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Related Concept Videos

Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

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Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
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When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
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The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
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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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Diagonal Method to Measure Synergy Among Any Number of Drugs
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Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization.

Pingjian Ding1, Cong Shen2, Zihan Lai2

  • 1School of Computer Science , University of South China , Hengyang 421001 , China.

Journal of Chemical Information and Modeling
|January 1, 2020
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Summary

Identifying effective drug combinations is challenging. A new computational method, DSGCR, uses graph co-regularization and multiple knowledge sources to accurately predict synergistic drug combinations, improving therapeutic strategies.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug combinations offer a promising strategy to enhance therapeutic efficacy and reduce toxicity for complex diseases.
  • Identifying effective drug combinations is challenging due to the vast combinatorial space.
  • Existing computational methods often fail to integrate diverse knowledge sources for synergy prediction.

Purpose of the Study:

  • To develop a novel computational method for predicting drug synergy.
  • To effectively incorporate multiple sources of biological and pharmacological knowledge.
  • To improve the accuracy and reliability of drug synergy predictions.

Main Methods:

  • Developed a graph co-regularization-based computational method named DSGCR.
  • Integrated drug-target network patterns, pharmacological patterns, and prior knowledge of drug combinations.
  • Validated performance using cross-validation and compared with existing methods.

Main Results:

  • DSGCR demonstrated superior performance in predicting drug synergy compared to existing methods, based on various metrics.
  • Analysis revealed the importance of different knowledge sources in DSGCR's prediction scenarios.
  • The method's potential was confirmed by successfully identifying three synergistic drug combinations.

Conclusions:

  • DSGCR provides an effective computational approach for predicting synergistic drug combinations.
  • The integration of diverse knowledge sources enhances the accuracy of synergy prediction.
  • DSGCR holds potential for advancing drug discovery and combination therapy development.