Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacovigilance01:19

Pharmacovigilance

1.8K
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...
1.8K
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

25
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
25
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

830
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...
830
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.2K
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...
2.2K
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

20
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
20
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

393
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
393

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Preconception body mass index and maternal thyroid function: a longitudinal observational study.

European thyroid journal·2026
Same author

Reply to "A Comment about AI Methods that May Help Advance Pharmacovigilance".

Clinical pharmacology and therapeutics·2026
Same author

Serious respiratory distress associated with bosentan administration in chronic thromboembolic pulmonary hypertension: A case report.

Therapie·2026
Same author

Coprescription of antipsychotics and benzodiazepines at hospital discharge.

L'Encephale·2026
Same author

Drugs associated with generalized nonallergic pruritus: A World Health Organization pharmacovigilance database analysis.

Journal of the American Academy of Dermatology·2026
Same author

Hepatobiliary adverse drug reactions during treatment with olaparib: an analysis of data from the EudraVigilance reporting system.

Frontiers in drug safety and regulation·2026

Related Experiment Video

Updated: Feb 21, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.7K

Language models in pharmacovigilance: Applications, promises and limits.

Francesco Salvo1, Lelio Crupi2, Clément Cholle3

  • 1Inserm, BPH, Team AHeaD, Inserm U1219, université de Bordeaux, 33000 Bordeaux, France; Service de pharmacologie médicale, pôle de Santé publique, centre régional de pharmacovigilance de Bordeaux - DROM, CHU de Bordeaux, 33000 Bordeaux, France.

Therapie
|February 19, 2026
PubMed
Summary

Language models enhance pharmacovigilance efficiency by automating tasks like adverse drug reaction extraction. However, challenges like transparency and regulatory acceptance require careful, interdisciplinary implementation for a sustainable system.

Keywords:
Adverse drug reaction reporting systemsArtificial intelligenceData annotationExpert systemSustainable development

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K

Related Experiment Videos

Last Updated: Feb 21, 2026

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

1.7K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K

Area of Science:

  • Pharmacovigilance and Artificial Intelligence
  • Natural Language Processing in Drug Safety

Background:

  • Language models offer potential to improve pharmacovigilance quality and efficiency.
  • Traditional NLP and advanced language models have diverse applications in drug safety workflows.
  • Practical constraints currently limit the widespread adoption of these technologies.

Purpose of the Study:

  • To examine the application of traditional NLP and advanced language models in pharmacovigilance.
  • To analyze their use in adverse drug reaction extraction, case processing, and evidence screening.
  • To identify challenges and opportunities for integrating these technologies.

Main Methods:

  • A systematic literature search on PubMed® was performed.
  • Expert-based selection and reference screening refined the literature set.
  • Analysis focused on traditional NLP techniques and advanced language models.

Main Results:

  • Traditional NLP methods provide transparency for structured data extraction but struggle with clinical narrative variability.
  • Advanced language models excel in contextual understanding of unstructured text but face regulatory hurdles (e.g., hallucinations, transparency).
  • Large language models have significant computational and environmental costs.

Conclusions:

  • Natural language processing and advanced language models are complementary tools for pharmacovigilance.
  • Integration requires interdisciplinary collaboration, human oversight, and sustainable implementation strategies.
  • A balanced approach can enhance scalability, trustworthiness, and clinical integrity in pharmacovigilance.