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Related Concept Videos

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...
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).
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.
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 its...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Pharmacodynamic Models: Overview01:27

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...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

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...

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Updated: May 10, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Model-based drug discovery: implementation and impact.

Sandra A G Visser1, Malin Aurell, Rhys D O Jones

  • 1Global Drug Metabolism and Pharmacokinetics, Innovative Medicines, AstraZeneca, Södertälje, Sweden.

Drug Discovery Today
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

Model-based drug discovery (MBDDx) enhances drug development by quantitatively linking exposure, efficacy, and safety. AstraZeneca

Related Experiment Videos

Last Updated: May 10, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Pharmacology and Drug Development
  • Quantitative Systems Pharmacology
  • Computational Toxicology

Background:

  • Model-based drug discovery (MBDDx) integrates quantitative understanding of drug exposure, efficacy, and safety.
  • AstraZeneca has systematically integrated MBDDx across its drug discovery programs.

Purpose of the Study:

  • To detail the implementation and impact of MBDDx at AstraZeneca.
  • To showcase the development of preclinical modeling and simulation capabilities.
  • To highlight the creation of an in vivo information platform and architecture.

Main Methods:

  • Systematic implementation of MBDDx across all drug discovery programs.
  • Focused investment in preclinical modeling and simulation.
  • Development of an in vivo information platform and architecture.

Main Results:

  • Established a robust MBDDx framework supporting target validation and lead optimization.
  • Defined compound property criteria for safety margins and dose selection.
  • Enabled prediction of human dose and scheduling for clinical candidates.

Conclusions:

  • AstraZeneca's systematic MBDDx implementation has significantly advanced drug discovery processes.
  • The developed preclinical modeling and in vivo data capabilities are crucial for efficient clinical translation.
  • Continuous improvement of MBDDx is key to optimizing drug development outcomes.