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Related Concept Videos

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure (CHF).
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

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

Structure-Activity Relationships and Drug Design

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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Pharmacodynamic Models: Overview01:27

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Related Experiment Video

Updated: May 20, 2026

In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing
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In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing

Published on: May 11, 2021

The current role of model-based drug development.

Satyendra Suryawanshi1, Liping Zhang, Marc Pfister

  • 1University of Tennessee Health Science Center, College of Pharmacy, Department of Pharmaceutical Sciences, 874 Union Avenue, Suite 5p, Memphis, TN 38163, USA +1 901 448 1206 ; +1 901 448 6940 ; bmeibohm@uthsc.edu.

Expert Opinion on Drug Discovery
|July 25, 2012
PubMed
Summary

Model-based drug development (MBDD) enhances drug discovery by using quantitative pharmacology for informed decisions. This approach accelerates development and improves cost-effectiveness, addressing industry challenges.

Related Experiment Videos

Last Updated: May 20, 2026

In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing
08:04

In Vitro Three-Dimensional Sprouting Assay of Angiogenesis Using Mouse Embryonic Stem Cells for Vascular Disease Modeling and Drug Testing

Published on: May 11, 2021

Area of Science:

  • Pharmacology
  • Drug Development
  • Quantitative Modeling

Background:

  • Drug discovery faces challenges of low productivity and high costs.
  • Model-based drug development (MBDD) offers a promising solution.
  • MBDD aims to improve efficiency and reduce expenses in pharmaceutical research.

Purpose of the Study:

  • Introduce the concept and iterative paradigm of MBDD.
  • Explain the role of MBDD in drug discovery and development.
  • Outline modeling and simulation tools for MBDD application.

Main Methods:

  • Review of MBDD principles and quantitative pharmacology.
  • Description of the 'learn and confirm' iterative process.
  • Summary of various modeling and simulation approaches.

Main Results:

  • Illustrated applications of MBDD in drug development.
  • Provided understanding of MBDD's potential benefits.
  • Highlighted limitations and implementation challenges of MBDD.

Conclusions:

  • MBDD is increasingly adopted across the drug development continuum.
  • Supports rapid and rational decision-making.
  • Has the potential to accelerate drug development and enhance cost-effectiveness.