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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Pharmacokinetics: Overview01:10

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Pharmacokinetics is a scientific discipline that focuses on the journey of a drug within the body, encompassing four key stages: absorption, distribution, metabolism, and elimination. The first stage, absorption, involves the drug's transfer into the bloodstream. Several factors dictate the extent and speed of this process. For example, the liver often metabolizes oral drugs before they reach systemic circulation, leading to only partial absorption. In contrast, intravenous (IV)...
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Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Biopharmaceutics and Pharmacokinetics: Overview01:28

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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...
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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge Graphs in Pharmacovigilance: A Scoping Review.

Manfred Hauben1, Mazin Rafi2, Ibrahim Abdelaziz3

  • 1Department of Family and Community Medicine, New York Medical College, Valhalla, New York; Truliant Consulting, Baltimore, Maryland.

Clinical Therapeutics
|July 9, 2024
PubMed
Summary
This summary is machine-generated.

Knowledge graphs show promise for predicting adverse drug reactions in pharmacovigilance. Further research is needed to compare their performance with existing methods and establish best practices.

Keywords:
Adverse drug reactionsDrug safetyGraph machine learningKnowledge graphsPharmacovigilanceScoping review

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Data Science and Artificial Intelligence
  • Biomedical Informatics

Background:

  • Pharmacovigilance systems are crucial for monitoring drug safety post-market.
  • Traditional methods for detecting adverse drug reactions (ADRs) have limitations.
  • Knowledge graphs offer a novel approach to structured data representation and analysis.

Purpose of the Study:

  • To critically evaluate the role and added value of knowledge graphs in pharmacovigilance.
  • To assess the capability of knowledge graphs in predicting adverse drug reactions.
  • To understand the current landscape of knowledge graph applications in pharmacovigilance signal detection.

Main Methods:

  • A systematic scoping review of peer-reviewed publications was performed.
  • Data extraction focused on objectives, technology, data sources, methodology, and performance metrics.
  • Included studies reported the use of knowledge graphs for pharmacovigilance signal detection.

Main Results:

  • 47 peer-reviewed articles were included in the review.
  • Knowledge graphs were applied to detect/predict single-drug ADRs and drug-drug interactions.
  • Reported performance metrics were variable, with limited comparisons to established methods.

Conclusions:

  • Knowledge graphs hold potential to enhance predictive signal detection in pharmacovigilance.
  • Further research is required with robust reference sets for ADRs.
  • Comparative studies against legacy pharmacovigilance methods are necessary to define best practices.