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Pharmacovigilance

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...

Structure-Activity Relationships and Drug Design

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...

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

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: Overview

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...

Factors Affecting Drug Response: Overview

Factors Affecting Drug Response: Overview

When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...

Drug Toxicity: Risk factors

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Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...

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Related Experiment Video

Updated: Jun 3, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

An algorithmic framework for predicting side effects of drugs.

Nir Atias¹, Roded Sharan

¹Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|March 10, 2011

Summary

This summary is machine-generated.

This study introduces a new computational method to predict drug side effects by analyzing drug-drug-side effect relationships. The approach effectively identifies potential adverse drug reactions, improving drug development efficiency.

Related Experiment Videos

Last Updated: Jun 3, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

Pharmacology
Computational Biology
Drug Development

Background:

Identifying drug side effects is crucial but costly in drug development.
Existing methods for predicting side effects are limited and often analyze each effect in isolation.
There is a need for more efficient and comprehensive methods to predict potential adverse drug reactions.

Purpose of the Study:

To develop a novel computational approach for predicting drug side effects.
To leverage information from known drugs and their side effects to predict those of new drug candidates.
To improve the efficiency and reduce the cost of identifying potential adverse drug reactions during drug development.

Main Methods:

A novel approach combining canonical correlation analysis and network-based diffusion was developed.
The method analyzes relationships between drugs and their known side effects.
Performance was evaluated using a dataset of 692 drugs and their side effects from package inserts via cross-validation.

Main Results:

The method successfully predicted known side effects for 34% of drugs in the top scoring prediction.
The approach demonstrated effectiveness in inferring side effects even on previously unseen data.
The proposed method outperformed prediction schemes that consider side effects independently.

Conclusions:

The developed method offers a promising strategy for predicting drug side effects.
This approach can potentially accelerate the drug development process and reduce associated costs.
The findings highlight the utility of network-based analysis for understanding drug-induced adverse events.