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Related Concept Videos

Pharmacovigilance01:19

Pharmacovigilance

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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...
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Drug toxicity: Drug–Drug Interaction01:30

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Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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Drug Toxicity: Risk factors01:24

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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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Pharmacokinetics: Drug–Drug Interactions01:25

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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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Pharmaceutical Poisoning: Potential Scenarios01:26

Pharmaceutical Poisoning: Potential Scenarios

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Pharmaceutical poisoning can occur through various channels, impacting an estimated 2 million hospitalized patients in the U.S. annually with serious adverse drug responses. These scenarios encompass both therapeutic uses, such as drug toxicity, where even standard dosages can lead to severe central nervous system depression, and non-therapeutic exposures, including accidental ingestion by children, and environmental and occupational exposures.Unintentional poisonings often involve exploratory...
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Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Data mining for potential adverse drug-drug interactions.

Felix Hammann1, Juergen Drewe

  • 1University Hospital of Basel, Division of Gastroenterology and Hepatology , Basel , Switzerland.

Expert Opinion on Drug Metabolism & Toxicology
|March 5, 2014
PubMed
Summary

Predicting drug-drug interactions (DDIs) is crucial for patient safety, especially for the elderly. While technology exists, data mining for DDIs requires significant human oversight to ensure accuracy and relevance.

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Area of Science:

  • Pharmacology and Bioinformatics
  • Computational Drug Safety
  • Health Informatics

Background:

  • Polypharmacy is common, particularly in elderly patients.
  • The number of potential drug-drug interactions (DDIs) escalates exponentially with each additional medication.
  • Computerized tools can aid clinicians in predicting and managing DDIs.

Purpose of the Study:

  • To review existing methods for detecting and predicting drug-drug interactions (DDIs).
  • To emphasize the role of data mining and network analysis in DDI prediction.
  • To discuss strategies for improving the efficiency and relevance of DDI mining efforts.

Main Methods:

  • Discussion of various DDI types and their occurrence levels.
  • Review of traditional pharmacovigilance alongside data mining techniques.
  • Exploration of network analysis and graph theory applications.
  • Strategies for focusing data mining efforts for meaningful outcomes.

Main Results:

  • Existing technologies for detecting adverse DDIs are advanced but often applied in lower-risk contexts.
  • Data mining for DDIs necessitates substantial human intervention for validation and relevance assessment.
  • Network analysis and graph theory show potential but are currently limited to descriptive analyses.

Conclusions:

  • The technology for identifying adverse drug-drug interactions is available and sophisticated.
  • Data mining for DDIs requires significant human input for result validation and relevance.
  • Network analysis and graph theory offer promising avenues for DDI research but need further development beyond descriptive capabilities.