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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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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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Therapeutic Drug Monitoring: Affecting Factors01:29

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Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
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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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DPDDI: a deep predictor for drug-drug interactions.

Yue-Hua Feng1, Shao-Wu Zhang2, Jian-Yu Shi3

  • 1Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.

BMC Bioinformatics
|September 25, 2020
PubMed
Summary

This study introduces DPDDI, a novel computational method for predicting drug-drug interactions (DDIs) using graph convolution networks. DPDDI effectively identifies potential DDIs by analyzing drug network structures, outperforming existing methods.

Keywords:
DDI predictionDeep neural networkDrug-drug interactionFeature extractionGraph convolution network (GCN)

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Network science

Background:

  • Drug-drug interactions (DDIs) pose risks in complex disease treatments.
  • Experimental DDI detection is costly and time-consuming.
  • Computational methods for DDI prediction are needed, but often rely on hard-to-obtain drug properties.

Purpose of the Study:

  • To develop a novel computational method for predicting DDIs.
  • To leverage network structure features of drugs, avoiding reliance on chemical or biological properties.
  • To improve the accuracy and efficiency of DDI prediction.

Main Methods:

  • Developed DPDDI, a method utilizing graph convolution networks (GCN) to extract drug network structure features.
  • Employed a deep neural network (DNN) predictor that concatenates latent drug features.
  • Trained the DNN model on drug pairs to predict potential DDIs.

Main Results:

  • DPDDI outperformed four state-of-the-art DDI prediction methods.
  • GCN-derived latent features captured more DDI information than chemical or biological features.
  • Concatenation proved to be a superior feature aggregation operator.

Conclusions:

  • DPDDI offers an effective and robust approach for DDI prediction using DDI network information.
  • The method successfully predicts DDIs without requiring drug chemical or biological properties.
  • DPDDI has potential applications in detecting adverse effects and guiding drug combinations.