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Agonism and Antagonism: Quantification01:14

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When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
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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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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
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DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.

Jie Yang1, Yihao Ding1, Siqu Long1

  • 1School of Computer Science, The University of Sydney, Sydney, NSW, Australia.

Frontiers in Digital Health
|May 11, 2023
PubMed
Summary

This study introduces DDI-MuG, a novel multi-aspect graph-based model for drug-drug interaction extraction from biomedical texts. DDI-MuG outperforms existing methods by integrating corpus-wide information for improved accuracy.

Keywords:
deep learningdrug-drug interactionsgraph neural networkmulti-aspect graphsrelation extraction

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Pharmacology
  • Bioinformatics

Background:

  • Drug-drug interactions (DDIs) pose significant risks, necessitating accurate extraction from biomedical literature.
  • Existing DDI extraction methods often overlook valuable corpus-level information, limiting their scope.

Purpose of the Study:

  • To develop a novel Multi-aspect Graph-based DDI extraction model (DDI-MuG).
  • To improve the accuracy and interpretability of DDI extraction by incorporating multi-aspect graph information.

Main Methods:

  • Utilized a bio-specific pre-trained language model for contextualized representations.
  • Employed two graphs: one for instance-level syntax and another for corpus-wide word co-occurrence.
  • Combined drug entity and verb token representations for classification.

Main Results:

  • Achieved superior performance on the DDIExtraction-2013 and TAC 2018 datasets.
  • Outperformed all twelve compared state-of-the-art models in DDI extraction tasks.

Conclusions:

  • DDI-MuG offers a more interpretable approach compared to black-box models by visualizing word relationships.
  • This work pioneers the use of multi-aspect graphs for DDI extraction, setting a foundation for future research.