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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.
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Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

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The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
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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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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.
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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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Knowledge Graphs in Pharmacovigilance: A Step-By-Step Guide.

Manfred Hauben1, Mazin Rafi2

  • 1Truliant Consulting, Baltimore, MD.

Clinical Therapeutics
|April 26, 2024
PubMed
Summary
This summary is machine-generated.

Knowledge Graphs (KGs) in pharmacovigilance (PV) are demystified, bridging knowledge gaps for professionals. This work synthesizes core concepts and implementation steps for advancing PV research and practice.

Keywords:
Adverse drug reactionDrug-drug interactionKnowledge graphsMachine learningPharmacovigilanceSignal detection

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Area of Science:

  • Pharmacovigilance (PV)
  • Information Technology
  • Machine Learning
  • Biology
  • Knowledge Graphs (KGs)

Background:

  • Pharmacovigilance professionals require a clearer understanding of Knowledge Graphs (KGs).
  • Existing knowledge gaps hinder the effective application of KGs in pharmacovigilance.
  • This work complements a scoping review on KGs in PV.

Purpose of the Study:

  • To demystify Knowledge Graphs (KGs) for pharmacovigilance (PV) applications.
  • To stimulate interest and facilitate engagement with KGs among PV professionals.
  • To bridge knowledge gaps and prepare readers for an in-depth scoping review.

Main Methods:

  • Elucidation of fundamental KG concepts and terminology.
  • Delineation of KG implementation steps: use case definition, data selection, sourcing, construction, embedding, and insight derivation.
  • Exploration of relevant information technology options and limitations.

Main Results:

  • Synthesis of core concepts for an intuitive exposition of KGs in PV.
  • Demonstration of the multi-disciplinary nature of KGs in PV, involving IT, ML, biology, and PV.
  • Provided a foundational understanding for KG application in pharmacovigilance.

Conclusions:

  • Demystified KGs for pharmacovigilance, laying groundwork for research and practice advancement.
  • Emphasized the importance of engaging vigilance experts for optimal KG assessment and deployment.
  • Enhanced knowledge sharing and collaboration for more effective pharmacovigilance.