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

2.8K
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...
2.8K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

356
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...
356
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

317
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...
317
Pharmacokinetic–Pharmacodynamic Relationship: Model Components01:14

Pharmacokinetic–Pharmacodynamic Relationship: Model Components

176
Pharmacokinetic-pharmacodynamic (PK–PD) modeling is essential in drug development and clinical pharmacology. It provides a quantitative framework to predict drug behavior and response over time. This approach integrates pharmacokinetics (PK), which describes the drug's absorption, distribution, metabolism, and excretion, with pharmacodynamics (PD), which characterizes the drug’s biological effects and mechanisms of action.The disposition kinetics of a drug determine its plasma...
176
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

858
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
858
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

2.3K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
2.3K

You might also read

Related Articles

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

Sort by
Same author

High-Temperature Tribological Behavior and Wear Mechanisms of Stellite 6 Alloy.

Materials (Basel, Switzerland)·2026
Same author

Neonatal muscle-derived extracellular vesicles containing miR-542-3p rejuvenate aged skeletal muscle via a functional microneedle patch.

Bioactive materials·2026
Same author

Phenotyping elucidates the association between traits and bioactive components in Notopterygium incisum.

BMC plant biology·2026
Same author

Neonatal small extracellular vesicle-loaded GelNB hydrogel reprograms the vascular-immune microenvironment for spinal cord injury repair.

Bioactive materials·2026
Same author

A sodium polyphosphate-induced gel interfacial layer enables a stable Zn anode under harsh conditions.

Chemical communications (Cambridge, England)·2026
Same author

Building with Light: Photoactive Colloids as the Next Generation Meta-Atoms.

Nano letters·2026
Same journal

Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-task Learning Method.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Multi-Modal Framework for Phage-Host Interaction Prediction Using Multi-View Contrastive Learning.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Decoding Gene-Disease Associations with Computational Methods: A Survey.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual Exclusivity, and Subnet Importance.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Prediction of GO Terms Based on Partitioning PPI Networks into Highly Connected Components.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems.

IEEE transactions on computational biology and bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 15, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.3K

A Multi-Channel Knowledge-Enhanced Model for Biomedical Relation Extraction.

Hongbin Lu, Lishuang Li, Jingyao Tang

    IEEE Transactions on Computational Biology and Bioinformatics
    |April 13, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel multi-channel model to improve biomedical relation extraction by integrating external and inherent dataset knowledge. The enhanced approach boosts understanding of complex biomedical texts for better knowledge graph construction.

    More Related Videos

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.9K
    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
    07:50

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

    Published on: September 20, 2018

    16.7K

    Related Experiment Videos

    Last Updated: Apr 15, 2026

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    2.3K
    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
    05:47

    Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

    Published on: June 13, 2025

    1.9K
    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
    07:50

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

    Published on: September 20, 2018

    16.7K

    Area of Science:

    • Biomedical informatics
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Biomedical relation extraction is vital for knowledge graphs and question answering.
    • Standard neural networks struggle with complex biomedical text without external or inherent knowledge.
    • Existing methods inadequately leverage external knowledge and underutilize inherent dataset knowledge.

    Purpose of the Study:

    • To propose a multi-channel knowledge-enhanced model for biomedical relation extraction.
    • To effectively integrate both external and inherent prior knowledge into a neural network.
    • To improve the understanding of biomedical entities and relations in text.

    Main Methods:

    • Developed a multi-channel neural network architecture.
    • Incorporated external entity knowledge through sequential and structural channels.
    • Utilized inherent dataset knowledge via an external attention mechanism in a prior knowledge channel.

    Main Results:

    • The proposed model demonstrated effectiveness in biomedical relation extraction.
    • Deep exploitation of external knowledge enhanced feature learning.
    • Effective utilization of inherent dataset knowledge improved model performance.

    Conclusions:

    • The multi-channel knowledge-enhanced model significantly improves biomedical relation extraction.
    • Integrating diverse knowledge sources leads to better comprehension of biomedical text.
    • The approach offers a promising direction for advancing biomedical NLP applications.