied the method to mass spectrometry collision energy (CE50) data.
  • Compared predictions of transformed data (Ecom50) using the novel method versus standard approaches.

Main Results:

  • The two-step method reduced the standard error of prediction by 21% for Ecom50.
  • A significant reduction in the confidence interval for prediction was observed.
  • The transformed data (Ecom50) exhibited a smaller data range (13.5% of CE50).

Conclusions:

  • The proposed two-step modeling strategy enhances prediction accuracy for transformed experimental data.
  • This approach is particularly effective when dealing with data sets exhibiting a wide initial range.
  • The method contributes to developing predictive models for human metabolites.