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

Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

282
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
282
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

25
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...
25
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

204
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...
204
Drug Concentrations: Measurements01:23

Drug Concentrations: Measurements

1.2K
Drug concentration is the quantity of a drug present in a biological sample. Measuring drug amounts in biological samples allows the clinician to understand how a drug is absorbed, distributed, metabolized, and excreted. Samples can be obtained through invasive or non-invasive methods. Invasive techniques involve surgical or parenteral interventions to gather blood, cerebrospinal fluid, or tissue biopsy. Conversely, non-invasive approaches provide samples like urine, feces, and saliva.
Plasma...
1.2K
Conserved Binding Sites01:49

Conserved Binding Sites

5.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
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...
290

You might also read

Related Articles

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

Sort by
Same author

Quantitative structure-insecticidal activity of essential oils on the human head louse (<i>Pediculus humanus capitis</i>).

SAR and QSAR in environmental research·2024
Same author

QSAR models for insecticidal properties of plant essential oils on the housefly (<i>Musca domestica</i> L.).

SAR and QSAR in environmental research·2021
Same author

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides.

SAR and QSAR in environmental research·2019
Same author

Sedentary behavior and compensatory mechanisms in response to different doses of exercise-a randomized controlled trial in overweight and obese adults.

European journal of clinical nutrition·2017
Same author

Raw sewage as breeding site to Aedes (Stegomyia) aegypti (Diptera, culicidae).

Acta tropica·2016
Same author

The Carbon Allotrope Hexagonite and Its Potential Synthesis from Cold Compression of Carbon Nanotubes.

Journal of chemical theory and computation·2015

Related Experiment Video

Updated: Feb 22, 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

14.7K

Predicting the bioconcentration factor through a conformation-independent QSPR study.

J F Aranda1, D E Bacelo2, M S Leguizamón Aparicio3

  • 1a Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP , La Plata , Argentina.

SAR and QSAR in Environmental Research
|October 3, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantitative structure-property relationship (QSPR) model using the ANTARES dataset to predict bioconcentration factors (BCF) in chemicals. The developed model enhances prediction accuracy for environmental risk assessment.

Keywords:
Bioconcentration factor (BCF)molecular descriptorspesticidesquantitative structure-property relationshipsreplacement method

More Related Videos

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.3K
Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry
13:26

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Published on: September 13, 2014

62.9K

Related Experiment Videos

Last Updated: Feb 22, 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

14.7K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.3K
Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry
13:26

Determination of Protein-ligand Interactions Using Differential Scanning Fluorimetry

Published on: September 13, 2014

62.9K

Area of Science:

  • Environmental Chemistry
  • Computational Chemistry
  • Toxicology

Background:

  • Bioconcentration factor (BCF) is a critical parameter for assessing chemical bioaccumulation in aquatic organisms.
  • Existing models for predicting BCF often have limitations in accuracy and applicability.
  • The ANTARES dataset provides a robust collection of experimental BCF data for diverse compounds.

Purpose of the Study:

  • To develop a novel, conformation-independent quantitative structure-property relationship (QSPR) model for predicting bioconcentration factors (BCF).
  • To explore and leverage a comprehensive set of molecular descriptors from various computational tools.
  • To improve the statistical performance and predictive power of BCF models.

Main Methods:

  • Utilized the ANTARES dataset comprising 851 compounds, including 159 pesticides.
  • Generated 27,017 molecular descriptors using freeware tools (PaDEL, Epi Suite, CORAL, Mold2, RECON, QuBiLs-MAS).
  • Employed the Replacement Method for variable subset selection to build multivariable linear regression models.

Main Results:

  • Identified complementary contributions of different molecular descriptor calculation tools.
  • Developed a QSPR model with improved statistical quality compared to previous models.
  • The best models were achieved using multivariable linear regression with the Replacement Method.

Conclusions:

  • The developed QSPR model offers enhanced prediction accuracy for bioconcentration factors.
  • The integration of diverse descriptor calculation tools proved beneficial for model performance.
  • This study provides a valuable tool for environmental risk assessment and chemical safety evaluations.