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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.
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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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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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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation.

Achilleas Chytas1, George Gavriilides1, Anargyros Kapetanakis1

  • 1Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.

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|February 8, 2025
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Summary
This summary is machine-generated.

A new data model, OpenPVSignal, transforms unstructured pharmacovigilance reports into a computable format. This enables easier integration of drug safety data into processing pipelines for enhanced drug safety surveillance.

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Data Science and Knowledge Representation
  • Computational Pharmacology

Background:

  • Pharmacovigilance signal report (PVSR) documents offer crucial initial insights into drug-related adverse effects but are typically unstructured.
  • The free-text format of PVSRs hinders their integration into data-processing pipelines for drug safety signal investigation and clinical decision-making.

Purpose of the Study:

  • To introduce OpenPVSignal, a data model designed for publishing PVSRs in a computationally exploitable format.
  • To ensure compliance with FAIR principles (Findable, Accessible, Interoperable, Reusable) for improved data access and reusability.

Main Methods:

  • Conversion of pharmacovigilance signals from the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model.
  • Development of a Knowledge Graph (KG) from the converted data.
  • Technical validation by KG engineers and qualitative verification by medical and pharmacovigilance experts.

Main Results:

  • A Knowledge Graph (KG) was successfully created from 101 PVSRs spanning 2011-2019.
  • The resulting KG is openly available, facilitating broader access to structured pharmacovigilance data.

Conclusions:

  • The developed Knowledge Graph (KG) is a valuable resource for data-processing pipelines.
  • This KG can significantly support systems involved in drug safety activities and signal detection.