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Related Concept Videos

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...
Biopharmaceutics and Pharmacokinetics: Overview01:28

Biopharmaceutics and Pharmacokinetics: Overview

Understanding drugs, drug products, and their performance in pharmaceutical science is pivotal. Drugs, whether simple molecules or complex compounds, are designed to interact with the body's biological systems to diagnose, treat, or prevent diseases. Drug products include various delivery systems such as tablets, capsules, injections, and inhalers. The performance of these drug products is gauged by their ability to deliver the active ingredient to the desired site of action at the appropriate...
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though pharmacologically...
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...
Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems01:22

Bioavailability Enhancement: Determination and Conceptual Approaches in Overcoming Bioavailability Problems

Bioavailability is a critical pharmacological concept that measures the extent and rate at which an active drug ingredient or therapeutic moiety enters the systemic circulation, remaining unchanged. It's a pivotal factor in determining a drug's efficacy and safety.The Biopharmaceutics Classification System (BCS) plays an essential role in drug development by categorizing drugs into four classes based on their solubility and permeability. This classification aids in understanding drug absorption...
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.

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Related Experiment Video

Updated: May 15, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Biomedical data integration in computational drug design and bioinformatics.

Jose A Seoane1, Vanessa Aguiar-Pulido, Cristian R Munteanu

  • 1Department of Information and Communication Technologies, Computer Science School, University of A Coruña, Spain.

Current Computer-Aided Drug Design
|January 9, 2013
PubMed
Summary

Biomedical data integration combines diverse omics data for analysis. This review explores common approaches like federated databases and semantic technology for better biomedical research and applications.

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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

Related Experiment Videos

Last Updated: May 15, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

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:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • The post-genomic era generates vast amounts of biological data from high-throughput technologies like proteomics and transcriptomics.
  • Analyzing and integrating this 'omics' data presents a significant challenge for researchers.

Purpose of the Study:

  • To review common approaches for biomedical data integration.
  • To analyze solutions developed using these approaches in recent years.

Main Methods:

  • Federated databases
  • Data warehousing
  • Multi-agent systems
  • Semantic technology

Main Results:

  • Identified and analyzed common biomedical data integration strategies.
  • Highlighted solutions developed using federated databases, data warehousing, multi-agent systems, and semantic technology.

Conclusions:

  • Biomedical data integration is crucial for leveraging omics data.
  • Various approaches facilitate querying heterogeneous and distributed biomedical data sources for applications in drug design, information retrieval, diagnosis, and systems biology.