Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

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.

The Two-State Receptor Model

The Two-State Receptor Model

The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with one...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

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...

Model Approaches for Pharmacokinetic Data: Compartment Models

Model Approaches for Pharmacokinetic Data: Compartment Models

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...

Model Approaches for Pharmacokinetic Data: Physiological Models

Model Approaches for Pharmacokinetic Data: Physiological Models

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...

Quantitative Aspects of Drug-Receptor Interaction

Quantitative Aspects of Drug-Receptor Interaction

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 Kd...

You might also read

Related Articles

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

Sort by

Same author

International Union of Basic and Clinical Pharmacology. CXXII. Applying an objective evaluation to the status of class A orphan G protein-coupled receptors.

Pharmacological reviews·2026

Same author

Electron-Transfer and Exchange-Interaction Model of the Ligand Hyperfine Structure of Alkylated Iron-Sulfur Clusters.

The journal of physical chemistry. A·2026

Same author

The Concise Guide to PHARMACOLOGY 2025/26: G protein-coupled receptors.

British journal of pharmacology·2025

Same author

G Protein-Coupled Receptor Signaling in CNS (Re)Myelination.

Journal of neurochemistry·2025

Same author

G Protein: β-Arrestin Bias Confers Differential Regulation of Gα<sub>q</sub> Signaling by GPR17 Antagonists.

ACS chemical neuroscience·2025

Same author

Large linear high-frequency strain by interlocked monoclinic polar nanoregions.

Nature materials·2025

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Matching models to data: a receptor pharmacologist's guide.

David A Hall¹, Christopher J Langmead

¹Respiratory CEDD, GlaxoSmithKline, Stevenage, Herts, UK.

British Journal of Pharmacology

|October 28, 2010

Summary

This review covers fitting mechanistic mathematical models to experimental data, emphasizing assay planning and direct fitting methods. It addresses common receptor pharmacology issues to enhance data analysis quality.

More Related Videos

A BW Reporter System for Studying Receptor-Ligand Interactions

A BW Reporter System for Studying Receptor-Ligand Interactions

Published on: January 7, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Related Experiment Videos

Last Updated: Jun 7, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

A BW Reporter System for Studying Receptor-Ligand Interactions

A BW Reporter System for Studying Receptor-Ligand Interactions

Published on: January 7, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Analytical Receptor Pharmacology
Mathematical Modeling
Drug Discovery

Background:

Part of a themed section on Analytical Receptor Pharmacology in Drug Discovery.
Addresses common challenges in receptor pharmacology and data analysis.

Purpose of the Study:

To discuss fitting mechanistic mathematical models to experimental data.
To highlight advantages of direct fitting over other analysis methods.
To improve assay planning, data analysis, and interpretation.

Main Methods:

Review of topics related to fitting mechanistic mathematical models.
Discussion of issues before experiments and model fitting.
Explanation of global data fitting and its applications.
Comparison of different model fits (e.g., one-site vs. two-site).

Main Results:

Provides insights into common receptor pharmacology issues with real-life examples.
Explains how to distribute dilutions along a concentration-response curve.
Details assumptions made when applying analysis models.
Clarifies the principles behind statistical comparison of data fits.

Conclusions:

Enhanced appreciation of assay planning and subsequent data analysis.
Improved quality of generated experimental data.
Better understanding of mechanistic mathematical model fitting in pharmacology.