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Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

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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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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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Factors Affecting Protein-Drug Binding: Drug Interactions01:23

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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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Renal clearance plays a pivotal role in drug elimination from the body and can be influenced by drug distribution and interactions. Understanding these factors is crucial in pharmacology as they impact the effectiveness and duration of drug therapy.
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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.
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Extraction of drug-drug interaction using neural embedding.

Wen Juan Hou1, Bamfa Ceesay1

  • 11 Department of Computer Science and Information Engineering, National Taiwan Normal University, No 88, Tingzhou Road, Sec. 4, Taipei 116, Taiwan R.O.C.

Journal of Bioinformatics and Computational Biology
|December 21, 2018
PubMed
Summary

This study introduces a machine learning approach using neural word embeddings to extract drug-drug interactions (DDIs) from text. The developed system shows competitive performance, highlighting deep learning

Keywords:
Drug–drug interactiondata abstractionlong short term memory (LSTM)neural networksword embedding

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Area of Science:

  • Pharmacology and Computational Linguistics
  • Drug-Drug Interaction (DDI) research
  • Natural Language Processing (NLP) for biomedical applications

Background:

  • Drug-drug interactions (DDIs) significantly impact drug efficacy and safety in the pharmaceutical industry.
  • Accurate identification of DDIs is crucial for preventing adverse drug events and optimizing therapeutic outcomes.
  • Manual extraction of DDI information from vast biomedical literature is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and evaluate a novel machine learning approach for automated extraction of drug-drug interactions (DDIs) from textual data.
  • To leverage neural word embeddings and deep learning techniques to enhance the accuracy and efficiency of DDI information extraction.
  • To demonstrate the utility of advanced machine learning methods in processing biomedical literature for DDI identification.

Main Methods:

  • Utilized neural word embeddings to represent words and capture semantic relationships within drug-related texts.
  • Trained a machine learning system, incorporating deep learning models, to identify and extract DDI information.
  • Evaluated the system's performance against existing methods for DDI extraction tasks.

Main Results:

  • The proposed system achieved competitive performance in extracting drug-drug interactions (DDIs) from text.
  • Significant improvements in DDI extraction were observed by incorporating word features and employing a deep learning strategy.
  • The study confirmed the effectiveness of neural networks and deep learning for information extraction in this domain.

Conclusions:

  • Machine learning, particularly deep learning and neural networks, offers an efficient solution for automated information extraction of DDIs.
  • The developed approach demonstrates strong potential for advancing DDI research and pharmaceutical applications.
  • This methodology can play a significant role in future research for understanding and managing drug interactions.