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

Drug-Receptor Interactions01:29

Drug-Receptor Interactions

Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue.
Pharmacokinetic–Pharmacodynamic Relationship: Problems01:24

Pharmacokinetic–Pharmacodynamic Relationship: Problems

The empirical approach to drug therapy optimization relies on correlating pharmacological response with administered dosage. Such an approach can be costly, time-consuming, and often yields poor correlation due to variables like formulation factors and drug elimination characteristics. A more precise approach correlates response with plasma drug concentration or the amount of drug in the body, rather than dosage. This is achieved through pharmacokinetic-pharmacodynamic (PK/PD) modeling, which...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

You might also read

Related Articles

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

Sort by
Same author

Development and validation of an Online Comprehensive Geriatric Screening (O-CGS) Tool.

BMC public health·2026
Same author

Unraveling the role of ChREBP in lung adenocarcinoma: Expression, regulatory networks, and potential functional impact.

PloS one·2026
Same author

Exploring the Impact of Expanded Hemodialysis with Super-High-Flux Dialyzer on Inflammation and Gene Expression: A Prospective Cohort Study in Prevalent Hemodialysis Patients.

Blood purification·2026
Same author

Genome Assembly of the Threatened Fea's Muntjac (Muntiacus feae) Reveals Adaptive Evolution and Comparative Population Genomics of Fea's and Red Muntjacs.

Animal genetics·2026
Same author

Genomic decoding of drug-resistant tuberculosis transmission in Thailand over three decades.

Scientific reports·2025
Same author

Factors Influencing Health Workers' Acceptance of Guideline-Based Clinical Decision Support Systems for Preventive Services in Thailand: Questionnaire-Based Study.

JMIR human factors·2025
Same journal

Qualitative Framework for Evaluating Clinical Data Science Systems: Beyond Technical Validation.

Healthcare informatics research·2026
Same journal

Multi-Agent System for Early Sepsis Management Support: A Follow-up Evaluation Study.

Healthcare informatics research·2026
Same journal

AI-driven Medical Care: Evaluation of Large Language Models in Generating Personalized Stroke Education Materials.

Healthcare informatics research·2026
Same journal

Accuracy of Orthodontic Malocclusion Detection Using Multiple AI Models: A Comparative Study.

Healthcare informatics research·2026
Same journal

Transferable Migration Framework Derived from a Large-scale Tertiary Hospital EHR System.

Healthcare informatics research·2026
Same journal

Nonlinear Interaction Patterns in Health Literacy Identified through Explainable Artificial Intelligence: A Focus on Age and Education.

Healthcare informatics research·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering.

Natthawut Adulyanukosol1, Krittaphas Chaisutyakorn2, Saknarong Sombutjaroan2

  • 1Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand. natthawut.adu@mahidol.ac.th.

Healthcare Informatics Research
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

THIRAWAT Mapper enhances medication concept standardization for research by combining semantic matching with deterministic rules. This approach improves drug mapping accuracy to RxNorm, crucial for interoperable analytics and observational studies.

Keywords:
Controlled VocabularyMachine LearningNatural Language ProcessingRxNormTerminology

Related Experiment Videos

Last Updated: May 23, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

  • Health Informatics
  • Computational Linguistics
  • Pharmacology

Background:

  • Standardizing medication concepts across diverse vocabularies is vital for interoperable analytics and observational research.
  • The Observational Medical Outcomes Partnership (OMOP) Common Data Model requires mapping local drug codes to standardized RxNorm concepts.
  • Automated drug mapping is challenging due to the complexity of drug strings encoding attributes like strength, dosage form, and brand.

Purpose of the Study:

  • To introduce THIRAWAT Mapper, a novel pipeline for automated drug concept standardization.
  • To improve the accuracy and efficiency of mapping local drug codes to RxNorm concepts within the OMOP Common Data Model.

Main Methods:

  • THIRAWAT Mapper utilizes a fine-tuned ColBERTv1 reranker (THIRAWAT) within a retrieval-reranking pipeline.
  • Candidate generation employs approximate nearest-neighbor retrieval with bi-encoders (SapBERT-XLMR or BioLORD-2023).
  • Reranking uses adapted Bidirectional MaxSim (BiMaxSim) pooling, with deterministic tie-breaking for clinically salient cues.

Main Results:

  • THIRAWAT Mapper achieved high Mean Reciprocal Rank (MRR@100) values (0.954, 0.898, 0.912) across different mapping settings.
  • Performance significantly outperformed a lexical baseline (MRR@100: 0.491, 0.216, 0.143).
  • Hits@1 scores also showed substantial improvement, indicating high mapping precision.

Conclusions:

  • BiMaxSim and deterministic tie-breaking enhance drug mapping to RxNorm while maintaining efficiency.
  • THIRAWAT Mapper provides a practical blend of learned semantic matching and deterministic lexical constraints.
  • The models and code are publicly available for use and further development.