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Related Concept Videos

Pharmacovigilance01:19

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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.
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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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Pharmaceutical Poisoning: Potential Scenarios01:26

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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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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

Pharmacokinetics: Drug–Drug Interactions

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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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Drug Toxicity: Overview01:00

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Drug toxicity quantifies the harm a compound causes to an organism, varying by dose and potentially impacting whole systems or specific organs like the liver. Toxic reactions may arise from venomous insect or spider bites, with effects ranging from mild symptoms to severe outcomes such as brain damage or death. Common forms of acute poisoning include ethanol intoxication and overdose of pain or fever medications, with substances like GHB and heroin being particularly lethal at doses close to...
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Efficiently mining Adverse Event Reporting System for multiple drug interactions.

Yang Xiang1, Aaron Albin2, Kaiyu Ren2

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|February 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to find multiple drug interactions and reactions in the Adverse Event Reporting System (AERS). The approach successfully identified many statistically significant and clinically relevant drug-related events.

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Area of Science:

  • Pharmacovigilance
  • Data Mining
  • Computational Biology

Background:

  • Mining drug interactions from adverse event data is complex.
  • Existing methods inadequately address multiple drug interactions and reactions.
  • The Adverse Event Reporting System (AERS) contains valuable but challenging data.

Purpose of the Study:

  • To develop an efficient method for identifying multiple drug interactions and reactions from AERS data.
  • To address the limitations of current approaches in analyzing complex drug-related events.
  • To discover statistically significant and clinically relevant associations.

Main Methods:

  • Utilized Unified Medical Language System (UMLS) mapping for data standardization.
  • Employed frequent closed itemset mining to identify patterns.
  • Implemented uninformative association identification and removal to refine results.
  • Applied the FCI-filter approach to the AERS database.

Main Results:

  • Successfully identified a large number of multiple drug interactions with associated reactions.
  • Statistical analysis revealed most identified associations possess very small p-values, indicating statistical significance.
  • Further analysis confirmed the clinical interest and potential relevance of many discovered interactions and reactions.

Conclusions:

  • The proposed FCI-filter approach is effective for mining multiple drug interactions and reactions from AERS.
  • The identified associations are statistically significant and clinically relevant.
  • Integrating external knowledge could further enhance the method's capabilities.