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Related Concept Videos

Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Pharmacodynamic Models: Overview01:27

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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...
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Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Structure-Activity Relationships and Drug Design01:28

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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.
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Principles of Drug Action01:24

Principles of Drug Action

Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
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Related Experiment Video

Updated: May 11, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

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

Published on: September 26, 2025

Pharmacophore modeling for antitargets.

Khac-Minh Thai1, Trieu-Du Ngo, Thanh-Dao Tran

  • 1Department of Medicinal Chemistry, School of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang St, Dist 1, 70000 Ho Chi Minh City, Viet Nam. thaikhacminh@gmail.com

Current Topics in Medicinal Chemistry
|May 9, 2013
PubMed
Summary
This summary is machine-generated.

Pharmacophore modeling aids drug discovery by assessing risks from interactions with key proteins like hERG and cytochrome P450. Considering substrate overlap in these targets improves drug safety assessments early in development.

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

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Pharmacology and toxicology

Background:

  • Pharmacophore modeling is crucial in modern drug research for predicting bioactivity and assessing risks.
  • Drug candidates can interact with antitargets such as P-glycoprotein, hERG, cytochrome P450, and pregnane X-receptor, leading to side effects and toxicity.
  • Understanding these interactions is vital for developing safer medicines.

Purpose of the Study:

  • To review and update the current state of pharmacophore modeling applied to promiscuous proteins in drug research.
  • To highlight the importance of considering the partial overlap in substrate properties of key off-targets.
  • To emphasize a systems-level approach for drug safety assessment on off-targets.

Main Methods:

  • Review of existing literature on pharmacophore modeling techniques.
  • Analysis of substrate properties and interactions related to P-glycoprotein, hERG, cytochrome P450, and pregnane X-receptor.
  • Integration of pharmacophore modeling with systems-level safety assessment strategies.

Main Results:

  • Pharmacophore modeling effectively profiles bioactivity and predicts potential toxicities arising from off-target interactions.
  • Significant overlap exists in the substrate properties of major drug-metabolizing and efflux proteins (hERG, P-gp, CYP450, PXR).
  • This overlap necessitates a holistic approach to drug safety evaluation.

Conclusions:

  • Pharmacophore modeling is a valuable tool for early-stage risk assessment in drug development.
  • Addressing the shared substrate properties of promiscuous proteins is essential for designing safer drug candidates.
  • A systems-level perspective on off-target interactions is key to accelerating the creation of new, safer medicines.