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Drug toxicity: Drug–Drug Interaction01:30

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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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Pharmacokinetics: Drug–Drug Interactions01:25

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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-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.
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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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Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
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Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration.

Guangsheng Wu1, Juan Liu2, Caihua Wang1

  • 1State Key Laboratory of Software Engineering, School of Computer Science, Wuhan University, Wuhan, 430072, People's Republic of China.

BMC Medical Genomics
|January 4, 2018
PubMed
Summary

This study introduces a novel computational method to predict drug-disease interactions, improving drug repositioning and disease treatment. The semi-supervised graph cut algorithm (SSGC) effectively identifies potential therapeutic relationships by integrating heterogeneous data.

Keywords:
Drug-disease interactionGraph cutGuilt-by-associationIntegration strategySimilarity

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Predicting drug-disease interactions is crucial for drug repositioning and developing new disease treatments.
  • Computational methods offer higher efficiency and lower cost than traditional experimental approaches.
  • Challenges remain in organizing heterogeneous data and improving prediction model performance.

Purpose of the Study:

  • To develop a novel computational method for predicting drug-disease interactions.
  • To address challenges in data organization and model performance for drug-disease association prediction.

Main Methods:

  • Hierarchically integrating heterogeneous data into three layers.
  • Calculating and linearly fusing drug-drug and disease-disease similarities.
  • Constructing a weighted drug-disease pair network and applying a semi-supervised graph cut algorithm (SSGC).

Main Results:

  • SSGC achieved superior performance compared to existing network-based methods, demonstrated by higher AUC scores and identification rates.
  • Integrating multiple data sources significantly improved prediction accuracy.
  • Case studies confirmed top-ranked drug-disease associations through external databases and literature.

Conclusions:

  • The proposed method, utilizing comprehensive similarity scores and a graph-cut algorithm, substantially enhances the prediction of drug-disease associations.
  • This approach offers a powerful tool for discovering novel therapeutic applications of drugs and new treatment strategies for diseases.