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Related Concept Videos

Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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...
Modified-Release Drug Delivery Systems: Site-Targeted01:24

Modified-Release Drug Delivery Systems: Site-Targeted

Site-targeted drug delivery systems enhance therapeutic efficacy while minimizing systemic toxicity and treatment costs. Unlike conventional methods, these systems ensure precise drug delivery, improving bioavailability and reducing side effects. Targeted drug delivery is classified into three levels. First-order targeting directs drugs to the capillary beds of specific organs or tissues. Second-order targets specific cell types, such as tumor cells, using receptor-mediated interactions.

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Related Experiment Video

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Drug target assessments: classifying target modulation and associated health effects using multi-level BERT-based

Jennifer Venhorst1, Gino Kalkman1

  • 1Biomedical and Digital Health, The Netherlands Organization for Applied Scientific Research (TNO), Utrecht 3584 CB, The Netherlands.

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Summary

Large Language Models systematically analyze drug target-health effects from literature. This AI approach accelerates drug discovery by providing mechanistic insights for efficacy and safety, outperforming manual methods.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Drug Discovery

Background:

  • Drug target selection is critical for successful drug development.
  • Manual risk/benefit analyses are time-consuming and prone to bias.
  • Large Language Models (LLMs) offer a systematic and efficient alternative for literature curation.

Purpose of the Study:

  • To develop and evaluate BERT-based LLMs for classifying drug target-health effect relationships.
  • To provide mechanistic insights into target modulation's impact on health and disease.
  • To create an AI-assisted tool for efficient drug target identification and evaluation.

Main Methods:

  • Developed BERT-models for multi-level classification of PubMed-indexed relationships.
  • Classified relationships based on causality, target modulation, and health effect direction.
  • Validated model performance with F1 scores ranging from 0.86 to 0.92.

Main Results:

  • Achieved competitive performance with F1 scores of 0.86-0.92.
  • Demonstrated applicability through case studies (ADAM33, OSM).
  • The developed pipeline is the first to offer detailed classification of these relationships.

Conclusions:

  • The LLMs provide mechanistic insights into drug target-health effects.
  • This AI-driven approach enhances drug target identification and evaluation efficiency.
  • The TargetTri platform offers a novel resource for artificial intelligence-assisted drug discovery.