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Related Concept Videos

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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Drug-Receptor Interactions01:29

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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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Pharmacokinetics: Drug–Food and Drug–Viral Interactions01:26

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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

Factors Affecting Protein-Drug Binding: Drug Interactions

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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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Drug-Receptor Interaction: Antagonist01:28

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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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Drug-Receptor Interaction: Agonist01:25

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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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Fast Inspection of Quality of Indigo Naturalis by Multiple Light Scattering
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Predicting Drug Interactions From Chemogenomics Using INDIGO.

Sriram Chandrasekaran1

  • 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. csriram@umich.edu.

Methods in Molecular Biology (Clifton, N.J.)
|December 7, 2018
PubMed
Summary
This summary is machine-generated.

Predicting antibiotic interactions computationally is key to fighting drug resistance. The INDIGO algorithm accurately forecasts synergistic and antagonistic drug effects, accelerating the discovery of effective antibiotic combinations.

Keywords:
AntibioticsChemogenomicsDrug combinationsDrug resistanceDrug synergyMachine learningMycobacterium tuberculosisStaphylococcus aureus

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Area of Science:

  • Microbiology
  • Computational Biology
  • Pharmacology

Background:

  • Combating antibiotic resistance requires effective combination therapies.
  • Identifying optimal antibiotic combinations is challenging due to the vast search space.
  • Computational methods are needed to predict drug interactions and prioritize combinations.

Purpose of the Study:

  • To outline a protocol for predicting antibiotic interactions using the INDIGO algorithm.
  • To demonstrate INDIGO's ability to predict synergistic and antagonistic drug effects.
  • To showcase INDIGO's applicability to diverse bacterial pathogens.

Main Methods:

  • Utilizing chemogenomics data to train the INDIGO algorithm.
  • Employing network conservation analysis to predict interactions across species.
  • Experimental validation of INDIGO's predictions in relevant bacterial models.

Main Results:

  • INDIGO accurately predicted novel drug-drug interaction outcomes.
  • High accuracy was achieved in experimental evaluations using E. coli and S. aureus.
  • INDIGO overcomes limitations of existing drug-interaction prediction algorithms.

Conclusions:

  • The INDIGO algorithm provides a powerful computational approach for predicting antibiotic interactions.
  • INDIGO's method enables the application of chemogenomics data to less-studied pathogens.
  • This approach accelerates the design of effective antibiotic combination regimens against resistant bacteria.