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Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

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Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
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A drug interaction occurs when the concurrent use of another drug, food, or an external substance alters the pharmacological activity of a drug. This interaction can modify the action of the original drug, affecting its effectiveness and safety.Drug–food interactions are significant as they impact drug absorption, metabolism, and excretion. For example, grapefruit juice is a well-known disruptor of drug metabolism. It inhibits the cytochrome P450 3A4 enzyme, crucial for the metabolism of...
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Position-aware deep multi-task learning for drug-drug interaction extraction.

Deyu Zhou1, Lei Miao1, Yulan He2

  • 1School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 210096, China.

Artificial Intelligence in Medicine
|March 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method for automatically identifying drug-drug interactions (DDIs) from scientific texts. The approach enhances DDI extraction accuracy by incorporating position-aware attention and multi-task learning.

Keywords:
ClassificationDrug–drug interaction extractionLong short-term memory networkMulti-task learning

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Computational Biology

Background:

  • Drug-drug interactions (DDIs) are critical for patient safety, but information is often dispersed in scientific literature.
  • Existing databases have limitations, necessitating automated methods for DDI extraction.
  • Accurate identification of DDIs is essential for healthcare professionals to prevent adverse drug events.

Purpose of the Study:

  • To develop and evaluate a novel position-aware deep multi-task learning approach for automated DDI extraction from biomedical texts.
  • To improve the accuracy and efficiency of identifying drug interactions and their types.
  • To address the challenge of extracting crucial DDI information currently embedded within scientific publications.

Main Methods:

  • Utilized a deep multi-task learning framework incorporating position-aware attention.
  • Represented sentences using a combination of word embeddings and position embeddings.
  • Employed an attention-based bidirectional long short-term memory (BiLSTM) network for sentence encoding.
  • Integrated relative drug position information into attention mechanisms.
  • Jointly learned tasks of DDI prediction and interaction type identification.

Main Results:

  • The position-aware attention mechanism alone improved binary DDI classification by 0.99% over the state-of-the-art.
  • The combined approach (position-aware attention and multi-task learning) achieved a micro F-score of 72.99% for interaction type identification.
  • This represents a 1.51% improvement over the state-of-the-art method in interaction type identification.
  • Demonstrated the effectiveness of the proposed deep learning approach for DDI extraction.

Conclusions:

  • The proposed position-aware deep multi-task learning approach significantly enhances the accuracy of drug-drug interaction extraction from biomedical texts.
  • This method effectively identifies both the presence and type of drug interactions, outperforming existing state-of-the-art techniques.
  • Automated extraction of DDIs using this approach offers a valuable tool for healthcare professionals and researchers.