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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.2K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.2K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

170
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
170
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

110
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
110
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

423
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
423
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

137
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
137
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

144
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...
144

You might also read

Related Articles

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

Sort by
Same author

Quality by design-guided development of silica-enabled lipid hybrid nanoparticles for enhanced olaparib dissolution.

Pharmaceutical development and technology·2026
Same author

In situ nasal gel loaded with Lactoferrin-Coated Brexpiprazole nanostructured lipid carriers for Schizophrenia: Cross-Species validation in Ketamine-Induced rat and zebrafish models.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V·2026
Same author

Assessing the impact of data harmonization on human liver microsomal stability prediction model performance.

Results in chemistry·2026
Same author

Hyaluronic Acid-functionalized Hesperidin-loaded Solid Lipid Nanoparticles for Mitigating Oxidative Stress: A Potential Strategy for Radiation-induced Skin Injury.

Applied biochemistry and biotechnology·2026
Same author

SETD5 dysfunction in human astrocytes drives IL-6-mediated neuronal impairments via the JAK/STAT signaling pathway.

bioRxiv : the preprint server for biology·2026
Same author

Depression patient-friendly formulation containing escitalopram and ascorbic acid: design, optimization, characterization, and in vivo taste assessment.

Naunyn-Schmiedeberg's archives of pharmacology·2026
Same journal

A Hybrid Experimental and in silico Platform for ITPK1 Chemical Probe Discovery.

SLAS discovery : advancing life sciences R & D·2026
Same journal

Tumor-versus-nonmalignant quantitative drug sensitivity profiling identifies capivasertib as a selective therapeutic candidate for nasopharyngeal carcinoma.

SLAS discovery : advancing life sciences R & D·2026
Same journal

ADCs for colorectal carcinoma: decoding clinical evidence for molecular design innovation.

SLAS discovery : advancing life sciences R & D·2026
Same journal

CellVision: A deep learning based image analysis platform to accelerate immuno-plaque assay data processing for dengue vaccine development.

SLAS discovery : advancing life sciences R & D·2026
Same journal

Use t tests to analyze counts of cells in two states.

SLAS discovery : advancing life sciences R & D·2026
Same journal

In silico prioritization and cheminformatics identify structurally diverse small-molecule inhibitors of Lassa virus glycoprotein-mediated membrane fusion.

SLAS discovery : advancing life sciences R & D·2026
See all related articles

Related Experiment Video

Updated: Oct 10, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

604

Validating ADME QSAR Models Using Marketed Drugs.

Vishal Siramshetty1, Jordan Williams1, Ðắc-Trung Nguyễn1

  • 1Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA.

SLAS Discovery : Advancing Life Sciences R & D
|June 28, 2021
PubMed
Summary
This summary is machine-generated.

Understanding drug absorption, distribution, metabolism, and excretion (ADME) properties through quantitative structure-activity relationship (QSAR) models aids drug discovery. Updated QSAR models for solubility, permeability, and metabolic stability improve prediction accuracy for new drug candidates.

Keywords:
ADMEPAMPA permeabilityQSARhigh-throughput screeningrat liver microsomal stabilitysolubility

More Related Videos

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.7K
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

14.2K

Related Experiment Videos

Last Updated: Oct 10, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

604
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

1.7K
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

14.2K

Area of Science:

  • Pharmacology and Cheminformatics
  • Drug Discovery and Development
  • Computational Chemistry

Background:

  • Drug Absorption, Distribution, Metabolism, and Excretion (ADME) properties are critical determinants of clinical drug success.
  • Understanding the relationship between chemical structure and ADME properties can mitigate risks in early-stage drug discovery projects.
  • Existing quantitative structure-activity relationship (QSAR) models require continuous updating with new experimental data.

Purpose of the Study:

  • To update and validate quantitative structure-activity relationship (QSAR) models for key Absorption, Distribution, Metabolism, and Excretion (ADME) endpoints.
  • To assess the in silico performance of these updated models using a set of marketed drugs.
  • To provide publicly accessible prediction services and datasets for the drug discovery community.

Main Methods:

  • Utilized in-house lead optimization data from Tier I ADME assays: kinetic aqueous solubility, Parallel Artificial Membrane Permeability Assay (PAMPA), and rat liver microsomal stability.
  • Developed updated quantitative structure-activity relationship (QSAR) models for the three selected ADME endpoints.
  • Validated the in silico performance of the QSAR models against a diverse set of marketed drugs.

Main Results:

  • Updated QSAR models demonstrated robust predictive performance, with balanced accuracies ranging from 71% to 85% for the tested ADME endpoints.
  • The validated models provide reliable predictions for drug candidates' ADME properties.
  • Publicly available prediction services and datasets were established at the ADME@NCATS web portal.

Conclusions:

  • The updated QSAR models and experimental datasets offer valuable tools for reducing risk in drug discovery projects.
  • The findings contribute to the ongoing efforts in developing accurate predictive models for drug ADME properties.
  • Enhanced computational tools and data resources are crucial for advancing the efficiency of the drug discovery pipeline.