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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.4K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.4K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

54
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
54
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

69
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
69
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

442
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.
442
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

388
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...
388
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

2.0K
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...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Antiviral Therapies for Adults With Mild to Moderate COVID-19 Infection.

JAMA·2026
Same author

Management of Crohn Disease in Adults.

JAMA·2026
Same author

Fertility Preservation in People With Cancer.

JAMA·2026
Same author

Management of Opioid Use Disorder.

JAMA·2026
Same author

Traditional Mediterranean physical activity: integration of active lifestyle behaviors and exercise with social interactions as part of daily life.

The Journal of sports medicine and physical fitness·2025
Same author

Late fluid flow in a primitive asteroid revealed by Lu-Hf isotopes in Ryugu.

Nature·2025

Related Experiment Video

Updated: Mar 17, 2026

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

2.6K

Predictivity of Simulated ADME AutoQSAR Models over Time.

Sarah L Rodgers1, Andrew M Davis2, Nick P Tomkinson2

  • 1Accelrys Ltd, 334 Cambridge Science Park, Cambridge CB4 0WN, UK phone: +44(0)1509644560; fax: +44(0)1509644576. Sarah.Rodgers@AstraZeneca.com.

Molecular Informatics
|July 29, 2016
PubMed
Summary

Regularly updating Quantitative Structure-Activity Relationship (QSAR) models improves their predictive accuracy for current drug discovery compounds. Random Forest (RF) models demonstrated the highest predictivity in this simulation study.

Keywords:
ADMEADMETBNNPLSQuantitative structure-activity relationships (QSAR)Quantitative structure-property relationships (QSPR)Random forest (RF)autoQSAR

More Related Videos

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
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Related Experiment Videos

Last Updated: Mar 17, 2026

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

2.6K
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
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Quantitative Structure-Activity Relationship (QSAR) models are crucial for drug discovery.
  • Automated QSAR (autoQSAR) model building and updating are essential for real-time project support.
  • Understanding model behavior over time is critical for reliable predictions.

Purpose of the Study:

  • To investigate the impact of regular model updating on QSAR model predictivity over time.
  • To evaluate the performance of different QSAR modeling techniques (PLS, RF, BNN) under dynamic updating conditions.
  • To assess the ability of QSAR models to predict future compound properties using real company data.

Main Methods:

  • Simulation study using three years of real company data.
  • Monthly updating of three global QSAR models: human plasma protein binding, aqueous solubility, and logD7.4.
  • Evaluation of model predictivity using monthly temporal test sets and a final terminal test set.
  • Comparison of Partial Least Squares (PLS), Random Forest (RF), and Bayesian Neural Networks (BNN) modeling approaches.

Main Results:

  • QSAR models can predict compound properties forward in time.
  • Regularly updating models significantly enhances their ability to predict current compounds.
  • The degree of improvement varies based on the specific property and modeling technique.
  • Random Forest (RF) models consistently showed the highest predictivity for both static and regularly updated models.

Conclusions:

  • Regular updating of QSAR models is crucial for maintaining and improving predictive performance in drug discovery.
  • The choice of modeling technique impacts the effectiveness of model updating.
  • RF models offer superior predictivity for the studied properties, highlighting their utility in dynamic QSAR applications.